02_pandas-primer.ipynb

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    "# Getting Started with Exploratory Data Analysis\n",
    "\n",
    "3 important Python packages\n",
    "1. NumPy for efficient computation on arrays\n",
    "2. Pandas for data analysis\n",
    "3. Matplotlib for plotting in the notebook"
   ]
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   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
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    "## Pandas\n",
    "\n",
    "Python module for manipulating tabular data\n",
    "\n",
    "## `pandas`\n",
    "\n",
    "- Provides python a `DataFrame`\n",
    "    - Table data structure\n",
    "    - Can be easily manipulated similar to lists and arrays\n",
    "- Structured manipulation tools\n",
    "- Built on top of `numpy`\n",
    "- Huge growth from 2011-2012\n",
    "- Very **efficient**\n",
    "- Great for *medium* data\n",
    "\n",
    "Resources\n",
    "\n",
    "- [pandas.pydata.org](http://pandas.pydata.org/)\n",
    "- [Python for Data Analysis](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793) by Wes McKinney\n",
    "- [Data Wrangling Kung Fu with Pandas](vimeo.com/63295598) by Wes McKinney\n",
    "- [Cheat sheet](https://s3.amazonaws.com/quandl-static-content/Documents/Quandl+-+Pandas,+SciPy,+NumPy+Cheat+Sheet.pdf) by Quandl"
   ]
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    "### Why `pandas`?\n",
    "\n",
    "> 80% of the effort in data analysis is spent cleaning data. [Hadley Wickham](http://vita.had.co.nz/papers/tidy-data.pdf)\n",
    "\n",
    "Efficency\n",
    "\n",
    "- Different views of data\n",
    "- [Tidy data](http://vita.had.co.nz/papers/tidy-data.pdf) by Hadley Wickham\n",
    "\n",
    "Raw data is often in the wrong format\n",
    "\n",
    "- How often to you download an array ready for array-oriented computing?\n",
    "- e.g. `scikit-learn` interface\n",
    "\n",
    "Storage may be best in a different format\n",
    "\n",
    "- Sparse representations\n",
    "- Upload to database\n",
    "\n",
    "\n"
   ]
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    "## Simple example using  *seal* oberservational data\n",
    "\n",
    "Data from: \n",
    "\n",
    "* Baker L, Flemming JEM, Jonsen ID, Lidgard DC, Iverson SJ, Bowen WD (2015) A novel approach to quantifying the spatiotemporal behavior of instrumented grey seals used to sample the environment. Movement Ecology 3(1):20. doi:10.1186/s40462-015-0047-4\n",
    "\n",
    "* Lidgard DC, Bowen WD, Iverson SJ (2015) Data from: A novel approach to quantifying the spatiotemporal behavior of instrumented grey seals used to sample the environment. Movebank Data Repository. doi:10.5441/001/1.910p0c20"
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    "### Reading a CSV file as text\n",
    "\n",
    "* Data is provided as a CSV file\n",
    "* You can just read the file in line by line\n",
    "* This will create a ```list``` of the lines in the file"
   ]
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       "['event-id,visible,timestamp,location-long,location-lat,manually-marked-outlier,sensor-type,individual-taxon-canonical-name,tag-local-identifier,individual-local-identifier,study-name\\r\\n',\n",
       " '677436629,true,2011-06-15 17:35:18.000,-59.97949982,43.92495728,,\"gps\",\"Halichoerus grypus\",\"106705\",\"E 87\",\"Grey seals (Halichoerus grypus) at Sable Island (data from Baker et al. 2015)\"\\r\\n',\n",
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       " '677436635,true,2011-06-15 19:06:20.000,-59.98566055,43.92499924,,\"gps\",\"Halichoerus grypus\",\"106705\",\"E 87\",\"Grey seals (Halichoerus grypus) at Sable Island (data from Baker et al. 2015)\"\\r\\n',\n",
       " '677436636,true,2011-06-15 19:22:18.000,-59.987854,43.92406082,,\"gps\",\"Halichoerus grypus\",\"106705\",\"E 87\",\"Grey seals (Halichoerus grypus) at Sable Island (data from Baker et al. 2015)\"\\r\\n',\n",
       " '677436637,true,2011-06-15 19:37:18.000,-59.98072815,43.92603302,,\"gps\",\"Halichoerus grypus\",\"106705\",\"E 87\",\"Grey seals (Halichoerus grypus) at Sable Island (data from Baker et al. 2015)\"\\r\\n']"
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     "execution_count": 2,
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   "source": [
    "f = open(\"Grey seals (Halichoerus grypus) at Sable Island (data from Baker et al. 2015).csv\", 'r')\n",
    "lines = f.readlines()\n",
    "lines[:10]"
   ]
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  {
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    "### Creating a `DataFrame`\n",
    "\n",
    "```\n",
    "df = pd.read_csv(filename)\n",
    "print df\n",
    "```\n",
    "\n",
    "**Why store it this way?**\n",
    "\n",
    "- Similar to a table\n",
    "- powerfull way to interact with the data\n",
    "\n",
    "### Converting the timestamp Column\n",
    "\n",
    "NumPy datetime64 dtype\n"
   ]
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    "?pd.read_csv"
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      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2902: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
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       "      <th>location-long</th>\n",
       "      <th>location-lat</th>\n",
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       "      <th>sensor-type</th>\n",
       "      <th>individual-taxon-canonical-name</th>\n",
       "      <th>tag-local-identifier</th>\n",
       "      <th>individual-local-identifier</th>\n",
       "      <th>study-name</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
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       "      <td>106705</td>\n",
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       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
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      "text/plain": [
       "    event-id visible           timestamp  location-long  location-lat  \\\n",
       "0  677436629    True 2011-06-15 17:35:18     -59.979500     43.924957   \n",
       "1  677436630    True 2011-06-15 17:50:19     -59.982738     43.925488   \n",
       "2  677436631    True 2011-06-15 18:05:32     -59.989689     43.925827   \n",
       "\n",
       "  manually-marked-outlier sensor-type individual-taxon-canonical-name  \\\n",
       "0                     NaN         gps              Halichoerus grypus   \n",
       "1                     NaN         gps              Halichoerus grypus   \n",
       "2                     NaN         gps              Halichoerus grypus   \n",
       "\n",
       "   tag-local-identifier individual-local-identifier  \\\n",
       "0                106705                        E 87   \n",
       "1                106705                        E 87   \n",
       "2                106705                        E 87   \n",
       "\n",
       "                                          study-name  \n",
       "0  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "1  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "2  Grey seals (Halichoerus grypus) at Sable Islan...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Grey seals (Halichoerus grypus) at Sable Island (data from Baker et al. 2015).csv\", \n",
    "                 parse_dates=[2])\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "### What data types are in the data frame?"
   ]
  },
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      "text/plain": [
       "event-id                                    int64\n",
       "visible                                      bool\n",
       "timestamp                          datetime64[ns]\n",
       "location-long                             float64\n",
       "location-lat                              float64\n",
       "manually-marked-outlier                    object\n",
       "sensor-type                                object\n",
       "individual-taxon-canonical-name            object\n",
       "tag-local-identifier                        int64\n",
       "individual-local-identifier                object\n",
       "study-name                                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
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   },
   "source": [
    "### Overview over the numerical types of the data"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event-id</th>\n",
       "      <th>visible</th>\n",
       "      <th>location-long</th>\n",
       "      <th>location-lat</th>\n",
       "      <th>tag-local-identifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.244350e+05</td>\n",
       "      <td>124435</td>\n",
       "      <td>124435.000000</td>\n",
       "      <td>124435.000000</td>\n",
       "      <td>124435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.455033e+08</td>\n",
       "      <td>0.999397</td>\n",
       "      <td>-60.352127</td>\n",
       "      <td>44.453460</td>\n",
       "      <td>106714.273621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.829974e+06</td>\n",
       "      <td>0.0245431</td>\n",
       "      <td>1.124249</td>\n",
       "      <td>0.837039</td>\n",
       "      <td>5.576758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.430037e+08</td>\n",
       "      <td>False</td>\n",
       "      <td>-75.647453</td>\n",
       "      <td>43.408535</td>\n",
       "      <td>106705.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>6.430355e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>-60.473623</td>\n",
       "      <td>43.928371</td>\n",
       "      <td>106710.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.430666e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>-60.064209</td>\n",
       "      <td>44.198532</td>\n",
       "      <td>106715.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>6.430977e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>-59.855791</td>\n",
       "      <td>44.689791</td>\n",
       "      <td>106718.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.774455e+08</td>\n",
       "      <td>True</td>\n",
       "      <td>-50.937111</td>\n",
       "      <td>59.374504</td>\n",
       "      <td>106724.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "           event-id    visible  location-long   location-lat  \\\n",
       "count  1.244350e+05     124435  124435.000000  124435.000000   \n",
       "mean   6.455033e+08   0.999397     -60.352127      44.453460   \n",
       "std    8.829974e+06  0.0245431       1.124249       0.837039   \n",
       "min    6.430037e+08      False     -75.647453      43.408535   \n",
       "25%    6.430355e+08          1     -60.473623      43.928371   \n",
       "50%    6.430666e+08          1     -60.064209      44.198532   \n",
       "75%    6.430977e+08          1     -59.855791      44.689791   \n",
       "max    6.774455e+08       True     -50.937111      59.374504   \n",
       "\n",
       "       tag-local-identifier  \n",
       "count         124435.000000  \n",
       "mean          106714.273621  \n",
       "std                5.576758  \n",
       "min           106705.000000  \n",
       "25%           106710.000000  \n",
       "50%           106715.000000  \n",
       "75%           106718.000000  \n",
       "max           106724.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Size of the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124435"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Indexing - very similar to Numpy arrays\n",
    "\n",
    "* 0 based indexing\n",
    "* last element not included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "      <td>E 87</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>677436633</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-15 18:36:31</td>\n",
       "      <td>-59.988968</td>\n",
       "      <td>43.925255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106705</td>\n",
       "      <td>E 87</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>677436635</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-15 19:06:20</td>\n",
       "      <td>-59.985661</td>\n",
       "      <td>43.924999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106705</td>\n",
       "      <td>E 87</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>677436637</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-15 19:37:18</td>\n",
       "      <td>-59.980728</td>\n",
       "      <td>43.926033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106705</td>\n",
       "      <td>E 87</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    event-id visible           timestamp  location-long  location-lat  \\\n",
       "0  677436629    True 2011-06-15 17:35:18     -59.979500     43.924957   \n",
       "2  677436631    True 2011-06-15 18:05:32     -59.989689     43.925827   \n",
       "4  677436633    True 2011-06-15 18:36:31     -59.988968     43.925255   \n",
       "6  677436635    True 2011-06-15 19:06:20     -59.985661     43.924999   \n",
       "8  677436637    True 2011-06-15 19:37:18     -59.980728     43.926033   \n",
       "\n",
       "  manually-marked-outlier sensor-type individual-taxon-canonical-name  \\\n",
       "0                     NaN         gps              Halichoerus grypus   \n",
       "2                     NaN         gps              Halichoerus grypus   \n",
       "4                     NaN         gps              Halichoerus grypus   \n",
       "6                     NaN         gps              Halichoerus grypus   \n",
       "8                     NaN         gps              Halichoerus grypus   \n",
       "\n",
       "   tag-local-identifier individual-local-identifier  \\\n",
       "0                106705                        E 87   \n",
       "2                106705                        E 87   \n",
       "4                106705                        E 87   \n",
       "6                106705                        E 87   \n",
       "8                106705                        E 87   \n",
       "\n",
       "                                          study-name  \n",
       "0  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "2  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "4  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "6  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "8  Grey seals (Halichoerus grypus) at Sable Islan...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:10:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event-id</th>\n",
       "      <th>visible</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>location-long</th>\n",
       "      <th>location-lat</th>\n",
       "      <th>manually-marked-outlier</th>\n",
       "      <th>sensor-type</th>\n",
       "      <th>individual-taxon-canonical-name</th>\n",
       "      <th>tag-local-identifier</th>\n",
       "      <th>individual-local-identifier</th>\n",
       "      <th>study-name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>124430</th>\n",
       "      <td>643119961</td>\n",
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       "      <td>-59.756832</td>\n",
       "      <td>43.974522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106724</td>\n",
       "      <td>F357</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124431</th>\n",
       "      <td>643119962</td>\n",
       "      <td>True</td>\n",
       "      <td>2012-01-06 03:33:53</td>\n",
       "      <td>-59.757130</td>\n",
       "      <td>43.974274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106724</td>\n",
       "      <td>F357</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124432</th>\n",
       "      <td>643119963</td>\n",
       "      <td>True</td>\n",
       "      <td>2012-01-06 03:52:42</td>\n",
       "      <td>-59.758202</td>\n",
       "      <td>43.970879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106724</td>\n",
       "      <td>F357</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124433</th>\n",
       "      <td>643119964</td>\n",
       "      <td>True</td>\n",
       "      <td>2012-01-06 04:10:06</td>\n",
       "      <td>-59.761147</td>\n",
       "      <td>43.971104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106724</td>\n",
       "      <td>F357</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124434</th>\n",
       "      <td>643119965</td>\n",
       "      <td>True</td>\n",
       "      <td>2012-01-06 04:25:06</td>\n",
       "      <td>-59.762070</td>\n",
       "      <td>43.971161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106724</td>\n",
       "      <td>F357</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         event-id visible           timestamp  location-long  location-lat  \\\n",
       "124430  643119961    True 2012-01-06 03:16:42     -59.756832     43.974522   \n",
       "124431  643119962    True 2012-01-06 03:33:53     -59.757130     43.974274   \n",
       "124432  643119963    True 2012-01-06 03:52:42     -59.758202     43.970879   \n",
       "124433  643119964    True 2012-01-06 04:10:06     -59.761147     43.971104   \n",
       "124434  643119965    True 2012-01-06 04:25:06     -59.762070     43.971161   \n",
       "\n",
       "       manually-marked-outlier sensor-type individual-taxon-canonical-name  \\\n",
       "124430                     NaN         gps              Halichoerus grypus   \n",
       "124431                     NaN         gps              Halichoerus grypus   \n",
       "124432                     NaN         gps              Halichoerus grypus   \n",
       "124433                     NaN         gps              Halichoerus grypus   \n",
       "124434                     NaN         gps              Halichoerus grypus   \n",
       "\n",
       "        tag-local-identifier individual-local-identifier  \\\n",
       "124430                106724                        F357   \n",
       "124431                106724                        F357   \n",
       "124432                106724                        F357   \n",
       "124433                106724                        F357   \n",
       "124434                106724                        F357   \n",
       "\n",
       "                                               study-name  \n",
       "124430  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "124431  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "124432  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "124433  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "124434  Grey seals (Halichoerus grypus) at Sable Islan...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Extracting the values of a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "124435\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-59.97949982, -59.98273849, -59.98968887, ..., -59.7582016 ,\n",
       "       -59.76114655, -59.7620697 ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longitude = df['location-long'].values\n",
    "print(type(longitude))\n",
    "print len(longitude)\n",
    "longitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Finding how many individuals are tracked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['E 87', 'S0749', 'S0757', 'F104', 'S0753', 'F122', 'K 88', 'K 11',\n",
       "       'S0751', 'S0758', 'S0756', 'F532', 'F719', 'F367', 'F357'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"individual-local-identifier\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Extracting columns for a new data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>event-id</th>\n",
       "      <th>visible</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>location-long</th>\n",
       "      <th>location-lat</th>\n",
       "      <th>manually-marked-outlier</th>\n",
       "      <th>sensor-type</th>\n",
       "      <th>individual-taxon-canonical-name</th>\n",
       "      <th>tag-local-identifier</th>\n",
       "      <th>individual-local-identifier</th>\n",
       "      <th>study-name</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>677436629</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106705</td>\n",
       "      <td>E 87</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>677436630</td>\n",
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       "      <td>106705</td>\n",
       "      <td>E 87</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    event-id visible           timestamp  location-long  location-lat  \\\n",
       "0  677436629    True 2011-06-15 17:35:18     -59.979500     43.924957   \n",
       "1  677436630    True 2011-06-15 17:50:19     -59.982738     43.925488   \n",
       "\n",
       "  manually-marked-outlier sensor-type individual-taxon-canonical-name  \\\n",
       "0                     NaN         gps              Halichoerus grypus   \n",
       "1                     NaN         gps              Halichoerus grypus   \n",
       "\n",
       "   tag-local-identifier individual-local-identifier  \\\n",
       "0                106705                        E 87   \n",
       "1                106705                        E 87   \n",
       "\n",
       "                                          study-name  \n",
       "0  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "1  Grey seals (Halichoerus grypus) at Sable Islan...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>2011-06-15 18:05:32</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-06-15 18:21:27</td>\n",
       "      <td>-59.990337</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-06-15 18:36:31</td>\n",
       "      <td>-59.988968</td>\n",
       "      <td>43.925255</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  location-long  location-lat  \\\n",
       "0 2011-06-15 17:35:18     -59.979500     43.924957   \n",
       "1 2011-06-15 17:50:19     -59.982738     43.925488   \n",
       "2 2011-06-15 18:05:32     -59.989689     43.925827   \n",
       "3 2011-06-15 18:21:27     -59.990337     43.926136   \n",
       "4 2011-06-15 18:36:31     -59.988968     43.925255   \n",
       "\n",
       "  individual-local-identifier   event-id  \n",
       "0                        E 87  677436629  \n",
       "1                        E 87  677436630  \n",
       "2                        E 87  677436631  \n",
       "3                        E 87  677436632  \n",
       "4                        E 87  677436633  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf = df[[\"timestamp\",\"location-long\",\"location-lat\",\"individual-local-identifier\",\"event-id\"]]\n",
    "sdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Using the timestamp as an index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>E 87</td>\n",
       "      <td>677436633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     location-long  location-lat individual-local-identifier  \\\n",
       "timestamp                                                                      \n",
       "2011-06-15 17:35:18     -59.979500     43.924957                        E 87   \n",
       "2011-06-15 17:50:19     -59.982738     43.925488                        E 87   \n",
       "2011-06-15 18:05:32     -59.989689     43.925827                        E 87   \n",
       "2011-06-15 18:21:27     -59.990337     43.926136                        E 87   \n",
       "2011-06-15 18:36:31     -59.988968     43.925255                        E 87   \n",
       "\n",
       "                      event-id  \n",
       "timestamp                       \n",
       "2011-06-15 17:35:18  677436629  \n",
       "2011-06-15 17:50:19  677436630  \n",
       "2011-06-15 18:05:32  677436631  \n",
       "2011-06-15 18:21:27  677436632  \n",
       "2011-06-15 18:36:31  677436633  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.set_index(\"timestamp\",inplace=True)\n",
    "sdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Adding a column\n",
    "\n",
    "* Want to add a behavior index to the data. \n",
    "* Data is in a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location-long</th>\n",
       "      <th>location-lat</th>\n",
       "      <th>individual-local-identifier</th>\n",
       "      <th>event-id</th>\n",
       "      <th>behavior</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-15 17:35:18</th>\n",
       "      <td>-59.979500</td>\n",
       "      <td>43.924957</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436629</td>\n",
       "      <td>1.497271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 17:50:19</th>\n",
       "      <td>-59.982738</td>\n",
       "      <td>43.925488</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436630</td>\n",
       "      <td>1.439458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 18:05:32</th>\n",
       "      <td>-59.989689</td>\n",
       "      <td>43.925827</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436631</td>\n",
       "      <td>1.859729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 18:21:27</th>\n",
       "      <td>-59.990337</td>\n",
       "      <td>43.926136</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436632</td>\n",
       "      <td>-0.526980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 18:36:31</th>\n",
       "      <td>-59.988968</td>\n",
       "      <td>43.925255</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436633</td>\n",
       "      <td>1.665737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     location-long  location-lat individual-local-identifier  \\\n",
       "timestamp                                                                      \n",
       "2011-06-15 17:35:18     -59.979500     43.924957                        E 87   \n",
       "2011-06-15 17:50:19     -59.982738     43.925488                        E 87   \n",
       "2011-06-15 18:05:32     -59.989689     43.925827                        E 87   \n",
       "2011-06-15 18:21:27     -59.990337     43.926136                        E 87   \n",
       "2011-06-15 18:36:31     -59.988968     43.925255                        E 87   \n",
       "\n",
       "                      event-id  behavior  \n",
       "timestamp                                 \n",
       "2011-06-15 17:35:18  677436629  1.497271  \n",
       "2011-06-15 17:50:19  677436630  1.439458  \n",
       "2011-06-15 18:05:32  677436631  1.859729  \n",
       "2011-06-15 18:21:27  677436632 -0.526980  \n",
       "2011-06-15 18:36:31  677436633  1.665737  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behav = np.random.randn(len(sdf))\n",
    "sdf.insert(4,'behavior',behav)\n",
    "sdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Renaming columns\n",
    "\n",
    "* location-long --> longitude\n",
    "* location-lat --> latitude\n",
    "* individual-local-identifier -> individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>individual</th>\n",
       "      <th>event-id</th>\n",
       "      <th>behavior</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-15 17:35:18</th>\n",
       "      <td>-59.979500</td>\n",
       "      <td>43.924957</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436629</td>\n",
       "      <td>1.497271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 17:50:19</th>\n",
       "      <td>-59.982738</td>\n",
       "      <td>43.925488</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436630</td>\n",
       "      <td>1.439458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 18:05:32</th>\n",
       "      <td>-59.989689</td>\n",
       "      <td>43.925827</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436631</td>\n",
       "      <td>1.859729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 18:21:27</th>\n",
       "      <td>-59.990337</td>\n",
       "      <td>43.926136</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436632</td>\n",
       "      <td>-0.526980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-15 18:36:31</th>\n",
       "      <td>-59.988968</td>\n",
       "      <td>43.925255</td>\n",
       "      <td>E 87</td>\n",
       "      <td>677436633</td>\n",
       "      <td>1.665737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     longitude   latitude individual   event-id  behavior\n",
       "timestamp                                                                \n",
       "2011-06-15 17:35:18 -59.979500  43.924957       E 87  677436629  1.497271\n",
       "2011-06-15 17:50:19 -59.982738  43.925488       E 87  677436630  1.439458\n",
       "2011-06-15 18:05:32 -59.989689  43.925827       E 87  677436631  1.859729\n",
       "2011-06-15 18:21:27 -59.990337  43.926136       E 87  677436632 -0.526980\n",
       "2011-06-15 18:36:31 -59.988968  43.925255       E 87  677436633  1.665737"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf = sdf.rename(columns={\"location-long\":\"longitude\", \n",
    "                        \"location-lat\":\"latitude\", \n",
    "                        \"individual-local-identifier\": \"individual\"})\n",
    "sdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Writing to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",timestamp,longitude,latitude,individual,behavior,event-id\r\n",
      "0,2011-06-15 17:35:18,-59.97949982,43.92495728,E 87,2.0087604594,677436629\r\n",
      "1,2011-06-15 17:50:19,-59.98273849,43.92548752,E 87,1.62864137037,677436630\r\n",
      "2,2011-06-15 18:05:32,-59.98968887,43.92582703,E 87,-0.291197184652,677436631\r\n",
      "3,2011-06-15 18:21:27,-59.99033737,43.92613602,E 87,-0.2584335661,677436632\r\n",
      "4,2011-06-15 18:36:31,-59.9889679,43.92525482,E 87,-1.19146486878,677436633\r\n",
      "5,2011-06-15 18:51:23,-59.98394394,43.92564011,E 87,0.670850786398,677436634\r\n",
      "6,2011-06-15 19:06:20,-59.98566055,43.92499924,E 87,-0.780565483811,677436635\r\n",
      "7,2011-06-15 19:22:18,-59.987854,43.92406082,E 87,0.338285593938,677436636\r\n",
      "8,2011-06-15 19:37:18,-59.98072815,43.92603302,E 87,-0.427790732073,677436637\r\n"
     ]
    }
   ],
   "source": [
    "sdf.to_csv(\"seal-behav.csv\")\n",
    "!head \"seal-behav.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Hierarchical columns\n",
    "\n",
    "## Reorder the organization of the table\n",
    "\n",
    "* Index: timestamp\n",
    "* Columns: the individuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">longitude</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">behavior</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>individual</th>\n",
       "      <th>E 87</th>\n",
       "      <th>F104</th>\n",
       "      <th>F122</th>\n",
       "      <th>F357</th>\n",
       "      <th>F367</th>\n",
       "      <th>F532</th>\n",
       "      <th>F719</th>\n",
       "      <th>K 11</th>\n",
       "      <th>K 88</th>\n",
       "      <th>S0749</th>\n",
       "      <th>...</th>\n",
       "      <th>F532</th>\n",
       "      <th>F719</th>\n",
       "      <th>K 11</th>\n",
       "      <th>K 88</th>\n",
       "      <th>S0749</th>\n",
       "      <th>S0751</th>\n",
       "      <th>S0753</th>\n",
       "      <th>S0756</th>\n",
       "      <th>S0757</th>\n",
       "      <th>S0758</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:07:27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.961720</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.504393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:24:28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.960075</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.351223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:41:25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.956333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.772029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:57:39</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.957340</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.139709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 20:14:14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.965260</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.324814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude                                          \\\n",
       "individual               E 87 F104 F122 F357 F367 F532 F719 K 11 K 88   \n",
       "timestamp                                                               \n",
       "2011-06-11 19:07:27       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2011-06-11 19:24:28       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2011-06-11 19:41:25       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2011-06-11 19:57:39       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2011-06-11 20:14:14       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "                                ...  behavior                                 \\\n",
       "individual               S0749  ...      F532 F719 K 11 K 88     S0749 S0751   \n",
       "timestamp                       ...                                            \n",
       "2011-06-11 19:07:27 -59.961720  ...       NaN  NaN  NaN  NaN  0.504393   NaN   \n",
       "2011-06-11 19:24:28 -59.960075  ...       NaN  NaN  NaN  NaN  2.351223   NaN   \n",
       "2011-06-11 19:41:25 -59.956333  ...       NaN  NaN  NaN  NaN  1.772029   NaN   \n",
       "2011-06-11 19:57:39 -59.957340  ...       NaN  NaN  NaN  NaN  0.139709   NaN   \n",
       "2011-06-11 20:14:14 -59.965260  ...       NaN  NaN  NaN  NaN -0.324814   NaN   \n",
       "\n",
       "                                             \n",
       "individual          S0753 S0756 S0757 S0758  \n",
       "timestamp                                    \n",
       "2011-06-11 19:07:27   NaN   NaN   NaN   NaN  \n",
       "2011-06-11 19:24:28   NaN   NaN   NaN   NaN  \n",
       "2011-06-11 19:41:25   NaN   NaN   NaN   NaN  \n",
       "2011-06-11 19:57:39   NaN   NaN   NaN   NaN  \n",
       "2011-06-11 20:14:14   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd = sdf.pivot(columns='individual') #row, column, values (optional)\n",
    "sd[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>individual</th>\n",
       "      <th>E 87</th>\n",
       "      <th>F104</th>\n",
       "      <th>F122</th>\n",
       "      <th>F357</th>\n",
       "      <th>F367</th>\n",
       "      <th>F532</th>\n",
       "      <th>F719</th>\n",
       "      <th>K 11</th>\n",
       "      <th>K 88</th>\n",
       "      <th>S0749</th>\n",
       "      <th>S0751</th>\n",
       "      <th>S0753</th>\n",
       "      <th>S0756</th>\n",
       "      <th>S0757</th>\n",
       "      <th>S0758</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:07:27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.504393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:24:28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.351223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:41:25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.772029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:57:39</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.139709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 20:14:14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.324814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "individual           E 87  F104  F122  F357  F367  F532  F719  K 11  K 88  \\\n",
       "timestamp                                                                   \n",
       "2011-06-11 19:07:27   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-06-11 19:24:28   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-06-11 19:41:25   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-06-11 19:57:39   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-06-11 20:14:14   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "individual              S0749  S0751  S0753  S0756  S0757  S0758  \n",
       "timestamp                                                         \n",
       "2011-06-11 19:07:27  0.504393    NaN    NaN    NaN    NaN    NaN  \n",
       "2011-06-11 19:24:28  2.351223    NaN    NaN    NaN    NaN    NaN  \n",
       "2011-06-11 19:41:25  1.772029    NaN    NaN    NaN    NaN    NaN  \n",
       "2011-06-11 19:57:39  0.139709    NaN    NaN    NaN    NaN    NaN  \n",
       "2011-06-11 20:14:14 -0.324814    NaN    NaN    NaN    NaN    NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd['behavior'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Extracting longitude and latitude for each individual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>individual</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-15 18:05:32</th>\n",
       "      <td>E 87</td>\n",
       "      <td>-59.989689</td>\n",
       "      <td>43.925827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-26 15:03:18</th>\n",
       "      <td>E 87</td>\n",
       "      <td>-60.023972</td>\n",
       "      <td>43.928467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01 14:24:30</th>\n",
       "      <td>S0749</td>\n",
       "      <td>-59.932743</td>\n",
       "      <td>44.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-17 15:09:15</th>\n",
       "      <td>S0749</td>\n",
       "      <td>-60.100521</td>\n",
       "      <td>44.495800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-07 01:14:48</th>\n",
       "      <td>S0757</td>\n",
       "      <td>-59.275227</td>\n",
       "      <td>44.682209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01 04:11:42</th>\n",
       "      <td>S0757</td>\n",
       "      <td>-60.867977</td>\n",
       "      <td>44.248436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-20 09:16:11</th>\n",
       "      <td>F104</td>\n",
       "      <td>-60.041412</td>\n",
       "      <td>43.927155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-16 19:40:27</th>\n",
       "      <td>F104</td>\n",
       "      <td>-60.496716</td>\n",
       "      <td>44.621536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-20 04:25:05</th>\n",
       "      <td>S0753</td>\n",
       "      <td>-59.538494</td>\n",
       "      <td>44.334286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12 08:00:38</th>\n",
       "      <td>S0753</td>\n",
       "      <td>-59.769253</td>\n",
       "      <td>44.785385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-23 10:07:36</th>\n",
       "      <td>F122</td>\n",
       "      <td>-59.639961</td>\n",
       "      <td>44.038654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-09 14:19:54</th>\n",
       "      <td>K 88</td>\n",
       "      <td>-59.081959</td>\n",
       "      <td>43.875942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-18 02:43:41</th>\n",
       "      <td>K 88</td>\n",
       "      <td>-61.186897</td>\n",
       "      <td>43.768154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-21 08:05:02</th>\n",
       "      <td>K 11</td>\n",
       "      <td>-60.871934</td>\n",
       "      <td>45.303607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-05 10:54:25</th>\n",
       "      <td>K 11</td>\n",
       "      <td>-60.863003</td>\n",
       "      <td>44.177757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-05 20:35:02</th>\n",
       "      <td>S0751</td>\n",
       "      <td>-64.866554</td>\n",
       "      <td>46.786152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-15 15:28:05</th>\n",
       "      <td>S0758</td>\n",
       "      <td>-60.021687</td>\n",
       "      <td>43.928288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-27 03:14:26</th>\n",
       "      <td>S0758</td>\n",
       "      <td>-59.938644</td>\n",
       "      <td>43.758900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-05 18:11:54</th>\n",
       "      <td>S0756</td>\n",
       "      <td>-59.991898</td>\n",
       "      <td>44.616425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-16 07:19:25</th>\n",
       "      <td>S0756</td>\n",
       "      <td>-60.324448</td>\n",
       "      <td>45.068775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-17 14:07:51</th>\n",
       "      <td>F532</td>\n",
       "      <td>-60.800690</td>\n",
       "      <td>43.785324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-24 18:04:44</th>\n",
       "      <td>F532</td>\n",
       "      <td>-60.248558</td>\n",
       "      <td>43.836136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-02 12:27:12</th>\n",
       "      <td>F367</td>\n",
       "      <td>-60.241093</td>\n",
       "      <td>44.314720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-13 11:41:55</th>\n",
       "      <td>F357</td>\n",
       "      <td>-60.123775</td>\n",
       "      <td>43.957939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-28 19:59:47</th>\n",
       "      <td>F357</td>\n",
       "      <td>-60.425831</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    individual  longitude   latitude\n",
       "timestamp                                           \n",
       "2011-06-15 18:05:32       E 87 -59.989689  43.925827\n",
       "2011-09-26 15:03:18       E 87 -60.023972  43.928467\n",
       "2011-07-01 14:24:30      S0749 -59.932743  44.020000\n",
       "2011-10-17 15:09:15      S0749 -60.100521  44.495800\n",
       "2011-07-07 01:14:48      S0757 -59.275227  44.682209\n",
       "2011-12-01 04:11:42      S0757 -60.867977  44.248436\n",
       "2011-08-20 09:16:11       F104 -60.041412  43.927155\n",
       "2011-11-16 19:40:27       F104 -60.496716  44.621536\n",
       "2011-07-20 04:25:05      S0753 -59.538494  44.334286\n",
       "2011-10-12 08:00:38      S0753 -59.769253  44.785385\n",
       "2011-06-23 10:07:36       F122 -59.639961  44.038654\n",
       "2011-08-09 14:19:54       K 88 -59.081959  43.875942\n",
       "2011-11-18 02:43:41       K 88 -61.186897  43.768154\n",
       "2011-08-21 08:05:02       K 11 -60.871934  45.303607\n",
       "2011-12-05 10:54:25       K 11 -60.863003  44.177757\n",
       "2011-09-05 20:35:02      S0751 -64.866554  46.786152\n",
       "2011-07-15 15:28:05      S0758 -60.021687  43.928288\n",
       "2011-10-27 03:14:26      S0758 -59.938644  43.758900\n",
       "2011-07-05 18:11:54      S0756 -59.991898  44.616425\n",
       "2011-10-16 07:19:25      S0756 -60.324448  45.068775\n",
       "2011-06-17 14:07:51       F532 -60.800690  43.785324\n",
       "2011-12-24 18:04:44       F532 -60.248558  43.836136\n",
       "2011-07-02 12:27:12       F367 -60.241093  44.314720\n",
       "2011-08-13 11:41:55       F357 -60.123775  43.957939\n",
       "2011-10-28 19:59:47       F357 -60.425831  45.067749"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longLat = sdf[['individual', 'longitude', 'latitude']]\n",
    "longLat[2::5000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Same for the pivoted data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th colspan=\"10\" halign=\"left\">latitude</th>\n",
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       "      <th>E 87</th>\n",
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       "      <td>44.525101</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-14 09:15:25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.762409</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.997272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-23 05:14:59</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.120434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-30 19:32:57</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.618286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-07 22:27:20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.082142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>43.939621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-16 20:55:54</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.684322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.005180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-24 16:26:58</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.989624</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.936295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-02 06:08:14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.808678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-10 07:23:12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.929222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-19 15:56:32</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.160984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-27 10:38:24</th>\n",
       "      <td>-60.009354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 14:57:37</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.485497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-15 00:27:56</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.398602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-22 13:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.025097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-29 02:48:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.784321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-04 21:24:41</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.117374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.948349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-13 00:09:13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.440189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-20 05:29:35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.555164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-27 13:48:57</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.507767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-05 03:15:54</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.880478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.168858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-13 01:49:53</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.117413</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-20 18:14:31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.806515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.659252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     longitude                                        \\\n",
       "individual                E 87       F104 F122       F357       F367   \n",
       "timestamp                                                              \n",
       "2011-06-11 19:07:27        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-06-22 03:16:32        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-06-29 01:15:33        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-07-06 09:42:34        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-07-14 09:15:25        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-07-23 05:14:59        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-07-30 19:32:57        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-08-07 22:27:20        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-08-16 20:55:54        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-08-24 16:26:58        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-09-02 06:08:14        NaN        NaN  NaN -59.808678        NaN   \n",
       "2011-09-10 07:23:12        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-09-19 15:56:32        NaN        NaN  NaN        NaN -60.160984   \n",
       "2011-09-27 10:38:24 -60.009354        NaN  NaN        NaN        NaN   \n",
       "2011-10-06 14:57:37        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-10-15 00:27:56        NaN        NaN  NaN -60.398602        NaN   \n",
       "2011-10-22 13:20:00        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-10-29 02:48:50        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-11-04 21:24:41        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-11-13 00:09:13        NaN -60.440189  NaN        NaN        NaN   \n",
       "2011-11-20 05:29:35        NaN -60.555164  NaN        NaN        NaN   \n",
       "2011-11-27 13:48:57        NaN        NaN  NaN -60.507767        NaN   \n",
       "2011-12-05 03:15:54        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-12-13 01:49:53        NaN        NaN  NaN        NaN        NaN   \n",
       "2011-12-20 18:14:31        NaN        NaN  NaN        NaN        NaN   \n",
       "\n",
       "                                                                      \\\n",
       "individual                F532 F719       K 11       K 88      S0749   \n",
       "timestamp                                                              \n",
       "2011-06-11 19:07:27        NaN  NaN        NaN        NaN -59.961720   \n",
       "2011-06-22 03:16:32        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-06-29 01:15:33        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-07-06 09:42:34        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-07-14 09:15:25        NaN  NaN        NaN        NaN -59.762409   \n",
       "2011-07-23 05:14:59        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-07-30 19:32:57        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-08-07 22:27:20 -60.082142  NaN        NaN        NaN        NaN   \n",
       "2011-08-16 20:55:54        NaN  NaN        NaN -59.684322        NaN   \n",
       "2011-08-24 16:26:58        NaN  NaN        NaN        NaN -59.989624   \n",
       "2011-09-02 06:08:14        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-09-10 07:23:12        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-09-19 15:56:32        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-09-27 10:38:24        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-10-06 14:57:37        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-10-15 00:27:56        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-10-22 13:20:00        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-10-29 02:48:50        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-11-04 21:24:41        NaN  NaN        NaN -60.117374        NaN   \n",
       "2011-11-13 00:09:13        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-11-20 05:29:35        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-11-27 13:48:57        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-12-05 03:15:54        NaN  NaN -60.880478        NaN        NaN   \n",
       "2011-12-13 01:49:53        NaN  NaN        NaN        NaN        NaN   \n",
       "2011-12-20 18:14:31        NaN  NaN -60.806515        NaN        NaN   \n",
       "\n",
       "                       ...       latitude                             \\\n",
       "individual             ...           F532 F719       K 11       K 88   \n",
       "timestamp              ...                                             \n",
       "2011-06-11 19:07:27    ...            NaN  NaN        NaN        NaN   \n",
       "2011-06-22 03:16:32    ...            NaN  NaN        NaN        NaN   \n",
       "2011-06-29 01:15:33    ...            NaN  NaN        NaN        NaN   \n",
       "2011-07-06 09:42:34    ...            NaN  NaN        NaN        NaN   \n",
       "2011-07-14 09:15:25    ...            NaN  NaN        NaN        NaN   \n",
       "2011-07-23 05:14:59    ...            NaN  NaN        NaN        NaN   \n",
       "2011-07-30 19:32:57    ...            NaN  NaN        NaN        NaN   \n",
       "2011-08-07 22:27:20    ...      43.939621  NaN        NaN        NaN   \n",
       "2011-08-16 20:55:54    ...            NaN  NaN        NaN  44.005180   \n",
       "2011-08-24 16:26:58    ...            NaN  NaN        NaN        NaN   \n",
       "2011-09-02 06:08:14    ...            NaN  NaN        NaN        NaN   \n",
       "2011-09-10 07:23:12    ...            NaN  NaN        NaN        NaN   \n",
       "2011-09-19 15:56:32    ...            NaN  NaN        NaN        NaN   \n",
       "2011-09-27 10:38:24    ...            NaN  NaN        NaN        NaN   \n",
       "2011-10-06 14:57:37    ...            NaN  NaN        NaN        NaN   \n",
       "2011-10-15 00:27:56    ...            NaN  NaN        NaN        NaN   \n",
       "2011-10-22 13:20:00    ...            NaN  NaN        NaN        NaN   \n",
       "2011-10-29 02:48:50    ...            NaN  NaN        NaN        NaN   \n",
       "2011-11-04 21:24:41    ...            NaN  NaN        NaN  43.948349   \n",
       "2011-11-13 00:09:13    ...            NaN  NaN        NaN        NaN   \n",
       "2011-11-20 05:29:35    ...            NaN  NaN        NaN        NaN   \n",
       "2011-11-27 13:48:57    ...            NaN  NaN        NaN        NaN   \n",
       "2011-12-05 03:15:54    ...            NaN  NaN  44.168858        NaN   \n",
       "2011-12-13 01:49:53    ...            NaN  NaN        NaN        NaN   \n",
       "2011-12-20 18:14:31    ...            NaN  NaN  44.659252        NaN   \n",
       "\n",
       "                                                                            \\\n",
       "individual               S0749      S0751      S0753      S0756      S0757   \n",
       "timestamp                                                                    \n",
       "2011-06-11 19:07:27  43.936630        NaN        NaN        NaN        NaN   \n",
       "2011-06-22 03:16:32        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-06-29 01:15:33        NaN        NaN        NaN  44.672211        NaN   \n",
       "2011-07-06 09:42:34        NaN        NaN  44.525101        NaN        NaN   \n",
       "2011-07-14 09:15:25  43.997272        NaN        NaN        NaN        NaN   \n",
       "2011-07-23 05:14:59        NaN        NaN  44.120434        NaN        NaN   \n",
       "2011-07-30 19:32:57        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-08-07 22:27:20        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-08-16 20:55:54        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-08-24 16:26:58  43.936295        NaN        NaN        NaN        NaN   \n",
       "2011-09-02 06:08:14        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-09-10 07:23:12        NaN        NaN        NaN  43.929222        NaN   \n",
       "2011-09-19 15:56:32        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-09-27 10:38:24        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-10-06 14:57:37        NaN        NaN        NaN  44.485497        NaN   \n",
       "2011-10-15 00:27:56        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-10-22 13:20:00        NaN  48.025097        NaN        NaN        NaN   \n",
       "2011-10-29 02:48:50        NaN        NaN  44.784321        NaN        NaN   \n",
       "2011-11-04 21:24:41        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-11-13 00:09:13        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-11-20 05:29:35        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-11-27 13:48:57        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-12-05 03:15:54        NaN        NaN        NaN        NaN        NaN   \n",
       "2011-12-13 01:49:53        NaN        NaN        NaN        NaN  44.117413   \n",
       "2011-12-20 18:14:31        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "                                \n",
       "individual               S0758  \n",
       "timestamp                       \n",
       "2011-06-11 19:07:27        NaN  \n",
       "2011-06-22 03:16:32  43.643261  \n",
       "2011-06-29 01:15:33        NaN  \n",
       "2011-07-06 09:42:34        NaN  \n",
       "2011-07-14 09:15:25        NaN  \n",
       "2011-07-23 05:14:59        NaN  \n",
       "2011-07-30 19:32:57  43.618286  \n",
       "2011-08-07 22:27:20        NaN  \n",
       "2011-08-16 20:55:54        NaN  \n",
       "2011-08-24 16:26:58        NaN  \n",
       "2011-09-02 06:08:14        NaN  \n",
       "2011-09-10 07:23:12        NaN  \n",
       "2011-09-19 15:56:32        NaN  \n",
       "2011-09-27 10:38:24        NaN  \n",
       "2011-10-06 14:57:37        NaN  \n",
       "2011-10-15 00:27:56        NaN  \n",
       "2011-10-22 13:20:00        NaN  \n",
       "2011-10-29 02:48:50        NaN  \n",
       "2011-11-04 21:24:41        NaN  \n",
       "2011-11-13 00:09:13        NaN  \n",
       "2011-11-20 05:29:35        NaN  \n",
       "2011-11-27 13:48:57        NaN  \n",
       "2011-12-05 03:15:54        NaN  \n",
       "2011-12-13 01:49:53        NaN  \n",
       "2011-12-20 18:14:31        NaN  \n",
       "\n",
       "[25 rows x 30 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd[['longitude', 'latitude']][::5000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Extracting Data from a Data Frame with a Condition\n",
    "\n",
    "- Extracting Seal F719 from the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event-id</th>\n",
       "      <th>visible</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>location-long</th>\n",
       "      <th>location-lat</th>\n",
       "      <th>manually-marked-outlier</th>\n",
       "      <th>sensor-type</th>\n",
       "      <th>individual-taxon-canonical-name</th>\n",
       "      <th>tag-local-identifier</th>\n",
       "      <th>individual-local-identifier</th>\n",
       "      <th>study-name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105100</th>\n",
       "      <td>643100631</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-14 19:59:29</td>\n",
       "      <td>-59.826447</td>\n",
       "      <td>43.937832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106721</td>\n",
       "      <td>F719</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105101</th>\n",
       "      <td>643100632</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-14 20:32:38</td>\n",
       "      <td>-59.829632</td>\n",
       "      <td>43.931831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106721</td>\n",
       "      <td>F719</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105102</th>\n",
       "      <td>643100633</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-14 20:48:07</td>\n",
       "      <td>-59.813534</td>\n",
       "      <td>43.936577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106721</td>\n",
       "      <td>F719</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105103</th>\n",
       "      <td>643100634</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-14 21:18:03</td>\n",
       "      <td>-59.812885</td>\n",
       "      <td>43.942875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106721</td>\n",
       "      <td>F719</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105104</th>\n",
       "      <td>643100635</td>\n",
       "      <td>True</td>\n",
       "      <td>2011-06-14 21:33:03</td>\n",
       "      <td>-59.808041</td>\n",
       "      <td>43.941067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gps</td>\n",
       "      <td>Halichoerus grypus</td>\n",
       "      <td>106721</td>\n",
       "      <td>F719</td>\n",
       "      <td>Grey seals (Halichoerus grypus) at Sable Islan...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         event-id visible           timestamp  location-long  location-lat  \\\n",
       "105100  643100631    True 2011-06-14 19:59:29     -59.826447     43.937832   \n",
       "105101  643100632    True 2011-06-14 20:32:38     -59.829632     43.931831   \n",
       "105102  643100633    True 2011-06-14 20:48:07     -59.813534     43.936577   \n",
       "105103  643100634    True 2011-06-14 21:18:03     -59.812885     43.942875   \n",
       "105104  643100635    True 2011-06-14 21:33:03     -59.808041     43.941067   \n",
       "\n",
       "       manually-marked-outlier sensor-type individual-taxon-canonical-name  \\\n",
       "105100                     NaN         gps              Halichoerus grypus   \n",
       "105101                     NaN         gps              Halichoerus grypus   \n",
       "105102                     NaN         gps              Halichoerus grypus   \n",
       "105103                     NaN         gps              Halichoerus grypus   \n",
       "105104                     NaN         gps              Halichoerus grypus   \n",
       "\n",
       "        tag-local-identifier individual-local-identifier  \\\n",
       "105100                106721                        F719   \n",
       "105101                106721                        F719   \n",
       "105102                106721                        F719   \n",
       "105103                106721                        F719   \n",
       "105104                106721                        F719   \n",
       "\n",
       "                                               study-name  \n",
       "105100  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "105101  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "105102  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "105103  Grey seals (Halichoerus grypus) at Sable Islan...  \n",
       "105104  Grey seals (Halichoerus grypus) at Sable Islan...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"individual-local-identifier\"] == \"F719\"][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>behavior</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>individual</th>\n",
       "      <th>F719</th>\n",
       "      <th>F104</th>\n",
       "      <th>F719</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:07:27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:24:28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:41:25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 19:57:39</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-11 20:14:14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    behavior latitude longitude\n",
       "individual              F719     F104      F719\n",
       "timestamp                                      \n",
       "2011-06-11 19:07:27      NaN      NaN       NaN\n",
       "2011-06-11 19:24:28      NaN      NaN       NaN\n",
       "2011-06-11 19:41:25      NaN      NaN       NaN\n",
       "2011-06-11 19:57:39      NaN      NaN       NaN\n",
       "2011-06-11 20:14:14      NaN      NaN       NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd[[('behavior', \"F719\"), ('latitude', \"F104\"),('longitude', \"F719\")]][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>behavior</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>individual</th>\n",
       "      <th>F719</th>\n",
       "      <th>F719</th>\n",
       "      <th>F719</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-06-14 19:59:29</th>\n",
       "      <td>-0.026851</td>\n",
       "      <td>43.937832</td>\n",
       "      <td>-59.826447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-14 20:32:38</th>\n",
       "      <td>-0.686117</td>\n",
       "      <td>43.931831</td>\n",
       "      <td>-59.829632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-14 20:48:07</th>\n",
       "      <td>-0.984824</td>\n",
       "      <td>43.936577</td>\n",
       "      <td>-59.813534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-14 21:18:03</th>\n",
       "      <td>-1.354056</td>\n",
       "      <td>43.942875</td>\n",
       "      <td>-59.812885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-14 21:33:03</th>\n",
       "      <td>0.057907</td>\n",
       "      <td>43.941067</td>\n",
       "      <td>-59.808041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     behavior   latitude  longitude\n",
       "individual               F719       F719       F719\n",
       "timestamp                                          \n",
       "2011-06-14 19:59:29 -0.026851  43.937832 -59.826447\n",
       "2011-06-14 20:32:38 -0.686117  43.931831 -59.829632\n",
       "2011-06-14 20:48:07 -0.984824  43.936577 -59.813534\n",
       "2011-06-14 21:18:03 -1.354056  43.942875 -59.812885\n",
       "2011-06-14 21:33:03  0.057907  43.941067 -59.808041"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd[[('behavior', \"F719\"), \n",
    "    ('latitude', \"F719\"),\n",
    "    ('longitude', \"F719\")]].dropna()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Simple Plotting\n",
    "\n",
    "- Plot the datafrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113b955d0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3U7ZsWerWretcf9NNNzFq1Cg++eSTHEU7MTHRY18yMjKIiIjIcx8LQodHlPJT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\nTUSkIRzaREQawqFNRKQh/wefgjwDVM1ucAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116740890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Plot the behavior of all seals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gcjGO16vH3KnH3CnDfKnH3CnDfPkuFlxERERELsY5XERERD6Ic7jU88g5XEIIPyHEL0II\nfW7fTURERORiuhdcAJ4EcASAV5bZHK9Xj7lTj7lThvlSj7lThvlqXGxsLIKDg2E2m2E2mxEWFoYz\nZ85g5syZ6NatG/z8/JCQkFDnuHfeeQfR0dEIDw/Hww8/jPLy8jpttm3bBpPJhH/84x/ueCl2dC24\nhBDtAdwJ4CMADrvgiIiIyHcIIbB+/XoUFRWhqKgIhYWFiI6ORt++fbFw4UL069cPQtiXDN999x3m\nz5+PLVu2IDMzE2lpaZg7d65dm4qKCjz55JO46aab6hzvDnr3cL0D4P8BsOgch8sMGzZM7xA8FnOn\nHnOnDPOlHnOnDPOl3qxZszBixAg0b968zr6EhAQ88sgj6N69OyIiIvCPf/wDS5cutWvz9ttv4/bb\nb0dcXJwuc9f83f6MVkKIuwDkSil/EUIMq6/d9OnTERsbCwCIiIhA3759bW/Ymq5ZPuZjPuZjPuZj\nPlb+2KiUFkRHjhzBPffcY3vcu3dv5OTkID8/H5GRkcjMzMSSJUuwb98+PP7445rEWJPD5ORkZGRk\nNNpet6sUhRBvAJgCoBJAcwBhANZIKafWauPxVykmJyfb3uCkDHOnHnOnDPOlHnOnjJHy1dhViuJV\nbYbd5Fxlf8djY2Nx/vx5+PtX9wkNHz4ciYmJtv1DhgzBjBkzMHWqrVxAly5dsHDhQtx6660AqocP\nAwMDkZGRgZiYGIwZMwaTJ0/GuHHj8OCDD6JDhw547bXXVL8mNVcp6tbDJaV8AcALACCEuBnAs7WL\nLSIiItKP0kJJK0IIrF27FiNGjHD6mNDQUBQWFtoe1/zbbDZj3bp1KC4uxrhx4wBU95751JCiA57d\nlVUPo3yT8UTMnXrMnTLMl3rMnTLMl2v06NED+/fvx9ixYwEABw4cQJs2bRAZGYmkpCTs3bsX0dHR\nAICCggL4+fnh0KFD+Oqrr9wWo8ltz9QAKeU2KeVoveMgIiIiY6qoqEBpaSksFgvKy8tRWlpq66ma\nOnUqPv74Y/z222+4ePEi5s2bh+nTpwMA5s2bh+PHj+PAgQPYv38/Ro8ejZkzZ2LJkiVujd8QBZc3\nM/rERCNj7tRj7pRhvtRj7pRhvtS75ZZbEBwcjF27dmHmzJkIDg7Gjh07AAC33XYbnnvuOQwfPhwd\nO3ZEp06d8OqrrwKoHm5s3bo1WrdujaioKAQFBSEkJAQRERFujd9IQ4pERETk49LT0x1ub6xYffrp\np/H00083en5392zV4L0UiYiIfBDvpaieR95LkYiIiMjbseByMY7Xq8fcqcfcKcN8qcfcKcN8+S4W\nXEREREQuxjlcREREPohzuNTjHC4iIiIiA2LB5WIcr1ePuVOPuVOG+VKPuVOG+fJdLLiIiIiIXIxz\nuIiIiHwQ53CpxzlcRERE5NFiY2MRHBwMs9kMs9mMsLAwnDlzBjNnzkS3bt3g5+eHhIQEu2MSEhJw\nww03IDw8HB06dMCcOXNQVVUFACgvL8fDDz+M2NhYhIWF4brrrsOmTZvc/rpYcLkYx+vVY+7UY+6U\nYb7UY+6UYb4aJ4TA+vXrUVRUhKKiIhQWFiI6Ohp9+/bFwoUL0a9fPwhh34l0+fJlvPfeezh//jx2\n796NpKQkvPXWWwCAyspKxMTEYPv27SgsLMS8efMwfvx4ZGZmuvV18V6KREREZHizZs0CADRv3rzO\nvscee8z277Zt22LSpEnYunUrACA4OBhz58617R85ciQ6deqEffv2oWPHji6O+g/s4XKxYcOG6R2C\nx2Lu1GPulGG+1GPulGG+nNPUuWXbtm1Dz549He7LycnBsWPH0KNHjyY9h1IsuIiIiKguIbT5UUhK\nibvvvhuRkZGIjIzEvffeq+j4Tz75BPv27cOzzz5bZ19FRQUmTZqE6dOn45prrlEcW1Ow4HIxjter\nx9ypx9wpw3ypx9wp41H5klKbH4WEEFi7di3y8/ORn5+PxMREp4/9+uuv8cILL2Djxo1o0aKF3T6L\nxYIpU6agefPmWLBggeK4mopzuIiIiMjjbdq0CTNnzsS3335bZ7hQSomHH34YeXl5+Pbbb+Hn5+f2\n+FhwuRjH69Vj7tRj7pRhvtRj7pRhvtSrqKhAVVUVLBYLysvLUVpaisDAQAghsGXLFkyaNAlr167F\nDTfcUOfYv/zlL0hNTcXmzZsRGBioQ/QcUiQiIiIPcMsttyA4OBi7du3CzJkzERwcjB07dgAA5s2b\nh6KiItxxxx229btGjhwJAMjMzMTixYtx4MABtGnTxrZ/5cqVbo2fK827WHJyMr/RqMTcqcfcKcN8\nqcfcKWOkfHGlefW40jwRERGRAbGHi4iIyAexh0s99nARERERGRALLhfzqDVXDIa5U4+5U4b5Uo+5\nU4b58l0suIiIiIhcjHO4iIiIfBDncKnHOVxEREREBsSCy8U4Xq8ec6cec6cM86Uec6cM8+W7WHAR\nERERuRgLLhczyorCnoi5U4+5U4b5Uo+5U4b5alxsbCyCg4Ntt+AJCwtDdnY2Bg8ejKuuugqRkZEY\nNGgQdu7caTtm6dKl8PPzsx1jNpuxfft2AEBWVpbddrPZDJPJhHfeecetr4s3ryYiIiLDEEJg/fr1\nGDFihG1bWVkZli5diq5duwIA1q5di1GjRiEvLw8mU3Xf0eDBg21FVm0xMTEoKiqyPc7IyECXLl1w\n3333ufiV2GMPl4txvF495k495k4Z5ks95k4Z5kudwMBAW7FlsVhgMpmQn5+PCxcu2No4e8VlQkIC\nbr75ZsTExLgk1vqw4CIiIiJDqa946t27N4KCgjBmzBjMmDEDV111FYDqXrFffvkFrVq1QlxcHObN\nm4eqqiqH5122bBmmTZvm0vgd4TpcREREPqixdbiERr1xUuG8tdjYWJw/fx7+/tWznoYPH47ExETb\n/vLyciQmJqK8vBxTp04FAKSnp8NkMqFjx444dOgQJkyYgClTpuD555+3O/eOHTtw5513IicnB8HB\nwapfk5p1uFhwERER+SCjLnzaqVMnfPzxx3ZzuBy59tprsWrVKvTu3bvOvs8//xxvvvkm9u7da7f9\nkUceQVVVFZYsWdKkGLnwqQFxvF495k495k4Z5ks95k4Z5ks7FRUVSE9Pr3f/lQXR5cuXsXr1al2G\nEwEWXERERGRwu3fvRkpKCsrLy3H58mXMnz8fubm5GDBgAABg48aNyMnJAQCkpqZi3rx5uPvuu+3O\n8dVXX6FFixa6Lc3BZSFcjGuuqMfcqcfcKcN8qcfcKcN8qVNWVobZs2cjLS0NAQEB6N27NzZs2IA2\nbdoAALZs2YIHH3wQxcXFiIqKwpQpU/DCCy/YnWPZsmWYMmWKHuED4BwuIiIin2TUOVyegHO4DIjj\n9eoxd+oxd8owX+oxd8owX76LBRcRERGRi3FIkYiIyAdxSFE9DikSERERGRALLhfjeL16zJ16zJ0y\nzJd6zJ0yzJfvYsFFRERE5GKcw0VEROSDOIdLPc7hIiIiIjIgFlwuxvF69Zg79Zg7ZZgv9Zg7ZZgv\n38WCi4iolkqLBfuKivQOg8hnxcbGIjg4GGazGWazGWFhYcjOzsbgwYNx1VVXITIyEoMGDcLOnTvt\njktLS8Ndd92FsLAwtGrVCnPmzLHtCw0NtZ3PbDbD398fs2fPduvr4hwuIqJa8isqcPXu3ciPj9c7\nFCKXMuocrk6dOuHjjz/GiBEjbNvKysqQlZWFrl27AgDWrl2Lhx56CHl5eTCZTCgvL0f37t3x17/+\nFY8++ij8/Pxw9OhR9OrVq875S0pK0KZNG2zcuBHxKj/nnMNFREREXicwMNBWbFksFphMJuTn5+PC\nhQsAgKVLl6J9+/Z46qmnEBQUhGbNmjkstgBg9erViIqKUl1sqcWCy8U4Xq8ec6cec6cM86Uec6cM\n8+Wc+nreevfujaCgIIwZMwYzZszAVVddBQDYtWsXOnbsiDvvvBOtWrXC8OHDcejQIYfnSEhIwNSp\nU10We3383f6MREREZHjJIlmT8wyTwxS1l1Li7rvvhr9/dYkyfPhwJCYmAgB+/fVXlJeXIzExEeXl\n5bZjTp48ieTkZKxbtw5/+tOf8O6772LMmDFITU1FQECArV1mZia2b9+OJUuWNP2FKcQ5XEREtXAO\nF/kKT5rD5ci1116Lzz//HL169cKYMWNQXFyMpKQk2/6IiAjs2LHDbmhx3rx5SEpKwtatW5sUI+dw\nERERkU+oqKhAWloaAKBPnz52++orJJctW4Zp06a5PDZHWHC5GMfr1WPu1GPu7H2d+jWe3vR0vfuZ\nL/WYO2WYL3V2796NlJQUlJeX4/Lly5g/fz5yc3MxYMAAAMDkyZOxa9cuJCUloaqqCu+++y5atWqF\n7t27286xc+dOnD59GuPGjdPlNXAOFxF5veLyYuRdytM7DCJSqaysDLNnz0ZaWhoCAgLQu3dvbNiw\nAW3atAEAXHPNNfj000/x2GOPITc3F9dffz2++eYb2zwwoLp367777kNISIgur4FzuIjI633666fY\n9PsmfHrvp4225Rwu8hVGncPlCTiHi4iIiMiAWHC5GMfr1WPu1GPulGG+1GPulGG+fBcLLiIiIiIX\n4xwuIvJ6nMNFVBfncKnHOVxEREREBsSCy8U4Xq8ec6cec6cM86Uec6cM8+W7WHARERERuRjncBGR\n11M6h6vTrl24OGSIGyIj0g/ncKnHOVzkUE55OeZnZekdBpFHcPibkoioiVhwuZgRxuvzKyqw5MwZ\nvcNQzAi581TMnTLMl3rMnTLMV+NiY2MRHBwMs9kMs9mMsLAwnDlzBiaTCaGhobbtM2fOtB3zzjvv\noHPnzggPD0e7du3wzDPPoKqqCgCQl5eH+++/H+3atUNERATi4+OxZ88et78uFlxERERkGEIIrF+/\nHkVFRSgqKkJhYSGio6MBAAcPHrRtX7x4se2YMWPG4JdffkFBQQEOHTqEAwcO4P333wcAFBcXY8CA\nAdi3bx/y8/Mxbdo0jBw5EiUlJW59XSy4XGzYsGF6h+CxmDv1mDtlmC/1mDtlmK+msVgsDrdfffXV\nCAsLs7URQuDEiRMAgE6dOuGpp55CVFQUhBCYMWMGysvLcezYMbfFDbDgIiIiIoOpbzL/0KFDER0d\njfvuuw+ZmZl2+z777DOEh4ejVatWOHjwIB599FGH59i/fz/Ky8vRpUsXzeNuCAsuDf1y5hdMXD3R\nbhvH69Vj7tRj7pRhvtRj7pTxpHwlJwtNfpSSUuLuu+9GZGQkIiMjce+99wIAtm/fjszMTKSmpqJt\n27a46667bPO0AOCBBx5AQUEBjh07hkcffRStW7euc+7CwkJMmTIFr7zyCsxms/rkqODv1mfzcqWV\npcgsyGy8oSstXw6EhwOjR+sbBxla+tx0+Jn9EPNsjN6hEJFBDRumz5IRQgisXbsWI0aMsNseb73d\nVnh4ON577z2EhYUhNTUVPXr0sGvXpUsX9OjRA7NmzcKaNWts2y9fvoxRo0Zh0KBBmDNnjutfyBVY\ncLmY28frf/0VcFDVeyLOdVCvsdxlvpYJv1AWXDX4XlOPuVOG+dKGlLLBdcQqKipsc7gAoKysDHff\nfTdiYmLwwQcfuCtMOxxSJCIiIkM7cuQI9u/fj6qqKhQXF+Nvf/sb2rdvj+7duwMAPvroI+Tl5dna\n/utf/8Kf//xnANXF19ixYxEcHIylS5fq9RJYcLmaJ43XGw1zpx5zpwzzpR5zpwzzpU5OTg4mTpyI\n8PBwdO7cGVlZWVi/fj38/PwAADt37kSvXr0QGhqKkSNHYuTIkXjjjTds+zZs2IAffvgBERERtnW8\nfvzxR7e+Bg4pEhERkWGkp6fX2TZ8+HCkpqbWe8wnn3xS776bb7653uUk3Ik9XC7G8Xr1mDv1mLtq\nJ08ChYWNt2O+1GPulGG+fBcLLiLyWn/7G7Bxo95RkCfauBEYOVLvKMibsOByMY7Xq8fcqefu3M2Z\nA2zZ4tan1BTfa+p5c+6+/Rao5yI41bw5X9QwFlxE1GTHjwMXL+odBRGRcbHgcjGO16vH3KnH3CnD\nfKnH3CnDfPkuFlxERFfQZ31tIvJmLLhcjOP16jF36ixeDCxZktxgG/9IfzRr18w9AXmA2u81IZTf\n+82X8XOu3JEyAAAgAElEQVSqDPPlu1hwEXmZb74Bzp5tuE3bR9uizbQ27gmIPNLBnIOYlDhJ7zCI\nvAYLLhfjeL16zJ16vXoN0zsEj8L3Wl2XKi7h9wu/N9qOuVOG+fJdLLiIiIjIMGJjYxEcHGy7BU9Y\nWBi2b99ue1zzYzKZ8NVXXwEADh06hNtuuw2tWrWCyVS3tPntt98wYsQIREREoGvXrvj666/d/bJY\ncLkax+vVY+7UO3gwWe8QPArfa+oxd8owX40TQmD9+vUoKipCUVERCgsLMXToUNvjoqIirF+/HqGh\nobj99tsBAM2aNcPEiRPx8ccf1zlfZWUlxowZg9GjRyM/Px+LFy/G5MmTcfz4cbe+LhZcRERE5FGW\nLl2KcePGISgoCABwzTXX4MEHH8S1115bp21qairOnDmDp556CkIIDB8+HIMHD8by5cvdGrPn3Lx6\n4UKgXTtgzBi9I1GE4/XqMXfqcQ6XMnyvqcfcKcN8OUc2sMR/SUkJ1qxZg/Xr16s+v8ViwaFDh1Qf\nr4auPVxCiA5CiK1CiMNCiENCiNn1Nv7tNyAry43RERER+S4hhCY/SkkpcffddyMyMhKRkZG49957\n7fYnJiaiVatWGDp0qFPni4uLQ+vWrfHmm2+ioqIC33//PbZv347Lly8rjq0p9B5SrADwtJSyB4Cb\nADwuhOiuc0ya4ni9elfmzlJpwQ7zDn2C8TDunsOl9f3m3I2fU/WYO2U8KV9SSk1+lBJCYO3atcjP\nz0d+fj4SExPt9ickJGDq1KlOny8gIABff/01NmzYgOjoaLzzzjsYP3482rdvrzi2ptC14JJSnpVS\n7rf+uxjAbwDa1tPYjZGRUVVdrtI7BKoH1wv1Xh9++CH27Nmjdxi6Kys7raqAIO1kZ2dj27Ztigou\nAOjVqxeSk5Nx7tw5bNy4ESdOnMCNN97ooigd07uHy0YIEQvgOgC7G2jkpmi0w/F69Zg79TiHSxm+\n1xq2ZcsWnDhxwuE+X8rdTz91AGBp0jl8KV+usHz5cgwePBidOnWqs6+0tBTl5eUAgLKyMpSVldn2\nHTx4EKWlpbh06RLeeust5OTkYPr06e4KG4BBCi4hRCiA1QCetPZ0EREREdlZvnw5pk2bVmd7RkYG\ngoOD0bNnTwghEBQUhO7du9sd17ZtW0RFRWHr1q344YcfEBAQ4M7Q9b9KUQgRAGANgE+llHVWIps+\nfTpiY2OB3bsRceYM+vbsafuGUDMWbpTH+37ah4LUAlvsycnJ2L9/P5566in3xZOVhWGtW9vtb9O/\nvy75aOrjd999F3379v1j/7Zk/Cp/xTAYIz6jPgaG4eDBZISGosH2P6X9hPTk9CY/n5/fMKSkAP36\nJSMyUtnxP1y4gMgbbsCzMTEuyUdubnU+ACDnUA6SWyQ7bF97Xs118fGaPb8nP/75p59ReLQQNY4c\nOYLk5Lr5qzlG73i1fvzrrzWv74/9+/dL3HwzmnT+mm16v77a8RhNenp6vft+++03h9tjY2NhsdTf\n+/jvf/8b//73v5scW201OUxOTkZGRkbjB2g1KU7lRDoBYBmAd+rZL21mzZJywQJpZDuzdsqbPrrJ\nbtvWrVvdG8Szz0r573/bbfqtuFjG7drl3jg0cGXuqiqq5Fa/rQ7b0h9GjpTyjTe2NtjmxPMnZMYb\nGZo837ZtUrZsKWViovJj/5OVJZ86flyTOBwZP17KVaukXH5guZy0ZlK97Wq/1y5WVMiw7dtdFpOn\n2JW9S9744Y1SSiknTpwoP/vsM4ft3P47zk2+/VZKQEqL5Y9tW7eapMVS2aTzGilfdn9jSZH6cmfd\n7rDm0XtIcTCAyQCGCyF+sf7crnNMmqr5NkHKMXfqcQ6XMnyvqcfcKcN8+S5dhxSllCkwyDwyIqIa\nvA6NiLTmOcWOh16Ka+RxcqNj7tTjvRSVqf1e87xrofXFz6kyzJfv8pyCC/DIZSGMoKiqCkfdvKKu\nS3hmza2J0sxSFOwqaLwhEREZkmcVXB7ICOP1p2qtReJJHOVOzW0ivEFBSgFO/e8pp9tzDpcyRvic\nGtmqVauwefNmh/uYO2WYL9/lEQVXZXElLOVcYZyIvE/mxUykZKXoHUajPvnkE71DIPJoHlFwZb+Z\njeIDJR45pMjxevWYO/U4h0sZPd9ru0/txvu739ft+ZuKn1NlmC/f5REFFxEZn5rvQz48LY+IfIzH\nFFzCQ69S5Hi9esydep40h8sI/dZ8r6nH3CnDfDknNjYWSUlJtserVq1CixYtsGPHjjptKyoqMHbs\nWHTq1Akmkwnbtm2z279161YMHz4cERERDu/B6C4eU3AB8MghRSIA6NoVqOeOFEREdAUhhO0iqYSE\nBDzxxBP49ttvMWTIEIfthw4dik8//RRt2rSpc3FVaGgoHnnkEbz55psuj7shnlVweSCO16vnTbn7\n/Xdgzx73PR/ncCnjTe81d2PulGG+nCelxAcffIBnn30W33//PW666SaH7QICAjB79mwMHjwYfn5+\ndfb3798fkyZN0rV3CzDAzauJiIiIrrRw4UL8+OOP2LJlC3r16qV3OE3mQQUX53D5GuZOPU+aw2UE\nfK+px9wp40n50moWj5op2FJKbN68GSNGjEDPnj21CURnnjWkyDlcvs0za24iIo8kpTY/agghsGjR\nIhw9ehSPPPKIti/MBX7+GahqZLlQzyq4rnCqrAz/e/Kk3mE0iOP16jnMHWtup3AOlzL8nKrH3CnD\nfDkvKioKSUlJ2LFjB2bNmqV3OA264QYgMbHhNh5TcJW17QV07my3Lae8HEvOntUpIiIibV28uAOX\nL2foHQaRYURHRyMpKQmbNm3CM888U2+7srIylJaW1vk3UD08WVpaioqKCkgpUVZWhvLycs1jbeyU\nHlNwFV9/P/DnP+sdhmKeNF5vNMydet48h+vYrGMo+rlI03Ma5b126tR/UVi4S+8wFDFK7jwF86Vc\nhw4dsGXLFqxevRovvviiwzZxcXEIDg7G6dOncdtttyEkJARZWVkAgG3btiE4OBgjR45EdnY2goKC\ncPvtt7vzJQDwqEnzRMZy7NIlvJWdjcVxcXqH4rHUTO+49NslVBZWah4LERlHenq63ePY2FhbAeVI\nRkZGvfuGDRsGi8WiVWiqeUwPl6fieL16Rs/dmrw8fHjmjN5hOOTuOVxNuRGEEablXfle4/UZzjP6\n59RomC/fxYJLjcpKoH17vaMgMhRvuYjYS14GERkMCy61nJysz/F69Zi7P0iLhKx0vt+l0TlcFgtQ\npX8Xu1Hwvea8k4Un8fuF322PmTtlmC/fxYKLyAP8/tTvyPsiT7sT/vQTUOvGsETOWn1kNRbsWaB3\nGEQexyMLrgsXvoOUnvHtnOP16tXJXa0OnktVVSgzwCRId6m8oGySONfhUoafU/WYO2WYL+8zf371\nfxubVmH4gmt3YeEfD159FThwAAcPjoKUvErJJ1nf0LOPH8dyD1uDraBA7wiIiEhr33/vXDvDF1w3\n7dv3x4OUFCA3F41dQ5SVBXTr5tq4nMXxevW8LXfZ2e57Lm9eh8sVvO291pB164CXXlJ/fJWlChVV\nFbbHvpQ7LTBfvsvwBVf96u+7kxK4fNmNoRA56S9/AUpK9I6CfNmFC00r/hMOJGDh3oXaBUTk4Zy9\nQtsjCy7ZlEV/3Izj9ep5Y+5WrWr89g9a0GsOV1oa8M9/Ot/eKJ9lZ99rJ07MQXb2u64NxsN44+fU\nlZgv3+WRBVc1rpZDZDR5ecA33yg7xiif5ONPHW+0TfXc0SrXB+MEKatw4cIPqo9/6623cODAAQ0j\nItJObGwskmpdSb1q1Sq0aNECO3bscNj+iy++QPfu3REWFoYePXpg7dq1dvtfeukltG/fHhERERg+\nfDiOHDmiWaxe3cPlSetAc7xePeZOPc7hUmbYsGE49d4pvcNQxGKpwMGDoxQfV1kJnD4NJCUl4fTp\n002Og59TZZgv5wghIKyVTEJCAp544gl8++23GDJkSJ22p06dwpQpU/Duu++isLAQb775Jh544AGc\nO3cOQHUxtmTJEqSkpODChQsYOHAgpkyZomGs9v+tj8cUXP7nM4D8fOsjC6qqtL15rVaOtpmMixUV\njTck8gFSViEn5zO9w6Ba3n0X2LxZ7yiIGielxAcffIBnn30W33//PW666SaH7U6ePImIiAjcdttt\nAIA777wTISEhOHHiBIDq+yzGx8cjNjYWJpMJkyZN0rSH6494G97vMQVXi+/fAH7+2fa4sHB3k895\ncftF5CfnN95QgaLmHVFeK+scr1ePuVPPKOtwWSwVSE19SO8wGuUr77UzZ4BDh7Q9p6/kTivMl/MW\nLlyIuXPnYsuWLejXr1+97fr374/u3btj3bp1qKqqwldffYXmzZujd+/eAICJEyfixIkTOH78OCoq\nKpCQkIA77rhDszidHVL01+wZ3UxocOO2i8kXISslIodFahDRHzxnwJN8VUFWGFp00nDlenj2vRSl\nxQLhOd8/Vbt0yfm2Fy5fwJ5Te1wXDBmeeFWbD7Wcq/yvopQSmzdvxogRI9CzZ88G25pMJkyZMgUP\nPPAASktL0axZM6xevRpBQUEAgDZt2mDw4MGIi4uDn58fYmJi7OaHuYvHFlzunmpbdqYMqVNT0eeH\nPg22E1LaXXnF8Xr1rsydUa5oq/F9vra9o1pqaA5XxfkKFGSGa15webJhN9+MbXA8GddXpZ5Ldapd\nfb/jsrPfQdu2j8HPL0jDqDyfJ/1NUFMoaUUIgUWLFuH111/HI488go8//rjetps3b8acOXOwbds2\n9OvXD3v37sXo0aOxceNG9OnTB6+99hr27t2LkydPok2bNli+fDlGjBiBw4cP24qypsXqXDvv/0qn\nEVkhcemoc18PjVUWeBkD9aIcdnJBLQ3mJWtKHmv8ajyiGmq/6GRmvgaLpVTjaMiXREVFISkpCTt2\n7MCsWbPqbbd//34MHTrUNux4ww03YMCAAbZerAMHDmDixIlo27YtTCYTpk2bhvz8fPz2229ueR01\nPLjgcv9f3ry8PKSkpDTSStoVXByvV6/e3Bn0HoorV67EypUr62yvWXfLnYVXg3O4Tp5UdK6CAiAn\np2nxaCnuoziY+5s1PeeV7zWj9aYaGX/HKcN8KRMdHY2kpCRs2rQJzzzzjMM2N954I3bs2GFb5uSX\nX37Bjh07bHO4+vfvjy+++AK5ubmwWCxYvnw5Kisr0aVLF01i9No5XFLH/iOLtKCqqvE1ePir2oXK\ny4FffwWaN9c7kjqOHj3a4H497n7Qd1FfbJm2BS2CWvyxMTsbQGenz7FqFbBvH/DBB9rHp0ZQZ+2H\nqCwVgJ/13wbqRCUiAB06dMCWLVswdOhQBAUF4Z9XrK48dOhQvPLKKxg7dixycnLQqlUrvPjii/jz\nn/8MAJgzZw5yc3PRt29flJSUoGvXrlizZg3CwsI0jbOxwsvjCi7j4xwurTB36tXM4TpZeBIWacwe\nQcA4X06GDR6KHfhR7zA0NWcOMH48cP31rn0efk6VYb6ck56ebvc4NjYWWVlZ9bZ//PHH8fjjjzvc\nFxgYiAULFmDBggWaxqiUBw4pGuVXtGNhlzMQaPLAtJLLGWlYTktNvTpRiyuOqa59+6rvm0hEruV1\nk+YDchserjGKrrmr0KpZM9tjjter5225c2fBpdc6XJ469Sl5xza9QwAAlJ64jNwvtHmjbN6s/Zpb\njnjb59TVmC/f5TkFV35NV6JAcHA3XWMhovqxw0o9WSUhy7SrWhsYgbHJzDTsdSi62rtX7wjIU3hd\nD1cNIUwIDGyvdxj29u+v99PJ8Xr1mDv1auZwnb98HqWVnn9p/qe/fopj54+57PzDhtzssnMbXffu\nQGkT3iLe+jl1VW+tt+aLGudxBZchffstkJio+LC33wYSElwQj7fy0OEqvZ0uql6PonfvP5ao8DQr\nD63E8fPev36YnldhE5E6XtXDZf7Zc29+29B4/ZkzQB4X+66Xo9wJ/kFyiqM5XEePum7oSPe5W3v2\nAM89V2ezxdJ4bEVlRUjenuyauNSQnjUmyzlJyjBf3qem4Gqs8PKIgqvlxlf1DoEaU1kJfPih3lFQ\nA4RwbWGk69ytCxeq12e7wurVjb8tw/8Vzp4lImqyelalsPGIgsuT+cx4fUUFMHu2pqf0mdy5gKN7\nKZpMBuiJMqibfXgOV1Pxc6oM8+V9ar5sNrYMCwsuIpXyKioUH6NnwSMEr0Yj5Zy5uwYRNc4jCy5P\nus+ZK8brN1+4gOT8fM3PazTeOtfBHUNvjuZwuXpIUQ0JoPhSru5XUm4zyDpcRtRYweWtn1NXYb6c\nk5KSgkGDBiEiIgItW7ZEfHw89lpXA/jss8/QsWNHhIaG4p577kF+rb+HoaGhMJvNth9/f3/MdjD6\n8tprr8FkMmHLli22badOncKYMWPQsmVLdOjQAR84eT8zr5o075hnTSx15KI4gfOWNMXHJV+8iJSC\nAhdERK6kokNMU0YsuADgm+2PY9+ZfXqH4XM2bdpku9kv2bt06XcEBmbrHYbPKiwsxF133YUnn3wS\n+fn5OHXqFObOnYvAwEAcPnwYjz32GFasWIGcnBwEBwdj1qxZtmOLi4tRVFSEoqIinD17FkFBQRg/\nfrzd+U+cOIHVq1ejbdu2dtsnT56Mzp07Izc3Fxs2bMALL7ygaYHsEQWXsOj8l6oJGhqvPxywFPsq\nVyg+pwH/Zip27tI5FJUVNdjG2+Y6OLMIpVYczeEyasF1201voFfrXrrGoHQO1+mi0/jvnv+6Jhg3\nfpfMyMho8jm87XMKAHv2dEWfPoNdcm5vzJfWjh07BiEEJkyYACEEmjdvjltuuQW9evXCihUrMHr0\naMTHxyMkJASvv/46EhMTUVJSUuc8q1evRlRUFOLj4+22P/HEE5g/fz4CAgJs24qLi7Ft2za8+OKL\n8PPzQ+/evTF27Fh88sknjcbrAz1cTWe+wQxzf3OTz/PL6SjsOtVBg4ic5+r7z40dC/zwg+vO3+rN\nVhi9arTrnsCAoqL0ff7bbgP8DXi7+qsiusIc2PTPoTvlFOfgo18+AgCUnS3DoXvccA8dciuT6bLe\nIfisuLg4+Pn5Yfr06di0aZPdkOGRI0fQp08f2+Orr74azZo1w7FjdRdGTkhIwNSpU+22ffnll2je\nvDnuuOMOu+01U5UstSa6WiwWHNLw/lg+XXC1vLMlrhp1lVNtL6ddhp/Fz+G+73/vhK+OXetwnyvG\n693RSVFSUr3SgyudLDzZ4H5vm+vgzoLL0RyuL78EvinOQY6f4/exWr/+CtT8PjRiD5ozmjKHS5ZJ\nFO1ruLfWm3nb59TVPCpfQmjzo5DZbEZKSgqEEJgxYwZat26NMWPGICcnB8XFxQgPD7drHx4ejqIi\n+89gZmYmtm/fjmnTptm2FRUV4cUXX8R7773n8DkHDx6M119/HWVlZdi3bx8SExNx+bJ2hbfnFVzf\nfKPL0/56668wVxjnW7jnz2BTwUP/mBvJ/546haxa3ehayM21f+wN91LkW4189LesPSm1+VGhW7du\nWLJkCbKzs3Ho0CGcPn0aTz/9NEJDQ1FwxRzmwsJCmM32f5+XL1+OIUOGoGPHjrZtr7zyCqZMmYKY\nmJhaL/GP+FasWIH09HR06NABjz/+OCZPnox27do1GqtXDinmxQOpV6/WOwxFlI7XHzsG7Gtk/rCv\n/DHgXAf1HM3h0ouUZbBYXNxd2kS153C5erje23jr59RVq2F4a75cKS4uDtOmTcOhQ4fQo0cPu4s9\n0tLSUFZWhmuuucbumGXLltn1bgHAli1b8P777yM6OhrR0dHIzs7G+PHj8eabbwIAYmJisG7dOuTm\n5uKnn35CXl4eBgwYoNnrMOCMjvrJAKAqwLvXhNm4EUhLA/r1a7gd/yToLyogADl6X3roBtOnA1Om\nNO0cVVVFMJkiNYmHyNXCwm7CiRMdG29ILnH06FFs2LABEyZMQLt27ZCdnY2VK1di4MCBmDRpEgYO\nHIiUlBRcd911ePnll3HfffchJCTEdvzOnTtx+vRpjBs3zu68SUlJqLTOlZFSon///njnnXds87lS\nU1PRrl07BAYG4osvvsAPP/yA1NTURuP1yh4uAB5XabhkDpenTpRRyOhzHQJMxv34OJrDpVZgIBAc\nrNnpDInrcKln9M+pGuHhQ3DuXCPfelXyxnxpzWw2Y/fu3RgwYABCQ0MxcOBA9O7dG2+//TauvfZa\nLFq0CJMmTUJUVBRKSkqwcOFCu+OXLVtWpwgDgBYtWqB169Zo3bo1oqKi4Ofnh8jISARbf8F99913\n6Ny5M1q0aIHFixfju+++Q8uWLTV7XR7Vw+UzY2lO8LC6kwCcPeue55k7F8h20RJCd6y4A4tGLkLH\niLrf/tWMxHnrRzq7tBQLTp3C/M6d9Q5Fc5kFmXqH4PVqvlT76vB227Zt8fnnn9e7//7778f9999f\n7/5FixY59Tzp6el2j5988kk8+eSTzgVZi/f2cHkYV4zXe+sfqSt521yHUjctpt6/P3DvvcMabXem\n+DQ+P1T/LzVHfr/wO8qrylVG5pir/6TUWki6XlrfS7GgshIbzp/X9JxGUVhWaPfY2z6nruZMvrZt\nM+HcuUTXB0OaYMHl5Xz1mw+kBeAq+5q4VHEZ6RfTG2/o5aSRvsK4OZTCwsLGG7nJ0aMzkZe3Ru8w\nDKO8PEfvEEhjnlVwueqX0a+/Vs9UdwFPXYfLCOrN3YkTbo1DSxZLFubOnePy5zHaPBGLRd1aNnsL\nC7Ht4kWNo6lr+47tLn8Op7nxu9TKlSubfI7a77XKykrk5eWpOk9VVTEslrImx2N0RvtsUtOxh0uJ\nDz8ENmzQOwpFfLR/y+NJeRGbN2/UOwy3q6oqVnVc8sWLWHfunMbRkKtkZWVpehk9kTfxuIJLCs/q\n33HF/IYQPz8EGfgKOa1429wQd47eeFvuXO3meG3ncCkR0TwCHcM9dwkCvteUYb68j7M9XLxK0QNs\n3159aX7NF8e5sbG6xqMXU3MTrsdjAF7WOxTVpCzCkSMH9Y3hrLEXIXVk9o2z0b1Vd5ed31JqabxR\nLVWlv+GOlmc0ee5bO9+KWzvfqsm5fJHFUo6CghRERo7Q7JynTi1E9+6tADyn2TmJvL+bRGdajNd/\n+y2wdWvTY/E0V+ZOmARC4NmXpEvpnm6uBt93pa4ruFyxRJwEcFuX2xATHtNoW7W2/ahsDpel6iI6\nBl1yUTSeRe85SZWVBThyZIKm57RYShAUlNt4QxX0zhfpx0MLLh/t6jKywMDqiw909MADQFaWriEY\nkrsXytXyAlqfvRqXiLyORxVctX/1uvsXsX87fzyIBxUf5zPj9SYT0LWrpqdUmru9e9231hUAnCxT\nc0WVn+ZxOOIz7zuN3Dx4qKbn86WvhHyvKcN8eR9epagxAYFyaLvgI/keP78eiI5uq3cYhvFIdDT+\n1qGD3mE0SX09iM5+Kdxzag/+9t3ftAypVgwuOa1PucSRY12kpKRg0KBBiIiIQMuWLREfH4+9e/cC\nAD777DN07NgRoaGhuOeee5Cfn287LjQ0FGaz2fbj7++P2bNnAwAyMjJgMpns9v/zn/+0HfvFF19g\n0KBBCAkJwfDhw52O1XsLLideWKWlEhUWYxRHHK9Xj7lTT4/cRdZzb+onnkhBfr7jXzUtAwLQulmz\nBs/rjt4ipXO46mhCYZN/OR+H8g417fl15O2f06oqbc/n7fnSQmFhIe666y48+eSTyM/Px6lTpzB3\n7lwEBgbi8OHDeOyxx7BixQrk5OQgODgYs2bNsh1bXFyMoqIiFBUV4ezZswgKCsL48ePrnL+mzYsv\nvmjb3rJlSzzzzDN4/vnnXfK6PKvgcvI3b1FZEXKKuUovkTsNHux4e3Z2XIN/tMZ/OR4HcxxfuWnU\nDpp/bPkHLlW4ouvDlwYjiRw7duwYhBCYMGEChBBo3rw5brnlFvTq1QsrVqzA6NGjER8fj5CQELz+\n+utITExESUlJnfOsXr0aUVFRiI+Pt9tusTi+KvlPf/oTxo4di+joaEXxem8Pl4fheL16zJ16npS7\n9IvpKK104+Q7B2rP4QoymbC7X78G218ovVBnKLGwcDcqK7W4CtWoZaZjer/XpKxARYXnLI6rd748\nQVxcHPz8/DB9+nRs2rTJbsjwyJEj6NOnj+3x1VdfjWbNmuHYsWN1zpOQkICpU6fW2d6xY0d06NAB\nDz30EM678Z6nnrUOFwB+A6T6HD8OXFZ3BxkyMHd/4k1CoGdoqOLjjh17DHFxn8Bsvs4FUTkntaQE\nH505g7e6dHGqfdeuXXHypHPn7t27dxMic53SUt4P1FWERsOfUmGRaTabkZKSgvnz52PGjBk4e/Ys\n7rzzTixevBjFxcUIDw+3ax8eHo6ioiK7bZmZmdi+fTuWLFli29aqVSvs3bsXffv2xblz5/D4449j\n0qRJ2LRpk+rXBnDhU8NITk7mNxqV6stdTkkOzhSfBcLC6uz7/Xeg1pcfn+Ut7zt39fVs+3E7AtDw\nXDJPcLGyEikKbu7evn37eguuTpGd7B5X1TMu7C3vNXfxpHwpLZS01K1bN1uxdPToUUyePBlPP/00\nQkNDUXDFe7ywsBBms9lu2/LlyzFkyBB07PjHXRxCQkLQz9p73bp1ayxYsADR0dEoKSlBSEiIi1+R\nRw4pelZ3O2kvteIsjp47qncYZOXmZb7ITaJDo3Fjuxttjw8fPqxjNO7H97VxxMXFYdq0aTh06BB6\n9OiBAwcO2PalpaWhrKwM11xzjd0xy5Ytw7Rp05w6/5VzupQuO+W1c7icvZeihMCAn392cTSN85Rv\nMlfatAnYtUvfGBzmrk8fFLdvDWe6OzMuZuClLS9pHpcn8NT3nWpbtwLffaf6cDX3UqyS9d8OyJf+\nVvvce62JmK/GHT16FP/5z39w6tQpAEB2djZWrlyJgQMHYtKkSVi3bh1SUlJQUlKCl19+Gffdd59d\nD6kStawAACAASURBVNXOnTtx+vRpjBs3zu68e/bswdGjR2GxWHD+/HnMnj0bw4cPt/WOWSwWlJaW\noqKiAhaLBWVlZaioqGg0Xu8suJT8FhPACY0m9GzP3I7Ccu1vyZKdDafmT+j1TevCBX2etzHCyV7O\n/Mv52HB8g4ujUebzz7k2klJOrZTvzhVvAQSYgBaNjEB6y/9mJd/2z507h/R0zqmipjGbzdi9ezcG\nDBiA0NBQDBw4EL1798bbb7+Na6+9FosWLcKkSZMQFRWFkpISLFy40O74ZcuW1SnCgOresDvuuANh\nYWHo1asXgoKCsHLlSrvjapaZ2LFjB4KCgvDoo49q9ro8aw6XTtLy0xBZWc8iQ41oaLz+1CnglJNr\nvHjjH+nGCqeGcuepPQi9erk+8qVLAX//ZEyePKzOvp8KC3EyMBBtXPTcWn85cN8crm2K5nB1CQE6\n15pXL6U05BDU8eOuf47an9OLFy+6/gmv4GA1gCZr2/Yv+OabAABAPSsIqOZJc7j00rZtW3z++ef1\n7r///vtx//3317t/0aJFDrdPnDgREydOrPe46dOnY/r06U7HWcPkZNeVR/VwhaYB0b+0A86eBZYt\n0zsc0otBis9OzZurOs7Vt6VauRI4fbr+/Rf8XHt7IW/8ctAYWSpResJ4l8geOaL9OTtHdtb+pE1Q\n7oI1rgMCWqG0tAUAoNA995snD+aVQ4pBZ4Crfr8KqKwEFFyJoyd+k1Gv4dwZsDvBQPr0GdZom1OI\nQJls+q+AC+IYcMVcCU9z82Dlc7jcJSkJWLdO7yj+0Nzf/osGf8cpw3z5Lo8quIgA5+dwudqVUaxc\nudJuPoAjTVzuRVMWCEgNclmJUuAq11016mul9ZXflv/v/4Aff9QnFiJqnFcOKXoi3jdLvYZyZ8Q/\nwseOHXO42nFtf/2rey6COHAgudE2AsbMY22uHn6tse3HbW55HodkOUJN+q603xT8HacM8+V9NBlS\nFEL4CyFWaBEQkVac/SPcKqQV7u9Z/8RKMn7B5S6VOs549ytYir+2beLNs2vh/1Mi99Kkh0tKWQmg\noxAiUIOYNOQ5v1I4Xq9eU3PXPqw9nhv8nDbBeBhn5nAB0GRI0eMFRuHZIHPj7aycWqZCAWHR7so+\nd/UI1qb37zg/vxaoqPCcuwTonS/Snpa39kkHkCKE+AbAJes2KaX8j7rQtOLeXyzhueG4Cle59TnJ\nsYbmcAUa7KuBkfnDgoa+cy1cCISGAg7u/epWWpQ3QRuCMGL9CODepp7Jud87nvOV0Bv4IS+vY+PN\nFDLiMh9kTFrO4ToBYIO1bSgAs/VHF8XFrVCWH2C37XDuYRzKPeTy5w5CkOJjOF6vXoO5c/DbsGtX\n4Iq7O/gsZ+ZwtUEBQkRlvfuzshpeXkIJPz/lN4MGtPtaFfJNCK7eeXW9+0t+3QdAZe9VPYf4St8h\nf8cpw3x5H816uKSUr1SfUJitj4saPMDFykrDUHHJfh2h1UdWwyIt6Nm6p8uedzqmIwIRLju/3vJK\n8pCUnoSJPetfFM4orhw2SU4GMjJ0CUWx0lLP+eZcXAwEBDTezhkmk7o1y1ztyi9qKQUFGBKh4nPu\nK9UVEdWhWQ+XEKKXEOIXAIcBHBZC/CyEcF1l0xid/lhVoPH7KTniKeP1JwtPYv6P8/UOw059uYsJ\ni0H7sA62x0ePAj/95KagaklTeTsZdxRcDc7hcrKK+u9/gfffb7xdTo5zManRLjAQXYKc61leAGDV\nKmU3WC6trP5/GNK7n9LQyKq+z2ll5UVUVen6/dyQPOVvgt5SUlIwaNAgREREoGXLloiPj8fevXsB\nAJ999hk6duyI0NBQ3HPPPcjPz7cdFxoaCrPZbPvx9/fH7NmzAQAZGRkwmUx2+//5z3/ajn3uuecQ\nExOD8PBwxMbG4n/+53+cilXLhU8XA3hGShkjpYwB8DfrNn01+gK1/cq5AAsQCH0mCHlKj4i7dGvV\nDde07Kp3GJ7Lv2l39JJXfOs5c6ZJp2vQfa1aYXb79k613QAgNTVL0fn9Tby7mWvp88uruPggiosP\n6vLc1HSFhYW466678OSTTyI/Px+nTp3C3LlzERgYiMOHD+Oxxx7DihUrkJOTY7v3YY3i4mIUFRWh\nqKgIZ8+eRVBQEMaPH1/n/DVtXnzxRdv2Rx55BEePHkVBQQF27tyJFStW4Kuvvmo0Xi0LrmAp5daa\nB1LKZAAh9TfXT5Wlym4OhpZX7EQiUtWCm1qN13vE7VLmzau+C4BGONdBvdpzuK4skJx1yy3AXXfZ\nb3P0Gaju4ap+Dk/7clBzm5qaOVy687D8Acb8nJ47l4i8vNV6h+GQEfNlNMeOHYMQAhMmTIAQAs2b\nN8ctt9yCXr16YcWKFRg9ejTi4+MREhKC119/HYmJiShxcFPN1atXIyoqCvHx8XbbLfXcIPOaa65B\nkLVHXUoJk8mEEydONBqvlpPm04UQ/xBCxAohOgkhXgKQ5tzpXaCBwqPlv1uioMx1t/xR+4fLE2jy\n2l5/Hahy8m7cLuBpf+z1dvlSw/u//x5Y4cQqfDln7T+URvxyUNG5AlLUfYOENquezG/AkElnrrhH\nIzknLi4Ofn5+mD59OjZt2mQ3ZHjkyBH06dPH9vjqq69Gs2bNHC46nZCQgKkOLrPu2LEjOnTogIce\negjnz5+32/evf/0LZrMZHTp0wKVLl/DAAw80Gq+Wy0I8BOBVAInWxzus28gJnjReb5Rb5tRQk7ua\nN77FUgnAApPJc9bn0ZIz63AVGWB6zYn/dwLN27t+Qn1F9wr8PuR3DMdwh/ur53B51l9YIUyIiprU\nYJu8PPXnt0gLKqoan7uq9++4Zs064ZVXtsGJv4uK5OZqe74aeudLiWSRrMl5hslhitqbzWakpKRg\n/vz5mDFjBs6ePYs777wTixcvRnFxMcLDw+3ah4eHo+iKX2iZmZnYvn07lixZYtvWqlUr7N27F337\n9sW5c+fw+OOPY9KkSdhU655rzz//PJ5//nns378fX3/9NcLCwhqNV8urFC8A+Ktzp/NO9fQ+koHl\n5CxHQcF2dOu2pPHGpJuzS8/C73m/xhtSHSZTM3Tr9nGd7bX78ZpSVCdnJOOXs7+oP4GbCBGAixej\n7bbl5HwKi6UMnTq9qlNU3kFpoaSlbt262Yqlo0ePYvLkyXj66acRGhqKggL7kazCwkKYzfarVS1f\nvhxDhgxBx45/rNEWEhKCfv2qL5Bp3bo1FixYgOjoaJSUlCAkxH6mVN++ffHdd99h7ty5ePvttxuM\ntclzuIQQ71n/u87BzzfOnd5FSkuBvHNue7qd6zurPpbj9eoxd+o5sw7X2TZrsel39XfT/jovD7sL\nC1UfDwCySsLip/83muKD+/AGumHzam3WwdB6NXpn6dFHrdXn1Gzuj8BA5y6QaMzly+koK8vW5Fxa\n4+815eLi4jBt2jQcOnQIPXr0wIEDB2z70tLSUFZWhmuuWIRx2bJlmDZtmlPnr29OV0VFhdvmcC2z\n/vften50U1bqj//P3r3HR1Gf+wP/PLlfSSBAkIvcr+GugiAIVFGxKLV4gUYu0mqtVTwee9qfaJVq\nbbWKba16UFs9QpXjOWov9Chq2wSMtCAoKqjBikEugoJASIAAyfP7Y2bDbm67M5nZmdl83q9XJLsz\nO/vN487sM9/vM9+Z/eLdXjYh4XTJ6YIFozhS3JZU5X6ID/d9aPv1q776Cu+0clyy/QXtcTLFuQst\nWqMW4mgdoL8G6P2vR49bkJ9/rtfNIB8oLy/HQw89hF27dgEAduzYgRUrVmDcuHEoLi7GypUrUVZW\nhurqatx5552YOXNmRA/V2rVrsXv3blxxxRUR212/fj3Ky8tRV1eH/fv3Y+HChZgyZQpyc3Ohqnj8\n8cdx8OBBqCrWr1+Pxx57DOeff37U9ra6h0tVN5q/FgD4h6qWhv2sjm3z7lAVrNl7dtzeL7e9MVfP\n57B+/XtQxuu75nbFjWNu9LoZEcJjV1cHvP66u+/3l337cNTDon8nxXovRScMG2b/tUX/XYQT6fbm\nuHOMSP08XLzuwrqgHOP8gvGKLjc3F+vWrcPYsWORk5ODcePGYfjw4ViyZAmGDBmCpUuXori4GIWF\nhaiursZjjz0W8fply5Y1SsIAozds2rRpaNeuHYYNG4bMzEysWLGifvkf//hH9O3bF+3atcOcOXOw\ncOFC3Hhj9O/FWHu4YimavwTAL0VkNYDnAawyb2rtGZE61GnTf6EbXfkjJu7C0mdWYcc8f3ZRtwV1\ndcC0acBJF6fcvW7rVmw44wxkJrOmyIrsKHftGTv2FaSlTW95JY/5pTcq+8DFkKreMa1bVwcMGQJ8\n9JHLjSKKs65du+L5559vdvns2bMxe/bsZpcvXbq0yednzZqFWbOavpuKiOCVV16x1tD618a2XtS8\nTFXnA+gH4AUAswFsE5HGlZo2iMhFIvKRiHwsIj+K9XVJoqhroumhebdycxXpZz/nRBNbjeP19sU7\ndonUuxFLDVe8LFo0FzFc6OMpq/NwbTwIbHXhKs+cr6YjrSr2G4I2cSV83MVzPz15sgp79jwTl/cK\n3UiipsbZ7cYar9raKPO2kG84OfEpVPU4gFcA/DeAjQC+YbdhISKSDONuHBcBGAJgtogMju21dajT\n5v/C/PaKjPMfa3Y5UXP80tNB/qYK1DbI0Gu7lKOyspX3mEqkrN8FtbWHsG3bori+Z1VVXN+uXk3N\nTm/emCxz8l6KF4vIfwH4GMDlAJ4EUNiKtoWMAfAvVa1Q1RMwkrkZsbwwOakWZV+/zIEmuI/j9fbF\nO3ZeXVnmhnjWcCUCL++l2OhzF7CsP9GPcQ3mxWy1RI9XW+RkD9ccAH8EMFBV56nqyw7VcHUDEF4U\ntdN8LipJAgbmeTfZfbwNGgT0tT8zRXzU1QV+amZF4L7rPBWRJjQxi3si65YJDHZomPTzKhdvRumC\nztmdvW5Cm5CWxjgHhWM9XKo6W1X/qKrHWtuohpt2eHu+5ER9w4IFwMyZrW+Lq445/fHwpv7Nyftv\neileNVwiEnGHgmB2EorlGq48Z6br8rXm9oXpA6bjlrNvqX/MOlVrYo1XSkq+uw0hxzg207yIjAPw\nMIw6qzQAyQCqVLW153e7APQIe9wDRi9XpPvuw2Lz13wAvat2o8B8vOGNN3B4xw5goPH4xCcnULam\nDOMnjgf0JIZ9/DFKT5yo78INfdAbPTa319xyhH3u33nnHUw655zI5aa3//E2Dn10agbc0tJSbNq0\nqdn3x4EKoC41Yv2PPwaSk6O0t6XHn32GyZ07R7avqMjW9nbuLEVpqYX3B4DVqzH5ggtiWv/Ix0dQ\nWlra7PJNmzZFPFYtRWlVFSYDWFtZicMbN6Jft24I/R88cqQU69YB/fsbj9ev/xx79jS//YaPa95+\nG28eOYJvTp0a0/ow2wfz8cMPP4xwofV79ZocegbAl42Wx9q+TdgElEZfPxSP0tJS4NMG7dm0CUAv\nAMCnB4HjnxyPXB6lPUc+PlXIe+TpMmxPTgcwsn75e+8BIqfWr609jp/97Dn86U/Am282vf0Nuzfg\nyyNf2vu8hz3eD6Ci4j0A05qMR/nB8kaft1PDeYIDeBPbt2Y2en34+rt27cDEiV0AhP73l2Ls6WPr\nH3/55VZccYVxNlm1cSNKq6tj+P+F+sd7P9mLM9uf2WT7m3q90fxTjz+sroZ26RLaYqPXG9MaTa5/\nz/B71DXc/rZ3tkV8furXSY9cP3xZ+ISUmzYBx4//Axdc0LPFv9/K4+PHv0ROTuT7DxnSeH1VwGhK\n7Pt/w8d79nwKI4anlm/apJg0CTG9Ppb/3y2tv3btVnTqZL/9VttD1oViuGZNKYCK6C9Q1RZ/YBTJ\n9wfwDoxk6xoA90V7XQzbTQHwCYyjfxqATQAGN1hHUVKiapw4qwK6b8R1+sZDffSr4iG6sbJSR731\nli4uWax3/v1Ozb8vX/cf2a/7qvdph/s7aMxuvFH14YebXfz0O0/rVc9cpQC0tLRU9cQJ1eTk+uVT\n+nyqRR336NrP1urZvz075rfFlDsU594d8dyvfqW6cGHsTW/kBz9Q/cUvIp76wxdfGHG0ADDCErPq\nauNFx47Ftv3F0P4P94958/UhHzFC9Z13FCUletaGDaqqunSp6nXXqfbrp7p1q7H+7t1P6Ycfzrfw\nB6h2KivTvTU1Ma+PkpKY4vrpp+Ef4R3apUs3S+1SVS1BiZYg+nupql5wgeqqVWYbF0P/seMfEW1+\n9L4/aAlK9J6J0M4PdLbUjv4P99fyfeWqqvqrWzboCzdu0q/Nel9xQ5Gqqq5Zozphwqn1T548qoDq\n1q0Hm90mFkPvXXOvpXY0snChXgToXXe9HLltM+4v/vRFfeLcJxq9rLauVnHfaTp01Vr9Gvboj5+s\navFtPv74Vt2+/QGd+SS0pASqqnrkkyNa1qVMS0qgO3c+qqqqb1dW6si33oqp6cv+Oqh+W9vv267/\n+uG/Ipb//OeqP/pR49fV1qqKRD63/tAhPdPcL0J/e7h//Sv8swidMmWKZmYau29DP3/j54rF0Jqa\nGoWRQ6qq6pK1S/SWVbc0+be8+uqr9euVlECPHq2I9udbcuzYTn3zza4Rz+3dq9qpU+R6f/97cn1M\n7di27U6dO3exAqqvv37q+ZKSJK2rO2l7u7EK/yy5CQ0/IBSz8NgtXhy5X2kzeU+sVyl+DCBZVWtV\n9WkYVxa2ihp1YDcCeBXABwCeV9UYpr1uPGYxrf80XNz/Ys9uvlyyrRe27HPiOgL7jtbW4tBJf8zY\nHVR+Hg37Emm4AuO8bkaCSoxhZKf57Wb2RH7lZNF8tYikA3hXRH4hIv8Oh45QqvqKqg5U1X6q+nO7\n2xnTbQzGdh/rRJMcF69u2//+4gv827/+FZf3ihcvurwT5SvGT/NwBUHVu9ZquNqC/IzYaog4NGUN\n45V4nEy45prr3QjgCIDuAOJawl0XUWmWKF+J5DcazIpvbyVcyBLuD7JtZJeRGNNtjNfNsKWuLgu1\ntWleN4NaoaysDOPHj0d+fj4KCgowYcIEbNiwAQDw3HPPoWfPnsjJycFll10WUYuYk5OD3Nzc+p+U\nlBQsXLgQAFBRUYGkpKSI5ffee2/E+/71r3/F6NGjkZOTgx49euB///d/o7bVsaJ5Va0wfz0K1Nev\nx5UmC3AymAfC+gJrl711+DCe27sXT8fl3eIjXrEL6ZSWhqQEuUqxpXm4pM6Z92hh7uGAEeSMMOfh\nqnMoOG1IvPfThjp1Aj5vMLPGwYO3YteuOpx3njdtaonX8QqCyspKTJ8+HY8//jiuvPJK1NTU4I03\n3kB6ejq2bNmC66+/Hi+//DJGjRqF6667DjfccEP9PRGrwmaqra6uRpcuXXDllVc22n5TV+F+8MEH\nKC4uxrJlyzB16lQcOnQoIplrTpJT91IUkQkA7oJR3B5aX1W1T2xv4TC/zPfTs6fXLYiwpboax9lD\n0yofjvHz2byD2Y2DOYVTrTpyBHjnHWDUKIc2aJFfcse8SXmoO8qkzwoRwM3bnybIOVigbN26FSKC\nq666CgCQkZGBqebV44sWLcKll16KCRMmAADuueceDB48GNXV1Y1uVv3CCy+gsLCwft2Quro6JDfx\nofnpT3+K66+/HhdeeCEAoH379mjfvn3U9jo5pPg7AA8BmADgLPPHz99M7ktJAT75JKZVOV5vH2Nn\nX9BquD7/HLj1Vu/ev+rdt6E+SLvyzs5D+ynRD/B+0pb20/Hj9yLGO+I1qy3Fy66BAwciOTkZ8+fP\nx6pVqyJ6mT744AOMGDGi/nGfPn2QlpaGrU3cWPSZZ57B3LlzGz3fs2dP9OjRAwsWLMD+sFsJrFu3\nDqqK4cOHo2vXrpgzZ058e7gAHFRVe7fQdoqFjpug1+F06QLwYkMKjGDvbo01k3Pt2bMMIrEcLg1O\nhuXYjmM4XH4cgM/vAN4GpKV19LoJcVVa6sxJyOTJ1vaI3NxclJWV4f7778e1116LPXv24OKLL8YT\nTzyBqqoq5OXlRayfl5eHw4cj7ya/fft2rFmzBk8/farQplOnTtiwYQNGjhyJffv24fvf/z6Ki4ux\natUqAMCOHTvw+9//Hq+99hpOO+00zJs3DzfddBN+//vft9jeVtdwicgZ5q8lIvIAgJcA1N83XVXj\ndlmP1KUDiD6TuR9nCbc6Xm/2oBJY69Aa4TVcacnuFA9H1nC1fEBNSspwpQ1Oqa/hasJ7e99DetUW\npKfkWNqmU0ejkwdP4tiOmugrmuJ90sn91JogxctqouSkQYMG1SdL5eXluPrqq3HLLbcgJycHhw4d\nili3srISubm5Ec8tX74cEydORM+w8p/s7GyMHm3s6507d8YjjzyC0047rX44MisrC9dccw369esH\nwBi+PP/886O2NdYerpZWWwLgQQBjYQwj/sx8LvTjqZ+/930cPdL8cq1T7NmzJ34NIvKhgswCnJ53\nusvvEj21yMtLd7kN7rn6patx4Gj0YQU/8N8pJ1HrDRw4EPPmzcPmzZtRVFQUcUeDbdu2oaamBgMG\nDIh4zbJlyzBv3ryYtl9nXiwzfPhwW+1rdQ2Xqk5W1SnmT/jvU1R1iq1WOejJ8mIcb6HTS6s1YpzX\nK/Ear0+0kR3Aeuyys2M/04gnJzperf7/9VMNV3b2QaTEPhrnCefm4fLhB9BlrEmyhvGKrry8HA89\n9BB2Gfeiwo4dO7BixQqMGzcOxcXFWLlyJcrKylBdXY0777wTM2fOjCiYX7t2LXbv3o0rrrgiYrvr\n169HeXk56urqsH//fixcuBBTpkyp7x275ppr8PTTT+PTTz/FkSNHcN999+GSSy6J2l7HiuZFpIuI\n/E5EVpmPh4jIt2PbvEN8OFQYLiWp1usmEIx7t/Xt63Ur3CMupNWtnk28QZOCWkLpxBEmeecw5OR4\nf5JHFHS5ublYt24dxo4di5ycHIwbNw7Dhw/HkiVLMGTIECxduhTFxcUoLCxEdXU1HnvssYjXL1u2\nrFESBhi9YdOmTUO7du0wbNgwZGZm1k8nARgJ19y5czF27Fj06tULmZmZje6R2xQni+b/C8DTAG43\nH38M4H9gXL0YF5IswInGz8fr4N4+oz265Xaz9dogjdf7DWNnX4vzcFk8P3j1VeCjj4Cbb264oWa2\n7+/zoya1VMNFLeN+ag3jFV3Xrl3x/PPPN7t89uzZmD17drPLly5d2uTzs2bNwqxZs1p878WLF2Px\n4sUxtTPEyWkhOqrq8wBqAUBVTwCI63V00kQr3Tjbb86MQTNw3ZDr4vZ+lMg+wp49uzxtQdIJaxnR\njh3A++83v7zQxm1Ej5w4gj999CfrL2yDgpjAUutlZg6IvhL5ghNF8yFVIlJ/LayInA3gUAvru888\nADU1b466lIg1vCoiVhyvt89K7IIzlGWv+NrPNVzdulp/zVdHv8L3X/6+c40oKrL1stAVfcY8XGQH\nj3HWxBKvrKwipKV1cb8x5Agne7huBfAnAH1EZC2A5QAW2m6ZQ6SJGed5d/u2qa30APjhz/zWsG8h\nL/3UHDitufHDnqo9eOytx6KvGItrr8XGThfaeqkfp5MhouBw7F6KAD4A8EcY91KsNH8vt9uw1gra\noZHj9fYlYuzS0oDjx91/n5ZquFpj8eTF9b+39l6KtVqLxzc+3rqNhIjA/tFBWlfD1UTSmZ2cjFE5\n1ubtCqrw/XTo0KH41re+5V1jAiARj2ttnZMJ1zIYida9MI5o34LRy3VFSy+KC44B1JtTWIiOqale\nN4MC6LPPPkPHjh2RlZXldVNaZfRo4Kyz7L221SdyDTYwMCsLvxs0qLVbDZyuXbvi2WefrX+syvtC\nUuJzsoarSFW/raolqvp3Vf0OAHsFEw6KZ9F8a8SrvuHarl3x0tChcXmveGFtiH1WarjmzJmDt956\ny/qb+GwXFLE/D5vVebgyUuI3kWtm/0x0mBr7/RXj/b8lfD+tqKiIehVYW8fjWuJxsofrbREZp6r/\nMDYsZwPYaL9pzglOoXQbYKMOpn2mezfpzcoaZOned/FQUAA0uN1XzJz8qMtxBwfmQw1rTTEXtSgp\nPRnJMY5ONqxHO+00FxrUgqNHj0bMAk7UFjhxL8X3w9Z5U0R2wDi8ng4Pa7hCgtLD5ch4/a23AjNm\nAOee2/ptuSUz0yhQitFNY25Cvw79WlynNbHLyxuHvLxxtl/vR1bSpJZquNL2OTMben0NVyuKzv1y\ns3mrNVxHaoNWTeoe1iRZw3i1XS11AbQ0n70/jpJNUNW4X3Xk+tt9/DFw8KDLb0JW9cnIwLZj0W+q\nHu6RR4DrPJ7SrS7Lt7tvYOw4moQPK4HJDmyrW2534MhHDmypsc8/d2WzRGRDS/dSrGjhZ3s8G9mc\nhifHfry8m+P19vk9dtd1tT4BVX6+Cw1pQqiGa//R/dh3ZF/EspM5LGSOpDj+/ibP3v203GDPtxS+\nn953H1BT411bgsDvxzW/KCsrw/jx45Gfn4+CggJMmDABGzZsAAA899xz6NmzJ3JycnDZZZfhwIFT\n8xvm5OQgNze3/iclJQULFxozWVVUVCApKSli+b333lv/2qKioohlqampuPTSS5tt465dwMsvx/43\nBfZOq07mVrve74Mv323n3AaJfOR4rYvzUCTAvRTl2G483K9vs932TZ3IdUhVDHbokJGRkTg3AH3/\nfaCO+Ty1UmVlJaZPn46bb74ZBw4cwK5du3DXXXchPT0dW7ZswfXXX49nn30We/fuRVZWFm644Yb6\n11ZVVeHw4cM4fPgw9uzZg8zMTFx55ZWNth9a5/bbb69/fsuWLfXPHz58GD169Gj02nB/+hNw552x\n/13+qir2yJEDudAD7kypwPF6+xi7cNbOMNyahytcc/NwWTkZcuvOEFZNOmcSfopDMbe9IM25dufk\nDEfHjpc5tr14435qDeMV3datWyEiuOqqqwAAGRkZmDp1KgBg0aJFuPTSSzFhwgQAwD333IPBLpjg\n1AAAIABJREFUgwejurq60c2qX3jhBRQWFtavG1JXV4fk5OQW27B69Wrs27cPM2fObHadLVuAzZuB\nkzHe7DAYPVzNHAWDeDYdJB06eN0C8jsfjuI3kpHhdQuIyIqBAwciOTkZ8+fPx6pVqyKGDD/44AOM\nGDGi/nGfPn2QlpaGrVu3NtrOM888g7lz5zZ6vmfPnujRowcWLFiA/fv3N9mGZ555BpdffjkyMzOb\nbefmzcYQenmMlxEGIuGqqu2NI4isl3H2KkX3MreWxuubuim3X2zfDoT1tHqCtQ72xfNein43OoYL\nEFe/uTqmbamegOoJrDuQghd3trJhCSJ8P23hu4lMQTquiYgjP1bl5uairKwMIoJrr70WnTt3xowZ\nM7B3715UVVUhLy8vYv28vDwcbjDnzvbt27FmzRrMmzev/rlOnTphw4YN+Oyzz7Bx40YcPnwYxcXF\njd7/yJEjePHFFzF//vwW27llCzBhAhDrTCg+/so/5fOT52M/Ii/xH5K/FclODogG4VQ9jk4/3dIs\nD9RW+aiXOR4Xzeza9TB27vwl9h1PwiOfuP52gfPb3zLpSiSq6siPHYMGDcLTTz+NHTt2YPPmzdi9\nezduueUW5OTk4NChQxHrVlZWIjc3N+K55cuXY+LEiejZs2f9c9nZ2Rg9ejSSkpLQuXNnPPLII3jt\ntddQXV0d8dqXXnoJBQUFODfKVEx1dcBFFwGbYrzmJhAJlyHyYPrSedehfUHj/5F+qQkJ4Xi9fVZj\n9+qrsY+lB43VT3U8aricuLFpbZJzM7a//PLLmDZtmq3XTjpnEoDW5Y8ZZdcgM7NPK7bgDKePgNFq\nXXiMs4bxsm7gwIGYN28eNm/ejKKioojJdbdt24aamhoMGDAg4jXLli2L6N1qSV2DKz2aG4psqKgI\nGDkSaJD/NSsQCVeTw4dNZM0SuFtbk5Muugh45RWvW+GeoEz2G7skfDjgF562wMkTtNStU5CWVujY\n9uxw4wgYLeEiclp5eTkeeugh7Nq1CwCwY8cOrFixAuPGjUNxcTFWrlyJsrIyVFdX484778TMmTMj\nCubXrl2L3bt344orIm/5vH79epSXl6Ourg779+/HwoULMWXKlIjesZ07d6K0tDSmZG3oUCCsnCyq\nQCRcfqOqOHHwREzrBmm83m/sxI4XUhhaquFSc6/PTM1En/ZO9cj4N/Br17a8XCAoXRNbDVeL/BuC\nZjkx0z+PcdYwXtHl5uZi3bp1GDt2LHJycjBu3DgMHz4cS5YswZAhQ7B06VIUFxejsLAQ1dXVeOyx\nxyJev2zZskZJGGD0hk2bNg3t2rXDsGHDkJmZiRUrVkSss3z5cowfPx69e/eO2s6iIqBbN+O2bbEI\n0LQQYedu5n3bhmZn4/+GDYt7S/S4Ym3hWkyqmRT3926rkpOBnTsBXOR1S+xL8cneVtOlFgAwqstI\nnBh4KV6BvW5BNf+TCD3LWhfAbCkAkpJY0EXWde3aFc8//3yzy2fPno3Zs2c3u3zp0qVNPj9r1qyo\nN1e/7bbbcNttt8XUzqIio/x7xAjg73+Pvn5ge7gEQFpSEk5Ld64GpLUGdRyEB6Y+EPEcx+vtC4+d\nCNAl2BNyI54fhbjUcMGdISwvTBpvFMdKTDfhjvyr/XI/SK+0dIxLSnJnfsMg43dC4igqMv4dOTK2\n9QObcPnxUN8+sz0mnD4h+opEFrXtr/QA8N/hiIhc1rmz8e83vhHb+gFJuIL7dcPxevsYu0hWvtNb\nquGqy/DT/qTIz8jH4NyxnrbijQ1vePbe2w5swwdffuDZ+7cW91NrGK/EM3FibOsFJOECNMavm7be\nvU8UTZ1D86tV9kzG4Ssibyg4ejTw+OPWttOvQz+cc1Y7/OUvzrTLDklq/vhyVdFVyEw1apFSUjog\nM9PZex9W1lTiwNED0VekmB08eBPKyxd63QyiCAFJuMKSqLraZteKx8SHVnG83j7Gzr541HD1euUY\n0j6qiXguNxcYNMj6tlKSgawshxpmw6QJzV8As2jiIrRLNxLLs8/ehhEjXo9XswAYdSKjRsX1LS1p\naT+NxwnwiRMH8dFHCyKeq6vrgOPHY7x0LM54XGu7fHLdVBQDBgB7a4H9AKqqvG4NeSktjXcFiLNp\n04BzzvG6Fa2jaYqTGdFnxY2WHqSk5EVZw3mXXBL3t/StpKRMdOwYWTCjWoP9+//PoxYRxS4QPVxy\n8bQGE10E5wuX4/X2NRm79eutzTSXIE7DMfwe62Je38l7KXbrBgwe3Pj5gtRUdExtxVVocUycj154\nFG9+981ml68uszYPV6jHKx6OHLF2nhnvoop4HuNSUztgwIBH4/Z+buB3QuLq1Knl5YFIuCL5r0Yr\nP+MYslKPe90MSmAC53fWbrndMaqL/bGqgVlZGNJgYsHAktjrRAE0iltSWhL63O/ObX0eeQS4++7Y\n1o3nqejevXvx3HPPxfEdifwt2lyLwUm4LORZ7dLb4alLn3L07VuqD7vx7I24dUxZk8s4Xm8fY2fP\nAw8A3/nO5KjrDew4ABf2u9D2+0TU58Q0f5V/hWq47Ha6SbKg3Zj49XoBQE1NDVSvcWXbD7z5AHYd\n3tXiOp999hl++ctfJvx+euSIs9tL9HhR84KRcFk8CKYmp2L86eMdbUJBrHP3+1DzlxlQIho+HOjQ\nIU5vJokx03wQ1dbWAmh+Nu5ozj33XJxxxhlNLnv2/WfxZfWXtredSHjhuzfKysowfvx45Ofno6Cg\nABMmTMCGDRsAAM899xx69uyJnJwcXHbZZThw4NRVvjk5OcjNza3/SUlJwcKFxhWrFRUVSEpKilh+\n77331r/2q6++wlVXXYWOHTuiU6dOuPrqq3H48OGY2xzthC0QCVfhnEJ07Pyx8aCFv+jlb72M9pnt\n49Sq2PhhvP7D6mqvm2CLH2IXVC3FTpPi9w0iIhBp7jDjn0TNag1XIhg7diw6h2ZubIWWPmvJyR5e\neupTPK5FV1lZienTp+Pmm2/GgQMHsGvXLtx1111IT0/Hli1bcP311+PZZ5/F3r17kZWVhRtuuKH+\ntVVVVTh8+DAOHz6MPXv2IDMzE1deeWWj7YfWuf322+ufv+OOO3Do0CFUVFTgk08+wd69e7F48eKY\n2x0t4QrEVYo5w3KA7K+irndG16bP1lrr7beB0tIcV7ZNFG+aAghiu/l6ayUlpSM5uYXbb3ncfcB5\n++yLHrskJCV5c+u14mLgqquc2VZurjPbodht3boVIoKrzP+JGRkZmDp1KgBg0aJFuPTSSzFhgnFX\nl3vuuQeDBw9GdXV1o5tVv/DCCygsLKxfN6Surg7JycmN3reiogLf+MY3kJNjfN9/4xvfwMqVK2Nu\nd0L0cHmtvBx44w17N2HleL19jJ19jF3sRKTFebioeSLiy89aVhbQzqGSOqfnh/NjvPxm4MCBSE5O\nxvz587Fq1aqIIcMPPvgAI8KuVO/Tpw/S0tKwdevWRtt55plnMHfu3EbP9+zZEz169MCCBQuwf//+\n+ue///3vY+XKlTh48CAOHDiAF198ERdffLFjfxcTrhjMng0sWRLceoacJjL5RMOOCgt8cMqekZKB\nUV1Ge92MekmZSVAfXgFN3nMqcQsiEWd+rMrNzUVZWRlEBNdeey06d+6MGTNmYO/evaiqqkJeXuR8\neHl5eY1qrbZv3441a9Zg3rx59c916tQJGzZswGeffYaNGzfi8OHDKC4url8+atQoHD9+HAUFBejY\nsSNSU1Pxve99z1K8WhLAhMs/dR+x8MN4fa+MDK+bYEusseM8qI21GLsMh4Z5WpGfdMzqiKdnPO1M\nOxxQurrt1XC1pFN2J6QkxVZx4odjnJvSHLoVVkiQ4qXqzI8dgwYNwtNPP40dO3Zg8+bN2L17N265\n5Rbk5OTg0KFDEetWVlYit8GJ5PLlyzFx4kT07Nmz/rns7GyMHj0aSUlJ6Ny5Mx555BG89tprqDbr\nnK+88koMHDgQVVVVqKysRJ8+fXD11VfH3OZo5dLBSbjYhRGb668HZs3yuhXxsW+f1y1o81pzO62U\npBSI+KeOSgAMzLBXOpBoXp/zOoZ2Hup1M4gAGEOM8+bNw+bNm1FUVIR33323ftm2bdtQU1ODAQMG\nRLxm2bJlEb1bLamrqwMAvPvuu/jud7+LzMxMZGdn47vf/S5efvnlmNv5VZRS8+AkXBEiD9BfVn+J\nJWuXxO3d647VQY/H9iXR0nh9UZFx1yJH9e0L9Ojh8Ea9wVoH+4IQu34F/ZCaZMxU7/V9UCdPnoxb\n8BFmXBz87tJ4JbBFRUVYtmxZID5rduS4dJ1UosbLSeXl5XjooYewa5cxF9yOHTuwYsUKjBs3DsXF\nxVi5ciXKyspQXV2NO++8EzNnzowomF+7di12796NK664ImK769evR3l5Oerq6rB//34sXLgQU6ZM\nqe8dO+uss/Dkk0/i2LFjOHr0KJ544omIerHWCmTC1fBwsv/ofjz59pMRzzW8VNRJdcfqHNlOYefo\ntwIgovjIxwlk51m/cDs5Mxntxrpb6BNrDhXPdDE7OxuD7Nyp3HGClBTnpwMKnQPEbU47qpebm4t1\n69Zh7NixyMnJwbhx4zB8+HAsWbIEQ4YMwdKlS1FcXIzCwkJUV1fjsccei3j9smXLGiVhgNEbNm3a\nNLRr1w7Dhg1DZmYmVqxYUb/8qaeeQkVFBbp3747u3bujoqICzzzzjGN/VyCmhYgwfBiA9VFXy83N\nxV133eV+e6IoLS3lGY1NiRa7ggIgzemCkGYkWuzcVrq61Dj7tNHTlt4tHUOeHeJ4m0Ja0/nnVi9N\nOK8/a2lpnTF27EeObzd0m1CnO1+9jlcQdO3aFc8/3/ykvrNnz8bs2bObXb506dImn581axZmtVBy\n06tXL/z5z3+OvaEWBa+HK5M1FhRMX/sa0LlzZ5x99tleN8UZZq/LBRcA7TtYH8byS+2WHV4PgcbT\nsWPHvG4CUUIITsJ1+unGv5dfbvwbkAMez2TsY+zsi1vsBOjeTWwPjftlL548abLXTfAt4xZCzeN+\nag3j1XYFJ+FyvLqcqI06yh4LIqJ4C07CFRKQnq2QIM254jfxiN1v3/4tdlbudP194q3F2J2Iz219\ngqR0danXTQgsHuOsYbzaruAlXCEBrv8g/3jy7Sexq3KX181wVU5aZOV00nFAkepRa/xJkoDueNHr\nZiSUwYOXwT+DxkTeC95Vik2IZ/Ht8OHDgeOxr8/xevsYO/tCsSvILEC79MgpC+SkQ1+Crdzt/HTK\nNGXKFECmAngs6rrx9s1vAjU1Xreiec3tp4WFxU0+39bFclwbPvwVpKUVut8YiivfJ1zj02O7DUk8\nrhrq3bs32rdvj+N7LWRcRImslbud11f7iQjG9xjvaRui6dvX6xZQvGVkxG/yaq/3wbbE90OKb3bt\nGvlEwD4bHK+3z07sund3vh1B1GLs2nl/82q/SElKwer5q2P6rFVWrkVNzR73GxUj44uyS6PnrfYc\nHj0KHG/FOSSPcdb4KV6qGvPP3poadCorw6hRf8Yll7xl6bXhP+sOHcJZGzZAVdGlSzV+9auu6Nev\nD0pKgJIS4KmSfphS8mM8uGYqQk8ePfoZni/pBPzyl+hV8hTWrRtSv0xVcaK2tv5xw58bblAAiilT\njH/ttrupH6t8n3DVY80WRTFgANBgYuGYqK8Gt+JAgrPb+8lXX61CVdXbAID2yVWYc7q37cnMzITI\ntojn7PZWVFU50aLEksoyx2Y51SkmkomUlHyEnyaI+ftQOwfzJiT56HDno6bEJqNbGlJRCXQMxj1x\nWIdkX7TYdT95stXvIUHrMo1RS7HTwO317ot9PzW+DLKSanAmb/kCIHGPcSkuFdwkarzsEUgzJ4Dd\nmyonGjnS8jsw4bKiwZBiVv8MpLc7DjS4KWVc+T9qCe/a8nJcdPSo180IpmQgGd53aQSxXzHIs+MT\nBUPL+9gX6Iw+/X4V89aYcFnRsF/XBwV+kipIzkuOaV0/jdcHDWNnXzxiF0o+OmR2wLWjr212vSee\nALKyml7m/d5siD1ezidc3dt1R5/2fRzfbjS9evVC14Y1sjZwP7WG8WpZi8eETZtwBNnIzz8v5u35\nKeHy/VWKRG5jr0UrCNApuxN+MP4Hza5y5ZVxbI/r6hzfYsesjqjNPc3x7UZzww03AADmzIn7WxPF\njZ8SLh81JTFxvN6+eMQuUS+JbjF2GRlxa0dQxPpZY3LeWKIf45zeXRI9Xm4RKGu4vJByLAUS1jnX\n5q4yI2oNn1x+Fcy9NpitJvt4fuIse3uQ/RNjP51TByfhCp1ZimDY8kHISxoasdivV5txvN4+xs6+\noMTOL3ttLPEqLJyH/Pwp7jcmYILyWfOLoMfLiU7e0H5veVubNhmvs/CSUA+XHzqnWcNFRPb44ADm\nmKQkoK7l+qzMzL5ITc2PU4P8Kz8jHzUnfXyvIYoD506V0tMVAwb0BfCpY9sM56chxeAmXH5IV2PA\n8Xr74hW7RBySbil2tdmCs/AdAN9p9fsEoQYuLy/6Oom0n7r9aV4wakHE40SKXTwwXpE6dUrBSy89\ng3/8o1v9c9Lcp7iZGq6WjkN+Srh81BQLAnCQj0VhF6B7HG6ZNSInB70SqBDhwIFSFGZ96Mi2/DoU\n7aqcbGQMDcbEwU4Y7+9bJToqlk9z797G7XzckJWVhTPPPNOdjVOb4tTJHBOu1ghYstXSeH2f3kDR\nEPfb0DszE5+efbb7b+Sw5mJXWbkWXbLK49uYgGmxTiQtDbjqqri1pTkZSUl4fcQIr5sBIHpdTY8e\nP0T37rfEpzEuE3G2EDw8dj179sTy5cud23gCCnoNl6fMGi4rmHARtYJICkRqvW4GUgKW/PtNsgjG\nxzLe5wMpKblIScnxuhlEvuB1RU9SjMfe0Tk5cUu4LrkE6Nev5XWCk3B5/X/YJo7X29dc7LKzi3Aw\ndyTQw5nxWLtzK93QtSvKRo1ypA1Oi8fnLmtQFlI7+WOKidbifmofY2dNUOMlALKSkiDi7Hdx374P\nIiWlfWwrmzVcyTEkXB1SUrDxzDPjlnD9+c/AoEEtrxPcovkwnIywbSko+DoqTu+Hjrm5rd5Wa+oE\nMpOTcU5Aemjc0Psnvb1uAlnUsSNw111et4KCqFNaGirGjcNo/NnR7XbocD6Sw+6Ut086O3rlB4cU\nXeDXq6U4Xm8fY2cfY2dNW4lXXh4wd66z22wrsXNK0OPlZP9GU9t6T85o/gWbNiEjxgzqu+Z9Qv2U\ncAWvh8uPidXQocAJ9rIRUXxlZnrdAmqLnOzgsLqp7BgzqJ/1MW4Iz4Qr0XToAJxselFQx+v9wErs\nOKociZ87a4IYr6QkoLrave2v3r4a63etj7peEGPnJcarZc3OwQXYupfijBnAe+8Bn33WikY5xEe5\nH5E9rT3ZSsSJT4la62RdM2eRRHGWnJyNv+E8W6/t3x9YtszhBtkUnIQroF0YQR+v91I8YpeoE5/y\nc2dNosSre3o67ukd34sZEiV28dLW4xXtIremlqemtsfjuN7WPFx+EpyEK8SPNVxERD7QITUVMzp2\nrH986aUeNoaoGW31W5w1XC5zZLz+j39sk4kmax3sY+ysYbzsY+ysYbxawUYNl58Er4erLUpKapMJ\nV7zEax63zp3j8jZERIHQ1KH3mGRb3k4SgIs6dGh9g1op2td0sBKuESOM+8ABEf+n/Fz03NbH61sj\nLjVccUxkHZinNWb83EWaePpE3DTmpmaXexmvrKyB6NTpcle2HY95eRP1s5aV5c52EzVedjU8BO9H\np+avVGymhktE8Mrw4fWP033aQRGsIcWLLwZSUppMIxO1+JmoNWYNnYX05HSvm+G5nvk90TO/p2Pb\na5feDnDodp7Z2UXIzi5yZmNhPvkE6NLF8c22GaFze2rIvQ6OXelTsPHkOJyBV9GaSq9u6Y2PeX64\n7i5YPVwBxPF6+xIldikenNaEYvfIxY8gNz2OXWsBZfWzNqnnJGSnWh/6iKc+fWLvpcnIsP8+ibKf\nxgvj1TyFNOpQ+XX4HaFZwxUnfkhPiWzo1s0YDafEISJIS06MLpDMTPeGz4icZrcMxA+jjMFJuAB/\nRMyilsbra44Dx4/Hry1BE69aBz/XANrFOhFrGC/7GDtrGK+mjRz5BjbkNb6zesS3fozzcDV1RPdD\nn02wEq4E8+abwD/Xed2Kto21f0RE3svPn4BjyYVeN8NVwSqaDyCO19vH2NnXXOxeLCrCee3bx7cx\nAcDPmn2MnTWMVyvEWMPV1Gm0HwbImHARtSHf7NTJ+OXcc4ETJ7xtTIAN7DwOaSfdmcqBqK1YtAjI\nyYnPe3FI0SHxmrjSjjY1Xu/wRFPxiF1qcmpCDitGjd255wLn2bsZbCKy+lnr3XkivjbyCXcaEzBt\n6hjngLYeryQRZCQZqcfNNwPZYRf7/qZ/f1x72mkR60d8u0ep4erZxHQQ8RStFy04PVxRkqp4TmBJ\nzdi3z+sWWPa3uX/zugmeGzt2LNpzmJGIYtDa/o0x7dqhdNSoJpelJiUhuYXv8u5paTjWurf3VHAS\nLsAfg7AWcbzePsbOPiux+8UvfuFeQwKCnzX7GDtrEiFeXnVwZJ5xBo6dPOnJe8ciWjKaEEOKRETk\nrEk9J2Fo56FeN4N8yC9VPA1v4RNqll+7Zphwuaytj9e3RiLF7o03gKFx/O5KpNi1xrnnAsXF0ddj\nvBpLSUpBVmr0GVEZO2uCHy/vsq0jGzfafu2ddwL33edgY2zwbEhRRB4AMB3AcQCfALhGVQ/F9OIV\nK4COHZ1rzBlnAn0zndseUQPxvHE1ndK+fWRRLlEsunb9LrZs8boVbVf4zatb6q2yMrR53nnuXycU\nrTle9nC9BqBIVUcA2Argtphf2aOHcT8Kp+TmuHZtaiKM13uFsbOPsbOG8bIvEWOXnt4VR450dWXb\niRgvd0RmL/O7dMG/TZ/uUVuc4VnCpaqvq2qd+XAdgO5etYWIiIj86+lBgzCvSxevm9EqfqnhWgDg\n5RbXaKFKLyUpBZ2yOjncJGcEf7zeOy3FLjMpCWkBvGo1Xvi5s4bxso+xs4bxsu/N1atbXL588GAA\n/p0mytUaLhF5HUBTKekiVV1prnM7gOOq+lxT25g/fz569eoFrF6N/JQUjCwtre+SDX1wJ0+ejL/P\n+3vE44bLW3rcDd1iWv/o0aMoLS3FOSPPiVgONL/9TZs2Nbu9QxUVOJaWWv+32m1/UB9/vPFjlB5t\n+v8nAGwyJ7lravmv+/dHaWkpSsvLEYr/kSOlWLcOGDjQH39fU49ramqwYsUKW6/fhE1Aqb/+Hice\nh7ixfWNqOH/9vX573Fx8Nv5jIyrLKxGutInjb/gyP/w9Tj3+4ovQ3+fs9kO8/vv82n6cfjoAYP36\nvcDBTUC/fvXLN7/7LjBunLHepk2oFTGujIlj+5qKR2lpKSoqKvD222iZqnr2A2A+gDcBZDSzXOvd\nfbfqHXeoG7Yu3Ko7frWjxXU++eQT7d27t6qqHj9wXNfkralf9pOfqP74x9bfd/wdd+j599xt/YUJ\n4KaXb9Jf//PXjmzrySdVv/1t1QEDVD/6yJFN+k4JSrQEJV43wxVnPH6GvrXrLVe2PWOG6h/+4Mqm\nE0Zmpmp1dePn/7njnzrmyTFaVVWlMC5Ni3/jPHT55apt7E+OyYgRf9AZM95xbfvLP/9czy9ZpFu2\nfEtRUqIP7zj13fxFTY12LCtTVVWUlGjG6tWNXo+SEu33z3+61r6WXHqphvaTJnMeL69SvAjAfwCY\npKpBnjyWiIiIHFBcWIjzMRhffVXR4nqjcnLQLjm5yWU5zTzvNS9ruH4DIAfA6yLyjog85mFbXNOw\nm5Nix9jZx9hZw3jZx9hZw3i1TESarcFa98Yb+HqHDsbvo0fj9REjGq3zr7FjsWr4cFfbaJdnPVyq\n2t+r9yYiIsPq1YDH9/wlatbgrFOT7+YkJ+O/zML41KSm+4v6OjlllMOCcy9Fv9xLwKJQgR1Zx9jZ\nx9hZ05bjddZZrXt9W46dHYxX7LRBrPweOz9PfGqdTy/1JCIiImpJsBKuAOJ4vX2MnX2MnTWMl32M\nnTWMl31Bjx0TLgq8IUOAr33N61YQESU+DWh5TzxEC01wargCyu9jzn4Wa+zGjzd+unc3foifO6sY\nL/sYO2sYL/uCHjsmXJQwzAmHiYgowNq3vxC5uWO9boZliVM036sX0KeP161o0q23Aj/8YdPLgj7m\n7CXG7pT0061dt8/YWcN42cfYWcN4RZeW1hFZWf0aPR/02AWnh2vuXK9b0KzsbK9bQIkuZ3gOaj6r\n8boZRERkU3ASroBqacx5aBGQntrs4jYv6OP1XmLsrGG87GPsrGG87At67JhweahzZyDNn7d8IiIi\nIgsSp4YroII+5uwlxs4+xs4axss+xs4axss+v8cu2rQQTLhskGRB7hm5XjeDiChuxo8f73UT4urq\nq71ugT/xhi/2cUjRhpTcFIz828iY1g36mLOXGDv7GDtrGK/oBgwY0OTziRq7884D7r/f+e0GPV7n\nnXceUlLSPHnvoMeOCRcREVEDOTnNT/fTlrVr1w51dV63Ipg4pOgyv485+xljZx9jZw3jZR9jZw3j\nZZ/fY8eieSIiIiKPMeFyWdDHnL3E2NnH2FnDeNnH2FnDeNkX9Ngx4SIioqimT5/udROIAo0JV4wK\nCgpwxx13WH6d38ec/Yyxs4+xs4bxii43t+mpcBg7axgv+/weu2gpAhOuGOXl5WHBggVeN4OIiIh8\n6IwzWl7OhMtlQR9z9hJjZx9jZw3j1bzs7Gz8+Mc/RseOHZtczthZw3jZF/TYcR4uIkpYw4YBzeQJ\nZMHdd9/tdROIAo89XC7z+5iznzF29jF2hnvuASZMiL4e42UfY2cN42Vf0GPHHi7yxIV9L0RBVoHX\nzQgOnhoREQUaEy6XBX3M2S1fH/D1qOswdqcMfGIgji06FvP6QYrd9Wdej665XT1tQ5CfT16ZAAAg\nAElEQVTi5TeMnTWMl31Bjx0TLqIASCtMQ1qhNzeMddt3Rn/H6yYQEbmOAxUuC/qYs5cYO/sYO2sY\nL/sYO2sYL/uCHjsmXEREREQuY8LlsqCPOXuJsbOPsbOG8bKPsbOG8bIv6LFjwkVERETkMiZcLgv6\nmLOXGDv7GDtrGC/7GDtrGC/7gh47JlxERERELmPC5bKgjzl7ibGzj7GzhvGyj7GzJhHiperN+wY9\ndky4iIiIKCYiXrcguJhwAeh2Yzd0nOnOHW6DPubsJcbOPsbOGsbLPsbOGsbLvqDHjjPNA8jqn+V1\nE4iIiCiBsYfLZUEfc/YSY2cfY2cN42UfY2cN42Vf0GPHhIuIiIjIZUy4XBb0MWcvMXb2MXbWMF72\nMXbWMF72BT12TLiIiIiIXMaEy2VBH3P2EmNnH2NnDePVWF5GHsZ2Gxt1PcbOGsbLvqDHjgkXERE1\nMqjjIDw87WGvm0GUMJhwuSzoY85eYuzsY+ysYbzsY+ysYbzsC3rsmHARERERuUzUq5sixUBE1M/t\na621O9YiWZIxtnv0OgkiIiKv3X03cPKk8S81JiJQ1SZvgMSZ5j00vsd4r5tAREREccAhRZcFfczZ\nS4ydfYydNYyXfYydNYyXfUGPHRMuIiIiIpexhouIiIhiwhqulrVUw8UeLiIiIiKXMeFyWdDHnL3E\n2NnH2FnDeNnH2FnDeNkX9Ngx4SIiIiJyGWu4iIiIKCas4WoZa7iIiIiIPMSEy2VBH3P2EmNnH2Nn\nDeNlH2NnTdDjVVwMzJ3rzXsHPXacaZ6IiIhi0rev1y0ILtZwERERETmANVxEREREHmLC5bKgjzl7\nibGzj7GzhvGyj7GzhvGyL+ixY8JFRERE5DLWcBERERE5gDVcRERERB5iwuWyoI85e4mxs4+xs4bx\nso+xs4bxsi/osWPCRUREROQy1nAREREROYA1XEREREQeYsLlsqCPOXuJsbOPsbOG8bKPsbOG8bIv\n6LFjwkVERETkMtZwERERETmANVxEREREHmLC5bKgjzl7ibGzj7GzhvGyj7GzhvGyL+ixY8JFRERE\n5DLWcBERERE5gDVcRERERB5iwuWyoI85e4mxs4+xs4bxso+xs4bxsi/osWPCRUREROQy1nARERER\nOYA1XEREREQeYsLlsqCPOXuJsbOPsbOG8bKPsbOG8bIv6LFjwkVERETkMtZwERERETmANVxERERE\nHmLC5bKgjzl7ibGzj7GzhvGyj7GzhvGyL+ixY8JFRERE5DLWcBERERE5gDVcRERERB5iwuWyoI85\ne4mxs4+xs4bxso+xs4bxsi/osWPCRUREROQy1nAREREROYA1XEREREQeYsLlsqCPOXuJsbOPsbOG\n8bKPsbOG8bIv6LFjwkVERETkMk9ruETkVgAPAOioql81sZw1XERERBQIvqzhEpEeAKYC2O5VG4iI\niIjiwcshxYcA/NDD94+LoI85e4mxs4+xs4bxso+xs4bxsi/osfMk4RKRGQB2qup7Xrw/ERERUTyl\nuLVhEXkdQJcmFt0O4DYAF4Sv3tx25s+fj169egEA8vPzMXLkSEyePBnAqWzX749D/NKeoDwOPeeX\n9gTp8eTJk33VHr8/Zrz4mI+D8TjET+0pLS1FRUUFool70byIDAXwNwBHzKe6A9gFYIyqftFgXRbN\nExERUSD4qmheVTeraqGq9lbV3gB2AhjdMNlKFA2zcoodY2cfY2cN42UfY2cN42Vf0GMX94SrCezC\nIiIiooTGeykSEREROcBXQ4pEREREbQ0TLpcFfczZS4ydfYydNYyXfYydNYyXfUGPHRMuIiIiIpex\nhouIiIjIAazhIiIiIvIQEy6XBX3M2UuMnX2MnTWMl32MnTWMl31Bjx0TLiIiIiKXsYaLiIiIyAGs\n4SIiIiLyEBMulwV9zNlLjJ19jJ01jJd9jJ01jJd9QY8dEy4iIiIil7GGi4iIiMgBrOEiIiIi8hAT\nLpcFfczZS4ydfYydNYyXfYydNYyXfUGPHRMuIiIiIpexhouIiIjIAazhIiIiIvIQEy6XBX3M2UuM\nnX2MnTWMl32MnTWMl31Bjx0TLiIiIiKXsYaLiIiIyAGs4SIiIiLyEBMulwV9zNlLjJ19jJ01jJd9\njJ01jJd9QY8dEy4iIiIil7GGi4iIiMgBrOEiIiIi8hATLpcFfczZS4ydfYydNYyXfYydNYyXfUGP\nHRMuIiIiIpexhouIiIjIAazhIiIiIvIQEy6XBX3M2UuMnX2MnTWMl32MnTWMl31Bjx0TLiIiIiKX\nsYaLiIiIyAGs4SIiIiLyEBMulwV9zNlLjJ19jJ01jJd9jJ01jJd9QY8dEy4iIiIil7GGi4iIiMgB\nrOEiIiIi8hATLpcFfczZS4ydfYydNYyXfYydNYyXfUGPHRMuIiIiIpexhouIiIjIAazhIiIiIvIQ\nEy6XBX3M2UuMnX2MnTWMl32MnTWMl31Bjx0TLpdt2rTJ6yYEFmNnH2NnDeNlH2NnDeNlX9Bjx4TL\nZQcPHvS6CYHF2NnH2FnDeNnH2FnDeNkX9Ngx4SIiIiJyGRMul1VUVHjdhMBi7Oxj7KxhvOxj7Kxh\nvOwLeux8Py2E120gIiIiilVz00L4OuEiIiIiSgQcUiQiIiJyGRMuIiIiIpcx4SLPiEg7ETnN/J2f\nRYtERMx/GbsoRKS7+W+TtRVE5C+JuK/yQN0KIpIrIlPN3xlLC0TkdgAfAPgPAFDVukTcwdwiIjcD\n+DZgxM7j5viWiKSKyKMAPhOR6aqq/JxZIyIjRaSj1+0IEsbMPhEZIyK5ABJuP2WSYJOIXARgO4BX\nRaQXv/RiZx6I+gH4jfl4emiRZ40KCDPJXwngVgCTROQs83nuy007G8CXAO4G8AMREeWVQjERkc4i\n8ncAbwI4X0RSvW6T3zFm9onIUPPY9hiAXwO4zeMmOY5XKdokIlcCqAAwC0AHVZ3Pg3nzRCRLVY+E\nPc4DkAxgLoC+AH6sqgcZw+hEZDKAQwDOg/HZW2Q+z9gBEJH2qnrA/D0TQL6qfi4iawGsUNXfMFbR\nicgZACYAyATQHsAyVd3ibav8jTGzR0R6AbgfwBpVfVRExgO4D8A1qvqJl21zEs+KYyQi3USkS9hT\nr6rqegB3AZgoIpM4XNGYiGSKyAoAz4d3savqIVX9CsBaAHUArjCf55dgGLNH63IRyQh7eo2qvgPg\nPQAd2ENoEJG+IvIXAL8PxUtVjwLYa65yG4AbRaQjP2dNM0+EQsoBPApgKYBCAOPNoR4Kw5g5YieA\nJ1X1UfNxhfmTUN+pTLiiEMMSAO8C+GnoeVU9ZJ4lHwbwCIDFIpLEA/kp5pfeAgBpAPJhDIElmctC\nn713zZ+zRCTHXJbsQXN9R0QmAvgYwHIAY5o48GyAUQc3zezVqWvjQ4u3AagGsAPA9wDjcxaKi6qu\nBrAJwB3mshTPWuozIjJJRD4H8FroOVWtUtWTqnoQwP8BGAtguFdt9BvGzD4RmS4im0SkLwCo6kkA\npWGr1AAYCaAmkb5T2/LBOVb5MM5UbgCQKSJfByKTAlX9JYBcAJeZy4Z40E7fEJHTAUBVjwF4XVVn\nwjjrmwugu7mszkxYawD8BcbZzH+JSBmM+i4CUgHMBHAngGIAHYBTRfJmD+FbAA4D+JqIjAFwmjdN\n9YaInBaWOP0EwM0AXoJRP9M7LAkNJatzAHxdRB4A8GcRGRD/VvuL2QMzA8AiGD2m3zKfTwlL8l8A\ncAzAmLDXtFmMmX1iXGi2EEZsfms+J2bSFTIIwG5V3eVBE92jqvxp8APjrKQ/gFzzcWcA2QCuB/A/\nAJLN5wVAivn7mQD2AXgHRsFfmtd/hwdx6wHjbG8NgF8AGNZg+f8CuAVAhvk4yfx3AICtAN4HMNvr\nv8PD+A0AcDuAr5mPQ5+tLAB/BXBlWMwk7HW3AzgAYDeAIq//jjjFajSMntGVAJ4JfabMZZ0A/BzA\ng03Eqi+Ms+c3AJzl9d/hYfxSzM9blvl4kPnvNwHsCltPwj5zvQE8Z+7j5QDyvP47GLNg/JgxyTR/\n7wJguPn7bgAzQvENW/9SAD8yX/dzAFd4/Tc48cMerjBmvdGjAJ4H8P8APAsAqvqFqlYDeB3AERiJ\nF8xlJ80zl+8AOA5giarerKrH4/4HeO8KGF+CF8GIxb+LyJlhy38N4BIYySz01JWdXwfwkqoOU9UV\ncWyvb5hnfWtgFNr+UEQWwehdhRoXGzwD4FswDuDQ0FHMOLO+C8C9qtpV20CBrtmDcDOAx1T1EgDp\nAH4pIlnmKvsB/AnAQBEZq6oqxvQQOQBmA7hOVSeq6lue/AEeE5FvwviiewBGvVt7Vf0IAFT1JQCf\niMjPzNVTAISGdMbDuEhoO4BzVPVQfFvuHcbMPjGmsHkTwKMiMkBV96jqe+biHwL4FVD/XRrKSS6A\nsa++ASAPYcO2QcaEK1I3GGctvVT12wDyROTfzSudAKM25M8ApgHGl5552W8BgDLzC+/3QJutQ5oC\n4E0zQXgUwBYAN4YWqmoZgH8AuMqM633m879U1f8HtOm6mqEAfqSqPwDwYxgHmX8PLVTV5QBOAJgE\n1F8NBQCvADhNVR80n0/4+JnJ5kkAX5hPXQ9jGPpCEUk2E/ktAF6EUSR/D4DL1Kiv+amqPuNJw31A\nRLJhDIVdoqozABwE8G8iUhS22vUw4pamqidg9O4DRtnEBFW9VlX3xbXhHmLM7DNPuC+BUU5SAeDH\nInJxaLn5fblPRH5sPq4zvzsHwEhSb1TVG9SsmY77H+CwNp9wNajhUABfikh/8/F/ADgfQBEAmL1W\nfwLwjoisEZHVMBK0irBEK8VctzZef4MXRGSiiLwqIj8TkUvMp/8Oo6cPqvo5jKLR9PAdDMBHMOoe\nvgFjeDZ0YYI0MY6fsERkrBiTI7Y3n+oCM5GHUdj9EoDhDXoIb4fR+1Vu/psBoFJV94dqRxIxfiIy\nR0T+T0TuFpGzzaerAKSKSKYaBcr/DaM+SwBAjYtZesAYhh2NyILcNkVE2oV+N3vqBwMIXTH8IIAM\nAOeFfYY+APAkgFVmj3+x+dqlqro2vq33BmNmn0ReuNMPxvDqvwDcA+PYdq6IDA5b5xoYyeoQEbkV\nxpQa31PVGaq6SUSSJEEuSGuzCZeInCUirwP4rYg8ICJjYRzEAaMIMkmNaR/KYe48plEwhs7SAdyt\nqu+HbzcRv/DCmQeYRTCuzFwGI4FaZiaaywHUisg3zNW/gFGXVWi+tgjAVQAWqOq5qvq2ebCqF/c/\nKM7EmBhxGYyD8y0whqkBo0ewu4iMNs+QP4YxxHiB+bpMGMNo7QH8RFWvUtVjocRejSujEip+YkyJ\nsQzGla4PwtjnrhGRDjCu0JwO87Olqr+DcXA/33ztVAAXA5iqql9X1S+aeIuEJyJ3Avi7iNwvIrPM\np/8AYKh5jPsAxvQiPQD0C/sMKYze1CpVfTzuDfcQY2af+d2wREQuNZ96C8ZdHkaYcXoVxpDr2NBr\nVHUzjNi9AyDb7In+xNxeiqrWaYJMLN4mEy4RmQRjnpTfwih4PAKjcG8vjG7Py2AU3gLALwFcJqfm\nkJoE4DeqOlZV/5YI3ZwWpQH4F4ALVPVZVV0GY5hwjtll/iKM7vYUVd0P46wwNIfUNlWdrqr/BdTv\nTAmVJLRERNJhJO97VXW4qs4DkCUiV6vqZwBehnH1DtS4AjE8NnkAVqlqoao+Z24voYetzV6qtwF8\nU1VLYNSxFcIovl0O47M4TcyrYmEM94fq3l5X1XGqusaDpntORLqIyPMwktBrYCQIN4tRb/o+jB7V\nSebqqwGcAWM+PIjIFTCGxE5X1R/Fu+1eYczsMzsw3oERu48AfF9E5sO4y8MXAM4B6pOrz831ICId\nReRJGPMx9lLVu8O3m2gdGG0q4QpLjjbC6CV43kwSPoQxZgwA/2n+Pk1EUs0vwtUwY6WqS9ScnK2t\nJQxAfQF3qaruFaMQORXAVzC6imEmYHtg9Bx+D8DXYE48qcYklPWJQqLtTNGoMQXG32DUaIU8hVPJ\n/TIAvUTkJvNxLsxaEDUKTVcCbWPYOmxY4glVPWDuax/AqJcMTUD8MIx99UEx7s15NYwvRjLmI/uT\nqs41e+Ffh9FrehqMXocvYRzjClR1J4yrXEPHwD+o6vc00S7Jj44xs68rgPtVdYHZu7cMwGhVrYSx\nT/YT43Z4gFF6cr7ZW7gPwF2q+k017gYRPq1GwmkTCZc5HBMqchdVrYJRXxSyE8aMtpmquh1G79eZ\nAF4Qkfdh9DR8Fba9NlNvFEqOwneCsOGZk+bwV3cA4V/+34HRW3M2gFvVuIoHYa9P2EQhBlvUmJ8s\n5DwYCSrMg/giGAejN2EMiT0LNIp/wn3uwmtmzH0rNNfYEfPfk2LUW9bATKrM2pjFAEpg9KReqG3g\nKs1YmL2DK8OfAjAMwEGzJ/9FGLUyK8xh2z4wrjBOyM9XNOZnjjGzKOy49DcY9xUOPT4NxpXqgJG4\nfgjgPjEmc/43GOUSoRPH3ea2khOxNCJcwl/RJCK3wSiuvV9V62etVdVaM8Oug3Hp7q5QD4yqviYi\nJQAuB/C5qpaGbzORPxAhZo/CSTNOEfdBDDET2IEA2qnqe2ZtTS9VfRtGQXx9UTyMuZASYhw+FqH4\nNXw+lGyavVQCY3LTv5rPpanqWrNupL+eunQ6oT9zIlIA42bci0SkH4x7bJaHLQ/d97A3gJ2qelyM\notsCNa58/U8v2u1XYXWRh8OeLoBxjPsCAFS1HMBNYkx3cDqAb5snT22C+eXe6MSPMYsubH9E2L9V\n5rIkGImqwJieBaq6B8CTIqIweqGPAri9Yezawol4wvZwyanL48sAnAtj5tqGQl9ipwF4yezOvEVE\nzlDVE6q6QlVLzQ6thK6XaSiULIjIFAD/IyKhWfQbxqE/gDIRuRFGt/s54QvNpFbbSrLVcLhURDrJ\nqdsZhd+d4CSMGqQvABw1h8TuN5cdDSVbksDTPITFaj+ModStMHoRmrtTw+kAks1Y/R5ATlwaGhAi\ncqGI5IZ68s3nQj0OvWHUzkBELhGRcwFjDilV/VVbShzMY1LoxGeYmXxp2HLGrAnm92BSg1g1zCFC\ny6bCKMWpn8JGVX8LY5qH61X1RFv7TgUSOOEKfeGp6hswrmi6Rpq4tYK5c/WGceuedTDqQzaHLzcT\nhoTOvhuOm4vIGDGmH5gD45Yyl5s9MLWhIVVz1SIYc22dAeOKsN+Eb6etJFohYQfyiWb8noBR7N3U\nGdx5MK60+wuMebgebbA8IYcqwobkQ7HqB2OfKwBwk6r+IXz9sAP8pTB6nbMATFLVVXFstq803F9N\n34MxXFMvLHYTYUzR8jsY090cQxulxlxPA0Tk/2Dcf/P0BssZswZCSakZu8Ei8h0RyWh4fDeT/Q4w\nerGOisj/APipiBSY+/yJsMQtob9Tm5KQCZcY83YUishdYszb8yCMG2GODz9QmTtWFxgH8joAc1X1\nR2oUN4evk/BCf6cYczsBxuX1S1R1AYyD0jGYNwSGMTwYisvHML78rlHVbSKS3MRZT8IK7/00//Yc\nEXkQxlVON8GYZbqv2SvT8IwwF8bFBveo6mxV/Veixy6sx1NF5HwRWQtjTrb7YdzC4z/M9VLDXhM6\nE34Kxmzdt4eGMNqa8F4BEUmXU5ffA0Zd6lfhPTZhJ0dDYfQ+b1BjSpb1cW24hxr2pIhIPoy7M/xF\nVb+lqp82WN7mY9aQeaKdISILYJxAzgHwCzGmU2p4AtAOxmSnywGsVtVpqro/fBiyrZ2IhyTEwV1E\nHhKRO8zfO5v/Mw/CSKamqHElxH8DmA/jvoih1yWrMUHn2eaOt8VM1hIiLtHIqaGu0L9X4FRSVQSj\nKBQwLs0vBXCxiHQ1z3JSgfru9TdCiYeq1raVnSkseagVkXTzb6+CcdXhEABbzeT9WgDXiki+nppJ\nGQBeNQ/kfzO3l5yIsROR00XkIoksjD8TwB0wrhZ+0IzdgwB6ishM80w4L7Q6AKjqH9SoD2xzzONS\nfa+g+eXVE0ZtzBXmidJxAGPMz2NouFbNdZ+CcW/TNlPvFkoCwnpSC8xFqQB6wbxwSkTSwl/XlmMW\n0kSSmgxj7sV/U9UxMCZpPgjjqs125glU6HszH8bJ0xg9dUV/mxs+bEqiJBZ/hHHfvoEw7tc01fyi\n+x+cuhz1cRhXmHxdTtXFhK6EWg8k3iRr0YT9naEvwjQYk/uNg1GIPFREuqlRSFoDI37zzNc2LHjU\nttJFLKeueg3NwXMTjDq2O0VkJoxemhMwJtBNU+PKufdh3Pqo/gvAPBFI2GkezCThfhi1HNfBuFQ8\ndL+5AgB7VPVVc9108/l7AdwqIv8J4GURydMEHFaNlZhlEOZxSUXkPBF5UUS+C6PXeRqMi37uh3EP\n2NHmSVEoyQhNZ/NHbeLCl0QlYbVGZk/qOgA/F5E5MCYPfhvmUKKa9701e77C98c2FbNwYZ+f/uaJ\nYi2M79NeItLdjMtaGPMDTg29zHztJrMX+oicmok/oY5tdgU+4TL/Z66BcXPL+2AU3c4BADUmS9wB\nY8gwFcDvYPRydTCXRwwXJvqBXUS+JiK9wx6ni/z/9u496KqqjOP499FSyRs2+kdMZomAV5BMRh0M\nZiryFohpF3WUSzgjKWoJljcI7yga1TCZxYSNodjYNMLQaAiGDI6EmjpjOoOgjjqlhTdMRfj1x7MO\n74EQeMXDOe/Zv88/HvZ7ztnLPfvsvfaz1nqeGEcpHgrMInPNDCJzzDwFzIyIk8hUD8uAHrULU9WU\nm90DZEmPncu2M4C+ZALdNWSHYhW57PnHZIqHQWRk9W+b+t42Pu/GAD3JbNynABcDZ0bEMDKB6Su1\nKJak9yJXw95FdrpWkvUPK1fsF9YPT59OlnDaqWw7iyyePJeMos4jh6QvJitgTCGPW61cVKXmUEZE\nj4joFx1FzImIgeT83O+QiXFvJkc+3gFOjojBEfHpyOSbp0Jb/x43q4wUXVFe946cf3Ub8LuIGCDp\nPvIeUcsjuIgs6D2kPJhro+/bQW2e5qGzunyHq85YMsnmWuCNcnGCzAEyFDhbmTjyLFWwzEfkRMY7\nyDI83yub3wceIYt0f6VcnOeQtef2JXNCzSFLGY0nOxEfKGvXVUZEdIusj3YNOX9hft2fjyRvgN8n\nz7OLS/TvRvLGN4o8N6dKenG7NryJSpRgCHBbGeLaTVlP7TKykO1ScoXr2RHRPSIOB6ZFxOGS5kq6\noYq/U1j/ELmWjBjsSSlXREZkpkiaIelqMnXGz8p7zyJ/z19vRpubqXROryHTq0wm65BeVv78STKa\n9U1y3tbU8oD+c2A5eV1bSFbB+PV2bnqr+SNwUYmqXgjcL2kwOdd0annIvAE4IiKOVtaYfBC4U5tI\n+Fqlzv7W6vIdrhJm31FZCuVm4EfkD25CRPQjJ+Q+QHYskLSyNrZfMWvJCNUsYHREjCzbl5IdqdpQ\n4UNkNGYk0FPST8lViAPJDscmozRtrgeZX+woSTOBd9WxsOKfwB+AF5SlZO6NiL7kcM9vyJxSF0iq\nz0nW9kqU4H06MnHXctzNLNv2J+dwHUDOr7wdWCDp8e3f2tYQESdFxMNkriLIh51XgGNLlKsHdTXo\nyJtfn8hUECuBK8ncZJVJ/lqmi7xc/vllMpJ1FTA+MqXNbsB3gT7AcZKuL3O5oszNGkMuxLhu+7e+\ndZRO/iKy83mjpLHAYxGxhKxx2A0YJ+k58ryspbB5pIwk2Vbo8h0u2GBOzE/IUHt3YCpZ+uN9ZamG\nv9e9v3IhzjI0s4osFTOOXH1zKXkO3AXsHRGXR8QJ5M3xz8AL5eMDyTD8YEl3bO+2t4B3gW5l+GEI\nWSdsUjlWc8mCrCsBImI0+fR8iKRZ1KXUgMqdewuBXpELWdZGR1qWecChZa7HODIq2FelRmSF/QsY\nAFweEReSFRzuITsNxwNXAyMi4rDy/oOBpWWOJZLe0YaJO6tgFbBPmTP0GvkTW0xWILiWHPZaQc43\ners8hM+lY/jw5Qoes80ZA5weEZ8nK4XcL+ki8uFxUkTsRw5dnwPVeYD8uES7XP/LePG6yCzdEyUd\nVCYs1yZEbjKzcJVEZkg+UNK1ZaL31WQB7wnkE+BkMnx8qaRldZ+r9LGLXJE5ihym+A85pNifvEEu\nJaN+k8k5XO+RaR4eLp8dQJYGebYJTW+qyFI85wPLS6S0tv0uYLqkB5vWuBYVEdPJFDY3kUNgE8gV\nw3uTHYhzgKPJB6d9gCslzWtOa1tDRNwNrJY0IrL+7Zqy/UVyqPV1cu5uHzJ6P7WiD46bVbvOl+HZ\n48h7wxfIUnfnkA8D5ylrmq6/5zatwV1Q23S4oCNJaUT8BbhV0t2Ry1HlEwPKCp1vkHNDDiXnGQ0H\n3iSfCF9SqfNXnlwqVY5nSyLiQOB5YBdlQeUxwEGSflDmN+wv6eny3h0oizeb2OSmK0M+k8lhiMfJ\nuW4CRqnUULMOEbEXeY71JudSDiM790+TOY1mlEUrgyT9qXktbR11x+xoZWqfXSWtjqx3uKQMHRIR\nfZTleWwLIqs+PETmWbwAuEmZtsW2QVt1uGD9Muo7yPw+y7b0/iopK8JWAL+XdF7Z1gv4nEouqLKt\n0hGtrVUu6MskTdtou49fnYg4hpxfcxQwT9KtTW5SSysRhoGSBpUVd9eRcyqfA4ZJer6pDWxBETEZ\n+KqkY+q2zQGukPRY81rWtdSNFJ0CXC+pd0TsJWlV+buvbdugHTtcg8mSKZN8Yvy/iLiFvOnd5x9P\n55SVd/sCJ5JDFU+T849ebWrDuohaBLrZ7egKIuJ5YLyk2SVaOpCMmC5qctNaVjlmI4B/AL8FXgPG\nqqKpRT6qupGi+cAvy0jRJ4C1/v1um7YrjCtpITlZ1zZtf2CX2KiWlW+GWybpgxJB7QtMKOeaj91W\n8jHqlEvIAt2zy7D+X5vcnq5gAjm/cimZjqTqaR4+ktLZ2h1YTUZVK5ub7OPWdpBrteIAAALJSURB\nVBEu27z68LBtmzLPrZJFWK3xIpMS/wLPBdxqZV7l7aqrh2ud55GixnCHq6K8wmTb+PiZmVlnuMNl\nZmZm1mBtkfjUzMzMrJW5w2VmZmbWYO5wmZmZmTWYO1xmZmZmDeYOl5m1jIjYMyLOLa8/U+rkNWpf\n/SLi+EZ9v5lZPXe4zKyV7AWMBZD0iqTTGriv/sAJDfx+M7P1nBbCzFpGRNwJDAWeIQvnHiTpsIgY\nAZwMfAroBdwE7AycCbwHnFAKivckk4XuA7wDjJH0TEScBlwJrAVeB74GLAd2AV4i6xWuAKaVbf8F\nRkp6thP7XkgW6B5EVvEYJWlpY46UmXU1jnCZWSu5BFguqT8wfqO/HQIMB44ErgHelvRFYAlZ2xLg\nV8D5kr5UPj+9bL8CGCLpcGCopDVl252S+kuaTdbgO7Z850Tg2k7uW0C30vaxwIxtPhpm1jbarpai\nmXVp8SGvARZIWg2sjog3gHvL9ieBvhGxK3AMcHdWXQJgp/LfxcDMiJgN3FP3/fX76A7cHhEHkJ2n\n+uvjZvdd975ZAJIWRcQeEbGHpDe38v/dzNqYO1xm1lXU18dbV/fvdeS1bAdgVYkwbUDSuRExADgR\nWBYRR2zi+68C5ksaHhH7AQs7se8P4zkbZgZ4SNHMWstbwO6d/EwASHoLWBERp0IWF4+IvuV1T0mP\nSJoIvAp8Fnhzo33tAbxcXo/szL7rXn+77G8g8Hppk5mZO1xm1jok/RtYHBFPAlPoiBCJDaNFG7+u\n/fsMYHREPA48RU7AB5gSEU+U710s6QlgAXBwRDwWEd8q+7suIh4FdvwI+xbwbvn8dGB0pw+AmbUt\nr1I0M/sYRMQC4IeSHm12W8ys9TjCZWZmZtZgjnCZmZmZNZgjXGZmZmYN5g6XmZmZWYO5w2VmZmbW\nYO5wmZmZmTWYO1xmZmZmDeYOl5mZmVmD/Q9nZqrdMilYBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113cebb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd['behavior'].plot(figsize=(10, 10))\n",
    "plt.ylabel('behavior')\n",
    "plt.title('Seal plotting exercise')\n",
    "plt.savefig('seal_behavior.png')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Plot the behavior of  seal K 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8WeMbBCecIN15Z/i6vD7zGenGG8NXtMPoqLTRRou3586V1luv2HB67lWvkr77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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ceb210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd[('behavior',\"K 11\")].plot(figsize=(10, 10))\n",
    "plt.ylabel('behavior')\n",
    "plt.title('Seal plotting exercise')\n",
    "plt.savefig('seal-k11.png')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Importing the image into the Markdown\n",
    "\n",
    "<img src='seal_behavior.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": true,
   "toc_section_display": "none",
   "toc_threshold": 6,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}