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Miki Tebeka committed 0799351 Merge

Merged hrojas/learn-pandas into master

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 Place for everything Pandas.
 
+How to install Pandas?
+-------
+
+* [Windows](http://www.youtube.com/watch?v=g4v9_K3Rq3Y)
+* [Linux](http://hdrojas.pythonanywhere.com/static/data/Data%20Analysis%20Kick%20Start.html)
+
 Lessons
 -------
 
-* [Lesson 1 V2](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%2520-%2520Lesson.ipynb)
-* [Lesson 2 V2](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%2520-%2520Lesson.ipynb)
-* [03 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/03%2520-%2520Lesson.ipynb)
-* [04 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/04%2520-%2520Lesson.ipynb)
-* [05 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/05%2520-%2520Lesson.ipynb)
-* [06 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/06%2520-%2520Lesson.ipynb)
-* [07 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/07%2520-%2520Lesson.ipynb)
-* [08 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/08%2520-%2520Lesson.ipynb)
-* [09 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/09%2520-%2520Lesson.ipynb)
-* [10 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/10%2520-%2520Lesson.ipynb)
-* [11 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/11%2520-%2520Lesson.ipynb)
+* [01 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%20-%20Lesson.ipynb)
+* [02 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%20-%20Lesson.ipynb)
+* [03 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/03%20-%20Lesson.ipynb)
+* [04 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/04%20-%20Lesson.ipynb)
+* [05 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/05%20-%20Lesson.ipynb)
+* [06 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/06%20-%20Lesson.ipynb)
+* [07 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/07%20-%20Lesson.ipynb)
+* [08 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/08%20-%20Lesson.ipynb)
+* [09 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/09%20-%20Lesson.ipynb)
+* [10 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/10%20-%20Lesson.ipynb)
+* [11 - Lesson](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/11%20-%20Lesson.ipynb)
+
+Exercises
+---------
+
+* [01 - Exercise](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%20-%20Exercise.ipynb)
+* [02 - Exercise](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%20-%20Exercise.ipynb)
+* [03 - Exercise](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/03%20-%20Exercise.ipynb)
+* [04 - Exercise](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/04%20-%20Exercise.ipynb)
+
+Cheat Sheets
+---------
+
+* [Pandas for Excel Developers](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Pandas%20for%20Excel%20Developers.ipynb)
+* [Pandas for SQL Developers](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Pandas%20for%20SQL%20Developers.ipynb)
+* [Dates](https://squareup.com/market/david-rojas-llc/data-analysis-dates)
+* [Plotting in Pandas](https://squareup.com/market/david-rojas-llc/data-analysis-plotting-in-pandas)

lessons/01 - Exercise.ipynb

+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from pandas import DataFrame"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# This is the data frame you are given"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create some data\n",
+      "d = [[1,0],[0,0]]\n",
+      "d"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "[[1, 0], [0, 0]]"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create the data frame\n",
+      "frm = DataFrame(data=d)\n",
+      "frm"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>1</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "   0  1\n",
+        "0  1  0\n",
+        "1  0  0"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# This is the data frame you have to replicate"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ans = [[0,1],[0,0]]\n",
+      "frm_ans = DataFrame(data=ans)\n",
+      "frm_ans"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>1</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 0</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "   0  1\n",
+        "0  0  1\n",
+        "1  0  0"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Start Coding..."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# How check your answer?  \n",
+      "\n",
+      "The correct answer will have \"True\" on all the rows."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# \"Yout data frame\" == frm\n",
+      "frm == frm_ans"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>1</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> False</td>\n",
+        "      <td> False</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>  True</td>\n",
+        "      <td>  True</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "       0      1\n",
+        "0  False  False\n",
+        "1   True   True"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

lessons/01 - Lesson.ipynb

 {
  "metadata": {
-  "name": "Lesson 1 V2"
+  "name": "01 - Lesson"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Pandas version 0.10.1\n"
+        "Pandas version 0.11.0\n"
        ]
       }
      ],
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "Location = r'C:\\Users\\David\\.xy\\startups\\births1880.csv'\n",
+      "Location = r'C:\\Users\\hdrojas\\.xy\\startups\\births1880.csv'\n",
       "df = read_csv(Location)"
      ],
      "language": "python",
        "stream": "stdout",
        "text": [
         "Names     object\n",
-        "Births     int64\n"
+        "Births     int64\n",
+        "dtype: object\n"
        ]
       }
      ],
       },
       {
        "output_type": "display_data",
-       "png": 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mzJjB0aNHK+8rKioiKCgIgKCgIIqKigDYv38/Xbt2rXxeSEgIBQUFNb7vsGHD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\nQn1NXdX5ulat7C9Qv/yiC+W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O02DYMLj+enjtNb3TiPrwhL6zLuw+Ynv88ceZO3cu3pV2+C0sLMTPzw8APz8/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\nIYRwK1LYhBBCuBUpbEIIIdzK/wOfDtU3W+HmjAAAAABJRU5ErkJggg==\n"
       }
      ],
      "prompt_number": 19

lessons/02 - Exercise.ipynb

+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from pandas import DataFrame"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# This is the data frame you are given"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create some data\n",
+      "d = [[2,2],[7,3]]\n",
+      "d"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "[[2, 2], [7, 3]]"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create the data frame\n",
+      "frm = DataFrame(data=d)\n",
+      "frm"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>1</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 7</td>\n",
+        "      <td> 3</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "   0  1\n",
+        "0  2  2\n",
+        "1  7  3"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# This is the data frame you have to replicate  \n",
+      " \n",
+      " * Create a sum column  \n",
+      " * The values are equal to the row sum  \n",
+      " \n",
+      " i.e. 2 + 2 = 4"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ans = {0:[2,7],\n",
+      "       1:[2,3],\n",
+      "       'sum':[4,10]\n",
+      "       }\n",
+      "frm_ans = DataFrame(data=ans)\n",
+      "frm_ans"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>1</th>\n",
+        "      <th>sum</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 2</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 7</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 10</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "   0  1  sum\n",
+        "0  2  2    4\n",
+        "1  7  3   10"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Start Coding..."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# How check your answer?  \n",
+      "\n",
+      "The correct answer will have \"True\" on all the rows."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# \"Yout data frame\" == frm\n",
+      "frm_ans == frm_ans"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>1</th>\n",
+        "      <th>sum</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "      0     1   sum\n",
+        "0  True  True  True\n",
+        "1  True  True  True"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

lessons/02 - Lesson.ipynb

 {
  "metadata": {
-  "name": "Lesson 2 V2"
+  "name": "02 - Lesson"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Pandas version 0.10.1\n"
+        "Pandas version 0.11.0\n"
        ]
       }
      ],
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "Location = r'C:\\Users\\David\\.xy\\startups\\births1880.txt'\n",
+      "Location = r'C:\\Users\\hdrojas\\.xy\\startups\\births1880.txt'\n",
       "df = read_csv(Location)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
+       "html": [
+        "<pre>\n",
+        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
+        "Int64Index: 999 entries, 0 to 998\n",
+        "Data columns (total 2 columns):\n",
+        "Mary    999  non-null values\n",
+        "968     999  non-null values\n",
+        "dtypes: int64(1), object(1)\n",
+        "</pre>"
+       ],
        "output_type": "pyout",
        "prompt_number": 17,
        "text": [
         "<class 'pandas.core.frame.DataFrame'>\n",
         "Int64Index: 999 entries, 0 to 998\n",
-        "Data columns:\n",
+        "Data columns (total 2 columns):\n",
         "Mary    999  non-null values\n",
         "968     999  non-null values\n",
         "dtypes: int64(1), object(1)"
      "metadata": {},
      "outputs": [
       {
+       "html": [
+        "<pre>\n",
+        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
+        "Int64Index: 1000 entries, 0 to 999\n",
+        "Data columns (total 2 columns):\n",
+        "0    1000  non-null values\n",
+        "1    1000  non-null values\n",
+        "dtypes: int64(1), object(1)\n",
+        "</pre>"
+       ],
        "output_type": "pyout",
        "prompt_number": 19,
        "text": [
         "<class 'pandas.core.frame.DataFrame'>\n",
         "Int64Index: 1000 entries, 0 to 999\n",
-        "Data columns:\n",
+        "Data columns (total 2 columns):\n",
         "0    1000  non-null values\n",
         "1    1000  non-null values\n",
         "dtypes: int64(1), object(1)"
         "count     1000\n",
         "unique       5\n",
         "top        Bob\n",
-        "freq       206\n"
+        "freq       206\n",
+        "dtype: object\n"
        ]
       }
      ],
        "output_type": "pyout",
        "prompt_number": 29,
        "text": [
-        "106817.0"
+        "106817"
        ]
       }
      ],
       },
       {
        "output_type": "display_data",
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+6l3sDvv27cPDwwMADw8P9u3bB8CePXvo0qWL9XVeXl7k5eXh6uqKl5eXdb2n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\nubl4eXnh6elpvaV8+vqyfXJycgAoKSn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uqNo10EOHDuHj4wOAj48Phw4dAuDgwYNYLBb7+ywWCzab7az5vr6+2Gw2AGw2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\n"
       }
      ],
      "prompt_number": 30

lessons/03 - Exercise.ipynb

+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from pandas import DataFrame"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# This is the data frame you are given"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create some data\n",
+      "d = {0:[2,7],\n",
+      "     1:[2,3],\n",
+      "     'sum':[4,10]\n",
+      "     }\n",
+      "d"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "{0: [2, 7], 1: [2, 3], 'sum': [4, 10]}"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create the data frame\n",
+      "frm = DataFrame(data=d)\n",
+      "frm"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>1</th>\n",
+        "      <th>sum</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 2</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 7</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 10</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "   0  1  sum\n",
+        "0  2  2    4\n",
+        "1  7  3   10"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# This is the data frame you have to replicate  \n",
+      " \n",
+      " * Change the column name  \n",
+      " * Change the index name  "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ans = {0:[2,7],\n",
+      "       'energy':[2,3],\n",
+      "       'sum':[4,10]\n",
+      "       }\n",
+      "\n",
+      "idx = [0,'row']\n",
+      "\n",
+      "frm_ans = DataFrame(data=ans, index=idx)\n",
+      "frm_ans"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>energy</th>\n",
+        "      <th>sum</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 2</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>row</th>\n",
+        "      <td> 7</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 10</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "     0  energy  sum\n",
+        "0    2       2    4\n",
+        "row  7       3   10"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Start Coding..."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# How check your answer?  \n",
+      "\n",
+      "The correct answer will have \"True\" on all the rows."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# \"Yout data frame\" == frm\n",
+      "frm_ans == frm_ans"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>energy</th>\n",
+        "      <th>sum</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>row</th>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "      <td> True</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "        0 energy   sum\n",
+        "0    True   True  True\n",
+        "row  True   True  True"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

lessons/03 - Lesson.ipynb

      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Pandas version: 0.11.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "html": [
+        "<pre>\n",
+        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
+        "Int64Index: 836 entries, 0 to 835\n",
+        "Data columns (total 4 columns):\n",
+        "State            836  non-null values\n",
+        "Status           836  non-null values\n",
+        "CustomerCount    836  non-null values\n",
+        "StatusDate       836  non-null values\n",
+        "dtypes: datetime64[ns](1), int64(2), object(1)\n",
+        "</pre>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 836 entries, 0 to 835\n",
+        "Data columns (total 4 columns):\n",
+        "State            836  non-null values\n",
+        "Status           836  non-null values\n",
+        "CustomerCount    836  non-null values\n",
+        "StatusDate       836  non-null values\n",
+        "dtypes: datetime64[ns](1), int64(2), object(1)"
+       ]
+      }
+     ],
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Done\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
      "collapsed": false,
      "input": [
       "# Location of file\n",
-      "Location = r'C:\\Users\\David\\.xy\\startups\\Lesson3.xlsx'\n",
+      "Location = r'C:\\Users\\hdrojas\\.xy\\startups\\Lesson3.xlsx'\n",
       "\n",
       "# Create ExcelFile object\n",
       "xlsx = ExcelFile(Location)\n",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "State             object\n",
+        "Status           float64\n",
+        "CustomerCount    float64\n",
+        "dtype: object"
+       ]
+      }
+     ],
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
+        "[2009-01-05 00:00:00, ..., 2012-12-31 00:00:00]\n",
+        "Length: 836, Freq: None, Timezone: None"
+       ]
+      }
+     ],
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Data Types\n",
+        "State            object\n",
+        "Status            int32\n",
+        "CustomerCount     int32\n",
+        "dtype: object\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>State</th>\n",
+        "      <th>Status</th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2009-01-05</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 721</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-12</th>\n",
+        "      <td> GA</td>\n",
+        "      <td> 2</td>\n",
+        "      <td>  86</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-19</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 441</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-26</th>\n",
+        "      <td> GA</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 992</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td> NJ</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 614</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "           State  Status  CustomerCount\n",
+        "StatusDate                             \n",
+        "2009-01-05    NY       1            721\n",
+        "2009-01-12    GA       2             86\n",
+        "2009-01-19    NY       1            441\n",
+        "2009-01-26    GA       2            992\n",
+        "2009-02-02    NJ       2            614"
+       ]
+      }
+     ],
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "array([NY, GA, NJ, fl, TX, FL], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 14,
+       "text": [
+        "array([NY, GA, NJ, FL, TX], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 17,
+       "text": [
+        "array([NY, TX, GA, FL], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": [
+        "<matplotlib.axes.AxesSubplot at 0x6cf0b90>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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2t8ujaGg2sG6avYlEAyTD4E00eJHBy6JocsHgeYlJLFOnwQcBPPWJIIBPksGb\n5qLhV2ISGTwv0dD1hAX4IcXg29qYDCFLqmQC8Ek4WQczg5eZjMEnAfC8ZihzsuoAntLb5jqK5uc/\nB/7xH833B/wSDcDO296ujoPXOVhpO5B7icZUg9c5WelzEgyechOR8Q9Osa/pGLypRGMC8Hx98s3g\nRQ0+DIMXJZoh6WSlDi5jKrnW4J9/3ptIS7TBoMHLzL9ajFeDz0UUjSmDp0U1RMB85x3bd0yUOPgD\nB9yYe1NTSTQ8gxc1eJ2DFSi8RCPT4IMkGv6hFTWKRrz3RNAAt69985vAwYPuA0U2jnIl0aj6+003\nAW+9xT4nGQevY/BBAC+GZQ95Bm86k7WlxW20OAD/qU+xSRpxGHxvL1ufNIrFYfAiM8x1HLwpg1dJ\nNDJACeNkJZM5nINMJtHoAB7Qx3ADZgy+vDyYwYtvFqKZaPCOwwB+9Gjv+cXPSTB48biyMhf4qK89\n+yybgZoUg1fNT8lm3QlrZKq+dOSIm6EyqiQY5GTlGTxPwlQAL9qQ1OCjSDQTJwJ/8RfyfXQm3lha\npCKOBv/aa8AXv2heB96iMng/IOVegw8CeJ7BywCesgDyFsXJGhbgS0rUEg05WWVx8EESDfU7vn1F\nDX7kSDOJRhcmaaLBt7YyQOEXPs9VFI14HN+m1BbUNnE0eH4dAhWDp/1NNXixfqJFiYMXl7WUMfiW\nFrcM3byIIQ3wYSSazk62tBkQDiR5IOns9GrOUY3yUESxXGvwjz4K/Pa3+rKSkmiCNHhVFE1YiSYs\nwI8c6c8HD3gZPM1GFNswLIMXJZqKivxo8KL+DuQuikY8jn84iQ893UOS77+6iU46iYb6gqkG393N\nADYfDF7lZNW1+5CUaKiDh5FodPvojL+x/BJzcTR4ykMRxaICvKkGv3078Kc/6csKAnia6EQgr1ph\niI+ikT2A6utt3zEk0agG3OHD/rUzwwJ8RQW710VF3ph2HuBTKe/1R9XgRYmmoiI/GrwYQQOoo2hy\nCfAqBh8nDl4H8DR5SGTwKpZMq16pTDXexZnucTR4XbsPSSdr1Cga1T46CwvwJhYV4Pv6ojMJnh0Q\n4PGdmjpWUIcGwjP4kpJoEk2UOPg1a/zyV1iAJ5AWQZTCJPkBydc5bhRNGIkmDMB3dwOXXOLd59Qp\nPYPnNfh8SDQ8g89lHLxqJTjVtXV3RyNV1H4UsmyaqkAG8C++qD5PNsvOMaQYfBSJRrePzniw44FZ\nBYImGnxDE+H3AAAgAElEQVRUgI8T+813niefBADLkziKB/igzmIyk5Wf6KQC+CCJZupUy3dMkEQj\nu7dhAT6VYiAk6tzFxaydZAMyahy8mIsmCYlGBPiuLpb3he+bPODw5yfjo2gKweDjxsGrnKzE4EXy\noOtPOrxQjXexPFGDV0k0tLTj6dPuNVxzjfr8uvFlYoMa4GUSjbjuo+zGFZrBR9XgozpYAa9E89hj\nwNSp8nzwURk8n4tGdLKqOiCluQ0bRaMDeFkbRYmiqajwg2gqpU/vmgSDN5FogqJoxDdbWdsHAbzM\nyZpUmCRfd1GDN8nGyZcZ1sna1iZPfazq8z090Ri8WL7I4GVO1qIiRpK6u939gxLPRZkXwtugBHha\nsUUm0fzwh95GkXXuqE7WQmvwUfV3wAWj9nbg6aeBhQttaZhkPiWaykrg9tvVcfC7d9u+Y/LB4AEG\nQjIQVWmmJhp8UC6aJKNoZGyc75umAJ9rJ2sYDT6Jmazt7fIkhVEZvGq8BwG8jMEDrpRKM1+DHqp9\nffEWYxmUAA8wNqUKB5M96XlLQqIphAYfh8FTx3nySWDhQjbBRRZFkyTABzF4ft8wM1l1Grzs3sqy\n8gWZTKKh86sYvMg+xcFp6mRNWqKJwuBlE51yCfAmGrw4rsU5A6YavAzgVX0+qgbP3+PeXlb3ESPY\nd5WTFXD7FN2fIJwZkhINGTWU+BrDM7Z8O1lzqcEnweAfewz45CeBGTPk+eApLExnSTF42lcl0VRX\nW779g5ysSTF4mURD5yfw4FM1iHHwI0b4gdo0TFIn0ZBvQ6f1iwBP5wvS4GVRNLlg8KKTta/Pr/mb\naPDi244pg5cx46Q1eP6aKfcOOVx1DJ6k1DAAP+QkGjLS4MUOVEiAN7FCafDNzcDGjWzCFw8CjuNl\n8EGWJIOnkMqwcfA6xiVaPiQawAuGspTNSTD4tjZ3wW2VJa3B5zoOnr+XpnHwUQE+LIOPqsHz95jP\nBQ94iamOwY8aNczgB1gib7kCeBOJZjBr8Fu2MHlmwgSmb1Pb8OXmG+D52HnxXu7da/v2D5JoknKy\nmko0fBw8D046gI8zkzVIngHMNPhTp8wkGv6+5kqi4ftcmDh48WHISzS6KJp8a/Diw7SoyHXYqzR4\nOiaozYc0gycNXmR6OoAfMSK3TlYTK5QG39PD5BnACwL8IEkC4PkVnei7SvahY8SJQ0C0maxi/Xt6\nooWXqiQax9Fr8CYAH0eiCYqgAcw0eMcxY/A8WOZKouHvjy6KRpzJqmLwQVE0+dTgKeRRbGuenPJ1\npTFgyuBJshyyDD6sRDNqVGE1+N5e80RCDz0E/Nd/ud/jMvh02s3HM2uW5dFZw5wjDIMH2HlVizXz\nuVD4xYgB4LzzLN/+YQE+CnsH1BINIJdoxDj4XEk0IoO/8kqWAE+snwzgxb5pAvA8WCYVJmnC4IPW\nZJW97Zg4WfOtwR8/Hgzw/NgoLWU4k8mY5VwKeqMNMsWLzuCwKBJNZWV4gKdBm0QUDf+QCLLdu73X\nEofBz5gB3HMPk2cALwjkAuB7ety2T6fV+VF4gMtkvA/UJKJoogL8Rz7iH5RkJnHwMoBPIkxSDJF8\n/nl5/YI0eMBMosk3gw8zkzUfGnxUBk/lHzzIItZUAC9KNCUlLLU17X9Wa/Ak0QwmBh+kwYeVZ/j9\n4zD48eO9K803NLgafGen67TLFYNXDTgdwDc22r7988Xgb7gBuOoq+TYZwItx8BUV/nU1k0g2lpQG\nD5hF0fAMPiqIiPdKdLKaavBBAJ+LMMkgJ6tqvFNdDhyQM3g+0kxk8O+9NwzwAFxWqNPgZZ0rDsDT\nQgVRGfyJE/rJMLL9yeIweNFEDZ5WoTfRq01SFZgCPC/RiACvywevqqfYRlEBXmc0IMV1WXmJZuRI\n77nTafXDkMwk2VgUgFf11UJp8KJEY8rggyQa01QFo0YlJ9GojGfwOolGZPClpSzajWLmTQCefFxR\nJLRBCfC7d7OVVlQSDc+cZJ3LFCj7+lznoON4EzRF1eBPnHAXmzaxpBi8aHPmeDV4AvioDF6WqkCU\naGSmY/BVVZZv/7Bx8FEmOQWZLIqG4uBVGrzubYcs11E0YTR4PoomLoMPkmhEBl9WZhYHX1Hh5m6h\nssQ6i9bWJo9O0Uk0OtITlIsmCOBlDL6z0/3NxMmaTnvz24SxQQnwv/wl8NRTfomGUrsmJdHwN7a1\nlXU8GpBxGDy/TFqQ8bJQrhg8Ly0MJolG1mHzJdHoTKXB82yMAJ7AR9VWYZONmQC8qQZPb6P8+cXP\nvNwx2Bh8cTEb8+IaDUFO1iTDJFWWzbJ8T2EZvBhoYMLgi4q8+W3C2KAEeB44+CgaE4AP42QV5Rk+\nvWpUDb65ORzA54rB79rlavAdHbkBeBMGr5NoDh2yffvny8mqM1UuGt6Ki9kftU8QgydAHTEid2GS\nYh3FyKYgiSZXycb4exZmTdZ02puuoBBOVl0c/MSJrG6HD4dj8PyDjPp5VRWbxyI7z5Bj8DzA54vB\ni8w7Xww+Hxp8EhJNLhh8EnHw+WTwovEyDc/MZABPA1W2di5vQYnGALM4eJnFlWiee46FbYomHscv\nosIzeLqnMv08m/XecxnAh2Hwphp8nJmsxcXA5MnAzp1qJ6tMg5cB/JQpgEwNomsdUgyeOoQ40ckU\n4E2BMgqDT1qDb2lxz5Ukg58710pMonGc3AD8+PGWb/8ggI/jZP3P/3SXddSZKh+8aHwkTZBE09fH\n+nPQottJavCiyaJoZAz+H/6B+cBEe/JJ4A9/0JcrGg+gvDwj1lWWxiIqgw+rwUeNgyeAf+ed8BOd\n+HIA/SLiNL6GGTzCMXhxFqsJwAdZWAZP5wZyG0VTVsaAOgzAU8eiBFj8TFZZHLzMRImGP39ScfD8\nOXT24IOMbQWZKopGND6SRueQBryMNIkomiANnt7YVCYLkyR7/nmgvt5/jA6EyHj2DngZvGkMPJUp\nPhD5N4F8pSpQGbXb5Mms7mEkGpkGrxo/RAz4hbzD2KAEeH69Q16Dp4GcpAZPZZpKNElr8HRuIFkG\nv3OnNw6+vNy8g/ADiIArCQYvhj82NdmefR2H/enCJGUALzoTdWbSBqo4eNF4iUYXUgq4jFRM8yCa\nqZNVFibJ11E1iYtMNtGJrL1d/pahusf8tYwe7d3GAygP8GJ7ygA+ikSThAbf3Ay89pq8nmL9qqvZ\n9zBOVplEo2vbgjD4kydP4hOf+ATmzJmD2tpabN++Hc3NzVi0aBFmzpyJxYsX4yQXIrJq1SrMmDED\ns2fPxjPPPGNWuTxE0dBALCSDDwPwfX36TINkPAiQRBPmzcYE4OOGSYo6KV0bgaDM4gK8ySBRrckq\nGg/wlEytr0+twVMSKllmTbI4Eg1vQQCvY/AqgDdh8DSTmkxk8Ko5IrK1AsJKNI7D9pVp8GEmOn31\nq8CCBfL9yXgNHojvZA0C+Lw7Wb/61a/iz//8z7Fz5068/vrrmD17NlavXo1FixZh165duO6667B6\n9WoAQF1dHR599FHU1dVh48aNuPPOO9Fn4LLnn4KAHODFXDVRAV5M+SkCzGuvMS930ho8HQO4N37N\nGrM662zePMsj0ZSXJw/wcSc6jRljefY1fR3ly8wXg1dp8NQXUymXnck0eB6UdA/oOFE0fB1NAV4G\nlnEAfsMG7zZTDb6z0z9nICyD7+xk90C2AlIYiYY/VqfBk0QDyNf3DeNkNWHweZNoTp06hd///vf4\n3Oc+BwAoLi7G6NGjsWHDBixfvhwAsHz5cjzxxBMAgPXr1+OWW25BJpNBTU0Npk+fjpdeeinwPKJE\nQ09/VZInIJyTlX+V7u72sgvxif/cc8Cvfx1cZhIMXqcCmQK8LA4+LsBHmckaJg6en9Sh68y8zpsv\ngJeZmK6AImlUYZKqNuItKQ3eVKKR6dkdHdEBnhZaJ+MZfNB6rPz4kwE8zWpXAXx7OwtDla0Ep2Lw\nb74J/Ou/en9TXSdvvJMV8AN8WAavOmdBJjrt2bMHEyZMwO23344PfvCD+OIXv4i2tjY0NTWhqqoK\nAFBVVYWmpiYAwKFDh1BNYhWA6upqHDx4UFr2bbfdBuAeAPfgN7+5H8eP81qyjR/9yB4A+JYWG4DN\nDUIb77xjc0BpezS03l7v9xdftNHXx753d7PcKJ2ddv++rLy6Ovf7vn027r///oHjxfJt28Z779kD\nAC/bzn9n+dDtAYBna5S61ys7/sgRewBodeW/9ZaNo0dtTw4Vdm3u/i+8ID+eBr1t28hm7QGA37qV\nbXcBnpXnAry/PGo/ADhxwsZrr7nfd+++f6C9AWDLFlYeSTSy6wPsAYBvbraxd689MO07qL1PnbKx\na5d6e3MzK58G5M6d9oCfwLZZe771lrt/S4uNl192vxcV2di82eZA08aLL7LtLAxQXz/btnHsmD0A\nFqrrIeJD36m/3H///QP7V1b6jwdY/wHY8e3tNk6csDmwZP23vZ2BpXj8vn2sfcT6uMBjY+tW7/n2\n73fH45tv2ujudtuzr8/G88+z72x2p42ODre9du+20dJicw8bG889Zw+Aa2enjW3b3PNt2mQjnXbv\n35Yt7v2R9adTp1j/BtjDuaHB7d9UR9V47+1l/bmxkfU/Gi+0PZ0GXnqJtSe1r23b2LPHbY+DB9l2\n2l92v3futAcmOm3d6m63bRu33XYbbrvtNtxzzz1QWaRskr29vdixYwd+/OMf4+KLL8Zdd901IMeQ\npVIppDTvoqptDz30ENauZZ9vvplJI9SBzjnHwrJlwKpV7PuoURYA/olo4dprgZUr+78Jr1fFxZYn\n1nT+fAsVFe5r2pw5Fp5+mq6RlVdby7739AATJ1oebU4s/4orLPT0uE9zcbv4vaaGfSeAnzBBv79l\nWaisdJm0rvyiIiaBWBbwq1+Rk9VfX55x0fEEUJZlYcwYl8Ffe62FdJq9ybCl2Nj+LsB729eyLA/r\nmDzZwowZ7veRIxegq8s94MMftlBc7DKeq66yPOyH6kcAP3ashdJSs/YAgNGjLZx/vnr72LHu9QDA\nZZdZePBBd/vEiRbmznW/T59uYcoU/nosfOhDwP/8DwE8a49332WAWl7ubx+xvr29LoNXXc+WLaw8\n+v7YY2z7ggULBn6rrJTJCxYmTWKf+vqAESNY/3fZo4Xx412JRjx+1ix5fR55xD3+6qu95xs3zmXP\nU6ey8smKiqyBhG+dnWw886tLzZ1roa2Nf5uwcPnlrt+grMzCpZe65c2fb2HsWHc95yuvtAbe7np6\n5P2BrKSE3U/LAn7xC+/1iddL9Zs0ycLNN2OgTfnt6TSwYIGFDRvc/mxZFhobWbhpSQkbD+SmTKfd\n4xsa3P3feIOFYabTwEUXuf3PsizP+e69917ILBKDr66uRnV1NS6++GIAwCc+8Qns2LEDkyZNwpEj\nRwAAhw8fxsSJEwEAkydPRmNj48DxBw4cwGR6t9GYKNEA/iRPgPc1OqoG393tffUXX+V7e+WdhDeS\nZ0ycoGQjRvg1eNM662zBAq8GT2GSZLo6ihJNZ6f3VbO42NvGUSWaykrLsy8vY+hkGv4+5VKiEddk\nFU2Vj0bnZA2ypOLgw0g0/L2j8NeoUTTiPmHi4IMkGr5+srrwfUGMOgm673yf4vusarxTu6VSEB5q\n3vMnpcHnNUxy0qRJmDJlCnbt2gUA2LRpE+bOnYsbb7wRa/vp99q1a7Fs2TIAwNKlS7Fu3Tp0d3dj\nz549qK+vx8KFCwPPIzpZAf1CC9QhogB8V1cwwAc1cBT9fcyYcFE0YTR4PkxSjF7QlSECfFsb+04P\nBfZ67O6flAYvPkRaWoC//mt/mbkGeD6KxnQmKxAM8EEafG+v3xekql8QeIVxsvL3iJK3yQBepWPz\ndREfYmHi4PltKoCn6C0VwJNcJ7ZRUBZVvk+ZjK+g+8lHmplMdBp0YZI/+tGP8Jd/+ZeYP38+Xn/9\ndfzd3/0dVqxYgWeffRYzZ87E5s2bsWLFCgBAbW0tbr75ZtTW1mLJkiVYs2aNVr4ZqJwQJgmwxk+l\n/PktANZ4JSXRnawygCfWSwzeq2l6LUoM/Jgx4SY6mQL8G2/Y0lQFZFEAnj/WFOB1UTQnTtiefSmK\nBmDna2gA/v3f/csB8kCQizBJ3snKr8kqmgnA0/WYOFmJvQcNDT4EtqHBJQh8HaOGSeoAXtU/+QeM\neI1iHDw9vMT2FBm8auavjsG3t3sZvElKZTK+n/J9XTXeZfMHeFM5WUtKwk90iuNkjaTBA8D8+fPx\nxz/+0ff7pk2bpPuvXLkSK0kcNzSZRAP4Bxaf8IkawoQx8TMOVQDP/z/TGLwo0fAWB+DjMHj+GmVx\n8LxE09jI7uPJk952lYVJ6pi2eG1BFjeKhvoVvcIDZhKNiTwDeO/tt77FFpAQzUSiIRAyZfAqgM81\ng29p8ZZnwuBFxps0g5fNH+AtrESji1AiJ+uQmclKxj8FeRMHFs/gaSKJycIWQRo836jUUYM0+LAx\n8DzAJ8ngP/hBeS4agHUYHdjkCuBLhBmqFRWWZ19RoiG3jQhgfN3zJdFYluWbZBbE4PlrN2XwQYnG\nqH50b3kA5fvmtGn6MoIYvCzPfhDAp1L+tw9eg+dBXBxHYh9VSTTd3a4TVTSRwUcFeBMNPiqDP2Mm\nOuXDSKIRmZ44sHiAB/yvQSoLAviyMm/+CxU43HUXkxEGG4OnDtHR4TIbIPh4E4DnO2lUiUYVB0/7\nEsAfO+a/NrJ8xsGL5xId/uKcAb5NkmbwfKoBsR0/8AHgvPP0ZagmOhGYRmHwsuszZfCmTtbOTnVf\n02nwYZyshWDwg2qiU75Mx+CDAF7WGUXwDQJ4Pn+LToN/4AG2PaoGnwsG/9prrgbPd3wgGYBPwsl6\n+rTt2TeIwVM/oOvq6/M/vIIsjAZfXOxm0rRt2wfAYRi8iWRoCvC8vsynjND5h0TjHxCiRFNSIgd4\nFQHROQr5FZOCctGYOFl1AK/T4KMyeJ0Gb+pkFRk871MatLlo8mFRNHhAzeAvv9z7XXSy8h0sDIMH\n2H5RGPzo0UxjzGZzp8HTDD8y3fE0W5A6JQG86ITKFYMXAZ4WKQa8+dYB17dgEn5IFkaiAbwsXpRQ\nwgC84yQH8Py91SUu05lOohk3LlkGLwuTlJVtwuC7uswYfFJhkiqTzQDmTcXg+fPw9TJxsg45Bi+L\nogHCSzQEEDRpiSzIySpj8Lwmt3o1W1qQtkfR4IuKGGicOqUeQJ/6lJvH3BTgP/QhywPwvLSgO54Y\nHWmpZWW5Y/Dl5ZZnX9HJun8/MHeuK9Hw6Xhl12ViYSQawI2ksSzLlycmCODFNuEB8P/9P5Z3nTeT\nPDRUDg/QdE06/5BoqjDJzs7oAC/rAyqJJqoGH4bBJ+Fk1WnwJhKNjMHzZsLgyck6JBm8CcCLDF7M\nubx1q1sebyYavC6K5o03gH373LKiMHjAzRGuYvCvvOI+pKJo8GEkGpGZ5FKiCYqDP3KE6ckqBi9e\nl4mFkWgA7wpMMomGd/brGLxY7sGDwIsvercnpcGbmGqiE6AGeFVEURgGr4rxDyPRqJizqMGHCZMM\ny+BNnawyDZ43kyiaIetkJYkmrpOVAF60oIlOvERDi17wmlxbmzfKJooGD7hsWafB0/WYAvwrr9iR\nJBpTgBdjn6NINK2ttmdfUaJxHGDePD/AUxlhHaxAeImG+iBp8DqJhti+CuBFAOyf9D1g+dTgVQwe\nYACvi6IR5yVEYfBBcfCDhcGr2tTUySqb6CSWQ/vLbEg7WUWJhnLQmGjwPFhu3QpcdJG//ChOVt7a\n270AH5XBk+k0eBHgaXEMlRGDoTqLQKsyU4DnLSqDD4qDLy0FZs50JZrubm+u+HwAPF830aErRtEQ\n2zdh8IAc4MOGSSatwQNqBk+gL3vzonqJxk900mWTFLfJluwDBo8GH8TgVU5WUYM3nck6ZBk8Afyn\nPw1cdhn7PQyD7+piEscll/jLD5rJKnOy8pqcDODDavC8dXUBP/kJcOON/m08wFdUsI7Mrafis4UL\nrf6MgX4Q1AE875cAWJu2tvqBmreoAF9SYnn25QdDJsOSOE2Y4GXwI0bEY/BBg0R1HRdfbA2koiWr\nqGDtSw9acsiqNHjx+/Hj/hnZKgDkTQXwYTR4XuJRAbxIIGjMiWCZKw1eNpPVlMEnlaogKBeNysJK\nNDon65Ce6CTL/xwmiqaxkYGEjBlFcbLyJko0STD4qir5jRQZfE0NsGePuizq4DKdmtI9yNZcSZrB\n69ZkDWLwBPC8k7WiwntPVAD/yitumKV4fTpTReTIHKDptDdtA/lSTCUawL8IuEmiOh7gZRq8Scy9\nTqIZNcqfUA4IBvh8RNGYxsGLYZK5YPBRnKx8AANfr7M2TFL2FOQBvqjIz+BFJ6uq8aJMdOI1OZ7B\nt7ayz2GdfmSO4wKYTKrp7vYypWnT9AD/8sv2AIOXAbxqWbxcSjTiTNb2dtuzr+hkPfdcYPx4L4Ov\nqBBnw8rP+4MfAL/7nfz6dKa6juees6X6ON8XicETcQiSaACgf8mEUMYPdpkGrwNSMp1EM2KEH1iB\nZBl8lDj4VErvZE16JqvjxM9FI2IXzbQnCzOTdcgxeF6DF1+NqbOl07mbycozeL6jkvEM/ujR8KmC\nectm2bHl5fLOSBNGqL7TpgF796rLI4auAnhVh0ka4IuK3DYx0eBFgB81ih3T0eFKNHy9VQAvMmP+\n+nSmug5a61M0PpKG1+DFmayqskUd3sRUYZJkumgVMgIfx/EzbxXAkwZvwuDps2rRbdFMGDy9LekY\nfBIAT/1V11dMJBqZBi+eyzSKZkgzeJ1Ew4fsBc1kFS1KmKRKgz96NJ7+ThOtVA8nEeCDJJrLLmMa\nvEynTgrgqePqAJ7OR//5gZZOW579RInm3HPZYCOZhhh8HIAPGiQqaWPWLEvK4EeO9AJ8WIkmCYAX\nNXgTLZ+XPkVSQgAvRtK0tbFrVDlZ+T5AYa4igycQl2nwQQyeJtiZSDRhwyRlfilxvPNm4mSVMXjA\ne53k5zCZ6DQkAT5Ig4/D4MM6WXVRNMTgoxqFaZoCfJBEQ28/IoMvLk4O4Ol/EMDTfkEMXibRAK5M\nIzpZgfwxeFUI49ixLDwWCNbgc8HgZRq8qUSjAikZg3cc1pdGjzbX4KuqXFklqF6qiU7U36nv5ypM\nUvbmHZfBy8gpIG8Dk4lOQ1qi0QF80EQnlZFWKtPBenr8TlZeg89m3ZhngGmpcR2sYRh8EMD/8Y9y\nDb6kJDzAt7fL82XHBfiuLtuzHy/RLF7sRj5RJA0thiHKdaI5TvIAv327LZVoxo1j0TBAcBSNjMEX\nSoPPZtUgNWIE+xOdm5kMKzeMBp/JuKkueJZuGgcPuA8bE4kmapikzHTrP5g6Wfk+TRYW4OMweAN/\nceFM1UhJa/BiHhogmMGLemSuGTy96vISzd696lh40uBFiYYcrGEA3nG8v1Fn5P9HkWh0UTRf+Yr7\nO0k0paVuzn9qIxnAnzypHtBRJZqODjmDFwE+TBx8Op2sRAOweyXrz7zRHAoVSJWX+xk8gaes39B3\nWdsRwJeUsP9h88FTfTo6giUaGYOnOpmkDxctiMGbOFllbSzGwtP+MhtyE514wNJJNKR7yhh8FICX\nJQESnay8JicCvK7zmlhYBj9qFBtwKqZ6xRVWYgwe8P5Gr7O8fhiFwQOWZz9VeB8v0ZSUeOsiA3hV\nmwDRGfzkyVZoBh8E8BMnJivRWJaF7m52Xl2YJN23MBINgaes3+gYPAE79duwa7ICfgYvqzONGToP\n30aqiLEg063/YMLgVU5WGU6cNU5WntWZSjRxnaw6gBcZPA0OMWQsSgfiLawGD+hlGpUGzwO8jNWY\nADxZXIDXMXjeeCcr1Z9MFpYaB+DDxMEDXoAXNfggiWbSpOQZvKk8Q8eqJBoZg6+okDNJXRx8JuOm\nIAbCzWSl8ngGr5JoxAl9PCCq+nqQ6fpKUk5Wvr4yG3LZJPkbUVTk5uPmG4CfIk4MfuFC4M/+jP2m\nY/C//70L/uRkdZxgBk//t2yxAfgZfJQOxFtYBg/oQyW3bdNr8GHi4IHg19EoEk1vr+3ZT8XgeQ2+\nUAy+rk4eBx+HwU+aFE2D52dp8mGStm2HioEPYvB8FA0BfFgGT/dcZPCyOPg4Grw4oY8H+DgMPolc\nNHE1+CGVTZK/EbQ0l9iYMga/aBFwwQXsN52T9YYbgGuvZQOLn8kqA3gxTBJwG1lk8HEBnhi8ONuT\nTAbwulBJYnmiBp+ERCNaGAbPX5vYYWUOKcAv0fBtEBbgo6YqaG+Xx8GH0eDFaxs9mt13WWKvoDrq\nGHxQDLxukhOgZ/AyZ18Qgwfc8aWqm4lEo9PgBxuD5/2HcaNohlSYpNioRUXs5uicrF1d/hmTKoBv\nbweuuQa4+GLg7bf9HZCvhyjRACy+nMrhf0+SwVNZnZ3uecJKNFdemTsNniyORMOOtTz7BUk09BDM\nJYNXSTSjR8vj4MeN84ZJUm4aYl28id9TKRZKGJbF6zR4kxh42l/H4MUoGupHUTR4QK/BO46ZkzUM\ngxc1eNX41CV302nwcRj8We1kFW9EOs3AjW+AkhI3UyIxeB74VBo8aejf/jZw//3A7t1upwhi8CLQ\nJw3wMg1+zRrgO99hn8NKNCYafL4lGv7hJcuDYyLRlJbGY/Bx4uBVDJ4AjiQackKbxMFH0eFFDZ63\nuGkKAHUUTZBEY8LgZXWjevB1EQGexrjKyapj8DqJRga2fL1UZupkHWbwgomNKgP4VMpl8eR4MWHw\n1AlSKeCmmxj7XbTIPUashxgmCbiaXFsbKydpBs9HDp044WaMDCvRbN1qD0g0YQGeP0euGDzrrLZn\nP1OJJojBv/eeOmQ1qkSzf79agyfjFwcBzGayRgV4XoMnS0KDp74RBeBVGjz/X6bBi/o7lckDPKB3\nsuo0eJ1EEwTwUXPRECCbTnRSlTXksknKAJ6YOm8E8DIGr9LgxU4wYYJbLn+jybEravD8NO32dqCy\nMoe2heMAACAASURBVHkGn0q5TLe93ZWiurv94FtTwzImykCLWJ7IbAqtwXsB3msqiWbcOPag6+jQ\na/Dr1jGmffQoC0GUWVSJRhUHz6en4NdvBcwYfBSJhkiA4/jfhMJE0cjqROMjSQZPAKXqJzJZSQbw\nOolG7OemYZJBqbNVZirRJDXRacg4WcVGLSpi4CY2Eg/wphq8LPEWfwwZ3RReUiCAv/hia6CsuAC/\nYwfw6qvsMz85herf3q7X4MvKGMAcOuQv++qrrUQlGt1AiBJFwwaf5dlPJdGk08A55zCmq2Pw//zP\nQH19PIBXXUdRkTwOvrTUrQOt6KQqKykGn0q5JIQ30uBNnaxiHVMpL8CLUTQjRujDJFUavMjeqa5k\nMsewisGrnKy5YvBRc9FQO8mSuUXR4IdMmGQSDF6lwZsCPIEc/+QXwybb2uID/P/8D7BhA/vMLxnI\nAzzP4GXL9dXUuPnSeSOWFzcOns6XG4nGayoGDzCZ5uBBPcD39ADvvgvs3OkF+JdeAh580L0+nanO\nr1sQm2SadFqe0kFXdlSAT6Xk9y+MRCPWMZ1WM3iTiU4qBi8DeN5yweCjavD8OhNxGbyMmAJnOYM3\n0eABdjMJEMSp9GIEAJlsdSMyGcDznbmnh7GMF16wB8riEy8lpcFTXUwYPMAcrTL7/e9tbZikaRw8\n5a+WAbxpNklTDV7F4AEmpx086Hey8vetpwf47W/ZZ1462bgR2LSJP6/aVOc/cUKuwQN+HR5geXSW\nLvXulxTAUz1FEmOqwaskGh7gxTFkEiap0uBlDtbBqsHPnMl8X0CwBm8C8LJ9zmonq0yiUTF4wP2d\nH/SyVKeAfHUjMjEJPzH4nh534W9eg0+CwfMWlcGrAF6VD37+fGDOHHOJBlADPLV9WIlGph0Daicr\n4AK8yOB56+lh+/B1A5hsIzrLVaa6DlUcPCAH+Pnz/QCvkmiiJhyTyZC5YvBRNXheojGNgac68jNZ\nAX0UjUhkeA2e7qmsz4kA39LirgQWxOCDnKxieDdZ2JmsQ8rJSpNGyHQMnrYDfgYvA/iwEg11ZtLb\nMhlgwQJroKxcMXiaECRj8GKnUgH8NddYcBz/Q+1LX2IRREkAPP1myuCLirzpJ0zj4AEm0Zw+7Xey\n8tbT4+Yo4m3XrngA390NOI6lBE4e4AmMTMuuqorO4MU+Z6rB79wpr1NRUTyAD8PgeW07KYlGxeAB\nNYuX9SUiCUEafFQGL9Pgg3LRDBkGv3699ztp8DInK20H/PlJkgJ4SjImavK5iqKhusiiaFQavMxo\nFnBLi1+DBwoD8ID78JJ11iCJhuqvGgzd3cD117szmgH2tsADfBSJpq2NsXfVal0yBi8zHcCr3mp0\n9YzK4P/yL93PIoMnMA2TTTKuBh/Gyeo4aolGpcFTPWT9XQa2Bw6w/7oxbeJkTYLBD7mJTr/8pfe7\nqUSTCwZPnYIH/O3bbQB+iSZK4/OWtAZv2zaKihijFTV4oDASDX3u6QkXBw94AV7H4Lu7gdmz3d+O\nHWMhljRYgx7EsutoaQEyGdu/od9MAV52bSNHsrY9fZpNWps6VV8/vqyoGjytcQt4r/f884GLLmKf\nRZkzajZJUw3elMGL5zl5Enj66egMXgbwPIPfuNHGP/yDfx9TJ2tSGnxBnKzZbBYXXnghbrzxRgBA\nc3MzFi1ahJkzZ2Lx4sU4SbN0AKxatQozZszA7Nmz8cwzzyjLFF9XTSUakcEn5WTlGTz/mpS0RBNV\ng58yRQ2KtJoOLxsUmsHT24lKg9dJNHS8ToMXAW7XLvbfJH00ID9/a6teeokj0QCuDv/WW8Dcueb1\njMrgVXW64grgn/6JfU4qm6QqTJK3ME5Wsc7btwP33usHeHHJPlW6giCAb24GVq3yP0xNJjolAfAF\nzSb5wAMPoLa2Fqn+d9fVq1dj0aJF2LVrF6677jqsXr0aAFBXV4dHH30UdXV12LhxI+688070Gb6P\nBgE8NbIJg9c5WXUMnrTvTAaYN88CkLxEw6csFhk8LeIgA/hMBqiu9pdnWZYnTIzfHzAPkwTiM3hR\nonEZvOXZz0SiEaNoeFMB/Pnnm98f2flbWoCqKsu/od/iMHiAyTSNjcwZPGeOeT2jaPDisBMlGjJd\nFM2WLV4iRmMgqgYvSjS0IInMySrWuaODgbAIuHEkmtOn2f+eHmD+fDaO6uu9++Q6TJLP2V+QbJIH\nDhzAk08+iS984Qtw+muzYcMGLF++HACwfPlyPPHEEwCA9evX45ZbbkEmk0FNTQ2mT5+Ol156yayC\nkmRjQP40eJJoxNjxfEk0AGM44opOvP3wh15ZgkxkxOm0d83TfDF4lUQj3lNTiUYVk5/NygF+7lxz\ngFcxeNWbHxBPgwcYg//DH9iDWvcGwFtUDZ7AS1Yn/rMuXfDDDwMPPOBui6rBk8NdlmisqMj1eegk\nmo4ON6xRvC6ZRLN7t79+vLW0uJ97e92ZyXV13v1MnKwyaRkInujET9QsmJP1a1/7Gr73ve+hiLur\nTU1NqKqqAgBUVVWhqT/+69ChQ6jmaGZ1dTUO0ntQgKkayiRMUlzOrqsrPIMXnaw7dtgA1AsQRzWV\nREO571UMHgA+9jH/dZHGKZNnADWj6ez0d0AVa47qZCWAT6Vsz366QRMk0RCAiwBXXx8f4Ds6gI4O\n27+h3+bPB/7iL9jnqAC/aZPXORxkMoA30eBFMOTbkgdoXTZJgKWF4FkmEMzg+baxbRsVFaxcXapg\nQC/REIMXx3o6zQhDRwfwxz+yenR1MSLEv8XoZrL29AAvvmgD8AN8UJhkWAYvzh4XAb683DsJy9Qi\nAfzvfvc7TJw4ERdeeOEAexctlUoNSDeq7XK7DcA9AO7B/fffj44Oe0CisW17ALgYwNs4cYJ9Ly52\nt2cy7Om/aZM94BRlZuPQIfe7bdvYupV9Lylh3zs77QFJ5oUXbPT02AM38+RJGw0NLLdAWxuwZ4+N\n1la3vMOH3fpR+arvrNlsOA773t3NyrNte2CZs74+G+XlNtrb2fb6ehsHD5qVT9ebTrvfi4rc7cXF\nQEOD//gdO2xMmuQtjxi8WH5np91/Duqg8vpQ57Vt1p6kwadSr/aXweydd2wcPiy/Hsbgbbz+us1l\nKHS3swFho7HRHhhAu3ax6yGAP3WK1VfVXs3Nbn+i7Z2dDDQzGXV7n3ce8M1vsu8tLery9+/3fm9q\nYt8nTQK2bQNGjjTvP+k0W1idv55XX30V777rXr+sPzQ2er83N7vfDx3yjq+WFvd7Wxvw6qs2jhxh\n3/fuBdasYdsJ4I8d89f3yBH3fp086d2eTtvYtMkeYPDbt9vo6LAHQI3qTwDPzm0PAHxnp41du9j4\nbGtji7JQ+UVFrD995zvse3Ex8NxzrP+RnTpl4/hx93tDg7c9337bRl0dG+91dd72zGaBP/1Jf39O\nnLDBL2rDjycA/WPZ3Z+2l/QTPNtm+JJOs5nZ777rlm/bNm677TbcdtttuOeee6AyzTNIbVu3bsWG\nDRvw5JNPorOzE6dPn8att96KqqoqHDlyBJMmTcLhw4cxsX+++OTJk9FIswfA5J3JkycrSn9o4NNd\ndwEPPeTGvvK6HQN4C/0vDMhkgA9/2N0+YgTLG+Odxm9h/nzum2UNaIklJew7JRgrLnZjyWmdy3PP\ntbBkCTtHezvLuX7ffW55555rwbKYs4zK95yd+87YkTXwGtrVBXzgA+z4n/yERQdUVFgYO9bNpDh5\nsqXUMcXv9JmfnDNiBCsfYNdTXe1+p2P+z/8BzjvPW8YPfsD2F883ejT77jJ4f3kAm0laXs6+jx7N\nwJYlVbvLw9ze/37L8xrKn6+8nLXH5ZczxxoAVFa67c0YuoXKSpchzZzJ7m9tLds+YYK6vQBg7FjL\n86ZC/aGzE5gyxXttsuMty8Latf7tr7/uXh9/SFUV+97QwPrcRz8qbz/Z96IiYM4c7/a77roLb7/t\nsmHx+FTKEli/hXPPdb/V1LjnZ+PAwpVXsu9tbcDixRZeeIF9nzcP2LXLwl//tcvgqf/z9d20CTh1\nin2fOtXdblkWJk5kb1f797PzXXKJhfJyV6qj+r/xBjtm2jT2nQC+rMwakO6am4HaWrf8dJr1b0od\nnckA73uftz1Gj7Zw/vnu9+pqtp3ky/PPt1Bba2H9egbwfHtms2zdY/54fjt74/eej7Y//TT7Pnmy\nu53Ht7Y2hgeWZQ1kmK2qAtrbve3Hn+/ee++FzCIx+H/5l39BY2Mj9uzZg3Xr1uHaa6/Fww8/jKVL\nl2Jtfw9fu3Ytli1bBgBYunQp1q1bh+7ubuzZswf19fVYuHCh0bmiTHQC1Dp8GIkmlWIdg1aT4TX4\nuBKN+LolTnQ6dYrVla5TJ9HojAdQ/hpVGvzhw/AMeoAl+pJN0zd1st57L/D5z7PPvEQjHqNzsgJM\nox41ym0DMQ8N4H3d7+pi1zh2bDyJJkxkSlQnK2AeQUNlhdXgi4r8Eo1Kg6fEYx0dDHApGovG2Sc/\nCfzmN+wz3cuwGvzIkUzzNpVoVBo8IL+ubNZto+JiuVY/ZYr7mfR24p6kwc+fz3IceZebTHaiE7+f\nKNEUFTEGf/RouLkSQEJx8CS3rFixAs8++yxmzpyJzZs3Y8WKFQCA2tpa3HzzzaitrcWSJUuwZs0a\nrXzDW5SJTkAyAA+w/wTwmQzwxhv2wAxR3ska1sQZl6IGf/Kku7IOEB7g6VUuLMAfOuQyeLJ/+ze2\n1KFopho876Tl0z+QPEWmc7ICwJ/+xOLEqSwVwBOQtLS4DwTTMEnZ+bu64HmV11lUDb64GJg1y+gU\nAKJp8EVF/jGhAniA9b22NnZMWZl3lapzz3Udtnystmi6OPiRI9k40K3mBPg1eDGKBnBX1eKvta/P\nbaNMxl1bgberrnIdq1QWAXxPD/O5nXMOIxcNDeo6iqbCLSA4iob8B3w++dJS1pfF6wyySBINb1df\nfTWuvvpqAMDYsWOxibI6CbZy5UqsXLkydPmqJyExSh2D7+jwRyWooiHEXDR8/hTKVkmgSDeOzy4Z\n1kSAF6NoCOCTYvBTp7oxzoAc4Ht72cQgYpRkqoeiKcDzRmCbTjOWyKc61sXBA+49D2LwMoCPw+Bl\njmeVRYmDnzkT+PKXzc9BZUVh8KKpwiQBlyTRAjvi/mQ6Bl9Sombwo0a5DJ6kFr48MhMGLwKfKYMH\n3H5FDJ7iQXp72fFlZUzmq6tzw1hNnayVlf5to0f7iQBfFq0JQTO+6Xpp7QAKODCxQTeTVTQKkwwT\nRQPEY/D8gC4udhMcZTLA9OmWdn1KU8s1gyd9jjpSaSnwv/6Xu10WB3/0KAv503Vc3kwlGt54iaal\nxfLUIUii4esOBAM8LbMXF+C7uph+bmJRJJrRo+Hx5ZiYjMEHxcHLzq9j8JSSm08DoAP4sHHwYRm8\nDuDFthABPpNRA7xYFi/RTJtmeQBeVUfRdBLN3LmuDs+PId5IppEBfBgb9ABfCA2+sdF9iss0eF1e\nDpX19HhZhk6DlzF4YhNhGbyqE8rCJGXyjM6iMnh3opPXgiQavgwAHoceDWSRwY8cGQ7gVRJNEhq8\naRuZWBQNXnZ+VZgk4I4hU4BXMXgaW2LbEIMXH0oieNI2WZhkZ6d8ecYoAE8Mft489r+nx334yADe\nZCarqj8Ts5ddE+AyeH5MRFreMdzu+bcgDZ7XdnmLA/D79mHAOy5q8Dt32tq8HCp7/HH2Gk4WlsED\nbKCF1eBVThlZ3cMCfBQGX1LCpypgdRRjfoOM7vn73uf+lmuJhg+v1ZlOojF5eJlaVA1etCAGHwbg\nZW33yU8Cd9/NPqs0+CAnayrFtqsYvCwgT9TgdRINGQH8okXA97/PxkddnS1l8CZOVtVEJ950AN/V\ndRYweFOJRux4qpzwcQCeZpPxy5eZzi47cMAL6iYafEWFV4YIA/BkYQBeFkGjs6QYPA08UwZvOtGJ\nAJ4eKiamYvCm+rgpg6fQ26iWLw2eJBoaN2EZ/PjxwPTp7LNMgzeRaABvBI/oZJWREhmDlzlZeRMX\nTKfkdWVlTHuvr3fHjEk2SdlyfaKpAP6skmhkKULLytiTPawGb5JsjAd40cl6/vmuBs+fP8iamrwD\nUpRokmbwpMGrHkBJMPg4AP+znwGUi4akqyAnK1+GaDoNnga7SYiZSoOvrbWCD4Y5wLe1qZcANLF8\nafCUDykqg+dNpsGbSDQAA3iZXm0K8GEYPOBKmJMmMQ1+xAgmkezZw7abMHixrjILkmjOCoDn/5OR\nZz8XGvzBg2qJprc3uMPLrKnJm5EubBQNkHsGnw+JhgD+4Yfd32jghXWy8kYAz4MFafA0n8GExasA\nPmgRDTJTJ6uYvzysRY2DF82UwYtvzLzpGDxvKgYfJNEAXulL1OBVAC+GSZo6WQGXwfN1q61lk+oo\nd78JwJsyeLFPqySaIafBUwPJGooHeBMGn06rAZIHeMfxOlkpiqa4mKULCHpllVkQwPf0uHXIZNj2\nfGvwUSQaWgA6LMD31xJAeInGlMGTROM/r9pk5+/sBHbvtoMPhnmYZGtr8gw+aQ0+rJM1DIMnDZ4Y\nPL9N1g9UAK/T4OMweBofu3e7qQVIhxeToclMxcxN9yOJRnSynjUMHghm8GJOeJJVZMYD/HnnecMk\neQafzSbD4GWJg3gGT/WNy+BzKdHwgzoawDPjAT6Mk5U3Gsj8m1AUgFcx+KQ1+LgMPooGn077+3+S\nYZKFYPC50uDpbZ00eMAL8EH91HRcpNPA5s0sVJa3s16iAcJLNLoBxQ9gPr+EGCZ53nmuBi87r8qC\nGDxfBx7g6TzpdDQNXsfgRcCLosHHA3hWx7ASjY7BA34N3n9etakA/oMftIIPRmElGhMNXgwpNA2T\n1PV3mvQT1Af4eokavImTlSwMwNM9Ly72Z5wUTWTwPT3AOedYA/WeO9cF+KBxH0aiueYa/+8lJaw+\nvKN24kSWlyrU0o7muxbG6OJknedHPwIuvBADiznzJgN4lf4OqAFenOhEmevCAHxfH7sxQQAvY/B0\nnpKSZDV4MQ6+t5cteN6fH87IogK8CExJRtEAbjtms97Zr3EkmqTj4HMh0QDBGrwI8HGdrNS/ZGNQ\nNF0UTRgGz9fBcZh0IXszEcMkg0zmZOXrNns28M476glM4vn5/ypT1au0lD28eCmotJTdhyCpibdB\nD/C6hrr6anbjZaCXFMATg6comj177NASzfHj7mIUZDKJxk2D69aXzlNS4tbDxMJq8DQFOkzoXnyJ\nhtUxrERjyuABl8GbhkqqGPzbb9vBB8M8Dj4JBi9ej4kGP3as97ewTlaxf/BgHFWDN5Fo7r0XA5kt\nZQ8iUeIQNXiT1FcyJ+vhw64GP2oUeygfOmQO8KZOVtGIwYvnCetoPWMAXtVQ6bS8kWRx8DqA5zuf\njsHzcfC0PciamtjA0jH4khK3E6oYPJA7DT6sPENlJKnBJxFFA8gBPq5EY9ru+dLgi4rk64QC6j4Z\nlcGbArzu3l16qTdzIwDjVAUAcMklLKuprJ7l5f4HF0k0QbIMbzIGz2vwgDtDNWjch3WyikaETmzT\nsI7WMwbgVQ1FwCtakhINr8FPnBheg29qYsm+ggBe/CwyeCBZDZ4H+LARNEA0Bu9l0qyOSThZTQDe\nJKOkSqK57DIr+GDkT6KROVkvucQK9AHoNHgdg+d9QbyZMvgf/YiNATLLsjzJxoIYPG/itrIy+YMr\n7BJ3/PgkBl9W5m/TME7WqAy+tJThl3iec85x8+ub2KAHeJ0GT7/LGimqkzWdBqZNc3/nnaxR4+Bl\nAC9KNCqAj8vgTQH+TGLwQRINPyDDavBx4+DzKdGI8kNQzpwlS4BvfMP7m0mYpK6/mzJ4mZWXs2to\nawtm8Ko6l5TIpSdKVRDVZBo8Xz9TJ2tcBi97mPFvGkE26AE+qKFUse2qMEmVEYA++6w3Lzcv0RQX\nAwcOyOPgdZ27qYm9nnZ1ua+MIoPnO3gSDD6sBl8YgGd1TMLJqlpnMymJ5pVX7OCDkd+ZrHQ9VO6W\nLbYW4KurgQ99SF0nWb6nMBKNaR8AWP9MpVgbZLPhAJ6vAz1Qq6u97RmFwYvnYAkC/W0aNIuVzg/E\n0+BlAF9e7sc1bfnmuxbGomrwUSUaPoEV4HWy6uLgdcB79CiTP4qK3FzzOomGysqnBn/4sH/w6+x3\nv2MD6vHH2fd8xsHr2oD3ZQDJTHTKZsPFwV9/fXDZra3JafDUZj09egYfdSZrJpMbBg+wPtTV5b1n\nQQ96vp4E8P/xH94yRIAPm9abxoeowQP5YfB8FA1vQ47Bm0g0cTV4fqUa0UQGP2aMXIPXgU5TE/N+\nl5a6g1KUaJJm8GHj4MMy+I9+lNWpEHHwusElC8Wj80bNRQOw9XlNrKgIeOqp4LKTnOhE5S5YEKzB\n6+oU18kahsFT/xw1yn/Pwkg0BPDizFIKkyQjQmW4kNyARJNK+dvUhMEn5WQdlmhCALxJojHRZBq8\nTKIJA/B9ff66qTT4TIZdY9IavBgHH0WiAZLR4Lu7/dOyg8pQGQ8WxcX+yWNBproO0zh407L7+pLT\n4KncIA0+F9kkk2DwYZ2Y/DbdpC6ewcvmneiMCJBKg09KotH1N5mTNaxEMyQAPq5Eoxv8YhTNkSPy\nOHgdqxQBvqPD32lUDJ6SquVag48SRQMko8Gfcw5j8XGiaMjECBpibNRuQQxONSC3bbODKxZgfNll\nZeEYr6wsfpYmALz4ol6Dz0U2yTgaPJAcg5ftEwfgXTJnSxl8kERjmqPprGfwQRJNVRWwYIH/dzEO\nfto09aLGYQCeNPg4DF7mYFMxePqfSw2+pyf8LFayKADPh/edPs1C3E6ciBdFQyYLkeSPMWVevOkk\nvDDGlxGHvQNyDZ7PwxN0fnrQhZ3opAuTLASDzxXAE4OX+TVMF6ZJp4edrIEW1HmmTAEeecT/OwE8\nRa387/8tP37CBBajqzI+H3wmA1RWWjh2zBzg+/qYk1XU4MW3CRmDp847YYI7Uy8XcfBNTewcYWax\nkoUF+LIy/sHLYqEJ4HPB4Mmo3UydY2KZphq8zvg+HCeCBpBr8HPmmGvw6bRfS1aFSabTudXgwwK8\nLIpGNFGDb2kxrxvgSrPptCV9qJmMlXR62MkaaFE6D+Bq10GTW9Jp4NOfVm8Xnaw9PeGiaI4fd1kK\nATx/PJnI4MvK3Ju7bRvw/vd7z2dqJgAfVZ4Bwt+fBQuAP/3J+1tYBm8K8DyImgK87PxJ6O9A8gw+\njgYvezMWrz2TcaUgKjcXGnwciSZIg587F7DtaAy+pUVevomTlcooNIMf9AAfJNHoTBYLH9Z4YMhk\nWFxsGCcr77zkAV7H4Csrgcsu814H5cIxjQIIo8FHdbAC4QF+5kyegbA68gw+rkTDD8ikGHxZmdue\ncSxpgBfj4HfsMNfg6bNOogHcSC6ZpAPE1+BzLdH09LDp/VE0+NZWIJ22fdvCSDTDGnyARWXwgHpV\npzDGO1GJwXd0uB0rKYDnGfyIESxHtLg9rP4O6DV4Aoh8AnwqBVx1lfe3fEo0UTT4pBh8khJN3Dh4\n2X2TXbu4LnDSDD6KRBMG4Lu72diJwuCzWfmYM3GyUh2STlUw5Bh8nM6TBMCLDD6VslBa6tYrCOAP\nHw7P4GUWFuDDaPBRJZqyMnfwFxUBd9xhdpwL8KyO+XCy8gu46Ewl0VB7xrEkGbxMg582zVyDD8vg\nyZLW4HMl0ZAGTwAP6Cc6ikbXOXq05ds2zOATtLgSTdIM/tQpb0fJBYOXWVQGbxIHH5XBL1gAbNjA\nPqdSwJo1ZsfpGHyhNXiVRJOE5UqioWsK0uBlentcBs/r0Ukx+KB+YOJk5dMF09gS35paW9XHU3+R\n3ft8OFlV2SSHLIMvNMBTFM2JE7a0w+sAntgxrbOYDwYfpMHTK6zjRAf4VIr5C8LaBRcM1BJAeIkm\n32GSpaXJaPBJSzQig3/1VVtbrkyiMWHwfF/VRZSEAXhqz5oaf3qQXEg0gL/Nm5qYPk/GtwV97umx\nfWUn6WRVlUNRNEOewccBeFlO+LDGM7/iYtZhziQGr9LgaRJGNhsviiaK0b2k/N65iqKJ4mQ9k6Jo\nxHJPnmThribHmDL4XEk0ZB/5CPCv/6ouU2amAC9KNDyY9/YCzc3e9uLZOvUX2ZiTjV9VHZKWaM66\nOHidJS3RsJttRQb4kpLBo8EDrg4fx8kax+bNswC4AG86uzMOg48q0SShwfN9OAkNnv7T57IySztZ\nLUiDl42xigrvohlJOVl17ZlkmCQB/K5drJ9R+7z3HksxzF9PWZnrjKVrqary1/PYMbb6WZDFcbIm\npcEPeoCnJEKm4YG8Je1kpZsRVqIZjBo84M7SbW6ONos1rlHnJYCfODE5Bv/1r3uXceMlGl0a2Xwx\n+CQkGiqTyn3vPXMGbxpFI/bTpBm8zJJi8LyENWOGd/uRI15GL5aVSrE+I3uAHDumb2e+DlEZPMm5\nZ0UUTdSOk0QcvJ/B28YMPpv16nz5jKIx0YyLi4GDB1lnTWpwhrHTp20AyTpZaUDOmOF9aMWdyToY\n4+ABdj1U7u7dduISTa7CJHXtmRTAd3SoiZOovwN+MGeTnfz1zBeDBwo0k7WxsRHXXHMN5s6diwsu\nuAA//OEPAQDNzc1YtGgRZs6cicWLF+PkyZMDx6xatQozZszA7Nmz8cwzz2jLFztdHIBPisHTRCMq\nl0yXbOy99xh40c0SAf7972crtQO5YfAm7GHfvsLIM4DbeXMVB8+baZhkLqNokpRoZAAfpMHLomjC\nOlkHA4M3TVVAKUZkduQISx/Cm1hWJiMfl6YAz98b3T4y41eYE+uYcwafyWRw33334a233sK2bdvw\nk5/8BDt37sTq1auxaNEi7Nq1C9dddx1Wr14NAKirq8Ojjz6Kuro6bNy4EXfeeSf6NNqBqAsWEuDF\nMEnAMpZo+Bh4wA/wDQ3Am2+623QWRYM3YQ/79+fXwcrb+PEWAKaVd3ayPxMWqMvSp2rHuE7W8ZK/\nCgAAIABJREFUpOPgk8hFQ//pmlparJxINHx/p31Ilx/MGryOwcskGhmDnzrVX898MHjqxwWJopk0\naRIW9KdwHDlyJObMmYODBw9iw4YNWL58OQBg+fLleOKJJwAA69evxy233IJMJoOamhpMnz4dL730\nkrJ8HshMGklluXGymkfR8CGSQP41+KB2y2SAxsbCMXjqvKkUY/HHj5vfa1VbBAF81DDJJCyXDJ6W\ng9SVG2Wi0w03sD/xOOJng1mDDwvwYlnFxWoN3hTgk2bwJSUsMMJ0OcLYGvzevXvxyiuv4JJLLkFT\nUxOq+t97qqqq0NTUBAA4dOgQqqurB46prq7GwYMHlWWKnS5qx8lFmCRgHgcvRqfEAfhLLgG++13z\netu2bczgCwXwJ07YA58J4E3vtWxgFBfHZ/AqiSZpDT4pJysBvOMAo0bZ2mCEsMnGAODDHwauuML/\nOwFMLjT4KEv2yfaJq8FnMsB77/nrmU+AF9shlQrH4mNF0bS2tuLjH/84HnjgAYziY9IApFIppDS9\nTb3tNnR31wAA7r//HDQ2LkA6bQFwOwW93gV9P3TIRmMjQFPiwx5v2zbefpsdX1wM/P73NoBXUVbm\nbj9wgG3PZIC9e23YNjBhAtu+davdPxDY98ZGG01NQHs7C7V0O7nlceTJ6jNiBJt0Ydvm9aeJRKrr\n7+mx8frrwFVXRW+fON9bWl4duJ4xY4D6ehuvvgpcdlnw8ZkMsHMnaw+6vqIiG/v3y6+XHOQsDE5e\nfnOzjbfeAm680d3e2QmUlsa73rFj2fdt22yMGcPOX1ERr/3YwLf7J92w7SUlbnvKjn/zTZoIZfUD\nj42XXwZmzWLbt2+30dAQfH7AQm8v0Ntr47XX3PO/+aaNkpLo/WH7dhsdHUA2y+onbt+6lX2n83V2\n2ti5003lzO/P4uDt/tna7nYmLVk4coQt3sO3V2ure30Au74TJ171HN/VBXR3Wxg/Pvh62tps7Nvn\nPT+//eBBtz7idkZUbDA3pnd7ebmF556z8etfPwQAqKmpgdKciNbd3e0sXrzYue+++wZ+mzVrlnP4\n8GHHcRzn0KFDzqxZsxzHcZxVq1Y5q1atGtjv+uuvd7Zt2+YrE4ADOM6ECY5DNfvpT9n3KPbv/+44\nluU4739/tOMdx3E2bGB1efllqqPj/OM/utu//W3221e+4jif+xz77c03Hae21nG+9CXHWbPG3fcn\nP3Gcv/orx7nqKsexbff3c891nF/8InodVVZZ6bajzGbPdpyaGsf57W+TP3eQAY7z8Y+736+/nv32\n0ktmx0+Y4Dj/9V/s849/zI6trGT9RWaPPML2ue46x5k5U77P2rWOs2+f97cpUxznW98yq5PKXnuN\nnfu999j38nLWR+LYN77ByqypcZwlS9jn669X719U5DhPPcU+A44zZw77v2cP+230aMdpajI7N+A4\n3/8+O+axxxznppscZ9Ysx9m8OdYlOQ0NjvO+97HxJWvzI0fYuVta2PeaGsd55RV5WXv2sH3nzvX+\nnko5TjbL+j5/D772Ncf59KfZMY2N7Lfp0x3nnnu8x593nuNkMo7T1xd8PVdc4Tj//M/q7Xfe6Tgc\nfHrs9GlWlxtv9G877zy3jmQqKI8k0TiOg89//vOora3FXXfdNfD70qVLsXbtWgDA2rVrsWzZsoHf\n161bh+7ubuzZswf19fVYuHCh0bkKHUUji5LhX/uSkmiS0nl5G+wSDX9fGbM1v9ey9s5k4kk0n/0s\nMHWq//dc5KJJWqIBgmOzZXJMkJNVZZTHaDBr8EB8J6vs3o8fbzYvJ8h/mE4HR33J2qG83FyiiQTw\nL7zwAh555BFs2bIFF154IS688EJs3LgRK1aswLPPPouZM2di8+bNWLFiBQCgtrYWN998M2pra7Fk\nyRKsWbNGK994KlgUz8maZD54ZrYHXPKlwYc1Uw2+r69wUTTHjtkDnwngTe+1DKhLStRgTO1rkiRK\ntFzkoslFmGRnp218fjqG2mP8+HAPMhnAJ6XBmwL8HXewFd10+8jGFS2bSX2OTBYmeeCAv54m+jvV\nQXcd3/oWcOut8m06gC8rM8e1SBr8hz/8YWWY46ZNm6S/r1y5EitXrgx9rmEGH92C2o1C3QoxixXw\nAkJYBi8D6rgMXmVJz2RdtChakjbeeICna6LcPkHH8J+pTvX14WaL55LBd3QA48apt9N57r47eB8Z\nwNMayeIDScbgZceHAXjdQ09XDs2klR2fcwafSxPDfwYfwFtGAN/by7zt/GSKfDJ41xGntuPH2f9C\nzGIFgPPOswY+h2XwUSWaKNeaVC4aOvf69fHvNw+q9PmSSyztMTKAp/4bNhVIXAava8/jx+XgR4TE\n5Dy0j6ydZZOcADmDp3xJvCXF4IOspCQ+gx90AC/evKKiwTGTNSyDP3qUsRD+uMGmwR89mvw5w5hM\ng48j0Qx2Bh9VatSVFUaDl0k0UceWLEwyKaKgCkOkc5rq34Aa4EX9HQinwZuYyUxWnfELC/F2RjN4\n0eJMdEoiDt4P4LYRwJ886Xde8gAvsoVCaPDd3ck5EKPY0aP2wOfB4GRVWS7i4OOaDOAbG22jY8Tj\noxgx+KgLfuja8/hxuURD5zSxIIlGBPiiInlitYYGfz2TkmiCbEgyeNHiSjS06k1Ui8rgATnAnz7t\n1U35bUmbSbslySrDGs/E4jJ4WnxEdJyRxZFocpFNMq7JAD6OBh/WeImG7kW+GLyJBUk0IsB/4xvA\n5z7n/a20VH7vTTJJAvElmiQY/KBPFxwX4OOaTIPXb3dNBvAnTsjrVQgNHgjHipK2KVOsgc9xNfiS\nEuC553In0SShwUdJea0yHqBpfNxwg6U9RhYSmQTAJ63BH/v/7Z15WFNn9se/YVNAtJTiOi2oIBSh\nCKi4IKi4zDyDfdyXn6KlWupGa6utjzMqWJFpa3mq4jJVn9FaKy44rVpG7GgFwSK4YKeWtQIWFSyK\nCshm4Pz+SHMNIYEs9yY38f38A7n35ubk5Obk3O973vPeV53BaxPg5TapSrwqKoCBA1tvUyXZ7Nyp\nuheNITN4Vc83qwxeXw1eX5QDw7RpsunbcuTbXV1l3SEVUQ7wNjbqA7wxNHjAuAFenzp45UDdnjwD\n6FcmyYeMxfdAtmKZo42NLOgo9r9XhVAZPJ8a/NOnMglT1XvRJcBrM8iqjJub/lU0Qg2ympUGr08v\nGn1RztCXL09Fnz5t9//lL4ByFag6iUY5wMfH81+LrokGD7S/IIjQlJencv/rm8F31IhNX4lGXw2e\nbylMUaKxs5NNWEtLS9XYBgsL2R2FrnYpB3hvb9kKSZqizp8PHsjOo8ouPgO8qoxdFars1GaQVZ/P\nvT2JRjGDf/y4HRt0f3nDoM9tjro6Vm1ob21G+WuoQ1WAB9oG+DlzdLOtIzRpdGTMDF5RsnBw0O7H\nXFUG3x76SjT6TpjjO4NXlmg0SWaUq2h0HWAFnl03sl49QFKS7udSpK4OcHFRvU+b2b/tafCqBlk1\nxd5e8+eOGwf4+Oj2OoBmGTwR4OGh/hyiD/D6SDSA/jKN8gCSsnbY3pdEOStXF+CFQBMN3trauAHe\n1XU0979EIvtB1FQOMWSA56MOXqgArzjI2pGNyhm8PjbJs+n79wGFRrEa056t6jJkFxegpkaz82s7\nyKoOZTuvXdP8h+aNNzQ7Th3qNHjFDL6wsH1p0qwlGoCfAG9pqX6AjI8MXig0aVVgTPr1a/34l180\nv83XVaIxVh083xKNogav6fdDOcDzkcFruj6pNrQngWiTxVtaqg7wEonuvYD07SGkDeokGsUMPi0N\nCAlRfw5RBvg33wRmz5b9b2+vX0DUV4dXXKoPaKvJqfuSWFi0bQFgyACvaS8aY1FdDXh6prbaptRx\nul2Ube9IitNVg//oI1mGqq8Gb4gMviMblato9LFJHuArKzXXpBVpz9b22hRog7oA36OH5hVNfMx/\n0BV1Eo1imWRHAV6UEs0fK/0BAIYMAY4f1/1c+s5mtbcHIiLU71cXJHv2bPvhGDqD10SDNxYODvqV\nDWqbwetaRaPvbbYcoQZZtQnU5pDBa4OFheoAr6v+bmg6muhEJAvwMTHqzyHKDF4RiaTj8q/24EOi\n2bXr2WNNNPi+fYHY2Lbb5V8qsWjwxpZo9NG1Q0KeLVgOCCvRAOLU4OVVMMbQ4PXN4HXR4LVFXQav\nTYDnY/6DrnQ00am4WFYF5+am/hyizOD5ROhgqipg2Nmpz/o7dWIaPB8sXtz6sZBlknwgRB18e5Ps\nVKFcRcNHgBcigxdaojGlDL69iU5yeabdZRqFM08c8B1MNdXg1WGoAJ+amipqiQbgV9/sKMDLB8p1\nfc9irINXrvDqyEa+JJru3YERI/DH8nq69bZvz1YxZfBi1uA70t8BFuD1RqwBHjDvDF6ZjgK8/Bhj\nvWchJBptZ5DyJdHcuwcsXSprVqfp6kbaILQGr8ksVjHQXhWNYgbfHmb0FVcN38FUmzp4VRgqwGta\nB29M+NQ3hQ7w+tpqiAy+IxuVq2j4+LHTVZ5pz1YxSTTG1ODby+ALCmRtHRTHoVTBAryeiDXAA89X\nBq/JjGVra/PU4HXJ4Lt1Q6uWG7rCV7YtxDktLVX/8JuKBj94sOrS4c6dgTt3gOnTO757MnuJho9+\nNIowDZ4/DKnBA7IfAWNp8EJINPpo8C+/DPDhfl0zeFW2ylt761M1pwgfZZLG1OD/7/+ASZPabpfH\ntI7kGeA5CPDPcwavrqeHHGMHeD4RuwYvhESjjwbPlw7NZwZfVSX7y5evVEk0nTubjgavDnk7D00C\nvBl9xVXzPGvww4YBn32m/hhjB3hjaPDu7q3nNWiKGOvglcskNa2Dv3OHP5mCTw2+tlY/W5RRFeAL\nC7Vr/2xMDV4djo6yuTbKPe1VwQK8nmgbJLt3b9vCQCg6d27/YjZ2gOcTbTL4UaOEt0cRKyv++/3r\no8Er90jSByE0eL5QFeB1aYwmNhwdgZs3NateYhKNluirwR8/Dgwfzp896tBEOzR2gDe0Bq9P1ZA+\ntr76KvDtt7q/tirkGXzXrs+W6tOmFw1f6BrgDaFtq9PgtcGYGnx7aFqaakY5nGrElsGLCVO2XRFN\n+6EbqyxUIgFeeYXfc8o1eG3kJr7HAQD+Z7HyiboyyecJM/mKq0dsGryh0EQ7NJc6+HnzNFuQQ5/3\nKzYtVtVMVG160fCFrhm8Ifzp6Kh+EXZNEdvnri0iDU/8wTJ49Ziy7Yp06aJZn+7OnY1XB883ukxU\nEnsGr7w+gL6kpwvznk0Js3/7YquDNxTPmwavCV9+CQwdqttzxabFqmo1oE0dPF9osw6rIqpsdXOT\ntcDlCz7er9g+d20RaXjiD6EzeEtLw1XF8I2xA7yh4TtDNCa6NAvjM8B37ixbaN7YMh+jfSREfP5m\n6odEIgHf5uTmAq+/Dvz6K6+nbcXTp6Z5ob/zDpCQwG/WxDAMZ8/KVpu6cEGz48eNA77/nkkWHSGR\nAGVlpldOqS52mv3H7eYGfPKJsK9hisEdkPmlpMTYVjB0oXdvwNtb8+PPnmXBXVPEXNuvLWb/kdvY\nANOm8Xc+U9HkNLHT1hZwdRXcFLWYii8B8dnq5QXs3Nl6m9hsbA+x2krUenKgWO3UFLMP8Hxz/fp1\nY5ugEaZgpynYKMcUbDUFG+WYiq2mYqc6DBrgU1JS4OnpCXd3d3witG4iEI8ePTK2CRphCnaago1y\nTMFWU7BRjqnYaip2qsNgAb65uRnLly9HSkoKcnNzkZiYiLy8PEO9PIPBYDx3GCzAZ2dnw83NDa6u\nrrC2tsbs2bNx4sQJQ708b5SWlhrbBI0wBTtNwUY5pmCrKdgox1RsNRU71WGwMsmkpCScOXMGe/bs\nAQAcPHgQWVlZSEhIeGYM34s7MhgMxnOCqlBusKkumgRvEZXkMxgMhsljMImmT58+KCsr4x6XlZXh\nT6Y2m4DBYDBMCIMF+MGDB6OoqAilpaVoamrCkSNH8Prrrxvq5RkMBuO5w2ASjZWVFbZv346JEyei\nubkZCxcuxKuvvmqolzdbiIiNXTAYDJWIqhcNQ3NSU1Ph5uaGXr16wdJceuAakbt376I3n2vZPedk\nZGSgf//+cHZ2hpWVFUtEjMRz1k9QPbW1tYiNjUWvXr0QGhoKb20afRiQ+/fvIzIyEvn5+QgICICd\nnR2++OILY5vVhtraWqxfvx49e/bExIkT4evra2yTVFJUVIS5c+eiZ8+e2LBhA/z8/EQZjGpqanDm\nzBlMmjQJnfhe4JVHcnNzsXr1apSXl8PDwwNdu3bFLl1WOTcAtbW1+PTTT+Hk5ITg4GD4+fkZ2yTe\nYa0KAJSUlGDkyJFobGxEly5dEBMTg++++w4A0NLSYmTrWpObm4vGxkbk5uZi586dyMrKwpEjRyCV\nSo1tGkdlZSXGjRuHpqYmEBHWrFmDU6dOARCXP5ubm3H27Fn0798f3t7eSE9PR2NjoyBdTfUhPT0d\n7u7uCA8PR1ZWlqhsU+T3339HQkICQkNDceXKFcTHxyMlJQU///yz6H4wk5KSEBAQgOrqapSXlyM2\nNhZZWVnGNot3LGNiYmKMbYSxycrKgqWlJeLj4+Hv74/bt28jLi4OUVFRorkwW1paIJFIcPv2beTn\n52PYsGFwcnKCs7MzDh8+jCFDhuBFXVdf4JkHDx4gLy8P//znPxEUFIROnTrh/fffx4oVK0TjTyKC\nhYUFXF1dMW/ePDx8+BDXr1+HnZ0d+vXrJxo7AaC4uBhz586Fm5sbUlNTMXLkSNgJvdCBDnTu3BmO\njo6YOXMmAMDOzg4FBQUYOHCg6CrmkpOTsWjRIixduhQBAQEoKCiAjY0NfHx8jG0arzyXGXxBQQF2\n796NK1euAACkUimOHTvG7XdycsLDhw/x8ccfAzBe1vmf//wH7u7uyMzMhMUfvV6JCESEqqoqAMC0\nadNgZWXF3XEYI7u7desWfvvtN+7x/fv3UVRUxN1VTJ8+Ha6urli/fj0AcfhTHsBffPFFWFhYYMKE\nCXjhhReQmZmJe/fuAZBl+MZA2Z9DhgzBiBEjsGzZMhQXF+PcuXOiuBNS9CcAWFpaYtiwYdz+xsZG\npKen44UXXgBg3Hkuyj6NiIjA8OHD0dLSAkdHRxQWFnJjWWK9Q9KF5y7AnzhxAiNHjsQvv/yCRYsW\n4dChQwgLC4OXlxfeeOMNREZGIjk5GVu3bsWpU6dQV1fHBVdDcvnyZezfvx89evRAXFwctz0oKAgS\niQQpKSlckH/77be5GcKGzDyJCNHR0RgwYAAiIiK47f7+/iAifPbZZ9y27du34/jx46iurhaVPy0t\nLdHS0oJu3bohODgYFRUVyM7OBgCD26nOn13+WHDWzs4OCxYswKFDh1Bi5Eb+6vxppbDMVFlZGXr0\n6AFPT08Axpmprs6nL730EncXRESwtbVF9z+WZhPT3Zu+PDcBnojQ0tKCjIwM7Nu3D1u3bkV0dDSy\ns7Nx5MgR/Pvf/8bChQvRr18/7NixA6NHj4aXlxeePn1qsF90IkJDQwMAoG/fvoiJiUFGRgZ+++03\nHDp0iDtu8eLFyMzMxH//+18AgJeXFzeGYEhqampQXV2N8+fPw8bGBl999RW3b8uWLdi8eTMeP34M\nAOjXrx8CAwNx584dg9mniT8VB1THjRsHPz8/pKenIywszOAdT9X5s7m5mbsGw8PDYW1tjbS0NABA\nTk6OwezT1J9yqqqqMGTIEDQ1NSEqKgp79+41mK1y1PlUfndpYWGBqqoq5OXlYcSIEQCA/Px8g9sp\nGGTmXLp0iQoLC6mmpoaIiJYuXUrLly8nIqL6+no6fPgwLVy4kHJzc1s9T77dUGzZsoWGDx9Ob775\nJhUUFLTal5SURK+99hrV1dVRS0sLEREdPXqU5s+fTzNmzCAXFxdav369QexU9ufdu3c5GwMCAujp\n06fcsZGRkRQeHk43btyg77//nkJCQqi6utogdmriz/r6em6b3O5ly5aRg4MDzZ07lyorKwW3U1N/\nNjc3k1QqJSKiwsJC8vDwoAEDBtCMGTOovr6euy6EQht/ym1Zs2YN9e3bl0aNGkWRkZFUVVUlqI1y\ntPEpEVF2djbNnj2bbty4QePGjaOVK1dSY2OjQWwVGrMN8HV1dbR06VJycXGhN998k8LCwoiIKDU1\nlebOnUt5eXlERFRQUEBr166lxMREIiIqLi6mN954g/r27Uvff/89EZHgX57Lly9TaGgoFRUV0YYN\nG2jevHmUnJzc6pgJEyZQdHR0q20PHz6k/fv307Vr1wS1j6itPydNmtRqv1QqpVmzZtHatWtbPSc+\nPp6mTJlCPj4+dPjwYSISrz/v3LlDEydOpEuXLnHb5EGAb7T1p9xn8ud1796dvv76a0FsU0ZXf0ZG\nRlJYWBjl5ORw24TyJ5HuPj1y5AhJJBIaMWKEwXxqKMw2wBcVFdHYsWO5x6NGjaKEhATKy8ujmJgY\nWrduHbfvrbfeol27dhERUX5+PheIhETxQk9MTKQxY8YQkeyi++yzz2j16tWt7iry8/PJy8uLMjIy\naM2aNXTjxo025xPyy6Psz+DgYIqPj2+VBV+6dIm8vb257Ofx48dERFReXi6YXXL09ecvv/zS5nzy\njFkIdPHnw4cPqbq6mk6cONHqXELYqY8/V69eTXfu3KE7d+60Op+Q1yeRbj5tbm6mY8eO0d///vdW\n5xLaVkNhVhp8YWEh979EIoGzszOKiooAAJs3b0ZKSgpqa2sxZswY5Obm4sCBAwCAbt26cdqih4cH\nZs2aBQCC1ZbHxcVh5cqVOHnyJABZlcQrr7yCn376CRKJBBMnToRUKsWlS5e453h4eKC2thbjx4+H\nlZUVBg4cyO2jP0r++B4U7MifZ8+exY0bNzgbAgMDMXXqVPj5+WHkyJG4fPkyAHCDV2L2p5eXF7ev\nubkZFhYWvM8Q1sefI0aMwLVr1+Dg4MD1cJL7k2879fWntbU1evfuzc0MlkqlglyfgH4+HT58ONLS\n0jB9+nTExsZytgKGH2AXCrN4F5cvX8b48eOxaNEifPDBB8jKyuIqD6qqqtDS0oLAwED0798fR48e\nRXBwMCIiIrBjxw4EBQXh3LlzKhufKVYE8GWnn58ffv31V3h6emLHjh3Yv38/nJ2d0b17d1y8eBEA\n4O3tjV69euHmzZsAgMePH2PdunXw9/fHr7/+io8++qjVefke9dfEn0OHDoWHhwe+/vprzobc3Fx8\n9913sLe3x8aNGxEaGgrg2ZfFVPzJd8Dkw5+xsbEYO3Zsq/OK1Z8bN24U1E65rfr6dNOmTRgzZgx3\nzpaWFkFsNSrGvYHQn9TUVPL396fDhw9TZWUlrV+/ntasWUNERKtXr6bVq1dTRUUFERGVlpaSi4sL\n3bt3j4iI7t27R9nZ2Qaz9dtvv+W0fiKigwcPUlRUFBERHThwgN577z06ffo0ERFdu3aNAgMDuVvF\n+/fvc897+vSpYLeQ2vjz1q1b5OLiwg1G7t+/n/bu3cudq6WlRVC9nfmTX0zBn0Sm5VNjY7IBXv6h\n1NTUtNIkExMTadq0aUQkC+hTpkyhffv2UVNTExERzZ8/X2V1hGL1h5C2VlVVcY83b95MK1euJCKZ\nTr17927y9fWlCxcu0Pz58+mDDz5oM5ovlC6sjz/lP5iKMH8yfwppp9h9KhZM7n6kvr4etra2XL+Q\nLl264K9//Su3/09/+hMkEgnq6+vh4uKCxYsX4+TJk/jmm29QXFyMgIAAbmadInzfmpFCfbX8r/wW\nUt52gIjg5OQEAOjZsyfeeustSCQSHDx4ELa2tti0aROsra1bnZdv+YAPfyq2SJC/b+ZP5k++MBWf\nihKj/KzoSFxcHG3YsIEaGhra7JPfEn7yySf07rvvttrX1NREhw4dovPnzwtuY0tLS5vbU+XH8kxk\n/PjxdPHiRSIiunLlCrdfnnkQCZsRMX/yC/Mn/5iCT8WMSQyyyke2g4KCcOHCBZUzzeRZSHl5OaZO\nnQqpVIrPP/8cV69ehbW1NebMmYPRo0eDiATrMdLc3AyJRAILCwvk5eVh7969aGhoaDMiL5FIUFVV\nBVtbW9ja2mLmzJlYu3YtHjx4ACKCtbU1N/NWiIyI+ZNfmD/5x1R8KnZMIsDLb6VGjRqFwYMHY9++\nfaipqWlzHBGhpKQEO3fuRGBgICoqKlr1dac/bs2EuigtLS3R0NCAf/3rX1iwYAG++uorfPjhh1wb\nUlKYxl1dXY1Tp04hPDwcISEhOH36NJycnFrdNgtVqsX8yS/Mn/xjKj4VPQa9X9CB5uZmqqiooJiY\nGMrMzKTKykoKCQmhlJSUNqPfd+/eJYlEQnPmzGkzEUgIlG9PpVIpLVy4kHx8fIiI6MmTJ7Ru3TqK\njo7mJv3IbytzcnLob3/7Gz158kTt+YSA+ZNfmD/5R8w+NTVEF+Dfe+892rhxIxERN/Ld0NBAixcv\npri4OCIi2rVrF82ePZsrhSJ6dvFlZWVx2wwxe45I1hvk4cOHRER05swZcnBwoLKyMiIiOn36NK1Y\nsYKSkpKISPU0/adPnwpWqsX8yS/Mn/xjij41FUS34IetrS3effddTJ48GatWrYKjoyMGDBgAe3t7\npKamolOnTpg1axYOHjwICwsL+Pj4wMLCAhKJBBKJBH369AEg0/AsLS15nwT0/vvvIzs7GyEhISgs\nLMSSJUtw+PBhJCcnw83NDcHBwSgtLcWFCxcwadIk9OrVC/n5+bh8+TL8/f3RtWvXVueT65hCtShl\n/uQX5k/+EbtPTRpj/8IoIs8SZs2aRZMnT6bExEQKDw/n9kdHR9OSJUuosbGRTp48SaNGjVJZ3yok\nFy5cIEdHR6qurqYlS5bQ7t27iYgoJCSEgoKCqKGhgW7evEkBAQH0448/EpEsw/jhhx8MaicR8yff\nMH/yjyn41JQRZYB/8OABde3alY4ePUrLly+nL7/8koiIMjIyqE+fPtxFW1JSYhT7pkwGOZ2JAAAG\nPUlEQVSZQm+//TYRyTrtDRs2jFasWEEBAQH06aefEpHswhw1apRB7VOG+ZNfmD/5R+w+NXVEFeCJ\nnulqMTEx5O/vTz/88AMNHDiQrl+/TqtWraLw8HC6fv06d7whpxnLX+v+/fvk4OBAJSUllJCQwHWm\n3LlzJ9nZ2VFpaSk9efKEa0lszKnQzJ/8wvzJP2L2qakjujJJeTlTdHQ0Kisr8ejRI6xcuRLvvPMO\nbGxscODAAfj6+nLHG1Jvk0gkaG5uhpOTE6KiojBt2jSu9Ky4uBi3bt1CYGAgnjx5Ajs7O3h6enKz\nAo0F8ye/MH/yj5h9avIY+xdGFfJR8MTERPL09CQiatXzwlDlWh3h7u5OERERFBcXRz169KDNmzcb\n2ySVMH/yC/Mn/5iKT00NUQZ4ome3YaGhoXT06FEikn3IYiiBkttw/Phxcnd3JyJqtRyZGC9G5k9+\nYf7kHzH71FQRnUQjRyKRoKamBnZ2dujXrx8A2a2cGBrxW1hYgIgwdepUvPzyyzh27BgcHR0hlUpB\nRKKcNcf8yS/Mn/wjZp+aKqJup3b16lX4+vpi0KBBxjalDfKL0d7enrsYxd6djvmTX5g/+UfMPjVF\nJEQKDSgYWpGamopz584hJiZGtFmRKcH8yS/MnwwW4BkMBsNMYeIWg8FgmCkswDMYDIaZwgI8g8Fg\nmCkswDMYDIaZwgI8w6TZtGkTvL294evrCz8/P2RnZ2Pr1q2or6/v8LlbtmzR6DhVpKamolu3bvD3\n94enpydCQkKQnJzc4fPS0tKQmZmp02syGNoi/sJYBkMNmZmZSE5ORk5ODqytrVFVVYWGhgZs2bIF\n8+bNg62tbbvP37p1K8LDwzs8Th3BwcE4deoUAOCnn37C5MmTYWtri7Fjx6p9zvnz5+Hg4IDhw4fr\n9JoMhjawDJ5hslRUVOCll16CtbU1AODFF19EUlIS7t69izFjxiA0NBQAsGTJEgwZMgTe3t6Qr2+z\nbdu2Nsd16dKFO3dSUhIiIiIAAMeOHYOPjw8GDRqE0aNHq7TF19cX69evx/bt2wEAp06dwrBhw+Dv\n74/x48fj999/R2lpKb744gt8/vnn8PPzw8WLF1FZWYnp06dj6NChGDp0KH788UchXMV4XjFWjwQG\nQ19qa2tp0KBBNGDAAFq6dCmlpaUREZGrqys9ePCAO07eh0UqldLo0aPp559/Vnlcly5duP+TkpIo\nIiKCiIh8fHzo7t27RETc2qXnz5+nsLCwVvbk5OTQq6++SkTELZFHRLRnzx5auXIlEcla4sbHx3P7\n5syZQxkZGUREdOvWLe75DAYfMImGYbLY29vj6tWrSE9Px/nz5zFr1iz84x//AACQwvy9I0eOYM+e\nPZBKpSgvL0dubi68vb07PL/8HCNHjsSCBQswc+ZMTJ06tcPjAaCsrAwzZ85ERUUFmpqauHYBysed\nPXsWeXl53OOamhrU1dXBzs5OAw8wGO3DAjzDpLGwsEBISAhCQkLg4+OD/fv3A3jWM7ykpATx8fG4\ncuUKunXrhoiICDQ0NKg8l2KfccXB1127diE7OxvJyckICAjA1atXVT4/JycHXl5eAICoqCisWrUK\nYWFhSEtLg7qlj4kIWVlZsLGx0fatMxgdwjR4hslSWFiIoqIi7nFOTg5cXV3h4OCA6upqAEB1dTXs\n7e3RtWtX3Lt3D6dPn+aOVzwOAHr06IH8/Hy0tLTgm2++4bbfvHkTQ4cOxYYNG+Ds7Izbt2+3seV/\n//sfYmNjsWzZMu51e/fuDQDcj478NWtqarjHEyZMwLZt27jH169f19UdDEYbWAbPMFlqa2sRFRWF\nR48ewcrKCu7u7ti9ezcOHTqEP//5z+jTpw/OnTsHPz8/eHp64uWXX0ZQUBD3/MjIyFbHffzxxwgL\nC4OzszMGDx6MJ0+eAAA+/PBDFBUVgYgwbtw4vPbaa0hNTUV6ejr8/f1RV1eH7t27IyEhAWPGjAEA\nxMTEYMaMGXB0dMTYsWNx69YtAMCkSZMwffp0nDhxAtu3b8e2bduwbNky+Pr6QiqVIiQkBDt37jS8\nMxlmCWs2xmAwGGYKk2gYDAbDTGEBnsFgMMwUFuAZDAbDTGEBnsFgMMwUFuAZDAbDTGEBnsFgMMyU\n/weSeOT2kicY4QAAAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>State</th>\n",
+        "      <th>Status</th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2009-01-05</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 721</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-12</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 368</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-12</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 103</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-19</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 441</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-01-26</th>\n",
+        "      <td> NY</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 408</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": [
+        "           State  Status  CustomerCount\n",
+        "StatusDate                             \n",
+        "2009-01-05    NY       1            721\n",
+        "2009-01-12    NY       1            368\n",
+        "2009-01-12    NY       1            103\n",
+        "2009-01-19    NY       1            441\n",
+        "2009-01-26    NY       1            408"
+       ]
+      }
+     ],
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th>Status</th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>State</th>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th rowspan=\"5\" valign=\"top\">FL</th>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 385</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-09</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 125</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-16</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 378</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-03-02</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 722</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-05-18</th>\n",
+        "      <td> 1</td>\n",
+        "      <td> 962</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": [
+        "                  Status  CustomerCount\n",
+        "State StatusDate                       \n",
+        "FL    2009-02-02       1            385\n",
+        "      2009-02-09       1            125\n",
+        "      2009-02-16       1            378\n",
+        "      2009-03-02       1            722\n",
+        "      2009-05-18       1            962"
+       ]
+      }
+     ],
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th>CustomerCount</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>State</th>\n",
+        "      <th>StatusDate</th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th rowspan=\"5\" valign=\"top\">FL</th>\n",
+        "      <th>2009-02-02</th>\n",
+        "      <td> 385</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-09</th>\n",
+        "      <td> 125</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-02-16</th>\n",
+        "      <td> 378</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-03-02</th>\n",
+        "      <td> 722</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2009-05-18</th>\n",
+        "      <td> 962</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": [
+        "                  CustomerCount\n",
+        "State StatusDate               \n",
+        "FL    2009-02-02            385\n",
+        "      2009-02-09            125\n",
+        "      2009-02-16            378\n",
+        "      2009-03-02            722\n",
+        "      2009-05-18            962"
+       ]
+      }
+     ],
+     "prompt_number": 21
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 22,
+       "text": [
+        "MultiIndex\n",
+        "[FL  2009-02-02,     2009-02-09,     2009-02-16,     2009-03-02,     2009-05-18,     2009-06-08,     2009-06-15,     2009-06-22,     2009-07-06,     2009-07-27,     2009-08-03,     2009-09-14,     2009-09-28,     2009-10-19,     2009-11-23,     2009-11-30,     2010-01-04,     2010-01-11,     2010-02-01,     2010-02-15,     2010-03-15,     2010-03-22,     2010-04-12,     2010-04-19,     2010-04-26,     2010-05-03,     2010-05-10,     2010-05-17,     2010-06-07,     2010-06-21,     2010-06-28,     2010-07-05,     2010-08-30,     2010-10-11,     2010-10-18,     2010-10-25,     2010-11-01,     2010-11-15,     2010-11-29,     2010-12-27,     2011-01-03,     2011-01-10,     2011-01-24,     2011-02-07,     2011-03-07,     2011-03-14,     2011-03-28,     2011-04-04,     2011-04-18,     2011-04-25, ..., NY  2012-10-22,     2012-11-12,     2012-11-26,     2012-12-03, TX  2009-01-05,     2009-01-19,     2009-03-02,     2009-03-16,     2009-04-13,     2009-04-20,     2009-06-01,     2009-08-03,     2009-08-31,     2009-09-21,     2009-12-14,     2010-01-04,     2010-02-15,     2010-04-19,     2010-05-31,     2010-06-07,     2010-06-14,     2010-06-28,     2010-07-05,     2010-08-09,     2010-08-23,     2010-09-06,     2010-10-04,     2010-11-01,     2010-11-08,     2010-12-13,     2011-01-17,     2011-02-14,     2011-02-28,     2011-03-14,     2011-05-16,     2011-06-13,     2011-09-12,     2011-09-26,     2011-10-17,     2011-11-07,     2011-11-21,     2011-12-05,     2012-01-30,     2012-03-05,     2012-03-26,     2012-06-04,     2012-07-02,     2012-07-30,     2012-10-08,     2012-11-12]"
+       ]
+      }
+     ],
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 23,
+       "text": [
+        "Index([FL, GA, NY, TX], dtype=object)"
+       ]
+      }
+     ],
+     "prompt_number": 23
     },
     {
      "cell_type": "code",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 24,
+       "text": [
+        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
+        "[2009-01-05 00:00:00, ..., 2012-12-31 00:00:00]\n",
+        "Length: 181, Freq: None, Timezone: None"
+       ]
+      }
+     ],
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "Daily.ix['FL'].plot()\n",
-      "Daily.ix['GA'].plot()\n",
-      "Daily.ix['NY'].plot()\n",
-      "Daily.ix['TX'].plot()"
+      "Daily.loc['FL'].plot()\n",
+      "Daily.loc['GA'].plot()\n",
+      "Daily.loc['NY'].plot()\n",
+      "Daily.loc['TX'].plot()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 25,
+       "text": [
+        "<matplotlib.axes.AxesSubplot at 0x5850bd0>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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LL7+M9PR09OrVC5s2bfI8v3v3bvTt2xfp6el4hM9SCnlwz0AdoWAgWw0+Azn4MI0M7rrr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\nn3/OkpIS+vj4MDAwkM899xzLy8ulvmfPnuWFCxf0Gq8SvCrNKSm8yoE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+      },
+      {
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9dYte23xyXlwcfUDLJc8VFTHN3whfzcyZss+vv1p2/mK0U76R//nzQGQkHaB1\nNDJPSwMCA4Fx46LvOfa3v9FCVt9/79g1pEapurQlOFsJ4T/gC9AHe3q68BQ/R1Fi33Kze82d7yuv\nROOnn+QPTswlH47mzYFp0/jX+2GavxO4epXeKHJH/rW1wLlz4mv4m+Ptze+Bdf48cP/9QFCQ45F/\naiqNoKzZ83//B8ybJ66+CKOe8nLan82a8Tu/SROgZ0/nZrQoFXPJhyM0lL4tp6XJe30ux98Ss2YB\na9bIMyObaf4mCMnz79hRfud/8SKN5Hx87j0mRjvlO+Br6vwdjfy5wV5r9j7wAE1rE7WghEwoVZe2\nBGerEMmHwxVF3pTYt+aZPhwHDhiMs33lxDTH35zQUDrB87vv7LcjpG/v3KFyk6+vgmSf5cuX4/77\n70ePHj3w9NNPo7KyEqWlpYiJiUFYWBhGjBiB6yaPqeXLlyM0NBTh4eHY7aSCHFevUilDbtlHSr0f\nEB75d+okb+TPsWIFsH07cPiwY9dqzIhx/q4u76wUzDN9THFGlU9rsg+HHPV+uOQADw+FyD7Z2dn4\n+uuvcerUKZw7dw61tbVISkrCihUrEBMTg4yMDAwfPhwrVqwAAKSlpWHTpk1IS0tDcnIyZs+ejToh\nibFm8NHMamvpgKi/v/yRvy3nL0Y7dXbkf+sWkJlJZStb9vr4AKtWAS++yD8bSU6UqEtbg7OV7+xe\nU7jI35kD7krsW07zNyc6OhqPPAIcOyZvSRJbsg8AjB0L5OcDp07ZbkdI35aW0ln/gEKcf+vWraHV\nalFRUYGamhpUVFQgMDAQ27dvx/Tp0wEA06dPx9atWwEA27ZtQ1xcHLRaLYKDgxESEoLU1FSpzWpA\naSl1Vk2bqjPyt+dci4tpbr9O53jkf+oU0L071ZftMXEi0KUL8MEH4q/XmBEy2MsRGEizTDIz5bFJ\nLdiK/Fu1omWWDxyQ7/q2ZB+A1vt56SVpo/9r1xo6f6Gav5d0plDatm2Lv/zlL+jUqROaNWuGkSNH\nIiYmBkVFRfDz8wMA+Pn5oaioCACQn5+PQYMGGT+v1+uRl5dnse0ZM2YgODgYAODj44PevXsbn5Sm\nWll0dLRx2/x4dHQ0rl4FWrQwoLQUqKm597iU26dPR6N3b8vHT58+jfnz5wtqT6uNRnW17fPPnwd0\nOgMOHAAefjgatbXAzp0GtGgh3P4TJ6IRFcXf3s8/j0bfvkDnzgYEBUnfn3y3V61aZfH+UOI29/+U\nFKBjR+F70RElAAAgAElEQVSfHzIEWLPGgFGjnGuvXO2L2T5xwnA366nhce6c0FAD/v1vYMwYea5/\n8qThbtkV6+dHRAArV0Zj5UrgzBnL7XH7+Fz/4EHA15duZ2YakJ9Pr28wGLDubl0Jzl9aRPBqwXb4\n448/SEREBCkuLibV1dUkNjaWrF+/nvj4+DQ4z9fXlxBCyNy5c8mGDRuM++Pj48n3339/T7t8TeWz\nAPL+/YQMHUpIXBwhJpeWnMJCQnx9Camrs2bHfsFtxsYSYqF7GvD554Q8/3z9dng4IefOCb4UIYSQ\np54iZP16+n++9n70ESHDhln/vZ2BUhcZtwRn66uvEvLxx8I//8knhLz4orQ22UKJfbtsGSFvvHHv\nfs7WX38lJDRUvut/9BEh8+bZP+/pp+m51hDSt//+NyHPPUf/n51NSFCQ5fOs+U7JZZ8TJ05gyJAh\naNeuHby8vDBhwgQcPXoU/v7+KCwsBAAUFBSg411xU6fTIcdEl8jNzYVOpxN9fe7JaAtOW+U7YUos\nZ87cW8PfFD62muPNY8CX0/s5HJF+TAd7+dr7yitUg01MFHdNKRDTt66Cs1XI7F5TnD3ZS4l9a032\n4Wzt1Yvq8nItrm5P9uGYM4dKP9aGNYX0bWkpzfQBFJLqGR4ejmPHjuH27dsghGDv3r2IjIzEuHHj\nkHjXGyQmJiI2NhYAMH78eCQlJaGqqgpZWVnIzMxElL3UEgfhtFUvL3kHfK1N7nIEPgO+5s6/c2da\nWE4oRUXUiYeGCvuclxfN/X/jjfrJdK7m7bfhlGn+jiAm2wegji0ry3kLEykRW5o/QAOwkSOBn36S\n5/r2sn04Bg+mYzR79zp+TVPNv1Wr+vWf+SK58+/VqxemTZuG/v37o2fPngCAF198EQkJCdizZw/C\nwsKwb98+JCQkAAAiIyMxadIkREZGYvTo0Vi9ejU0thYwtYOpdmYN08hfTudvb7CXj63miIn8w8OB\n338XfCmkptIlA7k/hxB7+/YFpk4FFiwQfl0pMLfVYAC2bXOJKXbhbBXr/LVaoF8/OmbgDMTct3Jj\nLdvH1FY5Uz7tZftw2Kv3I6RvTSN/T096fb7l3gGZ8vz/9re/4fz58zh37hwSExOh1WrRtm1b7N27\nFxkZGdi9ezd8TGY9LVy4EH/88Qd+//13jBw5Ug6TGsC9Xnt5ySv7SLF6lzn2Iv8rV+gDLSCgfl94\nOHDhgvBrmVfyFMo77wCHDgF79ohvQyry86ktSkas7AOwfH97kT8AxMTQIECOVGS+sg8APP008Msv\nwKVLjl3z2rV65w8IT/d0uxm+fDQzZ8g+t27RP655MTRTxGr+tm5eLuo3fXmKiBDn/M0ndwm1t0UL\nqm++/DJQUSH8+o5gaishQF4erYOjRGkkOjoadXU0Rbd9e3FtOFP3V6PmD9A1sMPD5ZmIyFf2Aej3\nYupUWhXXHCF9ayr7AMJ1f7dz/nxwxoDvb79Rp6vVStuuPZvNJR+Aav7Xrgl7JayrozVjHB1+GTOG\nSkfvvutYO45w4wbtt4EDlTsD+do1Gjl6i1zzYdAg+qbmwPxIVWNN9jFHLumHr+zDMWsW8M03jq21\nYSr7ACzy56WZOSPy5zO5S6zmzyfyN8XDg5ZfFqL7Z2bSiXCmGrRYrXfVKnqjnz0r6uOiMLU1L49O\nhho6VJnSj8FgcEjyAejfqX17cW94QlGi5m8t8je3ddQoeQZ9hcg+AF3cqWdPYPPmhvuF9K155C+0\nvo/bOX8+cJG/q52/GOwN+Fpy/oBw3Z9PPR+++PsD771HSz+4YtnH/Hw62/mhh5Tp/AHxg72mNOb6\n/taqepozYABNe6YToqRDiOzD4Wi9Hxb5m2FPM6utpbpYu3byyj58nL8Y7dTWgC8h1p2/UN3f0mCv\nI1pvfDx9cH3xhegmBGFqKxf5DxpE/y5ylNZ1hOjoaEmcv7MGfZWm+RNCZRdrtX1M8fIChg+XfmlF\n8/V7+TBuHK27dfp0/T6+fVtXR69pWi2Yaf52KCmhneTpKV/kz9Xwv5vpKim2In9udbK7VTQaEBEh\nTPaRMvIHqPT01Vc0A0iKZSWFkJdHI/+WLekCNzKXjhKFmLo+5jTWyP/WLVp7iu/4mhy6v5jI38tL\nfL2fGzfo9Tw96/c1etnHnmZmWjlRrsg/M7N+dR1biNFObUX+ljJ9OIRE/nfu0Lb69m2431GtNyKC\n5ji/8opDzfDC1FZO9gGo7n/woPzXFwKn+Tsa+XfvTn/XkhJp7LKG0jR/W5KPJVtHjqTpx1JKkEIH\nfDmef57W+ecidr59a1rRk6PRyz72MI2w5Ir85cjv57A14GtN8gHoLN3sbH45zmfO0AFi08WopeLN\nN+lDaMsW6du2Bif7AMod9JVC9vH0pG9rx45JY5NasLaQizV0Ovpz4oQ016+qojIMn8q35vj70zcR\noaVQzHP8Aeb87WpmphGWnM6fz2CvWM3f2tuKLeffpAmt7f/HH/avYW1ylxRab5MmVP559VX6pZUL\nU1tNI/8HH6TOUe51HITAaf6Oyj6Ac3R/pWn+tiZ4WbNVSumHk3zEFiYwrffDt2+tRf5M87eBaUqd\nXLKPXJk+gPjIH+Av/Uit95vz8MP01futt+S7himmkX/79oBeT99ulIQUsg/QOHV/vjn+pki5tGNW\nFi2eKJYHHqBBkZDaU5Yif6b529HMnCX78HH+Utb2sZXpw8F30Nea85dS6/3gA5rjLFc9Gs7Wmhr6\nN/f3rz+mNOnHYDBIIvsANKPp+HF532yUqPlbi/yt2frgg/T7Ulrq+PXtfe/sodHQQGjyZGDSJAOv\nhAjzHH+AyT52MR/wlfpLUlhI23SgKrVNrA34FhZSzdeWA+ET+ZeW0rYiIhyz0x5t2wIffQS88IK8\n9ZWuXKlP6+VQmvMHpNH8ARoNBgXRbLPGAp+6PuY0aULfQKWorpmWRrPIHGHyZBo0EkKzBJ97zvZ3\n1TzHH2DO365mZh75S+14uKifj/4nZT1/PtEHn4lex4/TCpGmKWQcUmu9U6ZQOeajjyRtFkC9rVya\npymc83fmure2ePDBaNy4cW8kJxa5dX+laf62ZB9btg4fTgu9OYoUzh+g0tF330Xjjz+Arl2B6Gjg\n8ccty3jWIn+m+dvAVPOXQ/aRo4a/KdYifz7OPyKCFjezVf/F0UqeQtBo6KSvlSuBixfluYbpYC9H\np040kyk9XZ5rCqWkhEZxlh64YuAWdW8siIn8AbretCPrW3M4KvuY07YtsGgRHUsYORJ49lkasOzY\nUf/dtRT5M83fxXn+QgZ7paztw+cGbNOGRki2NEVbg71yaL333UcXfZk1S9pInLPVdLDXFCVJPzt3\nGiSRfDgGD5Z30NcdNH+Alj0vKHDs2hUVNMDo2tWxdjhM7W3enJaAyMigGUF//zuVhBITqYJhHvm3\naEELxfH1aW7n/O0h94CvnJk+gPUHFt/ow5buT4hzI3+O116jD+X//Ef6ti1F/oCynP/169KkeXKE\nh9PIsKhIujaVjJhsH4AGBY7W+Pn9dyAkhPoSufDyohLpqVNUIv32W/oWYF7+W6Oh/cA3+pfF+V+/\nfh0TJ05EREQEIiMjkZKSgtLSUsTExCAsLAwjRozAdRNxavny5QgNDUV4eDh2O1h0w5bGV1NDO6Zd\nO7ottfO/eZNG1bZq+JsiVT1/Ppk+HLZ0/+xs2r61wWq5tF5u2cfXX5dudqqp5q/0yD8wMFrSyN/D\ng2b9yCX9KE3zF5PnD9BZ+FevOjbTNy1NWsnHlr0aDTBiBE0JTU+nKaLmCBn0lcX5z5s3D2PGjMGF\nCxdw9uxZhIeHY8WKFYiJiUFGRgaGDx+OFStWAADS0tKwadMmpKWlITk5GbNnz0adTEXJS0qoLsZp\nq1LLPufO0YEfOaMASwO++fk0e4HPQiC2Iv/UVOdH/RwDBtDo5vXXpW3X0oAvQB+CN29Ko/k6ilSZ\nPqY0ppW9xGr+Xl40EORqYolBqsFeoYSFWR4jEqL7S+78b9y4gUOHDmHmzJkAAC8vL7Rp0wbbt2/H\n9OnTAQDTp0/H1q1bAQDbtm1DXFwctFotgoODERISglQHKm/Z0vjMa6ZLHfkLlXykqu0jZMDJlvNP\nSbE9uUturffdd2lUs2+f421xtlqTfTQamuuthOj/+HGDpLIPIO9kL6Vp/rZkH3u2BgQ4Jv1IPdjr\naN8Kifwlj1GzsrLQoUMHPPfcczhz5gz69euHVatWoaioCH53y036+fmh6K4gmZ+fj0GDBhk/r9fr\nkZeXZ7HtGTNmIDg4GADg4+OD3r17G1+T7l2wm26bHv/1V6Bjx/rt334DamqirZ4vdPvHH4FRo/if\nf/r0acHX8/OLRnV1w+PnzwM+PgYYDPY/HxERjd9/t3x8zx7gk0+ktVfo9uefR+Pll4HPPjPA21t8\ne6fv1snNy4tGYKDl8wMDgUOHovH00/L9Pny2r18Hrl/n9/fju11ZacCJE0BVVTS8vV37+8m9feMG\nkJ5uwO3b9x7nsPb5wMBoFBSIv35aWjQiI6X7fezZa2+7TZto/PKLAYmJ6wDA6C8tQiTm+PHjxMvL\ni6SmphJCCJk3bx5ZtGgR8fHxaXCer68vIYSQuXPnkg0bNhj3x8fHk++///6edqUwNSmJkIkT67eP\nHiUkKsrhZo1ERRHyyy/StWeJzExCunRpuC8+npAvvuD3+bo6Qlq3JqS4uOH+qipCWrQg5MYNaex0\nhCefJGTRIsfbuXWLkCZN6O9siePHCbn/fsev4yixsYRYuOUdpmdPQlJSpG9Xafj5EZKfL+6zzz9P\nyFdfiftsRQUhTZvS745SmDaNkLVrG+6z5jsll330ej30ej0GDBgAAJg4cSJOnToFf39/FBYWAgAK\nCgrQ8a7IqdPpkGMivObm5kIn0/RY8/opUso+NTV03V45avib4m1hwPf8ef66o0ZjedD3t9/oWr9i\nsiak5l//Ar78kv5ejpCfTwd7rU24692bLqYhdwlke0hV1M2cxqL7i832ARxL90xPpymeUq/T7Qgu\nHfD19/dHUFAQMjIyAAB79+7F/fffj3HjxiHxbt3SxMRExMbGAgDGjx+PpKQkVFVVISsrC5mZmYhy\noKqY+euTKeaav5QDvhkZ1NEIWc3Hlq3WsGTz778LG3SypPvzSfEUY68YAgOBJUvoso9ix/4NBoNV\nvZ/Dy4tmxbh6UfdLl6TN8+eQS/eX4j44fJja5mhuR3U1DYaslR+3Z6sj6Z5yDPY62rcu1fwB4NNP\nP8UzzzyDqqoqdO3aFWvXrkVtbS0mTZqEb775BsHBwfjvf/8LAIiMjMSkSZMQGRkJLy8vrF69Ghqx\ntVHtcOUKXfCCQ8rIX+78fg7zyP/2bfrDpa/ywVKBN7kreQrlpZeADRuAr7+m/xeDtTRPU7jFXcaP\nF3cNKbh+XfpsH4BG/osWSd+uFMyaRRdAqa4GJk6kP0OG0DRVIXALuYh1GQEBwI8/ivuskDduZ9Gm\njYCHmfwqlDRIYeqTTxLy3//Wb6enExIa6nCzhBBC/vpXQpYulaYtW5SVEdKyZf12djYhQUHC2ti2\njZDRoxvuu/9+Qk6dctw+KTl3jpD27cXruStXEvLaa7bPMRikHfcRyp07hHh5WR+XcITaWkI8PAip\nrpa+bUcoK6PjS5WVhJw/T8g77xDSvTshAQGEzJ1LyIEDhNTU8Gvr4kVCgoPF25KaSki/fuI+Gxvb\n0J8oga+/JmTmzIb7rPnORjXD11xblbKwm5yrd5linupZVGR5zV5bmGv+ZWV0gpfpW5ES6N6dRv3z\n5on7vD3ZB6BvO7/9RteBdQXFxfSelONl18ODRsVyLpojhhMngF696FtsZCTwj3/QOTL79tF7+dVX\n6ZoLc+YA+/fbnoQlNsefwxHN31U5/rZw+SQvV2JP85djwJcQcbKPI5o/VwenqEi4ZNClCy3bXFFB\nt0+dogPV9gaunKX5m/LWW7RY3o4dwj5nMBh4yT7NmtG/m6uWPrxyBWje3CBb+76+tAKklDh6Hxw7\nRsdazAkPpzLV6dPAgQP0AfCXv9C/4csv0zkg5t9Xe4O99mwVO8v3zh2aLBAaKuxz9nCm5u92zt8W\nlgZ8pXD+XOQQEOB4W/bw9KRRInezion8vbxolgJX1TIjQ3kRDEezZnTZxzlz6IxcIfCJ/AHXlnq4\ncoXOypQLHx9hZX6dAZ/kgrAwut7zqVN0YPi++4CEBPode/FFYPduGgQ5GvlrtfQBKXSWb0YGtcnb\nW/y15UBIWWe3c/7cpAdzampop5hWwrMk+/TpIzxSElLDn4+t9jAd9BXj/IGGg75//knfBuwh1l5H\neeQRYNgwWtWQL9HR0bwif8D1zr9bt2jZ2pfD+TtyHxBiPfK3RteutPLr8eM0MSEsjN4LAQHAe+/Z\ndv58bA0MFC79yDXY6+h3zKXlHZRKSQl1/Kb1MMwj/7o64OxZqn8LwVmZPhym6Z5Xrohz/qa6P1/n\n70o+/BDYuJHqxXwgpD7P3x4PPECdirW1keVEqrV7rSGH7OMIly/TICkoSNzn77uP1n9KSQFOnqQr\nYMXFOWaTmBIPUhd0k4pGLftY08wsTaQxj/zLyugDgM8amqaIXcBFrL5nHvmLcR6muf58nb8rNH+O\n9u3poi8vvMBPqvvhBwOaNbOe/22Kjw+NLk+dctxOoVy5Aty8aZCtfTkif0fug2PHqOQjxQB3587A\nggXAY49ZP4ePrWIif7kGe5nmb4Xbt8V/1lKEZT7gy0VIQp2/syN/b+/6h5Yjsg/n/C9eVH7kD9AV\njdq1Az75xP65V68KW0fZVdJPY9P8U1KEST7OQEzkL3VBN6lo2pQGsHfu2D9XVc6fzzR8a5qZ+WAv\ncK/sU1pK/xXi/MvL6Y0TFsb/Mxxi9T3TdE+xzr9bN+r0r16lNwufSWKu0vw5NBpa9mH5cvvSnE4X\nrQrnf/UqMHRotGztyyH7OHIfcJG/s5BD86+spPef1Jk+gOPfMY2Gv+6vKudfXCz+s5ZkHw8P6vhM\n18UEhDn/s2dpBCBnDX9zTCN/sZp/8+aAvz9Nn+vSRZ48czkICaHpf/aWfeQ72MsxdCjwyy+OlxsQ\nihy1/E1RUuRfVQWcOQP07+9qSxoiNPLPzASCg+kaGkqEr/SjKufPJ/K3pplZkn00mobSz7VrtOOs\nVJS2iCOTu8Tqe1zkz6W6CSntYEp4OLBzJ3/Jx5Wavymvv04f0Js2WT/n8GGDoMg/IIAmBDhaTE4o\nV64Af/5pkK19JWn+Z87QsRUh9a8cRQ7NX07JR4rvGN90T7dz/tawVjnRNHOmtBTo0UNY5O9svR+o\nH/C9epU6fqH1UDgiIoBdu9Sh95ui1dKaPwsWWJc0iouFRf4A8NBDzpd+rl5tPJq/EvV+QHhxNyXO\n7DXFLWUfqTV/4N7Iv2dP6vxtSQqmOOL8HdH8q6vF6/0cERG0T/k6f1dr/qYMGgRMmAD87W+WjxMi\nTPMHnK/737pFJ+uNHh0t2zWUpPk7W+8H+NkqdJavnAXdpPiONVrZxxrW8qlNB31LS2n6mIcHv86r\nqaFRQI8e4u0SAxf5S+H8AfVF/hzLltE3F0sOm+/sXlM458/3we8oXEAi53gLi/ztw83yvXqV3/lK\nzfHncEvnz2fAV0ieP9Aw17+0lOq+ej0/6Sc9nZ7bsqX9c4XYag9uwFfsYC9HeDj9V22aP0fr1sCn\nn9Lp/pWVDY9lZRkEyz5du9LoT+gkP7Fwg71y9qtSNH8uK46755wFX1v5DvpWVQFZWeKy+/jANH8r\nyBH5m8s+bdvSiJHPoK/YyV2Owg34ip3gxdG+PZVNbC3zqXSeeIKmra5YUb+vuppO2BP6YNRonCv9\nyD27F1DODF/uLcd0hr2S4Dvom5kJdOpE8+mVCtP8TeCyYkzr+nCYyz6+vvwjf0cHex2p7SOF5g8A\n77/Pfxk6JWn+pnz2Gf3hahUVFtKF7sU4moceoou7OAPubVTOfm3WjL7N8Jn0wxcx9paU0GDD2fC1\nlW/kL/dgr1to/rW1tejTpw/GjRsHACgtLUVMTAzCwsIwYsQIXDd5L1m+fDlCQ0MRHh6O3bt3W21T\nbOTP1fWxlBXjiOzjikwfoGHk76jzdwf0eloT/qWXaJ6+GL2fw5mRv9w5/oCwST9yUlwsPiXZGfCN\n/JWe6QMowPl/8skniIyMNC7JuGLFCsTExCAjIwPDhw/Hirvv6Wlpadi0aRPS0tKQnJyM2bNno87K\nTBuxmr+t12vTyP/aNf6Rv9ga/vZs5QM34Ouo5i8UpWn+psyeTaPbtWupZOftbRDVTvfutF+LiiQ1\nzyLcfSl3v0ot/Yixt6TENc5fas1f7rIOqtf8c3Nz8eOPP+L5558HuZs6sX37dkyfPh0AMH36dGzd\nuhUAsG3bNsTFxUGr1SI4OBghISFITU212K7YyN/aYC8gPvLPy6OfdUYNf3OkSvV0Jzw9ae4/VwNe\nrMTg6UnXkv3lF2nts4St+1JKlJDx4yrZhy/uFPnzfdOTpSjBa6+9hpUrV6LMZP24oqIi+N31VH5+\nfii6G1rl5+djkEn+l16vR56V0dayshn4xz+C4eEB+Pj4oHfv3kaNjHtiWtqmKVwGGAz3HvfyikZN\nDbBnjwGVlUCLFtHQ64H0dMvnc9vr1xvQqRMA2L++rW0OIZ/39gbOnTMgJwfo2NGx6zvDXmduz5wZ\njQ8+AKZNo/vEtDd0KLBxowHt2slr7++/A1OmRCM6OlrW/vHxAQ4eNODOHWnaE2PvyZOGu2Mw0v9+\nUmwXFBjuLm5k/fyaGuDixWh06+Z6e21t//GHAWfPrsOMGUCwrWwOqRcQ/uGHH8js2bMJIYTs37+f\njB07lhBCiI+PT4PzfH19CSGEzJ07l2zYsMG4Pz4+nnz//ff3tAuAtG9PSFGRcJs++YSQOXMsHxsw\ngJCUFEIKCgjp2JHuu3qVkLvmWeXddwl54w3htkjBSy8R8vnndOHvqirX2KBUbt0i5L77CFm/Xnwb\nhw8T0qePdDZZo29fQo4fl/86kycTsnGj/NexxaxZhHz6qWttsMXly4TodLbPSUsjJCTEOfY4QmYm\nIV261G9bc/OSyz5HjhzB9u3bcd999yEuLg779u3D1KlT4efnh8LCQgBAQUEBOt4V4XU6HXJycoyf\nz83Nhc7KaF27dvalH/MIFbA+uxeol304yYe7TkWF7UW9pRjstWQrH7Ra+oraujX/TB0pEGuvM2ne\nnC7M4u9vEN1G//50mT65Fz53Rp4/QGUAJWj+rpB9+Nrq50f/HrZm+TpD8pHiXnCZ5r9s2TLk5OQg\nKysLSUlJeOSRR7B+/XqMHz8eiYmJAIDExETExsYCAMaPH4+kpCRUVVUhKysLmZmZiIqKsth2+/bi\nKnvaGvDl8vy5wV6AZkjo9bZz/V2V4w/QAd/cXKb3W6N9e8eqrHp70wfAkSPS2WQOIY1L81d6to+3\nN+0nW/5FqTX8zWnThgYu9maqy57nz2X7JCQkYM+ePQgLC8O+ffuQkJAAAIiMjMSkSZMQGRmJ0aNH\nY/Xq1cbPmMMn8uc0MFNsfcm4bB/TyB+wPeh74wbNJXe0nrclW/mg1VLb5E4TNEesva7AUVvlTvk8\nepTOrG7WTP5+teT8f/wRGDtW3NKVYux1VbaPEFvtZfw4I/KX4l7w9qY+oqLC9nmyOv+HH34Y27dv\nBwC0bdsWe/fuRUZGBnbv3g0fk1KGCxcuxB9//IHff/8dI0eOtNoeH+dvCXuRv7nsA9h2/mfP0no+\nrpqtyCJ/+ZHb+a9bB9xNfpMdS6meJ08C+/bR0hjOqGWk9GwfwH51TzVk+nDwyfVX1QxfOTR/LvI3\nlX0A27KPVJO7HNH8c3Kc7/zVoPlzOGrr4ME0ZVTKmbEct28DmzcDU6fSbWdo/uaR/6VLwHvvAb/9\nRldGE4JYzV/Jef6A7XTPmhpa2kHu2kRS3Qt8pD7VOX8xmj+fPH/zyF+nsx75u2pmL4e3Nx2MZpG/\nfLRqRaueHj8ufdtbtwIDBoifhSwUS47g8mX6+23fDnz1le2FcRylooLOvG7eXL5rSIEt2efiRfpw\nUPrvwOF2kX/79sI1/+pqWlHQUl0foOGAL1/Zx5HVu2zZyhdvb/qvs51/Y9L8Afmkn8REYMaM+m25\n+9WS7HPpEi1fHhhIHwCvvELHIfgg1F5O8nHFUqFCbLUV+ctZw98Uqe4Ft3P+YjT/4mLrdX2AhgO+\n5rKPJedfXQ1cuOD8Gv6mcOmdzh7wbWzI4fzz8mgq6t1kN6dgHvkTQiN/OkkR6NWLlsV48klarlhq\nXCX5CMVW5K/0Gv7mNErnb66Z2SubK3TA9/ffacQkxeufWH3PVZF/Y9L8AeDBB2k0zHeFJz5s2ABM\nnEizfDicrflfuQK0aEF/OB57jJbGGDvWvlYs1F5XpnlKpfk7a7CXaf5WEKP52xrsBawP+HbsSPeZ\nLxLiyvx+Di7yZ5q/vHToQKPBs2elaY8Q52b5cHCOgMvq4SQfc155BRg+HJg0qb7elRSoIdMHsB35\nqyXHn8PtIn8xmr+9iTSc5m8e+Xt60pvBPBKQcrDXUc2f5flbRypbpazvf/w4vdeGDGm4X+5+9fau\nTxIAqORjyfkDwEcf0eBi7lzrKaBiNH9XRf5CbPX3p/7CvKhwTQ2d8e2MVciY5m+Ftm1pNC4kL1ms\n7ANYzvhxdaYPQL+cLVuqJ/NAzUip+3NRvysGPk1lAGuRP0C/D0lJdLH1jz6S5trFxeqI/L29qdM0\nX8v3zz/pg8FUJlM6fEo8qMr5a7XU4dl6oplrZvYif25hlOvX6RfEFHPdX4oa/rZs5Yu3t2skn8am\n+QPSLep+5w7w3//SaqPmOKNffX0bOn9usNcSrVoBO3YAH39M01LNEWqvKyN/obZa0v2dOdgrpebv\nVpE/IDzjx57m7+VF3yaaN7+3SJq588/JAZo0cb3W7irn3xjp3Jn+zTMzHWvnhx9oVo0tpysnpsXd\nbGLglq0AACAASURBVEX+HEFB1PG/8AKdDewIasn2ASzr/mqa2cvhdrIPYL+4m7lmxkf2uXKl4WAv\nh7nzl1ryEavvDR4MvPuudHbwpTFq/oA00o95br8pzuhXvrKPKf370wVyHn+cBj4cfOzNyqKTx558\nEti2DejWTZzdjiK0by2VeHDmYC/T/G0gNPLnI/tcuWJ5Eph5iQepJnc5iq8v8Mgjrrai8fDQQ445\n/8JC4PBhYMIE6WwSiqnztzXga05sLDB/Pk0BLS+3fl5ZGZ0sNmcOLXg4eDD9nZ94gr41WSnUqzgs\nJXmoNfJ3K80fsO/8xeT523L+ckb+atLQAXXZK6WtQ4c6lvHzn/9QJ2ptwNBZmv+1a9SBV1YKk2H+\n8hdg4EBgyhSa+WIwGFBbSyerLV1KH446HfDpp0BwMK1blJ8PfPst8OyzdLDUVYjR/E0j/9paID2d\nlsJwBs7U/GVZxlFO5Ir8e/a895h5ts/p08DddecZjYiICBrZ5uUJr8fD5fZ/9pkspvGGi/y5wV4h\nGUcaDfD558CYMUBcHJVdz56lUfKIEcDChfQB4A7ZZwEBwJ499dtZWTR4bNnSdTaJgY/so0rnz1fz\nr64Gbt60rOdzeHnRhdAtRf4BAfRYTQ1t5+pVoGtX8bbbslUNqMleKW3VaOhs30OHaPQrhF9/pffO\n0KHWz3GW5p+Xx1/vN0erBb77jo41jRkTjZgY+masdBzV/J0t+Uh1L7RuTd/yzOcsmKI62YfPRC8O\nblq5tbo+AHX+ZWWWHxDe3vTzRUXAmTOureHPcC1iB3253H5b96Az4GQfIXq/OT4+wD//CTz3nDoc\nvxjMNX9nFXSTGk9PKjPaGqdRnfMXovnzWSaPS++0VvWT0/3lmNylJg0dUJe9UtsqxvlXVQEbN1rO\n7TfFGf1qKvuIdf4c7nwf+PvTYI+LmJ1d0E3KvrUn/Uju/HNycjBs2DDcf//96N69O/71r38BAEpL\nSxETE4OwsDCMGDEC102GopcvX47Q0FCEh4dj9+7dNtsXovnbG+wF6td6teX88/KUMbOX4Tr69AGy\ns+lMcL7s3Emjxi5dZDOLN+aaP8MyTZpQp8lJy2rM9OFwuvPXarX4+OOPcf78eRw7dgyff/45Lly4\ngBUrViAmJgYZGRkYPnw4VtwdOU1LS8OmTZuQlpaG5ORkzJ49G3U2hCohmr+QyN/auICckb+aNHRA\nXfZKbatWSzNeDh/m/5nERH5F3JzRr5zsI0Xk7+73ATfRq66OVvFVo+YPuMD5+/v7o/ddL9myZUtE\nREQgLy8P27dvx/S734Tp06dj691549u2bUNcXBy0Wi2Cg4MREhKC1NRUq+0L0fztze4F7Ef+Oh2t\n7ZGeDnTvzu+6DPdEiPRz9SpgMABPPSWrSbyRUvZxd7gSD9nZNNhs1crVFonDXllnWbN9srOz8euv\nv2LgwIEoKiqC392aBH5+figqKgIA5OfnY9CgQcbP6PV65FlZPHfGjBnQ6YJRVASsWuWD3r17G5+U\nplpZdHQ0DAYDTpwAwsIaHjc/38uLbl+8aICHx73H9fporF8PdOxoQGrqvccd2T59+jTmz58vWXty\nb6vJ3lWrVlm8PxzZbtUK2LWL3/lLlhgQFQW0amX/fPN7V47++O03AwoLgbq6aAQGOtaeM+yVatvc\nZj6fDwgA9u834PRpIDJS+faabxsMBqxbtw4XLgBr1wbDKkQmysvLSd++fcmWLVsIIYT4+Pg0OO7r\n60sIIWTu3Llkw4YNxv3x8fHk+++/v6c9U1ObNiXk1i3L192/f7/x/y+8QMgXX9i2c+NGQgBCsrOt\ntUePP/207XbEYGqrGlCTvXLYeusWIc2bW7/3TOndm5C9e/m164x+ra0lRKMhpFMnx9ty9/tg4UJC\nliwhZMUKQhYskN4mW0jZty+/TMjnnzf0nabIku1TXV2NJ598ElOnTkXs3fXq/Pz8UFhYCAAoKChA\nx7sjsTqdDjkmhUNyc3OhszOTxtagL/ckBKQb8AXkGew1tVUNqMleOWxt3pwWZ0tJsX3e2bP0/hw2\njF+7zuhXDw+a+y2F5OPu9wGX7umKwV4p+9bpmj8hBPHx8YiMjDRKBAAwfvx4JCYmAgASExOND4Xx\n48cjKSkJVVVVyMrKQmZmJqLsFAKxV9yNg8+Ar5cXzYm1NoOPew6xTB8GwK/UQ2IiTe/0UFgitY8P\n0/v5wE30UmuOP4c9zV/y2/Pw4cPYsGED9u/fjz59+qBPnz5ITk5GQkIC9uzZg7CwMOzbtw8JCQkA\ngMjISEyaNAmRkZEYPXo0Vq9eDY2duee2In9T7YzPgK9WS6N+a5ds1gwYMADo29d2O2IwtVUNqMle\nuWy1N+hbXU1r+djL7TfFWf0qlfN39/sgIICmd1+44HznL2Xf2ov8JR/wffDBB62mau7du9fi/oUL\nF2LhwoW8r8E315+v7GNN8uGwkXzEaGQ88ACtb1Ndfe/6DwDw00+0BEhYmPNts4evL8vx50NgIJXu\nOnSgDlStOF32cQZ8NP+qKlpTxXx1LnO8vW3X/pETNWmngLrslctWX1/gvvtozR5LiFmg3Vn9OmGC\n7RpDfHH3+8Dfn/oPVyzYLrXm71TZxxnYm+gF1K8bak93HTwY+PJL6WxjuD/W6vuXlAB79wKTJjnf\nJj688orzShOrmSZNqI9Rs94P2C/rrErnb2uiF6eZ8RnsBYCmTWkGhytQk3YKqMteOW21pvsnJQGj\nR9t/2zRHTf0KqMtesbYGBLjG+TtT81el8+ej+a9dyzJ0GPLAOX/zoS1bSzUy1MWUKfxTdZWKPeev\nIYQQ55kjHo1GA87UH3+kqwbt2mX53I0bgbfeogtPu0rPZ7g3XbvSZQs5XTgtDYiJoSWTWdlvhhIo\nL6dvMLdu1ftOU1Qb+VvT/M+fB159Ffj+e+b4GfJhLv0kJgJTpzLHz1AOLVsCd+5YP65K529N8y8r\nA0aNMmDlSmUstG4PNWmngLrsldtWU+dfUwOsXy88y4dDTf0KqMteNdkKSGuvRmO7KJ0qnb8lzZ8Q\nusJQ795Md2XIz0MP0Zm+hNAMn6AglknDUB625imoUvMnhObnV1TUT7T58ENg0yYajTVt6kJDGY0C\nQqieeuwYkJBAHwazZ7vaKgajIb17A2fOuJHmr9FQPZ9bVenAAer8N29mjp/hHDQaKv388AOQnCx8\nYXcGwxmMHm39mCqdP1Bf3C0/n063//ZbWrdETRqfmmwF1GWvM2wdOhRYvJhm+dgrEWILNfUroC57\n1WQrIL29y5dbP6Za59+uHVBYSGdTzpoFjBjhaosYjY2hQ+nbJxtjYqgRVWr+ABAbC1y8SKP97duV\nVz6X4f7U1gKLFgHvvlu/LgSDoTTMfadxv1qdf3w8sH8/m8jFYDAYtrDm/FUbL8+fD+zefa/jV5PG\npyZbAXXZy2yVDzXZqyZbAefaq1rn36MHEBJy7/7Tp0873xiRqMlWQF32MlvlQ032qslWwLn2Ksb5\nJycnIzw8HKGhoXj//fdFt3PdVgFrhaEmWwF12ctslQ812asmWwHn2qsI519bW4u5c+ciOTkZaWlp\n2LhxIy5cuOBqsxgMBsNtUYTzT01NRUhICIKDg6HVajFlyhRs27ZNVFvZ2dnSGicjarIVUJe9zFb5\nUJO9arIVcK69isj22bx5M3766Sd8/fXXAIANGzYgJSUFn376qfEce4u6MxgMBsMylty8IrKT+Th2\nBTyjGAwGw21QhOyj0+mQk5Nj3M7JyYFer3ehRQwGg+HeKML59+/fH5mZmcjOzkZVVRU2bdqE8ePH\nu9osBoPBcFsUIft4eXnhs88+w8iRI1FbW4v4+HhEsOLoDAaDIRuKGPAVypEjR9CiRQv06tXL1abY\npbKyEh4eHtBqtSCEKH7g+pdffkHz5s3Rt29fV5tilzt37sDT01M1fVtYWAh/f39UV1dDyy1EoVC4\n/qytrYWnCtamPHbsGFq2bInu3bu72hS7VFZWwsvLC56eni69b1Xl/K9cuYK4uDjU1NSgY8eOGDFi\nBEaOHIlOnTq52jSL/OMf/0Bqaiq6d++OxYsXo2XLlq42ySrXr1/HhAkTcPv2bTRr1gyxsbGIjY1V\nbN8mJCQgNTUVoaGh+OCDD9DG1pJFLubixYt4+umnkZGRgWvXrgEA6urq4KHQaoQffPABKioqsHjx\nYlebYpe0tDQsWLAAt27dgkajweTJkzFlyhS0a9fO1aZZZOnSpTh8+DC6dOmCZcuWufS+VebdZ4UD\nBw6gZ8+eOHDgAP7+97/j4sWL+Oyzz1xtlkU++OADXLhwAUlJSdBoNHj77beRkpLiarOscunSJXTt\n2hVHjx7F+++/j5KSEnzwwQeuNssiO3bswPnz57Fp0ybU1dVh0aJFOHjwoKvNsgghBN999x0mT56M\nqKgozJ8/37hfaVRWVmLixIlITEzEkSNHsGfPHgB0EqYSqaysxJIlS/Dwww/j0KFDSEhIwNmzZ1HK\nrfKkIIqKihATE4Nz585h9erVKCgowMKFCwG48F4gCic7O5tUVFQQQghZuXIlGT16tPHYnDlzyMCB\nA0lycrKrzGtAXV2d8f/z5s0jn3zyCSGEkNLSUtK3b1/y17/+lRQVFbnKvHu4c+eO8f+bN28mAwcO\nJIQQUlNTQ86ePUsmTJhAtm7d6irzrLJ06VISHx9PCKF9+84775C3336b5Ofnu9iyekz79vLly4QQ\nQnJyckjLli1JdnY2IYT2s9I4ePAgSU9PJ2vWrCFTpkwx7je9t12Nad9euHCBlJeXG7d79uxJDh06\n5AqzbFJUVES2bNli3M7NzSWdO3cmxcXFLrNJsZF/SkoKunbtirlz5+KJJ57A7du3ERsbi8rKSnz2\n2Wc4cuQISkpKMGLECPz0008utfXWrVt4+eWX8dZbb2H37t0AgE6dOqGwsBCFhYXw9fXFfffdh5s3\nbyqi0NSOHTswfPhwfPXVV8Z9jz/+OJo0aYIdO3bA09MTXbp0wWOPPYadO3e6NEotLy/HmjVrcPny\nZeO+Bx98EF5eXsjNzYWvry+GDRuGGzduKOLNylLfBgUFAQD0ej3i4+PxwgsvuMq8Bljq26FDhyIs\nLMzYx9988w0AKlO5Gkt9261bN7Rs2RJVVVWorKxEUFAQ2rVr5/I3K65vL126BADw9fXF8OHDAQBV\nVVXQarXo1asXWrRo4bK+VaTzr66uxpo1a7B48WL88MMP6NChA9555x00bdoUS5YsQXp6OhYvXoxn\nnnkGffr0gZcLV9IoKyvDxIkT4eXlhZ49eyIhIQE7d+7ElClTcPPmTcyYMQN9+vQxDqAWFhYCcN2r\n3p9//on33nsPer0e6enpOHPmDAA60W7atGlITEwEALRo0QKBgYHw8vJCeXm5S2w9efIk7r//frzx\nxhs4ePAgKioqAADNmzdHy5YtceDAAQD0YdC0aVPk5eUBUF7f1tXVGb/gH3/8MU6fPo0DBw7A09MT\nZWVlLrHVvG9v375ttBWgwcvjjz+OLVu24MqVK/D09HTpA8Ba33J/a29vb1y7dg03b95Ely5doNFo\nUFVV5RJbTfv20KFDuH37NrRaLVq1amW0taSkBBUVFdBoNC4b+/FcrJBRnbq6OuOot6enJ/7zn/+g\nS5cu6NOnDwYPHowffvgBlZWVGDNmDMaNG4epU6ciLCwMubm5OHPmDMaOHesSuysrK/Hzzz/j3Xff\nxeDBg6HT6ZCQkIAXX3wREyZMQGhoKJ555hk88cQTyM3Nxblz5zBq1CinjvCb9i0XKQ8bNgwZGRlI\nS0vDsGHD4OHhgYCAACQnJ+P8+fOIjo7GnTt3sGXLFkybNs0lGQnFxcUYPXo0Bg4ciNTUVAQFBSEg\nIAABAQH4448/cOHCBfj6+kKn0+Hq1avYuXMnJk+erJi+vXDhAoYNGwaNRmN0Rl5eXggJCcHcuXNR\nUFCAo0eP4oEHHnB6AGOtb7nfxcvLC61bt8alS5dw8eJFeHl5oaCgADqdzmk28u1bcjdjZu/evaiq\nqsJjjz2Gt956C3/++Sd69erl9Gwla31rypdffonOnTvjkUcewYEDB0AIga+TV6VSROT/9ddfo1+/\nfkhISMD//vc/AEDPnj1x+/ZtlJWVISAgAMOHD0dqaioKCgoAABUVFfjiiy8wa9YsDBs2zGm2ZmRk\n4L333oPBYAAhBOXl5aiqqkJFRQVqa2sxduxYREZGYsWKFQCAAQMGIDw8HOnp6di+fTsef/xxp9kK\nWO7brl274r777sOgQYNQVFRklM0CAgKwZMkSbNmyBa+88goee+wxDBo0yGm2cn27f/9+1NXVoUeP\nHnj44YcxefJk3LlzB4cPH0ZJSQk8PDwwatQoBAQEYPbs2Th+/DjWr1+P6Ohop0b99vq2sLDQKAPW\n1dXB29sbAL13//zzT+Tk5OD1119HkyZNZLfVXt/+8ssvxkwkrg/1ej3Cw8ORkJCACRMmODVCFdK3\n3IB0dnY2fvjhBwwZMgT5+fl45plnnJJSK6Rva2pqAFBZyMvLCzNmzMCrr76KO3fuyG7nPbhqsIEj\nNTWV9OvXjxw7doxs3ryZDBgwgBw+fJgcOHCAvPTSS+TIkSOEEEL+f3vnHhRV+f/x96LUoKLSiDKZ\no1mSGndXEEWuikxBOYWAJTBM2UXdrFhhtMZkgi+KibKRmuZl1Gw0RvKCl4IADQGTIcm8wJigBrKM\ngiILLJf37w9+ewJFRfOsAs/rL87u85z97GuXz7PnnOf5nKamJk6ePJm//vorSXL//v0MDg7miRMn\njBbrzz//zGHDhjEyMpK+vr6MjY2lXq/n3LlzuXTpUqndhQsXaGlpyerqapLk+vXraWVlxRUrVhgt\nVvJOty4uLjx06JD0vFar5cqVK6lSqTr0u3TpEvft28f8/Hyjxdre7YwZMxgXF8eqqirp+YMHDzI8\nPJy//PJLh34rV65kaGgoFy9ebLRYyYd3m5+fz4iIiCfSbXp6eod+P/30E62srLh8+XKjxUo+vFuV\nSkU7OzuePn3aaLE+rFtbW1taWFhw7dq1Rov1dh5L8m8/y+HAgQOMioqStrdt28bx48eTJKOiovi/\n//2P586dI0lGRkbyu+++M26w7UhMTOTWrVtJtn1BI