Canadian-Elections - Demographics v1.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# This is my title"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas.io.sql as psql\n",
      "from scipy.cluster.vq import kmeans,vq, whiten\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# http://www12.statcan.gc.ca/census-recensement/2011/dp-pd/prof/details/page.cfm?Lang=E&Geo1=FED2013&Code1=24028&Geo2=PR&Code2=24&Data=Count&SearchText=Hochelaga&SearchType=Begins&SearchPR=01&B1=All&GeoLevel=PR&GeoCode=028&TABID=1\n",
      "# download Federal electoral districts (2013 representation order)\n",
      "df = pd.read_csv(\"./data/98-316-XWE2011001-511.csv\", low_memory=False, skiprows=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "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>Geo_Code</th>\n",
        "      <th>Prov_Name</th>\n",
        "      <th>FED_Name</th>\n",
        "      <th>Topic</th>\n",
        "      <th>Characteristic</th>\n",
        "      <th>Note</th>\n",
        "      <th>Total</th>\n",
        "      <th>Flag_Total</th>\n",
        "      <th>Male</th>\n",
        "      <th>Flag_Male</th>\n",
        "      <th>Female</th>\n",
        "      <th>Flag_Female</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 10001</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td> Avalon</td>\n",
        "      <td> Population and dwelling counts</td>\n",
        "      <td>                            Population in 2011</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ..</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 10001</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td> Avalon</td>\n",
        "      <td> Population and dwelling counts</td>\n",
        "      <td>                            Population in 2006</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ..</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 10001</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td> Avalon</td>\n",
        "      <td> Population and dwelling counts</td>\n",
        "      <td>            2006 to 2011 population change (%)</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ..</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 10001</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td> Avalon</td>\n",
        "      <td> Population and dwelling counts</td>\n",
        "      <td>                       Total private dwellings</td>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ..</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 10001</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td> Avalon</td>\n",
        "      <td> Population and dwelling counts</td>\n",
        "      <td> Private dwellings occupied by usual residents</td>\n",
        "      <td>  3</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ..</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> ...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "   Geo_Code                  Prov_Name FED_Name  \\\n",
        "0     10001  Newfoundland and Labrador   Avalon   \n",
        "1     10001  Newfoundland and Labrador   Avalon   \n",
        "2     10001  Newfoundland and Labrador   Avalon   \n",
        "3     10001  Newfoundland and Labrador   Avalon   \n",
        "4     10001  Newfoundland and Labrador   Avalon   \n",
        "\n",
        "                            Topic  \\\n",
        "0  Population and dwelling counts   \n",
        "1  Population and dwelling counts   \n",
        "2  Population and dwelling counts   \n",
        "3  Population and dwelling counts   \n",
        "4  Population and dwelling counts   \n",
        "\n",
        "                                  Characteristic  Note  Total Flag_Total  \\\n",
        "0                             Population in 2011     1    NaN         ..   \n",
        "1                             Population in 2006     1    NaN         ..   \n",
        "2             2006 to 2011 population change (%)   NaN    NaN         ..   \n",
        "3                        Total private dwellings     2    NaN         ..   \n",
        "4  Private dwellings occupied by usual residents     3    NaN         ..   \n",
        "\n",
        "   Male Flag_Male  Female Flag_Female  \n",
        "0   NaN       ...     NaN         ...  \n",
        "1   NaN       ...     NaN         ...  \n",
        "2   NaN       ...     NaN         ...  \n",
        "3   NaN       ...     NaN         ...  \n",
        "4   NaN       ...     NaN         ...  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"Topic_Characteristic\"] = df.Topic + ' ' + df.Characteristic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census = df.pivot_table(index=['Geo_Code', 'Prov_Name', 'FED_Name'], columns='Topic_Characteristic', values='Total')\n",
      "census.reset_index(inplace=True)\n",
      "census.head()"
     ],
     "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>Topic_Characteristic</th>\n",
        "      <th>Geo_Code</th>\n",
        "      <th>Prov_Name</th>\n",
        "      <th>FED_Name</th>\n",
        "      <th>Age characteristics       15 years</th>\n",
        "      <th>Age characteristics       16 years</th>\n",
        "      <th>Age characteristics       17 years</th>\n",
        "      <th>Age characteristics       18 years</th>\n",
        "      <th>Age characteristics       19 years</th>\n",
        "      <th>Age characteristics    0 to 4 years</th>\n",
        "      <th>Age characteristics    10 to 14 years</th>\n",
        "      <th>...</th>\n",
        "      <th>Marital status    Married or living with a common-law partner</th>\n",
        "      <th>Marital status    Not married and not living with a common-law partner</th>\n",
        "      <th>Marital status Total population 15 years and over by marital status</th>\n",
        "      <th>Population and dwelling counts 2006 to 2011 population change (%)</th>\n",
        "      <th>Population and dwelling counts Land area (square km)</th>\n",
        "      <th>Population and dwelling counts Population density per square kilometre</th>\n",
        "      <th>Population and dwelling counts Population in 2006</th>\n",
        "      <th>Population and dwelling counts Population in 2011</th>\n",
        "      <th>Population and dwelling counts Private dwellings occupied by usual residents</th>\n",
        "      <th>Population and dwelling counts Total private dwellings</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 10001</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                             Avalon</td>\n",
        "      <td> 915</td>\n",
        "      <td> 915</td>\n",
        "      <td>  910</td>\n",
        "      <td>  885</td>\n",
        "      <td>  810</td>\n",
        "      <td> 4430</td>\n",
        "      <td> 4465</td>\n",
        "      <td>...</td>\n",
        "      <td> 44040</td>\n",
        "      <td> 24190</td>\n",
        "      <td> 68230</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 10002</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>          Bonavista--Burin--Trinity</td>\n",
        "      <td> 800</td>\n",
        "      <td> 930</td>\n",
        "      <td>  905</td>\n",
        "      <td>  860</td>\n",
        "      <td>  795</td>\n",
        "      <td> 2940</td>\n",
        "      <td> 3845</td>\n",
        "      <td>...</td>\n",
        "      <td> 44495</td>\n",
        "      <td> 22035</td>\n",
        "      <td> 66530</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 10003</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td> Coast of Bays--Central--Notre Dame</td>\n",
        "      <td> 845</td>\n",
        "      <td> 905</td>\n",
        "      <td>  905</td>\n",
        "      <td>  785</td>\n",
        "      <td>  840</td>\n",
        "      <td> 3335</td>\n",
        "      <td> 4170</td>\n",
        "      <td>...</td>\n",
        "      <td> 45715</td>\n",
        "      <td> 21265</td>\n",
        "      <td> 66970</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 10004</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                           Labrador</td>\n",
        "      <td> 415</td>\n",
        "      <td> 375</td>\n",
        "      <td>  360</td>\n",
        "      <td>  420</td>\n",
        "      <td>  355</td>\n",
        "      <td> 1830</td>\n",
        "      <td> 1835</td>\n",
        "      <td>...</td>\n",
        "      <td> 14020</td>\n",
        "      <td>  7395</td>\n",
        "      <td> 21415</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 10005</td>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>               Long Range Mountains</td>\n",
        "      <td> 990</td>\n",
        "      <td> 955</td>\n",
        "      <td> 1075</td>\n",
        "      <td> 1040</td>\n",
        "      <td> 1040</td>\n",
        "      <td> 3600</td>\n",
        "      <td> 4445</td>\n",
        "      <td>...</td>\n",
        "      <td> 47790</td>\n",
        "      <td> 27765</td>\n",
        "      <td> 75555</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 463 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "Topic_Characteristic  Geo_Code                  Prov_Name  \\\n",
        "0                        10001  Newfoundland and Labrador   \n",
        "1                        10002  Newfoundland and Labrador   \n",
        "2                        10003  Newfoundland and Labrador   \n",
        "3                        10004  Newfoundland and Labrador   \n",
        "4                        10005  Newfoundland and Labrador   \n",
        "\n",
        "Topic_Characteristic                            FED_Name  \\\n",
        "0                                                 Avalon   \n",
        "1                              Bonavista--Burin--Trinity   \n",
        "2                     Coast of Bays--Central--Notre Dame   \n",
        "3                                               Labrador   \n",
        "4                                   Long Range Mountains   \n",
        "\n",
        "Topic_Characteristic  Age characteristics       15 years  \\\n",
        "0                                                    915   \n",
        "1                                                    800   \n",
        "2                                                    845   \n",
        "3                                                    415   \n",
        "4                                                    990   \n",
        "\n",
        "Topic_Characteristic  Age characteristics       16 years  \\\n",
        "0                                                    915   \n",
        "1                                                    930   \n",
        "2                                                    905   \n",
        "3                                                    375   \n",
        "4                                                    955   \n",
        "\n",
        "Topic_Characteristic  Age characteristics       17 years  \\\n",
        "0                                                    910   \n",
        "1                                                    905   \n",
        "2                                                    905   \n",
        "3                                                    360   \n",
        "4                                                   1075   \n",
        "\n",
        "Topic_Characteristic  Age characteristics       18 years  \\\n",
        "0                                                    885   \n",
        "1                                                    860   \n",
        "2                                                    785   \n",
        "3                                                    420   \n",
        "4                                                   1040   \n",
        "\n",
        "Topic_Characteristic  Age characteristics       19 years  \\\n",
        "0                                                    810   \n",
        "1                                                    795   \n",
        "2                                                    840   \n",
        "3                                                    355   \n",
        "4                                                   1040   \n",
        "\n",
        "Topic_Characteristic  Age characteristics    0 to 4 years  \\\n",
        "0                                                    4430   \n",
        "1                                                    2940   \n",
        "2                                                    3335   \n",
        "3                                                    1830   \n",
        "4                                                    3600   \n",
        "\n",
        "Topic_Characteristic  Age characteristics    10 to 14 years  \\\n",
        "0                                                      4465   \n",
        "1                                                      3845   \n",
        "2                                                      4170   \n",
        "3                                                      1835   \n",
        "4                                                      4445   \n",
        "\n",
        "Topic_Characteristic                  ...                    \\\n",
        "0                                     ...                     \n",
        "1                                     ...                     \n",
        "2                                     ...                     \n",
        "3                                     ...                     \n",
        "4                                     ...                     \n",
        "\n",
        "Topic_Characteristic  Marital status    Married or living with a common-law partner  \\\n",
        "0                                                                 44040               \n",
        "1                                                                 44495               \n",
        "2                                                                 45715               \n",
        "3                                                                 14020               \n",
        "4                                                                 47790               \n",
        "\n",
        "Topic_Characteristic  Marital status    Not married and not living with a common-law partner  \\\n",
        "0                                                                 24190                        \n",
        "1                                                                 22035                        \n",
        "2                                                                 21265                        \n",
        "3                                                                  7395                        \n",
        "4                                                                 27765                        \n",
        "\n",
        "Topic_Characteristic  Marital status Total population 15 years and over by marital status  \\\n",
        "0                                                                 68230                     \n",
        "1                                                                 66530                     \n",
        "2                                                                 66970                     \n",
        "3                                                                 21415                     \n",
        "4                                                                 75555                     \n",
        "\n",
        "Topic_Characteristic  Population and dwelling counts 2006 to 2011 population change (%)  \\\n",
        "0                                                                   NaN                   \n",
        "1                                                                   NaN                   \n",
        "2                                                                   NaN                   \n",
        "3                                                                   NaN                   \n",
        "4                                                                   NaN                   \n",
        "\n",
        "Topic_Characteristic  Population and dwelling counts Land area (square km)  \\\n",
        "0                                                                   NaN      \n",
        "1                                                                   NaN      \n",
        "2                                                                   NaN      \n",
        "3                                                                   NaN      \n",
        "4                                                                   NaN      \n",
        "\n",
        "Topic_Characteristic  Population and dwelling counts Population density per square kilometre  \\\n",
        "0                                                                   NaN                        \n",
        "1                                                                   NaN                        \n",
        "2                                                                   NaN                        \n",
        "3                                                                   NaN                        \n",
        "4                                                                   NaN                        \n",
        "\n",
        "Topic_Characteristic  Population and dwelling counts Population in 2006  \\\n",
        "0                                                                   NaN   \n",
        "1                                                                   NaN   \n",
        "2                                                                   NaN   \n",
        "3                                                                   NaN   \n",
        "4                                                                   NaN   \n",
        "\n",
        "Topic_Characteristic  Population and dwelling counts Population in 2011  \\\n",
        "0                                                                   NaN   \n",
        "1                                                                   NaN   \n",
        "2                                                                   NaN   \n",
        "3                                                                   NaN   \n",
        "4                                                                   NaN   \n",
        "\n",
        "Topic_Characteristic  Population and dwelling counts Private dwellings occupied by usual residents  \\\n",
        "0                                                                   NaN                              \n",
        "1                                                                   NaN                              \n",
        "2                                                                   NaN                              \n",
        "3                                                                   NaN                              \n",
        "4                                                                   NaN                              \n",
        "\n",
        "Topic_Characteristic  Population and dwelling counts Total private dwellings  \n",
        "0                                                                   NaN       \n",
        "1                                                                   NaN       \n",
        "2                                                                   NaN       \n",
        "3                                                                   NaN       \n",
        "4                                                                   NaN       \n",
        "\n",
        "[5 rows x 463 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Compute male ratio\n",
      "ratio_male = df.ix[df.Characteristic=='Total population by age groups',['Geo_Code', 'Total', 'Male']]\n",
      "ratio_male['ratio_male'] = ratio_male.Male/ratio_male.Total\n",
      "\n",
      "census = pd.merge(census, ratio_male[['Geo_Code', 'ratio_male', 'Total']], on='Geo_Code', how='left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting the resulting variable\n",
      "census['ratio_male'].hist()\n",
      "plt.show()\n",
      "plt.boxplot(census['ratio_male'])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xc4efba8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qMGjGngP2A9wJTGdmRkTwo1bMHmACuCUzH4mIO4Fp4E+6T56bm2NmZoZGowHA6Ogo4+Pj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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13ab8f60>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = list()\n",
      "for i in list(census.columns.values):\n",
      "    if 'Age characteristics' in i:\n",
      "        l.append(i)\n",
      "        \n",
      "l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "['Age characteristics       15 years',\n",
        " 'Age characteristics       16 years',\n",
        " 'Age characteristics       17 years',\n",
        " 'Age characteristics       18 years',\n",
        " 'Age characteristics       19 years',\n",
        " 'Age characteristics    0 to 4 years',\n",
        " 'Age characteristics    10 to 14 years',\n",
        " 'Age characteristics    15 to 19 years',\n",
        " 'Age characteristics    20 to 24 years',\n",
        " 'Age characteristics    25 to 29 years',\n",
        " 'Age characteristics    30 to 34 years',\n",
        " 'Age characteristics    35 to 39 years',\n",
        " 'Age characteristics    40 to 44 years',\n",
        " 'Age characteristics    45 to 49 years',\n",
        " 'Age characteristics    5 to 9 years',\n",
        " 'Age characteristics    50 to 54 years',\n",
        " 'Age characteristics    55 to 59 years',\n",
        " 'Age characteristics    60 to 64 years',\n",
        " 'Age characteristics    65 to 69 years',\n",
        " 'Age characteristics    70 to 74 years',\n",
        " 'Age characteristics    75 to 79 years',\n",
        " 'Age characteristics    80 to 84 years',\n",
        " 'Age characteristics    85 years and over',\n",
        " 'Age characteristics % of the population aged 15 and over',\n",
        " 'Age characteristics Median age of the population',\n",
        " 'Age characteristics Total population by age groups']"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(85/5):\n",
      "    print('census[\"Age characteristics    {} to {} years\"] +'.format(i*5, i*5+4))\n",
      "    #census['ratio {} to {} years'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "census[\"Age characteristics    0 to 4 years\"] +\n",
        "census[\"Age characteristics    5 to 9 years\"] +\n",
        "census[\"Age characteristics    10 to 14 years\"] +\n",
        "census[\"Age characteristics    15 to 19 years\"] +\n",
        "census[\"Age characteristics    20 to 24 years\"] +\n",
        "census[\"Age characteristics    25 to 29 years\"] +\n",
        "census[\"Age characteristics    30 to 34 years\"] +\n",
        "census[\"Age characteristics    35 to 39 years\"] +\n",
        "census[\"Age characteristics    40 to 44 years\"] +\n",
        "census[\"Age characteristics    45 to 49 years\"] +\n",
        "census[\"Age characteristics    50 to 54 years\"] +\n",
        "census[\"Age characteristics    55 to 59 years\"] +\n",
        "census[\"Age characteristics    60 to 64 years\"] +\n",
        "census[\"Age characteristics    65 to 69 years\"] +\n",
        "census[\"Age characteristics    70 to 74 years\"] +\n",
        "census[\"Age characteristics    75 to 79 years\"] +\n",
        "census[\"Age characteristics    80 to 84 years\"] +\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census['Total by age'] = (census[\"Age characteristics    0 to 4 years\"] +\n",
      "    census[\"Age characteristics    5 to 9 years\"] +\n",
      "    census[\"Age characteristics    10 to 14 years\"] +\n",
      "    census[\"Age characteristics    15 to 19 years\"] +\n",
      "    census[\"Age characteristics    20 to 24 years\"] +\n",
      "    census[\"Age characteristics    25 to 29 years\"] +\n",
      "    census[\"Age characteristics    30 to 34 years\"] +\n",
      "    census[\"Age characteristics    35 to 39 years\"] +\n",
      "    census[\"Age characteristics    40 to 44 years\"] +\n",
      "    census[\"Age characteristics    45 to 49 years\"] +\n",
      "    census[\"Age characteristics    50 to 54 years\"] +\n",
      "    census[\"Age characteristics    55 to 59 years\"] +\n",
      "    census[\"Age characteristics    60 to 64 years\"] +\n",
      "    census[\"Age characteristics    65 to 69 years\"] +\n",
      "    census[\"Age characteristics    70 to 74 years\"] +\n",
      "    census[\"Age characteristics    75 to 79 years\"] +\n",
      "    census[\"Age characteristics    80 to 84 years\"])\n",
      "\n",
      "census['Ratio Age 0 to 14'] = (census[\"Age characteristics    0 to 4 years\"] +\n",
      "                                census[\"Age characteristics    5 to 9 years\"] +\n",
      "                                census[\"Age characteristics    10 to 14 years\"]) / census['Total by age']\n",
      "\n",
      "census['Ratio Age 15 to 34'] = (census[\"Age characteristics    15 to 19 years\"] +\n",
      "                                census[\"Age characteristics    20 to 24 years\"] +\n",
      "                                census[\"Age characteristics    25 to 29 years\"] + \n",
      "                                census[\"Age characteristics    30 to 34 years\"]) / census['Total by age']\n",
      "\n",
      "census['Ratio Age 35 to 54'] = (census[\"Age characteristics    35 to 39 years\"] +\n",
      "                                census[\"Age characteristics    40 to 44 years\"] +\n",
      "                                census[\"Age characteristics    45 to 49 years\"] + \n",
      "                                census[\"Age characteristics    50 to 54 years\"]) / census['Total by age']\n",
      "\n",
      "census['Ratio Age 55 to 84'] = (census[\"Age characteristics    55 to 59 years\"] +\n",
      "                                census[\"Age characteristics    60 to 64 years\"] +\n",
      "                                census[\"Age characteristics    65 to 69 years\"] + \n",
      "                                census[\"Age characteristics    70 to 74 years\"] +\n",
      "                                census[\"Age characteristics    75 to 79 years\"] +\n",
      "                                census[\"Age characteristics    80 to 84 years\"]) / census['Total by age']\n",
      "\n",
      "census['Ratio Age 85+'] = census['Age characteristics    85 years and over'] / census['Total by age']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "age_cols = ['Ratio Age 0 to 14', 'Ratio Age 15 to 34', 'Ratio Age 35 to 54', 'Ratio Age 55 to 84', 'Ratio Age 85+']\n",
      "for col in age_cols: \n",
      "    census[col].hist()\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xc5e00b8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xc4ef390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xc54ed30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xc8d2d30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x16505da0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marital_status_list = ['Marital status       Divorced',\n",
      "   'Marital status       Living common law',\n",
      "   'Marital status       Married (and not separated)',\n",
      "   'Marital status       Separated',\n",
      "   'Marital status       Single (never legally married)',\n",
      "   'Marital status       Widowed']\n",
      "\n",
      "census['Total by Marital status'] = 0\n",
      "\n",
      "for col in marital_status_list:\n",
      "    census['Total by Marital status'] += census[col]\n",
      "\n",
      "for col in marital_status_list:\n",
      "    census['Ratio ' + col] = census[col] / census['Total by Marital status']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "family_size_list = ['Family characteristics    Size of census family: 2 persons',\n",
      "       'Family characteristics    Size of census family: 3 persons',\n",
      "       'Family characteristics    Size of census family: 4 persons',\n",
      "       'Family characteristics    Size of census family: 5 or more persons']\n",
      "\n",
      "census['Total by Family Size'] = 0\n",
      "\n",
      "for col in family_size_list:\n",
      "    census['Total by Family Size'] += census[col]\n",
      "\n",
      "for col in family_size_list:\n",
      "    census['Ratio ' + col] = census[col] / census['Total by Family Size']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "first_language_list = ['First official language spoken \\xa0 English',\n",
      "       'First official language spoken \\xa0 English and French',\n",
      "       'First official language spoken \\xa0 French',\n",
      "       'First official language spoken \\xa0 Neither English nor French']\n",
      "\n",
      "census['Total by First official language'] = 0\n",
      "\n",
      "for col in first_language_list:\n",
      "    census['Total by First official language'] += census[col]\n",
      "\n",
      "for col in first_language_list:\n",
      "    census['Ratio ' + col.replace('\\xa0', '')] = census[col] / census['Total by First official language']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Creating new columns\n",
      "\n",
      "# Male ratio\n",
      "# 'ratio_male'\n",
      "# Total population\n",
      "# 'Total'\n",
      "# % by age\n",
      "#['Ratio Age 0 to 14', 'Ratio Age 15 to 34', 'Ratio Age 35 to 54', 'Ratio Age 55 to 84', 'Ratio Age 85+']\n",
      "# % by marital status\n",
      "['Ratio Marital status       Divorced',\n",
      "   'Ratio Marital status       Living common law',\n",
      "   'Ratio Marital status       Married (and not separated)',\n",
      "   'Ratio Marital status       Separated',\n",
      "   'Ratio Marital status       Single (never legally married)',\n",
      "   'Ratio Marital status       Widowed']\n",
      "\n",
      "# % size of family\n",
      "'Ratio Family characteristics    Size of census family: 2 persons',\n",
      "       'Ratio Family characteristics    Size of census family: 3 persons',\n",
      "       'Ratio Family characteristics    Size of census family: 4 persons',\n",
      "       'Ratio Family characteristics    Size of census family: 5 or more persons'\n",
      "# % by mother tongue (french, english, non-official language, multiple response)\n",
      "'Ratio First official language spoken  English',\n",
      "       'Ratio First official language spoken  English and French',\n",
      "       'Ratio First official language spoken  French',\n",
      "       'Ratio First official language spoken  Neither English nor French'\n",
      "\n",
      "\n",
      "# % Knowledge of official languages\n",
      "# % by Total number of occupied private dwellings by structural type of dwelling\n",
      "\n",
      "# To keep\n",
      "# Total Population\n",
      "# Average number of persons per census family\n",
      "# Average number of persons in private households"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndentationError",
       "evalue": "unexpected indent (<ipython-input-16-5b9934c954ee>, line 19)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-16-5b9934c954ee>\"\u001b[1;36m, line \u001b[1;32m19\u001b[0m\n\u001b[1;33m    'Ratio Family characteristics    Size of census family: 3 persons',\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cols_kmeans = ['Ratio ' + s for s in marital_status_list] + \\\n",
      "                ['Ratio First official language spoken  English','Ratio First official language spoken  English and French'\n",
      "                 ,'Ratio First official language spoken  French','Ratio First official language spoken  Neither English nor French'\n",
      "                 , 'ratio_male']\n",
      "census_kmeans = census[cols_kmeans]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cols_kmeans"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "['Ratio Marital status       Divorced',\n",
        " 'Ratio Marital status       Living common law',\n",
        " 'Ratio Marital status       Married (and not separated)',\n",
        " 'Ratio Marital status       Separated',\n",
        " 'Ratio Marital status       Single (never legally married)',\n",
        " 'Ratio Marital status       Widowed',\n",
        " 'Ratio First official language spoken  English',\n",
        " 'Ratio First official language spoken  English and French',\n",
        " 'Ratio First official language spoken  French',\n",
        " 'Ratio First official language spoken  Neither English nor French',\n",
        " 'ratio_male']"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = pd.tools.plotting.scatter_matrix(census_kmeans, alpha=0.2, figsize=(16,16))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/36XK275tgiD6Lt5+u4WhoZCPfCQsSpedvK+IlFRCG81D3ZUDInIfmsZwPTAO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F2J3kQujnKCtrXLlg8f3cXKzlWlMStAfcBct1a3OPLXWDHQT5Hj9uKqVC1i2bx3lsTLX0Nm7S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qUq82Jh\nKfF7V0rHL6X15OZKw8NCe3uQV6fefdf+aG3NsGUL3LljhvrxcYtpb2jQbB8y91kSo3j1Ucz4+Oab\n5ljZvdsycl26ZHP35uaA69c9rl1Ls26dT3e3fVtLo2ph0Z4XOluOHlVeeUX44AMTJYw7f9z9anVO\n/KBiOY0D24FPAceA48D/CmwQke9jxoKTqvrfy7lQNH1hdvsL2f9/tMCx/wT4J4t79MIoZDl1FlPz\nPNqiZHhYGBoyWmZTU8Dly8L27ZKzspnyup3f2am5hZijsZuImWbjsk0h12ic+c8ShRuMiinNLjdE\nzIjivF07dyqtrfYOlh9VOXzYLJUPP+zz5JNGPd+1yy0wU2zb5uc8uqWw0Hd15zl11UIL3UKUzrWK\n+npyVMBScJoFblIU95wEgfLOOyk8z+f+fWF62uPIEZ/WVgvF6Ow045ml9guYmYELF2zCMDVlDIZ0\nOsjm3nahAPkL/UIQMaZCvgGt/PoRrWtOr2B01ATR9uwxI92VK5ozZnieGVFUw4wEzz9v6Z1OnfJy\nmTvcgsR5O51IIZSXSmyl23KC5YP71i6FLthi2fVN7e2aE/Lq7dUc3X9w0NgEnZ1hKFBU2sDVw7Y2\noa3NzwslcP00wJ49cxX/jVUk+D5Z0U3LBHLmTArfV557LsixdaLvsVCk01HGUWFEr1+OwTb+bLXW\nniqdLHueZYKxFLuzbNhgnm6H5mZobp4hCMzTV1dHNl1meH6xe9Ri+awE1q+Hhx5SRkZ87t9PsW5d\neX6mWlr4VMpaiZ8TZ+3Mh+gY7HDtWoqGhlmeftpSDY+OhmOdpfoVjh4NClLQE6P40qDQ+mFiIoWI\ncvRowJkzyg9/aP37s88G2ewUlgp7/XonhmvZoK5ds9BJN6+przfdmLNnhbff9giCgIMHH+y+ZPG7\n5KIAACAASURBVDVg2YwDqjqLhQS8AfyGWCs/hIUA/B3gHwHzTAFqB4U6WbftKJy+D93dAZcvm4el\nowOuXDFrfxAI169Ddzd0dZkYWSplueA9z026yFnZXNo5MIEoFxtUSfzYSjXGuR5257XVrK6A5ed+\n5RWLl9y7F775TcsAsHmz4HnCY49l6Ooyr5XL111swR7/uxK4si5nUrlQ1Opkaz7jR6lnLja5dErr\nnudnBYbg8cf9bOyyhWoEAbzxhk1a33kHbtww9khDg9LYaDoI6bTm0rfZoltz1y/2LsXSmRV7h+i+\nqIo8KJOTQmurDX6e56iPtihratLc4so8r14u3akT5TxxIuDcOdM88DxbSDU0mFXevHVhGbpUYul0\n5RO5BGsPjuESTaPpQoD6+mwynUrZZNoZowYHJac07cYJN7EOgny2lWMlWAwwOQbBpUthxg1jzkhW\nN8YM2xMT1racsSwIgtyzVrN+VpoScSH3LuThrcU+GkKmRCH2w+yscPOmMblu3Qp/f+89uHWrjvp6\nn7a2gA0bQtZXfP6yVOPeasfwsDEgZ2Y8urvNMBAEkjOKRzMq1RLidXkh1PxK3ylaR+Nl8vjjswwM\neLz2mnL/vgkH79plKVfNoVZY6LGW2+RqR3wd4fS+jFkGzzwDTU0BIsqbb5rjprVV+cY3PJqbTTfC\npTK8ft329/eHIShgxsupKQshjbJr3H2Tb1tbWE7mAAAi0kjIHjgOdAJvAsWyGNQs4gty95uJRJkh\noL6ebH51n64umJjQrEfHUt6NjhqN+vJl4erVFLt2+dTVaZYOGsbnRBfClh4wn1LvYsBqtWFFn2vv\n3lBEy1KkCOfPw61bFoNeVxfg+5L1GCm7dsHx49ZhnT+vDA97+L7m6KtLmXFhqbAavlMl+0rBFrlg\n4nvuIkbDFLEcuv39aXzf5+pVGBtL09GRoaVF+cxn7OjXX5c8z2c5z7KQ9hBtxyKSY/SAGQNcuk0L\nKwjo7LT6e/Gi5rytju0TH/wmJswa39Rk2goiFjYzNZUCTLBptacSS7A0cJPtQpO4zs589trQkBlQ\n9+4lb/yIG2gdBgbg3XeF6ekUqpk8FgEoZ88KQeDx5JN+jnHQ0aFZGrCFN4DmjVfV1lOpRkrE+bBa\nPLzut7hzoL0dZmeVgYE6rlwRHnssjNicmAiZBB9+SFYTpToG9QcJ9+6luHPHY3bWFkTxFHuFUAsL\nn6jxa7nqeLEx7O5d4ebNNKo+d+74XLni0dBg82GnZO+eNTGKVx/xMaRQnfA8Y8mk0zYPqq9XHn5Y\nc/N1UKamYGjI5uJtbTZOeJ4d41jRJhRrRoPubqW72y8YLhJ9ngS1gWUzDojIVzCjwCbgu5gx4N8D\nH5abWrBWEVUzdxax48eDLLXTLGxjY/C971kaM5fz0zw0lrblkUcCxsbm0tjjDblQh6uqOSuda9xx\nkb5agr2XMDpqsbFXrsCtW0JHh6mftrTAU09pTlBrz55Qebmnx7wiYAaYMrKAJagBuPru++GExMVA\n+z74fkBDg7J9uwAZenuV1lZb6IB5L92g4oRA4wa0xSI+SDqvKlh7OnfOjBgQ8Mwzyvr1ti+VCoXY\nbEJjgofxyZdLQdraCs88Yxe+eFGAII/em2B+1KeEd8duFd3fvKWeli3rlvGJlhYuxCWu+xEXOHWI\np1GF/LrlMsfYsZaRZMcOo4mm02bA7ekxLZ2TJwFCsUIREx0VEVpbre6OjJi2hxPW9bzqG21XG8oV\n2Cp2XCE2Q/zvaH/V22vb9fUBjY0BTz8djps7dghBMMvt2ykmJ00HyYnpPujfqVy0t8PWrYrvB2za\n5OYlhTWP4t75YuW72DqyGtHSArduzdLQYOXV0GCaJlHUipFuraEc41BUjDnOonXstJkZy3DT3e0z\nOyuMjjpnn7Bjh4lOmvExAEwvLTpPS75p7WM5mQN/FRgCfgczDLyhqveW8f5LhiBwaclCJXTrzKPp\nnYyWBqbU6jIL7N4Np04Z3UbEBptjx4xyD3DunC2CXXhB3AodCuXlx0gPDJi6bNR7VEswz6yJ2DU0\nWI7smRmhrS1MnzU1ZYvA998XnnjCaTNEJ72SY0wkqG0EgVGVJyeVlhb7GyysZmgI6usD3n7b2s3T\nT8OmTbZo8X1j17S3a050zGWUMO97+bnNF4LoxO+hhyCVyjA+Dm+8YWFCBw5oLuTHCcbFDVZunxPX\n9DzTWrh2zdLNHTsW0ihryXtZy7h6N8OvfWuw6P5/8ZP7mLxVXOt2NRkP8tXC53p7Cxm13L74dRyi\nWTX27dNsSs5wAucU//ftM7HD8XFrp/lpRDUnVgqWhvT994VMxlgGqw3V9PCW247nO66U+G4cJnIK\nW7ZYujFLTWn72trsG4r43LolDA/bYjfpX8pHEMC9e8rmzUo263XRxVVc3X++b7uYOlIJaoHFcOOG\nzVmvXrUU0IcPW/hTlAWbYPkQNzL39YVC606nKW7sOn3aRDlbWwMmJ2F0NMWRIz6TkzbHSaXIrYOK\nOUsS1DZWQpDwKCZG+GkRuYAZCk4Cr6vqNRHJqz6qOidCSkR+E3gSOBMVJxSRXwH+THbzl1X1OyKy\nBfj/gB3Av1XV/1jtF/M85iih2/OQEyYzWnWQfacwTaEqXL7sce0a7NihucWv85AODwtTUx6Odhxn\nDrjtaIx0b6+iahRu1Xz13FqwQDuKrMuZbXmrNWc8eeMNC8m4f986nbo6SKczHDoEjpouYmnzks5m\n9UDVaPWzswHd3cqZMykuXlTWrQvo67P0OL29fs4wYMKcZkSK1nuj0Gp2cZIvHLkYxCdOhbw/jnLt\nhN/c72DPmUq5ON78Nmfq4Mr0tHDjhrBrV8DgoMflyx6f+5xflvBjgvIxn/HgSz++d9UYBxxEJJe+\nttLwn/kUy6NZMczYDVNTHp5nachUjeHi+8LYmKXlDcPjQsbc5csWNhOnrFeClRyjVtN4Ep/UDw3B\npk3K1av1XL+uPP98qEiYSsHwcD11dXDo0Azg5bJYrKZ3XklYvfS4fl3o6/OZnoY//+eLG+NqFSv9\nnIODHlevptm7N8PDD4e6KA61YMBYqyhWtsUYL27cCIIwC82ePTZG3Ljh0d4e0NKiXL1qmlIXL5o2\njdOfMSfg8rxbgupipQQJvxQTJPwdoFFE7gLro6cREykUkSPAJlU9LiJfEZFPqepb2d3/QVV/TUS2\nAX8EfAf465hx4PeBV0TkP2efpWoI6TeFYjvDnNIWf2PiZJ4Xip099ljAxIRZ8qPK5Z5nAlGqOm8D\ns0YYptmLxvVHFdJrxSvpeaGwlntG0Kxeg7Brl3L/vlBfn8HzTATI95X9+83DBfliSglqG9YuLHxg\nbMy+965dRjlrbrZY2fr6gPv3hZERcoYfz5M5MdWVpo+s9Dnznzm8V753dq74VNj+5orttLdbG25s\nDHLhMK2tYUhCoXsmdTsBlBd/XmkasqhYZ6H9FsZj7fPAgZC109dnorEikm2/Bic82tvr43lh2sNy\nn8khPkatVpT7TSr9tsUEY11Zb90K27dnCAJTDHepDK9dg40bM2zaZPo927fPTQmZoDTq62HrVh/w\nWLeOrMFs7viTP24svt2utTHB85QNG3z27jUhUzeOrqV3rGXMV9eiukfm2DDHTkdH6Ox84YWA7m7r\ndJzWQFsbXL3q0dzsZ5nOSzdPS7D0WAlBwgZMe+AEJkh4OLvrh8DPAHfmucTTwJ9k//4W8FngLQBV\nvZj9fYbQnfg08LdUNRCRd4GHgPcX/SIlUGrCBXPDA8wqByB5DcrzCqeIK3bPaOfqth2boFYnWvnv\nZBS848ddDLelgWxuhlQqIJWSgnnpE6wOWCYIZWJCGBvzOH48THm2b58yMGBMGdPlyFfrh9DItRTf\nvdy4X/dbqQlhlHUQtkUAi9fu6jJaXnt7wDPPWCqgQlb8BAkc5qsT5eyvJBPJgQP54lQONhYpmYxk\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C/Bjwd7Ke/mEsrOB/Bv5F9pgfx0IMwBb9W7N/b8PSIDpEu9yt2WO/DPwMcAb4moh0q+pQ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\nQyTZn2w2SglYrtdAch4oM2FtBIonISr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SzKt4B8TnTE9P5NWj2vxkXKfnLRc21iteUhlB\nl0QYI8RoiDyfIgVKa6t888FBYS6OHrWMjUXZSbRv2o9cLo6Q7Fo009sVv96oa9Klmdv3mL29WPD+\non/fOd+lUtJSXGjfLrSW12PN1LJsN0xBvYtkjzTs2WPZu9fw3e/CjRvSt61bl+8fO3fCnTsStnPv\nnseWLTA+7ofZU9IUKZX0a60VB0la7W+7uAhXr27B9+EHf3CJBx/018waWm1fKh2TSo1FhZ5JhiYm\n3/nwQ49DhwI++1nL+Dj51N/wyiuS+lDD8yDKqASljQRxfsRNNy1hbao0rlbJUov5tV7ntlu/y2e4\nHoB6Njc1BaGXsucFTEwYmpsla9WZMxkefjjg6adtnn8Xz+XWVp/h4SjtcpoBEu6Pc/mjQGuytVlr\nz+hvY8xvA43ALwD/L6I4+C1EeP9D4I77arIsY0wf8KC19pQx5jeNMZ+01r6av/1VxCvhEhK68GK+\njF+11v7LlfZj5VpDw+OP2xAJdHBQlAUi/Mtm0dYWxdvPznqAT3+/G5MbBzCU9ojGT0k2SHBj5rXt\nq3l4i7XQOpuPDUHnVMHR0CC4Ce5YtrbafJ9EKJyaMszPe0xPi1uTCpFuP9w+FHJzrKTd5dJ6b+y1\npjRtetK1tJBVScn9Np4nYHKaBmf/fnl5fFxAsTxP4l9BFA1dXdF87O6W7z8yIvfb2mxsLifrcq9L\nUTGvFi0nqehIMjZu3UFAHnxR8q+3t5Pvs2V0VPqbHDf1jNC+qIcM4LjmFVaErPdcS2MEXWFMGTiX\ngkA8hjKZSBmke1hHhw1DiHp6Ao4ciWdxSSr90srWNnieuE67bUujjRaHOnt7kZ//k5SBzdM//IGU\nwM77jIpZOtMouZZX+1uvdl2qrNUwBXefCAIJUzp8OGB2Fm7cyLBlS7DMIymbhU98wnLpkqy/3buD\nEKzYxYdZaR/WYqw11GqtlfJBIEr0bdt8Pv1pCdso5KG0ElqL+VRu+eXUX2hvLsYn7d1L6EHX3Q3K\n6gtQcBSOAHHQTS3bVRaWOrO7u21eQZ2Ok1EpqafjWs291ZoPSWWrGPFEOdDRId4xINhlyu94Huzf\nn2Pfvuj99nZobQ3yWQrEAKIA3YXau9HO4Y8irUcUyJeBL1lr/8gYE1hrf98Y8++Afdbaf1bG+58B\nvpn//S3gs4AqBx6z1r4CYIy5bYzZmf/7zxlj/gbwS9bab1fT6HKEwlIT3hjR0qkgMDEhrjsq/KhQ\npqitQeAzOSlAaoo6rPG+aQyQxj7rdTFryWosTm2/4CpEsekuKGM2K67pw8PiZn3oEGGohLb5+PHI\noihuSqL5FUYosjAnrb613mjqcQOrpVtbEhMirdykoqAQWJW+r/d1jnd0RGWLt4DcHxnRskFTP4rC\nLEr3mbS6u/M5meWjEip22OraSHPj035oqlAQRubgwSDmBilrNYhZsjVcJpkBY2LCI5cTa11DgzBH\ngl9hSyL7rqUAVKodKsAcPCix4MPDMkaCeGyZmsowOurT1RWlQRofBzChEii5Z6kFSV2pk2BTyT2t\nlBt3OftgPa75TVpO1YbTrCajvZrkgn2q1U/2IBmIP/9ziRN+6imf4WF46y0Rot2sMpOTElawbZvN\nK+kjYaoa5fp6rZVCQsdq74cNDXDkSI6bN+GNNww/8AOrK8DXqpzVCq+q9vt7nuHAAUnHrS7roOC1\nkkbPnbcgBqSeHg05jKdVTp7ZydS/Ml9MiIFUbX8iRUOUVauacaxmntZ6rypUf2trwMyMeDA3N0fP\nq2KyrU2wN2ZmhHc7ciR+7qbheCXPVuXf9MzePGvrk9YKc+BvEXkBNABtxpifAnxjzN8FJoHDZRb3\nEJC3LXILeNS5l3F+38o/+/vW2n9jjNkHfNMY86RNmOdeeOEFnn/+eTo7OwHYvXs3jz/+OP15lKyB\ngQGCAJqaTobXnkfB+zMz5zAGTp2K7vs+NDZG19ZCe7tcv/zyADMzhk98oh+w/Kf/9DKeBz/0Qydo\nbQ340z99mddeszzxxEmMgYWFcywsSP2eF5W/d+9J3njDY2HhLHNzltOno/omJw19fXL90ksDTE7C\nk0+epKfHcv78ABDvT6XX27dvByLvBk3ZKEJ95I7uWm2vXjX5lIzqTm65fBlef91j/34JM1BlyeSk\nuJu3tGj4RFxLXGsE1HLj8tzn3b+tlvKlVl4LrvbdVeqokFoofq/Qpp9MZaSHhCqyNHwmCCxDQwqS\naTl7Ni6EiyZaDnEXJyMIbBjHrxrucsch6TWjVM745XKiyPB9SdNorcnnXRcBPr9l5D1koveMUSwG\nGxvfQ4ciRdbhwwL689JLGd56K+CLX5SGzs2J+3B393LQtSTVy8EazU3FHZC1PD7uAQHNzRJuce6c\nwfcN/f0BExPQ2Rlw8ODy9K9aVjIVnItvkKRSVorCbY7mxv3kFXS/UNI66IabVPKdNrriR/fqpaUo\nhGZ8XMAIp6cNi4uWjg7L/HyGuTmPXG4pFLIEH8Xj9m3LnTsSO3/okHjptbQQejOWS+WeRWs95qtZ\nz+IijI56fPBBlp07l7h7N56toF7n12p8m2Lfv5SXaleX5coVcT83JuDqVcHf8byA6WmPj30sCPkT\noaj8kRHyvKAJM4G4deZyljNnDMaYMPNSKaFV3isvPWcxa7iWA6XB99Z7jri8m7ZZx1bu27yno+JI\neXiegIfPzMDsrCGXE2DlpCKnkOF0cNCEBsyLF718GufNc7ZeaS0xB6JAIPi7wDVgCfgHCA4Bxpj/\nEZjJP2cQzIF/lSjrFoJVALAbuOncc9nAXcCNfCpDrLULxpgrQDMSwhDSc889R19fX8HGqxCsAGlH\njvQvu6+xtaIk6A+FXc+Dp57q55134MIFWRCnT/fHrOEtLf00NhqmpmRhNTf3Y4wAbLW3W37gB/qZ\nmbFcumTYsweefbY/XJAAx4/3c+WKCNv79wf09Z2IWR1PnerPL1xpf2PjSRYXDZosQvuX7G8l1xcu\nXAg31ih2XMBMNMUbGAYHhaFZWJB329rg4EHZjECEvhs3DPv3R+B27e2WlhZhZGZnbd592IY55Nea\n0qzmG0nI0EPSmMhin8uJUmZ6WmLJ3MOtmjHWOnxfvu30tCh2Xn4Z3n47Q2+vWJHn5yMQQrXIDw4a\nRkejAz0ILK+9lmF8PAjRodNSJBVqR6XfxmUm1JOhtVXmr4YNTE6KS+/3vueRzVqOHQvC1D2izIi0\n4noAv/mm4dIl2LcPOjslE0FPj+Wtt4LQK0asIxsD3NIVugWhWhQkzc2yV1krir9czuPoUUtXl4QR\nXL8O3/kOtLTI+h0ZicIwJicltlGwVzzcDCsulWt9SX7/YoBbm1S/5FoHRQlZuZW7nvfkckh5jDff\nNOzbZ8P98+ZNSe/44Yce3/mO5e5dOHw4F9vDgwBu34YPPpA97IMPskxOLtHRIdhGmYxYV2s5NvfD\nmLsUBHDzZhaQdG3Dw/D44+neZvXe19VqbznlDg8LBk1joyiQR0ayHDvmc+AAgCgGNA2uAv1aK4Ko\nMSYWeusK/IJLA/PzXsg/KpU6I9wQtmJU7NzJ5SSlMRAqJuqZggDeftvw+useTU2a1tTQ0mLJZACi\nLFwdHQHj48K/fPihpEs1xobeA/rddXwKGdAmJwUXJZMJQo/BTapPWivMgaf1tzHmBQQw8L8EtgG/\nCvw4sAD8pZTXk8qBV4CfBr4BfA7BK1C6ZIx5CngT2GWtfd8Ys8ta+54x5gGgB5irth+lXBiT2kmI\nDvRLl0TT1tysyOw2lg9dLWZjY4amJhGANZ6wq0sWq7UiRIyMELoAapuMkVhvtbgm2+pqCsU6a6uO\n0S9G2awCt0UuwBpD5nk2jEsOAkNzswhUko3BcuKE9LO316e/X9GBg3B8QDSYqgNSB5BauxO6mu9y\nBYi0WPFisWnVaO1r3U8XYbi72zI97YeAlq5ngVtXofqjtGHLx2Fy0uRTcQojundvQEeHPN/YGAce\nUtL6RcFg8byA+XmPIPCdOVb60Fe39qQ7v1uHlpPmdqlrpacncusdHRUGZXFRvAlu37a89ZbM6Z4e\nG/Nq0DHs6bHMzFimp71wjIJA5viXviSNUIWfm1mhXpF9k94iQWBpbw/CMQ8CQ2OjeP9MThKGU506\nFTA6Gnl+aL80tCgIBISyrW15ytJajEep8up1vD/qJHNKfhtjYoCd7h79UfAiEKupxGT39Yk1b3YW\nFhYCRkczbNnixyzaSg88IAKLMYYtWwL27xdBQM5qE9vzS1E9702rRdu2QVvbEjdvGu7evf/6Xcma\n8DxieEGFykvjIWZn4b33LI2N4pG3a5cAFHd0wLvvmjDUEKJ0eRDHd0jjM6QeQ1+fpkFcfr/QnK0E\nt+p+++5CwmNr1i2IhwF0dQlo+uys7MUPPywKsslJuafZyASrID5GytcpHpnnCf+UTKO+kffk+5HW\nQ7f1z4GfA67n/+0B/iPw69bab5V62Vp70Rhz1xhzFrhorX3VGPMb1tqvALbWWbkAACAASURBVL8C\nfB14APha/pWfM8b8EOAB/9ha66+0A4UmsW6CSRyAyUm4dcujp0fQ+c+dkzirrq7IYj44KJbbxsaA\n48c1DZqLtipCyuSkZWnJMDgoyoXe3soO6rU41F3B6plnRGBQhUVvLxw8aBkYiNyWjBHBb2xMPASm\npw1jY7J5SIyYDdPRqRuZCCZyT+uqhLkpRK7mu7vbFnRDS84BBZtTF7hisWkr0drX4psl50AQiFKq\nrc3i+6INDoIo9ZymhnPrdxnyQn2JhGMTCsLPPGNDj5p33lGhUkGxNP4wfvBnMpKxAvxl7ShEQRCl\nvNL0eK4AqEo7BcTr7AzCteT2LzlOOnbiYmr59Kd9JiYkFMDzgtCrwVpx01NsjGxWAJgUuMfzCudj\nT7ah3g/LILC8/nqGvXslB/jsrORCbmsjPx5i5RwaknUiQGiRcvTQIbh6VfBX+vuDmFdUqbEo5+/F\n9rtCStRNqh9yLXKaylK/k3rpJHFokuTOA30PNoaVV8nzxErX3S0sjKbRFS8lcXv/kz+RNHFjY4Zc\nzsbCCvQc3r7d0tjoc/w47NqlYRoSuud5puw44HrgNdaSsllBd79927JzJzHhZqP1NY0HqGRN5HKC\nFwDLreRqyHHxg9yz9dVXDePjW7hzZymfiUrq++M/NiwteRw7FsTSQwsVbpPbj+Q3SJ4PpfaGlSgX\ns9niKRzXmkrxD54nPLXKJdpmfU89OYeHxSBy6JDlrbcMDz0En/+8pBg3Rs71N97I0NTkh96TWo7O\nE2MsLS1iBDh1yi4zYG40z5uPAq3HFP494P8D3kMUAwb4YeA/M8b8JNBsrf0VY0wbYKy1E8kC3PSF\n+euv5P+fRLwJ3Hu/DPxyrRqf5qYM6UKTChMtLeLCr94ASmfPyksdHUGI4G2MLMRsNtLMuuVbK671\nc3Me+/ZVh9q7FgvPHQc3TZK2NZfzyGQso6PqTWGZmPA4eTLA8+S370cuZW78WJoCYC03l7Q5UEhR\nlHyvXshVprg0Pa2u4cJwq4XXZRiTCpRideh3HxoiDAPxPGFkL12CsbEtwFIYuw+RVbmryy4TnLX+\nSud72vqcnDQsLEioTrL85FzSmERt15kzHkNDHh//eI7mZrHAqRKltzfu7hi1Q67dzCLJfOzJMXZz\nm9cTuQxVLgdTUwIwZQxcu5ZhcRE6Ovww1ECUpGKx1LzgrpWhrU32sbTUq4XWdqV/36SNTNGaKYY7\nUYzS9oCNRsrEu/uvgrN2dQkA6NxcAx9+6JPLRXaQ0VG4erWBBx/M0dUVMDKS4eWXLZ/7nICpahx3\nmkV2pe29X+juXXjttQwffpjh9OnFDa9YrHV7k3yBG8rqkrWGHTsCtmyxfP/7sLCQySuuRFBV5Xm5\niuCkYt3la4rx6oXKqKSfyfOlXs7pcs9APXPT3lMDl4QdiDFz3z7hCzVT0+KiGHWam31aW0V2UeOY\n4iwZI8YAwWUysbTZm1S/tGZT2RjjIYqAbQjWwJeA3wf+GvC/Ao/lf59APAB6gL8H/OhatbFScgUA\nN545Ccx25EgkOAgyu2ycExOEKYfa28Wt3vPEIpnLEYK1aSrDxUXyArPEHKplLtkml9ZjEbqCje/H\n3Y2CQCyqW7YEtLaKsiSTgZYWi7q4d3VF+A5pZcNy74xaMXxJLXI5GuW050pp5tfTyuC67LvtUQsc\nSNhKb2+k2ClESYucK7i7YQLufc073Nxs8bwl2tokdObAgSAEIZychIEBActSyzuUL/R53vKUV8mD\nr71d3NfVBdEVHtxQhCgmEZaWxCIHlocf9mlqEg8ftYK7WvckVkPatYyH1JfLxdvh5javJ1TfJJOW\nzcLJk4LF4PuGp5/28wxC5P00NSVWg9ZWGUffj8ALh4ZEKaWxpC65fa51CqmN4pnxUSfPIzXNXnLv\nKWZdTJZXSchYPVMuBxcveoDsc5mM4ciRRXbsiDP92SwcPbpINgu3bsn1jRtiFdTxPXRouUVvvane\n1mhDg2Xr1iUaG+unTaWonDGs1HpejpU8k7Fhil+3zGPHAhoaAvbsEWwZ3xfv0scekzP73XdZ5gWk\nZ3cQxPkBt2/KB6aBXyf5nY3y7WpN5cyFZJhWY6PgA0hYoPAp58+Lp/O2bZJ9TMM8R0YsIuYReoj2\n94ty0hgb8yzQulyPzFJt26S1o7XUc6moYJB0hl/OX/8HfcBa+0PGmBv5y+8gaQvrilQrpr9LUXJT\nCgKNsYeTJyN3rMuXxZVeBCMRioyxzMx4DA0JmrDG5p88GR3iaa45SeVEoXau9sFrrQAKel5kAY7i\nmJShkbiluTmTd2WPQGckzhtA+puGKOu6nOp4rqQ/1bqhuRrrYu+6ZawHuYdsBBwZWaSSsfxHjtjw\n0C2k+IDl3yYI5Lu4YJRunLAx0Ngo+BpdXZZz5wxzcx59fX74bScmDJOTKjhWB4yYZoFQSuJ26Pho\nqjBtr4yVgBWdOWN4912Pw4cDGhtFAB4bM6FwPzIi5agbZZKp8by4giMCYdS0TpEiwM1tXi+U5jWj\n/ROgLmKWnyAwXL0KV6/K75aWIK9IiFKdAiHWxdCQDa/ddZ4WplOModD9xt1z0vZK7Uc9jfEmLScN\nTYLaKVaTiv1alLkWlPTaaWjwGRnJ4nk+d+5Yhoe35PE/Is+Bri740z/Ncvt2wPbtAQp6OjMj4U6l\nvJPWQ0ivtzUq+DWGDz/M8OGHOWrtZbEalDaG5Vjgy6FiMf0QhVpq6KxSSwt861sZrDV8/vM59u2T\nWPatWy1jY1F4YVIRHAQ2xC9SPsX15pMwB1H6e55Z1p5a0UYQZNPaWO56UgOR5wn/190te+XgoNyz\nVowki4sZWltzHDwo742MyDc/fFjel0xF0N0d4Hkm5iWp4cECpO7R1mZ55JH1X+ObFNFaKgcO5f8f\nBf4h8DvAt4C/AnybeBpCEO+C5N8AMMb8GvAkcMENMTDGtAK/jQAefs1a+6Jz7w+AS9baf7CSTkSW\nT2FAXSYUXMVB8U3JXYBqWfM8WXQLCx5tbQGf/SwMDEgMvi44ELeclbovrcbBm9QCupZoN1a8txcO\nHRIgpYEBw7Vr8XIkTaGJjWFkvS2EKCuWy5UgLpczJsVinIu9W28Hilp2NesDpLc7CKLYwtOng4JK\nqVJov/qOOzcGByPLuM4Tpe7uKN5tYsILPUmSXgpu+cl2J9PjuXUrMKYyGfpOIWtid7fNZ3QQkEz1\neMlmbSi4Dg4SpjtUjwUtV9zrJUQgbWwg8iJS0tzmaf2rF1IvC822ADbEWtCwKGPgwQflt4wZgMkj\nzsuYHDok4yWKmUJgU8vrL7UO603A2KTKSZVDGpqUFmq0EtpIVsVIuS7/Z7OCmJ/L+Vy7Jmfmtm0B\n9+7B2BixrEX37sk6bW6WjDFzcwFHj6qlrzC/srmGhDwP7t3zuHPH8P77y8+KevNySKPV/JZu/13e\nL0meJ4j3IMqpXM4ijsWyX7e0RBkL4jhAZtkZuZxkf0hzXa+W/0r7ruUaBqupr1ZUbb0ReLMr29gw\nHbUx0NkJ7e1LZLPw3e9CLhdhN7kKKG2H8k/nzsXnnrWSccUYy5Ej9b12Pmq0ZsoBa+27AMaYJeBN\na+07xpg/Bn4NOZlGEq98Dsk6ECNjTB/woLX2lDHmN40xn7TWvpq//VXgF4FLwB8BL+bf+QQSzlAT\nNaK6PYswRCzXapLJUCWCKxydPh0wOKjAhCJc6HrMZCwtLX4YhtDZKS7MAuZW3I0rrilde6FCAeDU\n+plmiVYBbGRExi8IDI89ZsMNHQQcyZj4eMoYLUeUVfc2151stWilB2s9bHxJJUX5rrXqgpqufEl+\nG8+L5qseLmkeFcaItfnUKQA/D2AjB80zzwR5AK6AkZEIswDKByJLi+kPgkirrUK8+227umxYn6RH\nit7/4hdlro2NLfccgQhZOelloe30vHTLpzHiYZFE8K2HOeNS0hrjpquMACVl3GXcLAcOwM2bsHev\nJRlPmlTUFNq/aq1cqzdl3SYVJ8+TPaYQRkc15a2GVXE1KfJCEqWqMbLH9vTIvvPHf2y5etXjL/0l\nn+Zm2at1HUmYk58XvmBhwWdx0WN2VkL6XDyYeqF6W6PbtkFn5xKLi7B7t/xNjSH1qkCp/ryvjNI8\nylwh06XJScPhwz4f/7icw5mMANF6nqQ6ViVMJkNMyZ7NsixMwe1bOfzxSvtVbhn1OB/KWU9p82Vw\n0DA+HoUFHjok572GPg8Pe+zeHfCJT8QzoGm6aXBB1YMwBFPqAvDDv21S/dB6wGe8BfQjwIR/CvwU\noro+ZIz5OrDNGPMvEKyBL6S8/xngm/nf3wI+C6hy4DFr7SsAxpjbxpgd1tr3gZ8BfhP4ZCUNLaQx\nVO+AiQnRduqzybRpaRumliHMjgj+nkcYb6uWR42/d4XrchZPuQtstQ5esfrLbxchGeIu1jpuBw7I\nw1evun2NDhX3GxRqr5a/0v6sZEzqjZEpRsn5XKzd5ShfCpVRyrvF8wSLQg+UxUXxJLlyJcORI+IS\nW8yar4yZKpLc+l0vAUmNFG9vWgyze1/CLSxXrsiabG2Vw3BsLK6U0Hkct2ZGe4KWp+k9C80NY0z+\ngKx/gcUV7N1rzcSguBFDQ5arV0V5YK16CshLXV3RWChA4eHDxT2iyt3/knOxGGjoJm0MKraeV1Km\n0kbZu5OUy+m+bHnkEbh+XcKwPvMZy7ZtcYXsrl0wO2sYH7d5EGC/LGFxPc+2evoe2ayM4cKCeHgO\nD9tQWV7PVMl5X8s6C9XT0mLzWEJ63lnGxuScmJuTs+Cxx+L4Fy7fredLWp2V0npb99eaqpchxLov\nZ7XyNwLEnMtZDhyILP+KNeXy8Vpu0jigGVfKbdsmrR2th3LgE8Bjxpi/AjQDg4AP9ALfB94HrgKf\nAiZT3n+IyMvgFvCoc88NQ7gFPGyMaQfmgJuFGvTCCy/w/PPP05mHTN+9ezePPvo4TU0nAZidPYfn\nQX9/P0EAv/u7L2Ot5bnn+slmYWBggPFxaGw8SUsLXLt2jvl5OH68H4ALFwaYnbWcOiXvf+MbLxME\nlqNHTwKGl18+x8SE4ejRflpaLK+/PsD585b+fnn//PkBgPB6YKD86yCQa21/pe+Xe719+/Z8jHQQ\nopN/+9seCwuGJ5/0Y+6NxoiLUkuLxEJq1gbVEruCluuaVsvNo5q4u1IHaz1ubuUcfqXaXUr5UgnI\nTZolGARzY3wcrl0z9Pb6nDwp986cMczOehw9GvCxj0Xz4sABmwe5IbTuu23UOpL1qEIh6a6f/LZd\nXYIDcOmS52B4RIB4otCIvBi0bj0c9W+lPBvS6t4IpN9TBbbLl00IOtjbK3Pm4EH47nfFMvSFLwRs\n2ybvvP22pMns6Ag4dCieBrQWzNpKlQAfNYZxo1ByPRdSUlVbdpLqbR64fe/pCUJFycSEZXra48MP\nJaQgCDxGRyVNWZzELfj6dcO9e/CpT1l27xYmvxyA13Kp2nGrt/FOUhDAzZuGa9c8bt8OYn+vhz28\n3PFbSfuK8U1pOExppJm5XGBmiWeHT3wiwFpDb29cOZDLiYeZMSYM4xOhM46Or+WW08dyrPvVftd6\nmA+1IOWVurvFUDI5qRhLknnIGNi6Ne59qekrFbMpyYOl1bFJ9UfroRzIAP81sAPJUvArwOcRq///\npg8ZMcv9S8SzwKVbwK78793EhX5Xn7grf+8f5P89UqhBzz33HH19fbG/yUSX3/39/bEJ3NcnQrHG\nNR0/3s+3v224fDmDMTmefro/1G5KOrYTMev5E0+cyJckmucTJ/rDRdXebvmJnziB50Ub1/Hj8fpV\nKC91rYu1qelkbHGW+34l1xcuXMhvBBFS6bVrcphqWxTB3R1bfVY3lyR4IxCz0BaiSty4VuLytdKN\nbC0ZoJW4xEF5Qla5OA1pz7gHv1jjDU88ISlzXA10kjR2TQEMSzH27sFVrJ2usDEyIu6PmUyA70dp\nvtzMBCDeDjJ/Tdi2wUEJmWltlZCJQu1Kq3sjULpHlMYux/u4datcaIrCK1cEX8XayMuooyMIw6rW\n2xWzHt1BN2n5enb/vhrfq17ngbt33r0rXjeeZ2losAwNZTl4cImmpsCx2rnvWhobJVPI1JTHzIzP\n+LihqUmsd+qllRzjSuh+csNOUhDAnTseQeCRyWhOd0klqbzNeioGVnv8StVRroJNQe00TFE8AqS8\nAwckZG901IQ8YFLojDB8JERmtce82vLrcQ6XS2n77cyM4J1ppqG2NvJg4h4zMxAUcUNSfk4VPPW6\nxjcporVMZXgI4R4DxPrfBfwZcBb4JRzpL5/28OtAU0pRrwA/DXwDwSX4LefeJWPMUwhWwS5r7W1j\nTCfwr4E9wF5jzDettedKtbeQ5q+QhrStDcAP0wuqNU03tAhdVVIPiZJANKeKWK7KAteCVg1Fbj3V\nl1ENJTWEfX0BIOBtLso4xA+IW7ckHtklVRS4qPEroXKF8mqE90rKrhbwcK0o2UZtz2rl79UDI5Ox\nnD6taP5w4UKGxsYg70Hg8+67ccwBMDQ2Rm7syTWZNs6VrClrJWNGRwdo+lHPi5RXQSBM4RtvSDtO\nnZL6BZhRYu8j/IBI+XXlipTvAi1tZNL5MjWl6dCiMerqsjz8sDwXrXsJJTl4UDAsBgZknJPprtZ7\nHVRLG7Xd9Uxp+2axtfxR+AYK5js3J15Vkmpsibk58XRKA20MAsP165K+9fHHfWZmDNeuGdraZMAU\nM6i1NcIMKkYfhXF2qaEBPvYxn/Fxn507JezslVdgZsbjiSdWKZh/nakar0D37+nnsImlpQ0CUcTP\nzxtGR8Xo1tlpHR5Qztb2dvE0y2ajeHYX46hSS/39Yt2vNaUp/5UXFznH8vrrHkFgOHUqYHQ0YHJS\nBrC724Zemfrt9Bu5Cp7Vbj9sftOV0lp6Dgw5v3/N+f2jwAdAzhjzE8DvAv8OeBD4kWQh1tqLxpi7\nxpizwEVr7avGmN+w1n4F8UL4OvAA8LX88z8EYIw5DXyuHMWAUiWbzJEjNgQRLMW8GBP902c1nrur\ny4YAXa5bTrnkpvYTsKK13fzcetQakRYjquBS4+Mevi+YC2nuR+X2v9hG7252yjSlPVuN9r3wARi1\nqxKqpfa/0sMvGd8v4IDRXEpjFsutI5nVQ+tzD4y08nU9ReBGcRwENwtGKVKFlDIlbjuSZShQYjLd\noaswsRauX/fyVu8g/14EzJjsTxAsB0LcqLRc+JCYxGSfjJFxkpRr0VwRhtBjft7Q1ubHGLykB9Fa\njtNKGMZ0j4pNqhWpQAHxcU4D0NJ7K7H61bvgYK2huVnO+OFhuHMnwkNJI2MsCwsZfD9g/36JE+7o\niEBCpUzy4UHFFZjFvMHuVzdszxMgwulpD88TN/aZGY/mZn8ZkOx6tK3W45f2jdNCegrxP4V4YGPg\nypUMxohSXYxrliCwLCwY9u1T0Dt93nDokIz36KjUkwS6rrbf9TrX6o08T8Age3qUj5Hv2NAAjzwC\nXV0BAwNiJHjmGc1EEGWj0rnR2mpj37bWtBE8kDYKrWW2Ag/AGDMGzAC/Dvw/1trdxphu4A+AfwL8\nt8A14MestUsFyvq5xPVX8v9PIt4Eae+8BLy00n4UsmIkY551IxUUfle4kIUEcUWCu5HWUoBY7wMr\nKfC78XniTm5pb5d7Q0PxfMuVCralng2CtXFrqjXjtBIqt540BUq5lvZSYz44GNcgJ99tb49S/AWB\nrJHubhEYi80FTZmX9i0LjbOCiB45EvUx7VsVQlqGaK339logl8cjkHtpSpBkX9PatVEobbxEMRrf\n95Ta2y1TU4JC3dpKCFoUBMIQtrXZGGNdD+NSD23YpIg8jzDOeHg4AiZz769WvbWkWlq0VEGq5Xke\n7N1r2L9flJrDw8RSgylzb23AzIxhZsZy4oQAAasLt6YYXing40oUMvVOngeNjQHt7ZDJSBhcT48I\nSbWglcyRtRi/Sg0maefhiROW+fmAhQXC9NPd3RbfjxDxlTdOYgil/d60FNeW3HHXsU3yU88+G+09\nYtwUhc+ePQFBIDKPYJBF31/T0ep+s1JD2iatLq0X5sCPAH8FyBpjfop8tgLgnwH/BfDbwF/PW5f/\n1Tq0ccWUpjBw3aRUoNB7qu1fqQDhMg2r5QpeKaUJYSCbRCYjmsSREUItowpu1Qi2hWLhkhbJQu1c\nbde0Us/Ug/VEx97zajOXXA2yflutJwlK6IbhFGMGSrmopc0B97AqRuVasD1PLGyTkzo+Zlmfks8n\ngRDvB9K+pK1BSVUEL76YZXra0t3tk83KuGYyNtXDYiNYEdNoo7Z7I5B7HhQb53r9Bqth0XJxjAAa\nGgS7Y2bGw/ftMgZ8YsLDGAlBmJvzGB0NYpgokTJW88kXBxGrx3FeTQoCQdMfG8uwa5fPs8/GDT+1\nKL+erJ7lfONSz6Txcdu2wbFjltde87hwweTDT8Ujw1pJXZymLK6Vt+cmlSbPW+6F7J7Tiqmm2cc8\nz3LkiE97exSOJJkI4t/M9QCF2n+/j+K+tFq0JuKjMeZPrbWfz1/uB34fyTLwAPB/5P++DegD3gH+\nqvN6XSkH0iafK0xUS7poZJGtbHLXi1LAJRfsyCVjBOSkXMGtWtJDqpzDrpqyk9fVblDrtaEVavNK\n51IpoTypPS5ESe2yKtHSvBEKUdphVajf5TBDoG7zywEz7wfgwTSqdG4LngqMjS3/wMYImFqhejYi\nbdR21ztVMu8+it8gCAQ0taMjoKlJvAeS+1l7uw3jvZeWcMKmbFl7X5I+auOczcKjj1ru3YvG934f\ng2oMHuUoDLq7xTUdCHGD1ChWiOdIK6sQX7lJq0dJXk2/pwr3hfaStRLcN+dCbWitxMivO7//HMgB\nP4+EFvx3wH8OZKy1qSEBSTLG/BrwJHDBDTEwxrQiXgdbga9Za180xvwN4G/l//YvauGJUGjyJa2M\nhZQIQvF7abH29wupBdaYeHyjOz4rsarWI+NYST0rUSqthNIE7tWgNKE8Sfr90w6YcmIfi1EhpHO3\n7kJtKqU46O11w3+iNXw/WzMKjWGhb5LNwtNPB4yORi6Fm9r9Tfoo0WozxpmM4AdEueOX71lHjkSZ\ngg4cSMdF2bS8FactW+DTn/ZDgbaW9FEZe8UKUtd0HcdK+b9CfOUm1YbSvJCTvFg1nrZajl5/FOb8\nRqQ1+RzW2n8LYIzJAN8BbgP/F+It8E+BD4H/yhjzk8aY/8EY8zeMMXvSyjLG9AEPWmtPAQ3GmE86\nt78K/CLwg8D/lP/bv7PWngaOA3+n9r0rTGnCSCkBZXDQhK461VIxMJj1JLEURtdpG0WxDaJYvzaq\nFl83W91wa1FeOd/erXct5ko538fzlrvKrqQ8LWcl/SxVj9tmvV4p1ev6LUVpY6XjPzpa+tmNTBv1\nm20UWuv9ajVoteZ8LidAgp5nwjqK8RiTk4bJySjtaJol8H5am7UmF0i61nQ/jH2ptar30s7Oavi/\nJF9Z7nubVJqy2eJKsHLma3L8k/Njreb85jyojNYqrOBvESXC/hng55A0hnuB68A+4C+AS8AYksHg\n140xP2KtPZ8o7jPAN/O/vwV8Fng1f/2YtfaVfJ23jTE7rbW38/e2IlkRVoUq0YAFgcRXA7EY7FpQ\nPcZgFRqbctuqi3q9EMw3CtXjty+HXOVQWh/qUbtcqs21Qryvl/6uhKy1TEwYWlrunz65dD9+s03a\nGKQZZawV7xwo7Gat52g+M9zmmVoFWWvwfRgclLTTm+NWHbmpDMuhavmCzb25tlSN1+ZqyTqV0GYm\nocpprcIK/jqRz+1W4O8Bk879J4Exa+1x/UM+reH/CXwqUdZDwEj+9y0Eu0DJgdfhFrAbuG2M+Rrw\nt4m8CWL0wgsv8Pzzz9PZ2QnA7t27efzxx+nv7wdgYGAAoKLrIJBrz1t+/9y5AS5eNDz66El6eizn\nz0f3e3osAwMDzM9XVp97feGCXPf0nCj5fBDItedVX9/AwADbt29PG9qQqt0UXNATY+wyQJO05yut\nbz3RUt3N9vXXV6+etBCCQgqb5N9Wk8rdtKttz2ooFpJtFoY7mpub6LsReZ6kpbp61TA9DR/7WO0P\n5pWM9+a32hhUjwrCeiLPk33oyhW5TgK66j1rJWuI3nP3rU0qj2ZmorRsm7ScSq1VzTwyNJQOSKtU\nzt68uResPVXKW09MLFfOFMvqtHkm1wetiXLAWvu0/jbGzCGZCbYC44jSYDuSvtCl3wX+eUpxt4Bd\n+d+7gZvOPddpZJfes9b+sjHmnwAvGmN+11r7vlvgc889xxNP9AHpE1KF4HKvjx/vz7vNyILQ+zrp\nT57sp6lJmusK5Vr/qVOV1Zd8/yd+4kSsL4WeVyGnqelkTDCrtL/9/f1cuHCBSqhQipRC5Hnk48qK\nexhUqiWu9J3V2LhqVVYpDw1NJ6iHcSEXcFg/LXspxqKa8S/3m5b7fPJdyUAChw9Lu1cyhveLEOR+\np2wWOjuD8LrW9ZQ73sm5U6v5fr98s3qnzbGNSOdyNgunTwdh6M74uMEY2YuSZ4Ay6f39cm90NNq3\nNse2NHkedHRYLl40BIHH8eP+5rgVoFICvTHpGYygsKdoNXtsrfbmTYE1TuWOh6yZ6OwvZAxyebDV\n4EE3z+jKaT1w7ceAPcAdIsyDu0BT4rkvA0Mp778C/DTwDeBzwG859y4ZY54C3gR2WWvfN8Zstdbe\nA5YQ5UGqmny1haLkoujtlb+vhoW73id/JUx5ua5ja0H1IDyXosLupJaJifTDeD0p7fuupYtgmmdK\nJXPS/Vsag1NNG+vl21RLad+pmv1utdtU6ftQuP0r6dfM7XvM3l5Mvbfob9wgyYaM4Y2p26n3mnY2\n0Lxz6xq36P4gdy6rBU5Ti0F69hR5T/a50dEotXItU/Hd75TLwfnzhvn5DEeO+HWZFWojkOcVzmDk\nGjIERyP+XqVUC6F+I/B9a0nFxiPNQ9U9+9N49eR+tlr0Uf9uldJ6A+/jRgAAIABJREFUbG9ngR3A\nQSA/bTBArzHmO8BV4ED+3o8kX7bWXjTG3DXGnAUuWmtfNcb8hrX2K8CvIJkRHgC+ln/lq8aYpxFP\nhX/vYBCsGpUj0JY7UVdTo7ZRtGm6qaQJW8mNqhr01I0wBtVSpIG3tLauXBGzGu1by/pqRW57k4qC\n7m7L0JCM+0edmVjt77uSOVvuu6vNHM7eXuTn/yRNDw7/8Ae6alvZGtL1Ozl+6Vujqfd+9YcPbyoH\nVkhBAENDAsgWMdUWWD5H1ert+3I9MeHR3h5sorxXTIb9+30OHPho7+srpWQGo6TQaIwJPVqqHedN\noX5tqdB4J8e9WEjB/c6PbyRaM+WAI8D3AleA/wj/P3tvHh7HdR34/m51EwRXgCt2kCAAUhsli1Jk\ni6JIyfI4sZNM7ETOi+PsTibOfN9Tkpfnl3wTz0wySWYmzpvY40z8JnlyYk+WSZ6VxEm8jy1ZEi3Z\nlgxJ1EqC4IaN4E5xA8HuOu+P0xddXajeF3Q37u/78KG7qrrq1rn7ueecy4eA3wd+CHgjdawL+Cfg\nSyISdjUAILh9Yer7I6n/k6g1QfDcbwO/nS99lS6QUZ1zsc+ohUatHk3HwxRraVBKegq9rlEbrmDw\npErLr1SK7byrIf+FVgCluQKE6+pS9uO1Mq1VwLNC7put7DRaPXY4oupXuhzbdmehZdPwsCotk0lD\nb6+P7m7g/OYLJR6HvXt99u9X5crwsO/ajzKwsit3gafaNPK4rxqUKo9sLgVOvvVJLS0HfhZ4BHgA\n7cG+Hw0g+B7gNmAH8H8B3cBkNsVAtahFoSznGc1WgRrVl6jRZO/KTe3v2WwyL4V6fO9S0+Ty01Fv\nWCVvsFzmc2PyPF2Rjcfzx+9xRNPSAj09aonnZFcd6nFs6PI6k0qv+jv51h+L4VawAvhp4L+hQQO/\nF1Uc/CfgGLq14XpjzLPAT4jI8UVIY13gBqVKPjksZdkUQr3Kp5nKdzO9S6VoJpk0evodzUnYCiZf\nP9ks9XGx8LxMc3hH+VSzXLo8qi315rbqKJ3FyCIBrqY+J4C/RS0GBGgTkc3odoXPA5/JdhNjzMeM\nMU8ZYz4eOt5tjHncGPNNY8xDqWP/yhjzbOrv/VV4p6pRjs9VM+Hk0Jw0U74207tUCicTh6N2FOI2\n5upjeTgZVh4n06WBy+fGoZbZtDIVRDAB/CYalPAGcBOwDvBF5ApAaqvBXwfuirqRMWYXsEpE9gIt\nxpi7A6d/I3X/dwIfSR37iojcC9wP/FqlX8zhcDgcDofD4XA4HI5GppZuBR9M/V8GfBd1K9iNuhTA\nwm0LvwfdtjCKtwJfTX3+GnAvamkAcJuIPAtgjLlkjFkTcE1IosoJh8PhcDgcDofD4XA4HClqphwQ\nkU8DGGP+KLWKD/BpY8zvoVsQ/qgx5q+BCaAPeDfw18aY30nfQuz2hO3AkdTni8CtgUfFAp8vpq61\n2xd+CPhcxV6qABp1q7ZGxsm8fmjmvGjmd6sVToYOR2UopC65+lYZnBzLx8nQUS1c2SqfWm5luA3d\npcCkPgdpB64Bc8Am4DrwD0Ar0Jv6XXDPnYtoMEOANuBC4Fxwx9S1wPnU898KfB+6O0IGjz32GI8+\n+ij9/f16w7Y2du7cyZ49ewDYv38/QNHfd+/ew+ioYWRkPz09wt695d2vnr+vXLkyLNZFwe1tWz80\nc14087vVCidDh6MyFFKXXH2rDE6O5eNk6KgWrmxVhlq6FYxl+Qxq7n9eRH6mwHs9C/wi8FngIeDP\njTFdwBeA240x9wIHgLUicjkVo+ArwBvA24GvB2/28MMPs2vXrqwPs5PgYr9b7dWuXXsy9vUs9X71\n/H1kZASHw+FwOBwOh8PhcDQmtVQO3EBdAZYDP586ZlL/+0mZ/htj1gAfRhUGfyAiV0P3QUReMMbM\npgIcviAizxtj/hid+H8B+F10y0TrhvAZ1NrgOvCYMaZLRGar8I4ZuK07ao+Tef3QzHnRzO9WK5aC\nDC9fTzAyeYlrN/zI87d2rqpxiuqDlpjhpalLWc93rGmhc83yGqaosSmkLi2F+lYLnBzLx8nQUS1c\n2aoMRkTyX1WJBxmzEfhx4CfRnQr+B/AXIjJhjDkAvE9EDhpj/gTYDswCZ0TkJ4t8zhPAQyLiB449\nLiJvT33+J+ADIjI/Mvn6179eGyE4HA6Hw+FwOBwOh2NReOihh0z+q5YuNbEcMMaMAX+FWgp0pJ77\nG8DvGGMSQAvwVWPMAPDDwC3AVeBYhZKQK0ghQE63gjDBYBe+DwcPGiYmDH19Ptu3Z14zNqblb2BA\nSCT0Ny0taY1WIqF/c3P6vbU1fc739Xg8rn9zc3ouHl+4X6jv6589HhWQo5pBOkZGRoqSocWme3YW\nxsfh9dehvR3e8hY9bt/V91U28Xjm8eA9oLH3US1VhtWmmEBXwXIIWraD3+19bJ7a/7b82/tMToIx\nsH27Hjuc2stExGCMMDSkZSCKYuUY9lED/S4ibN0Kx47pdamQJPP1N1jf7H2C7xuWlz1v67C9tqUl\nXa6tLDxPy3tUWoNUq6yPjIywatVdqecJxsC2bZnp8zw4ntoHZnAwsw0LpzncLgXfN3iuEvW4XoIR\n1aI+W5mFy1xQnmHZg+ZVIqFlr6Vl4f08T685elSPb9kCK1emy6y9x/i4fh4czGyrs/l8Fhs0r17b\nxEZiZGSE6eldrF0LGzfCzTcvdooaj5GREQ4evJveXt/JsAxGRkY4eXIXa9ZAdzds2KDtT3Dc68hN\npdrEXP2DPW/7dNu2j44apqaE7m7o60u3/1u26Hnbp9jP9t72L5HaKy7cTwWP1QLnBp2fWrkVTKI7\nEABMAXcDM6irwTpUaTCDbl94XEROG2OWoQEJK0FkkEJLMQEJfR/+5//U7+9/v34fGXma06cNfX33\n4fvwt3+7n5kZwzvfuYd4XPjud/fz1a/ClSv7WLPGp739aTZuhDvv3MPBg/DFL+5nairG1q33cfPN\nPhMT32TZMqGnZw+vvBLnypVvsGULJBL3s2IFXLr0NJ2d8GM/tgfPgyef3M/UFGzevJfeXuHkyaeZ\nnDTceed9DA7Cd76j6e3ouB/fh5MnnyYWo+wAibt36/dnnik8IKFtCC5f1oZiagrOntXJ4MhIjM2b\nk6xeDd/5Drz5psfmzT6rV8ORIx4DAz5bt8K5c4bbbhN27NAG5dAhOHbMw/OEvr708VzPdx1RmrBM\ngt8TCZ2YG2My4maEO5PRUYPvC+PjhlOnDF1dPr4Phw55XLsmrF0rzM15rFjhs3kzrF4NJ04YNm6E\nS5eE6WlYtQpmZgyxmP5+yxbo7dUJysmT+hn0GSKwY0dlgs34PogIxpiAkkOYmoLjxw3Llul7f+1r\nHsb47NwJa9caTp/WTnJwUO8xNqbKrXPnDGvXQleXMDdnSCYN8bhw6ZIwM6Pv3tsLhw4ZrlyBu+8W\n7rgDJie14x0Z8Vi50vDBDybnO9rgpMv3wRiZz5NswceCeVo6wsSEIRYTxsdV2XnypKG93aetTfNw\n3To4c0Z48knD9esxHnggQTJpFTlw9qywejV0dsKZMwZjDHv3+hw5AuPjHv39wvCwMDam7YDnQVcX\nDA9nH7jkqt9WsROlQKrn+p9NoRtUroXlMDoK4+OGqSltKwcGBN/XwdrYmGF6Gvr6hMFBLcu+LyST\n8MwzcPKkx5Yt8L736U3n5mB6Wu/X0QGvviocOWK4ds3Q0yP8xE8I3/624dQpWLZMOHo0hjE+XV3C\niROqPBsc1HRZi8SgAlDrSHFB8xyVYWYGnn7asGqV8PDDmk9BpZAjP729Pp/7XJy2tgTve59TEJTK\nm2/CF76wjNlZw7veNYfnadsxNKQKyOBk0lE+2fqVgwd1zNHbK/T3q6I/2NccPw5HjsDcnI7T7rpL\nAGFszDAyInR16ZjtwAGPTZtg5Uqf06e1bVm1Cg4ditHe7tPdreOkLVu03wHo6jLE4zA4KAsUyS7f\n64OaKAdEZC+AMWYn8AQ6OX8NdS34e+BvgPtS3/9b6me7SG9XWCxhc5EDxpi3AS+TClIYPFlMQELV\nfO0DYG4uyfHjhl277mdgQOYHoh0de3jzzRjT00nuvBMmJ/dy/rzH8uVJzp3zgD2cOGH4/OeXsXHj\nDW69dQ++73HjBrz2mkcsdj99fT5nzsCNG8Lq1Xu5fl04d85jzZokN27sZW5OeOMNQcRw7dpeNm3y\nAZ9kEjo79zA97fGNb8ALL6gSYscOHUhOToLI/XR26gTI8zLfz/d10h+soFEBCIODuN279/Dii9Ga\nuOBgNpHQxiaRgC9+UY+tXw8vvRRnxQqf9vYkc3MwOhrHGJ/OTp8jR+KsW5ektdXnlVfiXLyYBAyn\nT3t4XpLhYRDRSeXJkx7nzycZGJAM64xgWqJWtOp5wlBtwpN/u0pvjJlvuK1VjD0noh3A0aMmNRER\nfF8H8mfOwIULuirw4oseJ096bNiQ4Phxw5o1WqZffnkZ7e1Jrl6FtjahtRVGR5fR05MgkYCrVw1g\nWLFC+MIX9FxLS5Jz53xuTW1aalfhgxYkpWAnLCKwdavMWygkk4ZkUt9lbk4nxNeuGa5ciXPtWpL2\ndmFiwmPdOp9XX1WFwOyscOWKVWolWbECkknh1CldIQF44404LS0+58/7vPLKslR99nnuObjtNr3m\n4kWPy5e1c56e1onyjh121VblLKKWFVGrxpWK1mvLg1XIHDpkSCZ9Ll6MISJcvixMTi7j2rUkV64k\nOX06RjwuPPGEx8qVwm23CZOThtHROJ4nrFuXxPNg3TrN81df9bhyxRCP+4ioAujUKcO5cx6+nySR\nMCnrEUmVNZl/1+Hh9MQ/XH9FhPFxDxF/wXW1jmKcr20JrvwfOUKGwidtlQa9vUIyaZiaMsRiPr5v\n2LRJFS8vvmg4dw5OnRK+8x1DPK7KtZkZj5YWnxMnVOnU2SkcP264eFGVOefOebS1JfjWt+DZZ+Nc\nv+7T3u6zbJlHIuFz5YqWMRE4f97jyJEkIvDaa3E8z6erK8nZs4bXX49x5UqS1lZVLIC2F8mkYXSU\nlOInU6nlqC2bNsFLL8W4eNHw+OM3eOMN2LdP+19HYaxZo+X37Nk4//iPugTqFATFc/So4epVQ3d3\ngpdfNkxMLKO/f46uLlVYdXfr37Zt+t22+dbaDJbmWC0f2ZQA4T7Pjp0mJrTf6OmBz37W4+JFQ09P\nkrNnIZn0uHhRiMWEyckY09PC1atJ2tvh/Hk4cqSFs2dv0N0tnD0bo7X1BhcuGMbHl3HhQpJt2yQ1\nHod4PMm5c6qcfuklj46OJJs2CfG49gkTE+nN6Dwv+4JHubJwFEcttzIcQYMEbgAGgeOktyf8EVRh\n8CEReSJ1LAn8ahH3jwNfBu4AvmyM+U3gJ0TkEeCjqOIhGKSwJDyP1EQ8XfCMSQ9eBwa0sp065TMz\nY3juOeHSJR2ELVsGly97JJNJLlzwSCRkXit3000+b74JFy7oKiPA5s2QTCa5ds2wfDmsXJnk6lUd\nHB46pBWvu1tX9kQ8br9dmJ4WzpyBjRt9QAfaExOGHTuEwUG978SEB0hGBcq3slNKZdOJvglMMDw8\nT99zZiZOS4uwcWMSgLNn42zbNsexY4br1w2dnWoCefiwx7lzws6dSS5eVG3lunV+SsmSnij4fpID\nBwwbN+pANB5fqAAIr7rZ40tt25OgC8DBg4apKZ18WFlYZYAqwoTubmHbNv3NiRMep08bIInvw/S0\nMDJiaG+Hjg6dMK5YIWzYAKtWCe3tSeJxaG0VVq2CU6c85uZ05bG9Xbh6NUYymaSrK0Frq3D+vEci\nYWhtFc6c8bh2DVpbk3iecPGiTj5iMZ0APv64x5Ytfk5LkUIxRsvN1JSht9cnFrODFF3JTyQMW7cm\nOXgwzoULujJ96lScdevmOHpUJ1zJpCq1BgeTnDzpcfw4dHb6bNwodHbCt7+tk78VK3T1tq1NlQyt\nrXD+fIyWlgRveQvMzCS4dg0OHFDlwLlz0NEBIyNal3p6BDAMDKgyY3zco7dXyrakCNdxq3DQ+gU3\nbsCpU9DRkeT6dYPnGbq7b9DSIpw/H6OjI8GKFTA97fHmmzFWrUoiIrS2JhExnDoVZ3g4QSwGTz2l\nK90ihtde0xXujRuFW24RZmbUdPell4QLF2LEYsmUxZMqAVX3q/kOWoZBVyE8T1egRPz5di5Yr2sV\nY8eSq22x9W9yktTEWVf57Tn7d+qU4cIFtb4xxrbXwje/6RGPM2/F4fswNbWMjo45Ll1SZVVbm97v\n9dc9Xn5ZmJ3VAffVqx69vQm6ugwnT0JLi8+bb8ZIJIRNm5JMT4PvG/r7hRs3hNZWw+nThu5uobf3\nBufOGVauhEuXIB4XTp/2uHJFuP12VViDll1bTu0Af3AQPC97OdXy5oJJVRp1X0qSTMaZmoJLlwzH\njgkf+IC68TkrgvwsXw7t7UmOHGnB9+O88kqC9eu1bXYUzrlzHjt2zNHaahgdjbFypSoSDx40XLsW\no6PjBjduGDZu9BgeTnLpkpbRXbvUAsZOICFTcbCU2ouosWwh41jfh9dfTytqZ2djnDiRZGbGsHq1\nz9mzcPy4x4oVwrJlwo0bEItpuT9xwvDyy6rIicd9ZmdjxGIJurtvsH69WoSsW5egpUXo64P29huI\n6Pjh1VcNZ84Y1q1Lcvq0x9SU8NBDms7ubj+V3soojZfimL4a1HK3grcEPh+OOG8CigFE5Plibi4i\nCeAdocPfSZ2bRLc8LAtbIe0Azg5kfB/eeEP4+tfjbN6c5Ad/UOMLvPwyXLjg8d73JpibMzz/vKGv\n7wa33AJbtyaZm0uydq2a5X73u2Y+9oDvq0b/5EnVsG7ZoqbMzzzj8dpr2ni2tOhA79IlVQRcueIh\noibY09MenZ2qDOjo0IG576vG1hhh3z6tjLpiqhM/uwoctbITVdmyDeLsgDaRgP37dYB4/ryaT69e\nrSbl167BtWv6bJ34JFm1KoHnwerVQmvrDTo74eJFWL8+QSymk6lt25J0dcHb3gbHj/vYZMbjqsG3\neXHsmE6eotJvJxBLrcEIuwCICCLq1pFMBjW3qijYuhX271dz5c5OH88Thod1YqplJF0WnnsuxvS0\nz+SkMDOj1586pefjcXX5aGvzuXYNNmwQ2toStLVpx/Pd78ZYtcpwzz1qit7RIVy6lEyZFwobN0Jn\nZ1qRtXmz/p+agiNHrBKo9PwM1uGxMX33gQFIJGTen+6OO3w6O21sgCQgXLpk2LZNteWzs7B2LczO\n+qxbJ6xdC1NThmvX9PdtbRpT4/LlGKtWJdi0Set3X1+SgQH1tzx5MkFXFxw7ZpieVuVIe7vw0ksx\njhyJcfr0HOvWqTz0/Q2eFz3RLWWClcucW+uXMDAgfP7zsGqVob/fZ/t2dbk4exYgyeuvw+ys4S1v\n8ZmaElasUDeC5cuho0PLRyIBly8bZmcNly+rxcbp0x6JhK5Cj48Lu3YJzz1nOH48Tn//jfk0eZ4O\nDoPKLbvyICIpqyiPBx9UiwFbpoOWMHbias/V0wpDV5cqNiCzvQK1DhgbMwwO+szOatmfnVVrgr4+\noaMDEgnDtm1zdHfDyy8LFy54GAP33ANTU8Jrr6k7iCp61ORzYEDdW+66y2ftWp9XXzXMzKjrx+rV\nWs6vXFE3LqtA1BU9tZxZtgzuvdfnu9/1aGnxuPdetfIC7UuSSeHoUf1s4xLkox7yotm47Tbti6en\nk1y+HOPGDdi4McmXvqQmwP/yXzoFQaF0diZYu1Y4cEBXXn/yJ4UCvSod6DgOVNHf35+ko0Nd+C5f\njrFyZZJYDK5c8Th/XtuO8fE4ngdPPOGzfLlhx45kSlEMb75paG8Xdu5Us3VIKwqauTyH3Qvtwl+Y\n8FhgdhYOHNB+9/bbVfF79qxhaCjJxo16zhjDqlU+K1dCPG5YvTrBunVw4gTMzek4buPGJK2t2sev\nXw9vvKEWa7t3+6xdqwsJ27dr3zwzA5s2CV1dms8HD6qbIujn6Wmhp2fhYp7rBxaXWioHUmuPfAEN\nOOgD19BghAIcqmFaCiK8sh5cBberUna1LhbTCcWxYzGOHk3OT4xAJxtHj8LQkMybTlmmptQfd9Wq\nGMmkDwiTk3EmJ5OpuAPw7nfrBCKZ9Onv9+ntVXNlXZlR/+W+Pk3sl78MV64IJ07o/R580MeYdKUz\nJm2ZYM1vk0k1H/I8k3dlJ0jUStjrr5uUaanPiRP6/rOz6m9+9SpcvCjcfDOcP6+ro55n6OgQ2tpU\ns7h+vU4+W1rsiobPlSuGgQGdiFj/bc/LHORb7bFOfFXWUe8QVgw020pVPtOyzE5E/c1UhqogCGrk\nLWfOpLX1N98s8yu2AAMD8D3fk0hZgxg2bBC6uvReIma+bI6P6yR540bh0CGPjg64dk1XlZcvF9as\n0Unirl06ET9wQMt4PK4TYd83bN4sbN+uExTtxLR+lBvUJqjsSiTg8cc9Dh82dHQkWbNG30UDCBru\nvFPYskVXbq9ehb/+aw/P08nvLbeoW8Hrr3t0dqrLS3e3PuPYMY8dO9QlZu1aQ0uLMDoaY/Vqn8uX\n1Uri+nXhzTdhzRrNh7vvViXYsWM66b35Zl0t18GPKseGhmB42I8s17nKRCkyWrkS3vMeSCT8VBrU\n2qKrS11LFOGee+D4cfVDP3dOXS9uukkVPydOqFtBW5vP9LS6X/T3J7l8WcvP9HScdet0ZToWE+69\n164260r2+vVq2TEwQMpUXejt1RdU80QlHteYFEFrqCi5RJlcliKrbL/L1bZ4HimLLlVMWasA6z5i\njCptPU+47TZ4+WVt2yYm4NSpGLt3JwA13xwZgfZ29SdXZRMcOSJMT8d47bUkP/ADQk+PpCb+wtat\nqoBbvVro6NDrNm1K0tGhypuODp+bb1Yl3Jkz6nJj+zERQ18frFrlpyyG4N3v9ueDFlqGhtSVbWJC\nLVuyKbMc1WfdOi0Ty5f7rFljuHABzp71uHZN8/DyZe1vm6EPrBaxmMpHxE9NPlUBfPw4FbFeWyoM\nD2ubvG2bz7p1EI/rgkJXV4KODm0HX301yQsvqBXppk0JLl82vPmmtlHxOLzwgkc87nPjBkxOxpmd\nTfL44+r+1NHhs2YN/PAPy4LAt2ELg3pSDpdD1Dg2PH9RF1JVpHd2akyeoSGfp582JBIemzb57Ngh\nxGJJzpzxWLNG2L1bF+ZmZmwMpQTPPGO4dMnj1luTJJPpGEHnzxuWLUu7UOq8SMdPvb3aj6vVp5+y\nftSYB6dPe/T0JOfTXe6qf66Fy/AxR3ZqphwQkWMAxpg/QHcPGESDEV5ALQkStUpLIWRbSfM8nRDB\nwoJ7111J5uZ08mpX4j1P/TwHByXlv6mVxQZui8e1ot5yi1aOnTvhK19JcvGix7ZtSXp60lrpWIz5\nSqb+v6QmdzpgTCQ0ENqddwqTkwk2bDAZUaTDFUbNbyWvuWehE2g1a9XBpIgH6MR+3Tro6fGZntZG\n6PJln3e+M71yHYtBMqkrGOfP6/e1a9V8/OabZT5dVp7WZcCmJypYWTEKgGZpLHLFVAj6pafLc1oR\ncPiwTvSCliEPPCAkEsnUyl+mnILuB3v2pH2mNbK9wfM0T6emdCDgeXDbbWkzMhvd/to1XUXo7dUJ\n7+Sk4aWX1HVm/XrDjRs6qbxxw9DTk/Y73LhRFQ02AFolgphllhvDhg2qmLhwIcaDDyYYHFQZHT2q\nvvRnzhje8Q6fc+d0YrpqldDWpkqNjg515bE+kyD09Rm2bRNaWiTlsqHmlJs3C8mkj+fpwEatJQwz\nM8KWLcI99yQZGtI6btsS0IliPkWe72vATlBNfrnm3Da6va1zQXnv2ZOWYSwG4LFunbB8uU4Q16+H\nd7xDf3jggDA769PRoYPsy5eFjg6fbduETZtgxw4fz1MLAC1XaauWWEwnzVZJ29WlSiNNSzr2S5SF\nU652I5c5Yq6BRa7f5Wtb0mVO4yskEsKTT6oV2N69fmo3CMOttwo336wxWb7yFTAmQXe3DrQPHhSe\nftojmYRnnzVs3Srcd59w/bpw6JDP5cuGycm0QlXbe5WDMZpvY2PqhtbXpxYCQ0Oazzt2pGPTHD6s\ngzlj1N1m7169l1W+tLZKxLtpGzE01DztbCOycqUGBF2+XMvYqVMeyaTm5fXrGpvIlqdCrDuWIsuX\nq6XmpUtxNm1KzrdFU1O6au0i7hdGT49aINqYQYOD/rzcbFvytrfJfKT8t71Ny+zERGJeQZNMCps3\n64LDt7/ts3y5cP68KlIvXYK5OY+jR5OMjxump1Xxvnq1cOutcOutaaWBtaLcti3dFjdCHmaOc6Jd\n1oILQlaRPjFhWLdO+9ijR3W8ZwM9Dwyk+7m1a/3UGF7nLR0danl04oQqETdvVguB1as9li/3uf12\nwfd1LOd5zC9cDA76jI0BaN7Y8Zrn6ff+fp++vrT1Z5S7RCnkkocLdFsYNe8GROTT9rMxpm6qYT6t\nUv5BpgaIsoNDrRDpe8fjpLY51EYxmUw/8+TJtPbtwAH167zrriSTkx4agEx/MzKiOzJu2ZJkaspw\n9qxhy5bkfNAoXWHVyVRHhzYKQSuF8LvZ1bVc753tt9mu6e3VweXEhJoU9/Wp2fr582qKvG6dMDMT\nY9myJB0dGsnd93WXgYEB4dlntWHZtEndKqyVgw101d+fGe8hXOmzTXAq1eA3mvbRrpwaIymlVnTa\no4KE2S00g5YCtjO1clBXFp3833ST7aQ0D3QQqh3B9esek5N+KpCeYXhYzdbuuCN9795enYicPu2x\nbp0OVmMxGwE9c3U86NpTSocS1qrbz/G4TmAfeEAP7t8vdHYm5juvqSmVpe9rEMIDB3TiNjur0d/v\nv18jxh87phN5a0E0PGzjA+ixlhb7DsKxYx7t7cIDD6TdfSye57F8udDamunuU2j5832NSQC6SpDr\nd8XcM9zRhhUQ27fD1q0+Tz+t13d2ynxZ8n2dZG7YoG3eK69bFdMYAAAgAElEQVTA2bOxlPuUTmJt\nuxRUQukgJ70SrbtECFNT2k5kU35EraTY/7mUBdnet5J1P23ZoPXT3tsYFihB5+bU7WTtWmHNGpMq\nr8LOncLwcJIjR9SlzF5/5QosW+axdaveY2ZGB3qdnXr/ZFLv0dKiFnDBVf6Wlkz3KzVh1V0L7ID7\nppsWWqwEKVTh5Cidk5euM3NpLvJcx5oWOtcsB9Jl6coVdWGcm1P3xP5+XembmVHzX7UecwqCKFpb\nYW4uzurVGoenrU0VtNPT8LnPGe68szLxb5YCU1MwPR1j82ZheNif39rXbn/X2grveIe2HXYif/Zs\nerKbbod10g/an/q+pCxWdaJqAyS3tmo/cf68be80HSI6YR4ftxZRC2P3hBdX6oFS06EWwsL0tJkP\nfmst7HSbQkN3t093N5w86afG3SpbVV7HuPXWJG99a3osZJXbdsehiQmPvr503+B5OmYcHTXzfYI9\nZ7d+h7SMXZ9RH9S8CzDG3AX8CbATWBY69wDQJSJ/a4xZDRDeWaBaFOJTHxwoWX99uyJi/4Kr9Loi\nC0HTd+v7H48LDzygPlU6IFNT2akpTYea6Waugm3alG4sgxo3myZjdHQbTJMl26S28hVQFRRWSaCm\nqOoP3NNDyhxYEyOijZVaC2jjYs2wBwfVhB3SPsPd3f68hjcb1WxQ6j3QSb6G1a7gR5lQF2JZYTtJ\n0Mlb2mw7fV3w9/G4TvC7uoREQs3gT57UfE/f28Y/MPMrmp6nHXtQxuHJqFWIBf3JC+28o7TqwecF\nTQ8feCBd5xIJ3c7KGDWz7ulJcuCAx6lT6Ym+jetht/ELyjCsgPE8VYypAkw16cH80TSlO9lsyslc\nWIVdIdeWivX7h7Q7j046rcJHLRyC1id2sLFtm05wN2zw6eqCZcvS5cPex65kh1eirXIzFrPbUGZ/\nz6BbWNrlqzgLo2yUO6DRAaqu0uzYITz4oFbM4AQtkYCnnjLMzHjccYfP0JAqoA4fTm8BdtNNsH27\nPz+Y3rBB62d3t7rCaB6Z+TIVVGCHZRvVT9g+zRjriqQsBausemXm0hwf/mJUCCf4g3cPzSsHQJVB\nV69Cf79w7doNVq1iPobP2JguTLz4osH30+5LjjQrVsCaNUkSCY19c889wre/rbvTtLdnBshzZMda\n0G7e7Ge4BR4+DCdP6hgwPBnP1cbaib49fsst+t9a7EGSnh74znd04cHieWnr2clJkzEusVjLOxv0\nN2xFXK95HTVesG23vqek2vH09bavTa/uq7WiiO48tWGDz7596tZx6pT229u2pRczbB8S7K8h7a4Z\nHCdCOo9rMa52SofiWYzm/++BLmAMGEJjDWxHVX1/CvQCf4vuF/hTwP+2CGkEchei9HZZOqALNhrB\nDiI9IZDA5EqPt7Solt6aVXke7Nvnc+iQ4dlnVWFg/W09D97+9vSgMahxsxUrODAPUqtJrX03NRXT\nz+o/q/ua2kH5jh2S2hJPo5p6nq5IaUNhUqarfsYq5LZtfsqkO/MdXKXPJNegPmzNUkqZsB2Kvd5a\nJdhyGn52upzq7266ST/bCYyd1AddHOxEM6jEsBO/+bulgmzawDaV6KiDK8fhAJZB3/Vt22Q+CGY8\nnvb9V3nYrR4lQ8kQVU7tVpHd3db8Lr1Kbst+ePIa9TkXnle4dVChhN9F82dhoD9tB/W4DZzk++lt\n7awFhbaBMq+AGRtLu7iEZRb+bttCe197TTYFwcSEltd8Vga53jebTEohOEANvlMUIhoLYPt2lZUI\n832Q3S4Q0u+/e7eNZWA4cUJSA7+0NVk22UL0JMeWR5V39p1tHPWKBm/bvl23NJ6d1TLU0qLue/G4\nrqJOTnp4Xvm7nzQbLS26wHHxolpbtLRonWxvh507Bd83jI25+pCP7dt1rArpNsX3SW1nm469Fd7W\nrtC2N3h9UHHwwAPg+35Gv2yt2axLWq5xhI6ZbABcG5Q38/pEIn3fbNaJtSI8Xsgcj6UVMqDHBwdl\nfvES0pa4VjFtjJdy9wtaN6rc7DQn2yKNXTSppBtosbg6WRyLoRzoBd4PfAt4BXgX8HXgsIi8yxhz\nPnXdN4D/t1aJKmaCGTWgy6YBCxb+8IQD0hYI1sRzeFhS/rrMV7hgMJVgGkAbI7vyWmj6q0V4ovj6\n67oS4XnwlrcsNGnWAIiSMQnUFcbwCuvC1etSJkrl0qiKCCu/fCbUhXRgQWVX8Fi2YGNBhVHw3okE\nC8zM7PVREeWtaXxwJd3z8m+NFkU2rbrt8Kw/dtAULu2jLRkWGNZMPp0e/Y1VYgVlGi7DwePVXKWr\ndFmNMs8Pu3lEtXVpdxN1K4jFdIUhKPeggiSc9lKtn4LxEdK7sRQ3KKlmfS/EvSseJ8OiIK1kXvge\n4UGsDf4UjhsSJceoPiwqv6NoNJerpYTWUc2g48fhlVfiLFvmp7Y8tu0g8647Lg8Xkkioq+TsrAck\niMdh7151x7Fxk2zbXm9m6PVGuD8Eu2VuemBSCf/zoPyz9bG58slOqIeGNDGHo410AC0fTzyhN9q3\nz5+fZEdZJy5WWxnuT+24ats2fz5Omi5kanqta6l1s7QytGMb6/bX2+tnjOeiyDZObNRxdbOzGMoB\nH/gIcFPq+YfRUOnrQ9ddBVbUKlHFFspC/fXDDaA9FmwgRNKBnqwPkK0sQc1e+Dl2QqMRQzMDyoXT\nsHiVz7BpUzIQcdwqRFR7bBub4ORs375M7W7QLxfI+p61oJEbr/DEIKwsyGdJEFWOcpWrYGf54IN+\nRl4fPkyqU1k4gQ7HlYC0oixbOool3yQpbG0RdcwStYJqB4hRg4KDB9MT4KC1j6339TyojConNu2w\nUPEUfherAPQ85uONBPE8FpglZntu+HfZIhQHf2fjvtSbfAtJT3hwq24AMu9iYd/fxnQ4dcrjLW9J\nzq+ejY4Wv7IZpejJpmysZ5crR9oV6/x5APWb7+zUc8G8q7SlUbOg8jC0tqYtOq3VhfZparJ98KD6\nbgctwBxpwqvGwe9pWUKxCtxqEezD0lYPpffT2drKWigMwmOtbC6H09Oq8LKWBuGtzoN9v3VFOHo0\n+pmFjBtdHak/Fsur7BbgSeBO4HnUhWBd6JrvAUZrnK6iKGYSHtaoWc2yNeWx8QKClSWo2csVTMzz\nMidPhaS1GuiqqTbs27drpzk8nIxcGc3WsNpro1ZZ9btxDUmFKEWOxa7aRvnxgWqR+/r8DJ+1gQGZ\n19QH7x1eNS017bmIqruFHMssm/bdoldzsz03eO9iJm3BdNUSicjUcDpyxYCwbkSggw5rlVGu0qeQ\n3zRL25GtbFp6ezUmhg0uZ8tLvtW4bOVbLX0074qJ7+GoP9atM6xfn6S/H+wWtkFcvkbT0gJ3363x\ncFpb08etvKIskmyAPRe/IU1YIRwkU5ZQLwoCS652LzxOyR3APJN8ytVK9PdRzwgqAoPWACKZYzc7\nDrdWzZZgX1BM7BnXxtQ/i9FkCfCPqEvBm8APpo4bY8x/AFqMMf8G+BDwC8Xc2BjzMeAuYEREfiVw\n/F7gv6A94adF5E/Kfoss5Cr04RWWoLmRXUGEzPO5gomFFQ71sCKmgbVUmRGPk7FbAmRvRKKOB2U1\nMKCyisWi/dsd5VGJ1fioe0aZhy8stzpgePJJDZgWtDKw1GJwVUinFj6WaVGQqV33PFngshC0DKr0\nykOtCLt9BAmnLUxYXpZ8/uulls9qlOt6IZeyOKxgg2ilVb77WgW23cmgu1vmg4C62C+NhY1RsWWL\nn4q/kY4q7vKuMLJNWjP7tHSw3iefVIFG9WlLlaCLYHgSbf83anmMcv2N+lzM++XrU8shnK7McpxW\ndhmj7s5q0bfQ4iN8L0fjsxjN1XHgd0XkRQBjzBZgF/BfgU2oRUE/8F4R+W6hNzXG7AJWicheY8wn\njTF3i8jzqdMfBn4UmASeRXdLqCtsxQyb5EaZ62ZrdBYba2JkP+e6Lkw+zWh6IpXdv91RHuWWpejy\nmX0wZbFBb2wgwkZj4WRKO9D8ZVlpNF/tctO5cBCSm3Ll0yhyrSRR7xw1sSlEtppfaUsf60tbyDMd\n9YMxaQvDqKjihdBobVUl8TwNSGg/R50PUgmf+WYkbOWaTZZR8aWahaiV9GpPsgt5Rrp+Z2aS7Tvs\nuMaV7eZnMZQDHwX+lzHmn1DLgU7g+4A/F5H/o4z7vhX4aurz14B7UZcFgLNAe+r/lTKeUTbhCppN\naxruhAsJFLXYhJUZhZJtJTQoC7uCVey9HdUjHI23kO1Asym4WloWug40Erk63lznsq0K5JusLVZd\nyJenxQ1A0scKlY+r+6WRzzIrl2yj+qxCB4f11kctVaw1iQ2s5vvpbVsLZanXxULHN0E57duXDqBr\nXQyWmtzC7Nvn5530L8WyVmj7W41ngJbPbFtDh9v8RrbucBRGzYbhxpij6JJFKxp88OdCl/yyMeZy\n6NgcMA58WURm8jyiHTiS+nwRuDVw7o+ALwMJ4LeKTnyFKWTlv5Btueqx8axGOuyODiILo9s7FodC\nTd3CK+S5ymwjKgWCFGstEyYqgGE511WLfHlayqDP1efqU46Mw78ttJzWYx+1VLHyDwZEdQHziqNY\nWdk+7dAhNafv7XVbRB454mRRLNWWUXDnAetqlK/Nd/nW3NRyON6DTt5bgD8FHgM+B7wX3bKwD3gQ\nVQb0owEJP4/GJPikMeZhEflSjvtfBNamPrcBFwLnPopaFpxCrRb+RkRm7cnHHnuMRx99lH6N0ENb\nWxs7d+5kz549AOzfvx+gpt8nJw27dmWe371bvz/zzH58Hzo67p8/73m1TV/4+8qVKykVp4VsfFwe\nlkYx5vVLEVeuqoeTraMYXHkpjLCcnAl28biyVns8L3qnIMfSxERFnq74Q4z5CPDfgR8HPo6a9x8A\n7kMn7g8DAyKyIvCbHwI+ICI/aoz5aeBXReQtOZ5xJ/CLIvIhY8wfo24Kz6fOPQW8W0QuG2MeB35I\nRC7Z337961+XXbt2VfitoynUzLKQ6+rJZHNkZIRKyTDKpSJIPbxvNaikDGtBKeUvkdD/1bQSaDQ5\nWorZH7vadb8YGUalJVf66qndqiaNWg4LIVseZmu7S83rZpZhJXhp6hIf/mL05ut/8O4h7uheMy/D\n8M4qbvWvcMoth+G2fam0gWFGRka4/XYti8FdqRyFU602MV+ZrHTbvpiMjIzw0EMPNWaQqxpRq2zt\nEZEzIvIJ4CrwLTT44DJ0V4JhILRJBl8A3pX6/FfAYK4HiMgLwGxKEZAQkeeNMZ9Inf594OvGmGeA\nx4OKgVpiTXdGR01BW0rlq3TN2LlHyci+ZzO+byNTbH5Yc3gb1d+Rxpb7YAT/XNRTXQinJV87V09p\nd5RGVB7marsdi0swb8BNzGqNje9gFQOFjgObkbExkzWoqWPxyNdWB88v9TK8FKiJW4GI/FLg66eA\n7wCn0Un7HUASCO9M8CHAqsQ3UkAgweD2hanvj6T+fwFVNjgcDofD4XA4HA6Hw+EIUdMQYMaYW4F/\nhcYZOAj8IXASuAn4uDFmAt1usAdVGPxw6qfbgX9by7RWg2x+VI1snlNpCvU1czJrTMJRcB1KOPZA\nPW/hVMi2o85fdOnheW5HmXrF1km3yrf4NFJbXw1cG9H4uLlM81PL3QpGgBWoK8P9wATpDZefRHcR\n+HGgG5gGnhWROQAReQp4qlZprSbZTDHBRXS2FOJv7WTWWBS6u8FSJmhyCvVZtovZ/s6xtFjsXTQc\n+XH5Ux80QltfLVwZbA7cXKa5qaXlwJ2Bz8eiLkgpAZoep12rLE6e9YnLl+KwQasaHZfvzYfL0+ZB\nRJbcanUjsJTqmCuDzUktAtw7akMtlQMfTv3/XeA3ARuRRNB4AvekrAs2kA6UKCLSX8M0Vp0o7Zoz\nwS2OsFme01bWH66cF0dQXvXsepEvH93qQfNRjLWIq+P1i3X70MCnrn4uNkt1HOPKYPMiEpzWORqZ\nmikHROT/BjDGfEFEXg+eM8b8JdAH/AfgL4CfRJUJf1er9C0mrnEsnmzbHDrqF1fOC6NeFQOWek6b\nY3FxZaO+qfe2ZamxFMcxrgw2Ly5fm4eaBiQEEJHXjTEdwFvRXQgM8EPAR0Tkc8YYP/X/OeCf0aCF\nTUM5qyulmp01srlaIfulu9WqylBKOcn2G7eKWBxR8grKtlHqcDH5Xq13ahRZ1ROJhP6PR4wIXF1u\nHkoJTOjqU/UJBvOE5g9SGLaOc2Vs8bDujOUqbVw/0VzUXDlgjPk14KOoUiC42enHgf8KXDLGtKNB\nCYdrnb5qk60RzNc4lmqu2yhmvlENVK60N8p7NQqlyDPfb0qZHNZykFCpZ1XqPtnK9+CgNFQQp2yK\nvGD9rlb9bbZ2oRb1IZGAJ55QmT34oGRVEDiah1ztSVgpWen65CaCmdi2cWzMkEiA5wmeZ5qi/Yoi\n2K9ZDh7UYzt2NOc71yu+r7KfmDD09fls3565GGEpNE9c3jUPNVcOAL8H/CPwEeBZ4A7ga+jWhgD7\ngT9G4xAcjLpBFMaYjwF3ASMi8iuB462p+20FXhGRXy7/FUojW0fbbAPaYvF9OHQIxsc9envFdRBL\ngKgyX8t6UKlnLfW6WwhRAxBHfmpVtnwfTp3yUp+T1XmIoyGo9q4yrr3MxMpDREgmYXLS4HnQ29vc\nPtsiGnfA82BgQJiYcGWiXgiWSREzbxHg8mVpsRjKgeXAj6AxBgzgA+8CvpU6/8vAfwTagJ8q5IbG\nmF3AKhHZa4z5pDHmbhF5PnX6EeCvROTxCr5DzSnVZKeRTX1ypb2R36seKUWeLg+qR1i2zSbnar1T\nM8qq2sTjsGtXcv6zo7kppo64+lQbjFFLgVhMJ2RDQ80rb+vWMjam3z0P+vr8+c+O2uF5aq1hJ/9R\nVgOOpcliDAUuA68CQ6hyYAx4MfUZEZkBPljkPd8KfDX1+WvAvYBVDuwDuo0x/xb4QxH557JSXwRh\n87lsHW2hHXCpDWe9N7ieB9u3w9CQv8DvKZfJY72/V6NRijzLKZNRZb5WkforNeitZgyRXPVgMSnW\nLDhqAFLM74ulnmRVDtWcmIXb0R070t8dzU2u+htV5pzyrnqE5aFWVbldPZqB6PdunvdrJKLG3Gmr\noeLrarOV1aVKTZQDxpi/CHz1gR3AN4BW4G2oO8B1Y8zPhX8rIn9WwCPagSOpzxeBWwPnBoGPAf8G\n+IYx5osiUnXbyWzmc4VMBJYihUwInUlic5HNx74a5qz5nl/r+zRqWS413U6ZVxrVkFlUHrq8WRoU\nUn9roZh1pMmnBG7UviIbxYyLHYuDc7N01MpyYAxVQRlgGeo2YCfo3wF+FOhEtzAk9XkQ+CZQiHLg\nIrA29bkNuBA696SI3DDGHAY6gKngjx977DEeffRR+vv79QZtbezcuZM9e/YAsH//foCivvs+dHTc\nP//d84r7faW/+75+97zq3H/lypVUGqeBrC9cfihODouPy4PycKajjkZgKdXzpfSu+XCyqBxOlo5S\nqIlyQER+y342xnwA+DsReTFw7HngT0TkwcCxnwNuKfARzwK/CHwWeAj488C5Z4A7jDEvoEEJT4V/\n/PDDD7Nr166sN7eT4GK/+76ugO7YUdrvK/V99+49jI4aRkdVm1eN542MjFBJsq1uOZPExaHaGuFG\nydtKyaFR3jdMPaTbrU6UjzHNHfDMEU091N9CWUr1PNe7NlKeFUK+d1lK+V5tai3LZiurS5maxRww\nxqxFdyjwgGeMMZ8AVgE/m/r/bOgnnwHOAP9nvnuLyAvGmFljzFPACyLyvDHmEyLyCPD7qXutBf5U\nRBIVe6k8VKtyNKomsBLpbrR3dqQpxse+USinTDfi+0Ll0t2o7VgzYIzdRbgySgKXl41Ds+VRM5W9\n4JavQZrh3SyluuGV+tulyGJahrk8ag5qGZDwj4GdaHyA08CPAf2oC8C3gSvGGFusVqIuBucLvXlw\n+8LU90dS/08C31tu4heTcvcdrgdtXq50RzX89ZBmR5py8yOY/7UKOlgKhSgwgjER3ApHaQS3Sxoa\nKi5KvmsbSiNYtispP7fS56gGucppcPLTDGXP87RfPHwYxsZMXfeR1cbKwn527UtxhOVV7rit1N86\nGptaKgf+BXAHMArcB5xAdy7YgsYFMEBwVX8S+IUapq8uqVSgtnqt3PnM6Rz1QyXyI7i/cb119IUO\nQuxx57ddHiLC+LiHiLBjR3FloZ7KTSPgBtiORiRfgD47iWwGPE8teuq5j6wFus1hbYMTNzMuuKCj\nFGq1W8FHgFUiMpMyZ/xVVBFwHvgw8P8AP4/uYgBwRURO1yJtjUajrpo1arodlcHmf3B/40bHlenS\n8TwYGlIFgZNdY+PqgWOxaKay14x9ZCVopjyuBU5ejkpQK8uBHiBmjHk7MA38a3TXggvAPcB64JqI\nHKtRehqGqIreqBU+Kt2uIVs61HtQyVLSVm/v0EjE47BjR32WhWaj2vXO5Z+jVtRzH1Iu9d5H1oJm\nGvMuFpWQ11Ivh0udWu1W8EvGmHcBn0odOpv6vzb11w1gjHk6+DNgDhgH/kFE/qkWaa1Hmr1iNvv7\nOTKp5/yu57Q1I07etcPJ2tEsNHtZbvb3y8dSf/96weXD0qVmWS8iW0VkAGgTkd7U30Dq2B+giopv\nAH8JPInGInge3XrwU8aYX69VWh0Oh8PhcDgcDofD4VhK1CrmwBsiclPqa7sxZjx0yWbAiMi/Dfzm\nL4HPiMhbjTF/B/wNui2hw+FwOBwOh8PhcDgcjgpSq5gDvxBwGRAWblHYA3wndOw4YBUKzwEd1Uue\nw+FwOBwOh8PhcDgcS5daxRx42hhj4w3cDfwXdOtCUGXBLwFHjTHDaIyBPuC3AKtQ2AlM1SKtDofD\n4XA4HA6Hw+FwLDVqEnPAGPM7wDZgEPg0MJT6bP/2owqAV4Grqf8x4GdSt7gOvD/PMz5mjHnKGPPx\niHPGGPOiMeaDlXgfh8PhcDgcDofD4XA4molauRX0oRYCliFgE7CctAXBBWBF6vhpEUnai0XkYK6b\nG2N2AatEZK8x5pPGmLtF5PnAJT+IBjaU6DssTXxf/zdiRNJGTnsj4eScHyej6uFku/i4PHBUG1fG\nyqMZ5deM71SPODk7oqiVW8HP2M/GmPegOxKMArcBr6T+PwvcBaxOXWd/+3gBj3gr8NXU568B96I7\nHVjejwY0NCwxslV834fRURXH8LA0VMOQLe2ukVtIOTJp5DJSKfLJz8moeiyWbOu5Hal12lz5dlQb\nW8ZEhKEhiNdqyapJaDb5+b7+jY25dqfa1Lp9r+e+1ZHJYjQjHwV+A/gi8ALwI8B/An4Y+GfUrSDI\nQAH3bAeOpD5fBG61J4wx70S3SEyyOO+bQS0rx1IZ2LnOJJp6zP9G6hwqKb9Geu9SaJb3q8c6Y6lG\n2pol3xzFU095LyKMj3uICDt21Fe9awTyya+e8jpXWmwb5/tgjMwvEjrqH7eQ0lwsxmR5GPhE6s8A\nh0mv6G8Avgn8kojMFHHPi8Da1Oc21EXB8kHgp8gRs+Cxxx7j0Ucfpb+/X2/Q1sbOnTvZs2cPAPv3\n7wco+/vu3XsYHTWMjOynp0fYu7ey9496HsDIyH5mZhY+z55/5pnyn7dy5cps4q04nqeNi8V1JtUh\nKOdKTUSarXMoREbN+N5BqvV+lS5/jkwKyTeXB81JPbVJngdDQzrBdWWsePLJr57yGgpLi+fB4CB4\n3uKnt5mpVPteb2XMUT6LoRw4DTwF/Drwd8DvAX8BJIDvAX4f+CRqUVAozwK/CHwWeAj488C57cDn\n0O0SjTHmaRE5FPzxww8/zK5du7Le3E6Ci/1uNWnh77t27cmY3JZ6/0K+Dw8Lw8P3ZVTWajxvZGSE\nWuJ5aYsB+911JplUouGvpizraTUjikLlV276610O2bDpria1lkk9T4arlTbbjuYaqDuah2C/WS/E\n47BjR33Wu0agGPktdn+Tq+zVc/vbrFRq4SdXH2Kf4/K2cVgM5cBy4O9FZMwY84eoYiCGWg8cBH4a\ntSYoGBF5wRgza4x5CnhBRJ43xnxCRB4RkTsBjDE/DcTCioFqEaVJW4zK0ayVMCjfwcG0fB2Z1JNM\noiw+oL41zZVIV65636ga93C6m6nTr+d3qGTaVKEqHD6sLlmNVP4cpVHP/Wa9pKNRyaXcq6d+15jc\nccFdOWgsrEuvMZJaoMt+rcvbxmExlANX0FgDiMj/MMY8iQYP3AhcBs4C64wx43qJ9BdyUxH5ldD3\nR0LfP1OBtJfNUl1lzEYl3qeeBjiOaML5XG8rV9moVH1r9vJZ6vs1W3vWaHgeWV2xXN40N67fzE89\n91PF1M966nfT7U3lNw9zbVbplCs7Ywye5zaEaxYWQznwceBxY8yfAePoNofLgGPAzwJ70K0Hf30R\n0lYxKunLY+/RqKuM2SjnfappYlvpey4FCgkyBItrRRNOU75n16K+LbYcSiUq3cXWnWZrzxaDctur\nbOXP5U1zUmp7sxT7xWAdqDdKrZ/WWsh+XgyGh6UqSgrXZpVOqbKz+diIYxhHbmqmHDDGrAU+gu4k\n8L+ALuBBdDeCVuBLIvINdGeB361VuqpJJawEghXWkUmlGyLXuZRGOQOVxaDe8nmxn18qbjK5uFRK\n5i6vlhbF5rer281DvezqVA9pcJSHaxeam1paDvwxsBP4KvB9wBiqKPgj1JqgzxjzCLpjwby6VkT+\nXQ3TWNcUovVvJA1/NawrHPmptbyW8uq4IzuLXS6aKS8r/S6LnTcOByxuHQ3WgRdfrP3zc1FM/Vwq\n7Zxrs0onHJciX3BBR/NTS+XAvwDuQLcZPAv8Z+ArQD/weeCu1P93A18E3gn8Yw3TV3dENXaLbQZd\naSptXVFu7IJm71yqUUYKkVs9ybOYfHar44VRat1x1iOlY2VerdXARpSJo/IsVr9YD3W0nutAIWmL\nkuFij3HKcW0pZOtVR2kU67pcD2XJUT1qmaWrRGQG+NU7gDEAACAASURBVAy6I8Ec8C3gCLrd4F+I\nyHuBq6n/D6PbGy4ZwlsM1UrjW49bGxVDIekv9B3rKXBPpalWPueLN1Dvsiw2jY3wTotBtrpTzXK3\n1PMhHFQum0wqJSsn8+YkX76WErxwMcqKK58LCcvE5mXUeLNeZFfPaWsmSpFreAvxQixXXN41HrW0\nHIgZY94O/ADwAeAvgadS5z6c+g7gG2NiwJeBv65h+haVqPgCxWrNS9Hk1YN2vlzybY1TzDs2gzyi\nqNYWVrnkVa+yDMui0FVXG8zJbf0WTVR+V6sMlHvfZlr1yGdBUKk8qNf67CiPauRrJe5ZbB115TOa\nqPFRJcabpZIvn3Klrd6232xkSgkUXWwdc3WycamlcuAU8Cl0Z4I/Ql0LPpU6twy1KOgDRoEfAs4A\n12uYvqZgKVa+am6N04y4zrV0PC/71m+OxqKZ6kAzvYvDYXHlunyaaXzkxi7Vx8nXATVUDojIVgBj\nzK8B7wM+AZxMnf5e4Hzq828Dfwe0AI/UKn21JmyKHaWxq0XwwUZfQbOrufZztmuK8TFvxq2ewu9V\nqfTmum89ly27AhH8XC3rnKVCthgppQQ6ylc+XT4sJJusC5WVk/nSpBr5GrRmqTTZyqkrnwuJkomV\nX3jcVCvZ5cunfGPhfG6M2c45Mimlby62juUqfy6P6ptaWg5Y/ndUhfl7Eef+s4h8yRizDmgRkUvF\n3NgY8zE0sOGIiPxK4Pi/RxUQAB8RkcdLS3plyGZqE9XZFXuPYtJgn9HIlbTQYFxBH7tCGsBi09AI\nplO5zIzLabBz3bfeZJHLpaBQ3KAkO9kGe6W69uRS3Cx1WUeRTdZRprv2evu9kPxxMm9OqqUIr3Sg\nzHzl1JXPhQRlYuXn++puYIzJaCMKHSNVOk3hY9nytRHdGOuZbP1FrjperFxdHjUmNVcOBCwIFhSL\nwLEbwA1jjCciBemejTG70KCHe40xnzTG3C0iz6dOf0ZEftsY0wb8E7CoyoFqUkjH3YgVtNzJVyO+\nc60oRzZLZVKcb7XCla3yiJKviDA6auZXH5xcK0MlFcuOpUUl41eAK0P1glUKVGPXk3zPzRd/AFw5\nqTRuPOPIR82UA8aYThE5GThUyE4EAsQKfMRbga+mPn8NuBd4HkBEjqWOz7HIjle2UpZrwpXNXCdf\npU4k6jNyaKmNVTbzxVp2Ko1mzhhlTlYKUYGDrKlivRLOq0LNrfMNYER0FaZW5a4eB035VhuymRdC\n9HZbOlitzPOXEtnKdb5o9IXWhUOH9PP27c6KphGpVf7kKoelTj48L78boX1GvmuWKsE+2vPSAf9E\nBJHq1+lC71VswLxsZvKuLGRi5SoiDA1BPB7tthH+jT2f7Z65zlsabay8lKml5cAhYG3g+3HgwcD3\nV4Dbyrh/O7otIsBF4NaIa34L+O/hg4899hiPPvoo/f39ALS1tbFz50727NkDwP79+wHK/r579x5G\nRw0jI/vp6RH27s1+ve/rd88r7v4AIyP7mZlZeP+3vW0PTzzh8eqrT3PHHcK+fcXdP9f3lStXRoi7\nMCqhqQybhxcbhTUqTYVeW8x19ULYnCyf330hncPYmB1g1PdqbyXNJ9PvDdu2Fb7zQbnPrDfNfj5z\nT8g+QYhSKBVbZ+tRJotJlAIrKJ8ohWqh7eL4uF44NOTnfc5Sz4d6o5D8KVbJl4tK538hq9uuDEaT\nzaXAYoxhaGjhOCCRgMOHyXBBKIeoBYVcY48wua6LGtfU2hqiERARxsc9RIQdO6IX3OznfPUpkaAo\nCz+XB41BLZUD4RDf7YEVfYwxEvxeAhdJKx/agAsZDzfmvcA6Efmb8A8ffvhhdu3alfXGdhJcynff\n10l7UCO3a9eejEY5/HurRBgd1cpWzPOGh4XBwfsyVufs8xMpW41bb72f++9PD+zKeT/7fWRkhGpR\nirYxSvtZ6OA31wCk2lroxdJyZ+ucc8mj0FWceiHXSnW2awudpBYzuKkFi1GORKK3zDp4UGUdHoRY\n8q0GNRLVkns17js2phOF4WEhXuBIwPOgu7v2dX78wizJiPIFsGJZjI7VLbVLTINi23LQuhqlHC1k\nYh22/Cm2HLjVw/oiV37oxA8mJjz6+nwSCb2m0PYiCt+X1P+FizrF9ruO4vE8GBrSNiDXeCYXwbHU\n4cMwPm7o6akPq1FnKVIZFiMgYbV4FvhF4LPAQ8Cf2xPGmNuBfw18fy0TFNXR1iISvm1wg1pTq519\n8EG9WTmNe6UpdPJlCcsj/HtVggjF7jgXpVmPOg/V00LXcsUjn9zT8tCBZLjMBK0FhoaCJn3116Hn\nW6kOlqls5ozZri/WTaFcCs03qO1qicjCLbN8HyYmotNS7IpRJVc0q0GUm0017lvK+wXlY+9p88YY\nmXcRKKSvicWyv1u18uFTz03yzPE3I8/98n19fP/NGyv3sCYkWIYGBnRcMDZWennKViaLMS8uhajy\nlW884FDCbQDkDwx78KBhctLQ1+ezdSs8+aT+4MEH/ZLHkLY/CPbD2RYhirVsscdr3Sc3GvG4Kush\n99jG/g+Wm0Qic46hlqKCdVFZTJzVUOWooylieYjIC8aYWWPMU8ALIvK8MeYTIvII8FFgM/AVY8xF\nEXnPYqWzUFO8YGUst7D7/kKzsHrTrhUzQcgVWdWen5jQDq3UtAwOkmrwSrpFw1CIhnhyUrXCKpPM\n87nM0+qVfGZzwRgCUeSLgVEL6lHO2Sbtth4GB/PhSXQhbiuFrmg6ogmX9cFBCShBpeCBVb5902tp\naeUonlwKuXImU9UamEdNVvI905W3aApRJgePT02p8jCq7y8dk0pL2r0p7ZKYe7JaaBlbjD65GchV\nn6IWz2x7YQzz/YijOailcmBlauJuRxarjTFPB86vKPcBwe0LU98fSf3/vnLvXQrldLRhrXipzww2\nvpZgJS/GpLQeierYPA96e3U1O59m3F5v/+fKr1pooWup5S5EAz84KExMGCYmPIaH/QXn85mn1QuF\n5l04hkAjshirJdme6XkauC5XWvIN+ILm0PVMteRezftqG5mpZK1UWtwqTv1QzGpqoflUrOVPKbgy\nVFuCY8XBQZlX7Nox4r596o5azpgxynrVuqSFLQaqZY211CmlXtlFE88zGYtn6T5+8eunsxSpHLWc\nFn4w9P1Toe+P1iohtaTcAhplDlrshDdqpTTKpLQRiHqXXOezUcqKQ67JdKVWyGqVD4V0DjZGRW8v\nZGv4c5mnLTa5VpzC2IFukCNHooMwNUIHtBjpsvXR+qVmk3u+OhwkvNJd7clIuVQrbYXet1CT2yh/\nXyhMxvUsf0d2bL7Zdr3UCV62CVs9KSUdhRMeYwaPW8UuRFt72fFoubK3Lmn5rDVdfleXbPINLpoM\nDS1cUMxn7RF1TbVw5aIy1Ew5ICKfDn4PWQ3YYz+/8Geyt5rpqieyVaIoc6rgOUuuqLJRk5ugSWkj\nkW+iVowWNJvpePA6S7ZJSrOsbgTL39wcPPWUQcSwb5/6FzbSe+VzP7HMzel2eZ6XrjNR1jZhGkkW\ntcL6qFqXHmu5Ex5khCl0wFfvioFaka2f8P382wxG/c7+NtsKnQs81zwkEvDEEx7GCHv3Ci2BOI6V\nGMQHFx8qkeeFlCFXtsonKMNsk0NIB7IEOHgQpqaguxt27Cg8H77xDW1nHnggOMnM7qJUyvjOkZtc\n9SqbfMMBh3P1Q263iMZnMQ3Kw5YDUTTWrLUM7MAaskf1Dl4brnDF+trH48WZAi2m72ghq2HF3i+o\nBc32+6ggdr4vHDxo5veGbdTGLmr11pa/wUHh0CHDzIxHR4efc1LWCIqRbMGOEgn4xjc8Tp827NqV\nnL8+ytqm3OdX4j6NhEjm9kaQzoOgz2KuwFOWRrAYqBW56pvv595mMEih1huVrt9LsS7UEzpp9zl5\nMsboqM/NN2eOHyB/PudqG4stL4WUB1dWaku2VeDBQWF0VANZDgyou+HBgzGSySSDg1LwAsKpUyZ1\n33S/YNut4eGF7VaUksKVicLJtehYDImESVkbq+LfTfqbm0VTDoQtCZY61swfiGxos5l+BfE83WZq\n27bCKn4xpqqLNQGs5rPthKPQAYp2jhqkp7c3My8acQIT7nQnJlRhAsL0NNx+u9+wDX/YAkBESCYX\n5o8xsGlTdMClXKZyhdIIypNK4Xmq2LTt1NhY2s3g6NF0wKkg+Vx1nL+pUmg8ANsu5VMuh6+pxip/\nVP4tlbpQj/i+1sPOTqGjI3u0+UK2q6tE3i2ltrFRiXLrgnRbk0z6dHQstL7Lxfr1YAOg2raokJ2l\nXHkpnlJlFu4jfB+mp+HsWcOWLfl/43aLaHzqJhSdMaYDeCuwgXTQQkTkzxYtUTXE8zSqt0j2hjaX\n6ZedvB4+rAOAfA1B1ABxKWllgx1drsYzPOk3Bnp7fYaHc7t7VItcplxRxwslWP5A3zMeF44ezVz9\nDT+jnht/m550FOTM7S3j8cK39rT7PRuT22R7qROsK7plmipnrKVA0FLH9xfH5HAx/SFLIdzGZItB\nY5Uz9nO2Nr6YwWI9129HacTjJqNfs+VkeFhIJArfri7K59yVl+YlnLeDgzpmePZZw6lTMXbtShbk\nTrJli8/UFDz1lEd/v8/27VrWbDmslEuKozSyxfnp7RV6e5MMD+t4yVrT5rM8cnnZmNSFcsAY8x7g\nL4FR4DbgldT//UBTKweCA7ihITXtnprK7RqQzafP88jrQ29///rrel0hZoWL2eFX+tn5JvNRMQaC\n19nAOYmEar6jBk/VmnBky6NyNOo2OJXnwbZtmeeMYV5ZEGWWb6n3xj+ZVIuIeFz35PX9tFsBLMzD\nKK356CiMjMTYvNnP6YoSZikOloNuHOPjwfgDaTPSbFY2QdlXezW7Uf0h87XXUMh5nQhan/Nc8QoK\nkUuh1ghLrS7UG8EBvW0DDx4084qlQpXO4fgiwTLj2sbmIVseWSWSRq8XOjqSJBKGN97QXX5aW7Pn\n6dyc4fp1aGlRpX0ioX1w0C02SgHvykvxFCOzsHWaiITcatNBI4PjwYGB/ItijaCEd2RSF8oB4PeA\nnxOR/88Yc15E7jTG/CyqIGhaogZwngddXQtdA4IVN6pTDmr/IXclTCTgxRe91PVJ4vHM+2cb6C0W\n1Xy2tbiw2AHy0FD262/cgH/4Bw/fN7zvfcmMAZeuMKfztJCI0IWYDJdD+P5WnnNz8PTT6kvW3y+M\njxt837B1a9CdoLHNuX0fJifVx/2+++DECZ0Qbtki7N+/MOBilPJIfeTV/aC7u/jyuJQ6RCs/31c3\njnD5OXgQJicNPT2ZLgjZJrxWduVGWG80SlGShBUf2YI/btkiPP209iF2ddj6/eoWnn5GoLp8si9m\n1Wgp1YVKcPLSdWYuzWU9P5csrPNIx5SReeXc8LAqPV980WPzZp/BwUxrKs8rPA5SqbjyUP/kyiNj\nDLt3C0ePwokTOmF85hm4/35hYABaWxf+5plntD/+sR+D554zTEzo9eWmxRFNIUqBYDwga51mF0XU\n3XmhRZq1yHzySQMsHEcFry8kOLSjvqiXoVYf8BVjzD1A3BjzdmAc+Bjwa4XcwBjzMeAuYEREfiVw\nvBu1SlgO/DsR+XqlE18sUYO29DFhZgZiMeZN1yFduVRLl9biRVXs4CAuaKoFzFfejo50IjLv3dyB\nRsIDbd/XHR5AV85tYJxt2/wFLgVWkTA7C6++qsesH6eIsHUrHD0Kk5Myb2EQjOIbZc4MafkXk3Z7\nv3yTh/RkTTtkYD6K/OgovP66Rq5esQLOn9cdLIJ+4cUqnuoF6+s+NwcXLwqnTsXYtMln+XKZ14if\nOmXYvNnPcOPx/fQuFkHt+NCQzAd5a4T3rzXB8pxIqH9iV5ewbBm89JIhmZRU/TKcOWPo6UlPQPIx\nN5cegOQzdQ6nJ9/qUz2W6WzWTdlW5e1v9C/tOmPre7jdGRvTPNi82c+wQuvuFqam1K3Nmo4GZZ9v\n1xIbyXwxZJlvEt2xpoXONctrmKLKMHNpjg9/8XDW8//+HQMF3Ud9hoXTpw3nzgkdHdDXp+c6O5N0\nd2uZCwbaTSTScZCilD5WuefaxKVHWInkeYZly7S9P33a8PnPaxyft78d2tszf3v1ql43OwsnTxou\nXvTo7U3MT0pdeaouURN3jTMiTEx489ub27bAugQG5wdqeWYwxvYzEuiDFm6TaxUJ9tkudkT9Uy/K\ngWvANHAJaEEn80lgTSE/NsbsAlaJyF5jzCeNMXeLyPOp078B/CZwAPg8sKjKgbQGP9252spkg6ad\nPOmRTAq+L8TjusppI7va3953n5+a4Kk/bzKp28rofdOV9OhRMz9YF0kPrh94IKwJtCa2WtE9r7AJ\nayMStsgIKgQ0yI4OkIH51RR77egojI/D6tUalFAnoNqonjghnDsHGzZoQzgxob6dLS2Zk/TBQfXl\n1/sXtzKfb4U1l0ZWRFdtRfS5U1OGZBLWrNFJwfXrHm++CatW+anOwsyn1SpGGqUhf/VVw0svaXlu\naRE2bEjyyv/P3puHx3mdh72/95sBSEIkwA0kQQDcwEWyTImGncqSYMq2Esdx4jS3YZo2uc5Nkxs7\nT9Oq6Zam142TNDc3XdI6VRPfG4eJ29h145pJ7cdqnThSLJGQ5NoSSIlaTIGLSGwEF5AguIDAzPfe\nP945mA+DmcEAmBVzfs+DBzPfcuac9+zvec97XhPGxmJ0dZmSYMMGpbUVBgcDOjrCiOO8uadY+AFL\nbqJtmpUR5coVGB4O2LJFuXlT+drX4tx/f5LGRmX9enI6gIxa4biwz5yBS5dsdTPbb0efd9fyDT6q\nxSKqELINtCBdHl1a3eDr/Pkg5ZTQBnROERZVJsRiyrp1woYNwpkzpmju6tKZ+j04mG6nnOxbW0Pe\nesv6kPvum2sRtX27PdvfLyVbZc7HfJPof/uR3TWpHCgWYQiXLpkCeHhYGBgIuOeeJCJCWxuAznG0\nGwTMbG/Mlp++Taxvom2ATSJNIXzypHL0aAOnTwtXr07xoQ8xy4ndxYtxJieFr30twerVIXv32ru+\nPJWezL7RFlGUoaGA9naloyOcdbx5EDgrWjuly7bSKidOmJXpAw8obW0wOAjPPBMQBEpnpxKLWZ46\nq7NkEoaGAmC2zxxP9VItyoFVwKdV9ZMi8lPAZ7HS+VsFvv8Q8I3U56eBhwGnHHinqr4IICITIrJG\nVSeKF/WFEYaztfGZg6xYzCaXACMjwpYtcOyYMDISsH17kpMnbRVndNSOXlOFjg4b3I2MCIODATt3\nhhw7ZhpA6/jnEp3wpleLbZK7davWjUYvCNIevp2mtL8fXn45hg2YQrZsEdx+6b4+YWjIJpONjcqx\nYzEaGkKam9OrZmEobN1q+Xf2rKaOjIzm/ex9n66xPHFiaWnJpTRwnYA17nYUTRDAli3K66/D6GhA\nPG5xuHvXVnoHBpQ1a0zhFAQ6s7Lkwqv2snHypFPGCLduCfv2JWltVU6dCojHQx57zNJ5+XLAgQNW\nl86eJeW4sHR73pcjrly7NuTECRgYiCGi3L0Lt27ByEhAGJo1zoYNuf0NZLNaCgI7ajKqqHPPL8cV\niEyrAEc+vx/u/tWrQiym3Htv9LSO2WHv3m0Dwldesfq8caPO5F8QmHJ0YEC4eFEJQ+HBB5N0dMAX\nvhBjYiIgHp+esYiydgXefjvg2jXYtElnWbx5qoNr1+A732lgelpoaEiwcWOYWvWzvBIh4hvE3gkC\nZvoun5+efLijsZ2y9vTpBOPjMU6caODkSeWXfilBR4fdu3kzIJGA69fNwvKjH1WamnwZKzeu/xwa\nglgsJO0Efa4fsu3brb8YGxP27LHxklmoKZcuwdmzwrVrQmMjXL0KDz5oYTz7bEAsFs6xRvXjquqn\n7MoBEdkA/BPgALA6dXkl8D4AVf1jEXkOswR4o8Bg1wJnU5/Hgfsj92KRz+OpZyumHMimjY9O4E6f\nNgWBiNLeDjt32gpcLGbbDYIA1q9PpiqbafrdYKy9HWzV38JVFfbsMUuBffvsXjaTXBEzD3JbFupJ\ng+vMI91nsEbPrVLa9gBNne9qK83j48rmzTZh/u53hampgC1bkrS2uryzLQoNDTqjhXV57Ey0lnL0\n4UIbV/dMPA6dncru3U4RAq+/rly5ImzYYNreFSusoU8mA7ZssUY9mUw78asVNm9WgiDJxYswNRVn\nbCxg3bokBw5Ms26d1auhofSZvefPp7cPZG4l8eTHtWnOemlqKsaOHUnGx03ZsmmTIDJFQ4Owfr0s\nyG/DYgYSy2HwEY13vpWWaFpNOZmctQqXTRY2kFcuXrR3OjpMGeC2qzlLteFhU4KCKX9aW5PEYrN/\n33x6CFev2jaF9vbqbCcaY8Irw9m7/VrdcrBQGhpCbtyI0dGhfPSj7vhW7yPCUxxce3PffTbOGBpK\n8JnPxJmYCOjrgy1b7LlVq5KsWhXyrndZ+5HPeaGnuGRTPIvIzIJgri17QWBbkYNAUA04cMD6e9t+\nqbS0wIYN5r/s9u2AkRFob3dbCoT2dqWrK/+2NE91UQnLgS9iWwf+G7adAGAQWCkigaqGqnp+gWGO\nA82pzy3A9ci9qC1qM3At8+UjR45w+PBhtm3bZgG0tLB//356enoA6O3tBSjK9yCAS5fs+733zr2/\neze8/LJ9f//7HyUehxUrjtLWBps3vw8IGR3tJRaDRx+18F54wZ5/5BEL74UXemlstPvx+PzxGR09\nBsC+fT3s26f09vZy+fLC0tfU1JQp1pohc3C0d69NEuc+Y/uubABsWtadO81uKrPRS6+6aMagfPa1\nYsQ5ei3XpCjbvZUr4W/8DQjDcJYZtx3Zl7Z4cHvyl6LQKDePPWZpmZqCqakEw8OmZGtvt2MoGxvh\ngx+0psFZjEBtpK3acGXdJqfC1q1Jtm83JRNYXXJblxobc5uP5iq/ufJkvvK+XMimKMgmH1N0zn0n\nmywaG2dvLduzJ5zz/L59du3MGbMw+Ot/HSA5k4fu2Y4Opb09bdlRjbIfu5Pg158+l/Xef/joniX5\nK8jn76B5ZZwbk4mc7853v1CHg/OxYQM88kgSSHLwIDQ3V2c+eWqfILDytXo1/NIvJTh+HFatCnBD\n8cces5Xnnh4zUa8XJ7PVQrTe5/Mbktm/2pgqbVmQdm5qyoWdKfcn586ZVdLu3ek+pFbGjZ40laiW\nDwObsNX7i6lrbdgKf0JEZvWGqlpIHF8EPgF8GXgc+Fzk3qsi8l7gJNCsqjczXz506BDd3d05A3eT\n4GJ9P3gw9/14HP72334USFemxx6z+87vwP33ly4+QZA/frm+9/X1sVzI1pBFrQvuuw/cBD9fx5Zr\nAlRK8oWfa5KQyf33z56AZFpW1AIuX1z61q6Fe++d7VAwmne1lLZqxMk1Wlbe8Y6Fl5uF5kO95dt8\n6V2IPOYr/+5aPsVZuUzP/+YDW/jIva1Z77WtaWTs9vSiw86nOID5lQdTyZBP/sXZrPd+9Xt35g27\nkPvFIB6Hj3wk/dnjKTVmUUZqa2vakewP/qD99+Ww8sw3aY/ey5Zf2SxvsymoPbWHqOY2WSzJD4r0\nAj8N/AA2YQfbYpAVVf2dAsP9HaAbOK6q/0BEnlTVJ0SkHfhjzK/Bp1T16cx3n3nmGe8hw+PxeDwe\nj8fj8XiWMY8//vjy9bpeBCqhu/sr4OvY6v6u1LUJQFX1jxYbaPT4wtT3J1L/hzBrgrzksxyoFqrZ\nAVdfX9+CZVjN6akEi5FhLVDufC6WHOu5fJajLC53+dZyfa6WvCmXDKslvaXAyfD0lducGMnud2H7\n2lV8T2dz1nue6qvLtVpeSynHWpXJQimFDOtFdo7lZOlcKiqhHDgIDAHfF7n2CPA2MKMcEJG/B/yy\nqnaUNXYej8fj8Xg8y4hXL97ks/9rOOu9x7vWeeWAx+PxeIAKKAdU9f2Z11J+Bh7IuPyHwJPliFOt\nsBy8cEdZbunxZKdW87lW410rePlWL/WWN/WWXk9t48vrXLxMFo+XnSeTihQDEdkgIj8lIr+UuqTM\nVQ5swh0I75lhuXn9XG7p8WSnVvO5VuNdK3j5Vi/1ljf1ll5PbePL61y8TBaPl50nStmLgog8BpwC\nfgL4ldTlEeA5EelMPdMJPAtkdeMrIp8WkaMpJ4TR6z8jImdF5PNZ3vmqiPxG8VLi8Xg8Ho/H4/F4\nPB7P8qASeqL/APwtVf0w4A74/T5gBXAhtcXgAtAEfG/myyLSDdyjqgeBRhF5T+T2V5nty8C98wCw\nEm+J4PF4PB6Px+PxeDwezxwqoRzYnuU4wbeA69iRhj8DHFDVzar6dpb3HwK+kfr8NPCwu6GqV4Fk\nlnf+PvAZwB9d4fF4PB6Px+PxeDweTwaVUA68KSIfFpHoRP17gZPAReAYcFNEukSkK8v7a7GjDwHG\nU99zIiL3Apcw5YPH4/F4PB6Px+PxeDyeDCpxlOE/Ap4CWoCEiHwW+LnUvYtZns9c7R8H3Jk7Lcyd\n9GduHfiHwKeA+3JF6MiRIxw+fJht27ZZoC0t7N+/n56eHgB6e3sB/Pc835uamjLF6vF4PB6Px+Px\neDyeGqESRxl+S0QeBH4BW/W/AAxjZv9/CkzOE8SLwCeALwOPA5/LuJ+pTNgO/CdgPbBBRL6hqsei\nDxw6dIju7u6cP+gmwf577u99fX14PB6Px+PxeDwej6c2KbtyQET+sar+OxH5aVXdmrr260CXqn53\nvvdV9biITIrIUeC4qr4kIk+q6hMi8kPAPwO6ROTLqvpjKceH7pSExzMVAx6Px+PxeDwej8fj8dQ7\nldhW8H+IyDjQICIbgGupeDxUaACq+osZ359I/X8K27KQ7Z3ngOcWG2mPx+PxeDwej8fj8XiWK5VQ\nDnw/8CzmeHAgdS0A3iEi00AYeVZVdWV5o+fxeDwej8fj8Xg8Hk99UQmfAyMi8mHgr4DXMB8DDwH/\nnrn+AjKdC3o8Ho/H4/F4PB6Px+MpMmVRDojIYXqtPAAAIABJREFUzzJ3on8X2Ab8KnAW6FfVP1pA\nmJ8G3g30RbcZiMjPAP8CeF5VP5a69nHg76QeeVJV/+ti0+LxeDwej8fj8Xg8Hs9yo1yWAx8jrRwQ\noA3YDVwGfh3YBPxvIvK/A62qul9EDgJbVPW/ZQYmIt3APap6UEQ+IyLvUdWXUre/ivkW+LXIK3+h\nqp8VkTjwLcArBzwej8fj8Xg8Ho/H40lRFuWAqr7ffRaRP8GUAeeAlcDV1Pc40ADsE5HPA6uBg8Ac\n5QC2DeEbqc9PAw8DL6V+66qIrMn4/fOpj0kgUZRE1ShhyqNDEFQ2HplUa7w8C6fe8rLe0lssvNxq\nk1rKt1qKq8dTauqlPtRLOpeCl5EnH5UoFh8GPgL8K+AFoB+zJvhd4BlsAn8GeBVoyhHGWmAi9Xk8\n9b0Qfh74yqJivQwIQ+jvF/r7ZaZhqAaqNV6ehVNveVlv6S0WXm61SS3lWy3F1eMpNfVSH+olnUvB\ny8gzH5U4reAycBx4EzgPdKSuf11VnxWRf6Cqv5Za/f+ZHGGMA82pzy3A9Yz7cxwZishDmGLiRzLv\nHTlyhMOHD7Nt2zYLsKWF/fv309PTA0Bvby9A2b8/8oh9f+GF4obX19fL6Khy8GDx4tvUlEuPU3q8\nBrR6qda8qdZ4LUe8rCuPz4M0XhaeemI5TP58na0efF7UB+VySPg46Qn7CuAKZilwMXXt3wFfFZEn\ngSYR+VVgJ/C1HEG+CHwC+DLwOPC5zJ/M+P124LeBH1bVOYqDQ4cO0d3dnTP+bhJczu9Oswc2qY9W\nxKWEv2ePsmfPo0ULz33v6+tjsQSBxct9XghROe3Zo77BqjDRvITqzJtilpmllN16IJesvdzKR72W\n92xx9f2Fp57ILO+1UnejLLTO1lIbVSkWKyPfftYP5bIc+EPSyoF2YAD4zcj9ezBLgP8Lm9j/Sur5\nj2ULTFWPi8ikiBwFjqvqSyLypKo+ISI/BPwzoEtEvqyqPwZ8CvNr8GciAvADqjpZ9FTWANVamas1\nXp6FEx2I1wO+7C4OL7fapJbyrZbi6vGUmnqpD/WSzqXgZeTJR7kcEu5wn0XkO8DvqOp/SX2PYVsN\n+oH3Adsx5cE+zLrgT3KE+YsZ359I/X8KeCrj3ieKlJSykU+ztxiznmowBSpWHKLheC1x6VlsvmXm\nTTWUQff7Sy0zmYqPSqepWilmO1Yt5afWWEh5X6yMqylv8sWl1P1FNcnBU3sUu/wUWt6rudwGAXR1\nVd84oppZiowq2X56qodK+Bz4XeD3ROTvAxcwZcA64B+r6igwCiAiV0j7I6hLslW+xZj1VIMpULHi\nkC0c30iVjqXmW7Wa8y7l911aVBVVmekwK52maqUY7Vi1lZ9ao5T9RDXlTSFxKVX8qkkOntqjVOWn\n1tvWMIQzZyx+XV0687ka41oNLCU/K9l+eqqLSmTzLwEfBH4PO37wPwJjmN8BAMRs//8RcKIC8fN4\nPB6Px+PxeDwej6euqITlQKeq9onIJWArMIQpCT4kInexIwqbsCMN/ypbACLyaeDdQF90e4GI/Azw\nL4DnVfVjqWtrgC9i1gm/r6qfL1nKSsRSzeirwRSoWObF1ZCWeqJY8s50VBiG1Zl/hZTF2WnxZXEx\nOBkW6pfC1/ulM1/ZXqyMqylvKhUXJ9tqkYOn9qhUH1lN9deRb8xbbXGtNpYio2rdCuopP5VQDlwU\nkePAfsxiYD3mc+Aa8BVgDXADUxokM18WkW7gHlU9KCKfEZH3qOpLqdtfBZ4Dfi3yys9hyoEvAd8U\nkT9R1emSpKwE5DOjX+ikeqHvFJtibZPIplxwHalvxBZGoZPhYoTnOptqMmGMxnchcct1z3emuckm\nm4WYiJZqv3w9EIZw6pTJet++7LJeivyqSea5+rpSlY9qa9M8tUs5+shs46VqKrPF3jpaj/3CUnzG\nLGYraD3KeLlTCeWAAruxEwUOAsew0wRGsJX9/zXP+w8B30h9fhp4GLM8QFWvpiwFMp//BVUNReQV\n4F7gZDESUklq1fdAsQlDeOstGBgI6OjQnANfz1yKXR5qrXxlxrfY4VV7+stJOQa8Xva5CUMYHMwt\nn+Umv2x1ezmlz+NZDLU+XvK+aorPUmXkZbw8qYRyoA34OPDTwPcCK1Lf/wD4OmZJkI+1wNnU53Hg\n/gKevxF5fu2CY1xBlrsZ1XJPn2c21Zzf1Ry35YiXd/kIAujsDGc+e4qHL8eeYlLv5ane018t+Hyo\nbyqhHPgWMKCq3y8iE6r6YRF5FHgR+J4C3h8HmlOfW4DrGfczlwDHU89dTr13LTPAI0eOcPjwYbZt\n22aBtrSwf/9+enp6AOjt7QWo2PcXXsh+/5FH8t/P9n3PHqW3t5fLl4sb36ampkyxFsxS97Lv3Qu7\nd4d+W8ECKXbjX2h41ZJH2eJbLL8K1ZLGaiGXbIolJy/7/Lh20n3Odn85yS9bekqZvuUgM0/1UKry\nVCvjpVzxWmg7tdzatVKQT0ZexvVLJZQDZ4G/EJG3gCYReQ3bZvAasFJEfgNQVf1UjvdfBD4BfBl4\nHPhcxn3J8vzjIvJl4ADw3cwADx06RHd3d84Iu0nwcvgeBHDwYM+sfWfFCL+vr49KUs0dXTVT6HaU\nQp+ttTxYznuvq41S71n0ss/PUn06LIZy7kXN/K3FDHQ9nuXOfGOlats/Pl+9no9qSUe1kc3PwGLx\nMl5+VCJLH0v97trU/11AI/AO0lsMPi4ix0RkIvNlVT0OTIrIUSChqi+JyJMAIvJDwOdJKwMADgM/\nCRwF/lBVEyVNXYVxk/58190eof5+KdhbeCXIlZbMZ06dEk6dqu60VBOFyDX6rCsriUTh79Uq89WN\nMKQu5FBOFtMeLaQMlzKMamGhaSlH2svZz1SqDFUibM/ypFRlZrH9fSXLr4tzZnx8vVo42WSWL5+9\njD1QGcuBLcBeVR0QkZ9OXft/gb+b5dnfzRZA9PjC1PcnUv+fAp7KuDcBfHSJca4JcjkGKbbjtXJQ\nqJOT+RxteWazWOcxYQinT4OI1K2cnSJqcFDo7AzZu9drzCtBMRwgLScnSt5J18IppQy8fD0LpVRl\nphbLYjTOXV2zj3ZcyOk2Ht83eBZPJZQD45iFAMD3Yw4FG4DvA6L6qh3A8bLGrE5YTnuEvKOt0uLK\nSrRjXs4sp7pRK3iZL0/Kma++DHk8S6fa6lE0Pp7iUW357Kk+KqEciAO/LyK/gikG1mHbC7qBL6We\nUaAX8yvgKZB8Tr+K6XitHCzEuV0+R1ue2SymU3B70uqlM8nnDGnfPi3K2cue2SxElsUoi8upPFez\nk65yyrbcZagSYXuWJ6UqM4vt7ytJvjj7erUwFups0LddHkcllAPtwFZs8i+kTxe4V1V/rZAAROTT\nwLuBvugWAxHZCnwBs0z4lKo+IyJ7gD9K/dZf5XF0uCzIN7GpNWqlM6s1ljKhqne8UqA6KEYeLKd8\n9E66Fk4pZeDl61kopSoztVgWl9M4ttL4vsGzGCpRDC5hzggfBl5W1QDbXnALQETaROSPReRzIrIl\n82UR6QbuUdWDQKOIvCdy+5eBTwIfAv5F6trfBf65qvYA7xWRZuqIenIuUk9pLRQvk6Xh5Vd/VHOe\nV3Pclhte1p5aYrmU1+WSDs9cfN7WDpVQDnwR+KvU/+dS1/4AuJD6/O8xiwYFPpvl/YeAb6Q+P40p\nGRzvVNUXVfUWMCEia4ArwFoRiaWeuVushFQ7hXher9WKmhn3avGyW00UQya1XEaWSqHyq2cZlZpy\ny7aa25FKxa0ey7eT9alTdkqLx1PNJBLmLLca262FUG+nI1UD5Wrfq7lv9cylEsqBTwOrsK0FP5+6\nthk4JyINmBXBJ1L3Hs3y/lrAHXE4nvruiEU+jwMt2JaCJ4HvAi+oat0oB/JRyxW1luNeS3g5z4+X\nUenwsq089ZwH7iSc06f9JMVTvbiThAYHl08ddWmqx3annNRz++7JTyV8DnwWsxr4LeBq6toY8CBw\nEHhdVSdEZAV2ikEm44DbGtACXI/cixbv5tSzTwI/BvQBfyoi21X1fDTAI0eOcPjwYbZt22aBtrSw\nf/9+enp6AOjt7QWoye979ii9vb1cvjz7fhjC5s3vm/keBPnDC0P7HgTZ7zc1NVEqXKM1n2NC70hl\nNouVie8kjGKVqULKryc75S6L1dyOVHPcCqXYdaFUdcvJWkQRibpG8niqDxE7Xnf37uLXhXL2X/Od\njuT70oVTLTJbDv1XPVEJ5cBfAz6iqqF1uoBN4H8LUxo4B4OPAm9mef9FzLLgy8DjwOci914VkfcC\nJ4HmlJKhGbimqioi48DqzAAPHTpEd3d3zgi7SXAtfg8COHgw+/0wtIq6b1/+8B55pCelXbTKne33\n+vr6KAXZzl3NdyqDZzYLlUmmvOu9MZ8v3fN1eP7c4KUhUv5JWTXnUbnjVswBXbHrQqnrVjzuTsLx\n9dZTvZRy0lWJ/ivXGM/3pQtnPpmVe8Lu86x2qERWXQT2ZFz7H0A/8Kiq/tfUtUHg/8x8WVWPA5Mi\nchRIqOpLIvJk6va/AX4T+MvUf4B/BXwh9fxdVX29qKmpMhayf6iWPK+r6qx01VLca5n55Fwv+5Hz\npdOXxaWTS74iQkSJ7KkAmeW7lur8UuPq67anFlhqOa3GOh1VCniKSzS/fRvnyUYlLAd+G3hKRH4L\niIvITwKfB/4n8B9FJLqyr9hWg1lEjy9MfX8i9X8IsyaI3vsO8EhRU1ACimH6UyrNajm0i7nSHwTQ\n1aX09wtnzniNcalZSF4vtbxV2tyt0N9fSjq9KV2aXPLOJV8vu/JRi3Whlqx2Kt3WeaqXSpeNfPWk\nXG1wNhlki5fvDxbGYi0wKl0mPZWn7MoBVf2jlHn/P8V8AnwMO0FgI/CfgTvRx8sdv0pQTYOYXJS6\nY5jP9KkaZbJcKYesK13my/n7vuwuXt5edqWnlutCLZSPSrd1nuqlFspGqeO0EBlUo3yqnaVuLfUy\nr0/KqhwQkTjmM6AVmATWquqHRWQK2IcpBgaoE6VAsVmumtXlmq5ap17ypV7SWSm8fGuHWsqrWoqr\nx1MpqrWeVGu8ah0vV08hlFU5oKoJEdkP/IKq/qGIXEvdCoE1wK9meW1n5gUR+TTwbqAvusVARLYC\nXwBWAJ9S1WdEJMB8ETwIjKnqjxc1UUWgmJW1Fit7IemvxXTVA4vNl0p3UAv9fV/+lsZ88vbyrRzL\nuS5UQ1wr3dZ5qpdqKRuV/u1cMvD1pTTMZ51RDWXSU1kq4XMgCbxPRJ4BiEzePwn8AeawECDr+UEi\n0g3co6oHReQzIvIeVX0pdfuXU+G8CjwFPAMcAt5Q1X9SwjQtmXqvhPWe/nqk0nle6d+vN7y8qxef\nN6XFy9eTC182vAyqDZ8fnkooB5qAn0r9CZBI/Qf4vizP/1HG94eAb6Q+Pw08DDjlwDtV9UUAEZkQ\nkTXADwGXReSbwH9R1cPFSshS8A4/lhe1lp+1Ft9iUs9prxV8Hi0NL7+5eJl4PGmWY31Yjmlabvg8\nqg0qkT0/C1wFrmM+Bn4bsyYYA3Zn/HVleX8tMJH6PJ767ohFPrt7m4A3ge8FflJENhUrIYvFOfzo\n75eqOKalGo+xKTalTGO15WcunAxqJb6loBhpr4f6UkkKzSOfD9kpZf2uVZlHZZJI1GYaPJ5iEYbw\n1lv2V411YTHtTD2PaxZLudtzn0e1Q9ksB0RkTFXXp04rOA4cAU4BzcDfxbYTvBdYD/wJdnpBtgOu\nx1PvALRgSgZHtLg1p+6NA8+palJEXsSUDpeiAR45coTDhw+zbds2C7Slhf3799PT0wNAb28vQFG/\nDw0J3d2lC7/Q72EIX/rS8wD8+I8/ShAsLrympiaqFe99dbYMurq8v8/F4stSdeDzofwsB5mr2rG4\nbl9tLabB41kqYQgDA1b4d+8Oq6oeLId2phbwcvbko5zbChrcB1U9LiLrgTZV/YiIPAb8KXAFaFfV\nnxeR9wP/GPhyRjgvAp9IXX8c+Fzk3qsi8l7gJNCsqhMi8gLmjLAf2A/8bmbEDh06RHd3d86Iu0lw\nsb4fPNiT0ppZhXT3nSat2L8333enpHAuHhYTXl9fH7VEMU2bas2BS7HjWytmYi6etZRXy41Cykqt\n1adyM58M601+CylTYQhnzpQnXh5PtRIE0NGx8DaiVH19McKtt3av3Pg8qi/K7nNARH4Dm4WuBN4h\nIv8S+DjwNeBvAtOpR7+F+ReYRUqxMCkiR4HjqvqSiDypqk9gjg3/GFgFfCr1yh8C/1lEfhH4c1Ud\nLmHyCiazYuTT4pVy8rUcK2umvDLTWAqNabXLrlT5HIZw6pTJct++6tU+z5fnhdax5VhfykWm9UoQ\nLP50knrNh0LbrnwyWWx/Uo0yzyaPXOlz5a3a0uDxlJsgsP7afc5FtC6VaqU52xhisXXU1+nCWYic\nXd6Hob0TX8LM0edRbVBO5YCIyC7gXZjjwRimJPgBYDPQhh1B2CQiP4A5HYxlCyh6fGHq+xOp/0OY\nNUH03k3gR4uakjJSDtOf5VRZc8lrOaVxsZRCBmEIg4O1bZq20DpWi2msJsIQTp8GEVlSmfH5sHCW\n2p9Uu8wLSV+1p8HjKQeFTgghPYksBdnGEL6OloeFWo0MDgoiyt69Po+WO+VUDjQBp5l9RKEA7059\n/n7MR0Aj8FuY/4CTZYxfRfErGuXDy7p4BAF0doYzn6sVn+eVZ7ZpdzZ3Mp758OV4NtmswjweT/Ep\nVdtTK2OIesblvYgikvWUec8yo2zKAVUNAETkBrAu5SDQdeWKKQrcyQMPAn/ObAeDy55cqxx+MFg4\nhcrLy7I4BAHs3Zv+XM34PdqVx5t2Lx2/53M20XQsx/R5PJUgW10qleVqrYwh6pl43OVTbVqIehZG\nJbL4DHY6AcCu1N+vAW8B/xo7xeAW8DDZjzJERD4tIkdF5Hcyrm8Vkb8SkedF5PGMe19N+TuoOcpl\nZlWrx1RlMp+8lks6o1QyTcvBDLAey0wlqWSZqfe8zCX7WpJLvrguh/bI46kG8tWlYrYX7ndqqQ2q\nR6LlwefV8qbsDgmBnwX+u4j8M2AIaMe2HHyfqr4sIgcxfwP/C3Mm+DPRl0WkG7hHVQ+KyGdE5D2q\n+lLq9i8DnwReBZ4Cnkm98wDmALEubGEKcTiV+Uy9HGuyEMd0lfDCP99vZrtfL3lXTrI5YlJVdu9m\nSc54PJUl1z7aYjrXKmZ4Swl3Ie/UUhvizmgHsu59rZXTUzyeaidXXZqvDi6GRAL6+0GkeGF6lka+\n/K/W/sK3/8Wh7OJT1T5gD/ATwKeBn8SUBO9K3T+KKQU+D3RmCeIhTHkA8DRmYeB4p6q+qKq3gAkR\nWZ26/veBz2BbF5Y1rtI6z6L5njl1Skgkyhu/aiJT8xmVnXVU+eVYCvL9ZiF5uxC85jc7Ts5vvcVM\n/VBVBgaCspcHT+nKaSnqUynajMWEW2g/UItl2Z3RPjAQzGm/K9VuezzLjcw2JNpe5KqDiyWRgO9+\nV+jrizE46OtuNZAv/6uVUvXB9UgljjIMVHUKOBq59iHgWRGZAP4U+CJwD/BDWYJYC5xNfR4H7o/c\ni55uMA6sE5EO4BLm7NCTIpGAoaHZnkeX+17N6Dn3Uado1ab5XCiLybtSr6DWOk4ZoKrs22cWA6q5\ny4nXVpcGN9lzZXyp8o3WlXomm1xrpf0PgrlntEeP2ko7zUrj66fHMz/5Voozx0uZdXCxOIuB4WGh\ntTWkvb22x2PLgWwLZ5n5Xyv9hWdxlFU5ICJxbEX/D4AeYANp64UA+FfA3wOuAj+iqtNZghkHmlOf\nW5g96Y/qippT934l9XdfrngdOXKEw4cPs23bNgu0pYX9+/fT09MDQG9vL0DFvz/yiH1/4YXczwcB\njI4eA2DfvtzhDQzA5s3vQ1Xo7T1GECwtfk1NTbnEWxClHrwVesRVtMGrROOX7zfzxWkpcSyWomS5\nDMCDgDnKgHg897nQSzWxWy5yKzbuyMOhIWhvL96EPirnYtbxUnrzXmi4+d5xch0cFDo6QsKwtvbp\nB0HuuhgE0NUFQZCuh9VsApsP3y7ULrWYd9nqSS5Far46uNDftLYooLMzpKvL+tpKbeusd5xSwI0H\nu7pyt5fVmC9eaVE8yqocUNWEiEwC3wO8gPkfOAx8GHgZuAH8KPBvgY+JCKr6RxnBvAh8Avgy8Djw\nuci9V0XkvdgRiM2qOiEi24D/BKwHNojIN1T1WDTAQ4cO0d3dnTPebhJcye/RhvuRR3pmFfzM5w8e\nnD+86B7q++5bevz6+vpYLJUYvOVqRHJ9Lhfz/Wax4lTsFdRaHYDnIpsyoBRpWm5yKwWqEIalOT6p\n2PIuVf4tJtx874g4xYBw5kztlb1sSoHlNCj07ULtspzyLp8itVjpEhE6O8NZ/nyWkwxrhWzWV1Gl\nca20r9Uev1qhEq61GjAHhN8LNGIWBEngxzCfANPA/4MpCl4HZikHVPW4iEyKyFHguKq+JCJPquoT\nwL8B/hhYBXwq9fyHAUTkMeDxTMVAtVOKfTPLbSBVCNnSnC3t9aStLuYKqtM414rcCsnnYqzS1hPF\nrjtpC476lms+FiNzV15thag08aoE+Va45quf9dTue+qbXGV9vnpSzLoRjYPvO6uLbNZX7nomvt1c\nvlRCObAK2w6gqb/1wEZsW8FqVb0lIvdgCoKd2QJQ1V/M+P5E6v8QZk2Q7Z3ngOeKlIaykKk9XWwj\nmq0CV1Nlns8ENtv1xf5OPupZW+3M+BYzwXdmaCKa6lRKE8diUYp8XmwY5Sr7paZUdSffdo58cVnI\n87XKUmTuVoSi1kO1pNxbKPnSVa52fyHl0k+aapfF5l052q35yno5ylq2OGSOPXz5Lx+LVdTU83i5\nHqiEcuA54DdV9RkR+RPMauBHgVeAQyKyFTu94LcwywFPisVUvlwVONogVMNgOpdWspyDtszPy41s\n+RxN71JlbWZote/srdx1o5Jlv9rIpcgsVHFVr3JbKJllvNDjXStNtBxUQ3wKZTHlspbS55nNYhTs\ntdxuLbZeuhNGzpyZ63S21mRQi0TLXVeXV8Z40lRCOfBzQExEDgLPYicSJIEuzJfAeeCjwH8EJioQ\nv6qhVNrTzAZhuXjtXwyZshCp/cltNnJp6zM7hsVQa1r++Vbrfd1YOMUoA/kUmbU8cC4VS1mhzHZS\nSSHPVlL2YWhnqw8MBHR02CkixbL6qaX2y+NZLKUo62EIp04Jg4PmO8CdfjVfHFzbMjRkW8c6OooT\nH8/CsbxIO4acLw/Bt5vLnUr5HPgatr1gAOjEfA/cxY43PA/swI4oTOQKREQ+Dbwb6ItuM0hZHnwB\nWAF8KmWh8HHg76QeeVJV/2uR01QyllrparkCVyLu6SOwlqeSIBdLlXUtlq1qphbrbTXEsxblthT8\n5Lg4lDrdXsaefJSzfFRD+YvGQdWco+7ZUx1xqycyFTWLed+zPKmEcuArwHHgzyPX/j9gDGgFHgCG\ngUPYiQRzEJFu4B5VPSginxGR96jqS6nbvwx8EngVeAp4BvgLVf1s6ijFbwEVUw4UYqK5UBOt+cyg\ns+0rq/SRfdnIlu5ydJRu1TweX9we3Goyu81FtnwuZL97tnCyPVfMVYhihpcr7EKdMkU/5zsDuhhx\nzgynmstToUTPS87WnmXecwOVKAtto5aD3BzZZFEMMi2FcoVbSBtRLnkHAezdC7t3hwX9fi20y4th\nuaarVilWfixk+1Q5KaSedXXpjGVRoWOmRAJ27lS6unTm+MJCf9NTHJycnW+fbFuPYf75ShSfZ8uD\nSigHuoAR4GOp71tS8WhX1Z91D4nI3wZO5wjjIeAbqc9PAw8DTjnwTlV9MRXGhIisUdXzqXtJ8lgj\nlJroUSF79ujMsS2ZzyzERGuxZtD5lAflJNqwlMJktJDfz5TZQkxpq8nsdj6yxS3bNZcmVUXV0uaO\ntimluXch9WOpYcP89SRb3SjU5D1bGAuNX7WXo0KxtgyGh+0owo4OZtXrXGnOljfLQR4LJVof3Han\n6HFfxQgzs05nI18bAeUtr+535usjq60+FSs+me1YoZMxT2lIJCw/nBJtKXlRbWUW5o9TIgHf/KZd\n/MAHwoIVA6dOwcsvxwgC5V3vCtm3L30v25jMU3yy5W10/OvGf24BLZdyP/NZn2fLg0pk4VvA/w08\no6ofUNX7gNeAQEQGReR1ERnEjjA8mSOMtaT9EYynvjtikc+Z934es1woG9GVM/d9cFA4fTp9bzFO\n8Bb7XrnCLiQM17CcOiUkKqaymU22/Kp1J4WLSYM19qWJTzaidSFaPzLvFwtLny453Gi83UDRTb7q\niWxyDENT9l26FMwpS9HnXT4sNPx6QhUGBqytzCeHpcgp17vVIvts8VCtjriVG7dH+M03q6fvrAei\nZdDlwcBA/bX3UKz2QmfemZoypV9m318sqqUdqyS5+un5+pTTp21sk0gsbd7iqR0qYTnwz4GvAiMi\n0gxsB+7DVvTPYr4IrgDH8oQxDjSnPrcA1yP3okW2GbgGICIPAR8GfiQzsCNHjnD48GG2bdtmAba0\nsH//fnp6egDo7e0FWPD3Rx7pob9f6Ovrpb1dOXiwhz17lOPHj3HlirB796OcOZO+39PTQxjCpUvH\naGyEvXt7CIK54R892svQkNDd3UNXlzI8bKLas8fC7+3t5fLlhcfX/f6XvvQ8AD/+449m/f1s35ua\nmtIZkEUjGTWXyzQfGxy0Y/B27zaT0XyrIcU2N4tuK4D0qprTlM6nwa6mbRnZmE/zH5VntLF3FgOz\n35l77m02M/Bc4Wf7nhlH5xTSfD/onPuL1UpH8wkgmRRU4fRp03jPZ6mQLZ9dp+niefasTVbmi182\nGeQKP/O5aiTXamYQQGen0t6etgTJtMzZudOsOPr7089kbuXI5c16IfFz4dUS0TKRSNgkZHiYnFZV\nzuoMZq8ERe+7e2myOyfNXEXKvF7udi/GqkvKAAAgAElEQVSatn37zLLMWQCdOTO3XMwXv3KXiWLK\nq6vL2txnnw24ehWCQNmzZ2kWJZ75CUOzblSFPXvs2uBgQBDozBjCjXEWQ6XHEoX2S+5Z1y489piN\n2eLxufU015ipqws6O5OcP29WZf39ytCQEIZCZ+fCrGIKqcsu74CCnO0tN6IWGW58G4/b/9On7Xq0\nz0j3ETrrvf5+F2K6P44+C5XffuYpDpXoTp7CHAn+ONAGnAJ+E/gp4CeArcCQqg7lCeNF7GSDLwOP\nA5+L3HtVRN6LWR00q+pNEWkHfhv4YdW566GHDh2iu7s754+5SfBCv7tK0d3dM1OB4nH4W38rfV9V\nZyb5b70FFy4EtLe/j/vuSzesmeE7pYOrrMnk+whD4dQpGzQdPLi4+Dq6u913Lfj9vr6+WdeiYnYd\nRqYZqGtc3GQwCMpvwh81YXODrqiyItvzUWrZrDNz4B/tOKLm3NHJarbBTy4FSmZ+wfz5F4+Tknvx\nTNMyO6dEAoaHzRRu61YYGbH8LsTLchQ3YVO1ci0i7N6df1CTrwznk10tlDGnLHGm6jB3kpp9xUEZ\nHAzm5EG63QARaG9fXJxqTY5RXHzjcdi2LZx1LZvibWDAynIYyixFS7SdyyeHbHkI1p5nmk+XU5au\nXQZTKMXjzNmnnMli6mApWervZLbXnZ0hV64Ix4+bInXfvtrti2qBMIQLF4QrVwQwB3qdnek6WYwy\nVan8K7RfykZU6Ts15dqg3HKYnISjR60taWtLh+/60a4uOHeuuNs6XV8NMrMAVS84GakqySSMjFhb\n2tHhxt+5HRE6OXV1KYkEHD1q/XRHhwIy65l8vw212f/WM5VQDmQzglPgTeBtzDHhehF5EbhPVTfO\neVj1uIhMishR4LiqviQiT6rqE8C/Af4Ys0D4VOqVXwE2AX+Wqgg/oKqTRU7XjHmf0+Bnal0zzaTP\nnBGSyXSlUYUrV+z5ri5YuXLub7j3RJRdu2y1Mtsz7jcXSjG016b0sNXZqSkLK5m0e84MNDro3bsX\nCpkMOmVKvsZsKWRTVmTmXyH7dXNZSZSDzLyfLz+jShybNNtELHOSWyrFTCIx13ojOolxz2WuJBRa\nxjNXMhwdHSFDQ0IsZp+jlgqZ4WfLzzB0lgLC1q1KVxfE43P3YeSLZ657tWay58pYdALqPqumlWzR\nlQeX32fOmKmiy4MwzFZWhfb26vZmXczVkVzWNtZOputNdADtrgeBtV+qMvOc6yOiVi2ZZTpbHro4\npK8XJz2LIQisjIBNLETgAx/QJfdVtYpbfX37bWFszGRw+7aNGbwFQemYnJzdZ7o6WW9kG9ueOiUM\nDEA8HtLWlvvd3l44cyZg9+5kqt904WWftEfN2J2FghtrZyrUc1mnur66vb1+J6jR8apTtEbHXtG+\nwVlZ7N5tyuJ0kVe2bGFmvJMp52g4UXItLJUTb8GwMCrRjXQDHwfeATRh2wAewnwDtKjqLRFZjVkT\nvDdXINHjC1Pfn0j9H8KsCaL3fr6YCYC5BW1qygYtoPT0WCedOcHIdC6VTNoKZixmA2g3uDtzxgI9\neDBtiptZoN3k1UzxFTe5NhNcCILZKz/5VlGiccz3bCEymZqC06eFgQFhfDzktdcAhM2blYceMtMx\nZwaaKy6ZcY6aRKmmJ67zpacQMju5ILAOP/skZX5cw1pqx4rZ0plrAp85wY3itg9Y+UkrXnKt9rr9\n4dmUD5Ce7EcnHG6fWuZpEKdOQV9fjE2bknR0WJl1HVUiYWXp7bdNllu3pmWZ2XnNt1LvOsKuLp2Z\nUO3c6eqMpBQP6d+F2dYk/f1pqxf3e2B1sL3dBjRnz1pczPpCZwY92Rz+ZE7ColtvJifh3DmIx2WW\nqWpmXleabEqozLKgqgwMBCSTafm+/HLA9esBTkc8OKiMjga0tSnf8z2a2qaRzteoB+VsSpRoHHK1\nZaU2Ly+G4iza9uWytnGrN729zvw2nHnXDeDa22ebeL71ljA87Fbk0ltnsrVRmeUzGi9nghptC+bL\nj2LJxoUThtZ/Xr0qqc/JRU2Ei6EALzdOtpltwrZtIYOD8D/+B6xeDbt2wQc/6BUEpeD2bXjzTeWe\ne2BgAEBmKZxrrUxFWUydyFb/L18O2LIlSSJhW8Wyj3+ElpaQ1lbr69xYNVpmXRvkxrMXL8LQUMCm\nTdDTE/L882YF8IEPhDPP9vfD0JApALJts+nsXJyCebFj5IX2LaXq56N568Y3HR06szXG9R1OZokE\nvP12QBDAjh0hAwMBEKIqXL0KsZjbkjBbUZPN2sMpHzK3LuSjFHLIZsXqyU8lupB/DTQCXwKiE/zb\nwJ9HVoU7cHYrFSJXIc30UAv2/dIlGB+PEYYh73+/zqzYRLXLySTEYrY66yYlqulJ1fr1EAQht2/D\nCy/A9LSwfbuZ2yYS6eNf7DfBdVBBYBOL3l64ckXYv1+ZmkpX2GyNdDQdCznpIBenTsErrwjT07Bx\no4URi8HEhHL8uHDjhrB3b0gYWkd7/nx2+YpYY7VzpzU+ac2lIjJ3MhDNp8UMRLM9l8tjuhugmwa7\nMlrohQ6483lUDgKbxLkGvq2NWSu9rtMVgV270nt8oyv9LrxTp5y5ms50wlNTcOyYWcj09MxVdq1Y\nkeTyZVP6dHZamO3tyksvmeKso8Pqx/CwlSVT3DhzfkgmdWYinW0CGQTMmkQlEta5RZUE6fhLKv4h\nrqxNTZmiyJVJJ4tt2yye58/Dq68Kra3Ktm32bF9fjIGBkJ6e2UoTF590+bVwnZxPnTKzvWTSPD+7\nOA0NmWx27oTGxqUNbopBto42c2UHbKKSTNqPDw1Z/rW2hqxYYdf6+sxqoLk55NixBkZHp9m0iZSS\n01aHM083iMbBWSbs2mXvRBU6mdYnhaQj3+CvlAqazHY4E+cctL8fzp8PGBtTWltDLlwwWbe1mXxV\nhUcesTrm/F9cvJie6J85A9u3uzpvSupk0voNZ6bv6sjbb9s7zjfH7t3K229b/Q5DG4Dv25ce0JfC\nssjh2qDhYdi6FQ4csARFfxsWZlVUisF9ISxmcgHpve4OZ+F165b1pVNTMD4OExP2vbl5bniepXHr\nFrS2wt278Oab1v5nU/Jmo5STykIoVZ2Ifre22kxE3famri7rs6J0d1tb8tpr6dMKpqbsnhsfOOeE\no6Nw5YqVa1XrP86dg8uXbYyZSKTbrcFB4fJlU86HIezbl+6XrO7IjJI6Mz25ZBBt16L9Sj7v/Y6F\nnGI0X/uZS+75wo2+Y30qPPecbYtxbajbbjE6GgBJOjtN8TU+HrJunSljt24NGRmx8cq6dSb706eF\n8+eVjg545ZWAjRtDtm1LKyCiW76CgLzWvtkWUXPJwVM+KqEceBzzGfATGdcbUv9fBo4DG4AfKGO8\nZpFr4OhWadwkKMrevXDlShKRgERCU9pl2LHDGsj2duXb37YwWluF06eVV16xPTxuotzWply4EGNw\n0CrL5s0h27bZAO4rXxGuXlX274e1a62Dam3VmUn0sWPw0ksx1qwJOXnSKn1jozWU7hk3oLIBqaVj\n61ZrZJ253FLMfwYGYrS0JOjosIlOPG5e4ScmAl57TWdWkF94wWRy8ybEYsLUlA129u61Buf4ceE7\n3xEefDBkbMzCXr/eJhiuMTFzcU35CDBLi2j+wdJXBKOycDLr64uxYYPywQ+GMx2fWx0PAptc79o1\n17HifBOPpRKdXEQ7kWh5jcYhkUhPcJ99NkAknCnT/f3C9LQSi9mRQxs3Krt22aQ5DOG737WJdDwe\n0t7OTKcyNqYkEqZpTiZtv/iNG8rYmE1gJietI3nnO+Gll4SJCWHjxpCtW9Py/da3Ypw9G3DgwDSd\nnbBzpw0I3JaUMDQl29iYlaXNm+fuk3aTnCCwSWoiAceOWRofecTi6n4PJLUqasq6wUFhyxaz4vnK\nV2wydOBASCIhXLxoJtt/+Zc20Ni4Ubl1yyZvR48KnZ2aGsBYPFyZfPbZgCtXlC1bdMbk8vx5oa1N\nZ0ztEwm4cSNgzZok27fbtQsX4OxZ4e234fhxeNe7TPHiylq0A85G1MqiWI6YnJLSlaEzZ8yc9OpV\n2LAB3vtea09GR+H48YCmppDLl01puGOHcvmy8Kd/GiMWS9LaqmzfDiMj01y6BOfPx+joCEkmdUZp\nlG6zTJmwYoXb76gMDJhstm83S5DoGMQpvTLN73PJK9fAZL4By1JWot3Ed3CQlPVMWgGZttaxSXos\npsRiSmursmKFKZOuXYvx2mvKhg12/emngxlZnDghrFypbN1qn8fGhHjcnmtutsH11avw3HMBLS3w\n7neHnD8vXLoUsGVLSGenMjRk+XvxopVbUyqEKYu37GUqs51b6pF7YWhKERFra5qa0mXf5U0ikXZo\nuZSj/nIN7hcT92wD+kLjlkik27SdO80CJwisTb1yBW7fVu65RxgfV27dgrGxGA0NIWfOKBMT8BM/\nMXdS5lkaN27Am28GJBIBXV0J7t7N3nfnyne3xSqXVYfr27IpG5Yynsm1arrQepEZN1Noy4xiwCwD\n3VjILFpOnYL775/9W7/3e8Lq1cr99ydRFVpb4atftbHgli2wcaPw9tvK2bMxtm9Psn69hZ1Mwvr1\naR80sZjy7LNCMils3x6ydauyaZPy+uumJAiCkOPHA1SVzZuV9nadNXF1cjFlQtqCdz6rmzC0PttZ\nPETzKGrRGJVdNivBQvMgW/7Nt7011/zl0qUAMKWw+SsTpqeFxsYkr74q/MVfmPJg1aqQ++5Tjh4V\nRkeFzZtD+vuhpQVWr7Y2SBXu3IFbt0ImJoQbN5T169N9mbMsCEPrn6NycuMHk01alqViKX10vVIJ\n5cAw0AX8ITAIdAL/EHNEeAfbcvAWsBP4ooj8Ruo9VdVPAYjIpzGnhn3R7QUishX4ArAC+JSqPiMi\na4AvAuuA31fVzy8m0umGxCqkMzEGt1dbOX06xt69SR5+OOTsWeGtt4SGhjDVeCnf/nbA2bMBO3cm\nGBqyRlFEiceVr3+9ERF45JEpRkeFFSvMeuDSJeHOHZvA2cpNI7HYFNPTAVevCrdvm6a2uxsuXbIG\ntrlZGRyMcfEidHQkSSTg2WetEX//+3VmNX5wMGDrVpvEHjtmpr0iZkadaWmQy2Q0em3nTmhrC4GA\n7dtD1q9XXnopjghs3jxNU5NpgcfHbQDa2JgkDAPWrw+ZnDQFQSIR8OqrIckkJBLKhQvC6tVCU1PI\n+HjA6tUhsZj91tCQ/fbISEAsBpCcMWOPThYX2hgHgfOinjaFAgvz4kWbdFy54lbQmTH1vXJFaG0N\naWuTmf3nrrMpZOJRKLkaulwDCxEzK96xgxmz7W3bTNk0NmZKg/Fx6ziuXw/ZudNk/7WvxWlsTLJ9\ne5IgkJlB+tiYTSwuXRJWrLA8PXECrl0zJc369ZraMmNKnjVrlIkJm+hNTcHoaIzbt5NcugRDQzHu\n3nXlLsaOHUkuXlRWrQpZvRpeeMEm3OZMiJmO/No1ZWAgzqZNCbq6lPPnZ8vwzTeFEyfMxDGt4Am5\neDHg3DkzRTxzxuIXhsLoqJJImKIMzGohHrdBeRgK+/eHjI7aiqwIXL8ODQ2WnslJYWIioLU1SWMj\nXL6snD8f5/r1kK1bw9SEVTl3Lsb4eJLNm+03zp+3MnTnjilJtmxJsn9/gljM6vqOHWZ2eeeOTegS\nCUujWegEbNyodHZqykdE9kGNWVkEqbKY3xFTIXXE2hJT/nR2WsfuBo5DQ8KVK7ai/eabcVavVvbs\nSXL5Mrz+egxQGhuTDA+b+WlrqymjXnkFLl4MEDHZJpO2UjQ2ZmWos9N++8QJ4dSpGB0dSQ4etHI6\nMACnT8eIxxNs3iypCZfObAc5cyY9gHKDWWDG4WsunzALZbErhomEyc1ZkoHF0ZXdzZslVRatTdyw\nwerwG2/EaWlJ0NKSZHg4xp07ptQaHY3xjnckaGyEkyfjJBIhHR0hO3ZoajuHcuMGjI7G6exMMD0t\nXL1q8nvzTVITT2HjRpP78LAyOQlvvWV53tGhTE3ZqQmZSiI3AIxOap1FRNT8eqEkEjYAffXVgJMn\nle5upa0t3S4nEnDiRMDISDijjHNK0Vx+YXLlVXTyvhTyKZQynTs6MvsEt5UpCGz17vjxgDA0C4Eg\nCDhwIMk3vwljYzbxnJqKcetWgps3rZ9KHb7kKRJhCFNTMW7fFsbHbQK7alXu0z2iimq3xUpVc1pD\nZU74or9brNXUQi0dMt9Jn2UfTafVu02bzIlgENhzw8M2Njh5MqCtLUlbG2yMeA67eTPG6KiwZcs0\nra3KV75ii18XLsR56aWA9vZpWlpgw4YkDzygvPaalec7dwKuXbO+e8MGS8PZszAyEmNsTFm5Url7\n1xSbabkqY2MxWlsTJBLCc8+lnWKD5cvQkI1NVOfmT2Yf0dWlnDqV3qqVKadoX5trspstP0s5eXWy\niMfN6ioMbZFtaMi136b4/fa3YXTULBdF4J57TO4jIwE3boRcvRowMsJMX2TKAVtEunkTXnmlkXXr\nQnbtSnD9ui1Obt2qXLxo1szr15vll4hy4kSM1tYw5WctoLs7mVXeS01zNAyvFFgYlVAOxIApTBEw\njR1feArYA4yk7t0LvATsB/pxy0SAiHQD96jqQRH5jIi8R1VfSoX9y8AngVexUxGeAX4OUw58Cfim\niPyJqk5nRiqzMOUaOAYBM6t6bgBiDYtpMMPQoppM2mrOtWtmGtXWZibQa9ZYhWhoCJlOxaKlBdav\nnyIIbHLf2prgvvus8b10CZ55RtixQ9m0CRKJ6VTlVlatUq5ds8nJ6dOmxdu+PYmIEIuFbNpkK0TJ\npK3qXbsW0N5uTmDAzK0znRqOjMjM0W75Vs6ydYKNjbayCbb/8Qd/UBkfn2ZiImDnThvY3r1rA/+7\nd60x3rpVuXsXGhvNYmBy0iaSa9cqTU1w+bKQSFiYltaAZNJWksEatU2bkojM3i8+MDB7T26+FZvM\nvHdmvmbWHs4yTRsdhS1bQjZvNrk+9xyMjlreNzSY0iCRsPwaHEx3NtHfKoalQKENnSvHrjMfHoYw\nNJleuwbf/W6cW7em2LTJVmNtUmGa3A0bEly9GmP16nDG5N+qoaSOqEsyPQ3PPhtjclLYuTOZchhJ\n6s8UX9euCatW2UBqzRqzLpietgnD6tXTNDSYourOHVMGtbSE9PTYtWvXzKTeTfDtlAHYtAnABh6N\njdmPARQJmZiwifb0tPCOd0AsZo4Id+5Ujh8PGBsT9u1LphxmSkrxZCu0AA8+mCQWs99YtUoZH7ej\nq+6/P+TqVUvnypWwenWSO3ec7Oy3165VBgct3AMHlOHhBNevC6+8YvWrocFW/aam4O5ds9LYtMk6\n7e98J70FKQztFITGRlMQBIFZYti+8+yDzug2BmcNMp9iINeevGiZvXkTbt60tF+6FLB5c8g736ms\nXWtpsEEcrFgRsmaNPT8+Dnv3Jrh92+S4aZMSBAkaG8NUfVEaGpwVSJJt2+DiRZvorluXoKfH4j44\nGDIyYsqm7dstrM5OJRZLsHmzeWGG2UcyAamBq22zevVVa6P37EnO2aKRa2CylAHLfKai5qPC2qps\nbYOIrfwPD9tqmIgNzDZuTOIsXvbuTXLqlClT2tqsPq1bB62t01y6FGNy0treDRtMURWPw61blv7p\nadi1K0FPjw3C169XNm405fX58/aePWcKxHjctomNjNiAfnBQuH3bZL9mDTz4oHL5slkk9ffbNrON\nG9M+OKIyLZQTJ0xOY2MxVJM4s2E3+e/qUkZGbE+scyLrLFoW67u2FIP1aFvsnDtmTtbcNVdv3QKE\ntSlmSbh5s403du2Cl182i5w1a8z099atGC0tSZ57Dj70IbOqKpSLE3cZnZjKeX/zmka2rFmxqLQv\nFzo6pmcUwxcuSKpMW58YPeXHrY5GzdBVZ9+P5n3U4s+1V8Uud0tFRNi1K9q22opye7vOOMl27df5\n8zYGvXxZ+Nzn4BOf0JmtLnv2JGhsFO7csZV25xMoHk+ybl3IqlVKMik88ID1gUNDAbdvC5s3J1Pb\nnISDB53/EyUeNwu08XFBRHjve208GY+7RZ5EVuVvEJhS3W1ZzWUx69519+PxdN3M7CeifW30XiFK\n6Fz5na0tmp2fcxfAZrc16fZl3z5rlxMJU/acPh2walUS589l795EZBHH5ixtbQmGhqz9WbPGrNYm\nJuDmTaGlxeQciyl37yZZvTrJxo02pwHl7FnhwgVoalLWr7c+pbERYjHrv4LA+ny3AJBPDoVSTGVa\nPVMJ5cBm4AXg+yLXGoHVwHrgfiDAnBT2qurfyXj/IeAbqc9PAw9jigSAd6rqiwAiMpGyGngI+AVV\nDUXkFUzxcDIzUrkcuTlcZTPzZNOENTfb6mJHh9LTo/T0WCPU2wv9/QE7dijr1iknTwpf/GKMrVuT\nrFplg/nbt+HGjRgrVyr33msVsrHRBg2XLzdy9eoUra1mNnrnTkAQJBgdtcp05441mtu3K42Nws2b\ncPVqjDCEbdsSNDTYoOzaNdi6Vejutv1Ar7yS5JVXAoaG7OxxZwq2d69pOoGZzm4xFSoIbEXOfW5u\nhp/+aTMxsxUlM0/dtk0ZGVE2bgzZuBF6extZsSJk5coEFy8KV682cPv2NG1tyrlzcTZvNhO+mzdj\nrF6d4Otfj5FMKn/tr9nq4Zkztp3iueeU++4LZywyMsm2mpRtUnT6tE1C29rSvh3c8YbnzgUcPx6n\nq2uKlha4etUcrHV1JXjHO0yZ4/wlROXotM6nTzuF0txzYk+cWLjMM+Wfa3IThswoK9rbbSL68MNK\nIjHF5csxpqdtRfv48YBnnomza9c0Bw7YoPP0aZmRZzyuPPCADfb7+61zTyZD1q0za4L+fuHcORs0\nnD8fcP260NwckkhYmb1wQbhzJ2Dt2pChoZCxMasD69ebyf2FCybPWCzJPfcok5PKU08FTEwIra1J\n3v9+02ZPTQXs3x/mPL7r8mUbLLz5ZsDduzaIe+MNTXnztrRs3Ajr1ye5csUUDj/8w+FMHfzOd2Js\n3BgSjysjI2kroJs3TWk1NASDg1Y2YzFoahIuXAhoaAhpbVVaWkJWrIDXXzdzxb4+Ux6dO9fA228n\nuXgxyeuvN7B2bYK2NiuzO3YIZ84ozz/fSDIJLS1T3LmjrFljW29GRmxCfuBAkljMlGjWqc6ur4kE\nfPObduEDHwhnlFMLrdNuQNTfb6sAU1Nw/rxy6lQj8XiCjRtt28Bf/qWwbl2SsTEzUQTbGtHUpLz2\nWgNTU7BxYwLVgMlJKysdHSHj48LFiwGbNikf/3jIG2/Aq68GnD5t5uNm6WB7G8EsXLZsSTI6atst\n9u4ltcXC6q8NzNID1V270j4u3CBJFbZsSeYdiC3k+lIJAlPSvv12QF+f0N2dTDlhtJWU/n5rM8G2\nkl2+bFvF9u2zFfyzZ4UbN+DmTdu+0dxsSpDLl+HSpTjNzUk6OpTJSSGZVK5dC1i3zgZ9b7wRZ9eu\nBPfeC5//fIxbtwKamxPcvBmjszPJf//vwvi4hbtqlXLunFnkXLsWsHKlcvlyyI0bpqy9ezfO+vWm\nVOjshPe8R3nxxYBr14S2tnBmpTzfKS+OTGWtWTg1sH79NFu2pM/ndtZCjY1mEQc6Y6V17lx6z/NC\nFanR314s+driXJO1zMmie88mUAEHDqQtfwYG4NvfDrj33pChIWdtE3L9esCbbzYyMBCydauNBQpl\ndGKKf/o/T+e8/28/srvulQNjY8L4eJympmnicevv7t41pZo7Hq6ry/xNnTkz+1i/6CJB5kp8vrJX\njHKZa6Ja6Lu5JprOqZ0pu9L1urFRaWlR3nijgcFB4dlnp/jIR+zZmzfNsqyhAZqakty9G7BmTYLm\nZtiwwZSQ09O27VAkbX2wZUt6P3s8bnHp6FAefdT6bVMaJmfahT17rL+/7z6XktnOV8HCSedLdrlk\njhHz5VOuvnapyuZ84WWLY6ZyIoobB7p+sanJtgKo2mR/aMgU0XfuCJs2JRkejtPYGLJlS8jkpCnu\nr11rYOvWae7cMSXB2rW2nfLSJeH55wPWrrXycveusG5dyLVrMW7cSDI5aX3Vrl2wf7+N348dE776\n1YAHHwy5917NagHpKT+VyIavAxPArwID2LaCl4FXVfVdInIDsyg4ClzI8v5azNoAYBxTJjhikc/j\nqWfXAjcyri0KV9lGR23Qs3Zt+siW558PZlbizew4xqVLSXbu1JmVjnPnYqgG7N49za1bAcmksHHj\nNCtXwrVrDVy7pjQ0hDQ06Mx+toYGZe3aaVasgPHxBtasSaRWcmKIJNi1y1Zq2tpMwbBmjTmLGhkh\nNclKexZNJoWRkbmazehnZ241X2OWb/ATxR3HaGmBtjZJaZpNJrduwerVidSqLExMxInHbVX19m1b\n8b11KyAMQ1atSs7sg1WVWR0rwMREwKVLdipCPO6coGjWzi0fzgx/1y4bZLqTC0TM1FbElmWTSXdm\nbphylGhmVCJKY+PcjsIGxzLrKKRsMlsK+SY3Tqsd9di7ezd861shZ87YCt/wsO1Tbm7WGadlsZjM\nPK9qZcoc+5iVxLZtIevXW8c9Pm73m5tt1TsILJ8mJwMaG0Pu3ImhKjQ1Jbh6NU4YWh7fvWsaZzP/\nFgYHA3butMHw9esBiQRMTMSIx22fYiyW/5xzt09bVVizxlbbVq82q4Z162xC8cEPmiOkP/szU2IM\nDCQIQzOLnJjQGcdeN2+ak6+bN2MEAaxdm6ShwSwJJidN4377tpmB370LIyNxNm2yyfGdOyaHO3ds\npTUWU5qawhlrmYYGK1duVcXJfMUKmwwOD8fYvTvJvn0WZxGbOa1bx4xCcb7JfyHlK1t9dvsxh4bM\n3PzKFdtXOD1t9bqpKeTiRZt93L2b5ObNBtasmU5ZbATE40nWrJlmcjLg1q0Yq1aZ1VRzM3zkI/Cd\n7yhvvKG0t4e0tNjgb2REGRsLWHnoTBQAACAASURBVLEi5Ed/1Py2OGeQro7ZCRvhTFzdYMJZRJ0+\nPduawhGP28p2Nk/WpSLfIDCqcHb+adx1N8kNAqtzZllgAzfbf2sTkTt3lBs3Yqxda74YBgdtINfe\nHhKPC7GYzPi5MKdebv9uQDIZ0Nhox83evh3j/2fvzaPrus7D3t8+9wIgQYLgDJEASJEASFEyNdCK\nZUkQNdixXSdx7FaOm7hj2jRJ13pq+vKSdqXN4Pa9tEnaunZqr6RLbuIkzbNqpclrWk+xrAmiZFuC\nZEquxQEESYAQSXAAR5DAved7f3x333vuwbkj7nz3by0snHPPOfvss+f97W9/X1eXbh9ascJq+Hgp\nryEq4Fq92qpWGzo7VfNtcdGwYYNw48YiGzfqYND3Da+9pto3IyPq+qpYooS1nufR06OacJs26apX\nPJ49SI9KX+vRpxRq0RaHr0UJCuw3qQZUVH+kbfLmzbrCd/myamd1d6vNgcVF1Si4dq1y39PunDsH\nN2/G0tu7Vq8GUCNtW7faFXBJ9/Og20+tEWWb51b4ZYxZ4jI4F7Uql6U8E4x3uAzb7XmrV0vKRkyG\n+XkVcGofLnR2Jli/HtatM1y7psaLh4d9btwQkkmPO+/UcWlGSyMYBx2vhNM2V9xzfUcpFFuvyw2j\nkkT164ODftq4qefZbR/C+fM6bltYgK4u7YutYeB163Qcd/lyDPDTab6woAuTxqjA/eLFDtas0b5q\nbs5j5Uqhp0cXeRIJk9bQtGVHtQ4AhJmZjGvz5aRPJYW87YwJT1Sq/kJjNgCfA/46KpxIoMYI3ysi\n3zbG3ERX9meBR0VkRej5fwzMisiXjTF/HegXkd81xmxBbRXEgVXAnwN/C7Vt8K+Az6JaA78qIl8I\nhvnzP//zcvHiHNu2bcMY6O3tZe/evYyOjgIwNjYGwOjoKL4Pf/qnLyECn/jEg3gePPXUS8zOwoc+\n9CC7dsHzz48xPQ379o3iefCNb4yxdi0kk/u5ds2wfv3zrF0L73nPKMbAH/7hSywuCtu2jbJqFczO\njnHtGnzwg6Ns2QIHDoxx6JDh1lsf5Lbb4NixMYyBRx4ZpbMTXnxR4/fgg/q+AwfGSCTgve8dJR6H\nV14Zw/PggQc0/i+9NJYy4Lf0+8o97+7uZt++fXnzPthwX72q3gricZ3ovP66bm9Q7QedDJw8qRZn\nb97UieeDD+q9CwtLXQktLGSk11Z7AaIlrOHfw78Fz+1x8H/YXV/UuwqtikXdNz4+XjANl0Ou/c/B\nvcNq90Enf55nVzjUUGRwwmLTJqi+HlSXVbsFmfDOn9cVf7VYr+quR45kJn0HD6rw6No1A6jtho0b\nSWmB2A4EwK4K5xYOjI+Pc+ed+9Lvh2wr/8EJJWTcB+ok1KS+UTsrNUyocZ+aUgHH3r0qJHr1Vf22\nzZut4Muudgj33ZftzvTGDd3TNzOjKne33aZlOR7XuHV2ZtwC6ZYZnez6frYleZv+Nt1zGR2z18ud\nBI+Pj7Nq1btT8df6ZIUEGzdmbAxcvChs3Qq9vWocra9P06y3V/NMhTSZMnP2rF2tsRPNTBrYc5sX\nuXyIB8ugPYfMeS7vHMXYVKgkpdTnoLvPcNsUVHcNth+2HAT3/b/wggpPHn7YTz9n67J9h+9r23v6\ntN2eo1s7dD+oxuHUKS2rXV36f80ak/a+cfSoruydP6/1fccO3WoXj0N/v7bNMzMeAwP+ku0EwbhH\nERYOvPHGOLfeuo+FBZ2M2Xpc2GhY/vc0KoX6p3B/FMzXK1fg7bft9jvVHvnEJ+DkSS2H//2ts/ze\nK6ci3/u+oXV8aPeGvJoDn/mxERaS0ePFVt9yMD4+zokT+9J9UTIJ992n7Zwtk+G+MUgx45BmoJh4\nB8cFc3PaB2zYoHV2fHycAwf2sWaNCom3bNF2qLMTHn44k3bB+p2rn88Vl2qkbSPlV65+pdg4BtuQ\noH0Y+2xmS4xe37gR7rqLtEaaLhRoGLfdpmO37m7tS956S+vEzp26zS2ZhFtuMWmvUMePW09tmXpj\n+zC9VryNmOUwPj7O+973vjI3nbUHNRcOpF9sTAzYhAoBFoB+4CxwE/hx4BTwuoh4oefuAX5WRH7O\nGPM54A9E5FVjTBfwH4H3AA8Dfykijxpj/inwAeD/Bv4tkBSRR4JhPvPMM1LKpCxqMplrwGqPg514\neK9r0DJ1sHMJhpdr8NgolDOxDQ9w1G1cZsKzsJA9+QkeFzPJbzaqLRwoh1LTNF+HHVXGLQuhra5R\nwpfw9VwspyyGfwvXccjUwaBgxP7PNykPTuTy1eNGKMfj4+PcfXd2GkZNVO3kPiw4Ws63VeL7GyUN\na12fixUKhfMSooUS9jg4IQ/+Hsx/G0ZUf1gKwbxrxDaxkfF9HZTPz8PKlTqwt2l44uI8h89dj3xu\nS08XSV/yCgd+/f07+NQ3JyOv5RMcAKxZEefyjURVrhd6tpDgohhbCzNHvs/tt+/j3Dktl5s31077\nqJUYHx9nYGAfi4s6iezuzu5XHYWpZJuYr58MLoQF2/5w+x5cLFtYyNwfHu8V875c1yqNEw4Upi7C\nAWNML7AbtTMAajzwM8BfAF8CtqDLVZ6ILGnVjTH/EdiHCg/+iTHmsyLyhDGmH9U6eBv1VvDNlN2B\nSdTo4e8DjwOfFJErNrxnnnmmPhISh8PhcDgcDofD4XDUBCccyE/NZXXGmL+Hbiu4Clgx9iLwf6Jb\nDf4D8D+A9wK/EhVG0H1h6vyJ1P9TKaOD7xMRP/XbFWPM90Xk4dT7fxi1O5AWDjz99NPMzem2Asi/\nrWC5574PX/qSnv/Nv6nbAOz1Bx4Y5fBh+OpXX8IY3aYwPAxPPTXGuXPQ1/cQvg9nz74IGO64Y5TB\nQeHsWX2+r+8hAM6ceRER3WYQjxeO3wsv6Pn+/Rq/sTHdhlDpbQXNgFVrFZGUKzndO2gt6g4MCAcP\nGta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wT4EfDt33Q7SB5kAU5VQgO+izLkTcBKZ4SklvY8wSTwaeF9yO0JyEJcfGqCaBbito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YaG0oSh8VUYSi4+h4vfMDMDly7Z2hOGMD4urFplY2F6Oh/bwISB8NWvBhw9Kjz2WGYW3Q8d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WC0e7568Ef73lfSiU3QyZbccyWl2Hdm/vdk7XSp05VMuO52/n9h0K6SpNW+/8w6nsdm/r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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x254ab710>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k_range = range(1, 11) \n",
      "\n",
      "features = census_kmeans.values\n",
      "whitened = whiten(features)\n",
      "\n",
      "k_wcss = list()\n",
      "\n",
      "for k in k_range:\n",
      "    centroids, _ = kmeans(whitened, k)\n",
      "    code, dist = vq(whitened, centroids)\n",
      "    k_wcss.append(dist.sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.ylim(0, 2000)\n",
      "plt.plot(k_wcss)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "[<matplotlib.lines.Line2D at 0x54f58780>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FXj17fyHmUiY/yuQvxFwhZvIRVLEXEZH0UM9eRCTLqWcvIiJAYMVePXt/IeZS\nJj/K5C/EXCFm8hFUsRcRkfRQz15EJMupZy8iIkBgxV49e38h5lImP8rkL8RcIWbyEVSxFxGR9FDP\nXkQky6lnLyIiwAQXezP7tJltNrNtZvat4ferZ+8vxFzK5EeZ/IWYK8RMPias2JtZDPjfwKeBs4E7\nzOys1HW2b98+UXG81dbWZjrCiELMpUx+lMlfiLlCzOSzoTyRW/aXAtudc+8753qAnwE3p67Q1tY2\ngXH8NDc3ZzrCiELMpUx+lMlfiLlCzLRhw4Zx15nIYj8L2J0yvydaJiIiaTaRxX7c037q6uomIscx\n+eCDDzIdYUQh5lImP8rkL8RcIWbyEZ/A59oLzEmZn0Ny637QvHnzuPfeewfnzz//fBYvXjwx6UZx\n8cUXs379+oxmGEmIuZTJjzL5CzFXCJlqamqGtG6KiorGfcyEnWdvZnFgC7AU+BBYC9zhnHt3QgKI\niExiE7Zl75zrNbOvA78CYsCPVOhFRCZGUJ+gFRGR9AjmE7TjfeAqA3keNbN6MwvmpFozm2Nmq8zs\nHTN728y+EUCmfDN73cxqzGyTmf3PTGcaYGYxM3vLzH6e6SwDzOx9M9sY5Vqb6TwAZjbFzFaa2bvR\ne/ixDOc5I3p9Bn6aA/m3fl/0f6/WzFaYWV4Ame6N8rxtZveOubJzLuM/JNs624FTgQRQA5yV4UxX\nAhcAtZl+fVIyzQAWR9PFJI+BZPR1irIURrdx4DVgSaYzRXn+Avgp8Fyms6Rkeg8oz3SOYZkeB76S\n8h6WZTpTSrYcYB8wJ8M5TgV2AnnR/JPAXRnOdC5QC+RHNfQlYN5o64eyZT/uB64mmnPuVeBgJjMM\n55yrc87VRNOtwLvAKZlNBc659mgyl+Q/uqYMxgHAzGYDNwD/DIx5gagMCCaPmZUBVzrnHoXksTXn\nXEifGroW2OGc2z3umunVAvQAhdHJJoUkzzDMpDOB151znc65PmA18NnRVg6l2OsDV8fIzE4luefx\nemaTgJnlmFkNUA+scs5tynQm4O+BvwT6Mx1kGAe8bGbrzOyrmQ4DnAY0mtljZrbezP7JzAozHSrF\nF4AVmQ7hnGsC/g74gOTZhIeccy9nNhVvA1eaWXn0nt0IzB5t5VCKvY4SHwMzKwZWAvdGW/gZ5Zzr\nd84tJvkP7Q/M7OpM5jGzzwANzrm3CGgrOnKFc+4C4Hrga2Z2ZYbzxIELgR845y4E2oBlmY2UZGa5\nwH8A/jWlDn+sAAABzElEQVSALPOA/0yynXMKUGxmf5TJTM65zcBDwIvAL4G3GGPjJpRiP+4HriTJ\nzBLA08BPnHPPZjpPqmj3/xfAxRmO8nHgJjN7D3gCuMbMlmc4EwDOuX3RbSPwDMkWZibtAfY4596I\n5leSLP4huB54M3qtMu1i4HfOuQPOuV7g/5L8d5ZRzrlHnXMXO+euAg6RPI43olCK/TpggZmdGo3m\ntwPPZThTcMzMgB8Bm5xz/yvTeQDMrNLMpkTTBcAnSW5hZIxz7q+cc3Occ6eRbAP8u3PuzkxmAjCz\nQjMriaaLgOtIHmDLGOdcHbDbzBZGi64F3slgpFR3kBysQ7AZ+JiZFUT/D68FMt6uNLOq6LYauJUx\nWl4TebmEUbkAP3BlZk8AVwEVZrYb+Gvn3GOZzARcAXwJ2GhmAwX1PufcCxnMNBN43MxySG48/Ng5\n90oG84wklDbhdOCZZK0gDvzUOfdiZiMB8OfAT6MNrR3An2Q4z8BgeC0QwnENnHMbor3DdSRbJeuB\nH2Y2FQArzayC5MHje5xzLaOtqA9ViYhMAqG0cUREJI1U7EVEJgEVexGRSUDFXkRkElCxFxGZBFTs\nRUQmARV7EZFJQMVeRGQS+P/HuQ2O12vpAAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5e3b5d68>"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k=3\n",
      "\n",
      "centroids, _ = kmeans(whitened, k)\n",
      "code, dist = vq(whitened, centroids)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans['cluster'] = code\n",
      "census_kmeans['cluster_distance'] = dist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans.head()"
     ],
     "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>Ratio Marital status       Divorced</th>\n",
        "      <th>Ratio Marital status       Living common law</th>\n",
        "      <th>Ratio Marital status       Married (and not separated)</th>\n",
        "      <th>Ratio Marital status       Separated</th>\n",
        "      <th>Ratio Marital status       Single (never legally married)</th>\n",
        "      <th>Ratio Marital status       Widowed</th>\n",
        "      <th>Ratio Family characteristics    Size of census family: 2 persons</th>\n",
        "      <th>Ratio Family characteristics    Size of census family: 3 persons</th>\n",
        "      <th>Ratio Family characteristics    Size of census family: 4 persons</th>\n",
        "      <th>Ratio Family characteristics    Size of census family: 5 or more persons</th>\n",
        "      <th>...</th>\n",
        "      <th>Ratio First official language spoken  Neither English nor French</th>\n",
        "      <th>ratio_male</th>\n",
        "      <th>Total</th>\n",
        "      <th>Ratio Age 0 to 14</th>\n",
        "      <th>Ratio Age 15 to 34</th>\n",
        "      <th>Ratio Age 35 to 54</th>\n",
        "      <th>Ratio Age 55 to 84</th>\n",
        "      <th>Ratio Age 85+</th>\n",
        "      <th>cluster</th>\n",
        "      <th>cluster_distance</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.042720</td>\n",
        "      <td> 0.085293</td>\n",
        "      <td> 0.560196</td>\n",
        "      <td> 0.020078</td>\n",
        "      <td> 0.225910</td>\n",
        "      <td> 0.065802</td>\n",
        "      <td> 0.508428</td>\n",
        "      <td> 0.243630</td>\n",
        "      <td> 0.201294</td>\n",
        "      <td> 0.046648</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000247</td>\n",
        "      <td> 0.490867</td>\n",
        "      <td> 81570</td>\n",
        "      <td> 0.166055</td>\n",
        "      <td> 0.221261</td>\n",
        "      <td> 0.314247</td>\n",
        "      <td> 0.298438</td>\n",
        "      <td> 0.015560</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3.630211</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.035921</td>\n",
        "      <td> 0.080860</td>\n",
        "      <td> 0.587961</td>\n",
        "      <td> 0.016382</td>\n",
        "      <td> 0.198617</td>\n",
        "      <td> 0.080259</td>\n",
        "      <td> 0.580985</td>\n",
        "      <td> 0.229258</td>\n",
        "      <td> 0.153235</td>\n",
        "      <td> 0.036522</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000394</td>\n",
        "      <td> 0.493676</td>\n",
        "      <td> 76690</td>\n",
        "      <td> 0.135205</td>\n",
        "      <td> 0.184976</td>\n",
        "      <td> 0.310200</td>\n",
        "      <td> 0.369619</td>\n",
        "      <td> 0.020427</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4.768561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.035314</td>\n",
        "      <td> 0.085710</td>\n",
        "      <td> 0.596834</td>\n",
        "      <td> 0.018665</td>\n",
        "      <td> 0.189861</td>\n",
        "      <td> 0.073615</td>\n",
        "      <td> 0.586778</td>\n",
        "      <td> 0.222114</td>\n",
        "      <td> 0.154641</td>\n",
        "      <td> 0.036466</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000516</td>\n",
        "      <td> 0.489306</td>\n",
        "      <td> 78080</td>\n",
        "      <td> 0.144946</td>\n",
        "      <td> 0.186843</td>\n",
        "      <td> 0.315800</td>\n",
        "      <td> 0.352411</td>\n",
        "      <td> 0.019187</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4.518964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.030345</td>\n",
        "      <td> 0.175770</td>\n",
        "      <td> 0.478758</td>\n",
        "      <td> 0.016106</td>\n",
        "      <td> 0.263772</td>\n",
        "      <td> 0.035247</td>\n",
        "      <td> 0.468673</td>\n",
        "      <td> 0.259828</td>\n",
        "      <td> 0.197789</td>\n",
        "      <td> 0.073710</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.006205</td>\n",
        "      <td> 0.506921</td>\n",
        "      <td> 26730</td>\n",
        "      <td> 0.199474</td>\n",
        "      <td> 0.283621</td>\n",
        "      <td> 0.319684</td>\n",
        "      <td> 0.197220</td>\n",
        "      <td> 0.004508</td>\n",
        "      <td> 2</td>\n",
        "      <td> 6.135575</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.046923</td>\n",
        "      <td> 0.093977</td>\n",
        "      <td> 0.538584</td>\n",
        "      <td> 0.018862</td>\n",
        "      <td> 0.227333</td>\n",
        "      <td> 0.074322</td>\n",
        "      <td> 0.576075</td>\n",
        "      <td> 0.239602</td>\n",
        "      <td> 0.148774</td>\n",
        "      <td> 0.035549</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000289</td>\n",
        "      <td> 0.484558</td>\n",
        "      <td> 87585</td>\n",
        "      <td> 0.139884</td>\n",
        "      <td> 0.199593</td>\n",
        "      <td> 0.310988</td>\n",
        "      <td> 0.349535</td>\n",
        "      <td> 0.018256</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3.813837</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 23 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "   Ratio Marital status       Divorced  \\\n",
        "0                             0.042720   \n",
        "1                             0.035921   \n",
        "2                             0.035314   \n",
        "3                             0.030345   \n",
        "4                             0.046923   \n",
        "\n",
        "   Ratio Marital status       Living common law  \\\n",
        "0                                      0.085293   \n",
        "1                                      0.080860   \n",
        "2                                      0.085710   \n",
        "3                                      0.175770   \n",
        "4                                      0.093977   \n",
        "\n",
        "   Ratio Marital status       Married (and not separated)  \\\n",
        "0                                           0.560196        \n",
        "1                                           0.587961        \n",
        "2                                           0.596834        \n",
        "3                                           0.478758        \n",
        "4                                           0.538584        \n",
        "\n",
        "   Ratio Marital status       Separated  \\\n",
        "0                              0.020078   \n",
        "1                              0.016382   \n",
        "2                              0.018665   \n",
        "3                              0.016106   \n",
        "4                              0.018862   \n",
        "\n",
        "   Ratio Marital status       Single (never legally married)  \\\n",
        "0                                           0.225910           \n",
        "1                                           0.198617           \n",
        "2                                           0.189861           \n",
        "3                                           0.263772           \n",
        "4                                           0.227333           \n",
        "\n",
        "   Ratio Marital status       Widowed  \\\n",
        "0                            0.065802   \n",
        "1                            0.080259   \n",
        "2                            0.073615   \n",
        "3                            0.035247   \n",
        "4                            0.074322   \n",
        "\n",
        "   Ratio Family characteristics    Size of census family: 2 persons  \\\n",
        "0                                           0.508428                  \n",
        "1                                           0.580985                  \n",
        "2                                           0.586778                  \n",
        "3                                           0.468673                  \n",
        "4                                           0.576075                  \n",
        "\n",
        "   Ratio Family characteristics    Size of census family: 3 persons  \\\n",
        "0                                           0.243630                  \n",
        "1                                           0.229258                  \n",
        "2                                           0.222114                  \n",
        "3                                           0.259828                  \n",
        "4                                           0.239602                  \n",
        "\n",
        "   Ratio Family characteristics    Size of census family: 4 persons  \\\n",
        "0                                           0.201294                  \n",
        "1                                           0.153235                  \n",
        "2                                           0.154641                  \n",
        "3                                           0.197789                  \n",
        "4                                           0.148774                  \n",
        "\n",
        "   Ratio Family characteristics    Size of census family: 5 or more persons  \\\n",
        "0                                           0.046648                          \n",
        "1                                           0.036522                          \n",
        "2                                           0.036466                          \n",
        "3                                           0.073710                          \n",
        "4                                           0.035549                          \n",
        "\n",
        "                                     ...                                     \\\n",
        "0                                    ...                                      \n",
        "1                                    ...                                      \n",
        "2                                    ...                                      \n",
        "3                                    ...                                      \n",
        "4                                    ...                                      \n",
        "\n",
        "   Ratio First official language spoken  Neither English nor French  \\\n",
        "0                                           0.000247                  \n",
        "1                                           0.000394                  \n",
        "2                                           0.000516                  \n",
        "3                                           0.006205                  \n",
        "4                                           0.000289                  \n",
        "\n",
        "   ratio_male  Total  Ratio Age 0 to 14  Ratio Age 15 to 34  \\\n",
        "0    0.490867  81570           0.166055            0.221261   \n",
        "1    0.493676  76690           0.135205            0.184976   \n",
        "2    0.489306  78080           0.144946            0.186843   \n",
        "3    0.506921  26730           0.199474            0.283621   \n",
        "4    0.484558  87585           0.139884            0.199593   \n",
        "\n",
        "   Ratio Age 35 to 54  Ratio Age 55 to 84  Ratio Age 85+  cluster  \\\n",
        "0            0.314247            0.298438       0.015560        1   \n",
        "1            0.310200            0.369619       0.020427        1   \n",
        "2            0.315800            0.352411       0.019187        1   \n",
        "3            0.319684            0.197220       0.004508        2   \n",
        "4            0.310988            0.349535       0.018256        1   \n",
        "\n",
        "   cluster_distance  \n",
        "0          3.630211  \n",
        "1          4.768561  \n",
        "2          4.518964  \n",
        "3          6.135575  \n",
        "4          3.813837  \n",
        "\n",
        "[5 rows x 23 columns]"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# number of FED by cluster\n",
      "census_kmeans.groupby('cluster').count().ix[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "cluster\n",
        "0           74\n",
        "1          149\n",
        "2          115\n",
        "Name: Ratio Marital status       Divorced, dtype: int64"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "['Ratio ' + s for s in marital_status_list]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "['Ratio Marital status       Divorced',\n",
        " 'Ratio Marital status       Living common law',\n",
        " 'Ratio Marital status       Married (and not separated)',\n",
        " 'Ratio Marital status       Separated',\n",
        " 'Ratio Marital status       Single (never legally married)',\n",
        " 'Ratio Marital status       Widowed']"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans.groupby('cluster').mean()[['Ratio Marital status       Divorced',\n",
      "         'Ratio Marital status       Living common law',\n",
      "         'Ratio Marital status       Married (and not separated)',\n",
      "         'Ratio Marital status       Separated',\n",
      "         'Ratio Marital status       Single (never legally married)',\n",
      "         'Ratio Marital status       Widowed']]"
     ],
     "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>Ratio Marital status       Divorced</th>\n",
        "      <th>Ratio Marital status       Living common law</th>\n",
        "      <th>Ratio Marital status       Married (and not separated)</th>\n",
        "      <th>Ratio Marital status       Separated</th>\n",
        "      <th>Ratio Marital status       Single (never legally married)</th>\n",
        "      <th>Ratio Marital status       Widowed</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cluster</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.069768</td>\n",
        "      <td> 0.218112</td>\n",
        "      <td> 0.347454</td>\n",
        "      <td> 0.015559</td>\n",
        "      <td> 0.290939</td>\n",
        "      <td> 0.058168</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.061612</td>\n",
        "      <td> 0.094196</td>\n",
        "      <td> 0.478463</td>\n",
        "      <td> 0.029431</td>\n",
        "      <td> 0.271434</td>\n",
        "      <td> 0.064864</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.050336</td>\n",
        "      <td> 0.071087</td>\n",
        "      <td> 0.525202</td>\n",
        "      <td> 0.025131</td>\n",
        "      <td> 0.282387</td>\n",
        "      <td> 0.045856</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "         Ratio Marital status       Divorced  \\\n",
        "cluster                                        \n",
        "0                                   0.069768   \n",
        "1                                   0.061612   \n",
        "2                                   0.050336   \n",
        "\n",
        "         Ratio Marital status       Living common law  \\\n",
        "cluster                                                 \n",
        "0                                            0.218112   \n",
        "1                                            0.094196   \n",
        "2                                            0.071087   \n",
        "\n",
        "         Ratio Marital status       Married (and not separated)  \\\n",
        "cluster                                                           \n",
        "0                                                 0.347454        \n",
        "1                                                 0.478463        \n",
        "2                                                 0.525202        \n",
        "\n",
        "         Ratio Marital status       Separated  \\\n",
        "cluster                                         \n",
        "0                                    0.015559   \n",
        "1                                    0.029431   \n",
        "2                                    0.025131   \n",
        "\n",
        "         Ratio Marital status       Single (never legally married)  \\\n",
        "cluster                                                              \n",
        "0                                                 0.290939           \n",
        "1                                                 0.271434           \n",
        "2                                                 0.282387           \n",
        "\n",
        "         Ratio Marital status       Widowed  \n",
        "cluster                                      \n",
        "0                                  0.058168  \n",
        "1                                  0.064864  \n",
        "2                                  0.045856  "
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "0 high ratio living common law/single\n",
      "2 married"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans.groupby('cluster').mean()[['Ratio Family characteristics    Size of census family: 2 persons',\n",
      " 'Ratio Family characteristics    Size of census family: 3 persons',\n",
      " 'Ratio Family characteristics    Size of census family: 4 persons',\n",
      " 'Ratio Family characteristics    Size of census family: 5 or more persons']]"
     ],
     "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>Ratio Family characteristics    Size of census family: 2 persons</th>\n",
        "      <th>Ratio Family characteristics    Size of census family: 3 persons</th>\n",
        "      <th>Ratio Family characteristics    Size of census family: 4 persons</th>\n",
        "      <th>Ratio Family characteristics    Size of census family: 5 or more persons</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cluster</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.534102</td>\n",
        "      <td> 0.213763</td>\n",
        "      <td> 0.179905</td>\n",
        "      <td> 0.072231</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.547009</td>\n",
        "      <td> 0.206222</td>\n",
        "      <td> 0.172734</td>\n",
        "      <td> 0.074036</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.420026</td>\n",
        "      <td> 0.235379</td>\n",
        "      <td> 0.239028</td>\n",
        "      <td> 0.105568</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 136,
       "text": [
        "         Ratio Family characteristics    Size of census family: 2 persons  \\\n",
        "cluster                                                                     \n",
        "0                                                 0.534102                  \n",
        "1                                                 0.547009                  \n",
        "2                                                 0.420026                  \n",
        "\n",
        "         Ratio Family characteristics    Size of census family: 3 persons  \\\n",
        "cluster                                                                     \n",
        "0                                                 0.213763                  \n",
        "1                                                 0.206222                  \n",
        "2                                                 0.235379                  \n",
        "\n",
        "         Ratio Family characteristics    Size of census family: 4 persons  \\\n",
        "cluster                                                                     \n",
        "0                                                 0.179905                  \n",
        "1                                                 0.172734                  \n",
        "2                                                 0.239028                  \n",
        "\n",
        "         Ratio Family characteristics    Size of census family: 5 or more persons  \n",
        "cluster                                                                            \n",
        "0                                                 0.072231                         \n",
        "1                                                 0.074036                         \n",
        "2                                                 0.105568                         "
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "0/1: 2 persons\n",
      "2: bigger families"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans.groupby('cluster').mean()[['Ratio First official language spoken  English','Ratio First official language spoken  English and French'\n",
      "                 ,'Ratio First official language spoken  French','Ratio First official language spoken  Neither English nor French']]"
     ],
     "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>Ratio First official language spoken  English</th>\n",
        "      <th>Ratio First official language spoken  English and French</th>\n",
        "      <th>Ratio First official language spoken  French</th>\n",
        "      <th>Ratio First official language spoken  Neither English nor French</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cluster</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.096168</td>\n",
        "      <td> 0.026562</td>\n",
        "      <td> 0.868888</td>\n",
        "      <td> 0.008382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.927498</td>\n",
        "      <td> 0.004259</td>\n",
        "      <td> 0.058972</td>\n",
        "      <td> 0.009271</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.931338</td>\n",
        "      <td> 0.008589</td>\n",
        "      <td> 0.027624</td>\n",
        "      <td> 0.032449</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 137,
       "text": [
        "         Ratio First official language spoken  English  \\\n",
        "cluster                                                  \n",
        "0                                             0.096168   \n",
        "1                                             0.927498   \n",
        "2                                             0.931338   \n",
        "\n",
        "         Ratio First official language spoken  English and French  \\\n",
        "cluster                                                             \n",
        "0                                                 0.026562          \n",
        "1                                                 0.004259          \n",
        "2                                                 0.008589          \n",
        "\n",
        "         Ratio First official language spoken  French  \\\n",
        "cluster                                                 \n",
        "0                                            0.868888   \n",
        "1                                            0.058972   \n",
        "2                                            0.027624   \n",
        "\n",
        "         Ratio First official language spoken  Neither English nor French  \n",
        "cluster                                                                    \n",
        "0                                                 0.008382                 \n",
        "1                                                 0.009271                 \n",
        "2                                                 0.032449                 "
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "0 FRENCH\n",
      "2 neiter english or french"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans['Prov_Name'] = census['Prov_Name']\n",
      "census_kmeans['FED_Name'] = census['FED_Name']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans.groupby(['cluster', 'Prov_Name']).count().ix[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 144,
       "text": [
        "cluster  Prov_Name                \n",
        "0        Quebec                       74\n",
        "1        Alberta                      10\n",
        "         British Columbia             24\n",
        "         Manitoba                      9\n",
        "         New Brunswick                10\n",
        "         Newfoundland and Labrador     6\n",
        "         Nova Scotia                  10\n",
        "         Ontario                      62\n",
        "         Prince Edward Island          4\n",
        "         Quebec                        1\n",
        "         Saskatchewan                 12\n",
        "         Yukon                         1\n",
        "2        Alberta                      24\n",
        "         British Columbia             18\n",
        "         Manitoba                      5\n",
        "         Newfoundland and Labrador     1\n",
        "         Northwest Territories         1\n",
        "         Nova Scotia                   1\n",
        "         Nunavut                       1\n",
        "         Ontario                      59\n",
        "         Quebec                        3\n",
        "         Saskatchewan                  2\n",
        "Name: Ratio Marital status       Divorced, dtype: int64"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_kmeans[(census_kmeans.Prov_Name=='Quebec') & (census_kmeans.cluster==2)]['FED_Name']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 153,
       "text": [
        "67         Lac-Saint-Louis\n",
        "87    Pierrefonds--Dollard\n",
        "99           Saint-Laurent\n",
        "Name: FED_Name, dtype: object"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census[census_kmeans.cluster == 1][['Prov_Name', 'FED_Name']]"
     ],
     "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>Prov_Name</th>\n",
        "      <th>FED_Name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                                            Avalon</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                         Bonavista--Burin--Trinity</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                Coast of Bays--Central--Notre Dame</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                              Long Range Mountains</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                                   St. John's East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td> Newfoundland and Labrador</td>\n",
        "      <td>                     St. John's South--Mount Pearl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </th>\n",
        "      <td>      Prince Edward Island</td>\n",
        "      <td>                                          Cardigan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </th>\n",
        "      <td>      Prince Edward Island</td>\n",
        "      <td>                                     Charlottetown</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td>      Prince Edward Island</td>\n",
        "      <td>                                            Egmont</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td>      Prince Edward Island</td>\n",
        "      <td>                                          Malpeque</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                                Cape Breton--Canso</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                                      Central Nova</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                            Cumberland--Colchester</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                           Dartmouth--Cole Harbour</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                                           Halifax</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                                      Halifax West</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                                      Kings--Hants</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                        South Shore--St. Margarets</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                                  Sydney--Victoria</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21 </th>\n",
        "      <td>               Nova Scotia</td>\n",
        "      <td>                                         West Nova</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                                  Acadie--Bathurst</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                                        Beaus\ufffdjour</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                                       Fredericton</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                                       Fundy Royal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                            Madawaska--Restigouche</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                             Miramichi--Grand Lake</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                        Moncton--Riverview--Dieppe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                           New Brunswick Southwest</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                              Saint John--Rothesay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31 </th>\n",
        "      <td>             New Brunswick</td>\n",
        "      <td>                                Tobique--Mactaquac</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>277</th>\n",
        "      <td>                   Alberta</td>\n",
        "      <td>                               Edmonton Strathcona</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>283</th>\n",
        "      <td>                   Alberta</td>\n",
        "      <td>                                          Lakeland</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>284</th>\n",
        "      <td>                   Alberta</td>\n",
        "      <td>                                        Lethbridge</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>285</th>\n",
        "      <td>                   Alberta</td>\n",
        "      <td>                    Medicine Hat--Cardston--Warner</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>287</th>\n",
        "      <td>                   Alberta</td>\n",
        "      <td>                           Red Deer--Mountain View</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>293</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                        Abbotsford</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>296</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                            Cariboo--Prince George</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>297</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>             Central Okanagan--Similkameen--Nicola</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>298</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                  Chilliwack--Hope</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>301</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                Courtenay--Alberni</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>302</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                       Cowichan--Malahat--Langford</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>305</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                       Kamloops--Thompson--Cariboo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>306</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                             Kelowna--Lake Country</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>307</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                Kootenay--Columbia</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>310</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                Nanaimo--Ladysmith</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>311</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                          New Westminster--Burnaby</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>312</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                           North Okanagan--Shuswap</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>313</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                   North Vancouver</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>318</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                         Esquimalt--Saanich--Sooke</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>319</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                             Saanich--Gulf Islands</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>320</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                            Skeena--Bulkley Valley</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>321</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                     South Okanagan--West Kootenay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>322</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                          South Surrey--White Rock</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>326</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                  Vancouver Centre</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>327</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                    Vancouver East</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>328</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                               Vancouver Granville</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>329</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                        North Island--Powell River</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>333</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td>                                          Victoria</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>334</th>\n",
        "      <td>          British Columbia</td>\n",
        "      <td> West Vancouver--Sunshine Coast--Sea to Sky Cou...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>335</th>\n",
        "      <td>                     Yukon</td>\n",
        "      <td>                                             Yukon</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>149 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "                     Prov_Name  \\\n",
        "0    Newfoundland and Labrador   \n",
        "1    Newfoundland and Labrador   \n",
        "2    Newfoundland and Labrador   \n",
        "4    Newfoundland and Labrador   \n",
        "5    Newfoundland and Labrador   \n",
        "6    Newfoundland and Labrador   \n",
        "7         Prince Edward Island   \n",
        "8         Prince Edward Island   \n",
        "9         Prince Edward Island   \n",
        "10        Prince Edward Island   \n",
        "11                 Nova Scotia   \n",
        "12                 Nova Scotia   \n",
        "13                 Nova Scotia   \n",
        "14                 Nova Scotia   \n",
        "15                 Nova Scotia   \n",
        "16                 Nova Scotia   \n",
        "17                 Nova Scotia   \n",
        "19                 Nova Scotia   \n",
        "20                 Nova Scotia   \n",
        "21                 Nova Scotia   \n",
        "22               New Brunswick   \n",
        "23               New Brunswick   \n",
        "24               New Brunswick   \n",
        "25               New Brunswick   \n",
        "26               New Brunswick   \n",
        "27               New Brunswick   \n",
        "28               New Brunswick   \n",
        "29               New Brunswick   \n",
        "30               New Brunswick   \n",
        "31               New Brunswick   \n",
        "..                         ...   \n",
        "277                    Alberta   \n",
        "283                    Alberta   \n",
        "284                    Alberta   \n",
        "285                    Alberta   \n",
        "287                    Alberta   \n",
        "293           British Columbia   \n",
        "296           British Columbia   \n",
        "297           British Columbia   \n",
        "298           British Columbia   \n",
        "301           British Columbia   \n",
        "302           British Columbia   \n",
        "305           British Columbia   \n",
        "306           British Columbia   \n",
        "307           British Columbia   \n",
        "310           British Columbia   \n",
        "311           British Columbia   \n",
        "312           British Columbia   \n",
        "313           British Columbia   \n",
        "318           British Columbia   \n",
        "319           British Columbia   \n",
        "320           British Columbia   \n",
        "321           British Columbia   \n",
        "322           British Columbia   \n",
        "326           British Columbia   \n",
        "327           British Columbia   \n",
        "328           British Columbia   \n",
        "329           British Columbia   \n",
        "333           British Columbia   \n",
        "334           British Columbia   \n",
        "335                      Yukon   \n",
        "\n",
        "                                              FED_Name  \n",
        "0                                               Avalon  \n",
        "1                            Bonavista--Burin--Trinity  \n",
        "2                   Coast of Bays--Central--Notre Dame  \n",
        "4                                 Long Range Mountains  \n",
        "5                                      St. John's East  \n",
        "6                        St. John's South--Mount Pearl  \n",
        "7                                             Cardigan  \n",
        "8                                        Charlottetown  \n",
        "9                                               Egmont  \n",
        "10                                            Malpeque  \n",
        "11                                  Cape Breton--Canso  \n",
        "12                                        Central Nova  \n",
        "13                              Cumberland--Colchester  \n",
        "14                             Dartmouth--Cole Harbour  \n",
        "15                                             Halifax  \n",
        "16                                        Halifax West  \n",
        "17                                        Kings--Hants  \n",
        "19                          South Shore--St. Margarets  \n",
        "20                                    Sydney--Victoria  \n",
        "21                                           West Nova  \n",
        "22                                    Acadie--Bathurst  \n",
        "23                                          Beaus\ufffdjour  \n",
        "24                                         Fredericton  \n",
        "25                                         Fundy Royal  \n",
        "26                              Madawaska--Restigouche  \n",
        "27                               Miramichi--Grand Lake  \n",
        "28                          Moncton--Riverview--Dieppe  \n",
        "29                             New Brunswick Southwest  \n",
        "30                                Saint John--Rothesay  \n",
        "31                                  Tobique--Mactaquac  \n",
        "..                                                 ...  \n",
        "277                                Edmonton Strathcona  \n",
        "283                                           Lakeland  \n",
        "284                                         Lethbridge  \n",
        "285                     Medicine Hat--Cardston--Warner  \n",
        "287                            Red Deer--Mountain View  \n",
        "293                                         Abbotsford  \n",
        "296                             Cariboo--Prince George  \n",
        "297              Central Okanagan--Similkameen--Nicola  \n",
        "298                                   Chilliwack--Hope  \n",
        "301                                 Courtenay--Alberni  \n",
        "302                        Cowichan--Malahat--Langford  \n",
        "305                        Kamloops--Thompson--Cariboo  \n",
        "306                              Kelowna--Lake Country  \n",
        "307                                 Kootenay--Columbia  \n",
        "310                                 Nanaimo--Ladysmith  \n",
        "311                           New Westminster--Burnaby  \n",
        "312                            North Okanagan--Shuswap  \n",
        "313                                    North Vancouver  \n",
        "318                          Esquimalt--Saanich--Sooke  \n",
        "319                              Saanich--Gulf Islands  \n",
        "320                             Skeena--Bulkley Valley  \n",
        "321                      South Okanagan--West Kootenay  \n",
        "322                           South Surrey--White Rock  \n",
        "326                                   Vancouver Centre  \n",
        "327                                     Vancouver East  \n",
        "328                                Vancouver Granville  \n",
        "329                         North Island--Powell River  \n",
        "333                                           Victoria  \n",
        "334  West Vancouver--Sunshine Coast--Sea to Sky Cou...  \n",
        "335                                              Yukon  \n",
        "\n",
        "[149 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "                 , 'ratio_male', 'Total', 'Ratio Age 0 to 14', 'Ratio Age 15 to 34', 'Ratio Age 35 to 54'\n",
      "                 , 'Ratio Age 55 to 84', 'Ratio Age 85+']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}