Dog-Analysis-Data-Sci.ipynb

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      "cell_type": "markdown",
      "source": [
        "#**Charlotte Law** | Dog Data Science Project"
      ],
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        "id": "sHYqz9W6mpfm"
      },
      "id": "sHYqz9W6mpfm"
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      "cell_type": "markdown",
      "source": [
        "##🌱 Step 1: Preparation\n",
        "> Importing all the relevant libraries and defining data manipulation functions to be used later."
      ],
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        "id": "HUwDU1RMokM2"
      },
      "id": "HUwDU1RMokM2"
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      "source": [
        "!pip install git+https://github.com/data-8/datascience.git"
      ],
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      "id": "G3bZumr1TpdE",
      "execution_count": null,
      "outputs": []
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      "id": "b951f6f6",
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        "id": "b951f6f6"
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      "source": [
        "from datascience import *\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings\n",
        "from pathlib import Path\n",
        "import matplotlib.pyplot as plots\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "plots.style.use('fivethirtyeight')\n",
        "plots.rcParams[\"patch.force_edgecolor\"] = True"
      ]
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      "source": [
        "def dollar_to_int(amount):\n",
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        "        return(amount.replace('$', '').replace(',', ''))\n",
        "    return(amount)"
      ],
      "metadata": {
        "id": "eiZh9aoeNEg5"
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      "id": "eiZh9aoeNEg5",
      "execution_count": 30,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "def value_to_int(amount):\n",
        "    if isinstance(amount, str):\n",
        "        return(float(amount))\n",
        "    return(amount)"
      ],
      "metadata": {
        "id": "kZ5g59EBnmuD"
      },
      "id": "kZ5g59EBnmuD",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categories = [\"LIFETIME COST\", \"POPULARITY IN US\", \"LONGEVITY\", \"INTELLIGENCE\", \"# OF GENETIC AILMENTS\"]"
      ],
      "metadata": {
        "id": "wtBguyLpcvO1"
      },
      "id": "wtBguyLpcvO1",
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##🕹️ Step 2: Data Loading\n",
        "> Loading in the dataset from an external csv file (dataset sourced from AKC, American Kennel Club)."
      ],
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        "id": "5QMxZkedpOGV"
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              "         Dog breed  Unnamed: 1                    category  Unnamed: 3  \\\n",
              "0  Additional info         NaN  American Kennel Club group         NaN   \n",
              "1    Border Collie         NaN                     herding         NaN   \n",
              "2   Border Terrier         NaN                     terrier         NaN   \n",
              "3         Brittany         NaN                    sporting         NaN   \n",
              "4    Cairn Terrier         NaN                     terrier         NaN   \n",
              "\n",
              "   datadog score                                   POPULARITY IN US  \\\n",
              "0            NaN  1- 173, 1 = most poular, US. American Kennel C...   \n",
              "1           3.64                                                 45   \n",
              "2           3.61                                                 80   \n",
              "3           3.54                                                 30   \n",
              "4           3.53                                                 59   \n",
              "\n",
              "                          POPULARITY IN US.1 LIFETIME COST  \\\n",
              "0  ranking within breeds with full data only           NaN   \n",
              "1                                         39    $20,143.00   \n",
              "2                                         61    $22,638.00   \n",
              "3                                         30    $22,589.00   \n",
              "4                                         48    $21,992.00   \n",
              "\n",
              "                                     5 LIFETIME COST  Unnamed: 9  ...  \\\n",
              "0  in descending rank (higher score = better),  n...         NaN  ...   \n",
              "1                                                48%         NaN  ...   \n",
              "2                                                14%         NaN  ...   \n",
              "3                                                16%         NaN  ...   \n",
              "4                                                22%         NaN  ...   \n",
              "\n",
              "  Unnamed: 59   Other regular costs, total per lifetime, $  \\\n",
              "0         NaN  if no average lifespan data, then 'no data'   \n",
              "1         NaN                                       13,095   \n",
              "2         NaN                                       14,643   \n",
              "3         NaN                                       13,514   \n",
              "4         NaN                                       14,476   \n",
              "\n",
              "                       total  per year, $ total, per year, £  \\\n",
              "0  converted to US $ using Google Finance                NaN   \n",
              "1                                   1,046              784.0   \n",
              "2                                   1,046              784.0   \n",
              "3                                   1,046              784.0   \n",
              "4                                   1,046              784.0   \n",
              "\n",
              "                 toys, presents, treats, per year, £ pet sitters, per year, £  \\\n",
              "0  averaged across all dog breeds. pet food exclu...                      NaN   \n",
              "1                                                121                    126.0   \n",
              "2                                                121                    126.0   \n",
              "3                                                121                    126.0   \n",
              "4                                                121                    126.0   \n",
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              "  grooming, per year, £ vet fees per year, £ kennels per year, £  \\\n",
              "0                   NaN                  NaN                 NaN   \n",
              "1                 244.0                177.0               116.0   \n",
              "2                 244.0                177.0               116.0   \n",
              "3                 244.0                177.0               116.0   \n",
              "4                 244.0                177.0               116.0   \n",
              "\n",
              "                                         one offs, $  \n",
              "0  Initial outlay: leash, collar, ID tag; food di...  \n",
              "1                                                200  \n",
              "2                                                200  \n",
              "3                                                200  \n",
              "4                                                200  \n",
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              "      <td>Additional info</td>\n",
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          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "dog_csv=\"dog_data.csv\"\n",
        "dog_df = pd.read_csv(dog_csv)\n",
        "dog_df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##💻 Step 3: Data Processing\n",
        "> Cleaning up the data by removing unnecessary columns and rows, for example, those with \"no data\" or \"NaN\". Creating a new dataframe with necessary rows and columns, and manipulating the data types to prepare for data analysis."
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        "#### Cleaning Data"
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      "id": "OSQ3UJjRp_kK"
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            "text/plain": [
              "         Dog breed                    category  datadog score  \\\n",
              "0  Additional info  American Kennel Club group            NaN   \n",
              "1    Border Collie                     herding           3.64   \n",
              "2   Border Terrier                     terrier           3.61   \n",
              "3         Brittany                    sporting           3.54   \n",
              "4    Cairn Terrier                     terrier           3.53   \n",
              "\n",
              "                                    POPULARITY IN US  \\\n",
              "0  1- 173, 1 = most poular, US. American Kennel C...   \n",
              "1                                                 45   \n",
              "2                                                 80   \n",
              "3                                                 30   \n",
              "4                                                 59   \n",
              "\n",
              "                          POPULARITY IN US.1 LIFETIME COST  \\\n",
              "0  ranking within breeds with full data only           NaN   \n",
              "1                                         39    $20,143.00   \n",
              "2                                         61    $22,638.00   \n",
              "3                                         30    $22,589.00   \n",
              "4                                         48    $21,992.00   \n",
              "\n",
              "                                     5 LIFETIME COST  \\\n",
              "0  in descending rank (higher score = better),  n...   \n",
              "1                                                48%   \n",
              "2                                                14%   \n",
              "3                                                16%   \n",
              "4                                                22%   \n",
              "\n",
              "                                INTELLIGENCE  \\\n",
              "0  1 = easiest to train / learn new commands   \n",
              "1                                          1   \n",
              "2                                         30   \n",
              "3                                         19   \n",
              "4                                         35   \n",
              "\n",
              "                 INTELLIGENCE (TRAINABILITY) ranking  \\\n",
              "0  in descending rank (higher score = better),  n...   \n",
              "1                                               100%   \n",
              "2                                                70%   \n",
              "3                                                80%   \n",
              "4                                                61%   \n",
              "\n",
              "                            LONGEVITY  ...  \\\n",
              "0  years, weighted average - see note  ...   \n",
              "1                               12.52  ...   \n",
              "2                               14.00  ...   \n",
              "3                               12.92  ...   \n",
              "4                               13.84  ...   \n",
              "\n",
              "                                food per lifetime, $  \\\n",
              "0  food per week x 52.1775 x average lifespan (co...   \n",
              "1                                              3,486   \n",
              "2                                              3,898   \n",
              "3                                              5,171   \n",
              "4                                              3,854   \n",
              "\n",
              "    Other regular costs, total per lifetime, $  \\\n",
              "0  if no average lifespan data, then 'no data'   \n",
              "1                                       13,095   \n",
              "2                                       14,643   \n",
              "3                                       13,514   \n",
              "4                                       14,476   \n",
              "\n",
              "                       total  per year, $ total, per year, £  \\\n",
              "0  converted to US $ using Google Finance                NaN   \n",
              "1                                   1,046              784.0   \n",
              "2                                   1,046              784.0   \n",
              "3                                   1,046              784.0   \n",
              "4                                   1,046              784.0   \n",
              "\n",
              "                 toys, presents, treats, per year, £ pet sitters, per year, £  \\\n",
              "0  averaged across all dog breeds. pet food exclu...                      NaN   \n",
              "1                                                121                    126.0   \n",
              "2                                                121                    126.0   \n",
              "3                                                121                    126.0   \n",
              "4                                                121                    126.0   \n",
              "\n",
              "  grooming, per year, £ vet fees per year, £ kennels per year, £  \\\n",
              "0                   NaN                  NaN                 NaN   \n",
              "1                 244.0                177.0               116.0   \n",
              "2                 244.0                177.0               116.0   \n",
              "3                 244.0                177.0               116.0   \n",
              "4                 244.0                177.0               116.0   \n",
              "\n",
              "                                         one offs, $  \n",
              "0  Initial outlay: leash, collar, ID tag; food di...  \n",
              "1                                                200  \n",
              "2                                                200  \n",
              "3                                                200  \n",
              "4                                                200  \n",
              "\n",
              "[5 rows x 61 columns]"
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              "      <th></th>\n",
              "      <th>Dog breed</th>\n",
              "      <th>category</th>\n",
              "      <th>datadog score</th>\n",
              "      <th>POPULARITY IN US</th>\n",
              "      <th>POPULARITY IN US.1</th>\n",
              "      <th>LIFETIME COST</th>\n",
              "      <th>5 LIFETIME COST</th>\n",
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              "      <th>LONGEVITY</th>\n",
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              "      <th>total  per year, $</th>\n",
              "      <th>total, per year, £</th>\n",
              "      <th>toys, presents, treats, per year, £</th>\n",
              "      <th>pet sitters, per year, £</th>\n",
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              "      <th>vet fees per year, £</th>\n",
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              "      <td>American Kennel Club group</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1- 173, 1 = most poular, US. American Kennel C...</td>\n",
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              "      <td>in descending rank (higher score = better),  n...</td>\n",
              "      <td>1 = easiest to train / learn new commands</td>\n",
              "      <td>in descending rank (higher score = better),  n...</td>\n",
              "      <td>years, weighted average - see note</td>\n",
              "      <td>...</td>\n",
              "      <td>food per week x 52.1775 x average lifespan (co...</td>\n",
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              "      <td>converted to US $ using Google Finance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>averaged across all dog breeds. pet food exclu...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Initial outlay: leash, collar, ID tag; food di...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Border Collie</td>\n",
              "      <td>herding</td>\n",
              "      <td>3.64</td>\n",
              "      <td>45</td>\n",
              "      <td>39</td>\n",
              "      <td>$20,143.00</td>\n",
              "      <td>48%</td>\n",
              "      <td>1</td>\n",
              "      <td>100%</td>\n",
              "      <td>12.52</td>\n",
              "      <td>...</td>\n",
              "      <td>3,486</td>\n",
              "      <td>13,095</td>\n",
              "      <td>1,046</td>\n",
              "      <td>784.0</td>\n",
              "      <td>121</td>\n",
              "      <td>126.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>200</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Border Terrier</td>\n",
              "      <td>terrier</td>\n",
              "      <td>3.61</td>\n",
              "      <td>80</td>\n",
              "      <td>61</td>\n",
              "      <td>$22,638.00</td>\n",
              "      <td>14%</td>\n",
              "      <td>30</td>\n",
              "      <td>70%</td>\n",
              "      <td>14.00</td>\n",
              "      <td>...</td>\n",
              "      <td>3,898</td>\n",
              "      <td>14,643</td>\n",
              "      <td>1,046</td>\n",
              "      <td>784.0</td>\n",
              "      <td>121</td>\n",
              "      <td>126.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brittany</td>\n",
              "      <td>sporting</td>\n",
              "      <td>3.54</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>$22,589.00</td>\n",
              "      <td>16%</td>\n",
              "      <td>19</td>\n",
              "      <td>80%</td>\n",
              "      <td>12.92</td>\n",
              "      <td>...</td>\n",
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              "      <td>177.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>200</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cairn Terrier</td>\n",
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              "      <td>3.53</td>\n",
              "      <td>59</td>\n",
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              "      <td>22%</td>\n",
              "      <td>35</td>\n",
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          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "dog_df.iteritems = dog_df.items\n",
        "\n",
        "for (columnName, columnData) in dog_df.iteritems():\n",
        "  if 'Unnamed' in  columnName:\n",
        "    dog_df = dog_df.drop([columnName], axis=1)\n",
        "\n",
        "dog_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dog_df = dog_df.drop([0])"
      ],
      "metadata": {
        "id": "NpXIwOeLwS51"
      },
      "id": "NpXIwOeLwS51",
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Creating new Dataframe\n",
        "1. **POPULARITY IN US:** 1 (most popular) to n (least popular)\n",
        "2. **LIFETIME COST:** Average lifetime cost of owning\n",
        "a specific dog breed\n",
        "3. **LONGEVITY:** Average life span of a specific dog breed\n",
        "4. **INTELLIGENCE:** 1 (most intelligent, easiest to train/learn new commands) to n (least intelligent)\n",
        "5. **Size Category:** small, medium, or large breed dogs."
      ],
      "metadata": {
        "id": "oR84ziKtuOqw"
      },
      "id": "oR84ziKtuOqw"
    },
    {
      "cell_type": "code",
      "source": [
        "new_dog_df = dog_df[[\"Dog breed\", \"POPULARITY IN US\", \"LIFETIME COST\", \"LONGEVITY\", \"INTELLIGENCE\", \"# OF GENETIC AILMENTS\", \"SIZE CATEGORY\"]]"
      ],
      "metadata": {
        "id": "gty2oHNWNSmJ"
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      "id": "gty2oHNWNSmJ",
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_dog_df.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RAZaQjqGNxQG",
        "outputId": "23759523-a5fd-40d2-d4e8-5e0a43a31ca5"
      },
      "id": "RAZaQjqGNxQG",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Dog breed POPULARITY IN US LIFETIME COST LONGEVITY  \\\n",
              "1                  Border Collie               45    $20,143.00     12.52   \n",
              "2                 Border Terrier               80    $22,638.00     14.00   \n",
              "3                       Brittany               30    $22,589.00     12.92   \n",
              "4                  Cairn Terrier               59    $21,992.00     13.84   \n",
              "5         Welsh Springer Spaniel              130    $20,224.00     12.49   \n",
              "..                           ...              ...           ...       ...   \n",
              "170                   Weimaraner               32       no data   no data   \n",
              "171                Welsh Terrier               99       no data   no data   \n",
              "172             Wire Fox Terrier              100       no data     13.17   \n",
              "173  Wirehaired Pointing Griffon               92       no data      8.80   \n",
              "174               Xoloitzcuintli              155       no data   no data   \n",
              "\n",
              "    INTELLIGENCE # OF GENETIC AILMENTS SIZE CATEGORY  \n",
              "1              1                     2        medium  \n",
              "2             30                     0         small  \n",
              "3             19                     0        medium  \n",
              "4             35                     2         small  \n",
              "5             31                     1        medium  \n",
              "..           ...                   ...           ...  \n",
              "170           21                     1         large  \n",
              "171           53                     0         small  \n",
              "172           51                     0         small  \n",
              "173           46                     0        medium  \n",
              "174      no data               no data        medium  \n",
              "\n",
              "[172 rows x 7 columns]"
            ],
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              "\n",
              "  <div id=\"df-044b0434-8ac1-46ca-b526-eccd3cda930d\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dog breed</th>\n",
              "      <th>POPULARITY IN US</th>\n",
              "      <th>LIFETIME COST</th>\n",
              "      <th>LONGEVITY</th>\n",
              "      <th>INTELLIGENCE</th>\n",
              "      <th># OF GENETIC AILMENTS</th>\n",
              "      <th>SIZE CATEGORY</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Border Collie</td>\n",
              "      <td>45</td>\n",
              "      <td>$20,143.00</td>\n",
              "      <td>12.52</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Border Terrier</td>\n",
              "      <td>80</td>\n",
              "      <td>$22,638.00</td>\n",
              "      <td>14.00</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brittany</td>\n",
              "      <td>30</td>\n",
              "      <td>$22,589.00</td>\n",
              "      <td>12.92</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cairn Terrier</td>\n",
              "      <td>59</td>\n",
              "      <td>$21,992.00</td>\n",
              "      <td>13.84</td>\n",
              "      <td>35</td>\n",
              "      <td>2</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Welsh Springer Spaniel</td>\n",
              "      <td>130</td>\n",
              "      <td>$20,224.00</td>\n",
              "      <td>12.49</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Weimaraner</td>\n",
              "      <td>32</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>large</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Welsh Terrier</td>\n",
              "      <td>99</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Wire Fox Terrier</td>\n",
              "      <td>100</td>\n",
              "      <td>no data</td>\n",
              "      <td>13.17</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Wirehaired Pointing Griffon</td>\n",
              "      <td>92</td>\n",
              "      <td>no data</td>\n",
              "      <td>8.80</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Xoloitzcuintli</td>\n",
              "      <td>155</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>172 rows × 7 columns</p>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-044b0434-8ac1-46ca-b526-eccd3cda930d');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### New DF of Cleaned Data"
      ],
      "metadata": {
        "id": "lUA4dgZWqSCW"
      },
      "id": "lUA4dgZWqSCW"
    },
    {
      "cell_type": "code",
      "source": [
        "for i in categories:\n",
        "    new_dog_df = new_dog_df[new_dog_df[i].str.contains(\"no data\") == False]"
      ],
      "metadata": {
        "id": "BCi0Bs31MJ6e"
      },
      "id": "BCi0Bs31MJ6e",
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Changing Data Types"
      ],
      "metadata": {
        "id": "0k_evW0MqHOV"
      },
      "id": "0k_evW0MqHOV"
    },
    {
      "cell_type": "code",
      "source": [
        "new_dog_df['LIFETIME COST'] = new_dog_df['LIFETIME COST'].apply(dollar_to_int).astype('float')\n",
        "\n",
        "for i in categories:\n",
        "    new_dog_df[i] = new_dog_df[i].apply(value_to_int).astype('float')\n",
        "\n",
        "new_dog_df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "eRSaTLY0NPox"
      },
      "id": "eRSaTLY0NPox",
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "small_dog = new_dog_df[new_dog_df[\"SIZE CATEGORY\"].str.contains(\"small\") == True]\n",
        "medium_dog = new_dog_df[new_dog_df[\"SIZE CATEGORY\"].str.contains(\"medium\") == True]\n",
        "large_dog = new_dog_df[new_dog_df[\"SIZE CATEGORY\"].str.contains(\"large\") == True]"
      ],
      "metadata": {
        "id": "itNwNWalSaYq"
      },
      "id": "itNwNWalSaYq",
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filepath = Path('sorted_data.csv')\n",
        "\n",
        "sorted_data = new_dog_df.sort_values(by=['POPULARITY IN US'], ascending=False)\n",
        "sorted_data.to_csv(filepath)"
      ],
      "metadata": {
        "id": "YTVh9cQqOkWF"
      },
      "id": "YTVh9cQqOkWF",
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##🔍 Step 4: Data Analysis\n",
        "> Analyzing the data through scatterplots and graphs."
      ],
      "metadata": {
        "id": "AHRRznvCqvGs"
      },
      "id": "AHRRznvCqvGs"
    },
    {
      "cell_type": "code",
      "source": [
        "df_scatter=sorted_data.plot.scatter(x=\"LONGEVITY\", y=\"POPULARITY IN US\", c=\"#5978ab\", figsize=(10,3))\n",
        "df_scatter.set_xlabel(\"Longevity\")\n",
        "df_scatter.set_ylabel(\"Popularity Rank\")\n",
        "df_scatter.plot()\n",
        "\n",
        "model = np.poly1d(np.polyfit(sorted_data[\"LONGEVITY\"], sorted_data[\"POPULARITY IN US\"], 2))\n",
        "\n",
        "polyline = np.linspace(6, 16, 50)\n",
        "plt.plot(polyline, model(polyline))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "f2NXElhGmAvf",
        "outputId": "fe17b7cf-46aa-4208-eb58-6f328f6a891e"
      },
      "id": "f2NXElhGmAvf",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 1 Axes>"
            ],
            "image/png": 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AgQMHYsOGDbj77rutx4miCJlMhmeffdYpDSYiIiJqDG+Yv+oPSwjxJgsR+ROHh0DffPPN2Lp1q03P7tdff40JEyZArVZDFEWIoogrr7wS3377LXr37u2M9hIRERE1iqfnr1qGYJ+9UI7CEi3OXSjnEGwiIg9zuAdYLpejS5cuNtvUajXmzJmD//73v7hw4QJUKhXCwsKa3UgiIiKiphreL7HWGrbunL/KIdjkKv4wsoDIUxwOwPWRSqWIibH9T1hcXIzw8HBXnI6IiIioFksgWP1HJioqqxCsCsIt/Tu6LSh4wxBs8j+WkQU1b64szTgMAAzBRI3gkgBcU3FxMebNm4dPP/0UJ0+edPXpiIiIiKxSk2OQEm9ejjEuLg5KpdJt5/b0EGzyTxxZQNQ8LgvAxcXFWLBgAT7++GOUlJS46jREREREXsnTQ7DJP3FkAVHzNCkAV1VVYe7cufjhhx9w8uRJBAcHo3v37pg6dSr69OkDADCZTFiwYAHeeecdFBcXQxRFREVF4YknnnDJL0BERETkjbiEELkCRxYQNU+jA7DJZMKoUaOwdetWiKIIAKisrMTatWuxadMmLFu2DKmpqbj33nuxc+dOiKKI6OhoPPHEE3jwwQehUqlc9ksQEREReSMuIUTOxpEFRM3T6AD89ddfY8uWLQCAkSNHomfPnqisrMT//vc/7Nq1C9OnT0dsbCz+/PNPhIeH49///jcmTZrE4EtERERE5CQcWUDUPI0OwCtXroQgCHjrrbfwr3/9y7r96aefxrhx47BmzRocPXoU11xzDZYsWVKrCjQRERERETUfRxYQOa7RAfjQoUMICwvDww8/bLNdEARMmzYNa9asgUQiwcKFCxl+iYgoIHAtTiIiIt/S6AB88eJFXHHFFZBIJLX2JSUlAQA6dOiA+Ph4pzWOiIjIW3EtTiIiIt9TO83WwWAw1Dmf17I9MjLSOa0iIiLycvWtxUlERETeqdEBmIiIiC7hWpxERES+p0nrAJeUlFgrQTuyv2/fvk05HRERkdfiWpxERES+p0kB+O+//8Ytt9xid58gCA3uLygoaHoLiYiIvBDX4iQiIvI9TQrAoig6fKLmPJeIiMjbcC1OIiIi39OkKtBERER0CdfiJCIi8i0+UQRr+fLlmDJlCgYOHIjo6GhoNBosWbKkzuNLSkrw4osv4sorr0R0dDS6du2Kl19+GWVlZXaPN5lM+Pjjj9GnTx/ExsYiMTERDz74IE6ePOmi34iIAtnuI3mYsXArXlrwB95YuBW7j+R5uklEREREAaFJQ6A95c0330ROTg4iIyMRExODnJycOo8tLy/H8OHDceDAAQwaNAh33nkn9u/fj7lz52LLli34+eefoVQqbZ4zZcoULFq0CF26dMEjjzyCc+fOYdWqVVi3bh1+//13JCZyPhcROQfXjiUiIiLyHJ/oAZ47dy7279+PrKwsPPDAA/Ue+/777+PAgQOYMmUKfvjhB7z22mv44YcfMGXKFOzevRsLFiywOX7Tpk1YtGgR+vTpg40bN+L111/HJ598giVLluDixYuYNm2aK381IgowXDuWiIiIyHN8IgAPHDgQ7dq1a/A4URSxePFihISE1Aqu06ZNQ0hICBYtWmSz3fLzSy+9BIVCYd1+4403ol+/fli3bl29Pc5ERE3BtWOJiIiIPMcnAnBjZWVl4dy5c+jVqxfUarXNPrVajV69euHkyZM4ffq0dfvmzZuhVqvRu3fvWq83ePBgAKh3bWMioqbg2rFEREREnuMTc4AbKyvLPISwQ4cOdvd36NABa9euRVZWFtq2bYvy8nLk5uYiJSUFUmntD5+W17G8bkO0Wq2DLXcNnU5n80jkLLy2HDekVxy+XZuJihrDoIOVMgzpGed17yHuxuuKXIXXFrkKry1yBV5XTXd5jaf6+FUALikpAQCEh4fb3R8WFmZznOXRsr2h4xty9uxZGI3eN4wxL48VZsk1eG01XYQSGNStBbb9XQS9wQS5TIK0LhpEKLWcblGN1xW5Cq8tchVeW+QKvK4aRyqV1tkBao9fBWBPa926taebYEOn0yEvLw8xMTE285uJmovXVvPExQGD0zzdCu/D64pchdcWuQqvLXIFXleu5VcB2NJjW1xcbHf/5T2+DfXwNtRDfLmmdL27k0Kh8Nq2kW/jtUWuwOuKXIXXFrkKry1yBV5XruFwEay5c+eioKDAmW1pNst6vdnZ2Xb3W7ZbjlOr1YiNjcWpU6fsDl2+/HgiIiIiIiLyXQ4H4FdeeQUpKSl44IEHsGHDBic2yXGJiYlo1aoVduzYgfLycpt95eXl2LFjB9q3b4+2bdtat/ft2xfl5eXYvn17rddbu3YtAKBPnz6ubTgRUYDbfSQPsxb/hU9/zsGsxX9h9xHOeyIiIiLnczgA9+7dGzqdDitXrsQdd9yBbt264Z133kFubq4z29ckgiDg/vvvR1lZGWbPnm2zb/bs2SgrK8P48eNttlt+/s9//mNTae23337D5s2bMWjQoEatQUxERI7ZfSQPSzMOI7egAqWVRuQWVGBpxmGGYAftPpKHGQu34qUFf+CNhVv570hERFSDUFRUJDr65MzMTHz11VdYvnw58vPzIQgCpFIpbrzxRowbNw5DhgyBRNL8pYYXLVqEbdu2AQAOHz6Mffv2oXfv3khISAAApKWlYdy4cQDMPb1Dhw7FwYMHMWjQIHTr1g379u3DunXrkJqaivT0dKhUKpvXf/LJJ7Fo0SJ06dIFQ4YMQW5uLlauXAm1Wo3ffvsNHTt2bPbv4AlarbmqbFxcHOcPkFPx2iJnmrFwK85eKK+1vXWUGi8/yBE4TWG5mVBeY5kttVKGMTelIDU5xoMt8yy+Z5Gr8NoiV+B15VrNKoLVsWNHzJgxA6+++irS09OxaNEibNiwAf/73/+QkZGB2NhYjB07Fvfddx/at2/v8Hm2bduGZcuW2Wzbvn27zbBlSwBWq9VIT0/HzJkzsXr1avzxxx+IiYnB448/jueee65W+AWA9957DykpKfjqq6/w0UcfQa1WY8SIEXj55ZetIZuIiFxDq7O/fJy2yvuWlfN26ZuzbMIvAJRrDUjfkhXQAZj80+4jeUjfnAWtzogghRQj+iXyOieiBjWrB9ie06dPY/HixViyZAnOnDkDQRAgCAIGDhyIcePGYfjw4ZDJ/Kr4tNfi3SNyFV5b5EzsAXaelxb8gcISba3tEWFK/Oex/h5okXfge5b/8ZbRDry2yBV4XblW88cnX6Zt27Z44YUXcODAAbz11luQSqUQRRHr1q3DxIkTkZKSglmzZqG0tNTZpyYiIh80vF8i1ErbG6NqpQzD+7ICf1MpFVL724PsbyfyVfWNdiAiqo/TA7BWq8WyZcswbNgwvPTSSzAYDBBFEb1790ZiYiLy8/Mxa9YspKWlITMz09mnJyIiH5OaHIMxN6UgNjIYYcFSxEYGB/ycVUfxZgIFCk6dICJHOW0s8t69e7F48WKsWLECpaWlEEURGo0Go0ePxoQJE9CpUycAwKZNmzBjxgzs2rUL06dPxzfffOOsJhAReSXOU2tYanIMUuLDOeSrmSzXVfqWLGirjFAGSTG8L6838j8c7UBEjmpWAC4uLsa3336LxYsX4+DBgxBF83Ti3r17Y8KECRg5ciSCgoJsnnPddddh9erV6Nq1q921d4mI/Im9eWpLMw4DAEMJuURqcgyvLfJ7w/sl2p0DzNEORNQQhwPwww8/jDVr1kCr1UIURYSHh+Oee+7BxIkTkZycXO9zlUolkpKSGICJyO+xKi8RkfNxtAMROcrhAPzdd98BAK699lpMmDABd9xxR5OGrA0ZMgTx8fGOnp6IyCdwnhqRe3CqQeDhaAcicoTDAXjSpEmYMGECrrjiCoeeP2XKFEdPTUTkMzhPjQKdO4IppxoQEVFjORyAZ8+e7cx2EBH5Jc5To0DmrmDqqakG7HUmIvI9Di+DFBERgWHDhjXq2BEjRiAyMtLRUxER+SzLEj+to9SICFOidZSaS/xQwHDXWq2emGpgCfdnL5SjsESLcxfKsTTjMHYfyXPZOYmIqPkc7gEWRdFa9bmxxxMRBSLOU6NA5a5g6ompBixwR0Tkm5y2DnB9qqqqIJO55VRERERUB3cP2XVXMPXEVAMWuCMi8k0uT6Xl5eU4fvw4h0ATERF5kCcKRbkrmHpiSRwWuCMi8k2NDsDp6en4+eefbbZlZ2dj8uTJdT6nsrISu3btQklJCQYOHOhwI4mIiJwhkIsWeWLIrjuDqbunGrDAHRGRb2p0AD5w4ACWLl1qs+38+fO1ttkTHR2NF154oemtIyIicpJAXyrHU0N2/XUOvCd6nYmIqPkaHYD79etn8/OsWbPQtm1bjB071u7xgiAgODgYCQkJGDRoEIKDg5vXUiIiomYI9KJFHLLrfP4a7omI/FmTAnDNEGwJwM8//7xLGkZERORMgV60iEN2iYiImlEE6+LFi85sBxERkUsFeg8oh+wSOdfuI3lY/UcmyiuqoA4+j1v6d+T/JyIfwLWJKOAEchEcokDGHlAO2SVylstrCpRWVgRUTQEiX9aoAJyTkwMAkMvliI2NtdnWFHFxcU1+DpEzBXoRHKJAxh5QInKWQK8pQOTLGhWAu3XrBgDo1KkTtm/fbrOtsQRBQEFBQRObR+Rc/INFFNjYA0pEzhDoNQWIfFmjArAoigAAk8lUa1tjNfV4IlfgHywiosDCeZrkCoFeU4DIlzUqANsreMUiWOSL+AeLiChw2JunuWjNQaxYexSCILAOBDmMNQWIfBeLYFFA4R8sIqLAYW/aS5XBhKrSKuvPrANBjrBcL6v/yERFZRWCVUEcXUDkIxwOwJMnT4ZEIsF///tfBAUFObNNRC7DIjhERIGjrmkvNbEOBDkqNTkGKfHhyMnJQVxcHJRKpaebRESN4HAA/vbbb9GpUyeGX/I5LIJDRBQY6pr2cjnWgSAiChwSR58YHR0NuVzuzLYQEREROc3wfolQKxu+1886EEREgcPhHuD+/fvjxx9/RElJCcLCwpzZJiIiInKj3UfykL45C1qd0a8KQ10+T1MikaKyyogq/aUeX9aBICIKLA4H4GeeeQarV6/GtGnT8OGHH0IicbgzmYiIiDzk8krJgH8Vhrp8nubhk8WsA0FEFMAcDsB5eXl45pln8J///AcHDhzA6NGjkZycjODg4Dqf07dvX0dPR0RERC5gr1KyPxeGYh0IIqLA5nAAHjFiBARBAAAcOXIEr776ar3HC4KAgoICR09HRERELlBXpWQWhiIiIn/kcABu27atNQATERGRb6qrUrKrCkP563xjIiLyDQ4H4AMHDjizHUREROQBw/sl1poD7KrCUP4+37guDP1ERN7D4QBMREQN4wdf8naW69EdhaECbb4xELihn4jIWzEAExG5CD/4kq9wV2GoQJxvHIihn4jIm3HtIiIiF6nvgy9RIHL3fGNvEIihn4jImzW7B/ivv/7CsmXLsH//fhQWFkKv19s9ThAE7N27t7mnIyLyGfzgS2TLnfONvUUghn4iIm/WrAA8Y8YMzJkzB6IoNngsK0YTUaDhB18iW+6cb+wtAjH0ExF5M4cDcEZGBt59911ERUVh+vTp+Oijj3DkyBGsWrUKFy9exM6dO7F06VJotVrMmDEDnTt3dma7iYi8Hj/4Nt2x0+VYuuEv6PQmFg1zMlEUIQIQRcBU/SgCMImACLHWdgAQAAiCeb6UIAACBAgAJILlZ9T4uXE3ut0139hbBGLoJyLyZg4H4C+++AKCIODTTz/FgAEDsGzZMgDAddddBwC47bbbMGXKFNxzzz148803sWnTJue0mIjIR/CDb9PsO56P3/cUQKszWbd5a9EwvUlEpUGE1lj70fJ9lRGoNIrQGUXoTCJ0JkBvFFFlEqE3AjqTiCqjCL0J1ftF6Kq3m48zn8ckAgaTCEP1o1EEDCbAKJq3Ga37AIMowlT9aDDVDLjuIwCQCoBUAggiIBpNAERIAagUUqgUUkgFQCYBZIJQfawAmcT8PJkgQC4BFFIBCokAhRRQSATIJQIUEkAuFRBUvd28rfZ2pVSAUipAJTM/CkYDisoFGEuNCDMarduVUveMUAu00E9E5M0cDsB79uxBy5YtMWDAgDqPadmyJT777DNcc801mD17Nj744ANHT0dE5JP4wbfxMrb/YxN+geZVy60yiijVm1CqE1FmEFGhN6HcIJq/9CIqDCLKDSbr9xUGEWV6k/X7msddHnCN7kyUPkYEzIHcMtVduFRvs0IPQO+pOfAqYE9Rra1B1YFZJRWglAkIlglQywQEyyRQyy3fW7abtwXXOE4tl1i/D5ELCFVIEFr9PE7/IiLyPg4H4KKiIlxxxRWXXkhmfqny8nKo1Wrr9vj4eCQnJ2P9+vXNaCYREfm7quqiYSYAekECnUQKvSBFuV6OH09WWsNsqd6EUr2IUp0JZQbzY4letG4r1ZuD7GVZmsiuKqP5Zkmxk/vJJQLMgVgmQahCQKhcQKhcYhOSQ+WXHsMUAsIVEoQrJNAEXfo+SMoQTUTkTA4H4IiICFRVVVl/btGiBQDg1KlTSElJsTnWZDLh/Pnzjp6KiIh8jNEkokhnQmGVCYVa82ORTkRRlQnFOvNXkU40P1Zv+0fVHpVKAQZJ7SJhP60v9MBvQeQ4kwiU6ESU6IxAheOvo5QCmuowbP4SEB4kqd52KSi3CDJ/RQRJEKE0PzI8O8fuI3lI35wFrc7I2gREfsDhANymTRucPHnS+vMVV1yBNWvWYM2aNTYBOCsrC5mZmdaATEREvqXKKOKC1oQLWiMKtSZcrKoOtjW+Lmptfy7WOdCbJsjME0iJyEprBHIrTcitbPqQBrVMsA3GNb5vobT8LKClUoqWSgkilRKEcOi2jd1H8moVM/TW2gRE1DgOB+A+ffpgz549OHXqFNq3b4+RI0di1qxZ+L//+z9UVFQgLS0NeXl5eOedd2A0GnH99dc7s91EROQgXY1Aa3402fycX2lCgdaEfK0RBVrz8GJynaAaRZ6CahZ2kl4qCCWTVBeLEszfyyyFo4Qa+6qLStUsLmWt1CxYqjULdio7V1dxhvkHy36gRkXo6krRloJalu2XV5G2VpkWgdyLFTiWUwSd0QQRAkQIkEglaBsbhrMF5ajUGWGCYH6OYN4vk0sR0zIEelON4mBGcwExXY3iYZyD3TjmOe9GnC5v/LzrICnQMkiKliqJNRS3VEoQpZRav6/5c6jcvwNz+uYsm/ALNK82ARF5nsMBeNiwYVi6dCk2bdqE+++/H507d8aTTz6J999/Hx988IG14JUoioiNjcXLL7/stEYTEZEtg8kcavMqjThfWeOxwvbn81ojShzpnQ0AMgE1ih5dKnakltlWFFZWF0uquc1SQEklNYfYmscGVX/JJagOtuaqxQqpOcD6b3jQYPcRqd0q6PZ61QQBGNyjHUYNjm7wlY2mGqG4RvXsKqP5y1ysDNAaRFQaRZRW6nDuQgFU4S1gFGTVVbrN+7SGS8dXGsyF0i4VRbN8b4LWU7W73KzKCJypMOJMReN+4SApEKWUIkYlQbTK/Bilsv05JliKKKUEarmk4Rf0Mlqd/X8HbVWAXBBEfsjhAJyWloasrCybba+99hq6du2KZcuW4dSpU1CpVOjbty+eeuopxMbGNruxRESBptIgIq/SiLPlRuRWGHGuOtReHnQLtCa3LnXjDYKksCkiFKoQECKXIKxmcSFLRV65gBCZuVpvsFxASI3KviHVVXwVnC/pdHVVQU9NjsGJM0VYu+sfiNUXrigC2w6cRUIbTYM9a1KJAJUEUDVyzLxWKyBHZkRcnApKpbLJvwdgDt01w3G5ncri5YbLCrVdVpjNvM2Esup9/qDKCJwut/Qy6+s9NkQmILpGII5RSREbLEVssAStgqXWr3CFZ3qV7c31VSpq1yQAAGWQ/e1E5P0cDsB1GTVqFEaNGuXslyUi8itGk4jzWhNyK6rDbaUR58pNOFdpxDlr2DXiYpV/fEi2J1QuXCrqo5AgTCZCqq9Ea00IIoPl1n2aIAnCqgv9mMOuObR6a4EfFsxpnMMnCqzh18Kbh5ZKJQLCFALCFM55PZNo7mEu04soqa5wXqSrUSSuSrR+X1xzX9WlAnK+NhS8zCCirNSI7NL6e09VUgGxwRLEVgfimgE5NliK1tXbgmXO61Gua65vWtfWKC6rstmuVsowvG+i085NRO7l9ABMRBTojCYReZUmnCk34ky5EafLDdbvz5QbcbbCiLxKE0w+9uG1LmEKwVpgx1Jkp2bRnQjlpe2WyrVhCglkEtsAq9VqkZNTjLg4tcO9dJ7GgjmNF+hDSyXCpUDdGk3vTRRFc89zsU7ExapLxekufyzUmswV2bWXthu8/L2n0ijiRKkRJxoIyuEKAW3UUrQJlpofa33JoJI17kZZXXN9D58swJibUuwO5Sci38QATETUBKJonmtrGfJ3xs7XuQqjz/XMWFgK4EQqJYhSmQvgRNWoEBtZI9BaQu3lQTaQsWBO43FoafMIgoAQuYAQOdBG3fC/mXlkwglU6owQFDL06ZGA1m0jUFhlKXpnQsFlhfAshfG8tRBesU5Esc6AwxcNdR4TESRBG7UUrdVStL0sIMdVb5dLhHpvyNQ1lJ+IfFOjAvCsWbOccrLnnnvOKa9DROQqJtHce/tPqQE55Ub8U2ZETpmh+tH8Velj6TYySGIuSBMsRbRSUl3dVWpTzdUScP29oqurBXqvZlMM75dYq7ecQ0tdw97IhPUb/saYm1IwuBHBrsoo2lSGr1k5Pr/ShPOV5lEt56trEnhTD7NlabYDhfbnJ0sEoHWwFKKyNeTQQm3UI9ikh9po/ormDRkiv9OoADxz5sxmfSASRRGCIDAAE5HHmUQR5ypMOFlqqBVu/ykzD1XWNX25TbcLlZuLyZirrEoRpTIXlImufrRUYI1SSSBnD63bsFez8Sw9ahxa6nrNHZkQJBXQurq3tCEmUURRlckaiPOqi/XlV9pWqc+vNIdoT2dlk2gu4gVBCajsT7345ptziAuRIk4tQ1yIFO1CpGgfKkN8qBRRMk//BkTUVI0KwPfeey97BIjIZ5TqTThVasTJUgNOlhoufV9mxKlSg1cH3CApEKsyf9CMVUnRSi1BK5UUrdSXir/EqHxzOZFAwF7NpuHQUvdw58gEiSAgQilFhFKKLi3k9R6rN4k4X2nCuQrz1JHc6q+zFSbr9+cqjCjy8NJt5hBvwq58+73I0QolOhwtRkJ4JeJDpYgPlaF9iPkxRiXhZ2giL9OoAPzhhx+6uh1ERI1mEkWcLTdXEj1lCbhlhurAa56z5o1qzkVrEyxFq+oqp5aw21othcZDy3+Qc1jC3HfrjqKkTAcAUMjZ+0ue5a0jE+QSwToftz4VBhPyKkzWkHy2wohzFSacrVFY0JO1F87rJDifb8D2/NpzkVVSAe1Dq3uMq3uOE0Kl6BAmQ/sQGZSNLNJFRM7DIlhE5JVMoogz5UZklxiQXWJEdqkBWSUGnCgx4ESpAVovm1IZphDQtkYl0tbVj21r/OzMJTvIu+l1Rpiqy3xfLK1iJWjyKF8fmRAskyAhTIKEsLo/thqs1fcNdosTnik3D8d2d0auNIo4UmTAkaLa4ViAuYBZYpgMHcKk6BAqQ4cw81d8aOMrWBNR0/hlAO7atStycnLs7uvbty/S09NttlVVVeG9997D8uXLcebMGbRo0QJDhw7F9OnTERUV5Y4mEwUko0nEmYoaIbekOuSWmr+8pW6QpUhKXIi0ev6XDO1CbKuJhnJIMlVjJWjyNoEw31rWiN5kvUnEuYrq5enKjOZCh9UFDy11INx5c1UErCsKbDxXe39btdTaW2z9qg7JDMdEjvPLAAwAYWFhePTRR2ttb9eunc3PJpMJY8aMwdq1a3Httdfi1ltvRVZWFhYtWoSNGzfi999/R8uWLd3VbCK/VFRlQmaJAceLDcgs1lc/GpDlJSFXJgBtQ8xLYrQLlZkfQ6SIqw66lmUyiBqDlaDJG3G+tXnItfkGpgyw809hWebOHIariyTWCMeny4xuXRLKEo7/yNXZbBdg/puVFCZDYrgMSWEyJIXL0DFchjZqKSScRkNUL4cDcERERJOOFwQBBQUFjp6uycLDw/HCCy80eNzSpUuxdu1a3Hnnnfj000+tc+8+//xzTJ06FW+++Sbee+89F7eWyPfpTSJOlhqs4fZ4scEaer1hTm6rYIlNYZL4UBnah5p7c2NVEkgZcH2aeY3TLGh1RgQppBjRz3O9W94635KI6icIAqJUUkSppEitYwBgUZUJp8rMtSdOlRmQVVSFoxcqcN4gR065yS1FFkXAuizfurNVNvtUUgGJ4TJ0DDMH4qTqgNwxXIYwBUcqEQHNCMCi2LQ7YE093l0WLVoEAHjllVdsCs9MnDgRH3zwAb777ju8/fbbUKlUnmoikVcp1ZtwoECPHXlSFBSUI6usHMeLzQWoPLn2o1pmLjRyeciNrw65HC7mv+ytcerJObe+Pt+SiOqmCZJAE6RAt0jzz1qtHDk5FxEXFw25IgjnKow4VWa0FmU8ZXksMyCv0vXpuNIo4mChHgftrHsco5KgY7gMyRo5OoXL0FkjQ2eNHLGsVE0BxuEAvG/fvjr3VVRUIDMzE5999hm2bt2K2bNnY+DAgY6eyiE6nQ5LlixBbm4uQkNDkZqaih49etgco9VqsWvXLiQlJdUaGi0IAq6//np88cUX2LNnD/r06ePO5hN5XIHWiKNFBhwrNuBIkR7Higw4WmTAmQrLMM4gAFq3tqlFkIAOoTIkhsmQUGM+VHyoFC2V/AMeqLxtzm0gzLckotqkEgFtQ2RoGyJD39igWvvL9SacKjPiRIkB2aUGmyKPp8uMLi/QZV7OSYctlw2pDpML6FQdhjuHy8zfh8vRLkTK0VHklxwOwJcHxsslJydjxIgReOONN/D8889j7dq1jp7KIXl5eZg8ebLNttTUVHz22WdISEgAAJw4cQImkwkdOnSw+xqW7VlZWY0KwFqte8NAQ3Q6nc0j0b7j+cjY/g+qqoeJDu3VDq3iInG02IBjxUYcKzHiWLERx0uMKKjyTHduhEJAfKgUHUIliA8xFwBJCJUgIUSKFkH2hm+ZAJhQVWVnF/kUR9+zKqtqV1cFgEqtwWPvyynx4UiJT7XZ5m1/IwIJ/x6SqzTl2pIC6KACOqgEDI6RA7i0TrLWKOKfMiNOlplwotS8zJ/l+9MVJphc+Ce5RC9iV76+1jrHSimQGCpFUpgUncLNj8kaGRJCJJAxGLsU37OaTqlUNvpYlxfBeu6557Bw4ULMmjULX375patPBwAYO3Ys0tLSkJKSArVajczMTMyfPx/Lly/Hrbfeiq1btyI0NBQlJSUAzPOF7QkLCwMA63ENOXv2LIxG7ytykpeX5+kmuMSx0+XY9ncRdHoTFHIJ0rpo0Kmt2tPN8lq7T1Vg1eFy5ItBKJYFocgUhAU7RVT9ddHtbQmWimivMqGdyvwYpxQRpxIRpzLB7ioXFUBZBVDm9paSJzT1PUsi2B9WKBFMda4IQIHJX/8ekuc549pSAegCoIsaQI2PMzoTcFYrIEcrIKdSglOVAk5VP17QuW5er9YIHCoy4lCR7WdbuSAiPlhEh2ATEoNNSAwWkag2oVWQCOZi5+J7VuNIpdI6OzTtcXkADgoKQseOHbFlyxZXn8rq+eeft/n5qquuwscffwwAWL58Ob766is8/vjjTj9v69atnf6azaHT6ZCXl4eYmBgoFApPN8ep9h3Px7p9p1FhGfZYacS6fRfRsmUkuiUF9tJV5QYRx4qNOFJswJEiI45Wf59bGQyEuq+iuUQA4tQSJIZK0TFMisRQKRLDpOgYKkWMSuBwZarF0fesW/or8e3azEvvBwCClTLc0j8RcXGB/X5AZv7895A8y13XVl0VBEr1JmSXmpBVYkRmqRFZJdVfpUZUuqhPRi8KOF4u4Hi5bfgOlgGdw2XoHCZFskaK5HApksNl/JvvAL5nuZZblkHKy8tDaWmpO05Vr4kTJ2L58uXYsWMHHn/8cWsPb3Fxsd3jLT2/luMa0pSud3dSKBRe2zZH/bojx+bDLgBUaA349c8c9Ooa56FWuZfRJCKrxIBDF83FLg5fNODvIj1Olrp3FEKYQkDncBk6hsvNyzBUL8fQIUyGICn/4FHTNeY96/Kqz326tsbhkwWcc0v18se/h+QdPHVtKZVAVCjQ67I+GJMo4lyFybr0oOXrWLEBp8td8zmhwgDsKTBgT4Ht5zONQkCXFnJcYfmKkCGlhRwhclalbgjfs1zD5QH4hx9+wNmzZ5GUlOTqUzUoMtJcsq+iogIAEB8fD4lEguzsbLvHW7YnJrJyp7cJtHU2L1aZcLBQbw27hy7q8fdFPbRu/HWjVRJ0qq4emaAW0aKqEH07tkI7jYp3dn2ENy0V1Bz2qj6XlFVhzE0pPvn7EBE5m0QQ0EYtRRu1FAMuC8dlehOOFxtwpMiAY0V6cx2QInNhLlfMNS7SidiWp8O2PNv5rAmhUlwZYQnFcnSNMBfe4jrG5GoOB+Bly5bVuU8UReTn5+PPP/9ERkYGBEHAPffc4+ipnGbXrl0ALhXwUqlUuOaaa7Bz5078888/NoW9RFHE+vXroVarcfXVV3ukvVQ3f11n02Dp1S3U4+BFvfmxsGblZddrqzYXueikqblUgtymAJVWq0VOjgkxXDrBZ3jbUkHN4W1Vn4kc5S83pci3hMgluLqlAle3tB1aW2U0fwY5VlS9+kOxAUeL9MgsMcAV/QsnSo04UWrE6lOXCgSGyASktJCbg3GEDFe2kKNLCznXMCancjgAP/bYYw1+8LWs/XvLLbfgqaeecvRUTXLs2DG0bdsWwcHBtba/9tprAIA777zTun38+PHYuXMn3njjDXz66afW3+mLL77AyZMnMWHCBK4B7IX8YZ3Ncr25V3d/oR77C8yB1529uqGiAV1ayNG7rRrJGhm6tDAPYeaQJP/kT6Ex0EaAkH/yp5tS5B+CpObwmdJCDnNJLjOjScSpMiP+vqjH30UG8+NFPY6XGKB38tLGZQYRf+br8Ge+bW9xfKgUXSPkuCpCjqsiFbgqkusXk+McDsB9+vSp86ITBAHBwcFITEzEzTffjP79+zvcwKb6/vvvsWDBAvTp0wdxcXEIDg5GZmYmfvvtN+j1ekydOhV9+/a1Hj9mzBisXLkSK1aswKlTp9C3b19kZ2dj9erVaN++PaZPn+62tlPj+do6mwVaI/YXXAq7+wv1yCw2uHzNPwCIDJIgpYU54Ka0kKOLRobkFnKE826qx7mz98efQqO/jgChwOJPN6XIv0klAjqEmWt7DG9/abu+etTa3xer65Bc1OPvIj2yS5y/pvHJUiNOXtZbHKWU4KpISyiW46oIBRLCOISaGuZwAE5PT3dmO5ymf//+OHbsGPbv349t27ahoqICkZGRuPHGGzFp0iQMGjTI5niJRIKlS5dizpw5WL58ORYsWIAWLVrg/vvvx/Tp09Gypfuq5lLTpCbHeN2HBFEUkVNuG3YPFOjdMoQ5VC6gi0aOLtVht4tGjpQWMkSpGAq8kTN6f5oSoP0pNPrDCBAif7opRYFJLhGQrJEjWSPH7QmXtlcaRBwt0uNIkSUcmwOysz8L5WtNWHumCmvPVFm3hcgEXBkhR9cawbiLRg4Fi3JSDW6pAu1O/fr1Q79+/Zr0nKCgIDz//PO1lk8iqo8omocE7b2gx54LOuwt0GN/oQ4Xq1zbryugRuGICDmurC4e0T5EyqFAPqS5vT9NDdD+FBp9bQQIkT3+dFOKqCaVTED3lgp0v2yO8cUqEw5Z65tYCnoaUGl03uemMoOI7ed12H7+0hBquQRIaSFH90g5ukcqcHVL87xirlQRuPwuABO5giiK+KfMiL0Feuy9oMOe6scinWvDbphcwBXVFRItlRK7tOA8XX/Q3N6fpgZofwuN3jgChKgp/OmmFFFjtAiSoF9sEPrFBlm3GU0isksNOFRowMEaK13klDmvt1hvAvYV6LGvQI+vYF4JRi4BrrCE4pYKdI80TxNjT3FgaHYALi0txeLFi/Hrr7/i2LFjKCsrQ0hICDp16oShQ4di7NixjV5Hl8gbWIYx772gx94CXfWjHoVVTq70cJmE6gIPV9YIvO3Yq+u3mtv740iAZmgkZ2H14ubzt5tSRI6QSgQkhcuRFC7HyIRLhbeKqkw4fPHS8o/7C81DqZ01Q0BvgrlTo0APHDOHYoUEuCLiUk9x95YcPu2vmhWAt2/fjgceeAC5ubnWis+AORSfO3cOmzZtwty5c7Fw4UL06dOn2Y0lcoX8SiP+uqDDX/nmocx7LuhR4MKwKxOAzhqZuYph9fyUKyMaV5SKHzr9R3N7fzh8kjyF1YudhzeliOzTBEnQJzYIfWr0FutNIo4VGXCg0DzlzFJvpcRJo/F0JmDPBT32XNADuBSKr4yQ45qWCqRGKXBNSzk6hstYaMvHORyAMzMzceedd6K8vBwajQbjxo1DcnIyoqOjcf78eRw5cgSLFy/GuXPncPfdd2P9+vVISkpyZtuJmqxcb8LeAj125+vw1wU9/rqgc+owm8sFywRc2aK6OmF1QYZkjRxKWdPfOPmh0780t/eHwyfJU1i9mIg8QS6pnhYWIce9MC93aqnHYgnDBwp0OFCox9kK53Rk6EzA7gt67L6gB46UAzBPT7u6pQKpLeXVoViB1mrefPYlDgfgmTNnory8HEOHDsVnn30GtVpd65hnn30WkyZNQkZGBmbNmoWFCxc2q7FETWEwifi7yFAddnX4K1+Hv4sMMLlo2m64QkC3SPM8EkvYTQyTQSpxzl1Cfuj0P83p/eHwSfKUQKtezJE3RN5LEATEh8oQHyrDrfGXhlDnVxrNPcXVw5z3FuhwstQ571ElehEbz1Vh47lL1adbBUuQ2lKBa6p7ibu3VHC5SS/mcADeuHEjlEolPvroI7vhFwDUajU+/PBDdOnSBRs3bnS4kUQNEUURp8uN2JWvw658PXZfMM/ddWZlwZrCFIJ5fkikHFe3NM8ViQ917XzdQPvQSQ3j8EnyhEAafh9oI28Y9slfRKmkGNRGikFtlNZtF6tM2Fdd22VP9eMpJ40CPFdhQvo/WqT/c2md4qRwGVJbynFtlALXRitwRQs5ZE7qFKHmcTgAl5WVITk5GRqNpt7jNBoNkpOTceTIEUdPRVRLpUHE3gIddp3X4c98HXbl63DOScNdLhcmF3BVpBxXt7QEXnPYdff8j0D60ElE3iuQht8H0sibQAv7FHhaBEkwsLUSA1tfCsWFWiP2VfcSW5a0/MdJofh4sQHHiw1YnlUJwDwtrnukHD2jFegRpUDPaAWiVfwM5wkOB+C2bduitLS0UceWlpYiLi7O0VNRgLPM79iVr8Of581hd3+BHgYXdO6qZQK6VYdcc8+uHB3CvKPYQSB96CQi7xVIw+8DaeRNIIV9IosIpRTXt5Hi+ho9xQU1QvFf+TrsuaBzypziCoOIrXk6bM27tEZxuxCpNRBfG6VA1whWnXYHhwPwyJEj8c4772Dnzp249tpr6zxu586dyMrKwrRp0xw9FQWYcr0Jewr0Nr275yud37srFcxrwF0TJbfO2+gc7rw5u84WSB86ici7Bcrw+0AaeRNIYZ+oPpHK2sOnz5YbsfuCOQz/dcE81c4Z1af/KTPin7JKrMg29xIHSYHukQpcHSFBR0GK+9h/6BIOB+BnnnkGmzZtwpgxYzBr1izcfvvtNvMfRVHEqlWr8Pzzz6NXr1545plnnNJg8j/nKozYkafD9vNV2HHe3Lvriqm7CaFSXBOlMIfdlnJ0jZQjWOZbBQoC5UMnEZE38MWRN47O4w2ksE/UVK3VUrRWqzCivbnQlkkUkVViwF/55hVFduebq0/rmtlfU2UEdpzXYcd54KpQGe7r3vy2U20OB+CpU6eiffv22LNnDyZNmoSXXnoJnTp1si6DdPz4ceTm5kIul6Ndu3Z4+umna72GIAiYN29es34B8i3G6srMO85XVYdendPmWtQUGSRBj6hL5elTW8oRoeQfcSIiajxfG3nTnHm8vhj2iTxFIghICpcjKVyOezual2SqMoo4VFgdiC+Yh08fKzY08Ep16xrmmto21IwAvHTpUgiCAFE0d9Xl5uYiNze31nE6nQ7fffed3ddgAPZ/5XoTduXrzYH3vA47z+tQondu965MMC9Sfm20ef5Ez2gF2oe4tiKzp7BCJxGRe/nSyJvmzOP1tbBP5G2CpAJSoxRIjVJYtxVVmbArX4ed1VP6duY3fuh011AGYFdxOAA/99xzzmwH+YnzlUZsy9Nha6458B4odP5w5hiVxFpS/tooBbq39L2hzI5ghU4iIqpPc+fx+lLYJ/IFmiAJbmirxA1tzfOJTaKI48UGa1HXnfk6/H3RAHsflRmAXcfhAPz88887sx3kg0RRxD9lRmzN02FbXhW25uqQWeL4UA975BLgqhq9u9dGKxCn9s/e3YawQicREdWH83iJvJtEENBZI0dnjRz3d1IDAEp0Juy5oKsRivVQSoHoIBcUxCEAzQjAFHhEUcTRYgO25eqwtTrwnqlw7vzdlkoJekUr0DvaPJS5W6QCSlnghV17WKGTiIjqw3m8RL4nTCHBgNZKDKhen1gURZwuqoT2QpmHW+a/nBqADQYDysrKEBISApmM2drXGUwiDhTqzWuW5VZhe54OBVXOHY7ROVyGXjGK6tAbhA5hgdm72xi8s09ERPXhPF4i3ycIAqJUEuR4uiF+rNkpNScnB/PmzcOvv/6KU6dOWbfHx8dj6NCheOyxxxAXx0WsfIHeJGJfgR6bz1Vhc3XgLTM4b/iFUgpc3dLcu9srRoGeUQpWZm4C3tknIqKGNGYeLwsqElEga1YAzsjIwMMPP4yysjJrNWiLEydO4OOPP8aSJUuwcOFCDBkypFkNJedzdeCNqh7O3CvG3LvbLVIOhZS9u47inX0iImouFlQkokDncAA+ceIEHnjgAVRWVqJ9+/Z49NFHkZKSgtjYWOTm5uLw4cP46KOPcPLkSUycOBGbN29GQkKCM9tOTeTqwNsuRIo+MQr0iQ1CnxgFEsNkHM7sZKzQSUTkf9zZI8uCikQU6BwOwO+//z4qKytx11134cMPP4RUemkoa1JSEvr3749Jkybh0UcfxXfffYcPPvgAc+bMcUqjqXH0JhEHSyVYdbgSOy6UOT3wJmtk6BMThLQYBdJiFGgb0vDlxGFXREREl7i7R5YFFYko0DkcgDds2AC1Wo13333XJvzWJJVK8e677yI9PR3r1q1zuJHkmA//1uKt/UoAFc1+LakAXBUptwm8kU2cv8thV0RERGaWG8K5hRUwmWxvTruyR5YFFYko0DkcgHNzc5GSkoKQkJB6jwsJCUHnzp1x+PBhR09FDkqLdnyKt1wC9IhSoG9MEPrEmtffDZVLmtUeDrsiIiKyf0P4cq7qkWVBRSIKdA4nJKVSiaKiokYdW1xcjKCgIEdPRQ7qFiGDUiJCa2p4Hq4l8PaLDUK/2CBcGy1HsKx5gfdyHHZFROQcnE7i2+zdEL6cq3pkWVCRiAKdwwE4OTkZf/75J/7880/07NmzzuO2b9+O7Oxs9O7d29FTkYMUUgHdwkzYUVT7j6i1hzc2CP2re3idHXgvx2FXRETNx+kknuOsGw913RC2cHWPLAsqElEgczjx3HXXXRBFEffddx9++uknu8f8+OOPGD9+PARBwF133eVwI8lx14Sb/8jKJUBajALPdAvFj0MjcWpsK/xvWBSmp4ZhQGuly8MvYB52pVba3nPhsCsioqapbzoJuY7lxsPZC+UoLNHi3IVyLM04jN1H8pr8WnXdEJZIBLSOUmPMTSkMqERELuJwD/D48eOxYsUKbN++HRMmTEDbtm2RnJyM6OhonD9/HkeOHMHp06chiiLS0tIwfvx4Z7abGmlIlBHXdwhD37Zqt4Tc+nDYFRFR83E6iWfUdePhi9UHADSt972uebgMvkRErudwAJbJZPjuu+/w7LPPYvny5cjJyUFOTo7NMRKJBKNHj8asWbPqrBRNrtVGKSIuVg6lh8OvBYddERE1D6eTeEZdNx4MRrHJQ9B5Q5iIyHMcLxMMc4XnBQsW4Pnnn8fatWtx/PhxlJWVISQkBJ06dcLgwYMRFxfnrLYSEREFFHtzTlnF1zNMoljnPkdWNOANYSIiz3AoABcXF+PEiRMAgISEBLRr1w4TJ050asOIiIgCWV3FrsbclIIxN6Ww99DNGlpPgUPQiYh8Q5MCcF5eHqZOnYpffvkFJpMJgHmY80033YR33nkHMTH840tEROQM9RW7evnBPi4JvFxeqW6CUH8E5hB0IiLf0OgAXFFRgeHDhyM7OxtijWFARqMRP//8M44dO4aNGzdCpVK5pKFERESBxN3Frri8Uv3qmnsNNG8IOm86EBG5V6MrIy1cuBBZWVkIDg7Ga6+9hvXr12PdunV49dVXERwcjMzMTCxcuNCVbSUiIgoY7i52xeWV6mdvKT8BgCYsyOHqzc5cWomIiBqn0T3A6enpEAQBH374IW655Rbr9quvvhoJCQmYMGEC0tPT8cQTT7ikoUTOxDvuROTt3F3sissr1c8VlZvru+nAv0lERK7R6AB87NgxREZG2oRfi9tuuw2RkZE4evSoUxtH5Aoc5kfuwhst1BzuXiqHyys1zNmVm3nTgYjI/RodgEtKSpCamlrn/vj4eOzdu9cZbSJyKd5xJ3fwtxstDPOe4c6lcri8kvvxpgMRkfs1OgCbTCbIZHUfLpfLrZWhibwZ77iTO/jTjRZ/C/Nkn7t7nIk3HYiIPMGhdYCJfBnvuJM7+NONFn8K81Q/d/Y4k/2bDinxkUjfnIXv1x3jaAsiIhdoUgA+ffo0Zs2aZXdfTk4OANS5HwCee+65ppyOyCV4x53cwZ9utPhTmCfn4tD45qt504GjLYiIXK9JAfjMmTN1BlzL2sAMwOTtOMyP3MGfbrT4U5gn52FYcz6OtiAib+VPNzwbHYD79OkDQRBc2RYit+EwP3I1f7rR4k9hnpyHYc35ONqCiLyRv93wbNI6wERE1Hj+cqPFX8K8P9299gYMa87H0RZE5I387YYni2AREVGDfD3M+9vda2/g7rAWCDcwONqCiLyRv93wZAAmIiK/5293r72BO8NaoNzA8JfRFkTkX/xtdAoDMBER+T1/u3vtDdwZ1gLpBoavjrYIhB56okDlb6NTGICJiDyEHxjdx9/uXnsLd4U13sDwboHSQ08UqPxtdAoDMBGRB/ADo3v5293rQBOoNzB85SZZIPXQEwUqXx2dYg8DMBGRB/ADo3v5293rQBOINzB86SYZe+iJyJcwABMReQA/MLqfP929DjRNuYFhr9c0JT7c3U1uNl+6SRaoPfRE5JsYgImIGuCKYYj8wEjUNI25gVFXr+ldgzsiQunqFjqXL90kC8QeeiLyXRJPN4CIyJtZPlCfvVCOwhItzl0ox9KMw9h9JK9Zrzu8XyLUStt7kPzASNQ8dfWaZmz/x0Mtcpwv3SRLTY7BmJtS0DpKjYgwJVpHqTHmphSv66kmIgLYA0xN5CsFOYicxVXDEDknlcj56uo1rapjuzfztV5VTjEgIl/BAEyN5ksFOYicxZXDEPmBkci56uo1Dapju7ey3GwWBAEyqYAghRThIUG8SUZE5AQMwNRovlSQg8hZfGkYIlGgq6vX9Kbe7QBose94Pn7dkePVo5js3WwOEsHwS0TkJJwDTI3mSwU5iJyFc3WJfEddc1G7JUXh2OlyfLs20+nz+Z2tvpvNRETUfOwBpkZjTxgFIs7VJfIt9qYWaLVabPu7CBVOHsXkiroYvNlMRORaDMA17N69G2+//TZ27NgBg8GAlJQUTJ48Gbfffrunm+YVfK0gB5GzcK5ubSyIR75GpzfZ3e5osHRVXQzebCYici0G4GqbNm3CqFGjoFQqcccddyAkJAQ//fQTJk6ciNOnT+OJJ57wdBM9jj1hRASwIB75JoVcAlTWDruOBktX1cXgzWYiItdiAAZgMBjw1FNPQSKRID09HVdddRUA4Nlnn8XgwYMxY8YM3HbbbWjXrp2HW+p57AkjIhbEI1+U1kWDX/8qgM5wqSc4SCZxOFi6aqgybzYTEbkWAzDMvb8nTpzA2LFjreEXAMLDwzF16lQ89thjWLZsGZ577jkPtpKIyDtwjiL5KkFoaEPjuXKoMm82ExG5DqtAA9i8eTMAYNCgQbX2DR48GACwZcsWt7aJiMhbcY4i+aJtfxeh6rJ5wFV6o8PVlVkhnojIN7EHGEBWlvmPX2Ji7T9aMTExCAkJQXZ2doOvo9Vqnd625tDpdDaPRM7CayuwDekVh2/XZtpU1A1WyjCkZ1yz3gd5XZGr6HS6OotgVWoNDl23KfHhuGtwR2Rs/wdV1cXgburdDinx4V73eYBch+9b5Aq8rppOqVQ2+lgGYAAlJSUAgLCwMLv7Q0NDrcfU5+zZszAavW8IYF6ed61xSP6D11ZgilACg7q1wLa/i6A3mCCXSZDWRYMIpRY5OTnNfn1eV+QKdRXBkggmh6/bCCUwZmB0jS3O+T9AvofvW+QKvK4aRyqVokOHDo0+ngHYiVq3bu3pJtjQ6XTIy8tDTEwMFAqFp5tDfoTXFsXFAYPTnPuavK7IVXQ6HdK6lGPdvkJUaC+F4GClDLf0T0RcXJQHW0e+jO9b5Aq8rlyLARiXen7r6uUtLS2FRqNp8HWa0vXuTgqFwmvbRr6N1xa5Aq8rcoVObdVo2TISv/6Zw+rK5HR83yJX4HXlGgzAuDT3NysrC927d7fZl5eXh7KyMqSmpnqgZUREROQs3ZKi0KtrnKebQUREHsQq0AD69u0LAFi3bl2tfWvXrrU5hoiIiIiIiHwTAzCAAQMGID4+HitWrMD+/fut24uLi/Huu+9CoVDg3nvv9WALiYiIiIiIqLk4BBqATCbDBx98gFGjRmH48OG44447EBISgp9++gk5OTmYMWMG2rdv7+lmEhERERERUTMwAFe77rrrkJGRgbfffhsrV66EXq9HSkoKXn/9ddxxxx2ebp7DpFKpp5tAforXFrkCrytyFV5b5Cq8tsgVeF25jlBUVCR6uhFERERERERErsY5wERERERERBQQGICJiIiIiIgoIDAAExERERERUUBgACYiIiIiIqKAwABMREREREREAYEBmIiIiIiIiAICAzAREREREREFBAZgP7V69WqMHDkSCQkJiImJwVVXXYUHH3wQp0+f9nTTyEeJooiffvoJI0aMQOfOndGqVSv06NEDU6ZMwcmTJz3dPPJyy5cvx5QpUzBw4EBER0dDo9FgyZIldR5fUlKCF198EVdeeSWio6PRtWtXvPzyyygrK3Njq8kXNPba0uv1+PHHH/Gvf/0LPXv2RJs2bdC2bVsMHjwYn332GYxGowdaT96qqe9ZNZ08eRJt2rSBRqPB008/7eKWkq9x5No6efIknnzySevfxKSkJIwYMQKrVq1yT6P9jMzTDSDnEkURTz/9NL788kskJCRg1KhRCAkJwblz57Blyxbk5OSgbdu2nm4m+aDp06dj/vz5iI2NxfDhwxEaGoqDBw/iq6++wvfff49ffvkFKSkpnm4meak333wTOTk5iIyMRExMDHJycuo8try8HMOHD8eBAwcwaNAg3Hnnndi/fz/mzp2LLVu24Oeff4ZSqXRj68mbNfbaOnHiBMaPH4+QkBBcd911uPnmm1FSUoKMjAz8+9//xq+//opvvvkGgiC4+Tcgb9SU96yaTCYTHn30URe3jnxZU6+t9evXY+zYsQCAm266CfHx8SgqKsKhQ4ewYcMGjBw50g2t9i8MwH7mo48+wpdffolJkyZh1qxZkEqlNvsNBoOHWka+LC8vDx9++CHi4uKwefNmhIeHW/fNnz8fL730EubPn4/58+d7sJXkzebOnYsOHTqgXbt2mDNnDl5//fU6j33//fdx4MABTJkyBa+99pp1+2uvvYb33nsPCxYswNSpU93QavIFjb22QkJC8N///hejR4+GWq22bn/zzTcxYsQI/PLLL/jxxx/5YZIANO09q6b58+dj586deOONN/Diiy+6uJXki5pybeXk5GD8+PFo1aoVVq1ahbi4OJv9/FzvGA6B9iOVlZWYNWsW4uPjMXPmzFrhFwBkMt7zoKb7559/YDKZ0Lt3b5vwC5jvRgLAhQsXPNE08hEDBw5Eu3btGjxOFEUsXrwYISEhmDZtms2+adOmISQkBIsWLXJVM8kHNfbaat26NSZNmmQTfgFArVZj8uTJAIAtW7a4pI3kexp7XdV07Ngx/Oc//8HTTz+Nrl27uqhl5Ouacm29++67KCkpwbvvvlsr/AL8XO8o/qv5kXXr1qGoqAhjx46F0WjEzz//jKysLISHh2PgwIHo0KGDp5tIPioxMREKhQLbt29HSUkJwsLCrPsyMjIAAAMGDPBU88iPZGVl4dy5cxg8eLDdoNKrVy+sXbsWp0+f5nQOchq5XA4Adm8cEzWG0WjEo48+ig4dOmDatGnYsWOHp5tEPk4URaxatQoREREYMGAA9u7di82bN0MURXTt2hXXXXcdJBL2ZTqCAdiP7N27F4D5D3jfvn2RmZlp3SeRSPDYY4/hzTff9FDryJdFRETg1VdfxfTp09GzZ08MGzbMOgd406ZNmDRpEh5++GFPN5P8QFZWFgDUecOuQ4cOWLt2LbKyshiAyWm+/vprAMCgQYM83BLyVe+++y727duH33//HQqFwtPNIT9w6tQpXLx4EVdffTWmTJmCL7/80mb/VVddhWXLlqFNmzaeaaAP420DP2IZgjp//nyEhYVh3bp1OH36NH7++Wd07NgR8+bNw2effebhVpKvmjx5Mj7//HOUl5fj888/x/vvv4+1a9eiR48euPPOOzkMh5yipKQEAGoNtbewjD6wHEfUXF9++SV+++03XHfddRgyZIinm0M+6MCBA/i///s/PPnkk+jevbunm0N+Ij8/HwCwf/9+rFixAvPnz8fJkyexb98+jB8/Hvv378f48eM93ErfxADsR0wmEwBAoVBgyZIlSE1NRUhICPr06YMvv/wSEokE8+bN83AryVfNmjULDz/8MKZOnYpDhw7h9OnT+N///getVosRI0bg559/9nQTiYiaJCMjA9OmTUNcXBw++eQTTzeHfJBOp7MOfX7uuec83RzyI5bP9UajES+++CLGjh0LjUaD9u3b4/3330ePHj2wa9cubNu2zcMt9T0MwH7E0jPSvXt3tGrVymZfSkoK4uPjceLECRQVFXmgdeTLNmzYgLfffhsPPfQQnn76abRp0wYhISFIS0vDN998A7lcjunTp3u6meQHLO9jxcXFdvdben5rzkMncsSvv/6K8ePHIzo6GqtXr0ZsbKynm0Q+6N1338Xhw4cxf/58BAUFebo55Edq/p0bNmxYrf2WIqR79uxxW5v8BQOwH0lKSgJQ99BBy3atVuu2NpF/+O233wAA/fv3r7UvJiYGSUlJyM7ORllZmbubRn4mMTERAJCdnW13v2W75TgiR/zyyy+4//77ERkZidWrVyM+Pt7TTSIftX//fphMJtxwww3QaDTWr1tuuQUA8MUXX0Cj0WDMmDEebin5moSEBGthPnuf7fm53nGctOdHLOHk2LFjtfbp9XpkZ2dDrVajZcuW7m4a+TidTgeg7qWOCgoKIJFIrJVUiRyVmJiIVq1aYceOHSgvL7epBF1eXo4dO3agffv2LIBFDvvll18wbtw4tGjRAqtXr+YKCdQs119/PSIjI2ttz8vLw6+//opOnTqhV69euOqqqzzQOvJlSqUSPXv2xLZt23DkyBGkpaXZ7D969CgANHm5LmIPsF9JSEjAoEGDkJ2dXWudzDlz5qC4uBjDhw9nsSJqst69ewMAFixYUGto6ueff44zZ86gZ8+eHP5FzSYIAu6//36UlZVh9uzZNvtmz56NsrIyFv0gh/32228YN24cNBoNVq9ezZEE1GwPPfQQ5s6dW+vriSeeAAD07dsXc+fOxUMPPeThlpIvevDBBwEAM2fORFVVlXX7sWPHsHTpUoSGhuKGG27wVPN8llBUVCR6uhHkPCdOnMCQIUOQn5+PoUOHIikpCfv378emTZsQFxeH33//HTExMZ5uJvkYo9GIW265BVu3bkVUVBRuvvlmhIeHY9++fdi0aRNUKhXWrFmDa665xtNNJS+1aNEia6GOw4cPY9++fejduzcSEhIAAGlpaRg3bhwAc0/v0KFDcfDgQQwaNAjdunXDvn37sG7dOqSmpiI9PR0qlcpjvwt5l8ZeW8eOHUP//v1RVVWFUaNGoWPHjrVeq127dhg7dqxb20/eqSnvWfb88ccfuOWWWzBx4kTMmTPHLW0m39CUa0sURUyYMAE//vgjkpKSMGjQIJSUlGD16tWoqKjARx99hLvvvttjv4uvYgD2Q6dPn8Zbb72FtWvXorCwEDExMbj55pvx7LPPIioqytPNIx9VVVWFBQsWYOXKlcjMzIROp0N0dDT69euHf//73+jcubOnm0he7NFHH8WyZcvq3D969Gh8+OGH1p+Li4sxc+ZMrF69Gnl5eYiJicHIkSPx3HPPITQ01B1NJh/R2GvLEkjq07dvX6Snpzu7ieSDmvqedTkGYKpLU68tg8GAjz/+GF9//TWys7MRFBSEHj16YOrUqejXr587mux3GICJiIiIiIgoIHAOMBEREREREQUEBmAiIiIiIiIKCAzAREREREREFBAYgImIiIiIiCggMAATERERERFRQGAAJiIiIiIiooDAAExEREREREQBgQGYiIiIiIiIAgIDMBEREXmERqOBRqPBqVOnPN0UIiIKEAzAREREjTR8+HBoNBq8/fbbnm6KX/vjjz/w9ttvY82aNZ5uChER+RkGYCIiIvKIpKQkJCUlQS6X22zfvHkzZs2ahfT0dA+1jIiI/JXM0w0gIiKiwLRz505PN4GIiAIMe4CJiIiIiIgoIDAAExERudDRo0cxefJkdO3aFdHR0Wjfvj2GDRuGRYsWwWg02n1OzeJQhw4dwsSJE5GUlITo6Gj06NEDs2bNgk6nq/Oce/fuxejRo5GQkIBWrVqhT58+mD9/Pkwmk3Ue85IlS+w+Nzc3F6+88grS0tLQpk0btG7dGn369MHMmTNRWlpqc+zWrVuh0WjQrl07aLXaOtvzxRdfQKPRIC0trc7fs+a2WbNmAQCWLVtmPcbyBQDPPPMMNBoNHnrooTrPCQA333wzNBoNPvjgg3qPIyKiwMEh0ERERC6ycuVKPPLII9DpdFCr1UhJScHFixexdetWbN26FStXrsTSpUuhUqnsPn/9+vV4/vnnIZPJ0LFjR8hkMmRmZuLtt9/GoUOHsGjRolrPSU9Px4QJE6DX66FWq5GcnIzCwkK89NJL+PPPP+tt78aNG3H//fejpKQECoUC7du3B2AO8TNnzsT333+Pn376Ca1atQIApKWloX379jh16hTS09MxatQou6/7zTffAADuvffeBv/NevfujdOnT+P06dOIiopCYmJirWMmTJiAhQsXYvXq1SgqKrIG45qOHz+Obdu2QS6XY/To0Q2el4iIAgN7gImIiFzg2LFjePTRR6HT6TBu3DgcO3YMGzZswL59+7Bq1SqEhYVh/fr1eOWVV+p8jWnTpuGRRx5BZmYmNmzYgL///huffvopBEHATz/9hE2bNtkcn5ubi0cffRR6vR533303jh49ivXr12Pfvn1YsWIF1q5dW2cIzs7Oxn333YeSkhI888wzyM7Oxs6dO7Fz504cOnQIN954I44fP45HHnnE+hxBEHDPPfcAuBRyL3fixAns2LEDEokEd999d4P/bhkZGRg7diwA4IYbbkBGRobNFwBceeWVuPbaa6HVarF8+XK7r2O5OTBs2DBERUU1eF4iIgoMDMBEREQu8MEHH0Cr1SIlJQXvv/8+1Gq1dd/AgQPx5ptvAgC+/PJL5Obm2n2NPn364PXXX4dSqbRuu+uuuzB06FAAsAZCi88//xwlJSVISkrCggULEBISYt13ww034I033oBer7d7LssQ50ceeQTTp0+3eW5sbCw+//xztG7dGps2bcJff/1l3WfpXV23bh3Onz9f63UtwXjgwIHWnmNnmDBhAgBg8eLFtfbp9XrrecePH++0cxIRke9jACYiInKB3377DQDwr3/9C4Ig1No/evRoREVFQa/XY/369XZfY9KkSXa39+zZE4C517amtWvXAgDGjBkDmaz2LKfRo0cjKCio1na9Xm9dc/fBBx+0e87Q0FAMHDgQgHmotEVCQgJ69eoFo9GIb7/9ttbzLNucPQz5jjvuQHh4OA4ePIjdu3fb7Pv555+Rn5+PuLg4a5uJiIgAzgEmIiJyuuLiYuTl5QEAUlJS7B4jl8uRlJSE/Px8HD9+3O4xHTt2tLvdMqS3rKzMZrvldbp27Wr3eSqVCh07dsShQ4dstmdlZaGiogIA8OSTT9p9LgDk5OQAAM6cOWOz/d5778WOHTvwzTff4PHHH7du3759O06cOIHQ0FCMGDGiztd1hEqlwj333INPPvkEixcvRmpqqnWfZfjz/fffD4mE9/qJiOgS/lUgIiJysprBNDo6us7jYmNjAaBWdWWL4OBgu9stoU4URZvt5eXlAGAzfPlyoaGhtbYVFRVZv9++fXudX5bgawnLFrfffjuCgoJw8OBBm3BtmZ9722231VnoqzkmTpwIAPj++++tbcrJycH69eshlUpx3333Of2cRETk2xiAiYiInKxmALU3L9bCMvfXXih1hGWe8eU9wzXZC9uW5wmCgIKCAhQVFdX79eGHH9o8X6PR4OabbwZgXroIAKqqqrBy5UoAjav+7IguXbqgd+/eKCkpsZ7r66+/hslkwg033IDWrVu75LxEROS7GICJiIicLDw8HDExMQCAw4cP2z3GYDBYhyx36tTJKedNSkoCABw8eNDu/srKSmRlZdXa3rFjRwQFBUEUxTrb2xBLyF2xYgWMRiMyMjJQVFSEdu3aoW/fvk16LXtzputSsxiWyWSyrm88bty4Jp2TiIgCAwMwERGRCwwZMgQA8NFHH9UaqgyYqyPn5+dDLpfj+uuvd8o5Bw8eDMDcC2swGGrtX758ObRaba3tKpXKWll67ty5Dp37hhtuQFRUFHJzc7FhwwZrFeZ77rmnSYEWuDT0u7KyssFjR44cCY1Gg+3bt+Ojjz7C6dOnERsba/19iIiIamIAJiIicoEnnngCSqUShw8fxpQpU6zzcwFzFeXp06cDMPdgWnqLm+uBBx5AWFgYjh49iscff9zmnOvWrcPLL78MuVxu97kvv/wyQkND8e233+Kpp56yFvGyMBgM2Lx5MyZPnoyzZ8/Wer5MJsOoUaMAAAsWLMDvv/8OwLHqzwkJCQCAXbt21TucGwCUSqX1HJY1lceOHWu3CjYREZFQVFRU+7Y0ERER1TJ8+HBs2bIFKpWq3qJObdu2xaZNm7By5Uo88sgj0Ol0CAkJQVJSEi5evIiTJ08CAK6//nosXbq01mtpNBoAwL59+9C+fftar79kyRJMnjwZffv2RXp6us2+9PR0jB8/HgaDodY5b731Vly4cAFbt27FRx99VGtu7h9//IEJEyagoKAAEokEiYmJ0Gg0KCsrQ3Z2Nqqqqupt1969e22WHerVqxd++eWXOv+d6vo9y8vLcfXVV+P8+fMIDw9HUlKSdfmmy39fADh69Ch69eoFwDx8es+ePYiPj6/zvEREFLjYA0xERNRElZWVKCwsrPPr4sWLAMzVkTdt2oQxY8ZAo9Hg0KFDKCwsRFpaGj744AOsWLHC6dWRhw8fjt9//x033XQTZDIZ/v77b6hUKsyYMQNffPGFtVqyvcJb/fv3x86dO/Hiiy8iNTUVeXl52L17N86ePYsuXbrgySefxC+//IJ27drZPXf37t1tln1ytPiVWq3Gjz/+iFtvvRVKpRJ79+7Fli1bsGXLFrvHd+7cGWlpaQCAAQMGMPwSEVGd2ANMREQUIIxGIxISElBSUoLNmzfjyiuv9HSTnKZHjx7IzMzE559/jjvuuMPTzSEiIi/FHmAiIqIAsXLlSpSUlCAiIgLJycmebo7TbNq0CZmZmWjZsiVGjBjh6eYQEZEXYwAmIiLyI7///jt++OEH63xdABBFEatXr8a///1vAOZiWf5SJKqyshIzZswAYP69FAqFh1tERETezD/++hEREREAIDs7G88++yyUSiUSExOhVCpx8uRJFBQUADDP8502bZqHW9l8c+bMwa+//orjx4/jwoULaN26NSZPnuzpZhERkZdjDzAREZEfGThwIB566CF06NAB586dw759+2A0GtGnTx+8++67+OGHH6wVlX3ZsWPHsG3bNuh0OgwZMgSrVq1CeHi4p5tFRERejkWwiIiIiIiIKCCwB5iIiIiIiIgCAgMwERERERERBQQGYCIiIiIiIgoIDMBEREREREQUEBiAiYiIiIiIKCAwABMREREREVFAYAAmIiIiIiKigMAATERERERERAGBAZiIiIiIiIgCwv8DVeQYXDHdsjgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_scatter=sorted_data.plot.scatter(x=\"LIFETIME COST\", y=\"LONGEVITY\", c=\"#5978ab\", figsize=(9,4))\n",
        "df_scatter.set_xlabel(\"Lifetime Cost\")\n",
        "df_scatter.set_ylabel(\"Longevity\")\n",
        "df_scatter.plot()\n",
        "\n",
        "model = np.poly1d(np.polyfit(sorted_data[\"LIFETIME COST\"], sorted_data[\"LONGEVITY\"], 1))\n",
        "\n",
        "polyline = np.linspace(12000, 28000, 50)\n",
        "plt.plot(polyline, model(polyline))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "bgdho7TxsvOF",
        "outputId": "4e664f8e-4f67-4141-ed82-b56bcfd575fa"
      },
      "id": "bgdho7TxsvOF",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x400 with 1 Axes>"
            ],
            "image/png": 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HCTQxSgzpl4Q+6XFBf53aOpvX7XVWW5sbdUW6jvY+dkpAUZUSmy+osOWCChU2/9e+urGTE6NSnLgj2YnuggSgGrgMlFz23K+j1TgUQlljlUqFXr16+fScqApddru98bLCZ599NqBjde/ePUij8p/dbkdFRQVSU1P9nrGjlrHG8mON5ccay481ltc3x85h8zclqBO/+2On1YnN31xCSkoyBmR1CeprxcWeRbW1rsn2WJ0Wer0+qK8VbjrS+7jeJeG/Zx1YU2LH56V2nBP9azmoVABDuqiRrddiUroGabEtB7aOVONQidQaR1XomjdvHg4dOoQNGzZAq9UGdCxfpwzlpNFowmo80Yg1lh9rLD/WWH6scfDtPVyB99YdQb3T80NxnViPDf8rwaAbghuEcob3bnJPV5ygRs7w3h3mexut72ObU8LWchtMFivWnrLiks2/oBWjBEZ00yLXqMOkDAEpgsrnY0RrjcNJpNU4qkJXUVERXC4XxowZ4/Xxd999F++++y4mTZqE5cuXt/PoiIiI6EoNTS2uDlwNRFvwL/NvaJaRv7MYos0JQatC9lB2L4xUdfUuFJTakGexYn2JiCqHf0FLqwJG93C3dp+gF5Ck9f8SRCJvoip03XHHHUhOTm6yvaKiAhs2bECfPn0waNAg3HjjjSEYHREREV0pf0dxk6YWVxK0vs8wtMXAvqkMWRGsyu7ChlJ3a/eCMhvq6v0LWnFqBcalC8g1ChibLiA+hkGL5BNVoevxxx/3un379u3YsGEDhg4dijfffLOdR0VERETeiPbmZ7LiBDWyh2a242gonF2yubD2lBUmi4it5SL8nQRN0CgwUe+e0RrVQ4BOzTW0qH1EROhaunQpCgsLAQCHDrnX0li2bBl27NgBABgyZAimT58esvERERGR7wSN95kstUqBByb052xUB3fO6kT+KfeM1rbTNvg5oYVrtEpkZwi406jD7d200KgYtKj9RUToKiwsxIoVKzy27dq1C7t27Wr8f4YuIiKiyJI9LLNJU4tYQY1pDFwdVlmtE3kW92LFhRV2uPwMWmk6JSYbdMgx6DA0TQO1kkGLQisiQtfChQuxcOFCv58/fPhwVFZWBm9AREREFLArm1pYxXooFS7kDGdTi47GXF2PPLMVn1ms2HPO4fdx9PEq5Bjclw7e2lUDpYJBi8JHRIQuIiIiik4NTS1EUURJSQn0+uCuy0Xh6UilA3kW96WDRRf9D1qZCSrkGnTINepwU3IMFAxaFKYYuoiIiCis7D1cgfwdxRDtTmg1KkwextmvSCdJEg5cqofJ7L508HBl810rW9M/SY0cow65Bh36d1YzaFFEYOgiIiKisPHNsXP4cNNxj/u8lq9zN9Fi8IoskiRh73kHTGYrTBYrTlb7v+7aTckxyDXqkGsQ0DsxJoijJGofDF1EREQUNtbtOtVk7a5asR75O4sZuiKA0yVh91k7TBYr1lhElNb6H7QGddUgxyAgx6CDoRM/slJk4zuYiIiIwkZ1rd3r9qoa79sp9BwuCTvP2GAyi1hzyoqzVpdfx1EqgKGpGuQadZhs0KFbrDyLYxOFAkMXERERhQ2bw/vMiGj3/x4gCj6bU8LWchtMFivWnrLiks2/3u4xSmBENy1yjTpMyhCQIjBoUXRi6CIiIqKwoY1Rod7ZNGBpm1lImdpPXb0LBaU25FmsWF8iosrhX9ASVMCoHu7W7hP0ApK0yiCPlCj8MHQRERFR2OgUp2lyTxcAJMZrQzAaqrK7sKHU3dq9oMyGunr/glacWoFx6QJyjQLGpguIj2HQoo6FoYuIiIjCxoTBGU26F8YJamQPzQzhqDqWSzYX1p6ywmQRsaVMhN2/W7SQoFFgot49ozWqhwCdmq3dqeNi6CIiIqKwMSCrC2JiNMjfWQzR5oSgVSF7KNfpkttZqxP5FhEmixXbT9vg54QWkrVKZBvcQev2blpoVAxaRABDFxEREYWZgX1TGbLaQVmtE3kWK0xmKwor7PAzZyFNp0SOQYccow63pWqgVjJoEV2NoYuIiIiogygVFTB9a8XnZVXYc87h93H08SrkGtyLFf+wqwZKBYMWUUsYuoiIiDqIvYcrkL+jGKLdCa1GhcnDeNleR3Ck0gGT2YrPTtbhQKUOQJ1fx+mdoEau0X3p4IDkGCgYtIjajKGLiIioA9h7uALL1x3yaFCxfN0hAGDwijKSJGH/RQdMFhF5ZiuOXPZ/jbP+ndXuGS2jDv2S1AxaRH4KOHTNnz8fDzzwAJKTk4MxHiIiIpJB/o7iJq3Ya8V65O8sZuiKApIk4avz7hmtPIsVJ6u9LzLdFjenxCDXoEOOQUDvxJggjpKo4wo4dP3xj3/EnDlzkJ2djenTp2PkyJFBGBYREREFk2j3/iFctPn/4ZxCy+mSsOusHSazFWssIsrq/PteKgAM6qpBjtEdtDLi3R8P9x6uwIsf8nJUomAIOHQNHjwYu3btwqefforVq1dDr9dj+vTpmDZtGtLS0oIxRiIiIgqQoFF53671vp3Ck8MlYecZGz4zW5F/SsRZq3+LaCkVwNBUDXKNOuQYdEiL9Xwf8HJUouAKOHR9/vnnOH78OJYsWYJVq1bh1KlTeOmll/Dqq69i7NixmD59OsaNGwelkiuPExERya25ZhnZwzKbfIjmosORweaUsKVcRJ5FxNpTVlyy+dfcPUYJ/DDRiXuzEpCb2QkpQvOBm5ejEgVXUBpp9O7dGy+++CKef/555OfnY+nSpdi6dSs+//xzrFu3DmlpaZg2bRoefPBBGAyGYLwkERERXaUtsxNcdDgy1NW7UFBqQ57FinUlIqod/gUtQQWM6iHgTqMOI7sAVRVl0OsFCC0ELoCXoxIFW1C7F6rVatx555248847UVpaimXLluH9999HWVkZ/vKXv2DevHkYOXIkpk+fjuzsbKjVbJ5IREQULK3NTnDR4fBWZXdhQ6kIk9mKgjIb6ur9C1rxagXG6QXkGASMTRcQH+O+2kgURVS18Ri8HJUouGRLPenp6fjtb3+L3/zmN3j77bfxxz/+EU6nE5s3b8aWLVuQkpKCRx99FE8++SQ6deok1zCIiIg6DM5OtCwc1ym7ZHNh7SkrTBYRW8pE2P27RQsJGgUm6t0zWnd0F6BTB9banZejEgWXbKFLFEV8+umnWLp0KXbv3g1Jcv+1ZvDgwTh//jyOHz+OuXPnYtmyZVi9ejV69+4t11CIiIg6hIZz7dU4OxFejSHOWp3It4gwWazYftoGPye0kKxVItvgXqz49m5aaFTBW0OLl6MSBVfQQ9e+ffuwbNkyfPTRR6iuroYkSUhKSsL999+PmTNnok+fPgCAbdu24cUXX8SePXvw+9//HitXrgz2UIiIiDqMvYcrUGd1NNmujVFxdgKhbwxRVutEnsUKk9mKXWftcPkZtNJ0SuQYdMgx6nBbqgZqpXyLFfNyVKLgCUrounz5Mj744AMsW7YMBw4c8JjVmjlzJqZMmQKtVuvxnNtvvx15eXm44YYbsGvXrmAMg4iIKCLIcZlb/o5i2OqbXpum06n5wRmhufTSXF2PPLMVn1ms2HOuaSBuK328CrkGHXINAn7YVQOlQr6gRUTyCDh0/eQnP8GaNWsgiiIkSUJiYiJ+/OMf4+GHH0bfvn1bfK4gCMjKymLoIiKiDkOuy9yaCxVK8AM60H6NIY5WOmCyuJthFF30P2j1TlAj1+i+dHBAcgwUDFpEES3g0PXhhx8CAH74wx9i5syZuPvuuyEIQpufP27cOBiNxkCHQUREFBHkusyN3eZaJldjCEmScOBSPUxmK/IsVhyurG/9Sc3o31ntntEy6tAvSc2gRRRFAg5djz32GGbOnInrrrvOr+fPnj070CEQERFFDLkuc2O3uZYFszGEJEnYe97RGLROVPv/vbs5Jea7Swd1yEzkUjpE0Srgn+7XX389GOMgIiLqEOSakWK3udYF0hjC6ZKw+6wdJosVaywiSmv9C1oKAIO6apBj1CHHICAjnkGLqCMI+Cf9mmuuweDBg7F27dpW9508eTIKCwtx4cKFQF+WiIgoZAJphCHnjBS7zQVXvUvCjjM2mMwi8k9ZUWH1bxEtlQIYmqZFrkHAZIMOabG85JOoowk4dEmS1Oy6IM3tT0REFKkCbYThy4xUOC7mK6ejpbVYvvUr2B2ukH29NqeEreU2mCxWrD1lxSWbf59bYpTAyG5a5Bh1yM4QkCwwaBF1ZO06p22z2aBWcxqdiIgiVzAaYbRlRiqcFvNtD98cO4eCry9AtH8/m9ReX29dvQsFpTbkWaxYXyKiyuFf0BJUwOgeAnKNOoxPF5CkVQZ5pEQUqdotAdXW1uLYsWNITk5ur5ckIiIKuuYaYVTV2IP6Ov6Gu0iZHbt6nFbR4RG4AHkXL66yu7Ch1N3avaDMhrp6/4JWvFqBcXp3a/cx6VrExzBoEVFTPoeu/Pz8JvdvnThxArNmzWr2OVarFXv27EFVVRVGjhzp8yCJiIjCRXONMGqtDvx2wTYoFYqghB1/uhxGyuyYt3E21xw9mIsXX7K5sPaUFSaLiK3lIvw9dKJGgYl694zWqO4CBHX0tnaPlBBPFO58Dl379+/H8uXLPbadPXu2yTZvunbtit/+9re+viQREVHYyB6WiX9/VoSrb1GWAFRW2xr/P9Cw40+XQ7nWAAs2b+Nsbp4p0K6OZ61O5FtEmCxWbD9tg58TWkgRlMjOcAet4WlaaFTRG7QaREqI94ZhkcKNz6Fr2LBhHv8/d+5cpKenY9q0aV73VygUiI2NRc+ePTFq1CjExsb6N1IiIqIwMLBvKmLXq1FrbXkR3EDDjj9dDuVaAyzYmhvn1fzt6lhe60SexQqTxYrCCjtcfgatNJ0SOQYdcow63JaqgVoZ/UHrSs2F+I82HwnrQBPJYZGil1+h68rg1RC6fvOb3wR1YEREROEqMU7baugCAgs7/qy7JdcaYMHW3DjjdUrExwqwO1w+rzNmrq5HntkdtL485/B7bPp41XeLFQv4YVcNlIqOFbSu1Fw4rqyy4RKCN6sbbJEy40sdS8CNNC5duhSMcRAREUUMb7NQ3gRjwWNfPiTKuQZYMHkbZ6ygxsgbO2P0kL4QBKFNxzla6YDJ4m6GUXTR/6DVO0GNXKO7GcaA5BgoOnDQulJz4fjqicNwCzSRMuNLHQv7txMRUUQLxb0bV89CSZBQV+eArf777ntxghr9jcl48d//bbex+TM7FgrexjnuVj2uEcQWnydJEg5cqofJbEWexYrDla3PNjanf2e1e0bLqEO/JDWDlhfewrEC3u+/C6dAEykzvtSx+BS6SkpKAAAxMTFIS0vz2OYLvV7v83OIiIiuFsp7N66ehdp7uMIjRPQ3JqNwf3m7j83X2bG2kCPYXj1OURS9fqaQJAl7zztg+u7SwZPV/n+4vzklBrkGHXIMAnonxvh9nI7CWziuE+s9GsY0CKdAEykzvtSx+BS6BgwYAADo06cPdu3a5bGtrRQKBS5cuODTc4iIKDoF+mE+nO7duDpEvPjv/4bN2AIRimDrdEnYfdYOk8WKNRYRpbX+B61BXTXIMQjIMehg6NQxL/Bp+Dmz2uqhVLiQM1zAoBva9gdwb39cCPdAEykzvtSx+PTbR/quP67L5WqyzddjEBFRxxaMD/P+rmXVHpcjRst9Je0VbB0uCbsrlZh/pgbryi7hrNXV+pO8UCqAoaka5Bp1mGzQoVts+MzAhIK3n7MPNh1HTIzGr+9fpAQaOWZ8iQLhU+jy1jSDjTSIiMgfwfgw7+u9G+05axMt95W0Fh4DCbE2p4St5TaYLFastVhxyS4AaHrpWmtilMCIblrkGnWYlCEgRYisGsvJ289ZXYChmYGGyHcdc56diIhCLhgzQb7eu9GelyNGy30lLYVHf0JsXb0LBaU25FmsWF8iosrh3xUwggoY1cPdcXCCXkCSVunXcaJdtMy4EkW6iAhdq1atQmFhIfbt24dDhw7BbrdjwYIFTRZkdjgcWLt2LT7//HPs3bsXZWVlUCgUuPbaa/HAAw9g5syZUKn41y8ionAQjJkgXy91as8PoJFyGVZrWgqPbQ2xVXYXNpS6W7sXlNlQV+9f0IpTKzAuXUCuUcDYdAHxMQxarYmWGVeiSBdw6Lrttttw//3347777kNqqjwnkjlz5qCkpATJyclITU1ttmPiyZMnMWPGDMTHx+P222/HxIkTUVVVhXXr1uFXv/oVNmzYgJUrV7ItLBFRGAjWTJAvlzq19wfQaLgMq6Xw+PHmo16fI9qcuGRzYe0pK0wWEVvKRNj9u0ULCRoFJurdM1qjegjQqXkO90Vza6JF2owrUaQLOHR9++23eP755/HCCy/gjjvuwP3334/s7GxotdpgjA8AMH/+fPTq1QsZGRl488038cILL3jdLz4+Hm+88Qbuv/9+xMXFNW6fM2cOJk+ejPXr1+Ozzz7DlClTgjY2IiLyTyhmgqLlkr9ga+m+rKsfu/J7dHWItSpVKNN2wmlNEv654jT8nNBCslaJbIM7aN3eTQuNikHLX1f+nFnFhu6FkTfjShTpAg5d8+bNw8qVK/G///0PBQUF2LRpE+Lj43HXXXfhxz/+MW677baABzly5Mg27de9e3c89thjTbbHxcVh1qxZeOyxx7Bz506GLiKiMNHea0pFyyV/wdTSfVkN/93cPVvZwzKxcO1hFKviUSJ0wvmYWEgNV5P4GLhSdQrcnmTHj/slY6Q+Hmolg1awNPycNayFptd3CfWQGoVicXOiUAg4dD388MN4+OGHYTabsWLFCnz44Yc4efIkli5dimXLliEjIwNTp07F1KlTYTQagzBk/8TEuBdB5D1dRETRqy2NHaLhkr9gaum+LEjw+tj7Oy3Y4YiFyaLAns69/X5tfbwKuQYdcg0Cbkhwoay0FPrUGAauDiKUi5sTtbegNdIwGo347W9/i9/+9rfYvXs3Vq5cidWrV8NiseC1117Da6+9hkGDBuH+++/H9OnTg/Wybfbee+8BAEaNGtWm/UVRlHM4bWK32z3+TcHHGsuPNZYfa/y9vO3HvYaEvO3H0d+Y6Pdxo7XG3xw7hzMX67w+Zr2qjpdVGpQICSjRdkKlUgfsqfLrNTM7KZGt1yJbr8GNnVXf3WctwW53AIi+GoeThtp+deg0Cr4qh+272aUJgzMwIKv9Z7/k+nkNpWj9XRFOwqXGgiD4tL8s3QsHDRqEQYMGYe7cufj888+xatUqFBQUYNeuXdi9e3e7h67Fixdj48aNuP322zFu3Lg2Pae8vBxOZ3i0U62oqAj1EKIeayw/1lh+rDFQW+d9jac6q63ZJky+iKYaHy2tRcHXF+Byeb8OUKFw4bxCgwNxCSgRElCl9v9e7d6xLtyR7MSolHpkxkpQKGqAWqC0tum+0VTjcNTwfRev6GyycuNRnD9/AX3S41p4ZvDJ/fMaSnwfyy+UNVapVOjVq5dPz5G1ZbxGo0Fubi46deoEu92OTZs2yflyXq1btw7PPPMM9Ho93nnnnTY/r3v37jKOqm3sdjsqKiqQmpoKjUYT6uFEJdZYfqyx/Fjj78XFnkW1tenMTaxOC71e7/dxo7HGy7d+5fHBG3DfhnVBrcOZ+EScT+iM0zb/L/PrEyvhR1lxmJSuQWZC65f2R2ONw43dbseSjXubfN9Fuwt7jtdi9JC+7ToeuX5eQ4nvY/lFao1lC13ffvstVqxYgY8++ghnzpxp3N6vXz+5XrKJDRs2YMaMGejatSvy8vKQlpbW5uf6OmUoJ41GE1bjiUassfxYY/mxxkDO8N5euxPmDO8dlNpEUo1ba1Bgd7g/eLsAnI+JRYnQCaXaBNSp3PdAw/skRPMkCd1cNlyrqMNPbumCSQPafs69UiTVOBI1fN+9bW/vusv98xpKfB/LL9JqHNTQdf78eXzwwQdYuXIlDhw4AACQJAldunTBPffcg6lTp2LAgAHBfMlmrV+/HtOnT0dycjLy8vJC2sSDiIjaR3t0J4yEbmutNShwuCRUaOKwr1MSSrUJEFX+fRxQKoBhaVrkGgRMNuiQFstmVeFOE6MErE1vnwjFYsnsJkodScChy263Iz8/HytXrsTmzZvhdDohSRK0Wi0mTpyIqVOnYsyYMe3aNbAhcHXu3Bl5eXk+X3NJRESRS87uhN8cO4cPNx0P+25r3joSVolOzN9ZjtjzGqw9ZcUlVSoQ6/uxY5TAiG5a5Bp1mJQhIEVg0IokQ/olYfM3l1AXJmvVsZsodRQBh66srCxUV1dDktw34g4aNAhTp07FXXfdhcTE9u88s3HjRkyfPh1JSUnIy8tDZmbHXvCSiIiCZ92uU822Vw+nD46i3T2TUQ8FTmvjUaJNQJk2HvVKFXDMe7fClggqYFQP92LFE/QCkrTKYA+Z2kmf9DikpCRjw/9KOLtE1I4CDl1VVVUwGAz48Y9/jPvvv1+Wy/iWLl2KwsJCAMChQ+6/KC5btgw7duwAAAwZMgTTp0/H0aNH8eCDD8Jms2HYsGH46KOPmhwrIyMD06ZNC/oYiYgo+tns3rvairbw6HYLAFV2F05qO2F/YgrKtfFwKvwLSHFqBcalC8g1ChibLiA+hkErWgzI6oJBN0RmowqiSBVw6Fq7di2GDBkSjLE0q7CwECtWrPDYtmvXLuzatavx/6dPn46KigrYbO47fz/++GOvxxo6dChDFxFRiETC/VAt0Wq8X0rX2v0wcn/dl2wurD1lhckiYkuZCLuyC+DH/eUJGgUm6t0zWqN6CNCpuUgxEVEwBBy65A5cALBw4UIsXLiw1f2GDx+OyspK2cdDRES+a625QySYMDijyT1dzd0P0xC0LtfaUCfWQ7piOaxgfN1nrU7kW0SYLFZsP21DvffltlqVrFUi2+AOWrd300KjYtAiIgq2oHYv/Prrr/HFF1+grKwMVqsV//jHPxofO3PmDBwOR8Suu0BERIHx1twhHO+HasmArC6IidG02m3NW8C8kr9fd1mtE3kWK0xmKwor7PAzZyFNp0SOQYccow63pWqgVjJoERHJKSihq6KiAj/96U+xbds2AO428QqFwiN0zZkzB8uXL8f69evxwx/+MBgvS0REEUSMgPuh2qIt3da8BcyrXfl1t3T5obm6HiazFSaLFXvOOfwetz5ehVyDDrkGAT/sqoFSwaBFRNReAg5dNTU1yMnJwbFjx9C9e3eMHDkSX3zxBcrLyz32mzp1Kt5//32sXbuWoYuIqAMS/LwfKhJV1dpb3afh6/Y2K7Zgw3EklLqwp1aF/Rf9D1q9E9TINbovHRyQHAMFgxYRUUgEHLoWLFiAY8eOYdy4cVi0aBHi4+MxceLEJqFryJAh0Gq1+OKLLwJ9SSIiikDZwzKbhItQrg8kJ9He8izXlV93/o5i1Ij1qFRrUaJNQImQgCq1FihxAXD5/Nr9O6vdM1pGHfolqRm0iIjCQMChy2QyQa1WY/78+YiPj292P5VKhV69euHkyZOBviQREUWghsvlWrsfKhpoNSrUW5sGLwWAbl3ikD00Ezdf2xV7ztmx2ZWIY8ndUKPW+P16N6fEfHfpoA6ZiUG9XZuIrhDpHVgpdAL+zWw2m9GrVy907dq11X3j4+NRU1MT6EsSEVGA9h6uwEebjuDyd5fBJcRp8KPR18r+4aEt90NFg8Q4LWq9hK7ULnEYlf0DrDJb8dAHFSircwIxST4fXwFgUFcNcow65BgEZMQzaBHJLRo6sFLoBPxb2pfLFiorKxEXFxfoSxIRUQD2Hq7A0vyDsDm+b+RQWW3D0jUHAPDDQzBceSmlC0CFJg5nYhNRoU3Cm5+f9+uYSgUwLE2LXIOAyQYd0mKj7144onAWDR1YKXQCDl0ZGRk4ceIE6urqEBsb2+x+Z8+eRXFxMX7wgx8E+pJERBSA/B3FHoGrga3exQ8PQXJdVlf0vOTC8m+rcBw62BTfBSQfe2LEKIGR3bTIMeqQnSEgWWDQIgqVaOnASqERcOgaM2YM/vGPf+Cvf/0rnnvuuWb3e/nllyFJEsaNGxfoSxIRUQCa++AA8MNDIOrqXSgotSHPYsW6EhHVDglQNH+vc3MEFTC6h4Bcow7j0wUkaZUyjJaIfNWROrBS8AUcumbNmoXFixfjjTfeQE1NDR555BGPxw8ePIi///3v+OCDD5CSkoLHHnss0JckIqIANPfBAWj9w0O030Tu69dXZXdhQ6kIk9mKgjIb6ur9W644Xq3AOL27tfuYdC3iYxi0iMJNR+rASsEXcOhKTU3FsmXL8NBDD+Htt9/G22+/3fhYcnIyJEmCJEno1KkTFi9ejKSkpEBfkoiIApA9LLPJPV0AoFUrW/zwEO03kbf09fU3JjZuu2RzYe0pK0wWEVvKRNh97+oOAEjUKDBR757RGtVdgKBma3eicNaROrBS8AWl3dGIESPwxRdf4JVXXkF+fj7q6uoAAJIkQavVYvz48fjDH/6A3r17B+PliIgoAA0fED7afASXa77rXhivwY9Gtdy9MNpvIm/p6+uSehM+Oa3Gf49XYWeFA35OaCFFUCI7wx20hqdpoVExaLW3aJ+tJXl1lA6sFHxB6zHbs2dPvPPOO6ivr0dxcXFjp8LevXtDEIRgvQwREQWBPx8cIvkm8rZ80L7666tTqlGiTcAXUiLmrb4ECRr43AkDQLdYJSZ/t4bWkFQN1EoGrVCJ9tlaIgpfQV/YQ61W49prrw32YYmIKMQi9Sbytn7QFjQq1KhiUKJNQIm2Ey5omu/I2xp9vAp3GnTINQq4pYsGSh+WVyH5RPtsLRGFL66mSEREbRJpN5E3zG6duVgHl8vzesArP2gfqXTAZLbis04GHA8gP2YlqpFrEJBj0GFAcoxP61hS+4jk2VoiimwBh64VK1a0eV+VSoVOnTrBYDCgb9++UCrZnYmIKFJE0k3k3ma3GkgAKtVaHHMmYM0nFThyuek+bdW/sxq5Bh1yjTr0S1IzaIW5SJ2tJaLIF3DoevLJJ/06yVxzzTV49NFH8X//93+IiYkJdBhERFEvHBoARMpN5FdfRiYBuKDWoUTohFJtAmrUGvcDfgSum1Ni3EHLoENmIi8YiSSRNltLRNEj4LPFbbfdBoVCgf/9739wOBzQarXo3bs3OnXqhOrqahQXF0MURWg0Gtxyyy2oq6tDcXExLly4gNdffx27d+/Gxx9/DJWKf2UiImpOW9uZB/s1Qx3y/CXanXABOB8T2xi06lT+/YFPAeCHKWoM7VSHB27oiqzkuKCOldpPJM3WElF0CTh0rVmzBg8//DC0Wi3mzp2L+++/36NboSiKWLlyJf74xz+iS5cuePfddyFJEj755BP83//9H7Zt24b33nsPM2bMCHQoRERRq6UGAP2NA4P+epHa5c3hkrDjtA3btV1wNEUHUeXfaU6lAIamaZFrEDDZoEOS0oGSkiro4/gHwkgXKbO1RBRdAg5dCxcuhMlkwkcffYRRo0Y1eVwQBMycORMZGRm499578dZbb2HWrFm49957odVqMX36dHz44YcMXUREaH52qb0bAERSlzebU8KWchEms4jPS6y4ZJMAZSefjxOjBEZ20yLHqEN2hoBk4fuAJYq+t4onIiJqEHDoev/992EwGLwGriuNGjUKBoMB77//PmbNmgUAyMnJQXJyMg4dOhToMIiIIl5Ls0vt3QAg3Lu81TpcKCizIc9ixfoSEdUO/1YrFlTA6B7uxYrHpwtI0rLBExERBV/AoevkyZPo169fm/bt3LkzDh8+7LHNaDSiqKgo0GEQEUW8lmaX2rsBQDh2eauyu7C+RITJYkVBqQ1Wp39BK16twDi9gFyDDmPStYiPYdAiIiJ5BRy6BEHA0aNHUVdXh9jY5heSrKurw7Fjxzzu9wLc93wlJCQEOgwioojX0uxSSw0ARFEM+ljCpcvbRdGJtSUi8sxWbCm3we7y7ziJGgUm6t0zWqO6CxDUbO1ORETtJ+DQNWjQIKxfvx6//OUv8dZbb3lde8vlcuFXv/oVamtrMWHChMbtdrsdxcXF6NWrV6DDICKKeK3NLrVnA4BQdnk7a3VijcU9o7X9tA1+TmghRVAiO8MdtIanaaFRMWgREVFoBBy6nn32WWzatAkffPABvv76azzwwAO44YYbEB8fj5qaGhw4cAArVqzAkSNHoFar8eyzzzY+Nz8/H6IoYtiwYYEOg4go4oXL7FKD9gx5pTX1yPsuaO2qsMPPnIVusUpM/m4NrSGpGqiVDFpERBR6AYeum266Ce+++y6efPJJHD16FC+88EKTfSRJQkJCAhYuXIgBAwY0bk9ISMBrr72GkSNHBjoMIqKI19HWEDpZVQ+TxQqT2YqvzvvfHTAjXuVerNgo4JYuGigVDFpERBReAg5dAJCdnY3//e9/WLRoETZv3oxjx46htrYWcXFxyMrKwujRo/HII48gNdXzg8Po0aMxevToYAyBiCjstWWx4WhfQ+hwpQMmsxUmi4gDF/0PWlmJauQaBOQYdBiQHAMFg1bEavi5sNrqoVS4kDNcwKAb9KEeFhFRUAUldAFAamoqnnvuOTz33HPBOiQRUdSI1MWGAyVJEoouOpBndl86ePRyfetPasZ1ndXINbovHeybpGbQigLefi4+2HQcMTGaqP65IKKOJ2ihi4iImhdJiw0HyiVJ2Hu+YUbLCnO1/2t7DUyJQa5BhxyDDpmJPGVFG28/F3VR+nNBRB0bz2BERO0g3BcbDpTTJWHXWTtMZivWWESU1fn5dUkSusOGO42xeOLWLsiI52kqmkX7zwURUYOgnc127NiBdevW4cSJE6ipqYEkee89pVAoYDKZgvWyREQRIRwXGw6UwyVhx2kbTBZ30Don+reIlhJAWn0dutddRrqtGjpXPRxVapzvrkQGZzuiWjT+XBAReRNw6HI4HHj88ccbg1RzYasBr8Enoo4o3NrB+8vmlLClXITJLOLzEisu2fxr7h6jBEZ20yLHqMO32/aj8kK1x+PReuklefL2cxEbgT8XREStCTh0vfnmm/jss8+gUCgwfvx43HrrrejSpYvXRZKJiDqqSG4HX1fvQkGpDXkWK9aViKh2+Be0BBUwuod7seLx6QKStO7zxO8KvHcx5CVm0e/Knwur2NC9MDJ+LoiIfBFw6Prwww+hUCjwr3/9C/fcc08wxkREFJUiqR18ld2FDaUiTGYrCspsqKv3L2jFqxUYpxeQa9BhTLoW8TFN/yDHS8w6toafC1EUUVJSAr2+S6iHREQUdAGHrlOnTqFbt24MXEREEe6SzYW1p9xraG0pE2H37xYtJGoUmKh3z2iN6i5AULd8WXm0XHpJRETUnIBDV2JiYpNFj4mIKDKctTqRb3GvobX9tA1+TmghRVAiO8MdtIanaaFRtf3+3Ui+9JKIiKgtAg5dQ4cOxaZNm2C326HRaIIxJiIiklFZrRN5FitMZisKK+zwM2ehW6wSkw3uxYpvS9VApWw+aO09XIH8HcUQ7U5oNSpMHuYZqiLp0ksiIiJfBRy6/u///g/r1q3Dq6++ij/+8Y/BGBMREQWZubq+cbHiPee8N65oi4x4FXINOuQaBdzSRQNlGzrS7j1c0eTyweXrDgEAgxYREXUIAYeuhIQEvPrqq3jmmWewb98+PProo+jduzdiY2ObfY5erw/0ZYmIqBVHKh3fBS0R+y/6H7SyEtXINQjIMegwIDnG56U/8ncUewQugC3hiYioYwk4dA0YMKDxv7du3YqtW7e2uL9CocCFCxcCfVkiIrqKJEkoumCHySIiz2zFkcv1rT+pGckuG37ygxTkGnTom6QOaI1F0e699TtbwhMRUUcRcOhqbTHkQPcnIqLmSZKEvecdWHEyBtv2VcJS42fLQQDXOKzQi1XQ26rQGfWo+m8ZPtzT9P4rX7ElPBERdXQBh65Lly4FYxxERNRGTpeEXWftMJmtWGMRUVbnBBADwLfApQBwfScg7uxZdK2pRJzr+5mxegAXq0QAgd9/xZbw7au1piVERNT+Ag5dREQkP4dLws4zNpjMItacsuKs1b8ZLZUCGJamRa5RQHaGDmmxKuw9rEb+TitEmxpVtTbUOz2vSAj0/iu2hG8/bFpCRBSeIiJ0rVq1CoWFhdi3bx8OHToEu92OBQsWYNq0aV73r6qqwquvvgqTyYSzZ88iNTUVU6ZMwbPPPov4+Ph2Hj0RkX+zDzanhC3lIvIsItaesuKSzb/Ls2OUwB3dtcgx6DApQ0Cy4HlZ35Xt2n/31vbGGa4rBXr/FVvCtw82LSEiCk9BDV1nz57Fli1bcOzYMVRXV6NTp07o06cP7rjjDnTp0sXv486ZMwclJSVITk5GamoqSkpKmt23trYW2dnZ2L9/P0aNGoV7770XRUVFmD9/Pnbu3Im1a9dCEAS/x0JE5CtfZh/q6l0oKLUhz2LFuhIR1Q7/gpagAkb3EHCnUYfxegGJGmXbnsf7ryIam5YQEYWnoIQum82GP/zhD1iyZAkcjqZtiWNiYvDwww/jhRdegFar9fn48+fPR69evZCRkYE333wTL7zwQrP7/u1vf8P+/fsxe/Zs/OlPf2rc/qc//Ql//etf8dZbb+GXv/ylz2MgotCL1HtVWpt9qLK7sKFUhMlsRUGZDXX1/gWtODUwXu9erHhMuhbxMW0LWlfi/VeRjaGZiCg8BRy6XC4X7r//fmzduhWSJKFLly7IyspCWloazpw5g2PHjuHcuXN45513cPz4cXz44Yc+tx4eOXJkm/aTJAnLli1DfHw8nnnmGY/HnnnmGfz73//G0qVLGbqIIlAk36vibfbBplDi63odflxwAVvKRNj9bDqYqFFgXHcNBumqcK0uBlu/PIwvDzlR5Gco5f1XkY2hmajjidQ/SHY0AYeu9957D1u2bEFCQgLmzJmD+++/H2r194d1Op1YsWIF/vCHP2Dz5s14//338eCDDwb6sl4VFxfj9OnTGD16NOLi4jwei4uLw6BBg7Bp0yaUlpYiPT1dljEQkTwi+V6VhtkHq1KFMm0nlGgTUKGJg6RQACVN759qTYqgRHaGgFyjDsPTtHA5bNhUWIrVuy6hLgihlPdfRS6GZqKOJZL/INnRBBy6Vq1aBYVCgaVLl2LEiBFNHlepVHjwwQeh1+sxZcoUrFixQtbQBQC9evXy+nivXr2wadMmFBcXtxq6RNH3D0LBZrfbPf5Nwccayy9YNbbavC/0axXrw+LntTnldU7UGfXYIok4o9IBfi4ynKZTYFK6FpP0GgzuooZa6T6Oy2GD3W5H4beVHoELcIfSvO3H0d+YGPDX0dFF0u+K/sZE9DcO9NgWzj8jDSKpxpGKNZZfe9c4b/txr3+QjObf/eHyPva1R0TAoevgwYMwGAxeA9eVRowYAaPRiIMHDwb6ks2qqqoCACQmen+TJSQkeOzXkvLycjid4XHjcUVFRaiHEPVYY/kFWmOlwvv1d0qFq8XmOqFQKiqw5bwKmy+ocKBaBUAJqGN9Pk43rQujUpwYlezE9Z1cUCpqATtwuqzpvnaH9/rUWW1hV59Ixt8V8mON5ccay6+9alxbZ/O6vSP87g/l+1ilUjU7ydOcgEOX1Wpt84t27twZp0+fDvQl20X37t1DPQTY7XZUVFQgNTUVGo0m1MOJSqyx/IJV45zhAj7YdNxjNidWUCNneCb0ev+7owbL0cv1yC+xI7/EjgOV/v/BpncnJbL17hmtGzur2nQPrN1uhyamDLA2fd1YnRZ6vd7v8ZAbf1fIjzWWH2ssv/aucVzsWVRb65psj+bf/ZH6Pg44dKWmpuLYsWOwWq3Q6XTN7ldXV4djx46ha9eugb5ksxpmsi5fvuz18YYZrob9WhJObeU1Gk1YjScascbyC7TGg27QIyZGEzb3qkiShP0XHTBZROSZrThy2fvlj23Rv7MauQYd7jTq0DdJ7XOzIQAY0i8Jm7/xvKcrTlAjZ3hvvreDiL8r5Mcay481ll971ThneG+vzXM6wu/+SHsfBxy6hg8fjhUrVuC5557Dm2++2ex+zz33HGpra3HnnXcG+pLNysx0d2c6ceKE18cbtjfsR0SRJdQNHiRJwlfnHTCZrcizWHGy2v8ZrZtTYpBrcLd3z0wMfPWOPulxSElJxob/lYRFKCUiIvmxeU7kCPhM/4tf/AIfffQRlixZgi+//BI/+9nP0L9/f3Tt2hVnz57FoUOHsHDhQnz77bfQaDR46qmngjFurzIzM9GtWzfs3r0btbW1Hh0Ma2trsXv3bhgMBnYuJKI2c7ok7Dprh8lsxRqLiLI6/4KWAsCgrhrkGHXIMQjIiA/q2vQAgAFZXTDohui8nISIiLwL9R8kqW0CPuv36dMHb7/9Np588kkcPHjQa6iSJAmCIGDhwoXo06dPoC/ZLIVCgYceegivvfYaXn/9dY/FkV9//XXU1NRwjS4iapXDJWHHaRtMFivyT4k4a/VvES2FJKG7S8S1ijo88cOuGHtj6O89IyIiovYXlD+13nXXXbj++uvxt7/9DQUFBR7dRFJTUzFu3Dj8v//3/5CVleXX8ZcuXYrCwkIAwKFD7rUHli1bhh07dgAAhgwZgunTpwNwz7ytXbsWf/3rX1FUVIQBAwbgm2++webNmzFw4EA88cQTgXypRIToXIjR5pSwpVyEySzi8xIrLtkkv44TowRuTlBAW34aXWouQyu5Z8bWb76AZI0i4utEREREvgva9S1ZWVn4xz/+AcDdsKKmpgbx8fEeTStGjBiBy5cvY9++fT4du7CwECtWrPDYtmvXLuzatavx/xtCV1xcHPLz8/Hqq68iLy8P27dvR2pqKn7+85/j2WefbbHZBxG1LpoWYqyrd2FjqQ15FivWl4iodvgXtAQVMLqHe7Hi8ekC5i/bhfLqWo99ImUhZyIiIgq+4N9UAHd3QG8dAktLS3Hp0iWfj7dw4UIsXLiwzfsnJibilVdewSuvvOLzaxFRy/J3FHtdiDFSAkWV3YX1JSJMFisKSm2wOv0LWvFqBcbpBeQadBiTrkV8jLLxMdHu/b4v0RYea/8RERFR+5IldBFR9IrEQHHJ5kL+KSvyzFZsKbfB7t8tWkjUKDBR757RGtVdgKD23tpd0Ki8b9d6305ERETRjaGLiHwSKYHirNWJNRb3jNb20zb4OaGFFEGJ7Ax30BqepoVG1foaWtnDMr2um5I9lMtVEBERdUQMXUTkk3AOFKU19cj7LmjtqrDDz5yFbrFKTP5uDa0hqRqolb4tVsx1U4iIiOhKDF1E5JNwCxQnq+qRZ7HiM7MVX513+H0cfbwKdxp0yDUKuKWLBkqFb0Hralw3hYiIiBowdBGRz0IdKA5XOmAyW2GyiDhw0f+g1TtBjVyjuxnGgOQYKAIMWkRERETeMHQRUdiTJAlFFx3IM7svHTx6ub71JzWjf2c1cg063GnUoW+S2u+gFY1rlREREZE8fA5dc+fO9fvFrFar388lovAjZ/BwSRL2nnfgM7MVeRYrzNX+d0e8xmGFXqzC9TE2LJgyJOCxRdNaZURERCQ/n0PXq6++6vdfhiVJ4uU7RFFCjuDhdEnYddYOk9mKNRYRZXV+Bi1JQorDCr2tCnqxGnEu9yWIaV3i/DveVSJ9rbJIxhlGIiKKRD6Hrttuu43BiYiCFjwcLgk7TttgsriD1jnRv0W0VApgaJoWuQYBBkcN1m8ula3DYiSuVRYNOMNIRESRyufQlZ+fL8c4iCjCBBI8bE4JW8pFmMwi1p6yotLuX3P3GCUwspsWOUYdsjMEJAsNa4XFI1mjkK3DYqSsVRZtOMNIRESRio00iMgvvgaP2noJ67+7P2t9iYhqh39BS1ABo3u4Fyseny4gSav0up+cHRbDea2yaMYZRiIiilQMXUTkl7YEjyq7C2vMNnx4VINdhRdh9fOzcbxagXF6d2v3MelaxMd4D1rtJdzWKusoOMNIRESRiqGLiPzSXPAwGlPw3rFa5Jmt2FJug90F+POrJlGjwES9e0ZrVHcBgjq87iUN9VplHRFnGImIKFIxdBGR3xqCR0WdE/mnRPzZYsX2XWfg9O/KQaQISmRnuIPW8DQtNKrwCloUWpxhJCKiSMXQRUR+Ka2pR57FvVjxrgo7/MxZiJfq0dNZi74KK35yUw/8sF/nNj2PrcPDnxzfI84wEhFRJGLoIqI2O1lVD5PFCpPZiq/OO/w+Tka8CoM6uSAesyCutgYN81mr1ldBpVC0+qGarcPDH79HRERE32PoIqIWHa50wGS2wmQRceCi/0Grs+TApHQNHh+YggHJMZizqBDltbUe+7S1/Tdbh4c/fo+IiIi+x9BFRB4kSULRRQfyzO5LB49erm/9Sc3oXC8iXayCXqxGgtMGXZUargwVFCmpAbX/DpfW4bzEsXnh8j0iIiIKBwxdRASXJOGrcw6YLO51tMzV/n8wvsZhhV6sgt5WhU5Oz5mxK2c6Amn/HQ6tw3n5XMvC4XtEREQULhi6iDoop0tC4Vk7TGYr1lisKK9z+XUcBYA0pxVpdZehF6sQ52p5ZqxhpiOQ9t/h0Dqcl8+1LBy+R0REROGCoYuoA3G4JGw/bYPJbEX+KRHnRP+ClkoBDEvTItcoIDtDh38t343yutrWn4jvZzoCaf8dDq3Deflcy8Lhe0RERBQuGLqIopxYL2FLuQiTRcTnp6yotPvX3D1GCdzRXYscgw6TMgQkC99fJuZtVkMbowIkCbb674Pd1TMdgbT/DnXrcF4+17pQf4+IiIjCBUMXURSqdbhQUOae0dpQKqLa4V/QElTAmB7uxYrH6wUUnziH/G3fYN5VjSOam9UAgLztx1FntSFWp0XO8N5R8yGcl88RERFRWzF0EUWJy3YX1peIMJmt2FRmg9XpX9CKVyswXi8gx6DDmHQt4mOUAFpvHNHcrEZ/YyJKSkqg1+shCIJfYwpHvHyOiIiI2oqhiyiCXRSdWFsiIs9sxZZyG+z+3aKFRI0CkzJ0yDUIuKO7AEGtaLIPG0c0xcvniIiIqC0YuogiTEWdE/mn3GtobT9tg58TWkgRlJic4b50cHg3LWKUTYPWldg4goiIiMg/DF1EEaC0ph55FnfQ2lVhh585C91jlZhs0CHXqMOQrhqoWglaV2LjCCIiIiL/MHQRhamTVfUwWawwma346ryj9Sc0IyNehVyDDncadfhBlxgoFW0PWldi4wgiIiIi/zB0EYWRw5UOmMxWmCwiDlz0P2hlJaqRa3A3wxiQHAOFn0HrSmwcQUREROQfhi6iEJIkCUUXHcgzuy8dPHq5vvUnNeO6zmrkGnXINejQN0kdlKB1NTaOICIiIvIdQxdRO3NJEr4654DJYkWexQpztf+NKAamxCD3u3u0eiXwx5mIiIgoHPFTGlE7cLokFJ61w2S2Yo3FivI6/3q7KwAMTtUgx6BDjkGAPp4/wkREREThjp/YiGTicEnYftoGk9mK/FMizon+BS2VAhiWpkWuUcDkDB1SY9ktkIiIiCiSMHQRBZFYL2FLuQiTRcTnp6yotPvX3D1GCdzRXYscgw6TMgQkCwxaRERERJGKoYsoQLUOFwrKbMizWLG+RES1w7+gpVMpMLqHFrlGHcbrBSRqlEEeKRERERGFAkMXkR+q7C6sL3F3HCwotcHq9C9oxasVGK8XkGvUYUwPLeJiGLSIiIiIog1DF1EbXbK5kH/KijyzFVvKbbD7d4sWkjQKTMzQIdcg4I7uAgR18Fu7ExEREVH4YOgiasFZqxNrLO4Zre2nbfBzQgspghKTM9wzWsO7aRGjZNAiIiIi6igYuoiuUlpTj7zvgtauCjv8zFnoHqvE5O/W0BrSVQMVgxYRERFRh8TQRQTAXO3E+mPVMJmt+Oq8w+/jZMSrkGvQ4U6jDj/oEgOlgkGLiIiIqKNj6KIO63ClA58cr8OnJwQcq630+zhZiWrkGgTkGHQYkBwDBYMWEREREV2BoYs6DEmSUHTRgTyz+9LBo5frv3vE946B13VWI9eoQ65Bh75JagYtIiIiImpWVIYuSZKQl5eHd955B8eOHUNVVRV69OiBYcOGYfbs2TAajaEeIrUTlyThq3MOmCxW5FmsMFc7/T7WwJQY5Bp0yDHokJkYlT86RERERCSDqPzk+Pvf/x4LFixAWloasrOz0alTJxw4cABLlizBxx9/jPXr16N///6hHibJxOmSUHjWDpPZijUWK8rr/OvtrgAwOFWDHIMOOQYB+vio/HEhIiIiIplF3afIiooKLFy4EHq9Hjt27EBiYmLjYwsWLMDvfvc7LFiwAAsWLAjhKCnYHC4JO07bYLJYscYi4pzoX9BSKYBhaVrkGgVkZ+iQFqsK8kiJiIiIqKOJutB16tQpuFwuDB482CNwAcCECRPwu9/9DufPnw/R6CiYbE4JW8pFmMwi1p6yotLuX3N3tULCiG4aTOkZh0kZApIFBi0iIiIiCp6oC12ZmZnQaDTYtWsXqqqqkJCQ0PjYunXrAAAjRowI1fAoQLUOFwrKbMizWLG+RES1w7+gJaiAMT0ETOyhRn/pLPr1TIEgCEEeLRERERFRFIaua665Bs8//zx+//vf49Zbb8WkSZMa7+natm0bHnvsMfzkJz8J9TDJB1V2F9aXuDsOFpTaYHX6F7Ti1QqM1wvINeowpocWcTFKiKKIkpIgD5iIiIiI6ApRF7oAYNasWejevTueeuop/Oc//2ncPmTIENx7771Qq1v/skVRlHOIbWK32z3+3ZFctLmwvsyO/BI7tp1xwO7fLVpIjFFgXA8NJus1GNEtBoLqu9buTjtEZ8eucXthjeXHGsuPNZYfayw/1lh+rLH8wqXGvl4hpaisrPRv2iCMzZ07F2+88Qaee+453HfffUhMTMT+/fvx3HPPYf/+/Vi6dCkmTZrU4jFOnDgBp9P/9uLku/N24IsLamy+oMJXlUo44d/aV51jJIy4xolRKfW4JdGFGN+X4SIiIiIi8kqlUqFXr14+PSfqQtfWrVsxZcoUPPnkk3j55Zc9HquoqMBNN92Ebt26Ye/evS0eJ1xmuioqKpCamgqNRhPq4ciirNaJtaXuGa3d5+rh75sxTafApHQtsvUaDO6ihkrZtsDWEWocaqyx/Fhj+bHG8mON5ccay481ll+41NjXma6ou7xw48aNAIDhw4c3eSw1NRVZWVkoKipCTU0N4uPjmz1OODVV0Gg0YTWeQJ2sqofJYoXJbMVX5x1+HycjXoVcgw65RgG3dNFAqfBvZgyIvhqHI9ZYfqyx/Fhj+bHG8mON5ccayy/Sahx1oavh+s7m2sJfuHABSqUSMTEx7TmsDu9wpQMmsxUmi4gDF/0PWlmJauQaBOQYdBiQHANFAEGLiKitvjl2Dnnby+CSTkPQqjF5WCYG9k0N9bCIiChCRF3oGjx4MP71r3/hrbfeQm5ursdaXf/5z39QVlaGwYMHQ6vVhnCU0U+SJBRddCDP7O46ePRyvd/Huq6zGrlGHXINOvRNUjNoEVG72nu4Ah9sOo468bvfY9U2LF93CAAYvIiIqE2iLnRNmTIFixYtwn//+1/ccsstmDhxIhITE/HNN99g27Zt0Ol0eOmll0I9zKjkkiR8dc4Bk8WKPIsV5mr/G5EMTIlBrkGHHIMOmYlR9zYlogiSv6P4+8D1nVqxHvk7ixm6iIioTaLu06xKpcKnn36Kt956C59++ik++ugj2O12dO3aFffddx9+9atf4dprrw31MKOG0yWh8KwdJrMVayxWlNf519tdAWBwqgY5Bh1yDAL08VH31iSiCCXavf8BSbSxwy0REbVNVH6y1Wq1ePrpp/H000+HeihRyeGSsP20DSazFfmnRJwT/QtaKgUwLE2LXKOA7Awd0mJVQR4pEVHgBI33302Clr+ziIiobaIydFHwifUStpSLMFlEfH7Kikq7f83dY5TAHd21yDHoMClDQLLADy1EFN6yh2Xi/XWHPC4xjBPUyB6aGcJRERFRJGHoombVOlwoKHPPaG0oFVHt8C9oCSpgdA8Bdxp1GK8XkKgJ3WrFew9XIH9HMUS7E1qNCuMH6XFN5HQbjXhX158d4CgSDOybCofDjrztxXBJSui+C1x87xIRUVsxdJGHy3YX1peIMJmt2FRmg9XpX9CKVyswTi8g16DDmHQt4mNCF7Qa7D1cgeXrDqH2ir9Wf7DpOEYN6Ay9PoQD6yC+OXYOH2467lF/doCjSDEgqwuuEUTo9fqIWheGiIjCA0MX4aLoxNoSEXlmK7aU22D37xYtJGoUmKgXkGvUYVR3AYI6vFq75+8o9vjADwB1Yj0Kv63E6CEhGlQHsm7XqSb1Zwc4IiIi6ggYujqoijon8k+J+MxsxY4zNvg5oYUUQYnsDHfQGp6mhUYVXkHrSs11IHPU+5kyySc2doAjIiKiDoqhqwMpqanHGot7seJdFXb4mbPQLVaJyQb3YsW3pWqgUoZv0LpScx3IYtShv/SxI9CyAxwRERF1UAxdUe5kVT1MFitMZiu+Ou/w+zgZ8SrkGnTINQq4pYsGSkVkBK0rZQ/LbHJPV6ygxpB+SaEbVAcyYXBGk3u62AGOiIiIOgKGrih0uNIBk9kKk0XEgYv+B62sRDVyDQJyDDoMSI6BIgKD1pUa7hvK31kM0eaEoFVh3K16XCOIIR5ZxzAgqwtiYjQe9WcHOCIiIuoIGLqigCRJKLroQJ7Zfeng0cv1rT+pGdd1ViPX6L50sG+SOuKD1tUG9k31+JAviiJKSkpCOKKO5er6ExEREXUEDF0RyiVJ+Oqco/HSQUuN/80IBqbEfHfpoA69EviWICIiIiIKJn7CjiBOl4TCs3aYzFassVhRXudf1z0FgMGpGuQYdMgxCNDH821ARERERCQXftoOc/UuYOtpO9aVW5F/SsQ50b+gpVIAw9K0yDUKyM7QIS2WHeOIiIiIiNoDQ1eYcrgk/GJXDT4v0aGqvtqvY8QogTu6a5Fj0GFShoBkgUGLiIiIiKi9MXSFqRilAt9W1qOq3rdGFjqVAqN7aJFr1GG8XkCihmtQERERERGFEkNXGMvWa1B0ydrqfvFqBcbrBeQadRjTQ4u4GAYtIiIiIqJwwdAVxrL1WrxS5D10JWkUmJihQ65BwB3dBQjq6GrtTkREREQULRi6wljvBBV6xrpwss49c5UiKDE5wz2jNbybFjFKBi0iIiIionDH0BXm7k6rR6U6AXdlxmNIVw1UDFpERERERBGFoSvMTe1eD70+DoKgDfVQiIiIiIjIDwxdRO1o7+EK5O8ohmh3QqtRYfKwTAzsmxrqYREB4PuTiIhILgxdRO1k7+EKLF93CLVifeO25esOAQA/2FLI8f1JREQkH/YWJ2on+TuKPT7QAkCtWI/8ncUhGhHR9/j+JCIikg9DF1E7Ee1O79tt3rcTtSe+P4mIiOTD0EXUTgSNyvt2rfftRO2J708iIiL5MHQRtZPsYZmIEzxvo4wT1MgemhmiERF9j+9PIiIi+bCRBkWtcOvE1vDa+TuLIdqcELQqZA9ldzgKD3x/EhERyYehi6JSuHZiG9g3lR9iKWzx/UlERCQPXl5IUYmd2IiIiIgoXDB0UVRiJzYiIiIiChcMXRSV2ImNiIiIiMIFQxdFJXZiIyIiIqJwwUYaFJXYiY2IiIiIwgVDF0UUX9rAsxMbEREREYUDhi6KGOHaBp6IiIiIqCW8p4siBtvAExEREVEkYuiiiME28EREREQUiRi6KGKwDTwRERERRSKGLooYbANPRERERJGIjTQorF3drXDIDd1xyHyBbeCJiIiIKGIwdFHY8tatsKrGhgcm9GfQIiIiIqKIwcsLKWyxWyERERERRQOGLgpb7FZIRERERNGAoYvCFrsVEhEREVE0iOrQlZeXhylTpqBnz55ITU3FjTfeiEcffRSlpaWhHhq1AbsVEhEREVE0iMpGGpIk4emnn8bixYvRs2dP3HPPPYiPj8fp06exc+dOlJSUID09PdTDpFY0NMvI31nMboVEREREFLGiMnS9/fbbWLx4MR577DHMnTsXKpXn5Wj19fXNPJPCzcC+qQxZRERERBTRou7yQqvVirlz58JoNOLVV19tErgAQK2OyqxJRERERERhKOrSx+bNm1FZWYlp06bB6XRi7dq1KC4uRmJiIkaOHIlevXqFeohERERERNSBRF3o2rdvHwBApVJh6NChOH78eONjSqUSTz75JObMmdPqcURRlGuIbWa32z3+TcHHGsuPNZYfayw/1lh+rLH8WGP5scbyC5caC4Lg0/6KyspKSaaxhMTTTz+Nd999FyqVCgMGDMAbb7yBPn36oKioCLNnz8bRo0fxl7/8BY8++miLxzlx4gScTq4HRURERERE31OpVD5fPRd1oesXv/gFlixZAp1Oh71796Jbt26Njx06dAjDhg2DwWDA119/3eJxwmWmq6KiAqmpqdBoNKEeTlRijeXHGsuPNZYfayw/1lh+rLH8WGP5hUuNfZ3pirrLCxMSEgAAN910k0fgAoD+/fvDaDTixIkTqKysRFJSUrPH8bWQctJoNGE1nmjEGsuPNZYfayw/1lh+rLH8WGP5scbyi7QaR133wqysLABAYmKi18cbtofDTBYREREREUW/qAtdw4cPBwAcPXq0yWMOhwMnTpxAXFwcUlJS2ntofvHW8p6CizWWH2ssP9ZYfqyx/Fhj+bHG8mON5ReJNY66e7oA4O6778bmzZvx97//HdOnT2/c/tprr+Hll1/Gfffdh3feeSeEIyQiIiIioo4iKkPXyZMnMW7cOJw7dw7jx49HVlYWioqKsG3bNuj1ehQUFCA1NTXUwyQiIiIiog4gKkMXAJSWluLll1/Gpk2bcPHiRaSmpmLixIn49a9/jS5duoR6eERERERE1EFEbegiIiIiIiIKB1HXSIOIiIiIiCicMHQRERERERHJiKGLiIiIiIhIRgxdREREREREMmLo8sGqVaswe/ZsjBw5El27dkVSUhLef//9Jvs5HA589tln+NnPfoZbb70VPXr0QHp6OkaPHo1FixbB6XQ2+xoffPABRo0ahe7du8NgMODHP/4x9u3b1+z+e/fuxY9+9CNkZGSge/fuGDNmDD799NNm9z9z5gx+/vOf49prr0VqaipuueUWvPHGG3A4HD7VQi5trbE3ZrMZPXr0QFJSEp5++ulm92ONfa+x2WzGU089heuvvx5du3ZFVlYWJk+ejNWrV3vdnzX2rcbFxcV48sknMXDgQKSlpaFfv36YMmUK1q5d2+xzOnKNy8vL8dZbb+Guu+7C9ddfjy5duqBPnz546KGHsGfPHq/PqaqqwnPPPdf4Hr7hhhvwhz/8ATU1NV73d7lc+Oc//4nbbrsNaWlpyMzMxKOPPgqz2dzsuDZt2oRJkyYhPT0der0ekydPxhdffNHs/sePH8fMmTPRq1cvpKWlYejQoVi0aBEkKfT9rXypMc95/vHnfXwlnvNa52+Nec5rO39q3FHPeexe6IMbbrgBJSUlSE5ORmxsLEpKSrBgwQJMmzbNY7+jR4/i1ltvRXx8PG6//XZkZWWhqqoK69atw+nTpzF+/HisXLkSCoXC43lvvPEG5syZA71ej9zcXNTU1OCTTz6B3W7HZ599hsGDB3vsv23bNtxzzz0QBAF333034uPjYTKZUFJSghdffBH/7//9P4/9KyoqMHr0aJSVlWHy5MnIzMzEzp078eWXX2LixIlYvnx5kzG1t7bW+GoulwvZ2dkoKipCbW0tHn74Ybz55ptN9mONfa/xli1bGh+bMGECjEYjKisrcfDgQfTr1w9//etfPfZnjX2r8Z49e5CTkwOHw4GJEyciMzMT586dQ15eHqqqqvCb3/wGv/nNbzye09Fr/Kc//Ql//etf0bNnTwwbNgwpKSkoLi5Gfn4+JEnCv//9b9x9992N+9fW1mLChAnYv38/Ro0ahRtvvBFFRUXYvHkzBg4ciLVr10IQBI/XeOqpp7B06VL069cP48aNw+nTp7F69WrExcWhoKAAmZmZHvuvWrUKP/3pT5GSkoK77roLAPDpp5/iwoULWLx4Me68806P/Q8fPoxx48ZBFEVMmTIF3bp1w4YNG/Dtt9/i8ccfx+uvvy5T9drGlxrznOcfX9/HV+I5r238qTHPeb7xtcYd+pxXWVkp8Z+2/bN69WqpqKhIqqyslJ5//nkJgLRgwYIm+x06dEh64403pLKyMo/tZWVl0s033ywBkBYvXuzx2FdffSWp1Wqpd+/eksViady+bds2SavVStdee6108eLFxu3nz5+XevbsKWm1Wmnbtm2N2y0Wi9S7d29Jo9E0jrXhn6lTp0oApHnz5jVuu3TpknTPPfdIAKR///vfEVPjq/958cUXJbVaLb388ssSAOnhhx9usg9r7HuN9+/fLyUkJEiZmZnS/v37mzx+/vx51jjAGo8dO1YCIL3//vse24uKiqROnTpJOp1OqqioYI2v+Gfp0qXSmjVrmmxfu3atFBMTIyUlJXnU7Ne//rUEQJo9e7bH/rNnz5YASH/84x89tptMJgmAdNttt0lnz55t3P7hhx9KAKRRo0Z57G82m6XExEQpOTlZOnjwYOP2gwcPSsnJyVJycrJUUlLi8ZzbbrtNAiB9+OGHjdvOnj0rDRkyRAIgbdiwIWJqzHNe+7yPr/yH5zx5asxznvw17sjnPF5e6IORI0ciIyOj1f26d++Oxx57DHFxcR7b4+LiMGvWLADAzp07PR57//33UV9fj1/96ldITExs3H7jjTfinnvuwZEjR1BYWNi4fdu2bTh58iTuvfde3HjjjY3bExMT8ctf/hJ2ux0rVqxo3F5dXY1PP/0URqMRDz/8cON2hUKB559/HgCwZMmStpRBVm2t8ZWOHj2Kl156CU8//TRuuOGGZvdjjd18qfG8efNQVVWFefPmQa/XN3lcrVZ7/D9r7OZLjc1mMxQKBcaOHeuxPSMjA/3794fVakVtbW3jdtYYyM3NxbBhw5psv+222zB8+HBUVlbi0KFDAABJkrBs2TLEx8fjmWee8dj/mWeeQXx8PJYuXeqxveH/f/e730Gj0TRuHzt2LIYNG4bNmzejpKSkcfvq1atx+fJl/OQnP0GPHj0at/fo0QOPP/44Lly4gDVr1jRuP378OP773/9i+PDhHt93jUaD3/3udwAiq8Y85/nHlxpfiee8tvO1xjzn+c7XGnfkcx5DVzuLiYkBAKhUKo/tO3bsAACMGjWqyXNGjx4NwPOk5ev+X375JWw2G+64444mU6QZGRnIysrC7t27W7z2Phw5nU488cQT6NWrV5MPVFdjjX0jSRJWr16Na665BiNGjMC+ffvwj3/8A/Pnz8fWrVvhcrmaPIc19l2/fv0gSRI2btzosb2kpASHDh3C9ddfj2uuuaZxO2vcsqt/xxYXF+P06dMYNGiQ11AwaNAgmM1mlJaWNm7fsWMH4uLimlyyAgSnxi3tP2TIEMTFxTUJKeGkufOYL/vyfdyy5urGc17wXF1jnvOCz9v7uCOf89St70LB9N577wFo+mYoLi5GfHw8UlNTmzyn4d6B4uJij/2vfOxKqampiI+Px4kTJ5rs36tXL6/j6tWrF44dO4aSkhIYjUYfvqLQmjdvHr755hsUFBR4/EXaG9bYNxaLBZcuXcLNN9+M2bNnY/HixR6P33jjjVixYoXHX/ZZY9/9/ve/x+7duzFjxgxMnDgRvXv3bry+3Wg04t133/XYnzVuXklJCbZu3Yq0tDRcd911ANr29WzatAnFxcVIT09HbW0tzpw5g/79+3sNFQ3HaWuNW/qeeBuTSqWCwWDA4cOHUV9f3+Qv66HmrcYt4TnPdy3VmOe84PBWY57zgqu593FHPueF12/zKLd48WJs3LgRt99+O8aNG+fxWFVVFbp06eL1eZ06dWrc58r9ASAhIaHZ53jb/8qp2Ss1HOfy5ctt+VLCwv79+/Haa6/hqaeewk033dTq/qyxb86dOwcAKCoqwrFjx7BgwQJkZ2fj8uXLmDdvHpYsWYIZM2agoKCg8Tmsse/69OmDjRs3YubMmcjLy2vcfs0112DatGno2bOnx/6ssXcOhwM//elPYbPZ8Kc//akxMLX162nYr7V6Xb1/a89p6XvS3Jg6deoEl8uFmpoaJCUled0nFJqrcXN4zvNdSzXmOS84mqsxz3nB09L7uCOf83h5YTtZt24dnnnmGej1erzzzjuhHk7Es9vtjZdYPPvss6EeTlRquJTC6XTiueeew7Rp05CUlASDwYC//e1vuOWWW7Bnzx6Pa6nJd1999RXGjRuHpKQkbN26FeXl5di3bx+mTp2K3/zmN3j00UdDPcSw53K58OSTT+K///0vZsyYgalTp4Z6SFHH1xrznOe7lmrMc15wtFRjnvOCo7XfFR35nMfQ1Q42bNiAGTNmoGvXrsjLy0NaWlqTfRISEjyS95Wqq6sb97lyfwAtPsfb/s0l9daSfriZN28eDh06hAULFkCr1bbpOayxb6782iZNmtTk8QkTJgAAvv76a4/nsMZt53A48Mgjj0CpVOK9997DTTfdhNjYWBiNRrz88svIzs7G6tWrsWvXrsbnsMaeXC4XZs2ahQ8//BD33Xdfk7bZbf16GvZrrV7e/ora0nNa+p40N6bq6mooFArEx8d7fby9tVbjq/Gc57vWasxzXuDa+rsC4DnPX63VuKOf8xi6ZLZ+/Xo89NBDSE5Obrxe1ZvMzEzU1NSgoqKiyWPerk/1dh1rg4qKCtTU1Hhcn9qw/5XXsV7pxIkT0Gg0SE9Pb9sXFmJFRUVwuVwYM2YMkpKSGv/JyckBALz77rtISkrCAw880Pgc1tg3PXv2bLwkwNsvm4Ztoig2bmONfXP06FFYLBb84Ac/QGxsbJPHhw8fDsD9fm/AGn+v4S+qK1aswL333ouFCxdCqfQ8rbXl67lyv7i4OKSlpcFisXi9cfrq/a/8b281bul74m1MTqcTFosFBoMhLO7nakuNr8Rznu/aUmOe8wLTlhrznBeYttS4o5/zGLpktH79ekyfPh2dO3dGXl5eszfpAcDQoUMBAJs3b27y2KZNmzz28Wf/W265BRqNBlu2bIEkea6HferUKRw7dgyDBg0Ki5N8W9xxxx146KGHmvzTcN9Aw2rod9xxR+NzWGPfCIKAW2+9FYB7IderHTlyBAA8WqOzxr5pWOn+/PnzXh9v2H7lX7ZZY7eGE/zKlStx991345///KfXe4wyMzPRrVs37N6926MNMeBeNHn37t0wGAweJ9ShQ4eitrbW46+tDRpqdtttt3nsDwTne1JYWIja2lqP/UOlrTVuwHOe79paY57z/NfWGvOc57+21rijn/MYumSyceNGTJ8+HUlJScjLy/PaReVK06ZNg1qtxl/+8hePKc6ioiJ8/PHHuPbaazFkyJDG7SNGjIDRaMRHH33k8ReBhhs+NRqNx3W0CQkJuPvuu2E2mz06w0iShD//+c8AgBkzZgT8dbeXxx9/HPPnz2/yT8Mq40OHDsX8+fPx+OOPNz6HNfZdw7XVr776Kmw2W+P2o0ePYvny5ejUqRPGjBnTuJ019k2/fv2QkJCA3bt3NzlBlJaWYvHixVAoFB4nCNb4+0tYVq5ciSlTpuCdd95pNgwoFAo89NBDqKmpweuvv+7x2Ouvv46ampomX0/D/7/00kuw2+2N2zdu3IgdO3Zg1KhRHh+87rrrLiQkJOCdd95BWVlZ4/aysjL861//QnJyMiZPnty4PSsrC7fddhu2b9/u0TbZbrfjpZdeAgBMnz7d17IElS81BnjO84cvNeY5zz++vo95zvOdLzXu6Oc8RWVlpdT6bgS4F8xsuIHy0KFD+OabbzB48ODGTitDhgzB9OnTcfToUQwfPhw2mw333HMPevfu3eRYGRkZmDZtmse2N954A3PmzIFer0dubi5qamrwySefwG6347PPPmuyZsy2bdtwzz33QBAE3H333YiPj4fJZEJJSQlefPHFxl/GDc6cOYMxY8agrKwMOTk56NWrF3bu3Ikvv/wSEyZMwIoVK5qsUdDe2lrj5mzfvh05OTl4+OGHvd53wBr7VmNJkjBz5kx89tlnyMrKwqhRo1BVVYW8vDzU1dXh7bffxn333edxfNbYtxovXrwYs2fPhlKpxPjx49GnTx9UVFRgzZo1qKmpwc9//nPMmTPH4/gdvcavvPIK5s6di/j4ePzsZz/zeoLPzs5uXBiztrYW48ePx4EDBzBq1CgMGDAA33zzDTZv3oyBAwciPz8fOp3O4/lPPfUUli5din79+mHcuHE4c+YMPv30U8TFxWHjxo1Nfq+vWrUKP/3pT5GSkoK77roLAPDpp5/iwoULePfddzFlyhSP/b/99luMHz8eoijirrvuQlpaGjZs2IBvv/0Wjz/+eJOA2N58qTHPef7x9X3sDc95LfO1xjzn+c7XGnfkcx5Dlw+eeOIJj1Wsr3b//fdj4cKFjb8EWzJ06FDk5+c32f7BBx9g4cKFOHz4MGJiYjB48GA899xzzbaH/eqrr/DKK6/gf//7HxwOB/r3749Zs2bh7rvv9rr/mTNnMGfOHGzYsAGVlZXQ6/WYOnUqfvGLX7S65kd7aGuNm9PaCQhgjX2tcX19Pf75z3/ivffew4kTJ6DVanHLLbfgl7/8pddV6AHW2Ncab9myBW+//Tb27NmDyspKxMXF4frrr8fMmTObnOAbdOQat1ZfAFiwYIHHh/zLly/j1VdfRV5eHioqKpCamoopU6bg2WefbWw7fCWXy4V33nkHS5YswYkTJxAXF4eRI0fiD3/4Q5OWxg0KCgrwl7/8BUVFRVAoFBgwYACeeeYZjBw50uv+x44dw5w5c7Bt2zbU1dUhMzMTjzzyCB599NGQ/+HAlxrznOcff97HV+M5r2X+1JjnPN/4U+OOes5j6CIiIiIiIpIR7+kiIiIiIiKSEUMXERERERGRjBi6iIiIiIiIZMTQRUREREREJCOGLiIiIiIiIhkxdBEREREREcmIoYuIiIiIiEhGDF1EREREREQyYugiIqI2y87ORlJSEt5//32vj69fvx6TJk2CXq9HUlISkpKSsH37dtnH9f777yMpKQnZ2dmyvxYREZGv1KEeABERhUZ2djZ27tyJZ599Fr/97W8DPt4XX3yBqVOnQpIk6PV69O/fHwqFAgkJCQEdd82aNdi/fz+GDRuG4cOHBzzOSOJ0OvHJJ5/g888/x1dffYXz58/D4XAgOTkZ1113HcaOHYsf/ehHuOaaa0Iyvo78vSEi8gVDFxERtVl6ejqysrK8BqlFixZBkiQ89thjeOONN4L2mvn5+VixYgUANPvBPiEhAVlZWUhPTw/a64baN998g0cffRTHjx8H4P4aDQYDNBoNzpw5g4KCAhQUFOCll17CwoULQzLL15bvDRERMXQREZEP/vnPfzb72JEjRwAA48aNa6/hNMrJyUFOTk67v65c/ve//2HKlCmoq6vDzTffjD/+8Y8YPnw41OrvT9vHjh3DkiVLsGjRIhQVFfHSSiKiMMbQRUREQWG1WgEAOp0uxCOJbFVVVZgxYwbq6uowduxYvPfee9BqtU32y8rKwpw5czBjxgwcOHAgBCMlIqK2YiMNIiJqM2+NNG644QYkJSXh1KlTANyzTg1NNK6efXE6nXjvvfeQm5uLXr16oUuXLujXrx8ef/xx7N+/32Nfi8WCpKSkxsvX5s6d23jcpKQk3HDDDY37ttRIo2F827dvx7Fjx/Doo4+iT58+6NatG4YOHYrly5c37ltVVYU///nPGDhwIFJTU3HdddfhD3/4A+rq6pqtidVqxVtvvYXx48fDYDCga9euuPHGGzF79myYzea2F/c7ixYtwunTp5GQkIB//vOfXgPXlbKysnDXXXc12V5aWopnnnkGP/jBD5CWloaMjAyMGjUK8+fPhyiKXo9VVVWFl19+GcOGDUOPHj3QpUsXXHvttRg5ciR+97vf4cSJEwB8+94QERFnuoiIKEADBw5Ejx498PXXX8Nms6F///6N93z179+/cb/Kykrcf//9KCwsBAB069YN6enpOHnyJD788EOsXr0ab7/9Nu655x4AgCAIGDx4MIqLi3Hu3Dmkp6d73LOVmprq0zj37duHuXPnAgB69+6N8vJyHDx4EE8++SQuXryIBx54AJMmTcKxY8fQt29fdOvWDRaLBfPnz8eRI0fwwQcfNDlmSUkJfvSjH+Hw4cNQKpXo3r079Ho9Tpw4gcWLF+Pjjz/G8uXLfbrfadWqVQCAH//4x343yNixYwceeOABVFVVQaPRoG/fvrBardi7dy/27t2LDz/8EJ988glSUlIan1NdXY2xY8fiyJEjUCgU6NmzJ5KSknDu3DkcPHgQ+/btw7XXXotevXoF/XtDRBTtGLqIiCggS5YsAeCeUSopKcHcuXO9hozHH38chYWFGDJkCP7yl780BjKXy4W3334bv//97zFr1iwMGDAAvXv3RmpqKtatW4cnnngCK1aswLRp0wLqsvjnP/8ZDz30EObMmYPY2FgAwMsvv4zXXnsNr776KrZs2YLExEQUFRWhR48eAICCggJMnToVGzZswNatWzFy5MjG49ntdtx///04fPgwJk2ahFdffRUZGRkAAJvNhldeeQV//etfMXPmTOzZswedO3dudYyXLl3C4cOHAfjfmOLChQuYOXMmqqqqMH78eCxcuLAxvO3btw8PPvggioqKMGvWrMaABwDLli3DkSNH0L9/f6xYsQIGg6HxMVEUsW7dOnTv3h0Agv69ISKKdry8kIiIZLd161Zs3LgR6enpWLFihccMmFKpxJNPPonHHnsMoihi4cKFsowhKysLb7zxRmPgAoBf//rX6NatG2pqarBz504sWrSoMXABwJgxYxovWVy/fr3H8VauXIkDBw7g5ptvxpIlSxoDFwBotVr86U9/woQJE3DhwgUsXbq0TWMsLy9v/G+j0ejPl4lFixbh/PnzSElJwbvvvusxW3bTTTdhwYIFjV/Pvn37Gh87duwYAOChhx7yCFyAe9ZxypQpuPXWW/0aExFRR8fQRUREsvvkk08AAPfeey+SkpK87pObmwvAvd6XHB566CEolZ6nPbVajeuuuw4AMHr0aK8t52+++WYAaLyfqUHD1/TQQw8hJibG62v6+jVVV1c3/nd8fHybnnO1DRs2AABmzpzpETAbjBgxAjfeeCMAzyCp1+sBAOvWrUNNTY1fr01ERN7x8kIiIpJdQ3e9vLw87Nq1y+s+Dc0dysrKZBlDr169vG5vuK+ptcdra2s9tjd8Tf/+97+93u8FAJcvXwbQ9q+pU6dOjf/tb/BpmLG6cjbxav3790dRUVHjvgDw4IMPYsGCBfjiiy/Qt29fjBw5EoMGDcLgwYMxcOBAqFQqv8ZDREQMXURE1A4qKysBAMXFxSguLm5x34bW88HmbdYHABQKRZselyTJY3vD13To0KFWX7ul7odXarhnCgDMZnPjjJQvGsJa165dm90nLS0NgOfMWteuXbFp0ybMnTsX+fn5WLNmDdasWQPAHTyfeOIJ/OIXv/BYK4yIiNqGvzmJiEh2cXFxAIB//OMfePDBB0M8muCIi4vD5cuXYTKZcPvttwflmJ07d0bfvn1x+PBhbN++vfHyRF/Ex8fj8uXLOHv2bLP7nDlzBoDnzBrgvo9s4cKFcDqd2L9/P3bt2oWNGzdi8+bNePHFF1FVVYUXXnjB5zEREXV0vKeLiIhk13Cp28GDB31+bsNMU7gJ5GtqyX333QfA3Tr+4sWLPj+/T58+AFqegWt4rGHfq6lUKtx000342c9+ho8//hivvfYaAOA///mPx4xfuH5viIjCDUMXERHJrmHx3pUrV7Y4A+NNw2V/cl126K+Gr+nf//53my8fbItHH30UqampqKqqws9+9jPYbLYW9z927BhWr17d+P/jxo0DACxevNjruLZt24aioiKPfVszaNAgAO7LEa+8JDFcvzdEROGGoYuIiGQ3YcIEjBo1CpcuXUJOTk7jAslXMpvN+Nvf/takvXrPnj0BAIWFhbDb7e0y3raYMWMG+vfvj+LiYtx9992NjTWu9O2332LOnDn4/PPP23zcxMRELF68GIIgYMOGDZg4cSK2bt0Kp9Ppsd/Jkyfx/PPPY8SIEfj2228btz/yyCNISUnB+fPn8cgjj3jMljWszwUA48ePx0033dT42AsvvIBFixY1CcWVlZV48803AbgXlW5Y+BoI3+8NEVG44T1dREQd3N///nf861//avbx9PR0bNu2LeDX+c9//oOZM2di69atmDhxIrp06QK9Xg+n04mysjKcP38eAPDss896PO/OO+/ESy+9hC+//BL9+/dHZmYm1Go1UlNT8Z///CfgcflLq9Xigw8+wAMPPIBdu3Zh2LBhSE9PR1paGmw2G06dOtXYvbBhbay2GjJkCD7//HM88sgj2Lt3L6ZMmYKEhATo9XpoNBqcOXMGp0+fBgAkJCR4hKfk5GQsXrwYDzzwANatW4d+/fqhb9++sFqtOHr0KAD3QtZXj+nIkSN488038atf/Qrp6elITU1FXV0dTpw4AZvNhri4OPztb3/zeE64fm+IiMINQxcRUQdntVpbvDysoQlGoJKSkvDJJ58gLy8Pq1atwt69e7F//36o1WqkpaVh5MiRmDhxIsaOHevxvPT0dHzyySf4y1/+gr179+LLL7+Ey+VqXFcqlNLT01FQUIBVq1bh008/RVFREfbt2wetVosePXogJycHkyZNwqhRo3w+9s0334wvv/wSH3/8MT7//HPs3bsXJ0+eRH19PZKTkzF27FiMHTsW9913X5O1z4YNG4adO3fi73//OwoKCnD48GHExMTg5ptvxt13343HHnsMOp3O4zm//vWv0b9/f+zcuROnTp3C/v37oVKpYDAYMGLECPz85z9vsmhyOH9viIjCiaKyslJqfTciIiIiIiLyB+/pIiIiIiIikhFDFxERERERkYwYuoiIiIiIiGTE0EVERERERCQjhi4iIiIiIiIZMXQRERERERHJiKGLiIiIiIhIRgxdREREREREMmLoIiIiIiIikhFDFxERERERkYwYuoiIiIiIiGTE0EVERERERCQjhi4iIiIiIiIZMXQRERERERHJ6P8DeUjxWsubZmkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_scatter=sorted_data.plot.scatter(x=\"# OF GENETIC AILMENTS\", y=\"POPULARITY IN US\", c=\"#5978ab\", figsize=(7,4))\n",
        "df_scatter.set_xlabel(\"# OF GENETIC AILMENTS\")\n",
        "df_scatter.set_ylabel(\"POPULARITY IN US\")\n",
        "df_scatter.set_xlim(-0.1,6)\n",
        "df_scatter.plot()\n",
        "\n",
        "model = np.poly1d(np.polyfit(sorted_data[\"# OF GENETIC AILMENTS\"], sorted_data[\"POPULARITY IN US\"], 1))\n",
        "\n",
        "polyline = np.linspace(0, 6, 50)\n",
        "plt.plot(polyline, model(polyline))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "NEmf1_tCV38p",
        "outputId": "515b1bf8-8dab-4024-8d70-be9a3b882095"
      },
      "id": "NEmf1_tCV38p",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_scatter=sorted_data.plot.scatter(x=\"INTELLIGENCE\", y=\"POPULARITY IN US\", c=\"#5978ab\", figsize=(8,3.5))\n",
        "df_scatter.set_xlabel(\"Intelligence\")\n",
        "df_scatter.set_ylabel(\"Popularity\")\n",
        "df_scatter.plot()\n",
        "\n",
        "model = np.poly1d(np.polyfit(sorted_data[\"INTELLIGENCE\"], sorted_data[\"POPULARITY IN US\"], 2))\n",
        "\n",
        "polyline = np.linspace(1, 83, 50)\n",
        "plt.plot(polyline, model(polyline))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "EN3qexFeSJwZ",
        "outputId": "b43ad5a0-20ab-47ac-cb90-b28794507826"
      },
      "id": "EN3qexFeSJwZ",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x350 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'Longevity': sorted_data[\"LONGEVITY\"],\n",
        "                   'Intelligence': sorted_data[\"INTELLIGENCE\"]}, index=sorted_data[\"index\"])"
      ],
      "metadata": {
        "id": "YFZ7peh88Pvn"
      },
      "id": "YFZ7peh88Pvn",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##🐕 Step 5: More Data Analysis\n",
        "> Comparing data between small, medium, and large dogs"
      ],
      "metadata": {
        "id": "Hk5liPJHiclw"
      },
      "id": "Hk5liPJHiclw"
    },
    {
      "cell_type": "code",
      "source": [
        "small_dog_avg = pd.DataFrame(columns = ['LIFETIME COST', 'LONGEVITY', 'INTELLIGENCE', '# OF GENETIC AILMENTS'])\n",
        "\n",
        "for i in categories:\n",
        "    small_dog_avg[i] = small_dog[i].mean()\n",
        "    small_dog_avg = small_dog_avg.append({i : small_dog[i].mean()}, ignore_index = True)\n",
        "\n",
        "small_dog_avg.drop([1, 2, 3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "3Mu71l2scWwE",
        "outputId": "e3c1c69d-4c98-4046-b594-0d099097e210"
      },
      "id": "3Mu71l2scWwE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LIFETIME COST  LONGEVITY  INTELLIGENCE  # OF GENETIC AILMENTS\n",
              "0   20599.935484  12.356452     45.322581               1.548387"
            ],
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              "      <th>LIFETIME COST</th>\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-c329df13-8f74-4310-852d-a51967597a7c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c329df13-8f74-4310-852d-a51967597a7c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 376
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "medium_dog_avg = pd.DataFrame(columns = ['LIFETIME COST', 'LONGEVITY', 'INTELLIGENCE', '# OF GENETIC AILMENTS'])\n",
        "\n",
        "for i in categories:\n",
        "    medium_dog_avg[i] = medium_dog[i].mean()\n",
        "    medium_dog_avg = medium_dog_avg.append({i : medium_dog[i].mean()}, ignore_index = True)\n",
        "\n",
        "medium_dog_avg.drop([1, 2, 3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "kOlWvqKthO3Z",
        "outputId": "ec2aaa43-66a4-4bee-cf18-3631feb5a0ba"
      },
      "id": "kOlWvqKthO3Z",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LIFETIME COST  LONGEVITY  INTELLIGENCE  # OF GENETIC AILMENTS\n",
              "0    19956.37931  11.226897     38.413793               1.482759"
            ],
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-ed0e87d0-4571-4619-aebe-ad4613ba5e37 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ed0e87d0-4571-4619-aebe-ad4613ba5e37');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 374
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "large_dog_avg = pd.DataFrame(columns = ['LIFETIME COST', 'LONGEVITY', 'INTELLIGENCE', '# OF GENETIC AILMENTS'])\n",
        "\n",
        "for i in categories:\n",
        "    large_dog_avg[i] = large_dog[i].mean()\n",
        "    large_dog_avg = large_dog_avg.append({i : large_dog[i].mean()}, ignore_index = True)\n",
        "\n",
        "large_dog_avg.drop([1, 2, 3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "aAXhAzQxhw5E",
        "outputId": "992a205a-d0cf-4c4c-a452-ab4fb78b42b6"
      },
      "id": "aAXhAzQxhw5E",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LIFETIME COST  LONGEVITY  INTELLIGENCE  # OF GENETIC AILMENTS\n",
              "0   18957.962963   9.578148     40.555556               2.074074"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>LIFETIME COST</th>\n",
              "      <th>LONGEVITY</th>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b940f58-142f-4546-86da-07a295e81ab9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8b940f58-142f-4546-86da-07a295e81ab9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 373
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.8.8 64-bit ('base': conda)",
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