Lego_Analysis_for_Course_(start).ipynb

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        "<a href=\"https://colab.research.google.com/github/jhenac/lego-analysis/blob/main/Lego_Analysis_for_Course_(start).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Introduction\n",
        "\n",
        "Today we'll dive deep into a dataset all about LEGO. From the dataset we can ask whole bunch of interesting questions about the history of the LEGO company, their product offering, and which LEGO set ultimately rules them all:\n",
        "\n",
        "<ul type=\"square\">\n",
        "<li>What is the most enormous LEGO set ever created and how many parts did it have?</li>\n",
        "\n",
        "<li>How did the LEGO company start out? In which year were the first LEGO sets released and how many sets did the company sell when it first launched?</li>\n",
        "\n",
        "<li>Which LEGO theme has the most sets? Is it one of LEGO's own themes like Ninjago or a theme they licensed liked Harry Potter or Marvel Superheroes?</li>\n",
        "\n",
        "<li>When did the LEGO company really expand its product offering? Can we spot a change in the company strategy based on how many themes and sets did it released year-on-year?</li>\n",
        "\n",
        "<li>Did LEGO sets grow in size and complexity over time? Do older LEGO \n",
        "sets tend to have more or fewer parts than newer sets?</li>\n",
        "</ul>\n",
        "\n",
        "**Data Source**\n",
        "\n",
        "[Rebrickable](https://rebrickable.com/downloads/) has compiled data on all the LEGO pieces in existence. I recommend you use download the .csv files provided in this lesson. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yme99OOQ_uq",
        "outputId": "48158210-6521-4bac-8ce8-efbbaf9c94d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "# Import Statements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5Wk7rs-IquQ"
      },
      "source": [
        "import pandas as pd\n"
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      "outputs": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5NQpJ_KIquT"
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      "source": [
        "# Data Exploration"
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        "tags": [
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        "id": "ffaG-UFYIquT"
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      "source": [
        "**Challenge**: How many different colours does the LEGO company produce? Read the colors.csv file in the data folder and find the total number of unique colours. Try using the [.nunique() method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nunique.html?highlight=nunique#pandas.DataFrame.nunique) to accomplish this."
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        "colors = pd.read_csv(\"data/colors.csv\")"
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      "outputs": []
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              "   id            name     rgb is_trans\n",
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              "f    107\n",
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      "source": [
        "**Challenge**: Change this into an h3 section heading: Understanding LEGO Themes vs. LEGO Sets"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0kxCh63uwOv"
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      "source": [
        "Walk into a LEGO store and you will see their products organised by theme. Their themes include Star Wars, Batman, Harry Potter and many more.\n",
        "\n",
        "**Challenge**: Display this image: https://i.imgur.com/aKcwkSx.png"
      ]
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        "tags": [
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        "id": "u_xkZUF8Iqug"
      },
      "source": [
        "A lego set is a particular box of LEGO or product. Therefore, a single theme typically has many different sets.\n",
        "\n",
        "**Challenge**: Display this image https://i.imgur.com/whB1olq.png "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Understanding LEGO Themes vs. LEGO Sets\n",
        "\n",
        "Walk into a LEGO store and you will see their products organised by theme. Their themes include Star Wars, Batman, Harry Potter and many more.\n",
        "\n",
        "<img src='https://i.imgur.com/aKcwkSx.png'>\n",
        "\n",
        "A lego set is a particular box of LEGO or product. Therefore, a single theme typically has many different sets.\n",
        "\n",
        "<img src='https://i.imgur.com/whB1olq.png'>\n"
      ],
      "metadata": {
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    },
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      },
      "source": [
        "The <code>sets.csv</code> data contains a list of sets over the years and the number of parts that each of these sets contained.\n",
        "\n",
        "**Challenge**: Read the sets.csv data and take a look at the first and last couple of rows. "
      ]
    },
    {
      "cell_type": "code",
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        "sets = pd.read_csv('data/sets.csv')"
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              "  set_num                        name  year  theme_id  num_parts\n",
              "0   001-1                       Gears  1965         1         43\n",
              "1  0011-2           Town Mini-Figures  1978        84         12\n",
              "2  0011-3  Castle 2 for 1 Bonus Offer  1987       199          0\n",
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              "           set_num                                 name  year  theme_id  \\\n",
              "15705      wwgp1-1  Wild West Limited Edition Gift Pack  1996       476   \n",
              "15706   XMASTREE-1                       Christmas Tree  2019       410   \n",
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        "**Challenge**: In which year were the first LEGO sets released and what were these sets called?"
      ]
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1949"
            ]
          },
          "metadata": {},
          "execution_count": 52
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              "      set_num                               name  year  theme_id  num_parts\n",
              "9521  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
              "9534  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
              "9539  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
              "9544  700.A-1              Small Brick Set (ABB)  1949       371         24\n",
              "9545  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJoK3M8TBAVU"
      },
      "source": [
        "**Challenge**: How many different sets did LEGO sell in their first year? How many types of LEGO products were on offer in the year the company started?"
      ]
    },
    {
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      },
      "source": [
        "sets.query('year==1949')['name']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9521           Extra-Large Gift Set (ABB)\n",
              "9534                 Large Gift Set (ABB)\n",
              "9539                Medium Gift Set (ABB)\n",
              "9544                Small Brick Set (ABB)\n",
              "9545    Small Doors and Windows Set (ABB)\n",
              "Name: name, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
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      "execution_count": null,
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            "text/plain": [
              "      set_num                               name  year  theme_id  num_parts\n",
              "9521  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
              "9534  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
              "9539  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
              "9544  700.A-1              Small Brick Set (ABB)  1949       371         24\n",
              "9545  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJMMYQYqIquo"
      },
      "source": [
        "**Challenge**: Find the top 5 LEGO sets with the most number of parts. "
      ]
    },
    {
      "cell_type": "code",
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        "id": "toJvjRuQIqup",
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          "data": {
            "text/plain": [
              "9987"
            ]
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          "execution_count": 23
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "        set_num                           name  year  theme_id  num_parts\n",
              "15004  BIGBOX-1  The Ultimate Battle for Chima  2015       571       9987"
            ],
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              "                                                     [key], {});\n",
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          "metadata": {},
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        "sets.sort_values('num_parts', ascending=False).head()"
      ],
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          "height": 206
        },
        "id": "uWccJz0Eaann",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        set_num                           name  year  theme_id  num_parts\n",
              "15004  BIGBOX-1  The Ultimate Battle for Chima  2015       571       9987\n",
              "11183   75192-1          UCS Millennium Falcon  2017       171       7541\n",
              "10551   71043-1                Hogwarts Castle  2018       246       6020\n",
              "295     10256-1                      Taj Mahal  2017       673       5923\n",
              "221     10189-1                      Taj Mahal  2008       673       5922"
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>set_num</th>\n",
              "      <th>name</th>\n",
              "      <th>year</th>\n",
              "      <th>theme_id</th>\n",
              "      <th>num_parts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15004</th>\n",
              "      <td>BIGBOX-1</td>\n",
              "      <td>The Ultimate Battle for Chima</td>\n",
              "      <td>2015</td>\n",
              "      <td>571</td>\n",
              "      <td>9987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11183</th>\n",
              "      <td>75192-1</td>\n",
              "      <td>UCS Millennium Falcon</td>\n",
              "      <td>2017</td>\n",
              "      <td>171</td>\n",
              "      <td>7541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10551</th>\n",
              "      <td>71043-1</td>\n",
              "      <td>Hogwarts Castle</td>\n",
              "      <td>2018</td>\n",
              "      <td>246</td>\n",
              "      <td>6020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>10256-1</td>\n",
              "      <td>Taj Mahal</td>\n",
              "      <td>2017</td>\n",
              "      <td>673</td>\n",
              "      <td>5923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>10189-1</td>\n",
              "      <td>Taj Mahal</td>\n",
              "      <td>2008</td>\n",
              "      <td>673</td>\n",
              "      <td>5922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6f6f1a7-9d80-4889-9919-446a34fde8e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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-d6f6f1a7-9d80-4889-9919-446a34fde8e3 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-d6f6f1a7-9d80-4889-9919-446a34fde8e3');\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": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSyhOzAHIqur"
      },
      "source": [
        "**Challenge**: Use <code>.groupby()</code> and <code>.count()</code> to show the number of LEGO sets released year-on-year. How do the number of sets released in 1955 compare to the number of sets released in 2019? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "qjdrktZAIqus",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2dbdff0-7824-4e53-fa3e-bdf66b29d473"
      },
      "source": [
        "sets_by_year = sets.groupby('year').count()\n",
        "sets_by_year['set_num'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year\n",
              "1949     5\n",
              "1950     6\n",
              "1953     4\n",
              "1954    14\n",
              "1955    28\n",
              "Name: set_num, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFInsHOkIqut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bed977db-3587-400f-88bf-e3dd6740cb24"
      },
      "source": [
        "sets_by_year['set_num'].tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year\n",
              "2017    786\n",
              "2018    816\n",
              "2019    840\n",
              "2020    674\n",
              "2021      3\n",
              "Name: set_num, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJrmIOULIquv"
      },
      "source": [
        "**Challenge**: Show the number of LEGO releases on a line chart using Matplotlib. <br>\n",
        "<br>\n",
        "Note that the .csv file is from late 2020, so to plot the full calendar years, you will have to exclude some data from your chart. Can you use the slicing techniques covered in Day 21 to avoid plotting the last two years? The same syntax will work on Pandas DataFrames. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nckj4lSGIquw"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZqAN-5MIquz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "6e30af19-289d-4654-d247-3d2f4d55a251"
      },
      "source": [
        "plt.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb981b65310>]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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AnDcgibT4CC+PyP80qSZl165dvPLKK/Tp04dvvvmG3/72t9x1113MmTMHgJycHACSkpLqPC8pKcn9WE5ODomJiXUeDwoKIj4+3n3MsWbOnElMTIz7lpaW1pRhi4iItLl9hRVY7Q5CgsykxIZ7ezh+qUkhxeFwMHLkSJ555hlGjBjBbbfdxq233sqrr77aWuMDYMaMGRQXF7tv2dnZrfp+IiIiLbXrUDkAPTtFqg9KMzUppHTu3JmBAwfWuW/AgAFkZWUBkJycDEBubm6dY3Jzc92PJScnk5eXV+fxmpoaCgoK3MccKzQ0lOjo6Do3ERERX7bzkHPXas8EdZNtriaFlNNOO42tW7fWuW/btm1069YNgB49epCcnMyCBQvcj5eUlLB8+XLS09MBSE9Pp6ioiNWrV7uPWbhwIQ6Hg7Fjxzb7RERERHzJztqZlF4JHbw8Ev/VpMLZe+65h3HjxvHMM89w1VVXsWLFCl577TVee+01AEwmE3fffTd//OMf6dOnj3sLckpKCpdffjngnHm54IIL3MtENpuN6dOnc8011zRqZ4+IiIg/0ExKyzUppIwZM4aPP/6YGTNm8OSTT9KjRw9eeOEFpk6d6j7mgQceoLy8nNtuu42ioiJOP/105s2b5+6RAvDOO+8wffp0zjvvPMxmM1OmTOHFF1/03FmJiIh42S7NpLRYk/qk+Ar1SREREV9WXGlj2B++BWDDExOICgv28oh8Q1N/f+vaPSIiIh62q3apJyk6VAGlBRRSREREPGyne/uxlnpaQiFFRETEw1wzKb0SVTTbEgopIiIiHube2aOZlBZRSBEREfEw986eRIWUllBIERER8aAau4M9h4+0xJfmU0gRERHxoH2FldjsBqFBZrrowoItopAiIiLiQa56lB6dIjHrwoItopAiIiLiQapH8RyFFBEREQ9yzaT0Uj1KiymkiIiIeJBmUjxHIUVERMSD1CPFcxRSREREPKSowsrhcisAPRO03NNSCikiIiIe4rpmT3J0GJGhQV4ejf9TSBEREfEQXbPHsxRSREREPERXP/YshRQREREPcc+kqB7FIxRSREREPGRXfu1MSoJmUjxBIUVERMQDauwO9h5WjxRPUkgRERHxgOzaCwuGBZvpHB3m7eEEBIUUERERD9iZd6SJmy4s6BkKKSIiIh6wK782pKho1mMUUkRERDxgZ15tPYqKZj1GIUVERMQDNJPieQopIiIiHuBq5KaZFM9RSBEREWmhwnIrBbqwoMcppIiIiLSQa6knJSaMiBBdWNBTFFJERERayH3NHi31eJRCioiISAvt1DV7WoVCioiISAvt0kxKq1BIERGRVvPh6n3MWbrH28NodUdmUhRSPEnVPSIi0iqqbHYe/PBnahwGp/XuRO8AvehedY2drMMVgHb2eJpmUkREpFXsK6ygxmEAkLHrsJdH03o27i+mxmHQMTKEzjG6sKAnKaSIiEiryCqocP952c7ADSkrdhcCMLp7HCaTLizoSQopIiLSKlxLIADLdh3GUTurEmhW7SkAYEz3eC+PJPAopIiISKvIKqh0//lwuZVteaVeHE3rcDgMVu11zqQopHieQoqIiLSKo5d7ADICcMlne14ZxZU2IkIsDEqJ9vZwAo5CioiItIrs2pByak/nDEMghpSVtUs9I7rGEmTRr1RP0ycqIiIeZxiGeybl6jFpACzfXRBwdSmukDK6m5Z6WoNCioiIeFx+mZVKmx2TCS4Y1JkOoUEUV9rIPFji7aF51Ko9znqUU3oopLQGhRQREfG47ELnLErn6DDCQyyM6R4HBNaSz/6iSvYXVWIxmxieFuvt4QQkhRQREfE4Vz1KWnwEAON6dQICq6mba+vx4JRoIkPVwL01KKSIiIjHuXqkdK0NKem9OgKwYncBNXaH18blSSt219ajaOtxq1FIERERj3MVzbpCyoDO0USHBVFWXcPGA4FRl+KqR1F/lNajkCIiIh7nDikdnSHFYjYxtqdzNiUQ6lKKK2xszXU2pxtdW28jnqeQIiIiHndsTQpAem1IWboz3ytj8qRVe51LPT07RdKpQ6iXRxO4FFJERMSjqmvsHCypAo4s9wCM6+0MKav2FGKt8e+6lJVa6mkTCikiIuJR+wsrMQyICLHQMTLEfX/fxCjiI0OotNn5eV+R9wboAe4mblrqaVUKKSIi4lFHF82aTCb3/WazKSBa5FcdFbLUxK11KaSIiIhH1VeP4uKqS/Hnfik/7yvGZjdIiAqts5wlnqeQIiIiHnXs9uOjufqlrNpbSJXN3qbj8hTXUs8p3ePrzBSJ5ymkiIiIR50opPRK6EBCVCjWGgdrs4raeGSeoXqUtqOQIiIiHpVVUAnUH1JMJpNfL/nYHQartbOnzSikiIiIxxiGccKaFDiy5LPMD4tnt+aUUlpdQ4fQIPonR3l7OAFPIUVERDymsMJGWXUNAKlx4fUe45pJWZtdSKXVv+pSXEs9I7rGEmTRr9DWpk9YREQ8xlWPkhwdRliwpd5junWMICUmDJvdcHdu9RdHF81K61NIERERj8lyL/XUP4sCzrqUU2uXfJZs958W+YZhHFU0q5DSFhRSRETEY05Wj+Jy/oAkAL5YfwCHw2j1cXnCvsJKckuqCbaYGJ4W6+3htAsKKSIi4jFZhxvefny0c/onEhUaxIHiKlZnFbbF0Frs03X7ARjcJYbwkPqXssSzFFJERMRjsgsbF1LCgi1MGJQMwGfrDrT6uFrCMAz++s1W/vrtNgAmDens5RG1HwopIiLiMSdq5HasS4enAPDlhoPY7L55VeTqGjv3vL+Ol7/fAcBd5/bmltN7eHlU7YdCioiIeITN7uBAUcON3I51Wq+OdIwMoaDcyk87fK+AtrjCxo1vrOCTdQcIMpt4dspQ7p3QT63w25BCioiIeMSBokocBoQGmUmICj3p8UEWM5OGOpdOPlvvW0s++wormPLqUpbtKqBDaBBv/GoMV41J8/aw2h2FFBER8Yijl3oaO9tw6TDnks83G3N85oKDG/cXc8U/lrIjr4zk6DDm3p7OmX0TvD2sdkkhRUREPKIp9SguI7vG0SU2nHKrnYVb8lpraE1y53/Xcqi0mv7JUXw8bRwDOkd7e0jtlkKKiIh4RFYje6QczWw2cUntbIov7PLJK6lid345ZhO8d9updI5puCmdtD6FFBER8YjsZsykwJEln4Vb8yipsnl8XE2xYX8xAL0SOhAbEeLVsYhCioiIeEhzlnsABnSOondiB6w1Dr7ZmNMaQ2s0V0gZkhrj1XGIk0KKiIh4hLvbbMemhRSTyeSeTfH2Lp8N+2pDSheFFF/QopDypz/9CZPJxN133+2+r6qqimnTptGxY0c6dOjAlClTyM3NrfO8rKwsJk2aREREBImJidx///3U1NS0ZCgiIuJFxRU2Sqqc/46nxTUtpMCRJZ+fduRzqLTao2NrCvdMikKKT2h2SFm5ciX//Oc/GTp0aJ3777nnHj7//HPmzp3LokWLOHDgAJMnT3Y/brfbmTRpElarlaVLlzJnzhxmz57NY4891vyzEBERr3It9SREhTbrujbdO0UyLDUGhwFfbTjo6eE1Sm5JFXml1ZhNMDBFO3p8QbNCSllZGVOnTuVf//oXcXFx7vuLi4t5/fXXee655zj33HMZNWoUb775JkuXLmXZsmUAfPvtt2RmZvL2228zfPhwLrzwQp566ilmzZqF1Wr1zFmJiEibam49ytEu8fKSj2upp3diByJCgrwyBqmrWSFl2rRpTJo0ifHjx9e5f/Xq1dhstjr39+/fn65du5KRkQFARkYGQ4YMISkpyX3MxIkTKSkpYdOmTfW+X3V1NSUlJXVuIiLiOzwVUkwmWL230L1TqC25lnoGa6nHZzQ5pLz33nusWbOGmTNnHvdYTk4OISEhxMbG1rk/KSmJnJwc9zFHBxTX467H6jNz5kxiYmLct7Q0tSYWEfEl7h4pcc3vK5IUHcapPToC8PnPbT+bsrE2pAxVSPEZTQop2dnZ/O53v+Odd94hLCystcZ0nBkzZlBcXOy+ZWdnt9l7i4jIyWU3o5FbfVxXRvZGYzdtP/Y9TQopq1evJi8vj5EjRxIUFERQUBCLFi3ixRdfJCgoiKSkJKxWK0VFRXWel5ubS3JyMgDJycnH7fZx/d11zLFCQ0OJjo6ucxMREd/hieUegAsHJxNsMbElp5TNB9tuab9O0WxnhRRf0aSQct5557FhwwbWrVvnvo0ePZqpU6e6/xwcHMyCBQvcz9m6dStZWVmkp6cDkJ6ezoYNG8jLO3KNhvnz5xMdHc3AgQM9dFoiItJWauwO9hdVAk3vkXKs2IgQzu2fCMDHa/e3eGyN5Sqa7ZMY1azdSdI6mlS+HBUVxeDBg+vcFxkZSceOHd3333LLLdx7773Ex8cTHR3NnXfeSXp6OqeeeioAEyZMYODAgVx//fU8++yz5OTk8MgjjzBt2jRCQ09+aW8REfEtB4ursDsMQixmkqJaXgpwxYhUvtmUy6fr9vPgBf2xmBt3ReWWUNGsb/J4x9nnn3+eiy++mClTpnDmmWeSnJzMRx995H7cYrHwxRdfYLFYSE9P57rrruOGG27gySef9PRQRESkDbjqUVLjwzF7IFCc0z+B2IhgckuqWbozv8Wv1xhHmripnMCXtHgj+A8//FDn72FhYcyaNYtZs2Y1+Jxu3brx1VdftfStRUSkmUqrbBwsrqJvUlSLX2vJDmeQaGk9iktokIWLh3bm7WVZfLRmP2f0SfDI657IkaLZ2FZ/L2k8XbtHRKQdevjjjUx4fjGLtx1q0ev8a/EuXvlhJwATBta/+aE5rhiRCsC8jTmUV7fuZVNyS6o45C6a1UyKL1FIERFph9ZkFQLwVsaeZr/Ga4t38vRXmwG467w+XHuK53pYjewaS/eOEVTa7HyzqXWvjPyzimZ9lkKKiEg7U2N3cLC4CoCFW/LIqf1zU7y2eCfPfLUFcAaUe8b3wWTyXIGryWRyz6Z8tKZ1d/moP4rvUkgREWlnXLtxABwG/G910xpk/nPRkYDyu/P6cO/5fT0aUFyuGNEFgJ925jcrSDXWRl352GcppIiItDPZhXWvi/P+qmwctaHlZF5dtJOZXx8JKPec39fj43Pp2jGCMd3jMAz4ZF3rzKYYhuFe7tH2Y9+jkCIi0s7sK3Q2XhvTPY6o0CCyCyrJ2HX4pM/795Jd/Kk2oNw9vnUDiotryefjNfsxjMYFqabILakmv6wai9mkolkfpJAiItLO7Kvta9InKYrLRjivlfPeyhMv+ezIK60TUO4e3/oBBWDSkM6EWMxszS0lsxXa5LvqUfokdlDRrA9SSBERaWeya2dSUuPCuWZMVwC+2ZhDYbm13uMNw+CJzzKpcRiMH5DYZgEFICYimPEDnW3yW6OAdsO+IkBLPb5KIUVEpJ3ZV1uTkhYXweAuMQxKicZqdzR4rZyvN+bw4458QoLMPHbxoLYcKnBkyefTdQeosTs8+tqumZSh2tnjkxRSRETamewC50xKWm2H2GvGOPubvLcy67i6jwprDX/8IhOA28/q1eILCDbHWX0TiIsIJr+smh93eK5NvmEYbNjvXELSTIpvUkgREWlHqmvs5JY6t/OmxoUDcOnwLoQFm9mWW8ba7KI6x//j+50cKK6iS2w4vz2rV1sPF4CQIDOXDnPWznhyySenpEpFsz5OIUVEpB05UFSFYUB4sIWOkSEAxIQHc9GQzgC8v+JIAe2e/HJeW7wLgEcvHujVwtIrRjqXfL7NzKHMQ23yN+w7UjQbFqyiWV+kkCIi0o64r1gcF16nAZurgPbznw+4Q8CTX2RitTs4o08nJg5KavvBHmVYagw9EyKpsjn48ucDHnlNNXHzfQopIiLtiKtHStoxVywe0z2Onp0iqbDa+WL9ARZszmXhljyCLSaeuHRQq3SUbQqTycRVo521M3OW7vVIz5Sf1Q7f5ymkiIi0I9nunT3hde43mUxcXVtA+/byvfzhc2ex7C2n96RXQoe2HWQDrh6dRmiQmcyDJe4LJDaXYRiaSfEDCikiIu3IkeWe43fpTBmVSpDZxMb9JWQVVJAUHcqd5/Zu6yE2KC4yxF1AO2fp3ha9lrNo1orFbGKAimZ9lkKKiEg7cmS5J/y4xzp1COX8gUdqTx6eNJDI0KA2G1tj3DiuOwBfbThIXknzLzq4ZJtzK7OKZn2bQoqISDviauRW30wKOEOAyQRn9OnEJUM7t+XQGmVwlxhGdYujxmHw3xVNu3qzS3GljWe/2QrAJbUzM+KbFFJERNqJSqud/DJn6/u0BkLKqT07svj+c/j3jaO9XizbkBvSuwHwzvK92JrRgfav32wlv6yaXgmR/PqMHp4enniQQoqISDvhmkWJCgsiJiK4wePS4iMIDfLdJZALB3emU4dQ8kqr+WZTTpOeuy67iLeXO+tZ/nj5EJ8+T1FIERFpN7JPstTjL0KCzPxyrLOvy1tNKKCtsTv4v482YBgweWQX0nt1bK0hiocopIiItBPuotm444tm/c3UsV0JMptYsaeAzAMljXrOnIy9ZB4sISY8mP+7aEArj1A8QSFFRKSdONH2Y3+TFB3GxMHJALyVseekxx8sruS5b53Fsg9d2J9OHUJbc3jiIQopIiLtxIm2H/ujX9VuR/5k3X6KKqwnPPbJzzMpt9oZ2TWWq2s714rvU0gREWknjnSb9f+ZFIDR3eIY0DmaKpuDuav2NXjcwi25fL0xB4vZxNNXDMFs9s1dS3I8hRQRkXYiu8A5k5IaIDMpJpOJG2u3I/9n2V7sjuOv51NptfPYp5sAuOX0Huou62d8q5WgiIi0ipIqG8WVNiAwalJcLhvehWe+2kxWQQWfrttP1/gI9hVWsq+wgv1FlWzcX8K+wkpSYsL43Xl9vD1caSKFFBGRdmBf7SxKXEQwHXys1X1LhIdYuHpMGv9aspt7P1hf7zFmE/zhssE+1+JfTk4/MRGRdsDVyC0tPnBmUVxuHNeduav3UVpVQ+eYMFLjwkmNiyA1LpwuseEMS4ulb1KUt4cpzaCQIiLSDmS7e6QEXkhJjYtg1cPjAQiyqNQykCikiIi0A0d6pARG0eyxFE4Ck36qIiLtgKtHSmoALvdI4FJIERFpB/YVBvZMigQmhRQRkQBnGIZ7uScQa1IkcCmkiIgEuKIKG+VWO6CZFPEvCikiIgHO1Q4/ISqUsGCLl0cj0ngKKSIiAc59YUHNooifUUgREQlwR7Yfqx5F/ItCiohIgHNf/ThALiwo7YdCiohIgNsXwN1mJbAppIiIBDgt94i/UkgREQlghmEcmUnRco/4GYUUEZEAdqismuoaByYTdI5RSBH/opAiIhLAsgucsyido8MICdI/+eJf9I0VEQlg7mv26MKC4ocUUkREApj76sdq5CZ+SCFFRCSAuWZStP1Y/JFCiohIAHPVpGgmRfyRQoqISAA70m1WMynifxRSREQClN1hcKDI1SNFIUX8j0KKiEiAyi2pwmY3CDKbSI4O8/ZwRJpMIUVEJEBlHigBoGt8BBazycujEWk6hRQRkQC1aNshAMb17ujlkYg0j0KKiEgAMgyDH7blAXB230Qvj0akeRRSREQC0J7DFWQXVBJsMZHeSzMp4p8UUkREAtCirc5ZlDHd44kMDfLyaESaRyFFRCQAuepRzuqb4OWRiDSfQoqISICpstnJ2HUYgLP6KaSI/1JIEREJMCv3FFBlc5AUHUq/pChvD0ek2RRSRCRgLdicy8UvLWHzwRJvD6VNLdp6ZKnHZFJ/FPFfCikiErBmL93Dxv0lfL0xx9tDaVNH6lG09Vj8m0KKiAQkh8NgXXYRALnFVd4dTBvaX1TJ9rwyzCY4vXcnbw9HpEUUUkQkIO3KL6O0qgaAnJL2E1IW186ijOgaR0xEsJdHI9IyCikiEpDWZhW5/5zbjkLK0fUoIv5OIUVEAtLa2qUeaD8zKTa7g5925AMKKRIYFFJEJCCtO2ompajCRpXN7r3BtJG1WUWUVtcQHxnCkC4x3h6OSIsppIhIwKmw1rAlx7nt2Fy7AzevpNqLI2obi2ovKHhGn06Yzdp6LP5PIUVEAs6GfcU4DEiODiMtPgJoH0s+aoUvgUYhRUQCjmvr8fC0WJKiw4DADyl5pVVs3O+cPTqjj0KKBIYmhZSZM2cyZswYoqKiSExM5PLLL2fr1q11jqmqqmLatGl07NiRDh06MGXKFHJzc+sck5WVxaRJk4iIiCAxMZH777+fmpqalp+NiAhHdvaM6BpLcm1ICfReKUu2OQtmh3SJISEq1MujEfGMJoWURYsWMW3aNJYtW8b8+fOx2WxMmDCB8vJy9zH33HMPn3/+OXPnzmXRokUcOHCAyZMnux+32+1MmjQJq9XK0qVLmTNnDrNnz+axxx7z3FmJSLt29ExKckz7mEnRUo8EoqCmHDxv3rw6f589ezaJiYmsXr2aM888k+LiYl5//XXeffddzj33XADefPNNBgwYwLJlyzj11FP59ttvyczM5LvvviMpKYnhw4fz1FNP8eCDD/LEE08QEhLiubMTkXbnYHElOSVVWMwmhqTGsPGAcwkkkEOK3WGwZHttSNFVjyWAtKgmpbi4GID4+HgAVq9ejc1mY/z48e5j+vfvT9euXcnIyAAgIyODIUOGkJSU5D5m4sSJlJSUsGnTpnrfp7q6mpKSkjo3EZH6uLYe90uKIiIkiKRo59JHIC/3bNhfTGGFjaiwIEakxXp7OCIe0+yQ4nA4uPvuuznttNMYPHgwADk5OYSEhBAbG1vn2KSkJHJyctzHHB1QXI+7HqvPzJkziYmJcd/S0tKaO2wRCXCuJm4jusYCHKlJKQ3ckLJws7Pu7/TenQiyaD+EBI5mf5unTZvGxo0bee+99zw5nnrNmDGD4uJi9y07O7vV31NE/JNrJmV47YyCa3dPbkk1hmF4aVStZ09+Of/+cTcA5w9MOsnRIv6lWSFl+vTpfPHFF3z//fekpqa6709OTsZqtVJUVFTn+NzcXJKTk93HHLvbx/V31zHHCg0NJTo6us5NRORYNruDn/cXAc4L7MGRkGKtcVBYYfPW0FpFjd3BvR+so8JqZ2yPeC4b3sXbQxLxqCaFFMMwmD59Oh9//DELFy6kR48edR4fNWoUwcHBLFiwwH3f1q1bycrKIj09HYD09HQ2bNhAXl6e+5j58+cTHR3NwIEDW3IuItLObc0ppcrmICosiJ6dIgEICTLTMdJZkJ8TYHUpr/ywkzVZRUSFBvG3q4ZhUZdZCTBN2t0zbdo03n33XT799FOioqLcNSQxMTGEh4cTExPDLbfcwr333kt8fDzR0dHceeedpKenc+qppwIwYcIEBg4cyPXXX8+zzz5LTk4OjzzyCNOmTSM0VHv7RaT51h619fjotvBJ0WEcLreSW1LFwJTAmIn9eV8Rf1+wHYAnLx9EalyEl0ck4nlNmkl55ZVXKC4u5uyzz6Zz587u2/vvv+8+5vnnn+fiiy9mypQpnHnmmSQnJ/PRRx+5H7dYLHzxxRdYLBbS09O57rrruOGGG3jyySc9d1Yi0i656lGO3eESaL1SKq127n5/HTUOg0lDOnO5lnkkQDVpJqUxRWdhYWHMmjWLWbNmNXhMt27d+Oqrr5ry1iIiJ7U2uxA4Uo/i4tqGHCjLPTO/3syuQ+UkRoXy9BWDMZm0zCOBSXvVRCQgFFfY2HXI2f162DEzKa7i2bwA2Ib8w9Y83srYC8BfrxxGbIQaYErgUkgRkYCwbl8RAN07RhAfWfcXt6tXir/PpBSWW3ngfz8D8Ktx3TlTLfAlwCmkiEhAOLY/ytGS3DUp1W04Is+qrrHzwIc/k1daTa+ESB68oL+3hyTS6ppUkyIi4qtc9Sj1hRR311k/LZzdfLCEe95fx5acUoLMJv5+zQjCQyzeHpZIq1NIERG/ZxiG+8rHxxbNwpGQUlBupbrGTmiQf/yCtzsMXlu8i+fmb8VmN4iPDOHZKUMZ3CXG20MTaRMKKSLiF/YeLuePX27mzD6duPaUrnWuUbPncAVFFTZCgswM6Hx8H5TYiGBCgsxYaxzklVSTFu/7PUX25Jdz39z1rN7rnCE6f2ASz1wxhIQo9ZOS9kMhRUT8wmuLdzE/M5f5mbnMXrqHhycN4Jx+iZhMJtbVLvUMTokmJOj4UjuTyURSdCjZBZXklFT5dEgxDIO3l2fxzJebqbTZ6RAaxOOXDOQXo1K11VjaHYUUEfELa2sLY4MtJnYeKufm2as4rXdHHr5ooPux4WnHL/W4JEeHOUOKj+/w+ccPO/nLN1sBSO/Zkb9cOVTdZKXdUkgREZ9XYa1hS04JAF/ddQb/W7OPN3/cw087DjPppSVEhjj/KRvRNbbB10jyg+LZ7IIKXqxtdX//xH789qxeddr7i7Q32oIsIj7v533FOAzoHBNGn6QoZlw4gAX3ncXFQztjGFBWXQPUv7PHxR92+Dz1RSbVNQ7Se3bkjrMVUEQ0kyIiPm9Nlqvdfaz7vrT4CF7+5UhuOq2Qlxdup0tcOKlx4Q2+RrKP90r5YWse32bmYjGb+MNlg1R/IoJCioj4AVfNych6theP6hbHmzedctLXcC/3+GBNSnWNnT98ngk4O8n2TYry8ohEfIOWe0TEpxmG4Q4pJ6o5ORlfvhLyGz/uYXd+OZ06hPK78X28PRwRn6GQIiI+bV9hJfll1QRbTAxKaX4Ts6SoIyGlMVd0b4p3l2dx13/XNmvn0MHiSl5a6CyW/b+L+hMdFuzRsYn4M4UUEfFprnqUgZ2jCQtufqfYxGhnEzRrjYOiCptHxgbOqy//4fNNfLb+AJe8/COr9xY06fnPfLWFCqud0d3iuGJEF4+NSyQQKKSIiE87stTTcA+UxggLthAX4ZylyC313JLPR2v3UV3jAOBQaTXXvLaM91ZkNeq5S3fm8/n6A5hNqFhWpB4KKSLi09a6r8kT2+LXchXPeqqhm2EYvLvcGUgeurA/Fw5OxmY3eOijDTz26UZsdkeDz7XZHTzx2SYApo7t1qKlLJFApZAiIj6rymYn80AxUP/OnqZyFc96qlfKqr2FbM8rIzzYwi/HdmXWL0dy3/l9AXgrYy/X/Xs5h8vqbnkur65ha04pz83fxrbcMuIjQ7hvQl+PjEck0GgLsoj4rE0HirHZDTp1CDlhD5TGSnbPpDTcK8XhMCi31hDViALW/9bOolwyrLO74PXO8/rQv3M097y/juW7C7j05Z8YmhrDvsJK9hVWUHhMPcwDE/sRGxHS3FMSCWiaSRERn3V0PYon6jXcyz0nmEl5/LNNjHrqOxZvO3TC1yqqsPLFhoMA/HJstzqPnT8wiU+mjaNHp0j2F1Xy9cYcNuwvdgeUmPBgBqVEc+e5vblqdFpLTkkkoGkmRUR8Vn2dZlviZNfvsdY4+Hjtfqx2Bw9/soFv7z6L8JD6dxR9uGY/1hoHAzpHMyz1+HqS3olRfHLHafxvzT7MJkiNiyA1LpwuceHaZizSSAopIuKz3DMpJ7i6cVMkxzi3ITdUOLtyT4H7OkDZBZW8/P127p/Y/7jjnAWzewH45diuDc7yxEQEc8vpPTwxdJF2Scs9IuKTDhZXcrC4CrMJhqV5ZueLayYlr4EtyAs25wHQs1MkAK8t3sX23NLjjlu5p5Cdh8oJD7Zw+fAUj4xNRI6nkCIiPmld7SxK/+RoIkI8M+nrKpzNL7Niram7PdgwDBZsyQXggQv6MX5AIja7wcOfbDyuQ61rFuXSYSmNKrAVkeZRSBERn+TpehSA+MgQQizOf/aOnU3ZeaicvYcrCLGYOb1PAk9cOojwYAsrdhfw4Zr97uMKy618tTEHcC71iEjrUUgREZ/kqU6zRzOZTO72+McWzy6snUUZ2zOeDqFBpMZFuC/298xXmykstwLw4Zp9WGscDEqJZmg9BbMi4jkKKSLic6w1DjbsdzVxi/XoazfUK8VVj3Je/0T3fbec3oO+SR0oKLfy53lbnAWztS3vrz2l4YJZEfEMhRQR8TlbckqornEQEx5Mj9oiVk+pr1dKcYWNVXudy0vn9k9y3x9sMfP0FUMAeG9lNv/4YSe7DpUTEWLhMhXMirQ6hRTxe8/P38aNb6ygymb39lDEQ9bsPVKP4unZivp6pSzafgi7w6BPYge6doyoc/yY7vFcXdtw7S/fbAXgsuEqmBVpCwop4tfsDoNXF+1k0bZDLNt12NvDEQ9xXVTQE9frOZarV8rRIWXhZmc9yrkDEut9zkMX9ndfQRmcSz0i0voUUsSv7S+spLp2K+mmAyVeHo14ypGi2ViPv/axV0KusTv4obYF/nlHLfUcLS4yhIcnDQRgeFosQ1M9Py4ROZ46zopf2553pNHWptqr5UrrKKmysSe/nCFdYlq1YDS/rJqsggpMJhiWFuvx108+ZrlnbXYRRRU2YsKDT1ik+4tRqXSNj/B4jYyINEwhRfza9rwy95837tdMyrFsdgeGASFBzZ80NQyDz9Yf4KkvMskvszL9nN78fmI/D46yLtcsSu+EDq1yjZvkmCOFs4ZhuHf1nN0vgSDLiT+nU3rEe3w8ItIwhRTxa9tzj4SUrIIKiiud/0Uszqv0Xj7rJxwGfPjbcSREhTb5Nfbkl/PIJxv5cUe++76Xv99BUkwY15/a7QTPbL61tU3cWqMeBY4s91TZHJRU1rj7o5zbv/56FBHxHtWkiF/bkVf3uiqZqksBnLMfD3+ykT2HK8gqqOB3763F7jBO/sRa1hoHLy3YzoQXFvPjjnxCg8zcP7Efd53bG4DHP93IN5tyWmXsrVmPAhAWbHEH2VV7C9iWW4bFbOKsvgmt8n4i0nwKKeK3DMNwL/f0THDWCaguxenTdQf48ueDWMwmwoMtLN15mL9/t61Rz12xu4CLXlzC3+Zvw1rj4Iw+nfj2njOZdk5v7jm/L9eekobDgLv+u5bVews8Ou6N+4tZVfuaI7u1zkwKHKlLeXe5szHbqG5xxEaEtNr7iUjzKKSI3zpQXEWF1U6Q2cTFQzoD2uEDsL+okkc/3QjA787rw5+mOJuRvbhwBz9szTvhc2f/tJurX8tgR14ZnTqE8PdrhvPWzafQraMzBJpMJp66bDDn9U+kusbBLXNWseOouqCWKK+u4a7/rsVmNzh/YBJ9Ejt45HXrk1Rbl/J97ecxvoGtxyLiXQop4re25zqXenp0inTvAmnvMykOh8HvP1hPaVUNI7rGcsfZvbhseBem1l4I757317G/qLLe5/3xi0ye+DwTw4DJI7uw4N6zuWx4l+N28gRZzLz0yxEMT4ulqMLGjW+sIO+Y6+A0x+OfbWJXfjmdY8J4dsrQVt1BlFx7/R7XCti5DWw9FhHvUkgRv+X6L/g+SR0Y3CXGfV+ltf12nn39x91k7DpMeLCF568a7t6t8ujFAxnSJYbCChvT3lmDtba3DECVzc60d9fw7x93A/DABf3425XDiIlouAA5IiSI128cTY9OkewvquTGN1dyoKiSgnLrcbfqmpP/PD5dt5//rd6H2QQvXD2cuMjWXXpxLfcAdOsYQa8EbSsW8UXa3SN+yxVSeidGkRgVSqcOIeSXWdmSU+LRK+f6iy05Je627Y9ePJDuR/XzCAu28I+pI5n04hLWZRfxzFebeeLSQRwuq+bWt1axJquIEIuZv1w5lMuGd2nU+3XsEMqcm05h8itL2XywhHF/WljvcREhFh68oD/Xn9oNs/n42ZG9h8t5+GPn8tSd5/ZhbM+OTT31JnMt94BzV48uFCjimzSTIn7LVTTbJ7EDJpOJQSnO2ZSNLahLWb7rMJfN+omf9xV5YohtprrGzt3vrcNqd3Be/0SuPSXtuGPS4iN47qrhAMxeuofXFu9kyitLWZNVRHRYEP+55ZRGBxSXrh0jmH3TGFLjwhs8psJq5/HPNnH9G8s5cMxSk7XGwV3/XUtZdQ2ndI/nztrdQ60tKepISGmoy6yIeJ9mUsQvGYbhrknpk+QssByUEs2ibYfIbEFdyp/nbWF9dhEvfLedN341xiNjbQvPfbuNLTmldIwM4U8nqOcYPzCJ28/qxauLdvLMV1sASI0LZ/ZNY+idGNWs9x7cJYYlD5xT72MOA95etpeZX2/mpx2HmfjCYp64ZBCTRzprXf727VbW7ysmJjyYF64ZftJmap7iuohgVGiQGrSJ+DCFFPFLh0qrKamqwWzC3abcVZfS3M6zO/JKWVPbo2PRtkMcKq1uVgO0trZqTwGvLdkFwMzJQ0465t9P6MuarEJW7C5gaGoM/75xNIlHzSw0R0OhyGKCG8d154w+nbj3g/Wsyy7ivrnr+TYzhwsHd+afi53jfvYXQ0mJbXg2xtP6JkXxzBVD6NYxokXdeEWkdSmkiF9yLfV07xhJaJAFcM6kAGzNKcVmdxDcxP8qn7t6n/vPdofB5+sPcPPpPTw04tbz0sIdGAZMGZnKhEHJJz0+yGJm9k1j+GnHYU7v3YnwEEurj7FnQgf+d3s6ry7ayQvfbeebTbl8s8nZ6fX6U7sxsRHj9rRfjtWVjEV8nf4TQvySa6mn91G9NLrGRxAVFoTV7qjTLr8xbHYHH67eD8DpvTsB8PHa/R4a7fH25Jfz9YaDGEbju8DWZ0deGYu2HcJkcvZEaayIkCDOH5jUJgHFJchiZvq5ffhk2mn0S3IuLfVLiuLhSQPabAwi4l8UUsQvbT9q+7GLyWRiYGfnbEpT+6Us2nqI/LJqOnUI4W9XDSPIbGLD/mJ3GPIka42D699Yzm/fWcNHa1oWhOYs3QM4iz9ddRa+bnCXGD678zRevW4k7912KmHBbReURMS/KKSIX9ru3n5ctyupqy6lqZ1nP1iVDcAVI7qQFB3G2f2c13H5qBVmU95flU12gXOXywsLttXpWdIUxZU2PlzjXKK6+bTunhpemwgNsnDB4M6t3g9FRPybQor4JXcjt2N2pLjqUpoyk5JfVs3CLc726FeOdm7dnTwyFYBP1u7H0YQL851Mlc3Oywu3A2A2QXZBpTsgNdUHK7OpsNrplxRFeq/W7y0iItLWFFLE7xwuq6ag3IrJBL0S6p9JyTxQ0uhw8cna/dQ4DIalxdK3tlbi3P6JRIUFcbC4imW7Dnts7G8v20tuSTVdYsN56ML+ALy0cDtVtqZ1ybU7DOZk7AHgptO6qxmZiAQkhRTxO66lntS48OMKP3t2iiQ0yEy51c6ew+UnfS3DMHh/pXMm46rRqe77w4ItXDzUedFCTy35lFXX8I8fdgLOItcbx3UnJSaM3JJq3l62t0mvNT8zl32FlcRFBHP5iKY1YBMR8RcKKeJ3GlrqAecOkgG1xbON6Ty7fl8x2/PKCA0yc8mwlDqPuZZ8vt5w0CPXA5r9024Kyq306BTJ5JFdCA2ycFftjpxXfthJeXVNo1/rzZ+c19m59pSuKjwVkYClkCJ+Z8dR7fDr05S6lLm19SAXDelMdFjdC+qN7hZHWnw45VY732bmtGTIFFfY3I3L7h7fx91ZdcqoVLp1jOBwuZXZtTt1TmbTgWKW7y7AYjZxfXq3Fo1LRMSXKaSI39med3yPlKO5d/icpPNspdXOZ+sOAHDlUUs9LiaTiStGOO8/0Vbhd5bv5drXlpGxs+HalX8t2UVpVQ39kqK4ZOiRGZtgi5l7xvcF4J+LdlJcaTvhmAFm/7QHgAsHJ9M5pu26tIqItDWFFPE7rkZtfZLqv9bM0TMpJ2qW9s2mHEqra0iNC+fUHvXvjrmitt5jyfZD5JVU1XnM4TB46otMHv54Ixm7DjP138v4+3fbsR9TsJtfVs0btcsz907oe9yVgC8ZlkLfpA6UVNXw79r29g05XFbNp+udweqm03y/G66ISEsopLRDOw+Vcf3ry/l+a563h9JkxRU28kqrgYZnUvomRWExmyissHGguKreY+BIb5QrR6UdFxxcenSKZETXWBwGfFYbDsC5lfiOd9bw+o/O8DG2RzwOA57/bhs3vLGcQ7VjBHj1h51UWO0MTY1hwsDjr7hrMZu493znbMobP+7mcFn1cce4vLs8C2uNg2GpMYzsGtvgcSIigUAhpZ1xOAzu+2A9S7bn88cvMlvclr2t7TjkXOpJiQmjQ2j9l54KC7a461U27a+/LiW7oIKlOw9jMsGUUSfeHeMqoHUt+Rwuq+bafy1j3qYcQixmXrx2BO//Jp2/XTmM8GALP+04zEUvLmHpjnxyiqt4q3bnzn0T+jW4VXjioGQGd4mm3Grn1UU76z3GWuPgP7WvddNpPbTtWEQCnkJKO/PflVmsyy4CYOehcvdVf1vDN5ty+PWclR5tLe9a6undwFKPy6CU2isiN7DD53+1FxM8vXcnUuNO3E7+4iGdCbaYyDxYwryNB5n8ylLWZhUREx7M278ey6W1u4KmjErl8zud16U5VFrN1NeX86s3V2CtcXBK93jO7NOpwfcwmUzcN6EfAG9l7OVAUSU1dked25cbDpBXWk1iVCgXDel8wjGLiAQCXQW5HTlUWs2fv94CQKcOoeSXVfO/1dmM6hbn8fdatusw099dg81usPNQOZ9NP42oY3bPNMf2k+zscRncJZoP10BmPTt81mcX8c7yLOBIh9kTiYsM4dz+iXyzKZfb314DQFp8OG/+6pTjlpx6J0bxybTT+MPnm3hvZTZbcpwB7b4JfU8683F23wRGd4tj1d5Cxv1pYYPHXXdqN0KC9N8XIhL49C9dO/LMV5spqaphcJdo/n7NcAA+X3+QCmvj+3M0xp78cm5/ezU2u4HJBLvzy3nww589srTU2JDinkk5aoePze7gufnbmPzKUvLLqunWMaLeGpH6uHb5AAxLjeGj357WYE1MeIiFP00ZygtXDyc+MoTJI7owtufJ29abTCZmXDTghAEkMSqUqWO7NmrMIiL+TjMp7cTSHfl8vHY/JhM8ffkQhqbG0DU+gqyCCr7ekMOUUcdvwW2O4gobN89ZSVGFjWGpMTx04QBueGM5X23I4c2f9nDz6S3bkbKjduno6Ksf12dg7Q6fnJIq8mvb6N/7wTp3aLlkWApPXTao0Y3Qzu2fyISBSUSHB/PkZYOICDn5/3UuH9GFy4annPS4o43qFsfPj0+g2lb/RQcjQi0EW/TfFiLSPiiktAPVNXYe+WQjANef2o1habEAXDkqlb/N38YHq7I9ElJsdgd3vLuaXYfK6RwTxr9uGE1idBgPXzSAJz7P5JmvNjMsLbbZy0tl1TXu3Tq9E05ck9IhNIgenSLZnV/Ok59nMm9TDtYaB7ERwTx12eDjusueTEiQmdduGN3kMTenuDUs2KIusiIiaLnHb6zcU8CLC7ZTUnXyZl/H+ueiXezKLychKpTfT+znvn/KqFRMJli+u4A9+Se/zs2JGIbBY59u4qcdh4kIsfD6jWNIjA4D4MZx3Zk0pDM1DoPp766hoNzarPfYWbvUkxAVSkzEyetbXP1SPlt/AGuNg3P6JfDt3Wc2OaCIiIh3KKT4gQ9WZnPNa8t4bv42fvmvZSfso3GsPfnlvPz9DgAevXhgndbvKbHhnNEnATiy26W5Xv9xN/9dkYXJBC9eM8K93ALO2YQ/TRlCz06RHCyu4u731zX6CsVHa2w9isuIrs4Zm8gQCzMnD+GNXx0JTiIi4vsUUnyYYRj87dutPPDhz9gdBsEWExv3l3DVPzM4UFTZqOc/+ulGrDUOzujTiUuGHr9t1XXl3w/X7DuuU2pjfZeZy9NfbQbg4YsGML6eYtSosGD+cd1IwoLNLN52yB2cmsLVDr+xIeXaU9KYOXkI8+4+k2tP6aq+IiIifkYhxUdZaxzc98F6Xlro/GV+57m9+fp3Z5ISE8bOQ+Vc+WoGu0+yRPPx2v0s2Z5PSJCZJy8bXO8v6fMHJhEbEczB4ip+3JHf5HFuPljC795bi2E4Q8EtJyiM7Z8czR8vHwI4O7O+vWwvG/cXU1xha9TOnx2N7JHiEhESxLWndCUt/sR9UERExDepcNYHFVfauP0/q8nYdRiL2cQzVwzm6jHObadzfzuO6/+9nF355Vz56lLeunlsnaUVu8NgfmYOb/y0hxW7CwCYdnZvenSKrPe9QoMsXDYshTkZe/lgVTZn9U1o9DjzSqu4ZfZKyq12xvXq2GAQOtovRqWyak8B763MdhfzAkSFBtElLpzUuAh6JkRydr8ETuke775aMDR9uUdERPybQoqP2V9UyU1vrmBbbhmRIRb+cd2oOsGhS2w4H9yezg2vryDzYAlXv5bB7JvG0Dsxig9WZjMnYw/7Cp1LQUFmE5NHduH2s3ue8D2vHJ3GnIy9zN+US2G5lbjIkJOOs8pm59a3VnOguIqenSJ5ZeqoRm+NfeLSQUSGBrFqbyH7CyvIL7NSWl3DlpxSZ/OzzfDa4l3ERgTXbv1N5pQe8WQXVgAKKSIi7YXJ8LeLtwAlJSXExMRQXFxMdHT0yZ/gBwzD4OuNOTz26Sbyy6pJig7ljV+NcTclO1ZxpY1fz1nJyj2FhAWbMZtMVFjtAMRFBPPLsV25/tTuJMc0rlD0or8vIfNgCU9cMpBfneTqug6HwZ3vreXLnw8SGxHMx3ec1uBMTWNUWu3sL6pgX2El+worWZddxILNuRRWHNnJFGQ2UeMwiI8MYc2j5zf7vURExHua+vtbMyk+ILuggsc/28TCLc6rEvdPjuKNX40hJTa8wefEhAfz1s1juf3t1SzadgiAvkkduPm0Hlw+okuT+2xcNTqVJz7PZO7qfScNKS8s2M6XPx8k2GLi1etGtSiggLNDa+/EKHonOmtNrju1GzV2B6v3FjI/M5dvMnPILnDODg1KCYxQKiIiJ6eZFC+y2R288eNuXvhuO5U2O8EWE789uzd3nN2r0SHDWuNg7upsuneMZFyvjs3ewVJYbmXsMwuw2h18cefpDO5S/wzOJ2v3c/f76wB49hdDuaoR175pKcMw2Jpbyso9hZzZpxPdOrYsFImIiHf41UzKrFmz+Mtf/kJOTg7Dhg3jpZde4pRTTvHaeHbnl7M2q5CSShslVTWUVNooraqhpMpGSZUNm90gIsRCeLCF8BDLUX8OqnN/eHDtY+4/Ox8PCz7ynHX7ivi/jza4L0B3So94nrliSIPXg2lISJCZqWO7tfjc4yJDOH9QEl/+fJD/rd5Xb0hZtaeAB/73MwC3n9WrTQIKOPus9E+Opn+y/wZSERFpOq+FlPfff597772XV199lbFjx/LCCy8wceJEtm7dSmJiolfG9OP2Qzz66aY2fc+4iGD+76IB/GJUqtf7eFw1Oo0vfz7If5btZX5mLlFhQUSHBxMdFkx0eBCLth7CancwYWASDxzVuVZERKQ1eG25Z+zYsYwZM4aXX34ZAIfDQVpaGnfeeScPPfTQCZ/bWss9i7Yd4t9Ldrl/KTv/N5jo2l/WFrOJSqudKpudCqvzdvyfa+rcX2mzU2k98uej/WJUKv930QDiG7Gbpi3YHQaTXlzint2pz6CUaObent6oC+yJiIgczS+We6xWK6tXr2bGjBnu+8xmM+PHjycjI+O446urq6muPtIKvqSkpFXGdVbfhCb1CWkqwzCosjmosNYQZDY36vozbcliNvHZ9NPZV1hxZJmr0vW/NixmE1NGpiqgiIhIm/DKb5v8/HzsdjtJSXXbpyclJbFly5bjjp85cyZ/+MMf2mp4rcZkMjnrVEJ89wq3IUFmeiaoD4mIiHifX7TFnzFjBsXFxe5bdna2t4ckIiIircwrMymdOnXCYrGQm5tb5/7c3FySk5OPOz40NJTQ0NC2Gp6IiIj4AK/MpISEhDBq1CgWLFjgvs/hcLBgwQLS09O9MSQRERHxMV6rgLz33nu58cYbGT16NKeccgovvPAC5eXl3HTTTd4akoiIiPgQr4WUq6++mkOHDvHYY4+Rk5PD8OHDmTdv3nHFtCIiItI+qS2+iIiItImm/v72i909IiIi0v4opIiIiIhPUkgRERERn6SQIiIiIj5JIUVERER8kkKKiIiI+CSFFBEREfFJXmvm1hKu1i4lJSVeHomIiIg0luv3dmNbtPllSCktLQUgLS3NyyMRERGRpiotLSUmJuakx/llx1mHw8GBAweIiorCZDK1yXuWlJSQlpZGdnZ2u+xy297PH/QZtPfzB30G7f38QZ9BS8/fMAxKS0tJSUnBbD55xYlfzqSYzWZSU1O98t7R0dHt8ovp0t7PH/QZtPfzB30G7f38QZ9BS86/MTMoLiqcFREREZ+kkCIiIiI+SSGlkUJDQ3n88ccJDQ319lC8or2fP+gzaO/nD/oM2vv5gz6Dtj5/vyycFRERkcCnmRQRERHxSQopIiIi4pMUUkRERMQnKaSIiIiIT2pXIWXx4sVccsklpKSkYDKZ+OSTT+o8npuby69+9StSUlKIiIjgggsuYPv27XWOOfvsszGZTHVut99+e51jsrKymDRpEhERESQmJnL//fdTU1PT2qd3Up44f4CMjAzOPfdcIiMjiY6O5swzz6SystL9eEFBAVOnTiU6OprY2FhuueUWysrKWvv0GqWln8GePXuO+/m7bnPnznUfF8jfgZycHK6//nqSk5OJjIxk5MiRfPjhh3WOCeTvAMDOnTu54oorSEhIIDo6mquuuorc3Nw6x/jqZzBz5kzGjBlDVFQUiYmJXH755WzdurXOMVVVVUybNo2OHTvSoUMHpkyZctz5NeY7/sMPPzBy5EhCQ0Pp3bs3s2fPbu3TOylPnf9dd93FqFGjCA0NZfjw4fW+188//8wZZ5xBWFgYaWlpPPvss611Wo3mifNfv3491157LWlpaYSHhzNgwAD+/ve/H/denvj5t6uQUl5ezrBhw5g1a9ZxjxmGweWXX86uXbv49NNPWbt2Ld26dWP8+PGUl5fXOfbWW2/l4MGD7tvRXzy73c6kSZOwWq0sXbqUOXPmMHv2bB577LFWP7+T8cT5Z2RkcMEFFzBhwgRWrFjBypUrmT59ep32xlOnTmXTpk3Mnz+fL774gsWLF3Pbbbe1yTmeTEs/g7S0tDo/+4MHD/KHP/yBDh06cOGFFwKB/x244YYb2Lp1K5999hkbNmxg8uTJXHXVVaxdu9Z9TCB/B8rLy5kwYQImk4mFCxfy008/YbVaueSSS3A4HO7X8tXPYNGiRUybNo1ly5Yxf/58bDYbEyZMqPMzvueee/j888+ZO3cuixYt4sCBA0yePNn9eGO+47t372bSpEmcc845rFu3jrvvvptf//rXfPPNN216vsfyxPm73HzzzVx99dX1vk9JSQkTJkygW7durF69mr/85S888cQTvPbaa612bo3hifNfvXo1iYmJvP3222zatImHH36YGTNm8PLLL7uP8djP32inAOPjjz92/33r1q0GYGzcuNF9n91uNxISEox//etf7vvOOuss43e/+12Dr/vVV18ZZrPZyMnJcd/3yiuvGNHR0UZ1dbVHz6Elmnv+Y8eONR555JEGXzczM9MAjJUrV7rv+/rrrw2TyWTs37/fsyfRQs39DI41fPhw4+abb3b/PdC/A5GRkcZbb71V57Xi4+PdxwT6d+Cbb74xzGazUVxc7D6mqKjIMJlMxvz58w3D8K/PIC8vzwCMRYsWGYbhPJfg4GBj7ty57mM2b95sAEZGRoZhGI37jj/wwAPGoEGD6rzX1VdfbUycOLG1T6lJmnP+R3v88ceNYcOGHXf/P/7xDyMuLq7O/+cffPBBo1+/fp4/iRZo6fm73HHHHcY555zj/runfv7taiblRKqrqwEICwtz32c2mwkNDeXHH3+sc+w777xDp06dGDx4MDNmzKCiosL9WEZGBkOGDCEpKcl938SJEykpKWHTpk2tfBbN15jzz8vLY/ny5SQmJjJu3DiSkpI466yz6nw+GRkZxMbGMnr0aPd948ePx2w2s3z58jY6m+ZpynfAZfXq1axbt45bbrnFfV8gfwcAxo0bx/vvv09BQQEOh4P33nuPqqoqzj77bCDwvwPV1dWYTKY6zazCwsIwm83uY/zpMyguLgYgPj4ecH6nbTYb48ePdx/Tv39/unbtSkZGBtC473hGRkad13Ad43oNX9Gc82+MjIwMzjzzTEJCQtz3TZw4ka1bt1JYWOih0becp86/uLjY/RrguZ+/Qkot1w9hxowZFBYWYrVa+fOf/8y+ffs4ePCg+7hf/vKXvP3223z//ffMmDGD//znP1x33XXux3Nycur8Hxdw/z0nJ6dtTqYZGnP+u3btAuCJJ57g1ltvZd68eYwcOZLzzjvPvWafk5NDYmJindcOCgoiPj7ep88fGv8dONrrr7/OgAEDGDdunPu+QP4OAHzwwQfYbDY6duxIaGgov/nNb/j444/p3bs3EPjfgVNPPZXIyEgefPBBKioqKC8v5/e//z12u919jL98Bg6Hg7vvvpvTTjuNwYMHA86xh4SEEBsbW+fYpKQk99gb8x1v6JiSkpI6NWze1Nzzbwx/+HfAU+e/dOlS3n///TrLmZ76+Suk1AoODuajjz5i27ZtxMfHExERwffff8+FF15Yp97itttuY+LEiQwZMoSpU6fy1ltv8fHHH7Nz504vjr7lGnP+rvX23/zmN9x0002MGDGC559/nn79+vHGG294c/ge0djvgEtlZSXvvvtunVkUf9bY83/00UcpKiriu+++Y9WqVdx7771cddVVbNiwwYuj94zGfAYJCQnMnTuXzz//nA4dOhATE0NRUREjR45s1KXnfcm0adPYuHEj7733nreH4hU6/5af/8aNG7nssst4/PHHmTBhggdH5xTk8Vf0Y6NGjWLdunUUFxdjtVpJSEhg7NixdaZsjzV27FgAduzYQa9evUhOTmbFihV1jnFVRScnJ7fe4D3gZOffuXNnAAYOHFjneQMGDCArKwtwnmNeXl6dx2tqaigoKPD584emfQf+97//UVFRwQ033FDn/kD+DuzcuZOXX36ZjRs3MmjQIACGDRvGkiVLmDVrFq+++mq7+A5MmDCBnTt3kp+fT1BQELGxsSQnJ9OzZ0/AP/5/MH36dHdBb2pqqvv+5ORkrFYrRUVFdf5rOjc31z32xnzHk5OTj9sRk5ubS3R0NOHh4a1xSk3SkvNvjIbO3/WYt3ni/DMzMznvvPO47bbbeOSRR+o85qmfv3/F/jYSExNDQkIC27dvZ9WqVVx22WUNHrtu3TrgyC/w9PR0NmzYUOcfqPnz5xMdHX3cL3df1dD5d+/enZSUlOO2q23bto1u3boBzvMvKipi9erV7scXLlyIw+FwBzp/0JjvwOuvv86ll15KQkJCnfsD+Tvgqr86dsbAYrG4Z9ra03egU6dOxMbGsnDhQvLy8rj00ksB3/4MDMNg+vTpfPzxxyxcuJAePXrUeXzUqFEEBwezYMEC931bt24lKyuL9PR0oHHf8fT09Dqv4TrG9Rre4onzb4z09HQWL16MzWZz3zd//nz69etHXFxcy0+kmTx1/ps2beKcc87hxhtv5Omnnz7ufTz2829Sma2fKy0tNdauXWusXbvWAIznnnvOWLt2rbF3717DMAzjgw8+ML7//ntj586dxieffGJ069bNmDx5svv5O3bsMJ588klj1apVxu7du41PP/3U6Nmzp3HmmWe6j6mpqTEGDx5sTJgwwVi3bp0xb948IyEhwZgxY0abn++xWnr+hmEYzz//vBEdHW3MnTvX2L59u/HII48YYWFhxo4dO9zHXHDBBcaIESOM5cuXGz/++KPRp08f49prr23Tc22IJz4DwzCM7du3GyaTyfj666+PeyyQvwNWq9Xo3bu3ccYZZxjLly83duzYYfz1r381TCaT8eWXX7qPC/TvwBtvvGFkZGQYO3bsMP7zn/8Y8fHxxr333lvnGF/9DH77298aMTExxg8//GAcPHjQfauoqHAfc/vttxtdu3Y1Fi5caKxatcpIT0830tPT3Y835ju+a9cuIyIiwrj//vuNzZs3G7NmzTIsFosxb968Nj3fY3ni/A3D+W/A2rVrjd/85jdG37593d8p126eoqIiIykpybj++uuNjRs3Gu+9954RERFh/POf/2zT8z2WJ85/w4YNRkJCgnHdddfVeY28vDz3MZ76+berkPL9998bwHG3G2+80TAMw/j73/9upKamGsHBwUbXrl2NRx55pM72saysLOPMM8804uPjjdDQUKN3797G/fffX2cromEYxp49e4wLL7zQCA8PNzp16mTcd999hs1ma8tTrVdLz99l5syZRmpqqhEREWGkp6cbS5YsqfP44cOHjWuvvdbo0KGDER0dbdx0001GaWlpW5ziSXnqM5gxY4aRlpZm2O32et8nkL8D27ZtMyZPnmwkJiYaERERxtChQ4/bkhzo34EHH3zQSEpKMoKDg40+ffoYf/vb3wyHw1HnGF/9DOo7d8B488033cdUVlYad9xxhxEXF2dEREQYV1xxhXHw4ME6r9OY7/j3339vDB8+3AgJCTF69uxZ5z28xVPnf9ZZZ9X7Ort373Yfs379euP00083QkNDjS5duhh/+tOf2ugsG+aJ83/88cfrfY1u3brVeS9P/PxNtYMWERER8SmqSRERERGfpJAiIiIiPkkhRURERHySQoqIiIj4JIUUERER8UkKKSIiIuKTFFJERETEJymkiIiIiE9SSBERERGfpJAiIiIiPkkhRURERHySQoqIiIj4pP8Hf0q7o9GHR6EAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrDeNYYXIqu1"
      },
      "source": [
        "### Aggregate Data with the Python .agg() Function\n",
        "\n",
        "Let's work out the number of different themes shipped by year. This means we have to count the number of unique theme_ids per calendar year."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "266a3f390c"
        },
        "tags": [
          "sample_code"
        ],
        "id": "qx8pTau4Iqu2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "71a562ec-d697-4560-b2d7-316635f4a868"
      },
      "source": [
        "themes_by_year = sets.groupby('year').agg({'theme_id': pd.Series.nunique})\n",
        "themes_by_year.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      theme_id\n",
              "year          \n",
              "1949         2\n",
              "1950         1\n",
              "1953         2\n",
              "1954         2\n",
              "1955         4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ac714d1-d3d9-4903-a00d-4a6a9b4d5106\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>theme_id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1949</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1953</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1954</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1955</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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        "themes_by_year.rename(columns = {'theme_id': 'nr_themes'}, inplace=True)\n",
        "themes_by_year.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      nr_themes\n",
              "year           \n",
              "1949          2\n",
              "1950          1\n",
              "1953          2\n",
              "1954          2\n",
              "1955          4"
            ],
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    {
      "cell_type": "code",
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        "themes_by_year.tail()"
      ],
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      nr_themes\n",
              "year           \n",
              "2017         89\n",
              "2018         93\n",
              "2019         78\n",
              "2020         82\n",
              "2021          1"
            ],
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              "      <th>year</th>\n",
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              "      <td>78</td>\n",
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              "      <td>82</td>\n",
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              "      <td>1</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "immCqqw1Iqu5"
      },
      "source": [
        "**Challenge**: Plot the number of themes released by year on a line chart. Only include the full calendar years (i.e., exclude 2020 and 2021). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2pamQEkIqu5",
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          "height": 471
        },
        "outputId": "a8495d2a-2a7d-4e78-ac0e-71dbb145330a"
      },
      "source": [
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('No. of Themes', fontsize=14)\n",
        "plt.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb97e54e7f0>]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBbt9-lJIqu7"
      },
      "source": [
        "### Line Charts with Two Seperate Axes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7lQ_amFIqu7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "bc8e0d3f-5930-466c-9af1-7a4a8b0973b2"
      },
      "source": [
        "ax1 = plt.gca() #get the axis\n",
        "ax2 = ax1.twinx() #create another axis that shares the same x-axis\n",
        "\n",
        "#Add styling\n",
        "l1 = ax1.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2], color='g', label='No. of Sets')\n",
        "l2 = ax2.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2], 'b', label='No. of Themes')\n",
        "\n",
        "ax1.set_xlabel('Year')\n",
        "ax1.set_ylabel('No. of Sets', color='green')\n",
        "ax2.set_ylabel('No. of Themes', color='blue')\n",
        "ax1.legend(handles=l1+l2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb97add1ac0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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t25flfRMSEoiKijI8ovM6QUAIIYQoAjZtUs+PGdFp1rIllCkDERGwY0fu13+45UM0NJ4Keopm5ZrlK06RP1ZN5t5//30GDBhArVq1sLe3p1GjRowcOZKBAwcCEBYWBoCvr2+G9/n6+hpeCwsLo2zZshlet7Ozw9vb23DNwyZMmICnp6fhod8ySgghhCguNC0tmUvXJ5ItW1t44sHuW7kNte68spPVZ1Zjq7Plyw5fEhoKTz2Vt/l2wnysmswtWbKERYsWsXjxYg4dOsS8efOYMmUK8+bNs+h9P/jgAyIjIw2PkydPWvR+AmJjY+nbty8eHh7odDoiIiJMaken0xmG2IUQQmTv5EkIC1O15Vq2NO49+qHWP/9UyWBWNE3jvU3vATC00VBq+NRg3DhYtgwmTjRD4CLPrJrMvfvuu4beuXr16jFo0CBGjRrFhAkTAPDz8wMgPDw8w/vCw8MNr/n5+XHz5s0MrycnJ3P37l3DNQ9zdHTEw8PD8HB3dzf3R7O6559/Hp1Ox8SH/mWtXLnSKvMa5s2bx44dO9i9ezehoaF4enpmeL1du3bodLpsH+3atSvwmIUQoijT98q1aQPGbjTTqZNK/q5cgaNHs77mrzN/sfvqbpztnPmk3SeEhsL8+eq1S5fyHbYwgVWTudjYWGxsMoZga2tLamoqAIGBgfj5+bF582bD61FRUezbt4+WD37NaNmyJREREYSEhBiu2bJlC6mpqTRv3rwAPkXh5eTkxKRJk7h37561Q+H8+fPUrl2bunXr4ufnlymhXL58OaGhoYSGhhoWwGzatMlwbvny5dYIWwghiqy8DLHqubhAly7qOKtBkJTUFD7c/CEAbzV/S+34MFWVNAG4dg2Sk00OWZjIqsncE088wZdffsmaNWu4dOkSK1as4Ntvv6V3796AGlIbOXIkX3zxBatWreLYsWMMHjyYgIAAej1YQ127dm26du3Kyy+/zP79+9m1axdvvPEGAwYMICAgwIqfzvo6deqEn5+foaczO8uWLaNOnTo4OjpSuXJlvvnmmzzfK6c22rVrxzfffMP27duz7WXz9vbGz88PPz8/ypQpA4CPj4/hnLe3t+Ha27dv07t3b1xcXKhevTqrVq3K0Nbx48fp1q0bbm5u+Pr6MmjQIG7fvp0hnhEjRjBy5EhKlSqFr68vP/30E/fv3+eFF17A3d2datWqsXbt2jy1u3TpUurVq4ezszM+Pj506tSJ+/fv5/nPUggh8ispCf75Rx3nJZmDtKHWBQsgXRUwde7fBZy4dQIvJy/GBI8hMhJmz057PTlZlUMRBUyzoqioKO2tt97SKlasqDk5OWlVqlTRPvroIy0hIcFwTWpqqvbxxx9rvr6+mqOjo9axY0ft9OnTGdq5c+eO9swzz2hubm6ah4eH9sILL2jR0dFGx3H16lUN0K5evZrptbi4OO3kyZNaXFzcg3g0LSbGOo/UVOP/bIcMGaL17NlTW758uebk5GT4bCtWrNDS/7UfPHhQs7Gx0caPH6+dPn1amzNnjubs7KzNmTPH6Hvl1sadO3e0l19+WWvZsqUWGhqq3blzJ8f2Ll68qAHa4cOHM70GaOXLl9cWL16snT17VnvzzTc1Nzc3Q5v37t3TypQpo33wwQfaqVOntEOHDmmPPfaY1r59e0Mbbdu21dzd3bXPP/9cO3PmjPb5559rtra2Wrdu3bQff/xRO3PmjPbaa69pPj4+2v37941q98aNG5qdnZ327bffahcvXtT+/fdf7fvvv8/2+/Dh7yshhDCnnTs1DTStdGlNS0nJ23ujojTN31+9/5NP0s7HJsZqFb6toPEp2qSdkzRN07SJE9V1QUGaFhiojrdtM9/nyE1OP79LEqsmc4VFXpK5mBj1zWqNR0yM8Z9Jn8xpmqa1aNFCe/HFFzVNy5zMPfvss9pjjz2W4b3vvvuuFhQUZPS9jGnjrbfe0tq2bWtUe7klc2PHjjV8HRMTowHa2rVrNU3TtM8//1zr3Llzhvfo/371vwS0bdtWa926teH15ORkzdXVVRs0aJDhXGhoqAZoe/bsMardkJAQDdAuXbpk1GeUZE4IYUmffqp+bvTvb9r7lyxR73dw0LT//lPnJuyYoPEpWoVvK2ixibFaXJym+fmp6+bO1bSOHdXxvHnm+xy5kWROkX03SoBJkyYxb948Tp06lem1U6dOERwcnOFccHAwZ8+eJSUlxaj2zdFGXtRPV/nS1dUVDw8PwyKYo0ePsnXrVtzc3AyPWrVqAWreXlZt2Nra4uPjQ7169Qzn9OVwjG23QYMGdOzYkXr16tGvXz9++umnQjFXUQhRMpkyXy69p56Cbt3UXLjXX4ebMbeYsFNN2fmyw5c42zuzYIFaLVu+PDzzDFSqpN57+bIZPoDIEztrB1DUuLiAtbZ9dXEx7X2PPvooXbp04YMPPuD55583a0zWYG9vn+FrnU5nWDQTExPDE088waRJkzK9z9/fP8c20p/TL9Awtl1bW1s2btzI7t272bBhA9OnT+ejjz5i3759BAYGmvhJhRAi76KjYe9edWxqMqfTwYwZUKcObNkCz336N1HuUTTya8TA+gNJSYGvv1bXjh4NDg5p+77KitaCJ8lcHul04Opq7SjybuLEiTRs2JCaNWtmOF+7dm127dqV4dyuXbuoUaMGtra2RrVtjjbMpXHjxixbtozKlStjZ2e+b29j2tXpdAQHBxMcHMy4ceOoVKkSK1asYPTo0WaLQwghcrN9u1qIULVqWoKVlSNhR/jjxB+MbjkaHxefTK9XqQLjxsGHH8LG2d1geCmmdJ6Cjc6GZSvh7FkoVQpeflldL8mc9cgwawlRr149Bg4cyLRp0zKcf/vtt9m8eTOff/45Z86cYd68ecyYMYN33nnHcE3Hjh2ZMWNGtm0b00ZBGT58OHfv3uWZZ57hwIEDnD9/nvXr1/PCCy/ka8g3t3b37dvHV199xcGDB7ly5QrLly/n1q1b1K5d24yfTgghcmfsEOu7G9/lq51f0WVhF6ISorK85u23wb38FbhflooHF9EhsAOaBvpBijfeADc3dSzJnPVIMleCjB8/3jBsqNe4cWOWLFnCb7/9Rt26dRk3bhzjx4/PMBx7/vz5DCU4HmZMGwUlICCAXbt2kZKSQufOnalXrx4jR47Ey8srU01Dc7br4eHB9u3b6d69OzVq1GDs2LF88803hn2GhRCioBiTzGmaRsgNVZ81JDSExxc/TmxSbKbr9oXuIPoxtcXmlS3d2L1blTw5cEAVFx4xIu1a/Zy5q1fBAtOlRQ50mpbdhh0lx7Vr16hQoQJXr16lfPnyGV6Lj4/n4sWLBAYG4mRsCW0hciHfV0IISwgLA39/NSXo1i3wyTx6CsDliMtU/l9l7GzscLV3JTIhki5Vu/DngD9xtHMEVMLX4pcW7L++n5o7d3B6U2vq1gU/P5UwDh+u5tXpJSerBC85WSV0D/04tYicfn6XJNIzJ4QQQhQT+g2TGjfOPpEDOBx2GIA6Zeqw5tk1uNi7sP78egYuH0hyqtrCYcmJJey/vh9Xe1eW/1yd0qXh+HGVyNnaqiHY9OzsoEIFdSxDrQVLkjkhhBCimDB2vtzhUJXMNfJvRHDFYFb2X4mDrQPLTi3jpVUvEZ8cz/ub3wfgveD3CKrky5Qpae9/+mnIaqG+zJuzDknmhBBCiGJA0/KQzD3omWvk1wiAx6o+xm99f8NWZ8u8o/No9lMzLkVcwt/Nn9Et1Yr8wYOhe3dVJuvDD7NuV2rNWYckc0IIIUQxcOaM2uje0REequOeycPJHEDv2r2Z03MOAMdvHgfgiw5f4Oqg6nHpdPDnnxAeDnXrZt2u9MxZhyRzRpJ1IsKc5PtJCGFu+l651q3VQoTs3I69zbWoawA08GuQ4bVBDQbxfffvAZXoDWkwJMPrdnZppUiyIsmcdUjR4Fzoi94mJibinNO/DiHyIDZWlQB4eCcKIYQwVV7ny1XzroaHo0em119v9jrtKrfD380fW5u8FX7XJ3MyzFqwJJnLhZ2dHS4uLty6dQt7e/t81SoTQtM0YmNjuXnzJl5eXgW+Q4YQonhKToatW9VxXufLZSWoTJBJcaSfM5eaCvIjs2BIMpcLnU6Hv78/Fy9e5LL8qiHMxMvLCz8/P2uHIYQoJkJCIDJSba/VKPscDTAumTNV+fKqbElioqp5FxBg9luILEgyZwQHBweqV69OYmKitUMRxYC9vb30yAkhzGrNGvXcoYNKpnJyJOwIoMqSmJudnUroLl9W8+YkmSsYkswZycbGRir1CyGEKHSuXoVvv1XHffrkfO39xPucvn0agIZ+DS0ST+XKKpm7fBlatbLILcRDZDRbCCGEKMLefBPu31flSAYMyPnaf8P/RUPDz80PPzfLTPXQz5uTFa0FR5I5IYQQoohatQpWrlTDm7Nn577gwJLz5fSkPEnBk2ROCCGEKIJiYuCNN9Tx229nX8g3PcM2XpLMFSuSzAkhhBBF0GefqflylSvDuHHGvcfQM2eBxQ96Umuu4EkyJ4QQIt80TdUVEwXj6FH47jt1/P33ar/U3CSlJHHs5jHAsj1z6WvNyWY3BUOSOSGEEPkSEQHlyuU++V6YR2oqvPoqpKRA377Qvbtx7zt1+xSJKYl4OHoQWCrQYvGVL6/m7sXHq31cheVJMieEECJfDh2C0FD44w84fdra0RR/P/0Ee/eCuzv873/Gv08/X66hX0NsdJb78e/goJJ7kHlzBUWSOSGEEPly61ba8bx51oujJAgPh/ffV8dffJGWNBmjIFay6sm8uYIlyZwQQoh8uX077XjBAjX8Jyzj7bfVsHbjxjB8eN7eW5DJnNSaK1iSzAkhhMiX9D1z166lbfguzOv+fVi8WB3Pnp37tl3paZpm2MbLUjs/pCflSQqWJHNCCCHyRZ/M6ZMLGWq1jGvX1OpQd3do1ixv770YcZGohCgcbB0IKhNkmQDTkWSuYEkyJ4QQIl/0w6z9+6vnZcsgKsp68RRX166p5/Ll8/5e/eKHumXrYm9rb8aosiZz5gqWJHNCCCHyRd8z16MH1KwJcXGwdKl1YyqO8pXMFeB8Ocg4Z05qzVmeJHNCCCHyRZ/MlSkDQ4aoYxlqNb+ilMxVqAA6nUrs08+pFJYhyZwQQoh8SZ/MDRqkfohv3w4XL1o3ruLGHMOsltzGKz1HRwgIUMcyb87yJJkTQghhMk1LmzNXurRKNDp2VF/Pn2+9uIojU5O58JhwQmNC0aGjvm998weWDZk3V3AkmRNCCGGyiIi0unJlyqjn559Xz/PmyX6t5mRqMqcfYq3hUwM3BzczR5U9qTVXcCSZE0IIYTL9EKu7uxpaA+jdW3198SLs3Gm92Iobk5O5Ah5i1ZPyJAVHkjkhhBAmSz/EqufiAv36qWNZCGEe8fFpf9am9swV1OIHPUnmCo4kc0IIIUyWfvFDevpVrX/8AbGxBRtTcXT9unp2doZSpfL23oLc+SE9mTNXcCSZE0IIYbLskrnWrSEwEKKjYcWKgo+ruEk/xKrTGf++6IRozt49CxR8z1xhqzWXkpLCxx9/TGBgIM7OzlStWpXPP/8cLV1wmqYxbtw4/P39cXZ2plOnTpw9e9aKURtHkjkhhBAmy2qYFcDGJq13bu7cAg2pWDJ1vtzR8KMAlHMvRxnXMrlcbV4VK6rn+/fhzp0CvXWWJk2axKxZs5gxYwanTp1i0qRJTJ48menTpxuumTx5MtOmTWP27Nns27cPV1dXunTpQnx8vBUjz50kc0IIIUyWXc8cwODB6nnzZrh61bJxTJoE771XfFfPmprM6YdYC3rxA4CTE/j7q2NLD7VGR0cTFRVleCQkJGS6Zvfu3fTs2ZMePXpQuXJlnnrqKTp37sz+/fsB1Ss3depUxo4dS8+ePalfvz7z58/nxo0brFy50rIfIJ8kmRNCCGGynJK5wEBo3lwNse3YYbkYbt6E99+HyZPh778tdx9rym8y19C3oVnjMVZBLYIICgrC09PT8JgwYUKma1q1asXmzZs5c+YMAEePHmXnzp1069YNgIsXLxIWFkanTp0M7/H09KR58+bs2bPHsh8gn+ysHYAQQoiiK6dkDqBWLdi3z7I/zHftSjueNAkef9xy97KW/NaYK+jFD3qVKsGePZZP5k6ePEm5cuUMXzvq6+Sk8/777xMVFUWtWrWwtbUlJSWFL7/8koEDBwIQFhYGgK+vb4b3+fr6Gl4rrCSZE0IIYbLs5szpFcSKxvTJ3M6dsHs3tGplufvlVUiISmpdXU1vw5RkLiklieM3jwPWGWaFguuZc3d3x8PDI8drlixZwqJFi1i8eDF16tThyJEjjBw5koCAAIboJ3gWUTLMKoQQwmS59cwVxA9zfWFi/fysSZMsd6+8WroUmjaF7t3zN5/PlGTuv9v/kZiSiIejB5W9Kpt+83woTOVJ3n33Xd5//30GDBhAvXr1GDRoEKNGjTIMyfr5+QEQHh6e4X3h4eGG1worSeaEEEKYzNrJXGwsHDqkjufMUWU7Vq2Ckyctc7+80DT44gt1vH07/N//mdZOYiLo84u8JHP6+XINfBtgo7POj/vCVDg4NjYWG5uMfw62trakPsiyAwMD8fPzY/PmzYbXo6Ki2LdvHy1btizQWPNKkjkhhBAmiY1NKwic3TCrvtbY5cuWWWl64AAkJUFAAHTuDL16qfNff23+e+XV+vVw9Gja12PGqMUaeRUaqhJDB4fs/5yzYq2dH9IrTLXmnnjiCb788kvWrFnDpUuXWLFiBd9++y29e/cGQKfTMXLkSL744gtWrVrFsWPHGDx4MAEBAfTSf2MVUpLMCSGEMIl+vpy9PWQ3Xal8eVVzLiHBtEQmN/r5csHBqlfuvffU14sWpQ1NWot+uPeNN6BhQ7h3D955J+/t6D9HuXLqz9JY1tr5IT19MhcdrT6/NU2fPp2nnnqK119/ndq1a/POO+/wyiuv8PnnnxuuGTNmDCNGjGDYsGE0a9aMmJgY1q1bh5OTkxUjz50kc0IIIUySfog1u10J7O3ThgYtMdSmny/XurV6bt4c2rZVvXXffWf++xlr/3745x+ws1M9cj/8oP6MFiyArVvz1pYp8+U0TSsUyZyzM+gXh1p73py7uztTp07l8uXLxMXFcf78eb744gscHBwM1+h0OsaPH09YWBjx8fFs2rSJGjVqWDFq40gyJ4QQwiT6nrns5svpWWreVGqqWrkKqmdOT9879+OP1usN0vfKPfssVKgAjzwCr72mzr36quqpNJYpydyVyCvci7+HvY09dcrWMf6NFrB4MRw8qFb0CsuQZE4IIYRJ9D1zuc3jSj9vypxOnIDISFXyo0GDtPNdu0L9+hATAzNnmveexjh9Om0/2jFj0s5/+SX4+cGZM3lbcWtKMqfvlQsqE4SDrUPOF1tYhw7QpInqpROWIcmcEEIIk+S2klXPUuUp9EOsLVqo4Uw9nS4tifrf/yAuzrz3zc2UKWqy/+OPQ510nWJeXmlDv199Bcbu356fZM6aQ6yi4EgyJ4QQwiR5TebM3TOnX/ygny+XXv/+qkfw1i2YO9e8981JaCjMn6+O338/67g6d1bDrK+/btwKT1OSucKwklUUHEnmhBBCmCS33R/0LDXMqu+ZSz9fTs/ODt5+Wx1PmQLJyea9d3amTlV14YKDs45Lp1NDv46OsGkT/Ppr7m1Kz5zIjSRzQgghTGLKMKu5ao1du6bas7FRw6xZefFF8PGBCxdg2TLz3DcnkZEwe7Y61i/CyErVqjB2rDoeNQru38/+2uRk1dsHxidz9+LucTlSjWk38GuQy9WiOJBkTgghhEmMTeYqVFA9UnFxae/JL/0Qa4MG4O6e9TWurjBihDqeNMnyRWtnz4aoKAgKgh49cr723XdVcnbzZsa9ZR8WHg4pKWBrm1biIzf6XrlAr0C8nLyMe5Mo0iSZE0IIYRJjS5M4OKiCt2C+odac5sul98Yb4OIChw+rYU1LiY9XQ6ygFl/kVtzX0RHatFHHBw5kf51+iDUgQCV0xpAh1pJHkjkhhBAmMbY0CZh/3lxO8+XS8/GBl15Sx8aWA7l5U+0gkZd5dgsWQFiY6m175hnj3tOsmXo2JpnL03y58COAJHMliSRzQggh8iw5Ge7eVce59cyBeVe0Rken7XmaWzIHMHq06tXavBlCQnK+NiVFDZE+95xaOGGMlJS0vWBHj1Y9kcbQJ3MHD2Z/TX4WP8hK1pJDkjkhhBB5dueOetbpwNs79+vNWWtu7161+0OlSsYlOZUqpfWW5dY7N3NmWnI1daoaPs3NypWqZlypUvDyy7lfr9eokRqOvX49bZHDw/KazCUkJ3Dy1klAeuZKEknmhBBC5Jl+vlypUhkL9mbHnD1zxs6XS09fRHjZMjh3LutrbtyAjz5Sx/b2avHBvHk5t6tpaQni8OHg5mZ8TK6uarEEZD/Umtdk7sStEySnJuPt7E15jzx054kiTZI5IYQQeWbsSlY9c86ZM3a+XHr16kH37qpHL7vh05Ej1RBu8+ZpCdqUKWoYNTv//KMSMSentJWzeZHbvLm8JnPph1h1Ol3eAxJFkiRzQggh8iyvyVz6nrn8lAhJTlbDrJC3njlIq/02d65arJDe2rXwxx9qbt0PP8CwYWr4+Nw5WL48+zb1Sd+LL0LZsnmLB8yfzB0OVTs/yBBrySLJnBBCiDwztiyJXsWK6jk2Nm2+nSmOHlVFdj09M+57aow2bVSB4YQEmDYt7XxsrBoiBXjrLVW7ztVVlTWB7GvUHTkC69ereW/63SbyKn0y9/A9UlPVfDpQtfqMIStZSyZJ5oQQQuRZXsqSgKqrFhCgjvMz1KqfL9eqVe613B6m06X1zs2cqQr8AnzxBVy8qBKmzz5Lu37ECHB2Vitgt2zJ3N7kyer56aehSpW8xaJXv75a/Xr3roohvVu3IClJfU4/v9zbStVSORqmlvnKStaSRZI5IYQQeZbXYVYwz7w5U+bLpffkk1Crltp664cf4MSJtLIi06dnXMBQujQMHaqOH14Fe/Ei/P67OtYvrjCFg4PqCYTMQ636IVY/P7UgIzcX7l0gOjEaR1tHapauaXpQosiRZE4IIUSe5XWYFfK/olXTTFvJmp6NjdpKC+C77+DVV9U8vCefhJ49M1//9ttqHt3GjXDoUNr5b75Rw6CdO6sSI/mR3bw5Uxc/1POth52NEUuMRbEhyZwQQog8y+swK+S/1tylS6p8iJ1dWgJkioED1ZBvaKjq6XN1Vb1yWalcGfr3V8f6YdVbt+D//k8d64dt88PcyZwMsZY8kswJIYTIs4IeZr12LS1xatxY7bdqKkdHGDUq7evPPktboJEV/TDqH3/A+fMq8YuLg6ZNoX170+PQa9pUPYeEZCyDYmoyJ4sfSh5J5oQQQuSZKcmcKcOs9+6pJK56dZVMgSobkl+vvKJqz3XoAG++mfO1DRpA165qWPWzz2DGDHX+vffUoor8ql1b9Q7evw+nT6edz3NZkjApS1JSSTInhBAiTzQtbc6cKcOsxtSai4tTw5pVqqjn+HhVWmT37rRFCfnh7g7//qv2azVmcYG+V3DBApVgVq8OvXvnPw5Qc/IaN1bH6Yda85LM3bx/kxvRN9Cho75vffMEJooMmSEphBAiT6KiVMkMyFvPnH4oMyZGJUTZ7em6bh289FJajbW6dWHiRLWDg7U2NWjbFh55BPbvV1+/845KwsylWTPYsUMlc0OGqHNZJXPLTi7jwI0DVPKsRGWvylT2qkwlr0qGkiTVfarj5pCHPcVEsWD1nrnr16/z3HPP4ePjg7OzM/Xq1eOgfpdjQNM0xo0bh7+/P87OznTq1ImzZ89maOPu3bsMHDgQDw8PvLy8GDp0KDExMQX9UYQQokTQD7G6uqo6bMZydgZfX3Wc3VBrUhI8+6xK5CpUULs1HDkCPXpYL5EDde/331fHfn4weLB52394EYSmZU7mYhJjGLBsAJN2TeL1v1+n++LuBM0MwvUrV3r+ppbiyhBryWTVZO7evXsEBwdjb2/P2rVrOXnyJN988w2lSpUyXDN58mSmTZvG7Nmz2bdvH66urnTp0oX4+HjDNQMHDuTEiRNs3LiR1atXs337doaZY1KFEEKITEwpS6KX27y57dtVr12ZMnDmjOqlMmcPWH706gVLlqgyJU5O5m1bn8wdOQKJiaqIsP7HnL7Y8unbp0lOTcbNwY2eNXvSwLcBHo4eAMQlxwHQvrIZVmSIIseqw6yTJk2iQoUKzJkzx3AuMDDQcKxpGlOnTmXs2LH0fFAAaP78+fj6+rJy5UoGDBjAqVOnWLduHQcOHKDpgyVB06dPp3v37kyZMoUA/b+CdBISEkhISDB8HR0dbamPKIQQxY4pZUn0KleGffuyT+b+/FM9P/GE+ROm/NLpoF8/y7RdpYoadr57F44dU+VXQO336uiojv+7/R8Ajf0bs3LASsN7I+IjuBRxifuJ92lRvoVlAhSFmlV75latWkXTpk3p168fZcuWpVGjRvz000+G1y9evEhYWBidOnUynPP09KR58+bs2bMHgD179uDl5WVI5AA6deqEjY0N+/bty/K+EyZMwNPT0/AICgqy0CcUQojix5SVrHo51ZrTtLRkrlcvUyIrunS6tBIlBw5kPV/u9B211LWWT60M7/Vy8qKhX0OCKwZja1NIujFFgbJqMnfhwgVmzZpF9erVWb9+Pa+99hpvvvkm8+bNAyAsLAwAX/0kiwd8fX0Nr4WFhVG2bNkMr9vZ2eHt7W245mEffPABkZGRhsfJkyfN/dGEEKLYys8wa0615o4cgStXVA25dL/Dlxi5JXP6njnZqks8zKrDrKmpqTRt2pSvvvoKgEaNGnH8+HFmz57NEP1yHgtwdHTEUd9vDUTpd1sWQgiRq/wOs0LWyZy+V65z57wtrCgu0i+C0M8QyrJnrnQthEjPqj1z/v7+mYY4a9euzZUrVwDw8/MDIDw8PMM14eHhhtf8/Py4efNmhteTk5O5e/eu4RohhBDmY6lh1pI6xKqnT+ZOnEgrHqxP5lK1VM7cOQNATR/pmRMZWTWZCw4O5nT6ctfAmTNnqPSgHz4wMBA/Pz82b95seD0qKop9+/bRsmVLAFq2bElERAQhISGGa7Zs2UJqairNmzcvgE8hhBAlS36SOf0wa2QkRESknb90SQ2z2tioMiQlUbly4O+vdppYv16d0ydzVyKvEJ8cj4OtA5W9KlstRlE4WTWZGzVqFHv37uWrr77i3LlzLF68mB9//JHhw4cDoNPpGDlyJF988QWrVq3i2LFjDB48mICAAHo9+NWtdu3adO3alZdffpn9+/eza9cu3njjDQYMGJDlSlYhhBD5k585cy4uae9LP9S6apV6bt3atOHb4kLfO6ef/aNP5vTz5ap7V5dFDiITqyZzzZo1Y8WKFfz666/UrVuXzz//nKlTpzJw4EDDNWPGjGHEiBEMGzaMZs2aERMTw7p163BKt2Z90aJF1KpVi44dO9K9e3dat27Njz/+aI2PJIQQxV5+5sxB1vPmSvoQq54+mdPTJ3Onb8t8OZE9q2/n9fjjj/P4449n+7pOp2P8+PGMHz8+22u8vb1ZvHixJcITQgjxkPwMs4JK5g4cSJs3d/cubNumjh+UFC2xHk7mypVTz4aVrDJfTmTB6tt5CSGEKDri49XeqmB6MvdweZK//4aUFLUHa5Uq+Q6xSEtXMhVvbzUsDbKSVeRMkjkhhBBG08+Xs7MDT0/T2nh4mFWGWNP4+IB+IySpMSeMJcmcEEIIo6WfL2fqxvfpk7n4eFi7Vn1d0odY9fRDrfpkLiohitCYUECGWUXWJJkTQghhtPzOl4OMtea2bIH799XcsCZN8h1esdChg3quW1c96xc/+Ln54elkYneoKNasvgBCCCFE0ZGfsiR6+jlz9+7B/PnquGdP03v6ipuhQ6FGDdCXSpX5ciI30jMnhBDCaPktSwLg5qbmhgEsXaqeZYg1jZ0dtG+ftvhBVrKK3EgyJ4QQwmjmGGaFtKHWlBTw8IB27fLXXnEmPXMiN5LMCSGEMJo5hlkhLZkD6N4dHBzy115xJj1zIjeSzAkhhDCaOYZZIW3eHMgQa05SUlM4e+csID1zInuSzAkhhDCauYdZ7e2hW7f8tVWcXY68TEJKAo62jlT0rGjtcEQhJcmcEEIIo5lrmLVlS/Xcp4/pxYdLAn1Zkho+NbC1sbVyNKKwktIkQgghjGauYdamTeHcOfD3z39MxZns/CCMIcmcEEIIo6SkwJ076ji/PXMAVavmv43iTp/M1fKR+XIiezLMKoQQwih374KmqWN9nThhWfqyJNIzJ3IiyZwQQgij6OfLlSqlFi4IyzP0zMlKVpEDSeaEEEIYxVzz5YRxIuIjCL8fDqgFEEJkR5I5IYQQRjFXWRJhHP1K1gD3ADwcPawcjTCHuDiIjU37+vJlmDoVNmzIX7uSzAkhhDCKucqSCOMY5svJzg/FRs+eMH++Oo6IgObN4Ztv1PlZs0xvV5I5IYQQRpFh1oIl8+WKn0OHoE0bdbx0Kfj6qt65+fNh2jTT25VkTgghhFFkmLVgSc9c8RMbC+7u6njDBlU028YGWrRQSZ2pJJkTQgiRq1u34O+/1bGfn3VjKSmkZ674qVYNVq6Eq1dh/Xro3Fmdv3kTPPIxLVKSOSGEEDmKilL7p547B+XLw4AB1o6o+EtOTebc3XOA1JgrTsaNg3feUXsTP/JI2rZ2GzZAo0amtys7QAghhMhWXBw8+SSEhKi5chs3qnk+wrIuRVwiMSURJzsnKnpWtHY4wkyeegpat4bQUGjQIO18x47Qu7fp7UrPnBBCiCwlJUH//rBtm5rns24d1JIRvwKhL0tSw6cGNjr5UV2c+Pmpf08bN6pflgCaNcvfvy35DhFCCJFJaioMHQp//QWOjuq5SRNrR1VyyHw5y7h+/TrPPfccPj4+ODs7U69ePQ4ePGh4XdM0xo0bh7+/P87OznTq1ImzZ8+a7f537qheuBo1oHt31UMH6t/a22+b3q4kc0IIITLQNBg5EhYsAFtb+OMPaNvW2lGVLLKS1fzu3btHcHAw9vb2rF27lpMnT/LNN99QqlQpwzWTJ09m2rRpzJ49m3379uHq6kqXLl2Ij483SwyjRqmt8K5cAReXtPP9+6ueb1PJnDkhhBAZjB8P06er47lz4YknrBpOiaTvmZNkznwmTZpEhQoVmDNnjuFcYGCg4VjTNKZOncrYsWPp2bMnAPPnz8fX15eVK1cywAwrfzZsUKtYy5fPeL56dSlNIoQQwkzOnoVPP1XH06bBc89ZNZwSS98zJ8OsxomOjiYqKsrwSEhIyHTNqlWraNq0Kf369aNs2bI0atSIn376yfD6xYsXCQsLo1OnToZznp6eNG/enD179pglzvv3M/bI6d29q6YzmCrPyVxcUhyxSWkbi12OuMzUvVPZcD6fG4sJIYSwunnz1HPXrjBihHVjKanuxd3j5v2bgFoAIXIXFBSEp6en4TFhwoRM11y4cIFZs2ZRvXp11q9fz2uvvcabb77JvAff9GFhYQD4PrRc29fX1/BafrVpk7adF4BOp+anTp4M7dub3m6eh1l7/taTPrX78GrTV4mIj6D5z82xt7Xnduxtvu38La81e830aIQQQlhNaqqaJwfwwgvWjSU7lyIuUcGjArY2ttYOxWL0vXLl3Mvh7uhu5WiKhpMnT1KuXDnD145ZdHOlpqbStGlTvvrqKwAaNWrE8ePHmT17NkOGDCmQOCdPVgsgDh6ExEQYMwZOnFA9c7t2md5unnvmDoUeok1FtbHY0pNL8XXz5fLIy8zvNZ9p+/OxsZgQQgir+ucfNTHby0vVlits1pxZQ+D/Avls22fWDsWiDPPlpFiw0dzd3fHw8DA8skrm/P39CQoKynCudu3aXLlyBQC/B1ubhIeHZ7gmPDzc8Fp+1a0LZ86oWnM9e6ph1z594PBhqFrV9Hbz3DMXmxRr+E1hw/kN9KnVBxudDS3Kt+ByRD5m7wkhhLAq/RBr//7g5GTdWLKy9dJWAH47/hvj24+3cjSWczj0MAB1ytSxciTFS3BwMKdPn85w7syZM1SqVAlQiyH8/PzYvHkzDRs2BCAqKop9+/bx2mvmG3X09ISPPjJbc4AJyVw172qs/G8lvWv1Zv359YxqMQqAm/dv4uGYj43FhBBCWE10NCxdqo4LaMQpzy7cuwDA2btnuR51nXIe5XJ5R9G048oOAIIrBFs5kuJl1KhRtGrViq+++oqnn36a/fv38+OPP/Ljjz8CoNPpGDlyJF988QXVq1cnMDCQjz/+mICAAHr16mW2OOLj4d9/1X6sqakZXzO1RzzPydy4tuN4dtmzjFo/io6BHWlZQW0stuH8Bhr552NjMSGEEFazbBnExqpipi1aWDuarOmTOVC9dM/VL35LbaMSojgafhSA1hVbWzma4qVZs2asWLGCDz74gPHjxxMYGMjUqVMZOHCg4ZoxY8Zw//59hg0bRkREBK1bt2bdunU4mamret06GDwYbt/O/JpOBykpprWr0zRNy+ubwmLCCI0OpYFfA8M2I/uv78fT0bNIjvFfu3aNChUqcPXqVco/XPxFCCFKgPbt1Zy5L7+EDz+0djSZaZqG50RPohOjAXih4Qv8X8//s3JU5rf+3Hq6LupKoFcgF966kPsbSrii9vO7enXo3BnGjTPvHsd5XgDx4p8v4mrvSiP/Rhn2i6tTpg6Tdk0yX2RCCCEKxKVLKpHT6WDQIGtHk7U7cXcMiRykzZ8rbnZe2QlAm0ptrByJsITwcBg92ryJHJiQzM07Oo+45LhM5+OS45h/dH4W7xBCCFGY6etedegAFSpYN5bs6IdYvZ29sbOx41LEJS5FXLJuUBagny/XuoIMsRZHTz2lfnEyN6PnzEUlRKFpGpqmEZ0QjZNd2vhxSmoKf5/9m7KuZc0foRBCCIvRtLRkrrAufAC4eO8iAEFlgkhJTWHPtT1svbiVFxoV0oJ4JkhMSWTf9X2AzJcrrmbMgH79YMcOqFdP7dOa3ptvmtau0cmc10QvdDodOp2OGjMyV6TWoeOzdsW79o8QQhQ3u3bB+fPg5qbqXRVW+p65KqWqUN69vErmLhWvZO5Q6CHik+PxcfaRbbyKqV9/VfuzOjmlTW3Q0+kKIJnbOmQrGhod5nVg2dPL8Hb2NrzmYOtAJa9KBLgHmBaFEEIIq9DXluvXD1xdrRtLTgzJnFcVgisG89XOr9hycQuapqFL/xOxCNtx+cEQa8XWxeYziYw++gg++wzefx9s8jzRLXtGJ3NtK7cF4OJbF6noWVG+0YQQooiLjYUlS9RxYR5iBbgQkdYz16pCK+xt7LkefZ1zd89R3ae6laMzj51XHyx+qCiLH4qrxERVlNuciRyYsACiklcldl7ZyXPLn6PVL624HnUdgAVHFxhW4QghhCj8Vq6EqCioXFltAF6YpR9mdbF3MdQ4LS6rWlO1VMPPUJkvV3wNGQK//27+dvOczC07uYwuC7vgbOfModBDJKQkABCZEMlXO74ye4BCCCEsQz/EOmSI+XsKzCkpJYkrkWr/zCqlqgDQvnJ7oPgkc//d/o+7cXdxtnOWAvzFWEoKTJ4MbdvCiBGqTEn6h6ny/M/3ix1fMPvx2fz05E/Y26YtwwiuEMyh0EOmRyKEEKLAXL8Omzap48GDrRtLbq5EXiFVS8XJzgk/N7XhuSGZu7gVE2rfFzr6XrkW5VvgYOtg5WiEpRw7Bo0aqV+ejh+Hw4fTHkeOmN5unrfzOn37NI9WejTTeU8nTyLiI0yPRAghRIFZuFDtC9mmDVSpYu1ocpZ+iFU/X7tF+RY42TkRfj+cU7dPEVQmyJoh5puhvpwMsRZrWy3UkZznnjk/Nz/O3T2X6fzOKzsN3d9CCCEKL03LOMRa2KVP5vQc7RwNG9FvvVj0h1oNOz/I4ocS4dw5WL8e4h7swZDfzuU8J3MvN36Zt9a9xb5r+9Ch40b0DRb9u4h3NrzDa01fy180QgghLO7AATh1CpydVUmSwi59WZL0isu8uWtR17gUcQkbnQ0tyrewdjjCgu7cgY4doUYN6N4dQkPV+aFD4e23TW83z8Os77d+n1QtlY7zOxKbFMujcx7F0c6Rd1q+w4jmI0yPRAghRIHQ98r16QMeHtaNxRjpy5Kk1z6wPWyFfy79Q6qWmmG/8KJE3yvX0K8h7o7uVo5GWNKoUWrXhytXoHbttPP9+6sFEN98Y1q7eU7mdDodHz36Ee8Gv8u5u+eISYwhqEwQbg5upkUghBCiwCQkqCr0UDSGWCHrYVaAZgHNcLV35U7cHY7fPE593/rWCC/f9MWCZYi1+NuwQQ2vli+f8Xz16nD5suntmvxrjIOtA0FlgvB19TWsNBJCCFG4rV4N9+5BuXLQoYO1ozFOdsmcva29YcHAlotbCjwuc9EXC5bFD8Xf/fvg4pL5/N274OhoertGJ3P/d/j/+HbPtxnODftrGFWmVaHerHrUnVmXq5FXTY9ECCGExemHWAcNAltb68ZijHtx9wyVEgJLBWZ6vUOgykiL6ry5iPgIjoUfAySZKwnatIH589O+1unUqvLJk6F9e9PbNTqZ+zHkR0o5lTJ8ve7cOuYcmcP8XvM58PIBvJy8+GzbZ6ZHIoQQwqLCw+Hvv9VxURti9XPzw8U+c5eGfhHEtkvbSElNKdDYzGH31d1oaFTzrmaooSeKr8mT4ccfoVs3tbXXmDFQty5s3w6TJpnertHJ3Nm7Z2ka0NTw9Z///UnPmj0ZWH8gjf0b81XHr9h8cbPpkQghhLCoxYtVBfrmzaFWLWtHY5zshlj1Gvk3wsPRg8iESI6EHSnAyMxDtvAqWerWhTNnoHVr6NlTDbv26aOKBletanq7Ri+AiEuKw8MxbdnT7mu7GdpoqOHrKqWqEBYTZnokQgghLKoo1ZbT0ydzgV6Zh1gB7GzseLTSo6w+s5qtl7bSJKBJQYaXb/piwbL4oeTw9ISPPjJvm0Ync5W8KhESGkIlr0rcjr3NiZsnDAUbAcJiwvB09DRvdEIIIczi6FH1cHBQZRCKitx65gA6VO7A6jOr2XJxC++0eqegQsu3+OR49l/fD0jPXEkSEQH798PNm2q+XHqmbq1ndDI3pMEQhv89nBM3T7Dl0hZqla6V4Teg3Vd3U7dsXdOiEEIIYVH6XrknnwRvb+vGkhcXIy4COSdz7QPVvLkdV3aQlJKUYd/wwizkRgiJKYmUdS1Lde/q1g5HFIC//oKBAyEmRtV4fLA7HaCOTU3mjJ4zNyZ4DC83fpnl/y3Hyc6JP/r9keH1XVd38UzdZ0yLQgghhMUkJam9WAGef96qoeSZMT1z9X3rU9a1LDGJMWy6sKmgQsu39Pux6tL/VBfF1ttvw4svqmQuIkKVCdI/7t41vV2je+ZsdDaMbz+e8e3HZ/n6w8mdEEKIwmHdOrh1C3x9oUsXa0djvOTUZC5HqkqqOSVzNjobng56mhkHZvDr8V/pVr1bQYWYL4ZkroIMsZYU16/Dm29mXWsuP4rm3idCCCGMph9iHTgQ7PK874/1XIu6RnJqMg62DgS4B+R47TP11MjQiv9WEJsUWxDhmUzTNCbvmszfZ1WdmLaV21o5IlFQunSBgwfN324R+mcthBAir+7eVfN0oGitYoWMK1lz23e1ZfmWVPaqzKWIS6w5s4Z+dfoVRIh5lpyazJtr32TWwVkAjGoxisb+ja0clbCkVavSjnv0gHffhZMnoV49tU9rek8+ado9JJkTQohi7LffVHHShg2hfhHbutSY+XJ6Op2OAXUGMHHXRH49/muhTObuJ95nwLIBrD6zGh06vu3yLSNbjLR2WMLCevXKfG58FjPWdDpVB9IURg2zRiVEmda6EEIIq5o7Vz0XtYUPkLdkDtKGWtecXWPYAqywCIsJo+3ctqw+sxonOyeWPr1UErkSIjXVuIepiRwYmcyVmlSKm/dvAtBhXodC949ECCFEZqdOwYEDap7cM0Ww2EBek7l6ZetRp0wdElMSWXFqhSVDy5OTt07S4ucWhISGUNqlNFuHbKVP7T7WDksUoA4d1OpVSzEqmXNzcONO7B0A/rn0D0kpSZaLSAghhFnoFz507w5ly1o3FlPkNZnT6XSGElm/Hv/VYnHlxeWIywT/XzCXIy9T3bs6e4buoUX5FtYOSxSwf/5R0x0sxag5c52qdKL9vPbULlMbgN6/98bB1iHLa7cM2WK+6IQQQpgkJQUWLFDHRW3hg15ekzmAAXUHMHbrWDZf3ExYTJjVN69fenIpEfER1Ctbjy1DtlDapbRV4xHFk1HJ3MLeC5l3dB7n755n26Vt1ClTBxd7MxdJEUIIYTabNsGNG+DjA48/bu1o8i4yPpI7cWpEKLt9WbNS1bsqzcs1Z9/1ffxx4g9GNB9hqRCNEhIaAkD/Ov0lkSvhTp6EsFy2sDd1kZJRyZyzvTOvNn0VgIOhB5n02CS8nLxMu6MQQgiL0w+xPvOM2o+1qNFv41XGpQzuju55eu8zdZ9h3/V9/Hr810KTzDUNaGrVOIT1dewImpb5vE6nzudnNWueS5NsHbLVcKw9iEq2IRFCiMIjMhJWPJj/X5KGWPWervM0ozeMZs+1PVy8d5HAUsb37JlTZHwkZ+6cAciwl7komfbtgzJlLNO2STtAzD86n3qz6uH8pTPOXzpTf1Z9FhxdYO7YhBBCmOCPPyA+HoKCoEkRzSHyk8z5u/vTvnJ7AH47/ptZ48qLw2GHAajkWUmGWAUVK0KlSjk/TJXnZO7bPd/y2prX6F6tO0v6LWFJvyV0rdaVV9e8ynd7vjM5kIkTJ6LT6Rg5cqThXHx8PMOHD8fHxwc3Nzf69u1LeHh4hvdduXKFHj164OLiQtmyZXn33XdJTk42OQ4hhCjq9LXlhgxRQzdFUX6SOaBQrGo9eEPt2yS9csLS8pzMTd8/nVk9ZjHpsUk8WfNJnqz5JJMfm8zM7jOZtn+aSUEcOHCAH374gfoPzfwbNWoUf/31F3/88Qfbtm3jxo0b9OmTVpsnJSWFHj16kJiYyO7du5k3bx5z585l3LhxJsUhhBBF3blzsGsX2NjAc89ZOxrTpd/KyxR9avfB3saeYzePcfzmcXOGZjT9fLkm/pLMlXRt21p27mqek7nQ6FBaVWiV6XyrCq0IjQ7NcwAxMTEMHDiQn376iVKlShnOR0ZG8ssvv/Dtt9/SoUMHmjRpwpw5c9i9ezd79+4FYMOGDZw8eZKFCxfSsGFDunXrxueff873339PoiULugghRCE1f7567twZAnLem75Qy2/PXCnnUnSv3h2AX49Zp3cu5IYsfhDK1q3g5WW59vOczFXzrsaSE0synf/9xO9U96me5wCGDx9Ojx496NSpU4bzISEhJCUlZThfq1YtKlasyJ49ewDYs2cP9erVw9fX13BNly5diIqK4sSJE9neMyEhgaioKMMjOjo6z3ELIURhk5qalswV1YUPACmpKVyKuASYnsxB2lDrbyd+MyzYKyiR8ZGcvXsWkJ45YXl5Xs36WbvP6L+0P9svbye4QjAAu67uYvPFzSx5KnOSl5PffvuNQ4cOceDAgUyvhYWF4eDggNdDqayvry9hDwq1hIWFZUjk9K/rX8vOhAkT+Oyzz/IUqxBCFHbbtsHly+DpCT17Wjsa012Pvk5SahJ2NnaU9yhvcjtP1HwCV3tXLty7wP7r+2levrkZo8zZodBDgFr84OPiU2D3FSVTnnvm+gb1Zd9L+yjtUpqVp1ey8vRKSruUZv9L++ldu7fR7Vy9epW33nqLRYsW4eTklNcw8uWDDz4gMjLS8Dh58mSB3l8IISxBX1uuf39wdrZuLPlx8Z6qMVfZqzK2NrYmt+Ni70KvWr0AWHRskTlCM5rUlxN6UVGWv4dJpUmaBDRhYZ+FhAwLIWRYCAv7LKSRf6M8tRESEsLNmzdp3LgxdnZ22NnZsW3bNqZNm4adnR2+vr4kJiYS8dDOtOHh4fj5qe1Z/Pz8Mq1u1X+tvyYrjo6OeHh4GB7u7nkrSCmEEJZy7RrUrg1du0JIiPHvi4mBpUvVcVEeYoX8z5dLb2C9gYBa1VqQ+4obVrLKEGuJV6oU3Lypjjt0gIfSGrMwKZkzh44dO3Ls2DGOHDlieDRt2pSBAwcaju3t7dm8ebPhPadPn+bKlSu0bNkSgJYtW3Ls2DFu6v+UgI0bN+Lh4UFQUFCBfyYhhMivX3+F//6D9euhaVMYMECtUM3N8uVw/z5Urw4P/osssgzJnFf+k7nHqj6Gr6svt2Nvs+7cuny3ZyzDSlYpS1LiubnBHbUzHf/8A0kW+J0iz3PmzMXd3Z26detmOOfq6oqPj4/h/NChQxk9ejTe3t54eHgwYsQIWrZsSYsWLQDo3LkzQUFBDBo0iMmTJxMWFsbYsWMZPnw4jo6OBf6ZhBAivzZtUs9BQXDqFPz+OyxbBsOGwbhx8NA0YQN9bbnBg4tubTm9CxHm65mzs7Hj2XrP8t3e71jw7wKeqPlEvtvMTUR8BOfuqgxceuZEp07Qvr3qcQfo3Tv7MiVbtph2D6slc8b47rvvsLGxoW/fviQkJNClSxdmzpxpeN3W1pbVq1fz2muv0bJlS1xdXRkyZAjjx4+3YtRCiKJg82a4cAEGDYICnrabrfh42LFDHS9ZAsnJ8MEHsHYtzJyp5sQNHgweHhnfl5ysSh/odOr1ok6fCJkjmQMYVH8Q3+39jlWnVxERH2HxvcX1ix8qe1WWxQ+ChQvVv93z59UipTp1wMXFvPfQaQW9XrsQunbtGhUqVODq1auUL2/6yikhRNGgaeDjA/fuQfnyMH68SoJsTZ9rbxZbt6o5NX5+cONGWg/bP//Ae+/B/v05v79DB5WkFrTElESeWfYMzcs1Z0zwmHy19e2eb3l7w9sAnBp+ilqla+U7Pk3TqD+7PsdvHufHx3/k5SYv57vNnHy962vGbBpD39p9Wfr0Uoveq6Qraj+/27dX+yabu+Zcoe6ZE0IIS7hzRyVyoBYcvPgiTJkCEybAE09Yb5hSP8TaqVPGGNq1g717YdUq1XOX1a/g9vbw0ksFEmYmOy7vYPmp5fz5358Mqj8If3d/k9r5Zvc3vLPxHQDGPTrOLIkcgE6nY1D9Qby36T0W/LvA4sncwVC1+EFWsoqHbd2adqz/d2yO/2/MtgBi5oGZjN8mw5tCiMLv0iX17Ourkjhvbzh5UtVma9NGbYdlDemTuYfpdCq+KVPgm28yPyZOhGrVCjZePX2B3xQthXlH55nUxpTdUwyJ3CdtP+Gz9uatBfpsvWfRoWPHlR2G0ieWot/5QebLiazMnw/16qnyQc7OUL8+LFiQvzbNlswtO7WMuUfmmqs5IYSwGH0yV6UKvP22msvywQfqP9Zdu6B1a1i8uGBjuncPDqoOHTp2LNh759fFiLTk6OdDP+d5t4Wvd33NuxvfBVQi92m7T80ZHgDlPcrTsYr6g13470Kzt693L+4e5++dB6Cxf2OL3UcUTd9+C6+9Bt27q3mxS5aoMkSvvgrffWd6u2ZL5jYP3syFty6YqzkhhLCYy5fVc+XK6tnLC776SpUA6dNHndOvDi0o//yjtuOqVUvN4ytK9D1zAOfvnWfb5W1Gv3fyrsmM2aTm2VkqkdMbVH8QAAv+XWCx7b30ix8CvQJl8YPIZPp0mDULJk2CJ59Uj8mT1QKnadNMbzdfyZymaQW+350QQuSXvmdOn8zpBQTAJ5+o4z171CrRgpLTEGthp++ZC3APAFTvnDEm75rMe5veA+DTtp9aNJED6FO7Dy72Lpy9e5b913NZTWIiqS8nchIaCq1aZT7fqpV6zVQmJXPzj86n3qx6OH/pjPOXztSfVZ8FR/M54CuEEAVEn8xVqpT5tTp1VOmPmBg4dqzgYirSydyDOWhj24wFYOnJpdyNu5vje1b+tzJDIvdJu08sGyTg5uBGn9qq63X+0fkWuYchmZP5ciIL1aqpodWH/f67Kvhtqjwnc9/u+ZbX1rxG92rdWdJvCUv6LaFrta68uuZVvtuTjwFfIYQoINn1zIEqT6L/zXnnzoKJ58oVOHMGbGzUytWiJD45ntAY1aXQr04/Gvg2ICElgUX/Zr8XamxSLG+tewuAUS1GFUgip6cfav3txG8kpiSavX39Nl6yklVk5bPPVPHvrl3h88/Vo2tXdT4/JXLznMxN3z+dWT1mMemxSTxZ80merPkkkx+bzMzuM5m2Px8DvkIIUQA0LfOcuYcFB6vnglrVqq8N98gj4OlZMPc0l8sR6g/TzcENH2cfXmqs6qP8dOinbKfhTNw5kSuRV6joWZEvOnxRYLECdAzsiL+bP3fj7rL27Fqztn0v7p5hKzJZ/CCy0rcv7NsHpUvDypXqUbq0qiHZu7fp7eY5mQuNDqVVhcwDvq0qtCI0Oh8DvkIIUQDu3YPoaHVcsWLW17RurZ537sy6ppu5FeUhVv3ih8peldHpdAysNxBHW0eO3Txm6KVK7/zd80zeNRmAbzt/i4u9mUvh58LWxpaB9QYCMP9f8w61pl/84O3sbda2RfHRpInaFSIkRD0WLoRGjfLXZp6TuWre1VhyIvOA7+8nfqe6Tz4GfIUQogCkrzHn7Jz1NY88AnZ2cP26GgK1JE0r2smcfvFDoFcgAKWcS/FU0FNA1gshRq0fRUJKAp2qdDLMXytogxqoodbVZ1ZzL+6e2dqVIVZhLXlO5j5r9xnjto6j68KufL7tcz7f9jldF3bls22fMb6dFA0WQhRuOc2X03NxgcYPRsksPW/u+HG4eVPds0ULy97LEtL3zOnph1oXH19MTGKM4fyaM2v468xf2NnYMa3rNHRW2mqjvm99Gvg2IDElMcvOCVPJ4gdhLXlO5voG9WXfS/so7VKaladXsvL0Skq7lGb/S/vpXTsfA75CCFEAcpsvp1dQ8+b0vXKPPgqOjpa9lyU83DMH0LZSW6p5VyMmMcaQLMUnxxsWPYxsPpLaZWoXfLDp6BdCmHOoVd8zJ2VJREEzqTRJk4AmLOyzkJBhIYQMC2Fhn4U08s/ngK8QQhQAY3rmIOO8OUsqykOskFaWJLBUWjKn0+l4qZHqndMPtX6z+xvO3zuPv5s/49qOK/hAH/JsvWex1dmy++pujoXnvwbN3bi7hsRWeuZEQTPbDhBCCFEU5FRjLj19z9zx4xARYZlYEhNh24PNEopqMpfVMCvAkIZDsNXZsufaHtaeXcuXO74EYErnKbg7uhdwlJn5u/sbRpO+P/B9vtvTL36oUqoKpZxL5bs9YXkTJ05Ep9MxcuRIw7n4+HiGDx+Oj48Pbm5u9O3bl/DwcOsFaSSjkzmbz2ywHW+b48NuvJ0lYxVCiHwztmfO11cV+NQ0tRuEJezbB/fvQ5kyauPtoiYmMYZbsbeAzMmcn5sfT9R8AoA+S/oQlxxHm4pteKbuMwUdZrbeaPYGoLb3ioiPyFdbhiFW6ZUrEg4cOMAPP/xA/fr1M5wfNWoUf/31F3/88Qfbtm3jxo0b9Olj+YU6M2fmr86c0dnXiv4rsn1tz7U9TNs3jVQt1fRIhBCiABg7Zw5U79y5c2reXLdu5o9FP8TasaMqGFzU6GvMeTl54eXklen1lxq9xMr/VhKfHI+NzoYZ3WdYbdFDVh6t9Ch1y9bl+M3jzD0yl5EtRprclqxkLTpiYmIYOHAgP/30E198kVbnMDIykl9++YXFixfToUMHAObMmUPt2rXZu3cvLSy4QmnZMrh4URUUNoXR/330rNUz06NW6VrMPTqXKbun0K9OP06/cdq0KIQQogBEREBkpDrObZgV0oZaLTVvrsjPl8ti8UN6Xap1oZx7OQCGNxtOfd/6WV5nLTqdztA79/2B703ukDhx8wR/nv4TgNYVW5stPmG86OhooqKiDI+EhIRsrx0+fDg9evSg00P/8EJCQkhKSspwvlatWlSsWJE9luqef2DzZrhwwfT3m/S74I3oG7y86mXqzapHcmoyR149wrxe86jkZcT/jkIIYSX6IdYyZVQpkNzoF0Hs36/mt5lTVJQaZoUinMxlsfghPTsbO+b3ns+oFqP4ssOXBRma0QbWH4inoyfn7p5jw/kNeX6/pmm8tuY1klOTeaLGE7Qs39ICUYrcBAUF4enpaXhMmDAhy+t+++03Dh06lOXrYWFhODg44OXlleG8r68vYWFhZo9Z08xXlDxPyVxkfCTvbXyPatOqceLWCTYP3sxfz/xF3bJ1zRONEEJYkLHz5fRq1gRvb4iLg8OHzRvLtm2QkqLm5RnTS1gYGRY/eFbO9poOgR34tsu3hWLRQ1bcHNx4oeELAMzYPyPP7593dB47ruzAxd6F6d2mF6ph5JLk5MmTREZGGh4ffPBBpmuuXr3KW2+9xaJFi3BycrJClMr8+WqOrLOzetSvDwsW5K9No5O5ybsmU2VaFVafXc2vfX9l99DdtKnUJn93F0KIApSX+XKg5rFZqt7c2gfbghbVXjlIN8yaTc9cUTH8keEA/H32b87fPW/0++7E3uGdDe8A8EnbT2R0yorc3d3x8PAwPByzKNoYEhLCzZs3ady4MXZ2dtjZ2bFt2zamTZuGnZ0dvr6+JCYmEvHQ8vXw8HD8/PzMEue338Jrr0H37rBkiXp07QqvvgrffWd6u0YvgHh/0/s42ztTzbsa847OY97ReVlet7z/ctOjEUIICzK2LEl6wcHw119q3tzo0eaJY+lS+OEHddy9u3natIbsypIUNdW8q9GtWjfWnlvLrIOzmNJ5ilHve2/Te9yJu0OdMnUY1WKUhaMU+dWxY0eOHctYU/CFF16gVq1avPfee1SoUAF7e3s2b95M3759ATh9+jRXrlyhZUvzDJ9Pnw6zZsHgwWnnnnwS6tSBTz+FUSZ+GxmdzA1uMFi6j4UQRVpeh1khbd7crl1qfkt+/xvcsAGefRZSU+Hll+Hxx/PXnjXltgCiKHnjkTdYe24tvxz+hfHtx+Nin/Okyl1XdvHL4V8AmP34bOxt7QsiTJEP7u7u1K2bcVqYq6srPj4+hvNDhw5l9OjReHt74+HhwYgRI2jZsqXZVrKGhkKrVpnPt2qlXjOV0cnc3F5zTb+LEEIUAqYkc02agIOD2j/13DmoXt30++/ZA717Q1IS9OunfkMvqr8jR8RHGGqzFfWeOYCu1bpSpVQVLty7wKJ/F/Fyk5ezvTYpJYlX17wKwNBGQ2UFazHy3XffYWNjQ9++fUlISKBLly7MnDnTbO1Xq6aGVj/8MOP533/P3/8tUuVXCFFimJLMOTlBs2aqZ27XLtP/w/33XzWkGhsLnTvDwoVga2taW4WBfoi1jEsZXB1crRuMGdjobBjebDhvb3ibGQdm8FLjl7IdjZq6dyrHbx7Hx9mHSZ0mFXCkwpz++eefDF87OTnx/fff8/33+d8VJCuffQb9+8P27Rnn427erJI8UxXBMpVCCJF3kZFp23LldfVofuvNnT8PXbqo+7dsCcuXq96+oiy3siRF0QsNX8DZzpl/w/9l55Ws/7IvR1zm022fAmprMh8XnwKMUBR1ffuqkkSlS8PKlepRurQqf9S7t+ntSjInhCgR9CtZfXzAzS1v700/by6vbtyAxx6DsDBVjmDNGnAt+h1ZxWbxQ3qlnEvxXP3nAJhxIK1MiaZphMeEs+/aPl5Z/QqxSbE8WulRhjQYYq1QRRHWpInqmQ8JUY+FC6FRo/y1KcOsQogSwZQhVj39hOX//oPbt9Vv0nqxsTBtGvzxh5oL97CwMLh1C6pWhfXroVQx2YO9OC1+SG94s+H8dOgnlp1cRrdF3bgUcYnLEZeJS44zXGNnY8esHrNkUaAoNCSZE0KUCPlJ5nx8oHZtOHUKdu9WpQSSk2HOHFVO4MaNnN8fEAAbN4K/f97vXVjpe+aKWzLXwK8Bj1Z6lO2Xt7Pu3DrDeR06ynuUp5JXJUY2H0lQmSArRimKGhub3Bc76XTq/xVTSDInhCgR9MOspu62EByskrkdO9R/uB9+CKcfbEdduTKMHZt1oqjTwSOP5H1ot7DT98wVp2FWvXm95vHrsV/xdfOlsldlKntVprxHeRxsi/hER2E1K1Zk/9qePap3P9W0rYEBSeaEECVEfnrmQM2b+/lnVcFd/59u6dLw8cfwyiuQRcH5YkvTtGK5AEKvsldlPmiTeTsoIUzVs2fmc6dPw/vvq6LkAwfC+PGmty8LIIQQJYI5kjlQiZyLi0rizp+HN98sWYkcwJ24O9xPug9ARc+KVo5GiKLlxg1VMLxePdXLf+QIzJuXvz2apWdOCFEi5DeZq1oVpkxRixlGjgQzbdVYJOl75QLcA3Cys96G5UIUJZGR8NVXakuvhg1Vbbk2ZtriXpI5IUSxFx0Nd++q4/z89vv22+aJp6grrosfhLCUyZNh0iT1S+Cvv2Y97JofkswJIYo9/eKHUqXAw8O6sRQHxXnxgxCW8P774OystvOaN089srJ8uWntSzInhCj28jvEKjIyLH6QnjkhjDJ4sGX3YZZkTghR7EkyZ16XIi8B0jMnhLHmzrVs+7KaVQhR7OW3xpzIqDiXJRGiKJJkTghR7EnPnPlomsblSJUdyzCrEIWDJHNCiGJPkjnzCYsJIz45HhudDeU9yls7HCEEkswJIUoAfTInw6z5p1/JWsGjAva29laORggBkswJIYq5+/fh9m11LD1z+aevMSeLH4QoPCSZE0IUa/rFD56e4OVl1VCKBVn8IEThI8mcEKJYk/ly5iW7PwhR+EgyJ4SwijVrICTE8veR+XLmJbs/CFH4SNFgIUSBO38ennhCDXuGhoKjo+XupR9mlZ4585CeOSEKH+mZE0IUuKNHQdPg3j345x/L3kuGWc0nJTWFK5FXAOmZE6IwkWROCFHg/vsv7fjPPy17L0nmzOd69HWSUpOwt7EnwD3A2uEIIR6QZE4IUeBOn047XrUKUlMtdy+ZM2c++iHWSl6VsLWxtW4wQggDSeaEEAUufc/c9euWWwgRFwc3b6pj6ZnLP31ZEhliFaJwkWROCFGgNC0tmatdWz1baqhVv/jB3R1KlbLMPUoSWfwgROEkyZwQokCFh0NUFNjYwNtvq3OWSuZOnVLPlSuDTmeZe5QkZ++eBaRnTojCRpI5IUSB0vfKBQZC795gawvHj6tyJeZy6xaMHAkDBqivq1UzX9sllaZpbLm4BYBmAc2sHI0QIj1J5oQQBUqfzNWsCd7e0Lat+tocvXP378MXX0DVqvC//0FiInTsCJMm5b/tku7YzWOExoTiYu/Co5UetXY4Qoh0JJkTQhQo/UrWWrXUc8+e6jk/yVxSEsyapZK4jz+G6Gho1Ag2bIBNm6B69fzFLGDduXUAdAjsgKOdBas8CyHyTJI5IUSB0vfMPZzM7dwJt2+b1uZzz8Hrr6v5eFWqwK+/wsGD8Nhj+Y9XKPpkrmvVrlaORAjxMEnmhBAFKv0wK6j6bw0bqlpzq1fnvb3Ll2HJEnU8bZpa9DBggFpgIcwjOiGanVd2AtC1miRzQhQ28t+dEKLAxMWllQvR98xB/oZa589Xzx06wIgR4OCQvxhFZlsvbSUpNYlq3tWo6l3V2uEIIR4iyZwQosCcPavqzJUqBWXKpJ3XJ3Pr10NsrPHtaVpaMjdkiPniFBnJEKsQhZskc0KIApN+iDV93beGDaFiRdVzt2mT8e3t3g3nzoGrK/TpY9ZQs7Tj8g52X91t+RsVIpqmsfbcWkCGWIUorCSZE0IUmIdXsurpdKYNtc6bp56fegrc3PIfX07uxt2l04JOdFnYhaSUJMverBA5e/cslyIu4WDrQLvK7awdjhAiC5LMCSEKzMMrWdPTJ3N//QUpKbm3FRcHv/+ujp9/3izh5WjftX0kpiQSkxhDaEyo5W9YSKw9q3rlHq30KK4OrlaORgiRFUnmhBAF5uGVrOk9+ih4eandG/bsyb2tlSvVtmCVKqn3Wtrea3sNx9ejrlv+hoXEuvMyX06Iwk6SOSFEgdC07IdZAeztoUcPdWzMUKt+iHXw4IIpQ7Lv+j7D8fXokpHMxSXF8c+lfwCZLydEYSbJnBCiQFy/rrbbsrNTOzVkRT/UunKlSv6yc+MGbNyojgcPNmuYWUrVUjMmcyWkZ2775e3EJ8dT3qM8QWWCrB2OECIbkswJITJYuBA++giSk83brn6ItUoV1QuXla5dVZ24c+fg2LGcY0xNheBgqFbNvHFm5cydM0TERxi+Lik9c+lLkujSLz8WQhQqkswJIQxSU+HVV+Grr9J2VTCXnIZY9dzdoUsXddy/f9bbe2la2hBrQSx8ALX4Ib0Sk8zp58vJEKsQhZokc0IIg4sX1VAowOTJOQ915lVOK1nTmzEDypdX13frphY5pHfwIJw8CU5O0K+f+eLLiX7xQzn3cgDciL5RMDe2oksRl/jv9n/Y6mzpWKWjtcMRQuRAkjkhhMGJE2nHR4+qHRmMER6uHjkxNpmrWFHNhytdWiVuPXtCfHza6/peud69wdPTuPjySz9frk9tVZm4JMyZW39O/eW3rNASLycv6wYjhMiRJHNCCIPjx9WzfnXopEm5v+f6dQgKgnr1IDo6++v0w6xZlSV5WK1asG6dGnb95x815JqcDAkJ8Ouv6pqC2r4rNimWf8P/BaBv7b6AGmbVzNltWQhJSRIhig6rJnMTJkygWbNmuLu7U7ZsWXr16sVp/f/4D8THxzN8+HB8fHxwc3Ojb9++hD/UBXDlyhV69OiBi4sLZcuW5d133yXZ3LO3hSgB9D1zr76qVp3+8w/s35/ze0aOhLt3VX24pUuzviYmBq5eVcfGJHMATZrAqlXg6Kiehw6F1avVvQICoFMn49rJr5AbIaRoKZRzL8cj5R4BVIIXmRBZMAFYQWJKIpsvbAagW/VuVo5GCJEbqyZz27ZtY/jw4ezdu5eNGzeSlJRE586dua+ftAOMGjWKv/76iz/++INt27Zx48YN+qTbhDElJYUePXqQmJjI7t27mTdvHnPnzmXcuHHW+EhCFGn6ZK5rVxg4UB3n1Dv3998ZEzj9EOjDzpxRz2XKgI+P8fG0awd//AG2tjB/Prz4ojo/aJA6VxD08+Wal2+Os70zpZxKAcV7qHX31d1EJ0ZT1rUsDf0aWjscIURutELk5s2bGqBt27ZN0zRNi4iI0Ozt7bU//vjDcM2pU6c0QNuzZ4+maZr2999/azY2NlpYWJjhmlmzZmkeHh5aQkKCUfe9evWqBmhXr14146cRomhJStI0BwdNA007f17TTpxQxzqdpv33X+br79/XtMqV1TXPPKOuA027cCHztYsXq9datzYttgUL1Pv1j5MnTWvHFH1/76vxKdqknZM0TdO0Ot/X0fgUbf259QUXRAF7b+N7Gp+iDVo+yNqhCJEj+fmtFKo5c5GRatjC29sbgJCQEJKSkuiUbjylVq1aVKxYkT0P9vvZs2cP9erVw9fX13BNly5diIqK4kT62dzpJCQkEBUVZXhE5zTRR4gS4vx5SEwEFxeoXFnNg3viCZU+TZmS+frPP4dLl6BCBfjxR+j4YMHj/PmZrzV28UN2nnsOpk9Xx23aQO3aprVjCn3PXIvyLQAo56FWtBbnnjlDfTkpSSJEkVBokrnU1FRGjhxJcHAwdevWBSAsLAwHBwe8vLwyXOvr60tYWJjhmvSJnP51/WtZmTBhAp6enoZHUJBUNhdC/7tP7dppCyDee089z58Poen2lj9+PC3Bmz4d3NzSFiTMn5+5pEl+kzmAN95QhYRXrTK9jby6FnWN69HXsdXZ0sS/CVD8y5PsvLKTo+FH0aHjsSqPWTscIYQRCk0yN3z4cI4fP85vv/1m8Xt98MEHREZGGh4nT560+D2FKOz0ydyD36UAtcNCcLDqsZs6VZ1LTYXXXlOrS3v2TNuCq3dvldRduAA7d2ZsOy8rWXNSty489LudRemLBdfzrYergyuQlswVx8LBUQlRDFoxCIDnGz5PGdcyVo5ICGGMQpHMvfHGG6xevZqtW7dSvnx5w3k/Pz8SExOJiIjIcH14eDh+fn6Gax5e3ar/Wn/NwxwdHfHw8DA83N3dzfhphCia9GVJ6tTJeF7fOzd7NkRGwpw5KllzdYVp09Kuc3WFp59Wx+kXQqSmGrf7Q2Gkry/XvFxzwznDMGsxTOZGrhvJpYhLVPaqzNSuU60djhDCSFZN5jRN44033mDFihVs2bKFwMDADK83adIEe3t7Nm/ebDh3+vRprly5QsuWLQFo2bIlx44d4+bNm4ZrNm7ciIeHhwyfCpEH+p65h5O5Hj3U/LmoKPjiCxgzRp3/7DNV4Dc9/VDrkiUQG6uOr1xRRX8dHNRcvKLk4flykK5nrpjNmVtxagVzjsxBh475vebj4ehh7ZCEEEayajI3fPhwFi5cyOLFi3F3dycsLIywsDDi4uIA8PT0ZOjQoYwePZqtW7cSEhLCCy+8QMuWLWnRQv3n2rlzZ4KCghg0aBBHjx5l/fr1jB07luHDh+Po6GjNjydEkZGUlFY+JP0wK6j5c/oEbsoUVeetQQN4663M7bRuDYGBqnjwihXqnL5Xrlo1VbuuqEhOTebgjYNA8e+ZC4sJY9jqYQC8F/webSq1sXJEQoi8sGoyN2vWLCIjI2nXrh3+/v6Gx++//2645rvvvuPxxx+nb9++PProo/j5+bF8+XLD67a2tqxevRpbW1tatmzJc889x+DBgxk/frw1PpIQRdLZsyqhc3dXq1Mf9swzar9UAJ0Ofvgh68TMxgYGD1bH+qFWcyx+sIZj4ceIS47D09GTmqXTJvsFuAcAEB4TTlJKkrXCMxtN03jxzxe5HXubhn4N+az9Z9YOSQiRR1b9PVkzYjscJycnvv/+e77//vtsr6lUqRJ///23OUMTokTRz5cLClLJ2sMcHGDsWLUzxJtvQvPmma/RGzxYDcFu2gTXrhXdZE4/X+6Rco9go0v7vbesa1nsbOxITk0mLCaMCp5ZZL9FyA8hP7D23FocbR1Z2HshDrYO1g5JCJFHhWIBhBDCurKbL5feK6+oHrzvvsu5rSpV4NFHVXmShQuL7uKHrObLAdjobPB38weKfnmSM3fO8PaGtwGY2Gkidcrm8A0ghCi0JJkTQmRZliQr1apl3XP3MP1CiLlz03rm8luWpKAZtvEql7kbsjjMm0tKSeK55c8RmxRLx8COvNn8TWuHJIQwkSRzQohsy5KY6qmnwNlZ9crpiw0XpWTuXtw9Tt9RXYrNy2eRzBXxFa1hMWH0WdKHAzcO4OXkxdxeczMMJQtRHE2YMIFmzZrh7u5O2bJl6dWrF6f1QwcPxMfHM3z4cHx8fHBzc6Nv376Zyp8VRvKvV4gSLiEBzp1Tx+ZK5jw8oE+ftK/9/cHT0zxtF4T91/cDULVUVUq7lM70elEuHLz05FLqzqzL6jOrcbB14P+e/D/Ke5TP/Y1CFHHbtm1j+PDh7N27l40bN5KUlETnzp25f/++4ZpRo0bx119/8ccff7Bt2zZu3LhBn/T/mRVSRahQgBDCEk6fhpQUtbNCQID52h0yBBYtUseFsVfuSuQV7sTeoZF/o0yv6Rc/PDxfTq8oDrPei7vHiLUjWHRM/aU09GvIgt4LqFs2l7F1IYqJdevWZfh67ty5lC1blpCQEB599FEiIyP55ZdfWLx4MR06dABgzpw51K5dm7179xpKohVG0jMnRAmXfvGDMfPhjNWhQ1o5k8K2+CFVS6Xd3HY0/rExXRZ24Vj4sQyv5zRfDtLKkxSVYdYN5zdQb1Y9Fh1bhI3OhrFtxrLvpX2SyIliIzo6mqioKMMjISEh1/dERkYC4O3tDUBISAhJSUl06tTJcE2tWrWoWLEie/bssUzgZiLJnBAlnLnny+nZ2sI776jj7t3N23Z+nb1zlosRFwGV6DT8oSHD/hpGWEwYmqbl3jNXhIZZ39/0Pl0WduF69HVq+NRg94u7+bzD51KCRBQrQUFBeHp6Gh4TJkzI8frU1FRGjhxJcHAwdR+s/AoLC8PBwQGvhzaA9vX1JSwszFKhm4UMswpRwhlTlsRUb70FQ4eCm5v5284Pfc9bfd/61PCpwdKTS/np0E/8evxXXmz4Infj7uJo60gDvwZZvl8/zFrYS5NsOL+BSbsmATDikRFM7DQRF3sXK0clhPmdPHmScuXKGb7ObQeo4cOHc/z4cXbu3Gnp0AqE9MwJUcIZW5bEVIUtkQPYc00NmXSp2oU/+v3Bjhd20CygGTGJMUzbPw2Axv6Ns+290vfMxSTGEJUQVTBB51FiSiJvrlXlRt5q/hbTuk2TRE4UW+7u7nh4eBgeOSVzb7zxBqtXr2br1q2UL5+2+MfPz4/ExEQiIiIyXB8eHo6fn5+lQjcLSeaEKMHi4uD8eXVsiZ65wurhgsCtK7Zm70t7WdRnERU9KwLQtVrXbN/v6uCKp6NanltY581N3TuV03dOU9a1LJ+1ky26hNA0jTfeeIMVK1awZcsWAgMDM7zepEkT7O3t2bx5s+Hc6dOnuXLlCi1btizocPNEhlmFKMFOnVI7Nfj4QNmy1o6mYMQkxnDsplrwkH5OnI3OhmfrPUvvWr05FHqIR8o9kmM75TzKEXkrkuvR16ldprZFY86r61HXGb9N7U89udNkPJ2KUF0YISxk+PDhLF68mD///BN3d3fDPDhPT0+cnZ3x9PRk6NChjB49Gm9vbzw8PBgxYgQtW7Ys1CtZQZI5ISwuNTXr8zqdeVePmsJSK1kLs4M3DpKqpVLRs6JhVWp6zvbOBFcMzrWdcu7lOHnrZKHsmXt347vcT7pPy/ItGdRgkLXDEaJQmDVrFgDt2rXLcH7OnDk8//zzAHz33XfY2NjQt29fEhIS6NKlCzNnzizgSPNOhlmFsJC7d6FtW7WqM6uHiwusWmXdGC09X64w2nNVzZfLbqWqsQzlSSywonXP1T3cib1j0nu3XdrGr8d/RYeOGd1nyM4OQjygaVqWD30iB+Dk5MT333/P3bt3uX//PsuXLy/08+VAkjkhLCImRpXj2L49+2vi4+HDD7PvuSsIlipLUpjtvf5gvly5/CVzltrSa8P5DbT6v1bUmVmH3Vd35+m9yanJvLH2DQBeafIKjf0bmzU2IUThJMmcEGaWkAC9esG+feDtDfv3w61bGR+XLoG7u+oZ+/tv68VqybIkhZGmaZkWP5jKUJ4kxrzlSWbsnwFA+P1w2s1tx08hPxn93pkHZnL85nG8nb35osMXZo1LCFF4STInhBklJ8Ozz8LmzeDqCmvXQrNmULp0xkelSvDqq+o9Eyfm756pqfDjjyppzIuYGJVUQslJ5i5FXOLm/Zs42Drku9fKEj1zVyOvsubsGgA6V+1MUmoSw1YPY/ia4SSmJOb43vCYcD7e+jEAX3X4Ch8XH7PFJYQo3CSZE8JMNA2GDYPly8HBAf78Ex7JYUHkyJHqul271MNUv/4Kr7wCXbpAeLjx7zt1Sj37+qoEsyTQ15dr5NcIR7uci4rmxhL7s/5y+Be11VjldqwbuI4v2n+BDh0zD87ksQWPcfP+zWzf+8HmD4hKiKKxf2NeavyS2WISQhR+sppVCDPQNLV11Zw5YGMDv/0GHTvm/J6AABg0CH75BSZNMm0xhKap9wJERMDbb8PChca9t0TOlzPTECuk9cyFxYSRnJqMnU3+/jtNTk3ml8O/ADCs8TB0Oh0fPfoR9X3rM3D5QLZf3k7TH5vy85M/k5KawuXIy1yKuGR46Lcgm9FtBrY2tvn7cEKIIkWSOSHMYMIE+PZbdfzzz9C7t3Hve/dd+L//g7/+UvPX8ppYrV0Lx46Bs7NaULFoEbzwQu6JJJS8+XJg3mSurGtZbHW2pGgphMeEG3rq0kvVUvlo80fULF2T5xs+n2N7a8+u5VrUNUq7lKZP7T6G80/UfIJ9L+2j5289OXv3LF0Wdsm2jRGPjKBlhcJd3FQIYX6SzAmRTytXwkcfqeNvv1XJlLFq1lSJ3/Ll8PXXMHdu3u6t75V7/XW18GLGDHjtNfj3X3Byyvm9Ja0sSVxSHIfDDgPQsnz+Ex5bG1v83Py4Hn2d69HXs0zmNp7fyMRdE7HR2VCvbD2aBDTJtr0fQn4A4PkGz2caAq5dpjb7X97PK6tfYfOFzZTzKEdlr8pU9qysnr0qU9W7KvV96+f7cwkhih5J5oTIB02DTz9VxyNHwqhReW/jvfdUMrdoEXz+OVSoYNz79u5VpU/s7dV93dxg2TI4e1YtqtDHlZ2S1jN3KPQQyanJ+Ln5Gbbsyq9yHuVUMhd1HTLncqw+sxpQPXSvrnmVvUP3ZjkEeiXyCmvPrQXg5SYvZ3kvLycvfn/qd7PELYQoXmQBhBD5sGEDHD2qVq5+/LFpbTzyCLRrp1bCfved8e/T98o99xyUKweenjB1qjo3YQKcOZP9eyMj4epVdVxSkrn0Q6w6M213oZ83dyM6c3kSTdNYfVYlczp0HLxxkNkHZ2fZzi+H1MKH9pXbU8OnhlliE0KUHJLMCZEP+oTq5ZdVTTlTvfeeev7xR7VzRG7++0+tlgU1706vXz/o2hUSE9Vwq6Zl/f59aq48AQHg5WVy2EWKuYoFp2coT5LFitaTt05yKeISTnZOTH5sMgAfbvmQ0OjQDNclpybz8+GfAVXoVwgh8kqSOSFMdOAAbN0KdnYwenT+2urSBRo0gPv3wZhtAL/+WiVqPXtC7XR7vOt08P33ar7cli1q6Da9sDA1v65HD/V1TqVTihv9Nl7mXCCQU3kS/RBrh8AOjGoximYBzYhKiGL0hozfLH+f/Zsb0Tco7VKaXrV6mS02IUTJIcmcECbS98o9+6zx89yyo9PBmDHqeNo0iIvL/trr12HBAnWs79FLr0qVtCHf0aPh3j2IilLnqlaFWbPUkG6PHjB9ev7iLiquRV3jevR1bHW2NPHPfhFCXuVUOFg/xPp49cextbFl9uOzsdHZ8Nvx39hwfoPhOv3ChxcavpDv2ndCiJJJkjkhTHDmjFq0AGlJWH49/TRUrqy2+5ozJ/vrpk6FpCRo0wZaZtPJ9M47EBSk2nrySZXEffEFxMZCixawbRusXg3ly5sn9sJOP1+uvm99XB1czdZudj1zd2LvGPZV7VFDdYM29m/MiEdGAPD6mteJS4rjcsRl1p59sPChcdYLH4QQIjeSzAlhgilT1DDn44+bbwGBnZ0q+gvw4YdqMURCQsZrIiLgB9WRk2WvnJ6Dg+qBA9i5E27fVmVQli+H3bvh0UfNE3NRYc76cukFuAcAmXvm1p1bR6qWSn3f+hlWzo5vP54A9wDO3zvPxJ0T+eXwL2hodAjsQHWf6maNTQhRckgyJ0Q6mqax9ORSrkZezfaa0FCYN08d55RQmeLFF6FpU7XadPRolYAtWAApKer1WbMgOlrVhuvePee2Hn0Uxo2DevXgp5/Ujg+9e6sh3ZJGv42XuZM5/TBrdGI00QnRhvPph1jT83D04H9d/wfAxF0TmXVQZdyy8EEIkR+SzAmRzuozq+n3Rz+GrR6W7TX/+59aLdqqFbRubd77u7jAnj0q+QoIgMuXYfBgaNRIFSf+n8oDGDPGuKTss89UAeGXXlI9fyVRYkoiITdCAPMUC07P3dEddwd3IK08SVJKEuvOrQPg8RqPZ3pP39p96VatG4kpidyOvU0ZlzKy8EEIkS+SzAmRzq7zR2D9FP5ZVp2ExNRMr0dGpg1fvv++ZWKws1PJl774r6en2rKrd28ID4eKFWHAAMvcuzg6GnaUhJQEvJ29qeZdzeztPzxvbtfVXUTER1DapTSPlMu8XFin0zGj+wyc7NQWHS80fAEHWwezxyWEKDkkmRMinTWLAmHP28SvmEbtoGSWLctYq+2HH9TK0KCgtPIeluLiooZxL1xQCxocHyx0fPddteuDMI4ligWn9/CKVn1Jku7Vu2e74X2VUlX45clf6FG9B6Nb5rOujRCixJNkTogHNA3ObHkwDGebwMXzDjz1lFr9+c8/ajGCfoeFMWPApoD+9Xh7q7py587B2rUwfHjB3NeSQm6E0HVhV95c+yapWuYeUHMyzJczY7Hg9B7umdMncw/Pl3vYs/WeZfWzq/F187VIXEKIkkOSOSEe2LM3mcSwqmAXB29WJXjQZlxdYf9+aN9ezVsLDVXlPJ55puDjK19e7e5grQUMVyOvMnzNcI6FHzO5jeiEaEauG8kjPz/C+vPrmb5/OqPWjULLbqsKM9D3zJmzWHB66Xvmzt45y+k7p7GzsaNz1c4WuZ8QQjxMkjkhHpjxU5Q6qLUCPK/j1vlrzp1TOybY2cGpU+rl0aNV6Y+SJDk1mX5/9GPmwZl0X9yd27G38/R+TdNYcWoFtb+vzf/2/Y9ULZVOVToBMG3/NKbsnmKJsAmPCedixEV06GgW0Mwi9zCUJ4m+zpqzawBoW6ktnk6eFrmfEEI8TJI5IVBDqH8tcwPAselvABwKPYSvr8b336tEbvBg6NMHhmW/0LXYmrBjAvuuqw1dr0Vd47nlzxk9PHol8gq9fu9FnyV9uB59naqlqrLhuQ1sHLSRbzp/A8CYTWNY9O+iXFrKu40XNgIQVCbIYslV+v1ZDUOsWaxiFUIIS5FkTgjUbggxUQ7gfo0nu7piq7PlVuwtQ7mJatVUbblly8DVfBsIFAkHrh/gs22fATC2zVic7ZxZf349X27/Mtf3Lji6gKDvg1h1ehX2NvZ81OYjjr12jMeqPgbA6JajGd1CLQB44c8X2HRhk9niDo0OZfR61XbPmj3N1u7D9HPmzt09x7bL2wBJ5oQQBUuSOSFIKwJM/YU0Ld+IWqVrAXA47LD1gioEYpNieW7Fc6RoKfSv05/x7ccz+/HZAHzyzyfZJl+apjF+23gGrxzM/aT7tK7YmiOvHuGLDl/gbO+c4dqvO39N/zr9SUpNovfvvTkcmv8/81QtlUErBnEr9hYNfBvwcduP891mdvQ9c3fj7pKcmkxNn5oWKYEihBDZkWROlHjh4fD33w++aDiPoDJBNPZvDKih1pLs3Q3vcubOGcq5l2Nmj5nodDoGNxjMS41eQkPj2WXPZtrKKikliRdXvcgn/3wCwPvB77Pt+W0ElQnK8h42Ohvm9ZpH+8rtiUmMofvi7ly8dzFfcU/eNZnNFzfjYu/Cb0/9ZqjpZgm+br7Y6NL+K5VeOSFEQSuhNeGFSLN4sdouS1d+P1qZ/wgqE0Qjv0Ys+HdBie6ZW3t2LTMPzgRgTs85eDt7G16b1m0aB0MPciTsCP2X9mfrkK3Y29oTGR/JU388xaYLm7DR2TCz+0xeaZr7VlWOdo6s6L+CNnPacOzmMTrM70Dripm317DR2dCjeg/6BfXLtmbc3mt7GbtlLADTu0039LJaip2NHX5ufoYheUnmhBAFTZI5UeLph1i1+nNxtnOmkmcls/XMHQo9RHJqcpY7ARRmt2Nv8+KqFwF485E3DXPc9JztnVnabymNf2zMrqu7+HDzh7zV4i26L+rOsZvHcLV3ZUm/JXSvnssGsul4OnmyduBaWv7SkksRl7gUcSnL6+Yfnc+S2kuY1WMWZVzLZHgtIj6CZ5Y9YxgWfqHhC3n74CYq516OG9E38HT0JLhCcIHcUwgh9CSZEyXa0aPqYWefQnLd36hVuha2NrY09GsIqJWYd2Lv4OPik+e278Teoc2cNiSnJnP+zfOU9yhv5ugtQ9M0Xln9CmExYdQuXZuJnSZmeV1V76rM7TmXPkv6MGXPFOYencvt2Nv4ufmx5tk1hoQ4L8p5lGPfS/tYenIpiSmJmV6/Hn2d6funs+zUMnZc2cFPT/zEkzWfzBD3pYhLBHoF8sPjP1hkx4es6MuTdK3WFXtb2Z5DCFGwJJkTJZq+V65mq7OccLlHUBnVk+Tp5EnVUlU5f+88h8MOG2qi5cWiY4uITYoFYPGxxYwJHmO2uC1p/tH5LD+1HDsbOxb2WZhpwUJ6vWv3ZnSL0Xy791tux94mqEwQfz/7N5W8Kpl8f393f0Y0H5Ht68/Vf45BKwZx8tZJev7WkxcavsDUrlNZcmIJS04swc7Gjl/7/lqgdd761O7DtsvbGN6sGGzPIYQocmQBhCixkpJg4UJ1XDZYrYBIP0lf37NkyupKTdP45fAvhq/nH51v0V0OzCU2KZbRG1Q5j/HtxhvVuzax00RebPgig+oPYteLu/KVyBmjsX9jQoaF8E7Ld9ChY86ROdSbVY83174JwOftP6d5+eYWjeFhgxsM5t5792hTqU2B3lcIIUCSOVGMbdoEjz0Gr7wC165lfn3dOrh1C8qWhcjyvwMZk7lGfo0AOBSW93lzh8MO82/4vzjYOuBo68iJWyc4EnbEpM9RkBYcXcDduLsEegUa3ZNob2vPLz1/YX7v+Xg5eVk2wAec7Jz4uvPXbHt+G4FegVyJvEJcchydqnQqMj2gQghhLpLMiWLn0CHo3Fklcps2wY8/QvXq8N57cO9e2nX6IdZnn9X4795xwHw9c3MOzwGgV61ehjldC/5dYMrHydXliMtU+V8Vhv2Vv60pNE1j2v5pAIx4ZAS2NrbmCM+i2lRqw9FXj/LmI2/SpWoX5vean6FMiBBClATyv54oNs6fh2eegSZNYONGsLeH116D1q0hPh4mT4YqVeDrr+H6dfjrL/W+x/rcIDYpFgdbB6qUqmJor5G/6pk7c+cMMYkxRscRnxzPomNqa6oXG77I4AaDATVvLjk12UyfNs0n/3zCxYiL/HToJ8Om8qbYdGETJ2+dxM3BjRcbvWjGCC3L3dGd/3X7H+ueW4e/u7+1wxFCiAInyZwo8iIiYMQIqFULflPbqjJwIJw+DTNnwvbtKnGrU0ddO2aM6qlLTISGDQG/owDU8KmBnU3amqCyrmUp514ODY2jYUeNjufP//7kXvw9ynuUp1OVTnSp2oUyLmUIvx/OxvMbzfa5AU7dOpWhx+/jrabvdPC/ff8D4PkGz8sm8UIIUYRIMieKvBdfhBkzIDkZunRRw6wLF0JgoHpdp4PHH1clSObOhQoVIC5OvTZkCJy8dRIgyx0K9L1zeak3939H/g9QSZGtjS32tvYMqDsAMP9Q66fbPiVVS6VVhVbY29iz6cIm/rn0T57bOXvnLGvOrgHIcSWpEEKIwkeSOVGknTwJK1aohO3vv9WihkaNsr7W1lYlb2fOwHffwZtvwrBh6ZK50pmTucZ+D+bNGbkTxNXIq4bet+cbPm84rx9qXfHfCqISooz9eDk6EnaEJSeWoEPHrB6zeLnxywCM3TI2zytnp++fDkCP6j2o4VPDLPEJIYQoGJLMiSLt66/Vc8+e0K2bce9xcoKRI+F//wMXF/P2zM07Og8NjbaV2lLVu6rhfBP/JtQqXYv45HiWnVxmXKC5GLd1HAD96/anvm99Pnr0I5zsnNh1dRfrzq0zup3I+EjmHFELNt5q/pZZYhNCCFFwJJkTRda1a7BIrTPgvfdMa0PTtByTOf2K1hO3TpCQnJBjW6laqiEpengBgU6nY1D9QYB5hlr3XtvLX2f+wkZnw6dtPwXULgT6orVjtxrfOzfnyBxiEmMIKhNkUnFkIYQQ1iXJnCiyvvtOFf5t2xZatDCtjevR14lOjMZWZ0t1n+qZXq/gUQFvZ2+SU5M5fvN4jm1tv7ydC/cu4O7gTt/afTO9/lz95wDYemkrVyKvZNvO7qu7DQlmdvQLHYY0GELN0jUN598Lfg83BzcOhR5ixX8rcmwDICU1xTDE+uYjbxbY9ldCCCHMR5I5USTdu6fqx4HpvXKgVoMCVPepjoOtQ6bXdTpdWr25XObN6XvlBtQdgKuDa6bXK3pWpF3ldgAs+ndRptc1TeOTrZ8Q/H/B1J9Vn693fU2qlprpun8u/cOmC5uwt7FnXNtxGV4r41qGkc1HAmoYNiU1JceY15xdw4V7FyjlVIpBDQbleK0QQojCSZI5kW9XIq+w++pus7QVHw/Ll0NMLmXdZs5U19SvD127mn6/nIZY9Qw7QeQwby4qIYo/TvwBZB5iTS/9UGv6YdDElESe//N5xm8fD0CKlsKYTWN44tcnuB1723CdpmmM3TIWgJcbv0xlr8qZ7vF2q7fxcvLixK0T/Hb8t2xjgbRyJC83fhkXe5ccrxVCCFE4STIn8iUyPpKWv7Qk+P+C2X99f77bGzwY+vaFTp2yT+ji4tTiBVA14/IzMpjTSlY9Y3rmfj/+O3HJcdQqXYvm5bLfF/SpoKdwsnPi1O1ThISGAOrPsNuibsw/Oh9bnS0/PP4DPz7+I052Tvx99m8a/dCInVd2ArDu3Dp2Xd2Fk50THz36UZb38HLy4t1W7wKqdElSSlKW1x0LP8aWi1uw1dky/BHZIF4IIYoqSeZEvozdMpYb0TcA+Cnkp3y19fff8Ifq3GLfPujdGxLSrTk4fvM4lyIuMXeu2lO1UiXo3z9ft+TkbZXM1S5TO9tr9D1zR8OOZjtsqa8t92LDF3Ocd+bh6EGvWr0AtQ/q1cirtJ7Tmi0Xt+Bq78pfz/zFsCbDeLnJy+x7aR81fWpyLeoa7ea2Y+LOiYzdqnrlhjcbToB7QLb3ebP5m5RxKcO5u+eYd3ReltdM26e27upduzcVPStm25YQQojCTafltSBVMXTt2jUqVKjA1atXKV++vLXDKTIO3jjIIz89gob6FnJzcCPs7bAs54vlJjZW7dBw6RL06qW247p/H/r0gd9/h3UXVvPkr0/iZOOGzy+3uXbZgWnT1M4PptI0DZ/JPtyLv8fhVw7T0K9hltelaql4TvQkJjGGE6+fyDQkezTsKA1/aIitzpZro6/h5+aX433/Pvs3PRb3wNvZGyc7J25E38DfzZ81z64xlELRi0mM4bU1r7Hw34WGc24Oblx86yKlXUrneJ/v9nzH6A2j8Xfz56mgpzJ99p8P/0x8cjw7XthB64qtc2xLCCEKI/n5rdjlfokQmaWkpvDq6lfR0Him7jMcuHGAc3fPsfTkUoY0HJLn9j7/XCVyFSrAggWwdy/06KHmz/UbfIcNdQagoRH3bzeuXXbA20fjxRfzt/Ly5v2b3Iu/hw4dNX1qZnudjc6GBr4N2HV1F4dCDxmSOU3TWPjvQkasVRlljxo9ck3kADpX7UxZ17LcvH8TgDpl6vD3wL+z7B1zc3Bjfq/5tK/cnuF/Dyc+OZ5RLUblmsgBvNbsNb7Z8w3Xo68bVqw+rIl/E4IrBOfalhBCiMJLkjlhklkHZxESGoKnoyffdfmOXw7/wkdbPmLOkTl5TuZOnIApU9Tx9Ong5qbmzP36K/Trp7HyVx9o+SmPvryaPT+NJQkI6LgMF5e+gOkJnX6+XJVSVXC2d87x2sb+jdl1dReHQw/zXP3nuHX/Fq+sfsVQ/qNF+RbM6DbDqPva2djxYsMXmbhrIh0CO7Ds6WV4OXlle71Op+PFRi8SXCGYXVd3GRZR5MbJzok1z65h+anlWa6KtbOx49l6z0o5EiGEKOIkmRN5diP6Bh9u/hCAiZ0m4uvmy+AGg/l468dsu7yNc3fPUc27mlFtpabCq6+qfVV79lQPvW5PxFFx0NdcmjcO9ryDp++bJF13APv7HK/8CrMP3uK1Zq+Z/DmMWcmqZ1jRGnaIP//7k5f/eplbsbewt7Hn03afMiZ4DHY2xv9z+rTdp/So0YPm5Zpjb2tv1Htqlq6ZoaacMRr4NaCBX4M8vUcIIUTRIgsgSiBNgx9+iWPN3ykYO2Py3j2YNg127oTR60cTnRhN83LNGdZkGADlPcrTpWoXAOYemWt0LHPmqDZdXVX7eqlaKkNWDuFS4Ce4PK5Wbf61UtWBa93rNLjcZeT6kRy4fsDoez0sL8mcfkXr9svb6fV7L27F3qJu2brsf3k/H7b5ME+JHICjnSOtK7Y2OpETQgghsiPJXAmjafDkC6d59SVnHu9hyyOt4tm1K/vr4+LU/qdVq8Jbb0GbNvD7h8+hu1mf2Y/PxkaX9i30QsMXAJXM5VasFtSK1DFj1PFnn0HFdFPGPtn6CX+c/AN7G3v+nt6ZsWoRJ7a2sGByI3rV6kViSiL9/ujH3bi7ef5zgLSVrMYkc0FlgnCwdSBVS0WHjveC3+PgywezXTQhhBBCFBRJ5koQTdPo8eoOVs97MFRnF8fBvU60bq2GN0+m20EqJUX1mtWooRKue/egcmAq2CTD2cfRZh1m6vsNuXw57T1P1nwSb2dvrkdfZ+OFjbnG8+67cPcuNGigEkW9hf8u5IsdXwDww+M/0LZyW8aPh59+UgsiKlfWMafnHKqUqsLlyMsMWTkkyzlhudHv/mBMMmdva8+IR0bQxL8J21/YzsROE3G0c8zzPYUQQghzk2SuhEhJTaH9G7+z9sc2ALQY+jtVPukCjX8Em2RWrYJ69WDoULXwoH59ePFFtZl9hQoqsRv442fwehDODf4CzYZ581Sy9/bbcOMGJMU78nS1FyHBlR/3LCImhmwfGzfCvHmq4O8PP4Ddg1HKXVd2MXTVUEDtM/pCI9Xbp9PBSy/Bk0+q67ycvFjabymOto6sPrOaybsm5+nP407sHcLvhwNQq3Qto94zpfMUDg47KGU8hBBCFCpSZ47iX6fmfuJ9Hn17FodmjAZseOyFvaz/pTm3Y2/TdVFXDv17H7t/JpN84skM7/P2hg8/hL5DwphzbBYTdk4gKTWJZU8vo0JMH957D7ZuzV9sr72mtuYCuHDvAs1/VnH1qtWLZU8vyzCMm5WfD/3My3+9jI3Ohrpl61LZqzKVPSur5weP2mVq42TnlOF9O6/spM2cNlTyrMSlkZfy9yGEEEJYRXH/+W0sWc1azIXFhNHmwwmcmzkFsKHrM+f5+5cW6HRqU/Ytg7fwxK9PsKNMTxxbP0rNI8u5cMKHESOg86Aj/N9/3/LBrN9ISlVbQvWt3ZfetXqj08HmzbBhg0r4DmW/bWm2qleHr75Sx5HxkYZ9SBv7N2Zh74W5JnIAQxsNJeRGCLNDZvNv+L/8G/5vpmtc7V3pVr0bvWr2onv17pRyLmVY/JDTzg9CCCFEUSA9cxTfzP5o2FG6fvUlYbPmQbIznXvdYu2yMtg8lCPFJsXS749+/H32b+xt7Pmw9Vi2XNrEjis7DNe0qtCKkc1H0rt27yxXbsbFpR3POjCLtzeMpr5vA/a+tDfb+BwdwcYGklOTeXzx46w/v54A9wD2v7Sfch7l8vRZT98+zYV7F7gUcUk9ItXz+bvnuRN3x3CdnY0dbSu1JT45nl1XdzG6xWi+6fJNnu4lhBCicCiuP7/zSpI5LPfN8M03cPUqfPQRlCljtmYBVZft7Fn49184dkwtUNBLSkni4I0DHAk7ivbvs5DgSfvOMaxf7YZ9NpUwElMSGbJyCL8d/81wzs7Gjv51+vNW87doVq6Z0bHdjbuL/zf+JKYkcmjYoUxbVD1sxN8jmHFgBi72Lux4YYehDIg5aJrGodBDrPxvJStPr+T4zeMZXv/5iZ8Z2nio2e4nhBCi4Egyp0gyh2W+GaKiVKmNyEhwd1crN0eNUrsb5NXNmypp0ydu//6rdk1Ivwl9Tlq2TmLzBnucc97kgJTUFN7d+C4r/lvBwHoDeb3Z6zlu5p6TAUsH8PuJ33mj2RtM7571VlIAM/bPMGyHtfzp5fSu3duk+xnr3N1z/Pnfn6w8vZKYxBjWDVyHr5uvRe8phBDCMiSZUySZw3LfDBs3wvvvp80nK102maEjb9Cx7wXup0Zgb2OPi70L/9/evQdFdd5vAH92uSwuuCwgLBIuSjEqogIaCUZRKwNaY0Lob3So8UKMmkbaKFEprVHTNmOMxuhEUy+NSY3JiMQYm7ZiqRBv2RJlwAAqAkIYDZcS5CIoC+z7+8Ny6gomCAvLgeczc2bCOe+efZ/3vOA357Jrb2sPext7WLXa42axBtev2uNqni1ychT45hugoqLj/dvb33sCddw4wN6pHilFJ3Dlv/eCaewc8TO/n2HyyJFYsuReQdmb/ln0T0QeioSTnRO+e/W7dg8gAEBKYQrmfDIHRmHEmzPfRMKUhN7tJBERyRqLuXv6zQMQu3fvxtatW1FeXo7x48fj3XffxaRJkyzap9e+fRLXoq8AvrOAU2+gqtIPW37rjS3bmoDp+wC7WqBi3P+WKk9AdHBIFEbYDimF2vM6HL1L4DzsJtx8y+Hm2QAHlRpKhRJ/vnQQ9QH1sFJYYfWTq7Fx+ho42HbhNKCZzBw+E54aT9you4FVKasQ6B4IrZ1WWhqbGzEveR6MwojYwFise2qdxfpKREQkZ/2imEtKSkJ8fDz27NmDkJAQ7NixA5GRkcjPz4ebm5vF+tXQ3IDbLXVAwBFg1DHYXFqJ1vTfwlg9Avjsk45fZFcN6L4xXdzyYLBthAFADYBvAeDWf5f7hDwWgr1P7+0T38VppbTCkvFL8Mezf8TezL0PbRfmE4Y9T+/hl70TERF1Ub+4zBoSEoInnngCu3btAgAYjUZ4eXnhV7/6FX7zm9+0a9/U1ISm+244u3nzJvz9/c1+mvZq1VVYK62htdPCUeUIGysb3L4NbN8O7NsHaLX3LpHevwz1MOJOSyMamxvRYGhAQ3NDh//d2NyIhuYGaf043TgsGLsAVkors/W/u+qb6vHW+bdws/4mau7WmCy37t5C8NBgHPm/I3BRu1i6q0REJENduczaF6/kdZfsizmDwQC1Wo1PP/0UUVFR0vrFixejpqYGx48fb/eaTZs24fXXX2+3fqBfcyciIpKTRy3mkpKSsGjRIpMrecnJyRa/ktddsv86r6qqKrS2tkKnM30iUafToby8vMPXJCYmora2Vlou3/+lpERERCQr9fX1qKurk5amh3zcw/bt27Fs2TLExsbC398fe/bsgVqtxoEDB3q5x+Yl+2KuK1QqFTQajbQM7u1HPYmIiMhs/P394ejoKC2bN29u18ZgMCAzMxPh4eHSOqVSifDwcOj1+t7srtnJ/gGIIUOGwMrKChUPfH5HRUUF3N3dLdQrIiIi6i2XL1/GY4/975uDVCpVuzY/dCXv6tWrPd7HniT7M3O2traYMGECTp06Ja0zGo04deoUQkNDLdgzIiIi6g2DBw82ueLWUTHXn8n+zBwAxMfHY/HixZg4cSImTZqEHTt2oKGhAbGxsZbuGhEREfUB/flKnuzPzAHA/PnzsW3bNmzYsAGBgYHIzs5GSkpKu1OpRERENDD15yt5/eLMHADExcUhLi7O0t0gIiKiPqq/XsnrN8UcERER0Q+ZP38+/vOf/2DDhg0oLy9HYGBgv7iSx2KOiIiIBoz+eCWvX9wzR0RERDRQsZgjIiIikjEWc0REREQyxmKOiIiISMZYzBERERHJGJ9mxb0PDQSAsrIyC/eEiIiIOqvt3+22f8cHKhZzgPTVHpMmTbJwT4iIiOhRVVRUwNvb29LdsBiFEEJYuhOW1tLSgqysLOh0OiiVvXPlub6+Hv7+/rh8+TIGDx7cK+/ZlzD/wM4PcAwGen6AYzDQ8wPdHwOj0YiKigoEBQXB2nrgnp9iMWchdXV1cHR0RG1tLTQajaW70+uYf2DnBzgGAz0/wDEY6PkBjoG58AEIIiIiIhljMUdEREQkYyzmLESlUmHjxo1QqVSW7opFMP/Azg9wDAZ6foBjMNDzAxwDc+E9c0REREQyxjNzRERERDLGYo6IiIhIxljMEREREckYizkiIiIiGWMx1w1nzpzB3Llz4eHhAYVCgc8//9xke0VFBZYsWQIPDw+o1WrMmjULBQUFJm2mT58OhUJhsrz00ksmbUpLSzFnzhyo1Wq4ublh7dq1aGlp6el4P8oc+QFAr9fjpz/9Kezt7aHRaBAWFoY7d+5I26urq7FgwQJoNBpotVosXboUt2/f7ul4P6q7+UtKStod+7YlOTlZatdXjz9gnjlQXl6OhQsXwt3dHfb29ggODsbRo0dN2vTXOQAARUVFeO655+Dq6gqNRoN58+ZJXzHYpq/m37x5M5544gkMHjwYbm5uiIqKQn5+vkmbu3fvYuXKlXBxcYGDgwN+/vOft8vXmTn+5ZdfIjg4GCqVCn5+fvjwww97Ol6nmGsMfv3rX2PChAlQqVQIDAzs8L2++eYbTJ06FXZ2dvDy8sJbb73VU7E6zRz5L126hJiYGHh5eWHQoEEYPXo0du7c2e69+uoc6AtYzHVDQ0MDxo8fj927d7fbJoRAVFQUrl+/juPHjyMrKws+Pj4IDw9HQ0ODSdtly5ahrKxMWu7/BW1tbcWcOXNgMBjw1Vdf4S9/+Qs+/PBDbNiwocfz/Rhz5Nfr9Zg1axYiIiLw9ddf48KFC4iLizP5WrUFCxYgLy8Pqamp+Nvf/oYzZ85g+fLlvZLxh3Q3v5eXl8lxLysrw+uvvw4HBwfMnj0bQN8+/oB55sCiRYuQn5+Pv/71r8jJyUF0dDTmzZuHrKwsqU1/nQMNDQ2IiIiAQqFAWloazp8/D4PBgLlz55p8cXhfzX/69GmsXLkS//73v5Gamorm5mZERESYHN/Vq1fjiy++QHJyMk6fPo3vvvsO0dHR0vbOzPHi4mLMmTMHM2bMQHZ2NlatWoUXX3wRJ0+e7NW8HTHHGLR54YUXMH/+/A7fp66uDhEREfDx8UFmZia2bt2KTZs2Yd++fT2WrTPMkT8zMxNubm44dOgQ8vLy8Lvf/Q6JiYnYtWuX1KYvz4E+QZBZABDHjh2Tfs7PzxcARG5urrSutbVVuLq6iv3790vrpk2bJl555ZWH7vcf//iHUCqVory8XFr3pz/9SWg0GtHU1GTWDN3R1fwhISFi/fr1D93v5cuXBQBx4cIFad2JEyeEQqEQN2/eNG+Ibuhq/gcFBgaKF154QfpZLsdfiK6Pgb29vTh48KDJvpydnaU2/XkOnDx5UiiVSlFbWyu1qampEQqFQqSmpgoh5JNfCCEqKysFAHH69GkhxL0sNjY2Ijk5WWpz5coVAUDo9XohROfm+Lp168SYMWNM3mv+/PkiMjKypyM9sq6Mwf02btwoxo8f3279e++9J5ycnEx+7xMSEsTIkSPNH6Ibupu/zcsvvyxmzJgh/SynOWAJPDPXQ5qamgAAdnZ20jqlUgmVSoVz586ZtP34448xZMgQBAQEIDExEY2NjdI2vV6PsWPHQqfTSesiIyNRV1eHvLy8Hk7RdZ3JX1lZiYyMDLi5uWHy5MnQ6XSYNm2ayfjo9XpotVpMnDhRWhceHg6lUomMjIxeSvPoHuX4t8nMzER2djaWLl0qrZPr8Qc6PwaTJ09GUlISqqurYTQacfjwYdy9exfTp08H0L/nQFNTExQKhckHptrZ2UGpVEpt5JS/trYWAODs7Azg3pxubm5GeHi41GbUqFHw9vaGXq8H0Lk5rtfrTfbR1qZtH31JV8agM/R6PcLCwmBrayuti4yMRH5+Pm7dumWm3nefufLX1tZK+wDkNQcsgcVcD2mbrImJibh16xYMBgO2bNmCGzduoKysTGr3i1/8AocOHUJ6ejoSExPx0Ucf4fnnn5e2l5eXm/yRAyD9XF5e3jthuqAz+a9fvw4A2LRpE5YtW4aUlBQEBwdj5syZ0n1F5eXlcHNzM9m3tbU1nJ2dZZ//Qe+//z5Gjx6NyZMnS+vkevyBzo/BkSNH0NzcDBcXF6hUKqxYsQLHjh2Dn58fgP49B5588knY29sjISEBjY2NaGhowJo1a9Da2iq1kUt+o9GIVatW4amnnkJAQACAe323tbWFVqs1aavT6aS+d2aOP6xNXV2dyf21ltbVMegMOfwtMFf+r776CklJSSa3EshlDlgKi7keYmNjg88++wzXrl2Ds7Mz1Go10tPTMXv2bJP7wZYvX47IyEiMHTsWCxYswMGDB3Hs2DEUFRVZsPfd15n8bfcErVixArGxsQgKCsI777yDkSNH4sCBA5bsfrd19vi3uXPnDj755BOTs3Jy19kxeO2111BTU4N//etfuHjxIuLj4zFv3jzk5ORYsPfd15n8rq6uSE5OxhdffAEHBwc4OjqipqYGwcHBHc6TvmzlypXIzc3F4cOHLd0VixnoY2CO/Lm5uXj22WexceNGREREmLF3/Zu1pTvQn02YMAHZ2dmora2FwWCAq6srQkJCTC6XPCgkJAQAUFhYiJ/85Cdwd3fH119/bdKm7Skgd3f3nuu8GfxY/qFDhwIA/P39TV43evRolJaWAriXsbKy0mR7S0sLqqurZZ//fp9++ikaGxuxaNEik/VyPv7Aj49BUVERdu3ahdzcXIwZMwYAMH78eJw9exa7d+/Gnj17+v0ciIiIQFFREaqqqmBtbQ2tVgt3d3f4+voCkMfvQFxcnPRghqenp7Te3d0dBoMBNTU1JmdmKioqpL53Zo67u7u3e/qzoqICGo0GgwYN6olIj6w7Y9AZDxuDtm2WZo78ly9fxsyZM7F8+XKsX7/eZJsc5oAlyet//WTK0dERrq6uKCgowMWLF/Hss88+tG12djaA/xU6oaGhyMnJMfljnpqaCo1G064I6qseln/YsGHw8PBo9xj7tWvX4OPjA+Be/pqaGmRmZkrb09LSYDQapcK3r+vM8X///ffxzDPPwNXV1WR9fzj+wMPHoO3+0AfPQllZWUlnbgfKHBgyZAi0Wi3S0tJQWVmJZ555BkDfzi+EQFxcHI4dO4a0tDQMHz7cZPuECRNgY2ODU6dOSevy8/NRWlqK0NBQAJ2b46GhoSb7aGvTtg9LMscYdEZoaCjOnDmD5uZmaV1qaipGjhwJJyen7gfpInPlz8vLw4wZM7B48WK88cYb7d6nL8+BPsHCD2DIWn19vcjKyhJZWVkCgNi+fbvIysoS3377rRBCiCNHjoj09HRRVFQkPv/8c+Hj4yOio6Ol1xcWForf//734uLFi6K4uFgcP35c+Pr6irCwMKlNS0uLCAgIEBERESI7O1ukpKQIV1dXkZiY2Ot5H9Td/EII8c477wiNRiOSk5NFQUGBWL9+vbCzsxOFhYVSm1mzZomgoCCRkZEhzp07J0aMGCFiYmJ6NWtHzJFfCCEKCgqEQqEQJ06caLetLx9/Ibo/BgaDQfj5+YmpU6eKjIwMUVhYKLZt2yYUCoX4+9//LrXrz3PgwIEDQq/Xi8LCQvHRRx8JZ2dnER8fb9Kmr+b/5S9/KRwdHcWXX34pysrKpKWxsVFq89JLLwlvb2+RlpYmLl68KEJDQ0VoaKi0vTNz/Pr160KtVou1a9eKK1euiN27dwsrKyuRkpLSq3k7Yo4xEOLe34GsrCyxYsUK8fjjj0vzqu3p1ZqaGqHT6cTChQtFbm6uOHz4sFCr1WLv3r29mvdB5sifk5MjXF1dxfPPP2+yj8rKSqlNX54DfQGLuW5IT08XANotixcvFkIIsXPnTuHp6SlsbGyEt7e3WL9+vclj5aWlpSIsLEw4OzsLlUol/Pz8xNq1a00+pkAIIUpKSsTs2bPFoEGDxJAhQ8Srr74qmpubezNqh7qbv83mzZuFp6enUKvVIjQ0VJw9e9Zk+/fffy9iYmKEg4OD0Gg0IjY2VtTX1/dGxB9krvyJiYnCy8tLtLa2dvg+ffX4C2GeMbh27ZqIjo4Wbm5uQq1Wi3HjxrX7qJL+PAcSEhKETqcTNjY2YsSIEeLtt98WRqPRpE1fzd9RdgDigw8+kNrcuXNHvPzyy8LJyUmo1Wrx3HPPibKyMpP9dGaOp6eni8DAQGFrayt8fX1N3sOSzDUG06ZN63A/xcXFUptLly6JKVOmCJVKJR577DHx5ptv9lLKhzNH/o0bN3a4Dx8fH5P36qtzoC9QCCGEuc7yEREREVHv4j1zRERERDLGYo6IiIhIxljMEREREckYizkiIiIiGWMxR0RERCRjLOaIiIiIZIzFHBEREZGMsZgjIiIikjEWc0REREQyxmKOiGRBCIHw8HBERka22/bee+9Bq9Xixo0bFugZEZFlsZgjIllQKBT44IMPkJGRgb1790rri4uLsW7dOrz77rvw9PQ063s2NzebdX9ERD2BxRwRyYaXlxd27tyJNWvWoLi4GEIILF26FBEREQgKCsLs2bPh4OAAnU6HhQsXoqqqSnptSkoKpkyZAq1WCxcXFzz99NMoKiqStpeUlEChUCApKQnTpk2DnZ0dPv74Y0vEJCJ6JAohhLB0J4iIHkVUVBRqa2sRHR2NP/zhD8jLy8OYMWPw4osvYtGiRbhz5w4SEhLQ0tKCtLQ0AMDRo0ehUCgwbtw43L59Gxs2bEBJSQmys7OhVCpRUlKC4cOHY9iwYXj77bcRFBQEOzs7DB061MJpiYh+GIs5IpKdyspKjBkzBtXV1Th69Chyc3Nx9uxZnDx5Umpz48YNeHl5IT8/H48//ni7fVRVVcHV1RU5OTkICAiQirkdO3bglVde6c04RETdwsusRCQ7bm5uWLFiBUaPHo2oqChcunQJ6enpcHBwkJZRo0YBgHQptaCgADExMfD19YVGo8GwYcMAAKWlpSb7njhxYq9mISLqLmtLd4CIqCusra1hbX3vT9jt27cxd+5cbNmypV27tsukc+fOhY+PD/bv3w8PDw8YjUYEBATAYDCYtLe3t+/5zhMRmRGLOSKSveDgYBw9ehTDhg2TCrz7ff/998jPz8f+/fsxdepUAMC5c+d6u5tERD2Cl1mJSPZWrlyJ6upqxMTE4MKFCygqKsLJkycRGxuL1tZWODk5wcXFBfv27UNhYSHS0tIQHx9v6W4TEZkFizkikj0PDw+cP38era2tiIiIwNixY7Fq1SpotVoolUoolUocPnwYmZmZCAgIwOrVq7F161ZLd5uIyCz4NCsRERGRjPHMHBEREZGMsZgjIiIikjEWc0REREQyxmKOiIiISMZYzBERERHJGIs5IiIiIhljMUdEREQkYyzmiIiIiGSMxRwRERGRjLGYIyIiIpIxFnNEREREMvb/lDAKnN3xB3YAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BHYaUf-Iqu9"
      },
      "source": [
        "**Challenge**: Use the <code>.groupby()</code> and <code>.agg()</code> function together to figure out the average number of parts per set. How many parts did the average LEGO set released in 1954 compared to say, 2017?"
      ]
    },
    {
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        "sets.head()"
      ],
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  set_num                        name  year  theme_id  num_parts\n",
              "0   001-1                       Gears  1965         1         43\n",
              "1  0011-2           Town Mini-Figures  1978        84         12\n",
              "2  0011-3  Castle 2 for 1 Bonus Offer  1987       199          0\n",
              "3  0012-1          Space Mini-Figures  1979       143         12\n",
              "4  0013-1          Space Mini-Figures  1979       143         12"
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              "      <th>3</th>\n",
              "      <td>0012-1</td>\n",
              "      <td>Space Mini-Figures</td>\n",
              "      <td>1979</td>\n",
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              "      <td>Space Mini-Figures</td>\n",
              "      <td>1979</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjbb3tZcIqu_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "ef7db3a3-8c5a-4c5f-8636-49fca4b77e2b"
      },
      "source": [
        "parts_per_set = sets.groupby('year').agg({'num_parts': pd.Series.mean})\n",
        "parts_per_set"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_parts\n",
              "year            \n",
              "1949   99.600000\n",
              "1950    1.000000\n",
              "1953   13.500000\n",
              "1954   12.357143\n",
              "1955   36.607143\n",
              "...          ...\n",
              "2017  221.840967\n",
              "2018  213.618873\n",
              "2019  207.510714\n",
              "2020  259.732938\n",
              "2021    0.000000\n",
              "\n",
              "[71 rows x 1 columns]"
            ],
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              "      <th>year</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1949</th>\n",
              "      <td>99.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1953</th>\n",
              "      <td>13.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1954</th>\n",
              "      <td>12.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1955</th>\n",
              "      <td>36.607143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>221.840967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>213.618873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>207.510714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>259.732938</td>\n",
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              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>0.000000</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\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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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84dd1813-2117-47a5-9061-b138da4aac80 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-84dd1813-2117-47a5-9061-b138da4aac80');\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",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAeTe2XqIqvB"
      },
      "source": [
        "### Scatter Plots in Matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAViZ_TYIqvB"
      },
      "source": [
        "**Challenge**: Has the size and complexity of LEGO sets increased over time based on the number of parts? Plot the average number of parts over time using a Matplotlib scatter plot. See if you can use the [scatter plot documentation](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.scatter.html) before I show you the solution. Do you spot a trend in the chart? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQNZ0D7JIqvB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "5b42952e-724a-4829-82b9-52da677d3ad0"
      },
      "source": [
        "plt.scatter(parts_per_set.index[:-2], parts_per_set.num_parts[:-2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb97b17eee0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xK226Ip-IqvE"
      },
      "source": [
        "### Number of Sets per LEGO Theme"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKHa1FePIqvE"
      },
      "source": [
        "LEGO has licensed many hit franchises from Harry Potter to Marvel Super Heros to many others. But which theme has the largest number of individual sets? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOBcNrC9IqvE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2eee5676-cde2-43c1-f4b4-492672100918"
      },
      "source": [
        "sets.theme_id.value_counts().head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "158    753\n",
              "501    656\n",
              "494    398\n",
              "435    356\n",
              "503    329\n",
              "Name: theme_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-i6JULGIqvG"
      },
      "source": [
        "**Challenge** Use what you know about HTML markup and tags to display the database schema: https://i.imgur.com/Sg4lcjx.png"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27oDwiPHIqvH"
      },
      "source": [
        "<img src='https://i.imgur.com/Sg4lcjx.png'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_0iuerKIqvG"
      },
      "source": [
        "### Database Schemas, Foreign Keys and Merging DataFrames\n",
        "\n",
        "The themes.csv file has the actual theme names. The sets .csv has <code>theme_ids</code> which link to the <code>id</code> column in the themes.csv. "
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**Challenge**: Explore the themes.csv. How is it structured? Search for the name 'Star Wars'. How many <code>id</code>s correspond to this name in the themes.csv? Now use these <code>id</code>s and find the corresponding the sets in the sets.csv (Hint: you'll need to look for matches in the <code>theme_id</code> column)"
      ]
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          "height": 206
        },
        "outputId": "b8d47b29-99cf-4df4-933e-d8f1e1e100e2"
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      "source": [
        "themes = pd.read_csv('data/themes.csv')\n",
        "themes.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id            name  parent_id\n",
              "0   1         Technic        NaN\n",
              "1   2  Arctic Technic        1.0\n",
              "2   3     Competition        1.0\n",
              "3   4  Expert Builder        1.0\n",
              "4   5           Model        1.0"
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      "source": [
        "themes[themes.name=='Star Wars']"
      ],
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id       name  parent_id\n",
              "17    18  Star Wars        1.0\n",
              "150  158  Star Wars        NaN\n",
              "174  209  Star Wars      207.0\n",
              "211  261  Star Wars      258.0"
            ],
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              "      <th>211</th>\n",
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              "      <td>Star Wars</td>\n",
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      "source": [
        "sets[sets.theme_id==18]"
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        {
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          "data": {
            "text/plain": [
              "       set_num                                name  year  theme_id  num_parts\n",
              "8786   65081-1  R2-D2 / C-3PO Droid Collectors Set  2002        18          1\n",
              "12051   8000-1                           Pit Droid  2000        18        223\n",
              "12058   8001-1                        Battle Droid  2000        18        336\n",
              "12066   8002-1                     Destroyer Droid  2000        18        567\n",
              "12071   8007-1                               C-3PO  2001        18        339\n",
              "12072   8008-1                        Stormtrooper  2001        18        360\n",
              "12073   8009-1                               R2-D2  2002        18        239\n",
              "12074   8010-1                         Darth Vader  2002        18        388\n",
              "12081   8011-1                          Jango Fett  2002        18        425\n",
              "12083   8012-1                  Super Battle Droid  2002        18        378\n",
              "15273  K8008-1      Darth Vader / Stormtrooper Kit  2002        18          0"
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              "      <th>12073</th>\n",
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              "      <th>15273</th>\n",
              "      <td>K8008-1</td>\n",
              "      <td>Darth Vader / Stormtrooper Kit</td>\n",
              "      <td>2002</td>\n",
              "      <td>18</td>\n",
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              "           set_num                                               name  year  \\\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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-e22fa110-5829-41ae-a472-81bd61b7c7f4 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-e22fa110-5829-41ae-a472-81bd61b7c7f4');\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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sets[sets.theme_id==261]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "THENiPcEptLv",
        "outputId": "77f3d93f-f2d6-4cc8-f4df-daed472e0692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      set_num                      name  year  theme_id  num_parts\n",
              "14630  9748-1       Droid Developer Kit  1999       261        658\n",
              "14636  9754-1  Dark Side Developers Kit  2000       261        578"
            ],
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              "\n",
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              "      <th>14630</th>\n",
              "      <td>9748-1</td>\n",
              "      <td>Droid Developer Kit</td>\n",
              "      <td>1999</td>\n",
              "      <td>261</td>\n",
              "      <td>658</td>\n",
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              "    <tr>\n",
              "      <th>14636</th>\n",
              "      <td>9754-1</td>\n",
              "      <td>Dark Side Developers Kit</td>\n",
              "      <td>2000</td>\n",
              "      <td>261</td>\n",
              "      <td>578</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      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-80f81e09-86e6-4512-83b2-b66ca739937c 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-80f81e09-86e6-4512-83b2-b66ca739937c');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmTCXWKKIqvQ"
      },
      "source": [
        "### Merging (i.e., Combining) DataFrames based on a Key\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esKQULhcIqvR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37444d36-8203-4703-8bc1-f29af093d9bf"
      },
      "source": [
        "set_theme_count = sets.theme_id.value_counts()\n",
        "set_theme_count[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "158    753\n",
              "501    656\n",
              "494    398\n",
              "435    356\n",
              "503    329\n",
              "Name: theme_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0LobgIvIqvT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b628f2f3-155c-4144-81f5-0b6d20bf65bd"
      },
      "source": [
        "set_theme_count = pd.DataFrame({'id':set_theme_count.index, 'set_count': set_theme_count.values})\n",
        "set_theme_count.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id  set_count\n",
              "0  158        753\n",
              "1  501        656\n",
              "2  494        398\n",
              "3  435        356\n",
              "4  503        329"
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              "    <tr>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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-9b0cee25-4b95-4409-af44-316ac246e0f8 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-9b0cee25-4b95-4409-af44-316ac246e0f8');\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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7UMP7VXIqvU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "a0af31a5-f709-42c7-b87e-afac200452bb"
      },
      "source": [
        "merged_df = pd.merge(set_theme_count, themes, on='id')\n",
        "merged_df[:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id  set_count       name  parent_id\n",
              "0  158        753  Star Wars        NaN\n",
              "1  501        656       Gear        NaN\n",
              "2  494        398    Friends        NaN"
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    .colab-df-convert {\n",
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              "\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-e5980e0a-6880-4edd-9d26-8b54b3908a6f 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-e5980e0a-6880-4edd-9d26-8b54b3908a6f');\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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "plt.xticks(fontsize=14, rotation=45)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.ylabel('No. of Sets', fontsize=14)\n",
        "plt.xlabel('Theme Name', fontsize=14)\n",
        "\n",
        "plt.bar(merged_df.name[:10], merged_df.set_count[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "Q7soidncAyD8",
        "outputId": "b962a963-2fd4-4e32-e349-afab52548179"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 10 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XPWeM8RVBFoW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}