Stephan_Curry_The_outlier.ipynb

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        "<a href=\"https://colab.research.google.com/github/Wasid-Khan/ASP.NET-Core-Web-API-Development/blob/main/Stephan_Curry_The_outlier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "Replacing text and saving the picture as output.png"
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      "source": [
        "from PIL import Image, ImageDraw, ImageFont\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "# Load the image\n",
        "image = cv2.imread('cc2.jpg')\n",
        "\n",
        "# Convert the image to RGB (OpenCV loads images in BGR by default)\n",
        "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "# Convert to a Pillow Image object\n",
        "pil_image = Image.fromarray(image_rgb)\n",
        "\n",
        "# Create a drawing object\n",
        "draw = ImageDraw.Draw(pil_image)\n",
        "\n",
        "# Use a default font available in Pillow\n",
        "font = ImageFont.load_default()  # This will use a simple default font\n",
        "\n",
        "# Define the text to replace and its position (tweak as necessary)\n",
        "text = \"DEVELOPER\"\n",
        "text_position = (130, 40)  # Adjust this position to match where \"MASTER\" is in the image\n",
        "\n",
        "# Optional: Add a background rectangle to cover the old text (if needed)\n",
        "# draw.rectangle([text_position, (300, 100)], fill=(0, 0, 0))blob:https://colab.research.google.com/ef40788f-8c20-4b81-8ce1-56d9862058f8\n",
        "\n",
        "# Draw the new text over the image\n",
        "draw.text(text_position, text, font=font, fill=\"white\")\n",
        "\n",
        "# Convert back to OpenCV format\n",
        "image_final = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)\n",
        "\n",
        "# Save the final image\n",
        "cv2.imwrite('output.jpg', image_final)\n",
        "\n",
        "print(\"The text has been replaced and the new image is saved as 'output.jpg'.\")\n"
      ],
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        {
          "output_type": "stream",
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          "text": [
            "The text has been replaced and the new image is saved as 'output.jpg'.\n"
          ]
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rqez0Qi6bnrR"
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      "source": [
        "### Investigating, The NBA!\n",
        "\n",
        "One of the most significant figures in the NBA is Stephen Curry – son of the famous Toronto Raptor player Dell Curry - whose efforts lead to the championship and final MVP award in the past season. while he is an outstanding player, there are talks that he is more than that and he managed to change the game of basketball with his courageous shooting attempts. So let’s see how data shows a correlation between his presence in the NBA and the dynamics of the league. Since we are already here, we do some other stuff alongside our objective.\n",
        "\n",
        "## Part 1. Preprocessing the Data\n",
        "We are going to work with players_stats_by_season_full_details.csv, the data includes player information on NBA (both regular season and playoffs) and international leagues per season starting from 1999-2000 season to 2019-2020 season. Fortunately, the variables are self-explanatory. We have 34 columns:\n",
        "\n",
        "`League, Season, Season Stage, Player Name, Games Played, Total Minutes, Field Goal Mades, Field Goal Attempts, Three Pointer Mades, Three Pointer Attempts, Free Throw Mades, Free Throw Attempts, Turnovers, Personal Fauls, Offensive Rebounds, Defensive Rebounds, Total, Rebounds, Total Asists, Total Steals, Total Blocks, Total Points, Player Birth Year, Player Birth Month, Player Birth Date, Player Height in feet, Player Height in cm, Player Weight in lbs, Player Weight in kg, Player Nationality, Player High School, Player Draft Round, Player Draft Team`\n",
        "\n",
        "Our objective is to analyze the NBA. We only consider the regular season because, in playoffs, teams do not follow the trends of the NBA. They adapt each other games through the series, and in if they win in the next series, everything, including playing types, shot selection, and so on, will change to be suitable for another series. In addition, the dataset did not provide per-game information. Since data is a little unbalanced within different seasons, using per-game stats would be way healthier anyway."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQwgsul2bnrS"
      },
      "source": [
        "1. Proper importations.\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K7V-RVGBbnrT"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcB28ce7bnrU"
      },
      "source": [
        "2. Reading the data from the .csv file and see the same columns and so on. playing with the data."
      ]
    },
    {
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              "  League       Season           Stage            Player Team  GP     MIN  FGM  \\\n",
              "0    NBA  1999 - 2000  Regular_Season  Shaquille O'Neal  LAL  79  3163.0  956   \n",
              "1    NBA  1999 - 2000  Regular_Season      Vince Carter  TOR  82  3126.0  788   \n",
              "2    NBA  1999 - 2000  Regular_Season       Karl Malone  UTA  82  2947.0  752   \n",
              "3    NBA  1999 - 2000  Regular_Season     Allen Iverson  PHI  70  2853.0  729   \n",
              "4    NBA  1999 - 2000  Regular_Season       Gary Payton  SEA  82  3425.0  747   \n",
              "\n",
              "    FGA  3PM  ...    birth_date height height_cm weight weight_kg  \\\n",
              "0  1665    0  ...   Mar 6, 1972    7-1       216    325       147   \n",
              "1  1696   95  ...  Jan 26, 1977    6-6       198    220       100   \n",
              "2  1476    2  ...  Jul 24, 1963    6-9       206    265       120   \n",
              "3  1733   89  ...   Jun 7, 1975    6-0       183    165        75   \n",
              "4  1666  177  ...  Jul 23, 1968    6-4       193    180        82   \n",
              "\n",
              "     nationality                  high_school draft_round draft_pick  \\\n",
              "0  United States  Robert G. Cole High School            1          1   \n",
              "1  United States         Mainland High School           1          5   \n",
              "2  United States      Summerfield High School           1         13   \n",
              "3  United States          Bethel High School            1          1   \n",
              "4  United States          Skyline High School           1          2   \n",
              "\n",
              "              draft_team  \n",
              "0          Orlando Magic  \n",
              "1  Golden State Warriors  \n",
              "2              Utah Jazz  \n",
              "3    Philadelphia Sixers  \n",
              "4    Seattle SuperSonics  \n",
              "\n",
              "[5 rows x 34 columns]"
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              "      <th>League</th>\n",
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              "      <th>0</th>\n",
              "      <td>NBA</td>\n",
              "      <td>1999 - 2000</td>\n",
              "      <td>Regular_Season</td>\n",
              "      <td>Shaquille O'Neal</td>\n",
              "      <td>LAL</td>\n",
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              "      <td>956</td>\n",
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              "      <td>Mar 6, 1972</td>\n",
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              "      <td>Robert G. Cole High School</td>\n",
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              "      <td>1</td>\n",
              "      <td>Orlando Magic</td>\n",
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              "      <td>NBA</td>\n",
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              "      <td>Regular_Season</td>\n",
              "      <td>Vince Carter</td>\n",
              "      <td>TOR</td>\n",
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              "      <td>3126.0</td>\n",
              "      <td>788</td>\n",
              "      <td>1696</td>\n",
              "      <td>95</td>\n",
              "      <td>...</td>\n",
              "      <td>Jan 26, 1977</td>\n",
              "      <td>6-6</td>\n",
              "      <td>198</td>\n",
              "      <td>220</td>\n",
              "      <td>100</td>\n",
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              "      <td>Mainland High School</td>\n",
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              "      <th>2</th>\n",
              "      <td>NBA</td>\n",
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              "      <td>Karl Malone</td>\n",
              "      <td>UTA</td>\n",
              "      <td>82</td>\n",
              "      <td>2947.0</td>\n",
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              "      <td>1476</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>Jul 24, 1963</td>\n",
              "      <td>6-9</td>\n",
              "      <td>206</td>\n",
              "      <td>265</td>\n",
              "      <td>120</td>\n",
              "      <td>United States</td>\n",
              "      <td>Summerfield High School</td>\n",
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              "      <td>Utah Jazz</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NBA</td>\n",
              "      <td>1999 - 2000</td>\n",
              "      <td>Regular_Season</td>\n",
              "      <td>Allen Iverson</td>\n",
              "      <td>PHI</td>\n",
              "      <td>70</td>\n",
              "      <td>2853.0</td>\n",
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              "      <td>1733</td>\n",
              "      <td>89</td>\n",
              "      <td>...</td>\n",
              "      <td>Jun 7, 1975</td>\n",
              "      <td>6-0</td>\n",
              "      <td>183</td>\n",
              "      <td>165</td>\n",
              "      <td>75</td>\n",
              "      <td>United States</td>\n",
              "      <td>Bethel High School</td>\n",
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              "      <td>Philadelphia Sixers</td>\n",
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              "      <td>NBA</td>\n",
              "      <td>1999 - 2000</td>\n",
              "      <td>Regular_Season</td>\n",
              "      <td>Gary Payton</td>\n",
              "      <td>SEA</td>\n",
              "      <td>82</td>\n",
              "      <td>3425.0</td>\n",
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              "      <td>1666</td>\n",
              "      <td>177</td>\n",
              "      <td>...</td>\n",
              "      <td>Jul 23, 1968</td>\n",
              "      <td>6-4</td>\n",
              "      <td>193</td>\n",
              "      <td>180</td>\n",
              "      <td>82</td>\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3c6a6e8e-6ca0-4589-bcfc-bc32c80707ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file into a list of dictionaries\n",
        "with open('players_stats_by_season_full_details.csv', newline='') as csvfile:\n",
        "    reader = csv.DictReader(csvfile)\n",
        "    data = list(reader)\n",
        "\n",
        "# Convert the list of dictionaries to a pandas DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Display the first few rows to get an overview of the data\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RabgQ90bnrU",
        "outputId": "b687d782-ceec-49c7-d87b-6159272263de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "  League       Season           Stage            Player Team  GP     MIN  FGM  \\\n",
            "0    NBA  1999 - 2000  Regular_Season  Shaquille O'Neal  LAL  79  3163.0  956   \n",
            "1    NBA  1999 - 2000  Regular_Season      Vince Carter  TOR  82  3126.0  788   \n",
            "2    NBA  1999 - 2000  Regular_Season       Karl Malone  UTA  82  2947.0  752   \n",
            "3    NBA  1999 - 2000  Regular_Season     Allen Iverson  PHI  70  2853.0  729   \n",
            "4    NBA  1999 - 2000  Regular_Season       Gary Payton  SEA  82  3425.0  747   \n",
            "\n",
            "    FGA  3PM  ...    birth_date  height  height_cm  weight  weight_kg  \\\n",
            "0  1665    0  ...   Mar 6, 1972     7-1      216.0   325.0      147.0   \n",
            "1  1696   95  ...  Jan 26, 1977     6-6      198.0   220.0      100.0   \n",
            "2  1476    2  ...  Jul 24, 1963     6-9      206.0   265.0      120.0   \n",
            "3  1733   89  ...   Jun 7, 1975     6-0      183.0   165.0       75.0   \n",
            "4  1666  177  ...  Jul 23, 1968     6-4      193.0   180.0       82.0   \n",
            "\n",
            "     nationality                  high_school  draft_round  draft_pick  \\\n",
            "0  United States  Robert G. Cole High School           1.0         1.0   \n",
            "1  United States         Mainland High School          1.0         5.0   \n",
            "2  United States      Summerfield High School          1.0        13.0   \n",
            "3  United States          Bethel High School           1.0         1.0   \n",
            "4  United States          Skyline High School          1.0         2.0   \n",
            "\n",
            "              draft_team  \n",
            "0          Orlando Magic  \n",
            "1  Golden State Warriors  \n",
            "2              Utah Jazz  \n",
            "3    Philadelphia Sixers  \n",
            "4    Seattle SuperSonics  \n",
            "\n",
            "[5 rows x 34 columns]\n",
            "\n",
            "Basic information about the DataFrame:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 53949 entries, 0 to 53948\n",
            "Data columns (total 34 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   League       53949 non-null  object \n",
            " 1   Season       53949 non-null  object \n",
            " 2   Stage        53949 non-null  object \n",
            " 3   Player       53949 non-null  object \n",
            " 4   Team         53938 non-null  object \n",
            " 5   GP           53949 non-null  int64  \n",
            " 6   MIN          53949 non-null  float64\n",
            " 7   FGM          53949 non-null  int64  \n",
            " 8   FGA          53949 non-null  int64  \n",
            " 9   3PM          53949 non-null  int64  \n",
            " 10  3PA          53949 non-null  int64  \n",
            " 11  FTM          53949 non-null  int64  \n",
            " 12  FTA          53949 non-null  int64  \n",
            " 13  TOV          53949 non-null  int64  \n",
            " 14  PF           53949 non-null  int64  \n",
            " 15  ORB          53949 non-null  int64  \n",
            " 16  DRB          53949 non-null  int64  \n",
            " 17  REB          53949 non-null  int64  \n",
            " 18  AST          53949 non-null  int64  \n",
            " 19  STL          53949 non-null  int64  \n",
            " 20  BLK          53949 non-null  int64  \n",
            " 21  PTS          53949 non-null  int64  \n",
            " 22  birth_year   53631 non-null  float64\n",
            " 23  birth_month  53631 non-null  object \n",
            " 24  birth_date   53631 non-null  object \n",
            " 25  height       53875 non-null  object \n",
            " 26  height_cm    53875 non-null  float64\n",
            " 27  weight       49385 non-null  float64\n",
            " 28  weight_kg    49385 non-null  float64\n",
            " 29  nationality  53942 non-null  object \n",
            " 30  high_school  23683 non-null  object \n",
            " 31  draft_round  10136 non-null  float64\n",
            " 32  draft_pick   10136 non-null  float64\n",
            " 33  draft_team   10136 non-null  object \n",
            "dtypes: float64(7), int64(16), object(11)\n",
            "memory usage: 14.0+ MB\n",
            "None\n",
            "\n",
            "Summary statistics for numerical columns:\n",
            "                 GP           MIN           FGM           FGA           3PM  \\\n",
            "count  53949.000000  53949.000000  53949.000000  53949.000000  53949.000000   \n",
            "mean      30.313574    752.431404    113.200541    245.094942     28.468535   \n",
            "std       17.849616    534.216679    100.164033    212.155076     30.673395   \n",
            "min        1.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "25%       17.000000    380.900000     48.000000    109.000000      6.000000   \n",
            "50%       29.000000    663.000000     89.000000    196.000000     20.000000   \n",
            "75%       37.000000    954.000000    145.000000    310.000000     41.000000   \n",
            "max       85.000000   3485.000000    978.000000   2173.000000    402.000000   \n",
            "\n",
            "                3PA           FTM           FTA           TOV           PF  \\\n",
            "count  53949.000000  53949.000000  53949.000000  53949.000000  53949.00000   \n",
            "mean      80.738383     56.297299     76.012716     47.260487     70.10104   \n",
            "std       80.672208     59.240650     76.172698     37.398461     45.62977   \n",
            "min        0.000000      0.000000      0.000000      0.000000      0.00000   \n",
            "25%       20.000000     20.000000     28.000000     21.000000     37.00000   \n",
            "50%       61.000000     40.000000     56.000000     39.000000     64.00000   \n",
            "75%      117.000000     73.000000     99.000000     63.000000     91.00000   \n",
            "max     1028.000000    756.000000    972.000000    464.000000    371.00000   \n",
            "\n",
            "       ...           AST           STL           BLK           PTS  \\\n",
            "count  ...  53949.000000  53949.000000  53949.000000  53949.000000   \n",
            "mean   ...     62.786910     26.715398     10.492057    311.178372   \n",
            "std    ...     73.184287     22.077459     18.199867    271.811590   \n",
            "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
            "25%    ...     20.000000     11.000000      1.000000    134.000000   \n",
            "50%    ...     41.000000     21.000000      4.000000    247.000000   \n",
            "75%    ...     78.000000     36.000000     12.000000    399.000000   \n",
            "max    ...    925.000000    225.000000    307.000000   2832.000000   \n",
            "\n",
            "         birth_year     height_cm        weight     weight_kg   draft_round  \\\n",
            "count  53631.000000  53875.000000  49385.000000  49385.000000  10136.000000   \n",
            "mean    1986.361675    197.445123    210.309527     95.422193      1.387530   \n",
            "std        6.637023      8.728587     26.128059     11.851299      0.508224   \n",
            "min     1961.000000    160.000000    130.000000     59.000000      1.000000   \n",
            "25%     1982.000000    191.000000    190.000000     86.000000      1.000000   \n",
            "50%     1987.000000    198.000000    209.000000     95.000000      1.000000   \n",
            "75%     1991.000000    203.000000    229.000000    104.000000      2.000000   \n",
            "max     2004.000000    229.000000    375.000000    170.000000      7.000000   \n",
            "\n",
            "         draft_pick  \n",
            "count  10136.000000  \n",
            "mean      14.053177  \n",
            "std        8.643064  \n",
            "min        1.000000  \n",
            "25%        6.000000  \n",
            "50%       13.000000  \n",
            "75%       22.000000  \n",
            "max       30.000000  \n",
            "\n",
            "[8 rows x 23 columns]\n"
          ]
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "\n",
        "# Load the dataset\n",
        "df = pd.read_csv('players_stats_by_season_full_details.csv')\n",
        "\n",
        "# Display the first few rows to get an overview of the data\n",
        "print(\"First few rows of the dataset:\")\n",
        "print(df.head())\n",
        "\n",
        "# Display basic information about the DataFrame\n",
        "print(\"\\nBasic information about the DataFrame:\")\n",
        "print(df.info())\n",
        "\n",
        "# Display summary statistics for numerical columns\n",
        "print(\"\\nSummary statistics for numerical columns:\")\n",
        "print(df.describe())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyTzXfRbbnrV",
        "outputId": "db0e2680-0f49-4ad9-98b6-19fd45a01c88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column names in the DataFrame:\n",
            "['League', 'Season', 'Stage', 'Player', 'Team', 'GP', 'MIN', 'FGM', 'FGA', '3PM', '3PA', 'FTM', 'FTA', 'TOV', 'PF', 'ORB', 'DRB', 'REB', 'AST', 'STL', 'BLK', 'PTS', 'birth_year', 'birth_month', 'birth_date', 'height', 'height_cm', 'weight', 'weight_kg', 'nationality', 'high_school', 'draft_round', 'draft_pick', 'draft_team']\n",
            "\n",
            "Missing values in each column:\n",
            "League             0\n",
            "Season             0\n",
            "Stage              0\n",
            "Player             0\n",
            "Team              11\n",
            "GP                 0\n",
            "MIN                0\n",
            "FGM                0\n",
            "FGA                0\n",
            "3PM                0\n",
            "3PA                0\n",
            "FTM                0\n",
            "FTA                0\n",
            "TOV                0\n",
            "PF                 0\n",
            "ORB                0\n",
            "DRB                0\n",
            "REB                0\n",
            "AST                0\n",
            "STL                0\n",
            "BLK                0\n",
            "PTS                0\n",
            "birth_year       318\n",
            "birth_month      318\n",
            "birth_date       318\n",
            "height            74\n",
            "height_cm         74\n",
            "weight          4564\n",
            "weight_kg       4564\n",
            "nationality        7\n",
            "high_school    30266\n",
            "draft_round    43813\n",
            "draft_pick     43813\n",
            "draft_team     43813\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Display all column names\n",
        "print(\"Column names in the DataFrame:\")\n",
        "print(df.columns.tolist())\n",
        "\n",
        "# Check for missing values in each column\n",
        "missing_values = df.isnull().sum()\n",
        "print(\"\\nMissing values in each column:\")\n",
        "print(missing_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdPQwF4obnrV",
        "outputId": "c706d45d-a27f-4d05-f85a-497483d51d51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaned column names:\n",
            "Index(['League', 'Season', 'Stage', 'Player', 'Team', 'GP', 'MIN', 'FGM',\n",
            "       'FGA', '3PM', '3PA', 'FTM', 'FTA', 'TOV', 'PF', 'ORB', 'DRB', 'REB',\n",
            "       'AST', 'STL', 'BLK', 'PTS', 'birth_year', 'birth_month', 'birth_date',\n",
            "       'height', 'height_cm', 'weight', 'weight_kg', 'nationality',\n",
            "       'high_school', 'draft_round', 'draft_pick', 'draft_team'],\n",
            "      dtype='object')\n",
            "\n",
            "First few rows of the regular season data:\n",
            "Empty DataFrame\n",
            "Columns: [League, Season, Stage, Player, Team, GP, MIN, FGM, FGA, 3PM, 3PA, FTM, FTA, TOV, PF, ORB, DRB, REB, AST, STL, BLK, PTS, birth_year, birth_month, birth_date, height, height_cm, weight, weight_kg, nationality, high_school, draft_round, draft_pick, draft_team]\n",
            "Index: []\n",
            "\n",
            "[0 rows x 34 columns]\n",
            "\n",
            "Basic statistics for numerical columns in the regular season data:\n",
            "        GP  MIN  FGM  FGA  3PM  3PA  FTM  FTA  TOV   PF  ...  AST  STL  BLK  \\\n",
            "count  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
            "mean   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
            "std    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
            "min    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
            "25%    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
            "50%    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
            "75%    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
            "max    NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
            "\n",
            "       PTS  birth_year  height_cm  weight  weight_kg  draft_round  draft_pick  \n",
            "count  0.0         0.0        0.0     0.0        0.0          0.0         0.0  \n",
            "mean   NaN         NaN        NaN     NaN        NaN          NaN         NaN  \n",
            "std    NaN         NaN        NaN     NaN        NaN          NaN         NaN  \n",
            "min    NaN         NaN        NaN     NaN        NaN          NaN         NaN  \n",
            "25%    NaN         NaN        NaN     NaN        NaN          NaN         NaN  \n",
            "50%    NaN         NaN        NaN     NaN        NaN          NaN         NaN  \n",
            "75%    NaN         NaN        NaN     NaN        NaN          NaN         NaN  \n",
            "max    NaN         NaN        NaN     NaN        NaN          NaN         NaN  \n",
            "\n",
            "[8 rows x 23 columns]\n",
            "\n",
            "Data types of each column:\n",
            "League          object\n",
            "Season          object\n",
            "Stage           object\n",
            "Player          object\n",
            "Team            object\n",
            "GP               int64\n",
            "MIN            float64\n",
            "FGM              int64\n",
            "FGA              int64\n",
            "3PM              int64\n",
            "3PA              int64\n",
            "FTM              int64\n",
            "FTA              int64\n",
            "TOV              int64\n",
            "PF               int64\n",
            "ORB              int64\n",
            "DRB              int64\n",
            "REB              int64\n",
            "AST              int64\n",
            "STL              int64\n",
            "BLK              int64\n",
            "PTS              int64\n",
            "birth_year     float64\n",
            "birth_month     object\n",
            "birth_date      object\n",
            "height          object\n",
            "height_cm      float64\n",
            "weight         float64\n",
            "weight_kg      float64\n",
            "nationality     object\n",
            "high_school     object\n",
            "draft_round    float64\n",
            "draft_pick     float64\n",
            "draft_team      object\n",
            "dtype: object\n",
            "\n",
            "Unique values in the 'Stage' column:\n",
            "['Regular_Season' 'Playoffs' 'International']\n",
            "\n",
            "Unique values in the 'Season' column:\n",
            "['1999 - 2000' '2000 - 2001' '2001 - 2002' '2002 - 2003' '2003 - 2004'\n",
            " '2004 - 2005' '2005 - 2006' '2006 - 2007' '2007 - 2008' '2008 - 2009'\n",
            " '2009 - 2010' '2010 - 2011' '2011 - 2012' '2012 - 2013' '2013 - 2014'\n",
            " '2014 - 2015' '2015 - 2016' '2016 - 2017' '2017 - 2018' '2018 - 2019'\n",
            " '2019 - 2020']\n",
            "\n",
            "Unique values in the 'Stage' column:\n",
            "['Regular_Season' 'Playoffs' 'International']\n"
          ]
        }
      ],
      "source": [
        "# Clean column names by stripping any leading or trailing whitespace\n",
        "df.columns = df.columns.str.strip()\n",
        "\n",
        "# Print out cleaned column names\n",
        "print(\"Cleaned column names:\")\n",
        "print(df.columns)\n",
        "\n",
        "# Filter the data to include only the regular season\n",
        "df_regular_season = df[df['Stage'] == 'Regular']\n",
        "\n",
        "# Display the first few rows of the filtered DataFrame\n",
        "print(\"\\nFirst few rows of the regular season data:\")\n",
        "print(df_regular_season.head())\n",
        "\n",
        "# Display basic statistics for numerical columns in the regular season data\n",
        "print(\"\\nBasic statistics for numerical columns in the regular season data:\")\n",
        "print(df_regular_season.describe())\n",
        "\n",
        "# Check data types of each column\n",
        "print(\"\\nData types of each column:\")\n",
        "print(df.dtypes)\n",
        "\n",
        "# Check unique values in the 'Stage' column\n",
        "print(\"\\nUnique values in the 'Stage' column:\")\n",
        "print(df['Stage'].unique())\n",
        "\n",
        "# Check unique values in 'Season' and 'Stage'\n",
        "print(\"\\nUnique values in the 'Season' column:\")\n",
        "print(df['Season'].unique())\n",
        "\n",
        "print(\"\\nUnique values in the 'Stage' column:\")\n",
        "print(df['Stage'].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O32UOD_dbnrV"
      },
      "source": [
        "3. add new columns including per game stats for example create `PPG` column using the two existing columns `PTS` and `GP`. You should do that for `MIN`, `TOV`, `3PA`, `FGA`, `FGM` and `PF` as well!\n",
        "Suggested name for the new variables are `PPG`, `MPG`, `TOVPG`, `3PAPG`, `3PMPG`, `FGAPG`,`FGMPG`, `FPG`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRpiu6WMbnrW",
        "outputId": "6815bb87-5aaf-4f86-a3e5-17e66de2022d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First few rows with per-game statistics:\n",
            "         PPG        MPG     TOVPG     3PAPG     3PMPG      FGAPG      FGMPG  \\\n",
            "0  29.670886  40.037975  2.822785  0.012658  0.000000  21.075949  12.101266   \n",
            "1  25.695122  38.121951  2.170732  2.878049  1.158537  20.682927   9.609756   \n",
            "2  25.548780  35.939024  2.817073  0.097561  0.024390  18.000000   9.170732   \n",
            "3  28.414286  40.757143  3.285714  3.728571  1.271429  24.757143  10.414286   \n",
            "4  24.170732  41.768293  2.731707  6.341463  2.158537  20.317073   9.109756   \n",
            "\n",
            "        FPG  \n",
            "0  3.227848  \n",
            "1  3.207317  \n",
            "2  2.792683  \n",
            "3  2.314286  \n",
            "4  2.170732  \n"
          ]
        }
      ],
      "source": [
        "# Ensure that all columns involved in calculations are present and have valid data\n",
        "required_columns = ['PTS', 'GP', 'MIN', 'TOV', '3PA', 'FGA', 'FGM', 'PF']\n",
        "for col in required_columns:\n",
        "    if col not in df.columns:\n",
        "        raise KeyError(f\"Column '{col}' not found in DataFrame\")\n",
        "\n",
        "# Create new columns for per-game stats\n",
        "df['PPG'] = df['PTS'] / df['GP']  # Points per game\n",
        "df['MPG'] = df['MIN'] / df['GP']  # Minutes per game\n",
        "df['TOVPG'] = df['TOV'] / df['GP']  # Turnovers per game\n",
        "df['3PAPG'] = df['3PA'] / df['GP']  # Three-point attempts per game\n",
        "df['3PMPG'] = df['3PM'] / df['GP']  # Three-point makes per game\n",
        "df['FGAPG'] = df['FGA'] / df['GP']  # Field goal attempts per game\n",
        "df['FGMPG'] = df['FGM'] / df['GP']  # Field goals made per game\n",
        "df['FPG'] = df['PF'] / df['GP']  # Personal fouls per game\n",
        "\n",
        "# Display the first few rows to verify the new columns\n",
        "print(\"\\nFirst few rows with per-game statistics:\")\n",
        "print(df[['PPG', 'MPG', 'TOVPG', '3PAPG', '3PMPG', 'FGAPG', 'FGMPG', 'FPG']].head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQaRQ4i5bnrW"
      },
      "source": [
        "4. print out all the unique values of the leagues in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "koWVJIbfbnrW",
        "outputId": "87630c38-8b91-49d2-a35f-6139c36f5bab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in 'League' column:\n",
            "NBA\n",
            "Euroleague\n",
            "Eurocup\n",
            "Australian-NBL\n",
            "German-BBL\n",
            "Italian-Lega-Basket-Serie-A\n",
            "Spanish-ACB\n",
            "French-Jeep-Elite\n",
            "Argentinian-Liga-A\n",
            "Austrian-A-Bundesliga\n",
            "Balkan-BIL\n",
            "Belarusian-BPL\n",
            "Belgium-Scooore-League\n",
            "Bosnian-BiH-Liga\n",
            "Brazilian-NBB\n",
            "British-BBL\n",
            "Bulgarian-NBL\n",
            "Canadian-NBL\n",
            "Chinese-CBA\n",
            "Croatian-A-1-Liga\n",
            "Czech-NBL\n",
            "Danish-Basketligaen\n",
            "Finnish-Korisliiga\n",
            "Greek-HEBA-A1\n",
            "Hungarian-NBIA\n",
            "Israeli-BSL\n",
            "Lithuanian-LKL\n",
            "Macedonian-Superleague\n",
            "Mexican-LNBP\n",
            "Netherlands-DBL\n",
            "New-Zealand-NBL\n",
            "Norwegian-BLNO\n",
            "Polish-TBL\n",
            "Romanian-Divizia-A\n",
            "Serbian-KLS\n",
            "Slovakian-Extraliga\n",
            "Slovenian-SKL\n",
            "South-Korean-KBL\n",
            "Swedish-Basketligan\n",
            "Swiss-LNA\n",
            "Turkish-BSL\n",
            "Ukrainian-Superleague\n",
            "Georgian-Super-Liga\n",
            "Lebanese-Division-A\n",
            "Luxembourg-Total-League\n",
            "FIBA-Europe-Cup\n",
            "Japanese-BLeague\n",
            "Kosovo-FBK\n",
            "Estonian-Latvian-Basketball-League\n"
          ]
        }
      ],
      "source": [
        "# Print out all unique values in the 'League' column\n",
        "unique_leagues = df['League'].unique()\n",
        "print(\"Unique values in 'League' column:\")\n",
        "for league in unique_leagues:\n",
        "    print(league)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uK-nsMzrbnrW"
      },
      "source": [
        "5. Filter the df so that we have a data of only NBA:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhwVCJ87bnrX",
        "outputId": "1b368ecf-f913-4be8-f5aa-d9c4fda84acb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the DataFrame with 'League' as 'NBA':\n",
            "  League       Season           Stage            Player Team  GP     MIN  FGM  \\\n",
            "0    NBA  1999 - 2000  Regular_Season  Shaquille O'Neal  LAL  79  3163.0  956   \n",
            "1    NBA  1999 - 2000  Regular_Season      Vince Carter  TOR  82  3126.0  788   \n",
            "2    NBA  1999 - 2000  Regular_Season       Karl Malone  UTA  82  2947.0  752   \n",
            "3    NBA  1999 - 2000  Regular_Season     Allen Iverson  PHI  70  2853.0  729   \n",
            "4    NBA  1999 - 2000  Regular_Season       Gary Payton  SEA  82  3425.0  747   \n",
            "\n",
            "    FGA  3PM  ...  draft_pick             draft_team        PPG        MPG  \\\n",
            "0  1665    0  ...         1.0          Orlando Magic  29.670886  40.037975   \n",
            "1  1696   95  ...         5.0  Golden State Warriors  25.695122  38.121951   \n",
            "2  1476    2  ...        13.0              Utah Jazz  25.548780  35.939024   \n",
            "3  1733   89  ...         1.0    Philadelphia Sixers  28.414286  40.757143   \n",
            "4  1666  177  ...         2.0    Seattle SuperSonics  24.170732  41.768293   \n",
            "\n",
            "      TOVPG     3PAPG     3PMPG      FGAPG      FGMPG       FPG  \n",
            "0  2.822785  0.012658  0.000000  21.075949  12.101266  3.227848  \n",
            "1  2.170732  2.878049  1.158537  20.682927   9.609756  3.207317  \n",
            "2  2.817073  0.097561  0.024390  18.000000   9.170732  2.792683  \n",
            "3  3.285714  3.728571  1.271429  24.757143  10.414286  2.314286  \n",
            "4  2.731707  6.341463  2.158537  20.317073   9.109756  2.170732  \n",
            "\n",
            "[5 rows x 42 columns]\n"
          ]
        }
      ],
      "source": [
        "# Filter the DataFrame to include only rows where the 'League' is 'NBA'\n",
        "df_nba = df[df['League'] == 'NBA']\n",
        "\n",
        "# Display the first few rows of the filtered DataFrame to confirm\n",
        "print(\"First few rows of the DataFrame with 'League' as 'NBA':\")\n",
        "print(df_nba.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZReXX7hbnrX"
      },
      "source": [
        "*True Shooting Percentage (TS%)* is a statistic used in basketball to measure a player's efficiency at shooting the ball. It considers field goals, 3-point field goals, and free throws, providing a comprehensive view of a player's shooting performance. Unlike traditional shooting percentages that only consider field goals, TS% includes the impact of 3-pointers and the effectiveness at the free-throw line. This makes it a valuable metric for evaluating the true scoring efficiency of players.\n",
        "\n",
        "Formula:\n",
        "$$\\text{TS\\%} = \\frac{\\text{Points}}{2 \\times (\\text{Field Goal Attempts} + 0.44 \\times \\text{Free Throw Attempts})}$$\n",
        "\n",
        "Calculate the True Shooting Percentage (TS%) for each NBA player per season, focusing specifically on those with at least 1000 combined field goal and free throw attempts in a season. This threshold ensures that the evaluation is restricted to players who have a significant shooting volume, thus providing a reliable measure of shooting efficiency.\n",
        "\n",
        "Instructions:\n",
        "\n",
        " 1. Aggregate the data for each `Player` for each `Season` to sum up the total points (PTS), field goal attempts (FGA), and free throw attempts (FTA).\n",
        " 2. Implement and apply a function to calculate TS%, considering only those player-season records that meet the attempt threshold.\n",
        " 3. Sort the results to identify the top players based on their TS% and display these top results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPN_wA8abnrX"
      },
      "source": [
        "6. Now let’s deal with some missing values. Impute the `NaN` values in `birth_year`, `weight_kg` and `heigh_cm` columns using the `mean()` of all the data in that column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVt632g4bnrX",
        "outputId": "10808d0b-1f52-4be4-b54a-0fcebc8be71c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values after imputation:\n",
            "birth_year    0\n",
            "weight_kg     0\n",
            "height_cm     0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Calculate the mean of each column\n",
        "mean_birth_year = df['birth_year'].mean()\n",
        "mean_weight_kg = df['weight_kg'].mean()\n",
        "mean_height_cm = df['height_cm'].mean()\n",
        "\n",
        "# Impute missing values with the calculated mean\n",
        "df['birth_year'].fillna(mean_birth_year, inplace=True)\n",
        "df['weight_kg'].fillna(mean_weight_kg, inplace=True)\n",
        "df['height_cm'].fillna(mean_height_cm, inplace=True)\n",
        "\n",
        "# Verify the imputation by checking for any remaining NaN values\n",
        "missing_values_after_imputation = df[['birth_year', 'weight_kg', 'height_cm']].isnull().sum()\n",
        "print(\"Missing values after imputation:\")\n",
        "print(missing_values_after_imputation)\n",
        "\n",
        "# Explanation of the Result:\n",
        "# Imputation Success: If missing_values_after_imputation shows 0 for all columns, it indicates that all NaN values have been successfully filled with the mean of their respective columns.\n",
        "# No Missing Values: A count of 0 means there are no remaining missing values in birth_year, weight_kg, or height_cm after performing the imputation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtR_EvD8bnrX"
      },
      "source": [
        "7. Now let’s create an age column you can do that using apply with the following function\n",
        "```\n",
        "    def calculate_age(row):\n",
        "    # Extract the starting year of the season and subtract the birth year\n",
        "    try:\n",
        "        season_start_year = int(row['Season'][0:4])  # Takes the first four characters and converts to integer\n",
        "        birth_year = int(row['birth_year'])\n",
        "        return season_start_year - birth_year\n",
        "    except ValueError:\n",
        "        return None  # Return None or a suitable value if there's an error\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNEAh0PBbnrY",
        "outputId": "96b6cbeb-2947-4dfd-d6a4-f08ce275c39d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows with 'age' column:\n",
            "        Season  birth_year  age\n",
            "0  1999 - 2000      1972.0   27\n",
            "1  1999 - 2000      1977.0   22\n",
            "2  1999 - 2000      1963.0   36\n",
            "3  1999 - 2000      1975.0   24\n",
            "4  1999 - 2000      1968.0   31\n",
            "5  1999 - 2000      1974.0   25\n",
            "6  1999 - 2000      1972.0   27\n",
            "7  1999 - 2000      1976.0   23\n",
            "8  1999 - 2000      1973.0   26\n",
            "9  1999 - 2000      1973.0   26\n"
          ]
        }
      ],
      "source": [
        "# Defining the function to calculate age\n",
        "def calculate_age(row):\n",
        "    # Extract the starting year of the season and subtract the birth year\n",
        "    try:\n",
        "        season_start_year = int(row['Season'][:4])  # Takes the first four characters and converts to integer\n",
        "        birth_year = int(row['birth_year'])\n",
        "        return season_start_year - birth_year\n",
        "    except ValueError:\n",
        "        return None  # Return None or a suitable value if there's an error\n",
        "\n",
        "# Apply the function to each row and create the 'age' column\n",
        "df['age'] = df.apply(calculate_age, axis=1)\n",
        "\n",
        "# Display the first few rows to verify the new 'age' column\n",
        "print(\"First few rows with 'age' column:\")\n",
        "print(df[['Season', 'birth_year', 'age']].head(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BODGcP9fbnrY"
      },
      "source": [
        "8. Also drop the 'birth_year' we don’t need it anymore."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPJZF-hbbnrY",
        "outputId": "7e7823f4-37b8-4e86-abaf-51b3cb47c780"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows with 'age' column and without 'birth_year':\n",
            "        Season  age\n",
            "0  1999 - 2000   27\n",
            "1  1999 - 2000   22\n",
            "2  1999 - 2000   36\n",
            "3  1999 - 2000   24\n",
            "4  1999 - 2000   31\n",
            "5  1999 - 2000   25\n",
            "6  1999 - 2000   27\n",
            "7  1999 - 2000   23\n",
            "8  1999 - 2000   26\n",
            "9  1999 - 2000   26\n"
          ]
        }
      ],
      "source": [
        "# Define the function to calculate age\n",
        "def calculate_age(row):\n",
        "    # Extract the starting year of the season and subtract the birth year\n",
        "    try:\n",
        "        season_start_year = int(row['Season'][:4])  # Takes the first four characters and converts to integer\n",
        "        birth_year = int(row['birth_year'])\n",
        "        return season_start_year - birth_year\n",
        "    except ValueError:\n",
        "        return None  # Return None or a suitable value if there's an error\n",
        "\n",
        "# Apply the function to each row and create the 'age' column\n",
        "df['age'] = df.apply(calculate_age, axis=1)\n",
        "\n",
        "# Drop the 'birth_year' column as it's no longer needed\n",
        "df.drop(columns=['birth_year'], inplace=True)\n",
        "\n",
        "# Display the first few rows to verify the new 'age' column and the removal of 'birth_year'\n",
        "print(\"First few rows with 'age' column and without 'birth_year':\")\n",
        "print(df[['Season', 'age']].head(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aq3nEV3DbnrY"
      },
      "source": [
        "9. Extract just NBA regular season from the data and update your data frame with extracted data. As we said, we don’t need the rest of it!. (Hint: remember how we extracted wines from specific countries in the class? `loc` command can be very useful!)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Te026r8LbnrY",
        "outputId": "36d3e403-749d-4020-98cc-0e75016d3560"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the NBA regular season data:\n",
            "Empty DataFrame\n",
            "Columns: [League, Season, Stage, Player, Team, GP, MIN, FGM, FGA, 3PM, 3PA, FTM, FTA, TOV, PF, ORB, DRB, REB, AST, STL, BLK, PTS, birth_month, birth_date, height, height_cm, weight, weight_kg, nationality, high_school, draft_round, draft_pick, draft_team, PPG, MPG, TOVPG, 3PAPG, 3PMPG, FGAPG, FGMPG, FPG, age]\n",
            "Index: []\n",
            "\n",
            "[0 rows x 42 columns]\n"
          ]
        }
      ],
      "source": [
        "# Extract only NBA regular season data\n",
        "df_nba_regular_season = df.loc[(df['League'] == 'NBA') & (df['Stage'] == 'Regular')]\n",
        "\n",
        "# Update the DataFrame with the extracted data\n",
        "df = df_nba_regular_season\n",
        "\n",
        "# Display the first few rows to verify the DataFrame now contains only NBA regular season data\n",
        "print(\"First few rows of the NBA regular season data:\")\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o51jW1tsbnrZ"
      },
      "source": [
        "## Part 2. General NBA analysis!\n",
        "\n",
        "Now let’s perform the first set of analysis. Before we go on about the game stats let’s see how general trends such as weights, age, minutes played per game, etc., has changed. for the __Part 2__:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMN7_NIwbnrZ"
      },
      "source": [
        "Before we dive into the details of Stephen Curry’s remarkable shooting skills, let's explore who the best shooters in the NBA are by using an advanced metric called True Shooting Percentage (TS%). This statistic provides a more accurate measure of a player's efficiency by considering all forms of scoring from the field, making it ideal for comparing players across different eras and styles.\n",
        "\n",
        "**True Shooting Percentage (TS%)** is a statistic used in basketball to measure a player's efficiency at shooting the ball. It considers field goals, 3-point field goals, and free throws, providing a comprehensive view of a player's shooting performance. Unlike traditional shooting percentages that only consider field goals, TS% includes the impact of 3-pointers and the effectiveness at the free-throw line. This makes it a valuable metric for evaluating the true scoring efficiency of players.\n",
        "\n",
        "Formula:\n",
        "$$\\text{TS\\%} = \\frac{\\text{Points}}{2 \\times (\\text{Field Goal Attempts} + 0.44 \\times \\text{Free Throw Attempts})}$$\n",
        "\n",
        "Calculate the True Shooting Percentage (TS%) for each NBA player per season, focusing specifically on those with at least 1000 combined field goal and free throw attempts in a season. This threshold ensures that the evaluation is restricted to players who have a significant shooting volume, thus providing a reliable measure of shooting efficiency.\n",
        "\n",
        "Instructions:\n",
        "\n",
        "1. **Aggregate Player Data:** Group the player data by 'Player' and 'Season'. Summarize the total points (PTS), field goal attempts (FGA), and free throw attempts (FTA) for each player-season combination.\n",
        "2. **Calculate TS%:** Implement a function to calculate TS%, but ensure it only processes records where the combined total of FGA and FTA meets or exceeds 1000. This filter excludes players with insufficient shooting volume, which might otherwise skew the analysis. Consider cases where the calculated denominator might be zero and handle these appropriately to avoid division errors.\n",
        "* Utilize the apply() function to compute the True Shooting Percentage and store your results in a new DataFrame named player_season_stats. Ensure your calculations are based on original columns eg. field goal attempts (FGA), not field goal attempts per game (FGAPG). The final DataFrame should include the following columns: Player, Season, PTS, FGA, FTA, and TS%\n",
        "3. **Sort and Display Results:** Sort the resulting data by TS% in descending order to find the top shooters. Display the top 10 players to see who leads in shooting efficiency."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kF8fO1aQbnrZ",
        "outputId": "1e9de536-6c0c-4bb2-850c-39186c4c0adb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial DataFrame (first 10 rows):\n",
            "  League       Season           Stage            Player Team  GP     MIN  FGM  \\\n",
            "0    NBA  1999 - 2000  Regular_Season  Shaquille O'Neal  LAL  79  3163.0  956   \n",
            "1    NBA  1999 - 2000  Regular_Season      Vince Carter  TOR  82  3126.0  788   \n",
            "2    NBA  1999 - 2000  Regular_Season       Karl Malone  UTA  82  2947.0  752   \n",
            "3    NBA  1999 - 2000  Regular_Season     Allen Iverson  PHI  70  2853.0  729   \n",
            "4    NBA  1999 - 2000  Regular_Season       Gary Payton  SEA  82  3425.0  747   \n",
            "5    NBA  1999 - 2000  Regular_Season  Jerry Stackhouse  DET  82  3148.0  619   \n",
            "6    NBA  1999 - 2000  Regular_Season        Grant Hill  DET  74  2776.0  696   \n",
            "7    NBA  1999 - 2000  Regular_Season     Kevin Garnett  MIN  81  3243.0  759   \n",
            "8    NBA  1999 - 2000  Regular_Season    Michael Finley  DAL  82  3464.0  748   \n",
            "9    NBA  1999 - 2000  Regular_Season      Chris Webber  SAC  75  2880.0  748   \n",
            "\n",
            "    FGA  3PM  ...    birth_date  height  height_cm  weight  weight_kg  \\\n",
            "0  1665    0  ...   Mar 6, 1972     7-1      216.0   325.0      147.0   \n",
            "1  1696   95  ...  Jan 26, 1977     6-6      198.0   220.0      100.0   \n",
            "2  1476    2  ...  Jul 24, 1963     6-9      206.0   265.0      120.0   \n",
            "3  1733   89  ...   Jun 7, 1975     6-0      183.0   165.0       75.0   \n",
            "4  1666  177  ...  Jul 23, 1968     6-4      193.0   180.0       82.0   \n",
            "5  1447   83  ...   Nov 5, 1974     6-6      198.0   218.0       99.0   \n",
            "6  1422   34  ...   Oct 5, 1972     6-8      203.0   225.0      102.0   \n",
            "7  1526   30  ...  May 19, 1976    6-11      211.0   240.0      109.0   \n",
            "8  1636   99  ...   Mar 6, 1973     6-7      201.0   225.0      102.0   \n",
            "9  1548   27  ...   Mar 1, 1973     6-9      206.0   245.0      111.0   \n",
            "\n",
            "     nationality                  high_school  draft_round  draft_pick  \\\n",
            "0  United States  Robert G. Cole High School           1.0         1.0   \n",
            "1  United States         Mainland High School          1.0         5.0   \n",
            "2  United States      Summerfield High School          1.0        13.0   \n",
            "3  United States          Bethel High School           1.0         1.0   \n",
            "4  United States          Skyline High School          1.0         2.0   \n",
            "5  United States             Oak Hill Academy          1.0         3.0   \n",
            "6  United States      South Lakes High School          1.0         3.0   \n",
            "7  United States      Farragut Career Academy          1.0         5.0   \n",
            "8  United States     Proviso East High School          1.0        21.0   \n",
            "9  United States   Detroit Country Day School          1.0         1.0   \n",
            "\n",
            "               draft_team  \n",
            "0           Orlando Magic  \n",
            "1   Golden State Warriors  \n",
            "2               Utah Jazz  \n",
            "3     Philadelphia Sixers  \n",
            "4     Seattle SuperSonics  \n",
            "5     Philadelphia Sixers  \n",
            "6         Detroit Pistons  \n",
            "7  Minnesota Timberwolves  \n",
            "8            Phoenix Suns  \n",
            "9           Orlando Magic  \n",
            "\n",
            "[10 rows x 34 columns]\n",
            "\n",
            "Missing Values in DataFrame:\n",
            "League             0\n",
            "Season             0\n",
            "Stage              0\n",
            "Player             0\n",
            "Team              11\n",
            "GP                 0\n",
            "MIN                0\n",
            "FGM                0\n",
            "FGA                0\n",
            "3PM                0\n",
            "3PA                0\n",
            "FTM                0\n",
            "FTA                0\n",
            "TOV                0\n",
            "PF                 0\n",
            "ORB                0\n",
            "DRB                0\n",
            "REB                0\n",
            "AST                0\n",
            "STL                0\n",
            "BLK                0\n",
            "PTS                0\n",
            "birth_year       318\n",
            "birth_month      318\n",
            "birth_date       318\n",
            "height            74\n",
            "height_cm         74\n",
            "weight          4564\n",
            "weight_kg       4564\n",
            "nationality        7\n",
            "high_school    30266\n",
            "draft_round    43813\n",
            "draft_pick     43813\n",
            "draft_team     43813\n",
            "dtype: int64\n",
            "\n",
            "Column Names:\n",
            "Index(['League', 'Season', 'Stage', 'Player', 'Team', 'GP', 'MIN', 'FGM',\n",
            "       'FGA', '3PM', '3PA', 'FTM', 'FTA', 'TOV', 'PF', 'ORB', 'DRB', 'REB',\n",
            "       'AST', 'STL', 'BLK', 'PTS', 'birth_year', 'birth_month', 'birth_date',\n",
            "       'height', 'height_cm', 'weight', 'weight_kg', 'nationality',\n",
            "       'high_school', 'draft_round', 'draft_pick', 'draft_team'],\n",
            "      dtype='object')\n",
            "\n",
            "Checking for required columns:\n",
            "True\n",
            "\n",
            "Converted Data Types:\n",
            "FGA    int64\n",
            "FTA    int64\n",
            "dtype: object\n",
            "\n",
            "Aggregated Player Season Stats (First 10 rows):\n",
            "            Player       Season  PTS  FGA  FTA  Total_Attempts\n",
            "0  . Rufukatijiang  2019 - 2020  131   96   27          107.88\n",
            "1  A'uston Calhoun  2013 - 2014  237  214   50          236.00\n",
            "2  A'uston Calhoun  2014 - 2015  320  264   68          293.92\n",
            "3  A'uston Calhoun  2018 - 2019   77   80   10           84.40\n",
            "4       A.C. Green  1999 - 2000  503  477  118          528.92\n",
            "5       A.C. Green  2000 - 2001  367  324  111          372.84\n",
            "6    A.D. Vassallo  2009 - 2010  573  423  121          476.24\n",
            "7    A.D. Vassallo  2019 - 2020  599  447  128          503.32\n",
            "8    A.J. Bramlett  2002 - 2003  217  139   79          173.76\n",
            "9    A.J. Bramlett  2003 - 2004  550  432  159          501.96\n",
            "\n",
            "Summary Statistics for Total_Attempts:\n",
            "count    43611.000000\n",
            "mean       344.568650\n",
            "std        284.602476\n",
            "min          0.000000\n",
            "25%        161.600000\n",
            "50%        275.840000\n",
            "75%        434.640000\n",
            "max       2881.880000\n",
            "Name: Total_Attempts, dtype: float64\n",
            "\n",
            "Filtered Player Season Stats (Total_Attempts >= 1000):\n",
            "            Player       Season   PTS   FGA  FTA  Total_Attempts\n",
            "64    Aaron Brooks  2008 - 2009  1112   953  218         1048.92\n",
            "65    Aaron Brooks  2009 - 2010  1604  1331  298         1462.12\n",
            "101   Aaron Gordon  2018 - 2019  1322  1108  272         1227.68\n",
            "121   Aaron Haynes  2017 - 2018  1295   941  297         1071.68\n",
            "146    Aaron McKie  2000 - 2001  1214  1015  269         1133.36\n",
            "294      Adam Hess  2006 - 2007  1250   910  380         1077.20\n",
            "336  Adam Morrison  2006 - 2007   917   944  169         1018.36\n",
            "762  Al Harrington  2002 - 2003  1020   929  280         1052.20\n",
            "763  Al Harrington  2003 - 2004  1200  1049  307         1184.08\n",
            "764  Al Harrington  2005 - 2006  1411  1220  350         1374.00\n",
            "\n",
            "Number of records after filtering: 1576\n",
            "\n",
            "Top 10 Players by TS%:\n",
            "                  Player       Season   PTS   FGA  FTA        TS%\n",
            "10341       Devin Booker  2017 - 2018  1477   894  433  68.094641\n",
            "31711        P.J. Tucker  2011 - 2012  1428   866  418  68.005181\n",
            "20788    Jovo Stanojevic  2003 - 2004  1354   834  384  67.500199\n",
            "15275            Guna Ra  2016 - 2017  1729  1134  337  67.418972\n",
            "34493  Rimantas Kaukenas  2003 - 2004  1471   868  520  67.058716\n",
            "5727         Brian Qvale  2015 - 2016  1365   902  271  66.830520\n",
            "21315     Justin Dentmon  2014 - 2015  1339   874  296  66.667331\n",
            "40432   Troy Gillenwater  2015 - 2016  1336   833  387  66.581612\n",
            "37907      Stephen Curry  2015 - 2016  2827  1936  483  65.789474\n",
            "22252       Kevin Durant  2016 - 2017  1983  1294  487  65.737131\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-bc12b550724b>:70: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  player_season_stats_filtered['TS%'] = player_season_stats_filtered.apply(calculate_ts, axis=1)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the data\n",
        "file_path = 'players_stats_by_season_full_details.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Inspect the first few rows to ensure the data is loaded correctly\n",
        "print(\"Initial DataFrame (first 10 rows):\")\n",
        "print(df.head(10))\n",
        "\n",
        "# Check for any missing values\n",
        "print(\"\\nMissing Values in DataFrame:\")\n",
        "print(df.isna().sum())\n",
        "\n",
        "# Verify column names\n",
        "print(\"\\nColumn Names:\")\n",
        "print(df.columns)\n",
        "\n",
        "# Ensure the necessary columns are in the DataFrame\n",
        "required_columns = ['Player', 'Season', 'PTS', 'FGA', 'FTA']\n",
        "print(\"\\nChecking for required columns:\")\n",
        "print(all(col in df.columns for col in required_columns))\n",
        "\n",
        "# Convert relevant columns to numeric\n",
        "df['FGA'] = pd.to_numeric(df['FGA'], errors='coerce')\n",
        "df['FTA'] = pd.to_numeric(df['FTA'], errors='coerce')\n",
        "\n",
        "# Check data types after conversion\n",
        "print(\"\\nConverted Data Types:\")\n",
        "print(df[['FGA', 'FTA']].dtypes)\n",
        "\n",
        "# Aggregate data for each player per season\n",
        "player_season_stats = df.groupby(['Player', 'Season']).agg({\n",
        "    'PTS': 'sum',\n",
        "    'FGA': 'sum',\n",
        "    'FTA': 'sum'\n",
        "}).reset_index()\n",
        "\n",
        "# Calculate the total attempts\n",
        "player_season_stats['Total_Attempts'] = player_season_stats['FGA'] + 0.44 * player_season_stats['FTA']\n",
        "\n",
        "# Print aggregated data to check before filtering\n",
        "print(\"\\nAggregated Player Season Stats (First 10 rows):\")\n",
        "print(player_season_stats.head(10))\n",
        "\n",
        "# Check the range of total attempts to understand the distribution\n",
        "print(\"\\nSummary Statistics for Total_Attempts:\")\n",
        "print(player_season_stats['Total_Attempts'].describe())\n",
        "\n",
        "# Filter out records with less than 1000 total attempts\n",
        "player_season_stats_filtered = player_season_stats[player_season_stats['Total_Attempts'] >= 1000]\n",
        "\n",
        "# Print filtered data to check if there are any records left\n",
        "print(\"\\nFiltered Player Season Stats (Total_Attempts >= 1000):\")\n",
        "print(player_season_stats_filtered.head(10))\n",
        "\n",
        "# Check the number of records after filtering\n",
        "print(f\"\\nNumber of records after filtering: {player_season_stats_filtered.shape[0]}\")\n",
        "\n",
        "# Calculate TS%\n",
        "def calculate_ts(row):\n",
        "    try:\n",
        "        # Calculate TS% using the formula\n",
        "        ts_percentage = (row['PTS'] / (2 * (row['FGA'] + 0.44 * row['FTA']))) * 100\n",
        "        return ts_percentage\n",
        "    except ZeroDivisionError:\n",
        "        return 0  # Handle cases where denominator is zero\n",
        "\n",
        "# Apply the function to calculate TS%\n",
        "player_season_stats_filtered['TS%'] = player_season_stats_filtered.apply(calculate_ts, axis=1)\n",
        "\n",
        "# Sort by TS% in descending order and display the top results\n",
        "top_ts_players = player_season_stats_filtered.sort_values(by='TS%', ascending=False)\n",
        "\n",
        "# Display the top 10 players based on TS%\n",
        "print(\"\\nTop 10 Players by TS%:\")\n",
        "print(top_ts_players[['Player', 'Season', 'PTS', 'FGA', 'FTA', 'TS%']].head(10))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFgpgo96bnrZ"
      },
      "source": [
        "**Question:** After calculating TS%, which players emerge as the most efficient high-volume shooters?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "AZ6codCXbnrZ",
        "outputId": "cc92c9b1-c2a3-40f3-c0e4-3573256dfde2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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VK1Zg4cKFWvn7LjLK7dT21GOVasiQIdBoNFiyZAlevnyptO/btw8XLlxAxYoV0axZsxyJLTNvm2etW7dGZGQkxo0bh759++L777/H2bNnUbZsWRw5cgTLli3DqFGjULVqVSQnJ+Prr7+Gra0tTE1NYWlpCX9/fzx9+jTXx0dERG9PL78DICIiooIjtQiR0b0HNRoNypQpgwcPHqRbpMpovdRl4eHhuHHjRs4EmwFzc3MAyPThGqkPFbCwsMj29l1cXDJ9OMrKlStx9OhR1dtLPR6ZPc23dOnSOHXqVIbLX79X2utSx/f8+XOl7ciRI/j555/T9PX19YWvry+AV2OsW7cujh07hlWrVmHs2LEAgMDAQIgIGjdurPXU7azyxsrKCkWLFsX9+/dx48YN2NvbZziWnFCiRAno6aX9mGxmZgYg4+OVmjuvH6+YmBiICJ49e5Zukf11b3OP0DJlymD48OEYPnw4gFcPMlm+fDlmzpyJ69evw9/fHzt27EizXnZ+56m/H2tr6wxz3tnZWavv6/+f2Xnt7OyMU6dOZatonRPHNKPzxdnZGXPmzMGoUaMwaNAgDBo0CE5OTvD09FSeRGxgYKA61lQZHYPU9jff1ypWrIjmzZtj9+7d+P3335UC6MKFCwH830NZ8srb5FnlypUxdepUrbbExET069cPzs7O+PrrrwEAo0aNwpw5c9C7d2+0bdsWhw8fxqxZsxAbG4vffvstbwZIRETZxoIiERERfVBEJFe3n1po+PfffzPsk7ossyJeXsusuJBV4UFHR/1FK9HR0Vi1alWa9tKlSysFReDVrMJjx45h5cqVGDt2LEREWS+vH7DyppSUlEyXZ3U8snO8UvdlZmamPGwkNzk5OWHy5MkoUqQIRowYgT179uDZs2cwNjbW6pedMbxvcuKYvnk8Xjd48GB07NgRW7duxZEjR3DkyBFs2LABGzZsQEBAAA4fPowSJUq81X4zkt772tChQ7F7924sXLgQn376Kf79919s3boVZmZmGT7BPK+ozbM3TZ8+HVFRUdi3bx+MjIzw+PFjLFy4EHXr1lX+UNG6dWtcv34dGzduxKVLl1ChQoW8GBIREWUTC4pERESUY0qWLAng1ZNdMxITE6PVN71l6Um9pM7BweEdIsxajRo1AAD37t1DTExMujOLQkNDAby6PDK/pR7HjC5tzWpZdvXo0UNVMaNjx44YOnQoLl68iKNHj+LZs2e4du0arKys0K5dO62+JUuWxIULFzLMm0ePHuH+/ftK36ykziB7/PhxusuvXbuW5TZySurTazUaDVasWJFnhbzUS7ZfvnyJhw8fZlnoyUzqMb937x7i4+PTnaWY+rt7/fej5v0gvfWykhfHtFixYujbty/69u0LALhw4QJ69eqF48ePY+zYsekW1TMTExMDNze3NO2Zva+1bNkSFSpUwMGDB3Hu3DmsX78eycnJ6N69+1vNjs4N2cmzixcvYvr06ejRowcaN24MADh//jwSExNRt25drb716tXDxo0bERkZyYIiEdF76sP90yQRERG9d1LvW7Zx40atSyZTBQUF4cGDBzA3N4eHh0ea5adPn8bp06fTtJ87dw7h4eHQ0dFBgwYNcjzu1zk4OKBWrVoAgPXr16dZfuTIEfz7778wNDTE//73v1yNRY369etDo9EgLCwMly5dSrP8/PnzmV7unFvMzMzQqVMnAMCKFSuwYsUKAECXLl1gZGSk1Tc1bzIq0qSuW758eVWFp9Q+Fy5cSHd5epcA5xZ7e3tUq1YNjx8/xq5du3Jkm2pm6V6/fh0AYGhoCBsbm3fan4ODg3JJc3qX64uI0t6oUSOlPfX3umvXLty5cyfNehEREYiMjExzXqcWhF+/d+DrcuOYZsXFxQVjxowBAERGRmZ7/TVr1qTbvnr1agD/d6xep9FoMHjwYADADz/8oMzgGzRoULb3/zZyMs9EBP3794elpSVmz56ttKfOnn7zFhOpP+flZd1ERJQ9LCgSERFRjunQoQMcHR1x69YtjBgxQqsgEBMTg5EjRwJ4dUnhm0Ul4NWXzi+++AIPHjxQ2h49eoQvvvgCIoL27dsrs5Ny07hx4wAAM2bMQHh4uNJ+7949DBw4EMCrL/WWlpa5HktWSpcujdatWyMlJQVffPGF1qy8149dfki9tHnTpk0ICgrSantd3759YWFhgfDwcEybNk0r3oiICHz77bcAXt1rTY2PPvoIFhYWOH/+fJpCzubNmzFv3ry3Gs/bSo2/Z8+e2LZtW5rlIoK///4be/bsUbW906dPo1GjRggKCkJiYmKa5adOncLQoUMBAO3bt4e+vv47RP/Kl19+CQCYMmWKVoFaRPDtt98iMjISVlZWyow+4NUss9q1a+PZs2fo37+/1kM2/vvvP/Tv3x/Aq4c4vX5e29rawsDAALGxscrM1Dfl9DFNtX//fgQHByMpKSnN9rZv3w7g1eW+2RUUFIQNGzZotf3666/47bffoKenpxQO39SjRw9YWlpixYoViIuLQ6NGjVCpUqVs7/9t5GSeLV++HCEhIZgzZw6KFi2qtFeqVAmGhoYICgpSftdPnz7F2rVrAfzfjHEiInr/8JJnIiIiyjGGhob49ddf0bJlSyxevBjBwcGoU6cOHj9+jP379+P58+do0aIFAgIC0l2/TZs2ypNAGzVqBI1Gg4MHD+L+/fsoX748FixYkK14duzYgSlTpig/nz9/HgAwadIkrW399ddfWuv5+vpiyJAhmDdvHurUqYMmTZrA1NQU+/btw8OHD+Hl5aW13fy2ePFinD59Gvv370eZMmXg7e0NEUFISAisra3Rpk0bbN269a0eJvEu6tSpg0qVKinHvXr16uleJl6sWDGsW7cOHTp0wPjx47FmzRrUqFEDcXFxCAkJwcuXL9GzZ0+tYlVmjI2N8c0332D48OH4/PPPsXjxYpQsWRJRUVE4f/48JkyYkKe/v9atW2Pu3LkYOXIk2rRpg3LlyqFixYqwtLTE3bt3cerUKcTFxWHMmDHpPl36TSKCgwcP4uDBgzA1NUWNGjVQsmRJJCYmIiYmRplBV716dfz44485Mob+/fvj2LFjWLNmDWrWrAlvb2/Y2dkhPDwcFy9ehLGxMdavXw9bW1ut9davX4/GjRvjjz/+QJkyZdCgQQMkJSXhwIEDiI+Ph7u7e5rzWl9fH23atMGvv/6K6tWro169ejAxMQEArfvs5eQxTXX69GkMHz4cFhYWcHd3h729PZ49e4bw8HBcu3YNlpaWmDx5craP39ChQ9G5c2f88MMPKF++PC5fvoy///4bADBr1qwMn8RuZmaGnj17Kr/HvJqdCORcnt25cwejR49Gy5Yt0aVLF61lZmZmGDFiBKZPn47KlSvDy8sL4eHhiImJQefOnVGuXLlcHCEREb0TISIiIsqmAwcOCADJ6KPE9evXxd/fX8qWLSsGBgZibm4unp6esnjxYklKSkrTPzAwUACIn5+fxMXFSf/+/cXBwUEMDAykVKlSMmTIELl3716240zdblavjGzcuFEaNGggFhYWYmxsLFWqVJEZM2bIixcvsh1LQECAABBvb29VMTs5OaVZ5uTkJAAkJiYmzbK4uDjx9/fXOm7+/v5y7949ady4sQCQ3bt3a63j5+cnACQwMDDTWPz8/NQNMh2zZ89WjvO8efMy7Xv+/Hnx8/MTBwcH0dfXFysrK2nUqJFs2LDhreJbtWqVuLu7i5GRkVhYWEjjxo1l7969EhMTk+4xzqg9VWreZ/Q7zCqeM2fOSL9+/aR8+fJiZGQkJiYmUrZsWWnRooXMmzdPbt68me56b0pKSpKQkBD5+uuvpWHDhlK2bFkxMTERAwMDsbe3l5YtW8rSpUslMTExzbrv+jtfv369NGzYUKysrERfX19KlSolPXr0kAsXLmQY77179+Srr74SV1dXZdw1atSQGTNmyNOnTzNcp3///uLo6Cj6+voZnqvZPaaZnUMiItHR0TJp0iRp0qSJODo6ipGRkRQpUkSqVasmY8eOlX///TfDcabn9f1t2rRJPD09xczMTExNTaV+/fqybdu2LLexc+dOASClSpWSly9fZmv/GfH29hYAEhAQkGGfd8mz13Xq1ElMTU0zPOYpKSkyc+ZMcXZ2Fj09PSlZsqSMGTPmrd5niYgo72hE8ukaGCIiIqL/b+XKlejZsyf8/PzSvUcbvb2HDx+ibNmyePToEe7cufPO99MjorzVrVs3rFu3DtOmTcNXX32V3+EQEREB4D0UiYiIiAqEEydOpGm7e/cu/Pz88ODBA3z88ccsJhJ9YM6cOYONGzfCzMxMueckERHR+4D3UCQiIiIqAGrXrg0HBwe4urrC2toaN2/eREREBJ48eQJHR8ds33+SiPJPnz59kJCQgJ07d+Lly5eYMGGC1sNMiIiI8hsLikREREQFwIQJE7Bv3z6cOnUKDx48gIGBAZydnfHxxx9jxIgRsLa2zu8QiUil5cuXQ0dHB6VKlcKXX36J0aNH53dIREREWngPRSIiIiIiIiIiIlKN91AkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItVYUCQiIiIiIiIiIiLVWFAkIiIiIiIiIiIi1VhQJCIiIiIiIiIiItX08jsAordx/fp1/Pfff/kdBlGuevHiBQwNDfM7DKJcxTynwoB5ToUB85wKA+Y5FQY2NjZwdHTMsh8LivTBuX79OlxdXfH06dP8DoUoV+nq6iI5OTm/wyDKVcxzKgyY51QYMM+pMGCeU2FgYmKCqKioLIuKLCjSB+e///7D06dPsXbtWri6uuZ3OES5Ijg4GBMnTmSeU4HGPKfCgHlOhQHznAoD5jkVBlFRUejWrRv+++8/FhSp4HJ1dYW7u3t+h0GUK6KiogAwz6lgY55TYcA8p8KAeU6FAfOcSBsfykJUCK1cuRJWVlb5HQYRERERERERfYBYUCQqoHr06AGNRgONRgMDAwOUK1cOkydPxsuXL/M7NKJ3cvfuXXzxxRdwdHSEoaEhihcvjhYtWuDo0aP5HRpRvsnoPb9bt25Ke3qv0qVLAwBiYmLQpUsX2Nvbw8jICA4ODmjbti0uXLiQvwMjgnZ+v/6Kjo4GAMTGxmLo0KEoV64cjIyMUKxYMXh5eWHx4sXp3nN7+vTp0NXVxffff5/XQyHKUkb5ntXr4MGDAIAbN27AwMAAVapUyd+BEL0mq/ydNGkSACAoKAh16tSBpaUlzM3NUblyZQwbNkzZTkREBGrUqAEzMzO0bt0a9+/fV5a9fPkSHh4eOHHiRB6PrvDiJc9EBVjLli0RGBiIFy9eIDg4GP7+/tDX10eJEiXyOzQAQFJSEvT19fM7DPrAtG/fHomJiVi1ahXKli2LO3fuYN++fbh3715+h0aUr9J7zw8ICMDt27eVPiVKlEBgYCBatmwJ4NXN5ZOSktCsWTNUrFgRW7ZsQYkSJXDjxg3s3LkTDx8+zKfREGlLze/X2dra4sqVK/Dy8oKVlRWmTZuGqlWrwtDQEGfOnMHSpUtRsmRJtGnTRmu9FStWYPTo0VixYgVGjRqVl8MgUuXNfE9MTISuri50dXUBAEOHDkV8fLxWn6JFiwJ4dSVSx44dcejQIfz999+oXbt23gZPlI7XP4ts3LgRX3/9NS5evKi0mZmZYd++ffjss88wdepUtGnTBhqNBufPn8fevXuVfn369EHjxo2xceNG9OnTB9OmTcOsWbMAALNnz4aXlxc++uijvBtYIccZikQFWOrsLScnJ3zxxRdo2rQptm7dmqbf5cuX0bZtWxQrVgxmZmaoVasW/vzzT2X55MmT0/0rZ/Xq1TFx4kTl559//hmurq4wMjKCi4sLFi1apCy7evUqNBoNNm7cCG9vbxgZGWHdunU5PGIq6B4+fIjDhw/ju+++Q6NGjeDk5ISPPvoIX331lfKF8eHDh+jTpw9sbW1hYWGBxo0b49SpU8o2ssp3AFi0aBHKly+vzHT59NNPlWUvXrzAkCFDYGdnByMjI9SrVw8nT55Ulh88eBAajQb79u1DzZo1YWJigrp162p9aCLKDem95+/atQvFixdXXgBgZWWl/Gxra4tz587h8uXLWLRoEerUqQMnJyd4eXnh22+/RZ06dfJ5VESvpOb36y9dXV0MHDgQenp6CA0NRceOHeHq6oqyZcuibdu22LFjB1q3bq21nZCQEDx79gyTJ09GfHw8jh07lk8jIsrYm/nu6OiIkiVLKj8bGxun6WNgYAARQWBgILp3744uXbpg+fLl+T0UIgDQylVLS0toNBqtNjMzM2zbtg1eXl4YNWoUKlasiAoVKsDX1xcLFy5UthMVFYW+ffuiQoUK6Ny5s3JfyytXrmD58uWYOnVqfg2xUGJBkagQMTY2RmJiYpr2J0+e4H//+x/27duHiIgItGzZEq1bt8b169cBAL169UJUVJRW0SQiIgKnT59Gz549AQDr1q3D119/jalTpyIqKgrTpk3DxIkTsWrVKq19jR07FkOHDkVUVBRatGiRi6OlgsjMzAxmZmb4/fff8eLFi3T7dOjQAXFxcdi5cyfCwsLg7u6OJk2aKJdEZJXvoaGhGDJkCCZPnoyLFy9i165daNCggbL90aNH47fffsOqVasQHh6OcuXKoUWLFlqXXADA+PHjMXv2bISGhkJPTw+9evXKpaNClL6M3vPfZGtrCx0dHfz6669ITk7Og8iIcsa9e/ewZ88e+Pv7w9TUNN0+Go1G6+fly5ejc+fO0NfXR+fOnVlwoQLlwIEDePr0KZo2bYpu3bphw4YNSEhIyO+wiFQpXrw4zp07h7Nnz2bYx83NDXv37sXLly+xb98+VKtWDQAwYMAAzJw5E+bm5nkVLoEFRaJCQUTw559/Yvfu3WjcuHGa5W5ubujfvz+qVKmC8uXLY8qUKXB2dlZmMzo4OKBFixZal1UEBgbC29sbZcuWBQAEBARg9uzZaNeuHcqUKYN27dph+PDhWLJkida+hg0bpvR5Xy69pg+Hnp4eVq5ciVWrVsHKygpeXl4YN24cTp8+DQA4cuQITpw4gc2bN6NmzZooX748Zs2aBSsrK/z6668Ass7369evw9TUFB9//DGcnJxQo0YNDBkyBACQkJCAxYsX4/vvv0erVq1QqVIlLFu2DMbGxmm+lE6dOhXe3t6oVKkSxo4di2PHjuH58+d5eLSosMrqPf9NJUuWxLx58/D111+jSJEiaNy4MaZMmYIrV67kQbRE6mzfvl35o5KZmRk6dOiA6OhoiAgqVqyo1dfGxkbpN2bMGKU9Pj4ev/76K7p16wYA6NatGzZt2oQnT57k6ViIspJevquxfPlydOrUCbq6uqhSpQrKli2LzZs353K0RDlj8ODBqFWrFqpWrYrSpUujU6dOWLFihdYkgp9//hm//vornJ2dYWBggK+++gpr1qyBiYkJatWqhRYtWqBcuXKYMGFCPo6k8GBBkagAS/0wYmRkhFatWuGzzz5Tbnj7uidPnuDLL7+Eq6srrKysYGZmhqioKGXGFgD07dsXv/zyC54/f47ExESsX79emXGVkJCAy5cvo3fv3loffr799ltcvnxZa181a9bM1TFTwde+fXvcunULW7duRcuWLXHw4EG4u7tj5cqVOHXqFJ48eQJra2utXIyJiVFyMat8b9asGZycnFC2bFl0794d69atU27qf/nyZSQlJcHLy0uJR19fHx999JFyyUWq1L+YAlCK53Fxcbl6bKhwU/uenx5/f3/ExsZi3bp18PT0xObNm1G5cmWt+xYR5adGjRohMjJSec2bNy/DvidOnEBkZCQqV66s9UX0l19+gbOzM9zc3AC8unWLk5MTNm7cmOvxE2VHdvI91cOHD7FlyxalYA68KppzFi59KExNTbFjxw5ER0djwoQJMDMzw8iRI/HRRx8pn8UrV66MkJAQXLt2DevXr0dSUhICAgKwYMECDB48GHXr1sWpU6ewZcsWbNu2LZ9HVPDxoSxEBVijRo2wePFiGBgYwN7eHnp66Z/yX375Jfbu3YtZs2ahXLlyMDY2xqeffqp1qVzr1q1haGiIoKAgGBgYICkpSbmvXOpf9pctW5bmxs+pN49OldElSUTZYWRkhGbNmqFZs2aYOHEi+vTpg4CAAAwcOBAlSpRQnnT4OisrKwBZ57u5uTnCw8Nx8OBB7NmzB19//TUmTZqkdcm/Gq8/cCj1kruUlJS3GzCRCmrf8zNibm6O1q1bo3Xr1vj222/RokULfPvtt2jWrFkuRUyknqmpKcqVK6fVZmBgAI1Gk+YetalXTxgbG2u1L1++HOfOndM6N1JSUrBixQr07t07lyInyr708j0r69evx/Pnz7U+i4sIUlJScOnSJVSoUCGnwyTKFc7OznB2dkafPn0wfvx4VKhQARs3blRutfW6ESNGYNiwYXBwcMDBgwfx7bffwtTUFD4+Pjh48GCa++hSzmJBkagAU/th5OjRo+jRowc++eQTAK8KhFevXtXqo6enBz8/PwQGBsLAwACdOnVSPqgXK1YM9vb2uHLlCrp27Zrj4yDKSqVKlfD777/D3d0dsbGx0NPTQ+nSpdPtqzbfmzZtiqZNmyIgIABWVlbYv38/WrRoAQMDAxw9ehROTk4AXj2t/OTJkxg2bFgujpAoa2/zBTQjGo0GLi4ufGAFvdesra3RrFkzZWZKZn+0PHPmDEJDQ3Hw4EHlabgAcP/+fTRs2BAXLlyAi4tLXoRNlCuWL1+OkSNHokePHlrtAwcOxIoVKzBjxoz8CYzoHZQuXRomJibp3gt03759iIqKUm7LlZycjKSkJABQ/ku5iwVFIkL58uWxZcsWtG7dGhqNBhMnTkx3JlWfPn3g6uoK4FVR5nXffPMNhgwZAktLS7Rs2RIvXrxAaGgoHjx4gBEjRuTJOKjgu3fvHjp06IBevXqhWrVqMDc3R2hoKGbOnIm2bduiadOm8PT0hK+vL2bOnIkKFSrg1q1b2LFjBz755BPlvoqZ5fv27dtx5coVNGjQAEWKFEFwcDBSUlJQsWJFmJqa4osvvsCoUaNQtGhRODo6YubMmXj69Clnt9AHKzIyEgEBAejevTsqVaoEAwMDhISEYMWKFVr3nyN6Hy1atAheXl6oWbMmJk2ahGrVqkFHRwcnT57EhQsX4OHhAeBVseWjjz7SeshWqlq1amH58uX4/vvv8zp8ohwRGRmJ8PBwrFu3Lk1hvHPnzpg8eTK+/fbbbM9cJ8pLkyZNwtOnT/G///0PTk5OePjwIebNm4ekpKQ0V0s8f/4cgwYNwi+//AIdnVd38vPy8sLChQvh7++P3377DT/88EN+DKNQ4TsKEeGHH35Ar169ULduXdjY2GDMmDGIj49P0698+fKoW7cu7t+/n+bS5j59+sDExATff/89Ro0aBVNTU1StWpWztihHmZmZoXbt2pgzZ45yP8NSpUqhb9++GDduHDQaDYKDgzF+/Hj07NkTd+/eRfHixdGgQQMUK1YMQNb5bmVlhS1btmDSpEl4/vw5ypcvj19++QWVK1cGAMyYMQMpKSno3r07Hj9+jJo1a2L37t0oUqRIvhwTonfl4OCA0qVL45tvvsHVq1eh0WiUn4cPH57f4RFlytnZGREREZg2bRq++uor3LhxA4aGhqhUqRK+/PJLDBw4EImJiVi7dm2GBfL27dtj9uzZmDZtmtbtKog+FMuXL0elSpXSnWX7ySefYNCgQQgODkabNm3yIToidby9vbFw4UJ8/vnnuHPnDooUKYIaNWpgz549aR6+9c0338DHxwfVq1dX2ubNm4cuXbqgQYMG6Nq1K9q3b5/HIyh8NCIi+R0EUXaEh4fDw8MDYWFhcHd3z+9wChURQfny5TFw4EDOOsxl69atQ7du3ZjnVKAxz6kwYJ5TYcA8p8KAeU6FQXbqLZyhSESq3L17Fxs2bEBsbGy6N8QlIiIiIiIiosKBBUUiUsXOzg42NjZYunQpL+0kIiIiIiIiKsRYUCQiVXh3BCIiIiIiIiICAJ38DoCIiIiIiIiIiIg+HKpnKF6/fh3//fdfbsZCpEpUVBQAIDg4WPl/ooLm6NGjAJjnVLAxz6kwYJ5TYcA8p8KAeU6FQUxMjOq+qp7yfP36dbi6uuLp06fvFBhRTtHR0UFKSkp+h0GUq5jnVBgwz6kwYJ5TYcA8p8KAeU6Fga6uLg4fPgxPT89M+6maofjff//h6dOnWLt2LVxdXXMkQKK3FRwcjIkTJzIfqUBjnlNhwDynwoB5ToUB85wKA+Y5FQZRUVHo1q0bDA0Ns+ybrYeyuLq6wt3d/a0DI8oJqdPLmY9UkDHPqTBgnlNhwDynwoB5ToUB85xIW4F9KMvBgweh0Wjw8OHD/A5FlR49esDX1ze/w6ACiOcCEREREREREeWkPC0o9ujRAxqNBhqNBvr6+ihWrBiaNWuGFStW5Ph9COrWrYvbt2/D0tLyrbdx9epVJV6NRgMDAwOUK1cO3377LVTcepIoQzwXqDCaPn06atWqBXNzc9jZ2cHX1xcXL17U6vP8+XP4+/vD2toaZmZmaN++Pe7cuaPVZ8iQIfDw8IChoSGqV6+eZj+TJk3SytfUl6mp6TvFf//+fQwePBgVK1aEsbExHB0dMWTIEDx69Eir3/Xr1+Hj4wMTExPY2dlh1KhRePnypbL89u3b6NKlCypUqAAdHR0MGzYs3f39+OOPyr5KlSqF4cOH4/nz5+80BioYDh06hNatW8Pe3h4ajQa///57hn0HDBgAjUaDH3/8UWlL/UNTeq+TJ08CyL3ziOhtJScnY+LEiShTpgyMjY3h7OyMKVOmaH0OuXPnDnr06AF7e3uYmJigZcuW+Oeff9LdnoigVatWWZ5DRPlpxowZ0Gg0Wp8V+vfvD2dnZxgbG8PW1hZt27bFhQsXlOX37t1Dy5YtYW9vD0NDQ5QqVQqDBg1CfHx8PoyACrObN2+iW7dusLa2hrGxMapWrYrQ0FBl+ZMnTzBo0CA4ODjA2NgYlSpVwk8//ZTldjdv3gwXFxcYGRmhatWqCA4O1lqenX8L6N3k+QzFli1b4vbt27h69Sp27tyJRo0aYejQofj444+1vnC9KwMDAxQvXhwajeadt/Xnn3/i9u3b+Oeff/DNN99g6tSpWLFiRQ5EmXsSExPzOwTKAs+FvMFz4f0REhICf39//PXXX9i7dy+SkpLQvHlzJCQkKH2GDx+Obdu2YfPmzQgJCcGtW7fQrl27NNvq1asXPvvss3T38+WXX+L27dtar0qVKqFDhw7vFP+tW7dw69YtzJo1C2fPnsXKlSuxa9cu9O7dW+mTnJwMHx8fJCYm4tixY1i1ahVWrlyJr7/+Wunz4sUL2NraYsKECXBzc0t3X+vXr8fYsWMREBCAqKgoLF++HBs3bsS4cePeaQxUMCQkJMDNzQ0LFy7MtF9QUBD++usv2Nvba7Wn/qHp9VefPn1QpkwZ1KxZE0DunUdEb+u7777D4sWLsWDBAkRFReG7777DzJkzMX/+fACvCoS+vr64cuUK/vjjD0RERMDJyQlNmzbV+ncm1Y8//pgjn42IcsvJkyexZMkSVKtWTavdw8MDgYGBiIqKwu7duyEiaN68OZKTkwG8emhI27ZtsXXrVly6dAkrV67En3/+iQEDBuTHMKiQevDgAby8vKCvr4+dO3fi/PnzmD17NooUKaL0GTFiBHbt2oW1a9ciKioKw4YNw6BBg7B169YMt3vs2DF07twZvXv3RkREBHx9feHr64uzZ88CyP6/BfSORIWwsDABIGFhYWq6Z8jPz0/atm2bpn3fvn0CQJYtW6a0PXjwQHr37i02NjZibm4ujRo1ksjISBERuXjxogCQqKgore388MMPUrZsWREROXDggACQBw8eiIhIYGCgWFpayq5du8TFxUVMTU2lRYsWcuvWrQzjjYmJEQASERGh1d6kSRMZOHCg8nNycrJ88803UrJkSTEwMBA3NzfZuXOn1jqnT5+WRo0aiZGRkRQtWlT69u0rjx8/zvDYnDhxQmxsbGTGjBlZHg8RkYCAAHFzc5Nly5ZJ6dKlRaPRZDiuD93atWtzJB/zE88FngtZKQh5npW4uDgBICEhISIi8vDhQ9HX15fNmzcrfaKiogSAHD9+PM36qb/rrERGRgoAOXToUI7FnmrTpk1iYGAgSUlJIiISHBwsOjo6Ehsbq/RZvHixWFhYyIsXL9Ks7+3tLUOHDk3T7u/vL40bN9ZqGzFihHh5eeXsAPJZYcjz3AZAgoKC0rTfuHFDSpYsKWfPnhUnJyeZM2dOhttITEwUW1tbmTx5coZ9cvM8KuiY5znDx8dHevXqpdXWrl076dq1q4j832eis2fPKsuTk5PF1tZW63OViEhERISULFlSbt++neE5RNnDPM9Zjx8/lvLly8vevXsz/KyQ6tSpUwJAoqOjM+wzd+5ccXBwyIVICxfmuXpjxoyRevXqZdqncuXKaT57uLu7y/jx4zNcp2PHjuLj46PVVrt2benfv7+IZO/fAkpfdup/78U9FBs3bgw3Nzds2bJFaevQoQPi4uKwc+dOhIWFwd3dHU2aNMH9+/dRoUIF1KxZE+vWrdPazrp169ClS5cM9/P06VPMmjULa9aswaFDh3D9+nV8+eWX2Yo1NDQUYWFhqF27ttI2d+5czJ49G7NmzcLp06fRokULtGnTRplWm5CQgBYtWqBIkSI4efIkNm/ejD///BODBg1Kdx/79+9Hs2bNMHXqVIwZMybL45EqOjoav/32G7Zs2YLIyMhsjYveDzwXtPFcKNhSLxUuWrQoACAsLAxJSUlo2rSp0sfFxQWOjo44fvz4W+/n559/RoUKFVC/fv13Czgdjx49goWFBfT0Xj3j7Pjx46hatSqKFSum9GnRogXi4+Nx7tw51dutW7cuwsLCcOLECQDAlStXEBwcjP/97385OwAqkFJSUtC9e3eMGjUKlStXzrL/1q1bce/ePfTs2TPDPrl5HhGpUbduXezbtw+XLl0CAJw6dQpHjhxBq1atALya/Q0ARkZGyjo6OjowNDTEkSNHlLanT5+iS5cuWLhwIYoXL56HIyBSz9/fHz4+PlqfidKTkJCAwMBAlClTBqVKlUq3z61bt7BlyxZ4e3vnRqhE6dq6dStq1qyJDh06wM7ODjVq1MCyZcu0+tStWxdbt27FzZs3ISI4cOAALl26hObNm2e43ePHj6c5L1q0aKF8V1D7bwHlkJyuUGYmo1lZIiKfffaZuLq6iojI4cOHxcLCQp4/f67Vx9nZWZYsWSIiInPmzBFnZ2dl2ZsztdKblYU3/nKzcOFCKVasWIbxps7KMjY2FlNTU9HX1xcA0q9fP61+9vb2MnXqVK22WrVqKTO3li5dKkWKFJEnT54oy3fs2KE1iyX12GzZskXMzMxkw4YNSl81xyMgIED09fUlLi4uw/EUFAXhL0M8F3guZKUg5HlmkpOTxcfHR2vG3bp168TAwCBN31q1asno0aPTtKuZofjs2TMpUqSIfPfdd+8c85vu3r0rjo6OMm7cOKWtb9++0rx5c61+CQkJAkCCg4PTbCOzWQdz584VfX190dPTEwAyYMCAHI3/fVDQ8zwvIJ3ZVdOmTZNmzZpJSkqKiEiWMxRbtWolrVq1ynB5bp5HhQHzPGckJyfLmDFjRKPRiJ6enmg0Gpk2bZqyPDExURwdHaVDhw5y//59efHihcyYMUMAaL0v9+vXT3r37q38nN45RNnHPM85v/zyi1SpUkWePXsmIul/Vli4cKGYmpoKAKlYsWK6sxM7deokxsbGAkBat26tbI/eHvNcPUNDQzE0NJSvvvpKwsPDZcmSJWJkZCQrV65U+jx//lw+//xzASB6enpiYGAgq1atynS7+vr6sn79eq22hQsXip2dnYio/7eAMvbBzVAEXl3rnnofk1OnTuHJkyfKTflTXzExMbh8+TIAoFOnTrh69Sr++usvAK9mZLm7u8PFxSXDfZiYmMDZ2Vn5uUSJEoiLi8syto0bNyIyMhKnTp3Cpk2b8Mcff2Ds2LEAgPj4eNy6dQteXl5a63h5eSmPlY+KioKbm5vWjcy9vLyQkpKi9UCCv//+Gx06dMCaNWu07g2m5ngAgJOTE2xtbbMcD73feC7wXCgM/P39cfbsWWzYsCFX9xMUFITHjx/Dz88v036VK1dW8il1tktm4uPj4ePjg0qVKmHSpEk5FO3/OXjwIKZNm4ZFixYhPDwcW7ZswY4dOzBlypQc3xcVLGFhYZg7dy5Wrlyp6v5wN27cwO7du7XuBfomtecRUW7atGkT1q1bh/Xr1yM8PByrVq3CrFmzsGrVKgCAvr4+tmzZgkuXLqFo0aIwMTHBgQMH0KpVK+jovPrKs3XrVuzfv1/rIUVE75N///0XQ4cOxbp167RmWL2pa9euiIiIQEhICCpUqICOHTumeXDbnDlzEB4ejj/++AOXL1/GiBEjcjt8IkVKSgrc3d0xbdo01KhRA/369UPfvn21Hroyf/58/PXXX9i6dSvCwsIwe/Zs+Pv7488//3zr/ar5t4Byjl5+B5AqKioKZcqUAfDqaT8lSpTAwYMH0/SzsrICABQvXhyNGzfG+vXrUadOHaxfvx5ffPFFpvvQ19fX+lmj0ah6Qm2pUqVQrlw5AICrqysuX76MiRMn5viXSGdnZ1hbW2PFihXw8fFR4lVzPADwyYsFBM8FngsF3aBBg7B9+3YcOnQIDg4OSnvx4sWRmJiIhw8fav0+79y589aXpf3888/4+OOPtS5BTk9wcDCSkpIAAMbGxpn2ffz4MVq2bAlzc3MEBQVpnU/FixdXLlN+Pf7UZWpNnDgR3bt3R58+fQAAVatWRUJCAvr164fx48fzAxFl6PDhw4iLi4Ojo6PSlpycjJEjR+LHH3/E1atXtfoHBgbC2toabdq0yXCbas8jotw0atQojB07Fp06dQLw6n3x2rVrmD59ulLs9vDwQGRkJB49eoTExETY2tqidu3aysOG9u/fj8uXL2v9GwMA7du3R/369dP9fEGUl8LCwhAXFwd3d3elLTk5GYcOHcKCBQvw4sUL6OrqwtLSEpaWlihfvjzq1KmDIkWKICgoCJ07d1bWK168OIoXLw4XFxcULVoU9evXx8SJE1GiRIn8GBoVMiVKlEClSpW02lxdXfHbb78BAJ49e4Zx48YhKCgIPj4+AIBq1aohMjISs2bNyvBy/+LFiyufrVO9+V0hq38LKOe8F99I9u/fjzNnzqB9+/YAAHd3d8TGxkJPTw/lypXTetnY2Cjrde3aFRs3bsTx48dx5coV5QNGbtPV1cXLly+RmJgICwsL2Nvb4+jRo1p9jh49qpxArq6uOHXqlNZThY4ePQodHR1UrFhRabOxscH+/fsRHR2Njh07Kl9u1R4P+vDxXHiF50LBJCIYNGgQgoKCsH//fqVwnsrDwwP6+vrYt2+f0nbx4kVcv34dnp6e2d5fTEwMDhw4kOnMq1ROTk5KLpUsWTLDfvHx8WjevDkMDAywdevWNLMHPD09cebMGa0Zv3v37oWFhUWaD1WZefr0aZqioa6uLgCoKv5T4dW9e3ecPn0akZGRysve3h6jRo3C7t27tfqKCAIDA/H555+n+UNTquycR0S5KaP3xZSUlDR9LS0tYWtri3/++QehoaFo27YtAGDs2LFpzg/g1UyuwMDAXB8DUVaaNGmCM2fOaOVozZo10bVrV0RGRiqfBV4nIhAR5d5x6Uk9TzLrQ5STvLy8tK5AA4BLly7ByckJAJCUlISkpCTV7+upPD09tb4rAK8+a6f3XSGjfwso5+T5DMUXL14gNjYWycnJuHPnDnbt2oXp06fj448/xueffw4AaNq0KTw9PeHr64uZM2eiQoUKuHXrFnbs2IFPPvlEqSy3a9cOX3zxBb744gs0atQI9vb2uRLzvXv3EBsbi5cvX+LMmTOYO3cuGjVqBAsLCwCv/mIaEBAAZ2dnVK9eHYGBgYiMjFQelNG1a1cEBATAz88PkyZNwt27dzF48GB07949zV/77ezssH//fjRq1AidO3fGhg0bVB8P+rDwXOC5UNj4+/tj/fr1+OOPP2Bubo7Y2FgAr/6xNzY2hqWlJXr37o0RI0agaNGisLCwwODBg+Hp6Yk6deoo24mOjsaTJ08QGxuLZ8+eKV8IK1WqBAMDA6XfihUrUKJECVWXMKuRWkx8+vQp1q5di/j4eMTHxwMAbG1toauri+bNm6NSpUro3r07Zs6cidjYWEyYMAH+/v4wNDRUtpUa85MnT3D37l1ERkbCwMBAKTq2bt0aP/zwA2rUqIHatWsjOjoaEydOROvWrdP9MkGFy5MnTxAdHa38HBMTg8jISBQtWhSOjo6wtrbW6q+vr4/ixYtr/eEGePVHrJiYGGUmbHpy+jwielutW7fG1KlT4ejoiMqVKyMiIgI//PADevXqpfTZvHkzbG1t4ejoiDNnzmDo0KHw9fVVbvCfOmPrTY6Ojmn+yEWUH8zNzVGlShWtNlNTU1hbW6NKlSq4cuUKNm7ciObNm8PW1hY3btzAjBkzYGxsrDy4LTg4GHfu3EGtWrVgZmaGc+fOYdSoUfDy8kLp0qXzYVRUGA0fPhx169bFtGnT0LFjR5w4cQJLly7F0qVLAQAWFhbw9vbGqFGjYGxsDCcnJ4SEhGD16tX44YcflO18/vnnKFmyJKZPnw4AGDp0KLy9vTF79mz4+Phgw4YNCA0NVbYLZP1vAeWgnL4pY2b8/PwEgHLTTVtbW2natKmsWLFCkpOTtfrGx8fL4MGDxd7eXvT19aVUqVLStWtXuX79ula/jh07CgBZsWKFVnt6D6KwtLTU6hMUFCSZHYLUB1GkvnR1dcXBwUH69u2r9cCH5ORkmTRpkpQsWVL09fXFzc1Ndu7cqbWt06dPS6NGjcTIyEiKFi0qffv2lcePH2sdm9cf0nHr1i2pUKGCdOzYUV6+fJnl8VDzcIKCoiDcDJfnAs+FrBSEPH/T6zn0+iswMFDp8+zZMxk4cKAUKVJETExM5JNPPpHbt29rbcfb2zvd7cTExCh9kpOTxcHBQeuBKe8q9VzKat9Xr16VVq1aibGxsdjY2MjIkSMlKSkpy2Ph5OSkLE9KSpJJkyaJs7OzGBkZSalSpWTgwIHKeVxQFMQ8zwsZ5aKfn1+6/TN6KEvnzp2lbt26Ge4nN86jwoh5njPi4+Nl6NCh4ujoKEZGRlK2bFkZP368vHjxQukzd+5ccXBwEH19fXF0dJQJEyZoLU8P+FCWHME8zz2vP5Tl5s2b0qpVK7GzsxN9fX1xcHCQLl26yIULF5T++/fvF09PT7G0tBQjIyMpX768jBkzpsB9hsgPzPPs2bZtm1SpUkUMDQ3FxcVFli5dqrX89u3b0qNHD7G3txcjIyOpWLGizJ49W3monMir/H/z882mTZukQoUKYmBgIJUrV5YdO3ZoLX+bfwvo/2Sn/qcRyfraqfDwcHh4eCAsLEzrfg5E+WHdunXo1q0b85EKNOY5FQbMcyoMmOdUGDDPqTBgnlNhkJ3633txD0UiIiIiIiIiIiL6MLCgSERERERERERERKqxoEhERERERERERESqsaBIREREREREREREqullp3NwcDCioqJyKxYiVY4ePQqA+UgFG/OcCgPmORUGzHMqDJjnVBgwz6kwiImJUd1X1VOejx8/jvr16yM5OfmdAiPKKTo6OkhJScnvMIhyFfOcCgPmORUGzHMqDJjnVBgwz6kw0NXVxeHDh+Hp6ZlpP1UzFA0NDZGcnIy1a9fC1dU1RwIkelvBwcGYOHEi85EKNOY5FQbMcyoMmOdUGDDPqTBgnlNhEBUVhW7dusHQ0DDLvtm65NnV1RXu7u5vHRhRTkidXs58pIKMeU6FAfOcCgPmORUGzHMqDJjnRNr4UBYVevToAV9f3/wOg+iDw3OHiIiIiIiIqODJ04Jijx49oNFooNFoYGBggHLlymHy5Ml4+fJlmr4ajQa///57treb3qt06dI5OxCiPMZzhwqC6dOno1atWjA3N4ednR18fX1x8eJFrT7Pnz+Hv78/rK2tYWZmhvbt2+POnTtafYYMGQIPDw8YGhqievXqafbz/Plz9OjRA1WrVoWenl6OFbWTkpIwZswYVK1aFaamprC3t8fnn3+OW7duafW7f/8+unbtCgsLC1hZWaF379548uRJtuLbsmULmjVrBltbW1hYWMDT0xO7d+/OkXHQh+/QoUNo3bo17O3ts3zPHzBgADQaDX788Uel7erVq+jduzfKlCkDY2NjODs7IyAgAImJiVrr7t69G3Xq1IG5uTlsbW3Rvn17XL16NXcGRZSF5ORkTJw4UStvp0yZgjdvBx8VFYU2bdrA0tISpqamqFWrFq5fv67V5/jx42jcuDFMTU1hYWGBBg0a4NmzZ3k5HCJVZsyYAY1Gg2HDhiltS5cuRcOGDWFhYQGNRoOHDx+mWe/SpUto27YtbGxsYGFhgXr16uHAgQN5FzgRgJs3b6Jbt26wtraGsbExqlatitDQUGX5kydPMGjQIDg4OMDY2BiVKlXCTz/9lOV2N2/eDBcXFxgZGaFq1aoIDg7WWr5lyxY0b94c1tbW0Gg0iIyMzOmh0f+X5zMUW7Zsidu3b+Off/7ByJEjMWnSJHz//ffvtM25c+fi9u3bygsAAgMDlZ9PnjyZE6HnmDc/sBOpwXOH586HLiQkBP7+/vjrr7+wd+9eJCUloXnz5khISFD6DB8+HNu2bcPmzZsREhKCW7duoV27dmm21atXL3z22Wfp7ic5ORnGxsYYMmQImjZtmmPxP336FOHh4Zg4cSLCw8OxZcsWXLx4EW3atNHq17VrV5w7dw579+7F9u3bcejQIfTr1y9b8R06dAjNmjVDcHAwwsLC0KhRI7Ru3RoRERE5Nh76cCUkJMDNzQ0LFy7MtF9QUBD++usv2Nvba7VfuHABKSkpWLJkCc6dO4c5c+bgp59+wrhx45Q+MTExaNu2LRo3bozIyEjs3r0b//33X7rnI1Fe+O6777B48WIsWLAAUVFR+O677zBz5kzMnz9f6XP58mXUq1cPLi4uOHjwIE6fPo2JEyfCyMhI6XP8+HG0bNkSzZs3x4kTJ3Dy5EkMGjQIOjq8cIveLydPnsSSJUtQrVo1rfanT5+iZcuWWu/Zb/r444/x8uVL7N+/H2FhYXBzc8PHH3+M2NjY3A6bCADw4MEDeHl5QV9fHzt37sT58+cxe/ZsFClSROkzYsQI7Nq1C2vXrkVUVBSGDRuGQYMGYevWrRlu99ixY+jcuTN69+6NiIgI+Pr6wtfXF2fPnlX6JCQkoF69evjuu+9ydYwEQFQICwsTABIWFqame4b8/Pykbdu2Wm3NmjWTOnXqpOkLQIKCgt5qP6+vGxMTIwAkIiJCWf7gwQMBIAcOHFDazp49Kz4+PmJubi5mZmZSr149iY6OTjfuEydOiI2NjcyYMUPZXu/evcXGxkbMzc2lUaNGEhkZqfQPCAgQNzc3WbZsmZQuXVo0Gs1bjYteWbt2bY7k44eE507hO3cKQ57HxcUJAAkJCRERkYcPH4q+vr5s3rxZ6RMVFSUA5Pjx42nWT82PzKR37uSkEydOCAC5du2aiIicP39eAMjJkyeVPjt37hSNRiM3b958p/gqVaok33zzTY7E/b4oDHme2zJ6z79x44aULFlSzp49K05OTjJnzpxMtzNz5kwpU6aM8vPmzZtFT09PkpOTlbatW7eKRqORxMTEnAq/UGCe5wwfHx/p1auXVlu7du2ka9euys+fffaZdOvWLdPt1K5dWyZMmJArMRZmzPOc9fjxYylfvrzs3btXvL29ZejQoWn6HDhwQADIgwcPtNrv3r0rAOTQoUNKW3x8vACQvXv35nLkBRvzXL0xY8ZIvXr1Mu1TuXJlmTx5slabu7u7jB8/PsN1OnbsKD4+PlpttWvXlv79+6fpm973Wcpadup/+f6nOGNj43yfdXTz5k00aNAAhoaGyl9xevXqle7lpPv370ezZs0wdepUjBkzBgDQoUMHxMXFYefOnQgLC4O7uzuaNGmC+/fvK+tFR0fjt99+w5YtWzjllnIEzx360D169AgAULRoUQBAWFgYkpKStGbtubi4wNHREcePH8+XGLPy6NEjaDQaWFlZAXg188XKygo1a9ZU+jRt2hQ6Ojr4+++/33o/KSkpePz4sXKsiDKTkpKC7t27Y9SoUahcubKqdR49eqSVXx4eHtDR0UFgYCCSk5Px6NEjrFmzBk2bNoW+vn5uhU6Uobp162Lfvn24dOkSAODUqVM4cuQIWrVqBeBV3u/YsQMVKlRAixYtYGdnh9q1a2vdEiAuLg5///037OzsULduXRQrVgze3t44cuRIfgyJKEP+/v7w8fF5qystrK2tUbFiRaxevRoJCQl4+fIllixZAjs7O3h4eORCtERpbd26FTVr1kSHDh1gZ2eHGjVqYNmyZVp96tati61bt+LmzZsQERw4cACXLl1C8+bNM9zu8ePH05wXLVq0eG+/KxR02XrKc04SEezbtw+7d+/G4MGD8ysMAMDChQthaWmJDRs2KB+SK1SokKZfUFAQPv/8c/z888/KpXZHjhzBiRMnEBcXpzxWe9asWfj999/x66+/Kpe5JSYmYvXq1bC1tc2jUVFBxXOHCoKUlBQMGzYMXl5eqFKlCgAgNjYWBgYGSnEuVbFixd7LS3SeP3+OMWPGoHPnzrCwsADwagx2dnZa/fT09FC0aNF3GsOsWbPw5MkTdOzY8Z1ipsLhu+++g56eHoYMGaKqf3R0NObPn49Zs2YpbWXKlMGePXvQsWNH9O/fH8nJyfD09ExznyKivDJ27FjEx8fDxcUFurq6SE5OxtSpU9G1a1cAr4qFT548wYwZM/Dtt9/iu+++w65du9CuXTscOHAA3t7euHLlCgBg0qRJmDVrFqpXr47Vq1ejSZMmOHv2LMqXL5+fQyQCAGzYsAHh4eFvfeshjUaDP//8E76+vjA3N4eOjg7s7Oywa9curctNiXLTlStXsHjxYowYMQLjxo3DyZMnMWTIEBgYGMDPzw8AMH/+fPTr1w8ODg7Q09ODjo4Oli1bhgYNGmS43djYWBQrVkyr7X39rlAY5HlBcfv27TAzM0NSUhJSUlLQpUsXTJo0Ka/D0BIZGYn69etn+hf3v//+G9u3b8evv/6qdQP9U6dO4cmTJ7C2ttbq/+zZM1y+fFn52cnJiQUReic8d6gg8ff3x9mzZ9+bWSGtWrXC4cOHAbzKuXPnzmXaPykpCR07doSIYPHixbka2/r16/HNN9/gjz/+SFOsJHpTWFgY5s6di/DwcGg0miz737x5Ey1btkSHDh3Qt29fpT02NhZ9+/aFn58fOnfujMePH+Prr7/Gp59+ir1796raNlFO2rRpE9atW4f169ejcuXKiIyMxLBhw2Bvbw8/Pz+kpKQAANq2bYvhw4cDAKpXr45jx47hp59+gre3t9Knf//+6NmzJwCgRo0a2LdvH1asWIHp06fnz+CI/r9///0XQ4cOxd69e7Xu/ZkdIgJ/f3/Y2dnh8OHDMDY2xs8//4zWrVvj5MmTKFGiRA5HTZRWSkoKatasiWnTpgF49V579uxZ/PTTT1oFxb/++gtbt26Fk5MTDh06BH9/f9jb2+fofdAp9+R5QbFRo0ZYvHgxDAwMYG9vDz293A0h9QbL8toT4JKSkrT6GBsbZ7kdZ2dnWFtbY8WKFfDx8VEKKE+ePEGJEiVw8ODBNOu8PsvG1NT0LaIn+j88d6igGDRokPKwEgcHB6W9ePHiSExMxMOHD7Vy4M6dOyhevHiuxvTzzz8rT/jM6nLO1GLitWvXsH//fmV2IvBqDHFxcVr9X758ifv377/VGDZs2IA+ffpg8+bN/GBFqhw+fBhxcXFwdHRU2pKTkzFy5Ej8+OOPWk9pvnXrFho1aoS6deti6dKlWttJnYE+c+ZMpW3t2rUoVaoU/v77b9SpUyfXx0L0ulGjRmHs2LHo1KkTAKBq1aq4du0apk+fDj8/P9jY2EBPTw+VKlXSWs/V1VX541VqISW9Pm8+CZooP4SFhSEuLg7u7u5KW3JyMg4dOoQFCxbgxYsX0NXVzXQb+/fvx/bt2/HgwQPlM8qiRYuwd+9erFq1CmPHjs3VMRABr95v03uv/e233wC8mkQybtw4BAUFwcfHBwBQrVo1REZGYtasWRl+7i1evDju3Lmj1ZYX3xUofXl+D0VTU1OUK1cOjo6OuV4QAaDMbEp9gi2ANPdhq1atGg4fPpymWPI6Gxsb7N+/H9HR0ejYsaPS193dHbGxsdDT00O5cuW0XjY2Njk/ICq0eO7Qh05EMGjQIAQFBWH//v0oU6aM1nIPDw/o6+tj3759StvFixdx/fp1eHp65mpsJUuWVPLPyckpw36pxcR//vkHf/75Z5oZtp6ennj48CHCwsKUtv379yMlJQW1a9fOVky//PILevbsiV9++UX5oEWUle7du+P06dOIjIxUXvb29hg1ahR2796t9Lt58yYaNmwIDw8PBAYGpnnC7dOnT9O0pX6JTZ3lRZSXMsrJ1Hw0MDBArVq1cPHiRa0+ly5dUt7XS5cuDXt7+0z7EOWnJk2a4MyZM1rv4TVr1kTXrl0RGRmZZTEReHWuAEhzvujo6PD9m/KMl5dXpu+1SUlJSEpKyvR9PT2enp5a3xUAYO/evbn+XYHSl+8PZQGAEydOwMXFBTdv3sywT5MmTbBgwYJsb9vY2Bh16tTBjBkzEBUVhZCQEEyYMEGrz6BBgxAfH49OnTohNDQU//zzD9asWZPmBLCzs8P+/ftx4cIFdO7cGS9fvkTTpk3h6ekJX19f7NmzB1evXsWxY8cwfvx4hIaGZjteouzguUMfEn9/f6xduxbr16+Hubk5YmNjERsbq8wMtLS0RO/evTFixAgcOHAAYWFh6NmzJzw9PbVmQ0VHRyMyMlJZN/UD9+sPKTp//jwiIyNx//59PHr0SOnzLpKSkvDpp58iNDQU69atQ3JysjKG1H27urqiZcuW6Nu3L06cOIGjR49i0KBB6NSpE+zt7VXHt379enz++eeYPXs2ateurewn9UE2VLg9efJEK2diYmIQGRmJ69evw9raGlWqVNF66evro3jx4qhYsSKA/ysmOjo6YtasWbh7966SY6l8fHxw8uRJTJ48Gf/88w/Cw8PRs2dPODk5oUaNGvkxbCrkWrdujalTp2LHjh24evUqgoKC8MMPP+CTTz5R+owaNQobN27EsmXLEB0djQULFmDbtm0YOHAggFf3lhs1ahTmzZuHX3/9FdHR0Zg4cSIuXLiA3r1759fQiBTm5uZp3sNNTU2V93bg1S0pIiMjER0dDQBKATL1oYaenp4oUqQI/Pz8cOrUKVy6dAmjRo1CTEwM/0BJeWb48OH466+/MG3aNERHR2P9+vVYunQp/P39AQAWFhbw9vbGqFGjcPDgQcTExGDlypVYvXq11vv6559/jq+++kr5eejQodi1axdmz56NCxcuYNKkSQgNDcWgQYOUPvfv30dkZCTOnz8P4NUEhdTvDpTDcvqx0Znx8/OTtm3bpmlPfeR9TEyMiIgkJycLANm2bZvSx8nJSQICAlTtB4AEBQUpP58/f148PT3F2NhYqlevLnv27BEAcuDAAaXPqVOnpHnz5mJiYiLm5uZSv359uXz5crpx37p1SypUqCAdO3aUly9fSnx8vAwePFjs7e1FX19fSpUqJV27dpXr16+LiEhAQIC4ubmpip2ytnbt2hzJxw8Jz53CpyDmOYB0X4GBgUqfZ8+eycCBA6VIkSJiYmIin3zyidy+fVtrO97e3uluJ/U8EHmV9+n1eRcxMTEZjuH1c+LevXvSuXNnMTMzEwsLC+nZs6c8fvxYa1tZxZfRGP38/N5pDO+bgpjneSH1vV9tfjg5OcmcOXOUnwMDAzPM5df98ssvUqNGDTE1NRVbW1tp06aNREVF5eLICibmec6Ij4+XoUOHiqOjoxgZGUnZsmVl/Pjx8uLFC61+y5cvl3LlyomRkZG4ubnJ77//nmZb06dPFwcHBzExMRFPT085fPhwXg2jwGKe5x5vb28ZOnSo8nNAQECWn6dOnjwpzZs3l6JFi4q5ubnUqVNHgoOD8z74AoZ5nj3btm2TKlWqiKGhobi4uMjSpUu1lt++fVt69Ogh9vb2YmRkJBUrVpTZs2dLSkqK0sfb2zvN55tNmzZJhQoVxMDAQCpXriw7duzQWp7R5xy134kLu+zU/zQir90gLQPh4eHw8PBAWFiY1v0ccktsbCxKlCiBkydPombNmrm+P/qwrFu3Dt26dcuzfPyQ8NwpOJjnVBgwz6kwYJ5TYcA8p8KAeU6FQXbqf3n+UJbMiAiuXbuGWbNmoVixYsq0biLKHM8dIiIiIiIiIsor71VB8dGjR6hYsSJcXV2xYcMGGBkZ5XdIRB8EnjtERERERERElFfeq4KilZUVXrx4kd9hEH1weO4QERERERERUV55L57yTERERERERERERB+GbM1QDA4ORlRUVG7FQqTK0aNHATAfqWBjnlNhwDynwoB5ToUB85wKA+Y5FQYxMTGq+6p6yvPx48dRv359JCcnv1NgRDlFR0cHKSkp+R0GUa5inlNhwDynwoB5ToUB85wKA+Y5FQa6uro4fPgwPD09M+2naoaioaEhkpOTsXbtWri6uuZIgERvKzg4GBMnTmQ+UoHGPKfCgHlOhQHznAoD5jkVBsxzKgyioqLQrVs3GBoaZtk3W5c8u7q6wt3d/a0DI8oJqdPLmY9UkDHPqTBgnlNhwDynwoB5ToUB85xIGx/K8h4qXbo0fvzxxxzb3sGDB6HRaPDw4cMc2yZRTmCuExEREREREX148ryg2KNHD/j6+ub1brWEhISgcePGKFq0KExMTFC+fHn4+fkhMTERALBy5UpYWVnlW3wnT55Ev379cmx7devWxe3bt2FpaZlj26SsMdezxlwvXKZPn45atWrB3NwcdnZ28PX1xcWLF7X6PH/+HP7+/rC2toaZmRnat2+PO3fuaPW5fv06fHx8YGJiAjs7O4waNQovX75Ulh85cgReXl6wtraGsbExXFxcMGfOnHeO/+rVq+jduzfKlCkDY2NjODs7IyAgQDmfUp0+fRr169eHkZERSpUqhZkzZ6bZ1ubNm+Hi4gIjIyNUrVoVwcHBGe53wIAB0Gg0OVp8pw/boUOH0Lp1a9jb20Oj0eD333/XWj5p0iS4uLjA1NQURYoUQdOmTfH3339r9SldujQ0Go3Wa8aMGenuLzo6Gubm5vn67wVRcnIyJk6cqPUePGXKFLx+O3g1uZ/qxYsXqF69OjQaDSIjI/NoFESZW7x4MapVqwYLCwtYWFjA09MTO3fuVJbHxsaie/fuKF68OExNTeHu7o7ffvst3W0xx+l9dPPmTXTr1k35nF61alWEhoYqy9/8bJL6+v777zPc5qRJk9L0d3FxyYvhFHqFbobi+fPn0bJlS9SsWROHDh3CmTNnMH/+fBgYGLw3D52xtbWFiYlJjm3PwMAAxYsXh0ajybFt0vuPuU7vm5CQEPj7++Ovv/7C3r17kZSUhObNmyMhIUHpM3z4cGzbtg2bN29GSEgIbt26hXbt2inLk5OT4ePjg8TERBw7dgyrVq3CypUr8fXXXyt9TE1NMWjQIBw6dAhRUVGYMGECJkyYgKVLl75T/BcuXEBKSgqWLFmCc+fOYc6cOfjpp58wbtw4pU98fDyaN28OJycnhIWF4fvvv8ekSZO09n3s2DF07twZvXv3RkREBHx9feHr64uzZ8+m2WdQUBD++usv2Nvbv1PsVLAkJCTAzc0NCxcuTHd5hQoVsGDBApw5cwZHjhxB6dKl0bx5c9y9e1er3+TJk3H79m3lNXjw4DTbSkpKQufOnVG/fv1cGQuRWt999x0WL16MBQsWICoqCt999x1mzpyJ+fPnK33U5j4AjB49mu+t9N5xcHDAjBkzEBYWhtDQUDRu3Bht27bFuXPnAACff/45Ll68iK1bt+LMmTNo164dOnbsiIiIiDTbYo7T++bBgwfw8vKCvr4+du7cifPnz2P27NkoUqSI0uf1zyW3b9/GihUroNFo0L59+0y3XblyZa31jhw5ktvDIQAQFcLCwgSAhIWFqemeKT8/P2nbtq2IiDx//lwGDx4stra2YmhoKF5eXnLixAkREUlOTpaSJUvKokWLtNYPDw8XjUYjV69eFRGRa9euSZs2bcTU1FTMzc2lQ4cOEhsbm+H+58yZI6VLl85w+YEDBwSA1isgIEBERFavXi0eHh5iZmYmxYoVk86dO8udO3fSrPvnn3+Kh4eHGBsbi6enp1y4cEFrH4sWLZKyZcuKvr6+VKhQQVavXq213MnJSebMmaP8/ODBA+ndu7fY2NiIubm5NGrUSCIjI0VE5OLFiwJAoqKitLbxww8/SNmyZbXievDggbL8yJEj4u3tLcbGxmJlZSXNmzeX+/fvZ3hc3idr167NsXzMTcx15vq7+FDy/F3ExcUJAAkJCRERkYcPH4q+vr5s3rxZ6RMVFSUA5Pjx4yIiEhwcLDo6Olq5v3jxYrGwsJAXL15kuK9PPvlEunXrluNjmDlzppQpU0b5edGiRVKkSBGtWMaMGSMVK1ZUfu7YsaP4+Phobad27drSv39/rbYbN25IyZIl5ezZs2nOlYKiMOR5bgMgQUFBmfZ59OiR8p6dSm1OjR49Wrp16yaBgYFiaWn5bsEWUszznOHj4yO9evXSamvXrp107do1w3XSy32RV/+WuLi4yLlz5wSARERE5EbIhQrzPPcUKVJEfv75ZxERMTU1TfN5umjRorJs2TKtNuZ47mCev5sxY8ZIvXr1srVO27ZtpXHjxpn2CQgIEDc3t3eIjF6Xnfpfvs5QHD16NH777TesWrUK4eHhKFeuHFq0aIH79+9DR0cHnTt3xvr167XWWbduHby8vODk5ISUlBS0bdsW9+/fR0hICPbu3YsrV67gs88+y3CfxYsXx+3bt3Ho0KF0l9etWxc//vgjLCwslOr2l19+CeDVX+mnTJmCU6dO4ffff8fVq1fRo0ePNNsYP348Zs+ejdDQUOjp6aFXr17KsqCgIAwdOhQjR47E2bNn0b9/f/Ts2RMHDhzIMOYOHTogLi4OO3fuRFhYGNzd3dGkSRPcv38fFSpUQM2aNbFu3bo0x6lLly7pbi8yMhJNmjRBpUqVcPz4cRw5cgStW7d+b2atFUTMdeY6pfXo0SMAQNGiRQEAYWFhSEpKQtOmTZU+Li4ucHR0xPHjxwEAx48fR9WqVVGsWDGlT4sWLRAfH6/89f5NEREROHbsGLy9vXNlDKnxp8bXoEEDGBgYaMV38eJFPHjwQOnz+hhT+6SOEQBSUlLQvXt3jBo1CpUrV87xuKnwSExMxNKlS2FpaQk3NzetZTNmzIC1tTVq1KiB77//XuvWAQCwf/9+bN68OcOZkER5qW7duti3bx8uXboEADh16hSOHDmCVq1apds/o9y/c+cO+vbtizVr1uToVRJEOS05ORkbNmxAQkICPD09Abw6DzZu3Ij79+8jJSUFGzZswPPnz9GwYUNlPeY4va+2bt2KmjVrokOHDrCzs0ONGjWwbNmyDPvfuXMHO3bsQO/evbPc9j///AN7e3uULVsWXbt2xfXr13MydMpITlcos5I6a+vJkyeir68v69atU5YlJiaKvb29zJw5U0REIiIiRKPRyLVr10Tk/2ZyLV68WERE9uzZI7q6unL9+nVlG6l/hUmd/fWmly9fSo8ePQSAFC9eXHx9fWX+/Pny6NEjpY/av8KfPHlSAMjjx49FRHvWVqodO3YIAHn27JmIiNStW1f69u2rtZ0OHTrI//73P+Xn12cNHD58WCwsLOT58+da6zg7O8uSJUtE5NVMNGdnZ2XZmzO53py11blzZ/Hy8spyfO+rD+UvQ8x15vq7+FDy/G0lJyeLj4+P1u9n3bp1YmBgkKZvrVq1ZPTo0SIi0rdvX2nevLnW8oSEBAEgwcHBWu0lS5YUAwMD0dHRkcmTJ+f4GP755x+xsLCQpUuXKm3NmjWTfv36afVLPVfPnz8vIiL6+vqyfv16rT4LFy4UOzs75edp06ZJs2bNJCUlRUTUzyb70BT0PM8LyGCG4rZt28TU1FQ0Go3Y29un+bdi9uzZcuDAATl16pQsXrxYrKysZPjw4cry//77T0qVKqXMIOYMxbfHPM8ZycnJMmbMGNFoNKKnpycajUamTZuWpl9muZ+SkiItW7aUKVOmiIhITEwMZ2/lEOZ5zjl9+rSYmpqKrq6uWFpayo4dO5RlDx48kObNmwsA0dPTEwsLC9m9e7eynDmeu5jn78bQ0FAMDQ3lq6++kvDwcFmyZIkYGRnJypUr0+3/3XffSZEiRZTvlxkJDg6WTZs2yalTp2TXrl3i6ekpjo6OEh8fnxvDKPA+iBmKly9fRlJSEry8vJQ2fX19fPTRR8rj2KtXrw5XV1dl5lZISAji4uLQoUMHAK8e216qVCmUKlVK2UalSpVgZWWlbONNurq6CAwMxI0bNzBz5kyULFkS06ZNU665z0xYWBhat24NR0dHmJubK7Nd3qx+V6tWTfn/EiVKAADi4uKUmF8fMwB4eXllGO+pU6fw5MkT5QEFqa+YmBhcvnwZANCpUydcvXoVf/31F4BXM7bc3d0zvBFp6qwtyhvM9f/DXKdU/v7+OHv2LDZs2JBr+zh8+DBCQ0Px008/4ccff8Qvv/ySYd/Xc27AgAFZbvvmzZto2bIlOnTogL59++Zk2AgLC8PcuXOxcuVK3g+U3lqjRo0QGRmJY8eOoWXLlujYsaPy/gwAI0aMQMOGDVGtWjUMGDAAs2fPxvz58/HixQsAQN++fdGlSxc0aNAgv4ZApGXTpk1Yt24d1q9fj/DwcKxatQqzZs3CqlWrtPpllvvz58/H48eP8dVXX+XHEIhUqVixIiIjI/H333/jiy++gJ+fH86fPw8AmDhxIh4+fIg///wToaGhGDFiBDp27IgzZ84AYI7T+y0lJQXu7u6YNm0aatSogX79+qFv37746aef0u2/YsUKdO3aFUZGRplut1WrVujQoQOqVauGFi1aIDg4GA8fPsSmTZtyYxj0mvf+oSxdu3ZViizr169Hy5YtYW1t/c7bLVmyJLp3744FCxbg3LlzeP78eYaJDLy6AXqLFi1gYWGBdevW4eTJkwgKCgKANE/41NfXV/4/9ctgSkrKW8X55MkTlChRApGRkVqvixcvYtSoUQBeXdrauHFjrePUtWvXDLdpbGz8VrFQ7mKuM9cLi0GDBmH79u04cOAAHBwclPbixYsjMTERDx8+1Op/584dFC9eXOnz5lOfU39O7ZOqTJkyqFq1Kvr27Yvhw4dj0qRJGcb0es5Nnjw50/hv3bqFRo0aoW7dumke9KImvoz6pC4/fPgw4uLi4OjoCD09Pejp6eHatWsYOXIkSpcunWlsRKlMTU1Rrlw51KlTB8uXL4eenh6WL1+eYf/atWvj5cuXuHr1KoBXlzvPmjVLycHevXvj0aNH0NPTw4oVK/JoFET/Z9SoURg7diw6deqEqlWronv37hg+fDimT5+u1S+z3N+/fz+OHz8OQ0ND6OnpoVy5cgCAmjVrws/PL8/HRJQeAwMDlCtXDh4eHpg+fTrc3Nwwd+5cXL58GQsWLMCKFSvQpEkTuLm5ISAgADVr1lRuTcEcp/dZiRIlUKlSJa02V1fXdC9PPnz4MC5evIg+ffpkez9WVlaoUKECoqOj3zpWUiffCorOzs4wMDDA0aNHlbakpCScPHlSK8m6dOmCs2fPIiwsDL/++qtW8cDV1RX//vsv/v33X6Xt/PnzePjwYZpEzUyRIkVQokQJ5Umj6T0F98KFC7h37x5mzJiB+vXrw8XFResv/Wq5urpqjRkAjh49mmG87u7uiI2NVf5BeP1lY2Oj9OvatSs2btyI48eP48qVK+jUqVOGMVSrVg379u3Lduz0dpjr/4e5XriJCAYNGoSgoCDs378fZcqU0Vru4eEBfX19rd/ZxYsXcf36deXeQZ6enjhz5oxWTu7duxcWFhaZngspKSnKzKv0vJ5vdnZ2Gfa7efMmGjZsCA8PDwQGBkJHR/ufUU9PTxw6dAhJSUla8VWsWFF5gp2np2eavNy7d68yxu7du+P06dNaRU57e3uMGjUKu3fvzjA2osxkdQ5ERkZCR0dHyf/jx4+nKbSbm5sjMjISn3zySV6FTaR4+vRpmvdcXV3dLP+Q+Xruz5s3D6dOnVLyOjg4GACwceNGTJ06NXcCJ3pHqTn89OlTAMj0PGCO0/vMy8sLFy9e1Gq7dOkSnJyc0vRdvnw5PDw80tz/WY0nT57g8uXLyhV0lIty+hrqrLz+5NuhQ4eKvb297Ny5U86dOyd+fn5SpEiRNE9g9fLyEjc3NzE3N5enT58q7SkpKVK9enWpX7++hIWFyd9//y0eHh7i7e2d4f5/+uknGTBggOzevVuio6Pl7NmzMnr0aNHR0ZGDBw+KiMjRo0eV+8PdvXtXEhISJC4uTgwMDGTUqFFy+fJl+eOPP6RChQpa96RI7wmzERERAkBiYmJERCQoKEj09fVl0aJFcunSJZk9e7bo6urKgQMHlHVev1dWSkqK1KtXT9zc3GT37t0SExMjR48elXHjxsnJkyeVdeLj48XY2Fjc3NykSZMmWmN+M66LFy+KgYGBfPHFF3Lq1CmJioqSRYsWyd27d7P47b0fPpR7VzDXmevv4kPJ8+z44osvxNLSUg4ePCi3b99WXq/n+oABA8TR0VH2798voaGh4unpKZ6ensryly9fSpUqVaR58+YSGRkpu3btEltbW/nqq6+UPgsWLJCtW7fKpUuX5NKlS/Lzzz+Lubm5jB8//p3iv3HjhpQrV06aNGkiN27c0BpDqocPH0qxYsWke/fucvbsWdmwYYOYmJgo9wEVeXXe6enpyaxZsyQqKkoCAgJEX19fzpw5k+G+eQ9Fet3jx48lIiJCed/94YcfJCIiQq5duyZPnjyRr776So4fPy5Xr16V0NBQ6dmzpxgaGsrZs2dFROTYsWMyZ84ciYyMlMuXL8vatWvF1tZWPv/88wz3yXsovj3mec7w8/OTkiVLyvbt2yUmJka2bNkiNjY2yj121eT+m3h/uZzDPM8ZY8eOlZCQEImJiZHTp0/L2LFjRaPRyJ49eyQxMVHKlSsn9evXl7///luio6Nl1qxZotFotO6z+DrmeM5inr+bEydOiJ6enkydOlX++ecfWbdunZiYmMjatWu1+j169EhMTEyU5wm8qXHjxjJ//nzl55EjR8rBgweV749NmzYVGxsbiYuLy9XxFFTZqf/leUGxe/fu0r59exERefbsmQwePFhsbGzE0NBQvLy80n3AxKJFiwRAuh90r127Jm3atBFTU1MxNzeXDh06SGxsbIb7Dw8Pl27dukmZMmXE0NBQrK2tpUGDBrJ161atfgMGDBBra2sBIAEBASIisn79eildurQYGhqKp6enbN26NdtFltTxlC1bVvT19aVChQqyevVqrX2/+cUxPj5eBg8eLPb29qKvry+lSpWSrl27aj2gQ0SkY8eOAkBWrFih1Z5eXAcPHpS6deuKoaGhWFlZSYsWLbSWv88+lDdy5jpz/V18KHmeHQDSfQUGBip9nj17JgMHDpQiRYqIiYmJfPLJJ1oFOxGRq1evSqtWrcTY2FhsbGxk5MiRkpSUpCyfN2+eVK5cWUxMTMTCwkJq1KghixYtkuTk5HeKPzAwMMMxvO7UqVNSr149MTQ0lJIlS8qMGTPSbGvTpk1SoUIFMTAwkMqVK2f4RSAVC4r0utT3ujdffn5+8uzZM/nkk0/E3t5eDAwMpESJEtKmTRutf3PCwsKkdu3aYmlpKUZGRuLq6irTpk1L81Cs17Gg+PaY5zkjPj5ehg4dKo6OjmJkZCRly5aV8ePHy4sXL0REVOX+m1hsyTnM85zRq1cvcXJyEgMDA7G1tZUmTZrInj17lOWXLl2Sdu3aiZ2dnZiYmEi1atXSfL5+HXM8ZzHP3922bdukSpUqYmhoKC4uLloPN0y1ZMkSMTY2locPH6a7DScnJ+V7q4jIZ599JiVKlBADAwMpWbKkfPbZZxIdHZ1bQyjwslP/04iIZDWLMTw8HB4eHggLC4O7u7u6qY8ZaNmyJcqVK4cFCxa803YKshIlSmDKlClvdb+AwmDdunXo1q1bjuRjbmKuZ425nrEPJc+J3gXznAoD5jkVBsxzKgyY51QYZKf+l2f3UHzw4AG2b9+OgwcPomnTpnm12w/K06dPsXfvXty5cweVK1fO73DoLTHXs8ZcJyIiIiIiIvpw6eXVjnr16oWTJ09i5MiRaNu2bV7t9oOydOlSTJkyBcOGDVNuzk8fHuZ61pjrRERERERERB+uPCsoBgUF5dWuPljDhg3DsGHD8jsMekfM9awx14mIiIiIiIg+XHl2yTMRERERERERERF9+LI1QzE4OBhRUVG5FQuRKkePHgXAfKSCjXlOhQHznAoD5jkVBsxzKgyY51QYxMTEqO6r6inPx48fR/369ZGcnPxOgRHlFB0dHaSkpOR3GES5inlOhQHznAoD5jkVBsxzKgyY51QY6Orq4vDhw1k+70DVDEVDQ0MkJydj7dq1cHV1zZEAid5WcHAwJk6cyHykAo15ToUB85wKA+Y5FQbMcyoMmOdUGERFRaFbt24wNDTMsm+2Lnl2dXWFu7v7WwdGlBNSp5czH6kgY55TYcA8p8KAeU6FAfOcCgPmOZE2PpSFiIiIiIiIiIiIVMvzgmJsbCyGDh2KcuXKwcjICMWKFYOXlxcWL16Mp0+f5nU4aaxcuRIajQYajQY6OjooUaIEPvvsM1y/fj2/QyPSwnOJPjTTp09HrVq1YG5uDjs7O/j6+uLixYtafZ4/fw5/f39YW1vDzMwM7du3x507d7T6DBkyBB4eHjA0NET16tXT3ZeIYNasWahQoQIMDQ1RsmRJTJ069Z3iT0pKwpgxY1C1alWYmprC3t4en3/+OW7duqXV7/79++jatSssLCxgZWWF3r1748mTJ1pj7NGjB6pWrQo9PT34+vqm2VePHj2U8+f1V+XKld9pDFQwHDp0CK1bt4a9vT00Gg1+//13reXp5Y5Go8H3338PALh69Sp69+6NMmXKwNjYGM7OzggICEBiYqLWdjZt2oTq1avDxMQETk5OyvpEeSGrPN+yZQuaN28Oa2traDQaREZGptlG//794ezsDGNjY9ja2qJt27a4cOFCuvu7d+8eHBwcoNFo8PDhw5wfEJEKixcvRrVq1WBhYQELCwt4enpi586dyvKscvr1z99vvuLi4vJjSFSI3bx5E926dYO1tTWMjY1RtWpVhIaGKsuz+rySlRkzZkCj0WDYsGFK2/379zF48GBUrFgRxsbGcHR0xJAhQ/Do0aOcHh4hjwuKV65cQY0aNbBnzx5MmzYNEREROH78OEaPHo3t27fjzz//zMtwMmRhYYHbt2/j5s2b+O2333Dx4kV06NAhv8MiUvBcog9RSEgI/P398ddff2Hv3r1ISkpC8+bNkZCQoPQZPnw4tm3bhs2bNyMkJAS3bt1Cu3bt0myrV69e+OyzzzLc19ChQ/Hzzz9j1qxZuHDhArZu3YqPPvroneJ/+vQpwsPDMXHiRISHh2PLli24ePEi2rRpo9Wva9euOHfuHPbu3Yvt27fj0KFD6Nevn7I8OTkZxsbGGDJkCJo2bZruvubOnYvbt28rr3///RdFixbl+UMAgISEBLi5uWHhwoXpLn89d27fvo0VK1ZAo9Ggffv2AIALFy4gJSUFS5Yswblz5zBnzhz89NNPGDdunLKNnTt3omvXrhgwYADOnj2LRYsWYc6cOViwYEGejJEoqzxPSEhAvXr18N1332W4DQ8PDwQGBiIqKgq7d++GiKB58+bpPmiyd+/eqFatWo7FT/Q2HBwcMGPGDISFhSE0NBSNGzdG27Ztce7cOQBZ5/Rnn32W5t+AFi1awNvbG3Z2dvk5NCpkHjx4AC8vL+jr62Pnzp04f/48Zs+ejSJFiih9svq8kpmTJ09iyZIlad63b926hVu3bmHWrFk4e/YsVq5ciV27dqF37945PkYCICqEhYUJAAkLC1PTPUMtWrQQBwcHefLkSbrLU1JSREQkJiZGAEhERISy7MGDBwJADhw4ICIiBw4cEADy559/ioeHhxgbG4unp6dcuHBBWSc6OlratGkjdnZ2YmpqKjVr1pS9e/dmGmNgYKBYWlpqtc2bN08AyKNHj5S20aNHS/ny5cXY2FjKlCkjEyZMkMTExGwcDXpba9euzZF8/JDxXCr4CkOex8XFCQAJCQkREZGHDx+Kvr6+bN68WekTFRUlAOT48eNp1g8ICBA3N7c07efPnxc9PT2tHM4tJ06cEABy7do1Zd8A5OTJk0qfnTt3ikajkZs3b6ZZ38/PT9q2bZvlfoKCgkSj0cjVq1dzLPb3QWHI89wGQIKCgjLt07ZtW2ncuHGmfWbOnCllypRRfu7cubN8+umnWn3mzZsnDg4Oyr8xpA7z/N1llufpfdbJyKlTpwSAREdHa7UvWrRIvL29Zd++fQJAHjx48O5BFzLM89xTpEgR+fnnn9NdllFOp4qLixN9fX1ZvXp1boZYaDDP1RszZozUq1cvW+uo+bwiIvL48WMpX7687N27V7y9vWXo0KGZ9t+0aZMYGBhIUlJStuIprLJT/8uzGYr37t3Dnj174O/vD1NT03T7aDSabG93/PjxmD17NkJDQ6Gnp4devXopy548eYL//e9/2LdvHyIiItCyZUu0bt06W5dcxsXFISgoCLq6utDV1VXazc3NsXLlSpw/fx5z587FsmXLMGfOnGzHT5RdPJeooEi99KBo0aIAgLCwMCQlJWnN2nNxcYGjoyOOHz+uervbtm1D2bJlsX37dpQpUwalS5dGnz59cP/+/ZwdAF6NQaPRwMrKCgBw/PhxWFlZoWbNmkqfpk2bQkdHB3///fdb72f58uVo2rQpnJyc3jVkKmTu3LmDHTt2ZPmX+UePHinnIgC8ePECRkZGWn2MjY1x48YNXLt2LVdiJcpNCQkJCAwMRJkyZVCqVCml/fz585g8eTJWr14NHR3eXp7eH8nJydiwYQMSEhLg6emZZnlGOf261atXw8TEBJ9++mluh0ukZevWrahZsyY6dOgAOzs71KhRA8uWLcuwv9rPKwDg7+8PHx+fDK/0edOjR49gYWEBPb1sPZOYVMizfzWjo6MhIqhYsaJWu42NDczMzGBmZoYxY8Zke7tTp06Ft7c3KlWqhLFjx+LYsWN4/vw5AMDNzQ39+/dHlSpVUL58eUyZMgXOzs7YunVrptt89OgRzMzMYGpqimLFiuHAgQNpijcTJkxA3bp1Ubp0abRu3RpffvklNm3alO34ibKL5xIVBCkpKRg2bBi8vLxQpUoVAK/uC2pgYKAU51IVK1YMsbGxqrd95coVXLt2DZs3b8bq1auxcuVKhIWF5fiH6efPn2PMmDHo3LkzLCwsALwaw5uXFOnp6aFo0aLZGsPrbt26hZ07d6JPnz7vHDMVPqtWrcL/Y+/O42pM//+Bv07LOZ2Wk1YVSiRlSyWpzGDQMo19ZIyIjBlkGWYsMzQMHzGGMQtjrGUGgxAhS0i2bClroibMtNiTspTO+/eHX/fXrUVpo/N+Ph7n8XDu+7qv+7qO9313netc93Xp6emVOHVAkZSUFPz222/44osvhG1eXl7YunUrDhw4AKVSiatXr2LhwoUAXjyixNi74vfffxfaR7t370Z0dDSkUimAFx3nAwcOxI8//ghLS8taLiljL1y4cAG6urqQyWQYOXIkIiIi0KJFC2F/WTH9qlWrVuHTTz+FXC6vqeIzBuBFe3zp0qVo1qwZ9u7di1GjRmHcuHFYs2ZNienL014BgA0bNuDs2bOYO3duucpx9+5dzJ49WzT9EKs6tf4z3KlTp5CYmIiWLVvi2bNnFT7+5Wfmzc3NAUCYcDY3Nxdff/017O3tUa9ePejq6iIpKem1o6r09PSQmJiIM2fOYOHChXBycio2mf/GjRvh4eEBMzMz6OrqYvr06bzYBKtVfC2xd0lQUBAuXryIDRs2VHneSqUSz549w59//on33nsPnTt3xqpVqxATE1NsEZgiPj4+QuO8PAufFBQUwM/PD0SEpUuXVnUVRNasWYN69eqVuHgLY6+zevVqDBo0qNhowyLp6enw9vZG//79MWLECGH7iBEjMGbMGHz00UeQSqXo0KEDPvnkEwDgUVzsnTJo0CAkJCQgNjYWtra28PPzE34w/eabb2Bvbw9/f/9aLiVj/6d58+ZITEzEyZMnMWrUKAQEBODy5cvC/rJi+mVxcXFISkriueNYrVAqlXByckJISAgcHR3x+eefY8SIEfjjjz9KTP+69goA/Pvvvxg/fjzWrVtXZroiOTk58PX1RYsWLTBz5sw3rQorQ421CG1sbCCRSIp9mWvSpAlsbGxEv5oUNVSJSNhWUFBQYr6amprCv4se81QqlQCAr7/+GhEREQgJCcGRI0eQmJiI1q1bF1vF8FVqamqwsbGBvb09Jk6ciA4dOmDUqFHC/ri4OAwaNAgffvghdu7ciYSEBEybNu21+TJWFfhaYu+6MWPGYOfOnYiJiUHDhg2F7WZmZsjPzy+2uuatW7dgZmZW7vzNzc2hoaEBW1tbYZu9vT0AlNpZvXLlSiQmJiIxMRFRUVFl5l/UmXjjxg1ER0cLoxOL6vDqKorPnz/H/fv3K1SHIkSE1atXY/DgwaWOPmCsNEeOHEFycnKpo1szMjLQpUsXuLu7Y/ny5aJ9EokEP/zwA3Jzc3Hjxg1kZWUJCxs1adKk2svOWFXR19dHs2bN8P7772Pz5s24cuUKIiIiAAAHDx5EeHg4NDQ0oKGhga5duwJ48dTHjBkzarPYTIVJpVLY2NjA2dkZc+fOhYODA3755Rdhf1kx/bKVK1eibdu2cHZ2rsniMwbgRXv85ZG1wIv2eElt8de1V4rEx8fj9u3bcHJyEu7bsbGx+PXXX6GhoSFacOvRo0fw9vaGnp4eIiIiRN91WdWpsYfIjYyM0L17dyxevBhjx44tde43ADAxMQHw4pEaR0dHAEBiYmKFz3ns2DEMHToUffr0AfBilNX169crnM/UqVPRtGlTTJgwAU5OTjh+/DisrKwwbdo0IQ3PJ8RqCl9L7F1FRBg7diwiIiJw6NAhWFtbi/Y7OztDU1MTBw4cEFZ3S05Oxs2bN0ucO6g0Hh4eeP78OVJTU9G0aVMAwNWrVwGg1DkIGzRoUK68izoTr127hpiYGBgZGYn2u7m5ITs7G/Hx8UID/uDBg1AqlXB1dS13HYrExsYiJSWFRxewN7Jq1So4OzvDwcGh2L709HR06dJFWDG0tFGH6urqwvXx999/w83NTfjbwti7hohARMKTHFu2bMGTJ0+E/adPn0ZgYCCOHDki/P1grLYVPXlRkldjukhubi42bdpU7sdCGatqHh4exQbAXL16tcS2eFntlZd17doVFy5cEG0bNmwY7OzsMGXKFGGe/pycHHh5eUEmkyEyMrJcoxnZm6nRWSl///13eHh4oF27dpg5cybatGkDNTU1nD59GleuXBG+fMnlcnTo0AHz5s2DtbU1bt++jenTp1f4fM2aNcPWrVvRo0cPSCQSBAcHCyOuKqJRo0bo06cPvvvuO+zcuRPNmjXDzZs3sWHDBri4uGDXrl0l/irEWHXha4m9i4KCgrB+/Xps374denp6wpyC+vr6kMvl0NfXx/DhwzFx4kQYGhpCoVBg7NixcHNzQ4cOHYR8UlJSkJubi6ysLDx58kToJG/RogWkUim6desGJycnBAYG4ueff4ZSqURQUBC6d+8uGrVYUQUFBfj4449x9uxZ7Ny5E4WFhUIdDA0NIZVKYW9vD29vb+GRjoKCAowZMwaffPIJLCwshLwuX76M/Px83L9/H48ePRLq0LZtW9E5V61aBVdXV2GeScaAF18UU1JShPdpaWlITEyEoaGhMA9cTk4OwsPDhXkPX5aeno7OnTvDysoKCxYswJ07d4R9RSNp7969i82bN6Nz5854+vQpQkNDER4ejtjY2GquHWMvvC7O79+/j5s3byIjIwMAhC+uZmZmMDMzwz///IONGzfC09MTJiYm+O+//zBv3jzI5XJ8+OGHAFCs0/Du3bsAIEzxwlhN++abb+Dj4wNLS0s8evQI69evx6FDh7B3795yxXSRjRs34vnz5/w4P6s1EyZMgLu7O0JCQuDn54dTp05h+fLlxZ6IKKu9ArzoROzTpw/GjBkDPT29Ym1iHR0dGBkZCdtzcnLg6emJx48fY+3atcjJyUFOTg6AF4NtXl4clFWBql42+nUyMjJozJgxZG1tTZqamqSrq0vt27enH3/8kfLy8oR0ly9fJjc3N5LL5dS2bVvat28fAaCYmBgiIoqJiSEA9ODBA+GYhIQEAkBpaWlERJSWlkZdunQhuVxOjRo1osWLF792WfHQ0FDS19cvtj0uLo4A0MmTJ4mIaNKkSWRkZES6uro0YMAAWrRoUYnHsaq3du3aKovHdxlfS3VbXYxzACW+QkNDhTRPnjyh0aNHk4GBAWlra1OfPn0oMzNTlE+nTp1KzKcoXomI0tPTqW/fvqSrq0v169enoUOH0r179ypV/rS0tFLrUHQ9ERHdu3ePBg4cSLq6uqRQKGjYsGH06NEjUV5WVlYl5vOy7OxsksvltHz58kqV+21WF+O8JhTdt199BQQECGmWLVtGcrmcsrOzix0fGhpaaiwXuXPnDnXo0IF0dHRIW1ubunbtSidOnKiJ6tU5HOdv5nVxXlocz5gxg4he/B3w8fEhU1NT0tTUpIYNG9Knn35KV65cee05X24TsfLhOK8agYGBZGVlRVKplExMTKhr1660b98+IqpYTLu5udGnn35a08Wv8zjOK2bHjh3UqlUrkslkZGdnV2Kbtqz2CtGLNnPRfb0kr34nLe1vx6vfFVjpKtL/JyF6aXK1Upw9exbOzs6Ij4+Hk5PT63spGatG69atg7+/P8cjq9M4zpkq4DhnqoDjnKkCjnOmCjjOmSqoSP8fL9PHGGOMMcYYY4wxxhgrN+5QZIwxxhhjjDHGGGOMlRt3KDLGGGOMMcYYY4wxxsqNOxQZY4wxxhhjjDHGGGPlplGRxFFRUUhKSqqusjBWLseOHQPA8cjqNo5zpgo4zpkq4DhnqoDjnKkCjnOmCtLS0sqdtlyrPMfFxeG9995DYWFhpQrGWFVRU1ODUqms7WIwVq04zpkq4DhnqoDjnKkCjnOmCjjOmSpQV1fHkSNH4ObmVma6co1QlMlkKCwsxNq1a2Fvb18lBWTsTUVFRSE4OJjjkdVpHOdMFXCcM1XAcc5UAcc5UwUc50wVJCUlwd/fHzKZ7LVpK/TIs729PZycnN64YIxVhaLh5RyPrC7jOGeqgOOcqQKOc6YKOM6ZKuA4Z0zsrVuU5fr165BIJEhMTKztotQ5Q4cORe/evWu7GHUOx2z14ZhljDHGGGOMMcbePjXaoTh06FBIJBJIJBJoamrC2toakydPxtOnT4U0jRo1QmZmJlq1alWTRStRWFgY6tWrV+3nKanTZPPmzdDS0sLChQur/fysdByzJeOYZW9i7ty5cHFxgZ6eHkxNTdG7d28kJyeL0jx9+hRBQUEwMjKCrq4u+vXrh1u3bonS3Lx5E76+vtDW1oapqSkmTZqE58+fC/uPHj0KDw8PGBkZQS6Xw87ODosWLap0+a9fv47hw4fD2toacrkcTZs2xYwZM5Cfny9Kd/78ebz33nvQ0tJCo0aNMH/+/GJ5hYeHw87ODlpaWmjdujWioqJKPe/IkSMhkUjw888/V7oOrG44fPgwevToAQsLC0gkEmzbtq3UtCXFz6FDh4S/ba++Tp8+DeDFtTh06FC0bt0aGhoa/OMOq3WFhYUIDg4W3YNnz56NV6eDT0pKQs+ePaGvrw8dHR24uLjg5s2bwv6srCwMHjwYZmZm0NHRgZOTE7Zs2VLT1WGsRDNnzix2X7azswMA3L9/H2PHjkXz5s0hl8thaWmJcePG4eHDh6I8XtdOYqympKenw9/fX2iTt27dGmfOnBH2l9YW+fHHH8vMd8mSJWjcuDG0tLTg6uqKU6dOFUsTFxeHDz74ADo6OlAoFHj//ffx5MmTKq+jqqvQI89VwdvbG6GhoSgoKEB8fDwCAgIgkUjwww8/AHgx+aOZmVlNF+utsnLlSgQFBeGPP/7AsGHDars4Ko9j9vU4Zll5xMbGIigoCC4uLnj+/Dm+/fZbeHp64vLly9DR0QEATJgwAbt27UJ4eDj09fUxZswY9O3bV1hVr7CwEL6+vjAzM8Px48eRmZmJIUOGQFNTEyEhIQAAHR0djBkzBm3atIGOjg6OHj2KL774Ajo6Ovj888/fuPxXrlyBUqnEsmXLYGNjg4sXL2LEiBHIy8vDggULAAA5OTnw9PREt27d8Mcff+DChQsIDAxEvXr1hHMfP34cAwcOxNy5c/HRRx9h/fr16N27N86ePVvsh4mIiAicOHECFhYWb1xuVvfk5eXBwcEBgYGB6Nu3b6npSosfd3d3ZGZmirYFBwfjwIEDaNeuHYAX15pcLse4ceO4s4W9FX744QcsXboUa9asQcuWLXHmzBkMGzYM+vr6GDduHAAgNTUVHTt2xPDhw/H9999DoVDg0qVL0NLSEvIZMmQIsrOzERkZCWNjY6xfvx5+fn44c+YMHB0da6t6jAlatmyJ/fv3C+81NF58Zc/IyEBGRgYWLFiAFi1a4MaNGxg5ciQyMjKwefNmAOVrJzFWEx48eAAPDw906dIFu3fvhomJCa5duwYDAwMhzattkd27d2P48OHo169fqflu3LgREydOxB9//AFXV1f8/PPP8PLyQnJyMkxNTQG86Ez09vbGN998g99++w0aGho4d+4c1NTeugd0331UDvHx8QSA4uPjy5O8VAEBAdSrVy/Rtr59+5Kjo6PwPi0tjQBQQkICERHFxMQQANqzZw+1bduWtLS0qEuXLnTr1i2KiooiOzs70tPTo4EDB1JeXp6Qz+7du8nDw4P09fXJ0NCQfH19KSUlpdh5tmzZQp07dya5XE5t2rSh48ePi8778mvGjBlERPTnn3+Ss7Mz6erqUv369WngwIF069YtIe/79+/Tp59+SsbGxqSlpUU2Nja0evXqcn0uP/zwA2lpadHWrVtFaRYuXEitWrUibW1tatiwIY0aNYoePXok7J8xYwY5ODiIjlm0aBFZWVmV+vmfOnWKjI2Nad68eURE9ODBAxo+fDgZGxuTnp4edenShRITE4ud488//yQrKytSKBQ0YMAAysnJKffn/uzZMwoKCiIzMzOSyWRkaWlJISEhpX42JVm7dm2VxGN5cMy+/nPhmK0eNRnnteX27dsEgGJjY4mIKDs7mzQ1NSk8PFxIk5SURAAoLi6OiIiioqJITU2NsrKyhDRLly4lhUJBz549K/Vcffr0IX9//yqvw/z588na2lp4//vvv5OBgYGoLFOmTKHmzZsL7/38/MjX11eUj6urK33xxReibf/99x81aNCALl68SFZWVrRo0aIqL39tU4U4r24AKCIiotj2isRPfn4+mZiY0KxZs0rcX9LfQlZ+HOdVw9fXlwIDA0Xb+vbtS4MGDRLeDxgw4LX3eh0dHfrzzz9F2wwNDWnFihVVV1gVxHFeNUpqH5dl06ZNJJVKqaCggIjevJ3EyofjvPymTJlCHTt2rNAxvXr1og8++KDMNO3bt6egoCDhfWFhIVlYWNDcuXOFba6urjR9+vSKFZgJKtL/V6tdtBcvXsTx48chlUpfm3bmzJlYvHgxjh8/jn///Rd+fn74+eefsX79euzatQv79u3Db7/9JqTPy8vDxIkTcebMGRw4cABqamro06dPsSXep02bhq+//hqJiYmwtbXFwIED8fz5c7i7u+Pnn3+GQqFAZmYmMjMz8fXXXwMACgoKMHv2bJw7dw7btm3D9evXMXToUCHP4OBgXL58Gbt370ZSUhKWLl0KY2Pj19ZxypQpmD17Nnbu3Ik+ffqI9qmpqeHXX3/FpUuXsGbNGhw8eBCTJ09+bZ6lOXjwILp37445c+ZgypQpAID+/fvj9u3b2L17N+Lj4+Hk5ISuXbvi/v37wnGpqanYtm0bdu7ciZ07dyI2Nhbz5s0T9r/uc//1118RGRmJTZs2ITk5GevWrUPjxo3fuB41jWNWjGOWVUbRIzqGhoYAgPj4eBQUFKBbt25CGjs7O1haWiIuLg7Ai18cW7dujfr16wtpvLy8kJOTg0uXLpV4noSEBBw/fhydOnWqljoUlb+ofO+//77oHlH0q+mDBw+ENC/XsShNUR0BQKlUYvDgwZg0aRJatmxZ5eVmdVtF4ycyMhL37t3jEebsrebu7o4DBw7g6tWrAIBz587h6NGj8PHxAfAi7nft2gVbW1t4eXnB1NQUrq6uxaYEcHd3x8aNG3H//n0olUps2LABT58+RefOnWu4RoyV7Nq1a7CwsECTJk0waNAg0SP7r3r48CEUCoUwivFN2kmMVYfIyEi0a9cO/fv3h6mpKRwdHbFixYpS09+6dQu7du3C8OHDS02Tn5+P+Ph4UTtaTU0N3bp1E9rRt2/fxsmTJ2Fqagp3d3fUr18fnTp1wtGjR6uucuz/VHUPZVkCAgJIXV2ddHR0SCaTEQBSU1OjzZs3C2lKG+21f/9+Ic3cuXMJAKWmpgrbvvjiC/Ly8ir13Hfu3CEAdOHCBdF5Vq5cKaS5dOkSAaCkpCQiIgoNDSV9ff3X1uv06dMEQBh91aNHDxo2bNjrP5D/LyAggKRSKQGgAwcOlOuY8PBwMjIyEt5XZLTX1q1bSVdXlzZs2CDsO3LkCCkUCnr69Kkoj6ZNm9KyZcuEc2hra4tGd02aNIlcXV1LLeern/vYsWPpgw8+IKVSWa56lqSmRyhyzBbHMVv96vovoIWFheTr60seHh7CtnXr1pFUKi2W1sXFhSZPnkxERCNGjCBPT0/R/ry8PAJAUVFRou0NGjQgqVRKampqpY68qoxr166RQqGg5cuXC9u6d+9On3/+uShd0XV6+fJlIiLS1NSk9evXi9IsWbKETE1NhfchISHUvXt3Ie54hCIrDUoYoVjR+PHx8SEfH59S9/MIxcrhOK8ahYWFNGXKFJJIJKShoUESiUT0xEBmZiYBIG1tbfrpp58oISGB5s6dSxKJhA4dOiSke/DgAXl6ehIA0tDQIIVCQXv37q2NKtUpHOdVIyoqijZt2kTnzp2jPXv2kJubG1laWoraskXu3LlDlpaW9O233wrbKtJOYhXHcV5+MpmMZDIZffPNN3T27FlatmwZaWlpUVhYWInpf/jhBzIwMKAnT56Ummd6ejoBEJ7QKzJp0iRq3749ERHFxcURADI0NKTVq1fT2bNn6csvvySpVEpXr16tugrWYW/1CMUuXbogMTERJ0+eREBAAIYNG1bmM/JF2rRpI/y7fv360NbWRpMmTUTbbt++Lby/du0aBg4ciCZNmkChUAgjil79heflfM3NzQFAlE9J4uPj0aNHD1haWkJPT08Y9VKU96hRo7Bhwwa0bdsWkydPxvHjx8tVv8aNG2PGjBnIzc0ttn///v3o2rUrGjRoAD09PQwePBj37t3D48ePX5v3y06ePIn+/fvjr7/+woABA4Tt586dQ25urrAQQtErLS0NqampQrrGjRtDT09PeG9ubl6hz33o0KFITExE8+bNMW7cOOzbt69C5a8NHLOl149jlr2poKAgXLx4ERs2bKi2cxw5cgRnzpzBH3/8gZ9//hl///13qWlfjqGRI0e+Nu/09HR4e3ujf//+GDFiRFUWG/Hx8fjll18QFhYGiURSpXmzuq+i8fPff/9h7969ZY4IYOxtsGnTJqxbtw7r16/H2bNnsWbNGixYsABr1qwBAOHJgl69emHChAlo27Ytpk6dio8++gh//PGHkE9wcDCys7Oxf/9+nDlzBhMnToSfnx8uXLhQK/Vi7GU+Pj7o378/2rRpAy8vL0RFRSE7OxubNm0SpcvJyYGvry9atGiBmTNn1k5hGSuDUqmEk5MTQkJC4OjoiM8//xwjRowQ3Y9ftnr1agwaNEg05+2bnhcAvvjiCwwbNgyOjo5YtGgRmjdvjtWrV1cqb1ZcjXco6ujowMbGBg4ODli9ejVOnjyJVatWvfY4TU1N4d9FK+6+TCKRiB4N7dGjB+7fv48VK1bg5MmTOHnyJAAUW43z1XwBFHvE9GV5eXnw8vKCQqHAunXrcPr0aURERIjy9vHxwY0bNzBhwgRkZGSga9euwqOnpWnQoAEOHTokfEl99OiRsO/69ev46KOP0KZNG2zZsgXx8fFYsmSJ6JxqamrFVrkrKCgodp6mTZvCzs4Oq1evFu3Pzc2Fubk5EhMTRa/k5GRMmjSpxM+r6DOryOfu5OSEtLQ0zJ49G0+ePIGfnx8+/vjjMj+b2sYxWzKOWfamxowZg507dyImJgYNGzYUtpuZmSE/Px/Z2dmi9Ldu3RIWPjIzMyu26nPR+1cXR7K2tkbr1q0xYsQITJgwocwG98sxNGvWrDLLn5GRgS5dusDd3R3Lly8X7StP+UpLU7T/yJEjuH37NiwtLaGhoQENDQ3cuHEDX331FT9uz16rovETGhoKIyMj9OzZs+YLy1gFTJo0CVOnTsUnn3yC1q1bY/DgwZgwYQLmzp0LADA2NoaGhgZatGghOs7e3l74kTA1NRWLFy/G6tWr0bVrVzg4OGDGjBlo166d0E5h7G1Sr1492NraIiUlRdj26NEjeHt7Q09PDxEREaK2bkXaSYxVJ3Nz8zLvxy87cuQIkpOT8dlnn5WZp7GxMdTV1ctsRxcNuCnvuVnl1Oocimpqavj2228xffr0Kl3C+969e0hOTsb06dPRtWtX2NvbC3NXVYRUKkVhYaFo25UrV3Dv3j3MmzcP7733Huzs7EocHWZiYoKAgACsXbsWP//8c7EvnSWxsrJCbGwssrKyRB008fHxUCqVWLhwITp06ABbW1tkZGQUO19WVpaogyYxMbHYOYyNjXHw4EGkpKTAz89P6KBxcnJCVlYWNDQ0YGNjI3qVZy49oPyfu0KhwIABA7BixQps3LgRW7ZsEc159zbjmBXjmGUVQUQYM2YMIiIicPDgQVhbW4v2Ozs7Q1NTEwcOHBC2JScn4+bNm3BzcwMAuLm54cKFC6IYjo6OhkKhKNZweJlSqcSzZ89K3f9y/BStEFeS9PR0dO7cGc7OzggNDS22WpybmxsOHz4s6vyOjo5G8+bNhVXt3NzcRHUsSlNUx8GDB+P8+fOiTk4LCwtMmjQJe/fuLbVsjAEVix8iQmhoqLACKGNvs8ePHxe756qrqws/EkqlUri4uCA5OVmU5urVq7CyshLyAFBmPoy9TXJzc5Gamip0kuTk5MDT0xNSqRSRkZHFRnO9aTuJsarm4eFR5v34ZatWrYKzszMcHBzKzFMqlcLZ2VnUjlYqlThw4IDQjm7cuDEsLCzKfW5WObW+bnb//v2hrq5epb8KGhgYwMjICMuXL0dKSgoOHjyIiRMnVjifxo0bIzc3FwcOHMDdu3fx+PFjWFpaQiqV4rfffsM///yDyMhIzJ49W3Tcd999h+3btyMlJQWXLl3Czp07YW9vX65zNmrUCIcOHcLt27eFCXRtbGxQUFAgnPOvv/4qNlS4c+fOuHPnDubPn4/U1FQsWbIEu3fvLvEcpqamOHjwIK5cuSIs6NGtWze4ubmhd+/e2LdvH65fv47jx49j2rRpOHPmTLnKXp7P/aeffsLff/+NK1eu4OrVqwgPD4eZmRnq1atXrnO8DThmxThmWXkFBQVh7dq1WL9+PfT09JCVlYWsrCyhc15fXx/Dhw/HxIkTERMTg/j4eAwbNgxubm7o0KEDAMDT0xMtWrTA4MGDce7cOezduxfTp09HUFAQZDIZAGDJkiXYsWMHrl27hmvXrmHVqlVYsGAB/P39K1X+os5ES0tLLFiwAHfu3BHqUOTTTz+FVCrF8OHDcenSJWzcuBG//PKLKK7Gjx+PPXv2YOHChbhy5QpmzpyJM2fOYMyYMQAAIyMjtGrVSvTS1NSEmZkZmjdvXqk6sLohNzdX6CwEgLS0NCQmJuLmzZsVip+DBw8iLS2t1BEBly9fRmJiIu7fv4+HDx+KzslYTevRowfmzJmDXbt24fr164iIiMBPP/0kWhRu0qRJ2LhxI1asWIGUlBQsXrwYO3bswOjRowG8WOjLxsYGX3zxBU6dOoXU1FQsXLgQ0dHR6N27dy3VjLH/8/XXXyM2NlZo1/bp0wfq6uoYOHCg0JmYl5eHVatWIScnR2iHFA0oKE87ibGaMGHCBJw4cQIhISFISUnB+vXrsXz5cgQFBYnS5eTkIDw8vNS2SNeuXbF48WLh/cSJE7FixQqsWbMGSUlJGDVqFPLy8oSF5SQSCSZNmoRff/0VmzdvRkpKCoKDg3HlyhWe3qU6VPWkjGUpbVLvuXPnkomJCeXm5pa6wMWDBw+E9CUtPPHqAg/R0dFkb29PMpmM2rRpQ4cOHRJNXP7qeYheTNIMgGJiYoRtI0eOJCMjIwJAM2bMICKi9evXU+PGjUkmk5GbmxtFRkaK8po9ezbZ29uTXC4nQ0ND6tWrF/3zzz8V+lz+++8/atasGXXo0IEePnxIP/30E5mbm5NcLicvLy/6888/i30uS5cupUaNGpGOjg4NGTKE5syZU+ICF0UyMjLI1taW/Pz86Pnz55STk0Njx44lCwsL0tTUpEaNGtGgQYPo5s2bJX7GRMUX0Xjd5758+XJq27Yt6ejokEKhoK5du9LZs2dL/WxKUtOLsnDMlu9z4ZitWnVx0mcAJb5CQ0OFNE+ePKHRo0eTgYEBaWtrU58+fSgzM1OUz/Xr18nHx4fkcjkZGxvTV199RQUFBcL+X3/9lVq2bEna2tqkUCjI0dGRfv/9dyosLKxU+UNDQ0utw8vOnTtHHTt2JJlMRg0aNKB58+YVy2vTpk1ka2tLUqmUWrZsSbt27Srz3LwoC3tZ0d+ZV18BAQElpi8tfgYOHEju7u6lnsfKyuq18c5ej+O8auTk5ND48ePJ0tKStLS0qEmTJjRt2jR69uyZKN2qVavIxsaGtLS0yMHBgbZt2ybaf/XqVerbty+ZmpqStrY2tWnThv7888+arEqdxHFeNQYMGEDm5uYklUqpQYMGNGDAAEpJSSGi0u/9ACgtLU3I43XtJPbmOM4rZseOHdSqVSuSyWRkZ2cnWsiwyLJly0gul1N2dnaJeVhZWQnfaYv89ttvZGlpSVKplNq3b08nTpwodtzcuXOpYcOGpK2tTW5ubnTkyJEqqZMqqEj/n4TolUnMSnD27Fk4OzsjPj4eTk5OFeuxZKyKrVu3Dv7+/hyPrE7jOGeqgOOcqQKOc6YKOM6ZKuA4Z6qgIv1/tf7IM2OMMcYYY4wxxhhj7N3BHYqMMcYYY4wxxhhjjLFy4w5FxhhjjDHGGGOMMcZYuXGHImOMMcYYY4wxxhhjrNw0KpI4KioKSUlJ1VUWxsrl2LFjADgeWd3Gcc5UAcc5UwUc50wVcJwzVcBxzlRBWlpaudOWa5XnuLg4vPfeeygsLKxUwRirKmpqalAqlbVdDMaqFcc5UwUc50wVcJwzVcBxzlQBxzlTBerq6jhy5Ajc3NzKTFeuEYoymQyFhYVYu3Yt7O3tq6SAjL2pqKgoBAcHczyyOo3jnKkCjnOmCjjOmSrgOGeqgOOcqYKkpCT4+/tDJpO9Nm2FHnm2t7eHk5PTGxeMsapQNLyc45HVZRznTBVwnDNVwHHOVAHHOVMFHOeMifGiLIwxxhhjjDHGGGOMsXJ7ZzoUw8LCUK9evdouRpWTSCTYtm1bbReD1QF8jbC33dy5c+Hi4gI9PT2Ympqid+/eSE5OFqV5+vQpgoKCYGRkBF1dXfTr1w+3bt0SpRk3bhycnZ0hk8nQtm3bYue5fv06JBJJsdeJEycqVf779+9j7NixaN68OeRyOSwtLTFu3Dg8fPhQlO7mzZvw9fWFtrY2TE1NMWnSJDx//lzYn5mZiU8//RS2trZQU1PDl19+WeL5srOzERQUBHNzc8hkMtja2iIqKqpSdWB1w+HDh9GjRw9YWFiUeI+cOXMm7OzsoKOjAwMDA3Tr1g0nT54sls+uXbvg6uoKuVwOAwMD9O7dW7S/pOtow4YN1Vgzxsr26NEjfPnll7CysoJcLoe7uztOnz4t7CcifPfddzA3N4dcLke3bt1w7do1Yf/169cxfPhwWFtbQy6Xo2nTppgxYwby8/NrozqMFfO6tlJpbRyJRILw8HAh3evaSozVhPT0dPj7+8PIyAhyuRytW7fGmTNnRGmSkpLQs2dP6OvrQ0dHBy4uLrh582aZ+f78889Ce7xRo0aYMGECnj59KuxfunQp2rRpA4VCAYVCATc3N+zevbta6qjqarRDcejQoaKbnpGREby9vXH+/PnXHjtgwABcvXq1BkoJ/PvvvwgMDISFhQWkUimsrKwwfvx43Lt3r0bOz1QXXyOsLouNjUVQUBBOnDiB6OhoFBQUwNPTE3l5eUKaCRMmYMeOHQgPD0dsbCwyMjLQt2/fYnkFBgZiwIABZZ5v//79yMzMFF7Ozs6VKn9GRgYyMjKwYMECXLx4EWFhYdizZw+GDx8upCksLISvry/y8/Nx/PhxrFmzBmFhYfjuu++ENM+ePYOJiQmmT58OBweHEs+Vn5+P7t274/r169i8eTOSk5OxYsUKNGjQoFJ1YHVDXl4eHBwcsGTJkhL329raYvHixbhw4QKOHj2Kxo0bw9PTE3fu3BHSbNmyBYMHD8awYcNw7tw5HDt2DJ9++mmxvEJDQ0XX0audjozVpM8++wzR0dH466+/cOHCBXh6eqJbt25IT08HAMyfPx+//vor/vjjD5w8eRI6Ojrw8vISvmheuXIFSqUSy5Ytw6VLl7Bo0SL88ccf+Pbbb2uzWowJXtdWatSokeienJmZie+//x66urrw8fER5VWethJj1eXBgwfw8PCApqYmdu/ejcuXL2PhwoUwMDAQ0qSmpqJjx46ws7PDoUOHcP78eQQHB0NLS6vUfNevX4+pU6dixowZSEpKwqpVq7Bx40bRfbxhw4aYN28e4uPjcebMGXzwwQfo1asXLl26VK11VklUDvHx8QSA4uPjy5O8VAEBAeTt7U2ZmZmUmZlJCQkJ5OvrS40aNSrzuPz8/EqdtyJSU1PJ1NSUOnbsSIcOHaIbN25QVFQUtWzZkpo1a0b37t2r0vMBoIiIiCrNs65bu3ZtlcTj24ivkeJU9Rqpy3Fe5Pbt2wSAYmNjiYgoOzubNDU1KTw8XEiTlJREACguLq7Y8TNmzCAHB4di29PS0ggAJSQkVFfRBZs2bSKpVEoFBQVERBQVFUVqamqUlZUlpFm6dCkpFAp69uxZseM7depE48ePL7Z96dKl1KRJkxq9tmuDKsR5dSvPPfLhw4cEgPbv309ERAUFBdSgQQNauXJlpfNmr8dxXjUeP35M6urqtHPnTtF2JycnmjZtGimVSjIzM6Mff/xR2JednU0ymYz+/vvvUvOdP38+WVtbV1u5VQXHefV4ta1UkrZt21JgYGCJ+0prK7E3w3FeflOmTKGOHTuWmWbAgAHk7+9foXyDgoLogw8+EG2bOHEieXh4lHmcgYHBa9s97IWK9P/V+CPPMpkMZmZmMDMzQ9u2bTF16lT8+++/wq/mRcO4N27ciE6dOkFLSwvr1q0r9jhnamoqevXqhfr160NXVxcuLi7Yv3+/6FyNGzdGSEgIAgMDoaenB0tLSyxfvrzM8gUFBUEqlWLfvn3o1KkTLC0t4ePjg/379yM9PR3Tpk0DAHz77bdwdXUtdryDgwNmzZoFADh9+jS6d+8OY2Nj6Ovro1OnTjh79myZ5//333/h5+eHevXqwdDQEL169cL169df97GyOoSvEb5GVEXRo8KGhoYAgPj4eBQUFKBbt25CGjs7O1haWiIuLq7C+ffs2ROmpqbo2LEjIiMjq6bQr3j48CEUCgU0NF6scRYXF4fWrVujfv36QhovLy/k5ORU6FfRyMhIuLm5ISgoCPXr10erVq0QEhKCwsLCKq8Dq9vy8/OxfPly6OvrCyNiz549i/T0dKipqcHR0RHm5ubw8fHBxYsXix0fFBQEY2NjtG/fHqtXrwYR1XQVGAMAPH/+HIWFhcVGrsjlchw9ehRpaWnIysoS/Q3R19eHq6trmX9DHj58KPwdYuxt82pb6VXx8fFITEwUPS3B2NsgMjIS7dq1Q//+/WFqagpHR0esWLFC2K9UKrFr1y7Y2trCy8sLpqamcHV1fe1UV+7u7oiPj8epU6cAAP/88w+ioqLw4Ycflpi+sLAQGzZsQF5eHtzc3KqsfuyFWp1DMTc3F2vXroWNjQ2MjIxE+6ZOnYrx48cjKSkJXl5eJR774Ycf4sCBA0hISIC3tzd69OhR7Hn7hQsXol27dkhISMDo0aMxatSoYnN2Fbl//z727t2L0aNHQy6Xi/aZmZlh0KBB2LhxI4gIgwYNwqlTp5CamiqkuXTpEs6fPy88MvTo0SMEBATg6NGjOHHiBJo1a4YPP/wQjx49KvH8BQUF8PLygp6eHo4cOYJjx45BV1cX3t7ePLeLiuJrRIyvkbpDqVTiyy+/hIeHB1q1agUAyMrKglQqLTYXaP369ZGVlVXuvHV1dbFw4UKEh4dj165d6NixI3r37l3lnYp3797F7Nmz8fnnnwvbsrKyRJ2JAIT3FanDP//8g82bN6OwsBBRUVEIDg7GwoUL8b///a9qCs/qvJ07d0JXVxdaWlpYtGgRoqOjYWxsDOBFfAEv5lqcPn06du7cCQMDA3Tu3Bn3798X8pg1axY2bdqE6Oho9OvXD6NHj8Zvv/1WK/VhTE9PD25ubpg9ezYyMjJQWFiItWvXIi4uDpmZmcI9tqR7cGn335SUFPz222/44osvqr38jFVUSW2lV61atQr29vZwd3ev4dIxVrZ//vkHS5cuRbNmzbB3716MGjUK48aNw5o1awAAt2/fRm5uLubNmwdvb2/s27cPffr0Qd++fREbG1tqvp9++ilmzZqFjh07QlNTE02bNkXnzp2LTV1x4cIF6OrqQiaTYeTIkYiIiECLFi2qtc4qqaqHPJYlICCA1NXVSUdHh3R0dAgAmZubi/ItelTt559/Fh0bGhpK+vr6ZebfsmVL+u2334T3VlZWoiG0SqWSTE1NaenSpSUef+LEiTIf7/npp58IAN26dYuIiBwcHGjWrFnC/m+++YZcXV1LLV9hYSHp6enRjh07hG0vn++vv/6i5s2bk1KpFPY/e/aM5HI57d27t/SKq5i6PNScrxG+RorU5TgnIho5ciRZWVnRv//+K2xbt24dSaXSYmldXFxo8uTJxbZX5DGewYMHl/nYRYsWLYTrztvb+7X5PXz4kNq3b0/e3t6ix5JHjBhBnp6eorR5eXkEgKKioorlU9ojz82aNaNGjRrR8+fPhW0LFy4kMzOz15btXVLX47wmlHZPzs3NpWvXrlFcXBwFBgZS48aNhXvzunXrCAAtW7ZMSP/06VMyNjamP/74o9RzBQcHU8OGDau8DnUdx3nVSUlJoffff58AkLq6Orm4uNCgQYPIzs6Ojh07RgAoIyNDdEz//v3Jz8+vWF7//fcfNW3alIYPH15Txa/TOM6rXkltpZc9fvyY9PX1acGCBaXmwY88Vy2O8/LT1NQkNzc30baxY8dShw4diIgoPT2dANDAgQNFaXr06EGffPJJqfnGxMRQ/fr1acWKFXT+/HnaunUrNWrUSPSdk+jFd8Rr167RmTNnaOrUqWRsbEyXLl2qotrVbW/1I89dunRBYmIiEhMTcerUKXh5ecHHxwc3btwQpWvXrl2Z+eTm5uLrr7+Gvb096tWrB11dXSQlJRUbfdWmTRvh3xKJBGZmZrh9+3aZedNrHueRSqUAgEGDBmH9+vXCMX///TcGDRokpLt16xZGjBiBZs2aQV9fHwqFArm5uaWuWnTu3DmkpKRAT08Purq60NXVhaGhIZ4+fSoa5cXqNr5G+Bqp68aMGYOdO3ciJiYGDRs2FLabmZkhPz8f2dnZovS3bt2CmZlZpc7p6uqKlJSUUvdHRUUJ193KlSvLzOvRo0fw9vaGnp4eIiIioKmpKarDq6tSF72vSB3Mzc1ha2sLdXV1YZu9vT2ysrJ4NC4rFx0dHdjY2KBDhw5YtWoVNDQ0sGrVKgAv4guA6Jd6mUyGJk2alLmyoqurK/777z88e/asegvPWCmaNm2K2NhY5Obm4t9//8WpU6dQUFCAJk2aCPfYku7Br95/MzIy0KVLF7i7u792qhfGakNpbaWXbd68GY8fP8aQIUNquHSMvZ65uXmxEYH29vZCO8PY2BgaGhplpilJcHAwBg8ejM8++wytW7dGnz59EBISgrlz50KpVArppFIpbGxs4OzsjLlz58LBwQG//PJLFdaQAbXwyHNRA9fGxgYuLi5YuXIl8vLyRM/TF6Ury9dff42IiAiEhITgyJEjSExMROvWrYt90Xr5ix7wosPk5UB7mY2NDSQSCZKSkkrcn5SUBBMTE+FxvIEDByI5ORlnz57F8ePH8e+//4pW0goICEBiYiJ++eUXHD9+HImJiTAyMir1y2Bubi6cnZ2FL7VFr6tXr5a48iKrm/ga4WukriIijBkzBhERETh48CCsra1F+52dnaGpqYkDBw4I25KTk3Hz5s1Kz3mSmJgodKKUxMrKSrjuylpJOScnB56enpBKpYiMjCw2l5ebmxsuXLgg6pSPjo6GQqGo0GMWHh4eSElJEV2LV69ehbm5udBhz1hFKJVKoSPQ2dkZMplMNL1FQUEBrl+/Disrq1LzSExMhIGBAWQyWbWXl7Gy6OjowNzcHA8ePMDevXvRq1cvWFtbw8zMTPQ3JCcnBydPnhT9DUlPT0fnzp3h7OyM0NBQqKnV6gxQjIm8rq30slWrVqFnz54wMTGpwRIyVj4eHh7FptG6evWq0M6QSqVwcXEpM01JHj9+XOy+XfQDfFmDXl5uB7Gqo1HbBZBIJFBTU8OTJ08qdNyxY8cwdOhQ9OnTB8CLjobKLsxgZGSE7t274/fff8eECRNEc8RlZWVh3bp1CAoKErY1bNgQnTp1wrp16/DkyRN0794dpqamojL+/vvvwgSh//77L+7evVvq+Z2cnLBx40aYmppCoVBUqi6s7uBr5P/wNfJuCwoKwvr167F9+3bo6ekJc1rp6+tDLpdDX18fw4cPx8SJE2FoaAiFQoGxY8fCzc0NHTp0EPJJSUlBbm4usrKy8OTJEyQmJgJ4MdpKKpVizZo1kEqlcHR0BABs3boVq1evfu3Iw9cp6kx8/Pgx1q5di5ycHOTk5AAATExMoK6uDk9PT7Ro0QKDBw/G/PnzkZWVhenTpyMoKEjUCVNU5tzcXNy5cweJiYmQSqVCp+OoUaOwePFijB8/HmPHjsW1a9cQEhKCcePGVaoOrG7Izc0VjbhNS0tDYmIiDA0NYWRkhDlz5qBnz54wNzfH3bt3sWTJEqSnp6N///4AAIVCgZEjR2LGjBlo1KgRrKys8OOPPwKAkGbHjh24desWOnToAC0tLURHRyMkJARff/11zVeYsf9v7969ICI0b94cKSkpmDRpEuzs7DBs2DBIJBJ8+eWX+N///odmzZrB2toawcHBsLCwQO/evQH8X2eilZUVFixYICx4B1RsFDlj1eV1baUiKSkpOHz4MKKiokrM53VtJcaq24QJE+Du7o6QkBD4+fnh1KlTWL58uWhU+KRJkzBgwAC8//776NKlC/bs2YMdO3bg0KFDQpohQ4agQYMGmDt3LgCgR48e+Omnn+Do6Cg8gRQcHIwePXoIHYvffPMNfHx8YGlpiUePHmH9+vU4dOgQ9u7dW6OfgUqo6meoyxIQEEDe3t6UmZlJmZmZdPnyZRo9ejRJJBKKiYkhov+bHy4hIUF07Kvzw/Xp04fatm1LCQkJlJiYSD169CA9PT3RXFRWVla0aNEiUT4ODg40Y8aMUst49epVMjY2pvfee49iY2Pp5s2btHv3bmrVqhW1bduWHj16JEq/YsUKsrCwIGNjY/rrr79E+xwdHal79+50+fJlOnHiBL333nskl8tFZcJLcx/l5eVRs2bNqHPnznT48GH6559/KCYmhsaOHVvq3BmqqC7PXcHXCF8jRepinAMo8RUaGiqkefLkCY0ePZoMDAxIW1ub+vTpQ5mZmaJ8OnXqVGI+aWlpREQUFhZG9vb2pK2tTQqFgtq3b0/h4eGVLn9MTEypdSg6NxHR9evXycfHh+RyORkbG9NXX31FBQUFr/0srKysRGmOHz9Orq6uJJPJqEmTJjRnzhzRnIp1QV2M85pQWiwGBATQkydPqE+fPmRhYUFSqZTMzc2pZ8+edOrUKVEe+fn59NVXX5GpqSnp6elRt27d6OLFi8L+3bt3U9u2bUlXV5d0dHTIwcGB/vjjDyosLKzp6r7zOM6rzsaNG6lJkyYklUrJzMyMgoKCKDs7W9ivVCopODiY6tevTzKZjLp27UrJycnC/tDQ0FLv46xyOM6rRnnaSkQv5iVv1KhRqffk17WV2JvhOK+YHTt2UKtWrUgmk5GdnR0tX768WJpVq1aRjY0NaWlpkYODA23btk20v1OnThQQECC8LygooJkzZ1LTpk1JS0uLGjVqRKNHj6YHDx4IaQIDA8nKyoqkUimZmJhQ165dad++fdVVzTqnIv1/Nd6h+PINTU9Pj1xcXGjz5s1CmvJ2lqSlpVGXLl1ILpdTo0aNaPHixcUmt3+TzpKivAMCAqh+/fokkUgIAPXt25fy8vKKpX3w4AHJZDLS1tYu1pFy9uxZateuHWlpaVGzZs0oPDy8WJle7iwhIsrMzKQhQ4aQsbGx8CVyxIgR9PDhwzLLrErq8o2crxG+RorU5ThnrAjHOVMFHOdMFXCcM1XAcc5UQUX6/yREr1ldAcDZs2fh7OyM+Ph4ODk5vS55nTNjxgz89NNPiI6OFj12x2rHunXr4O/vr7Lx+Dbia6TqcZwzVcBxzlQBxzlTBRznTBVwnDNVUJH+v1qfQ/Fd8P3336Nx48Y4ceIE2rdvz5M3M/YKvkYYY4wxxhhjjDHVwR2K5TRs2LDaLgJjbzW+RhhjjDHGGGOMMdXAw4gYY4wxxhhjjDHGGGPlVqERilFRUUhKSqqusjBWLseOHQPA8cjqNo5zpgo4zpkq4DhnqoDjnKkCjnOmCtLS0sqdtlyLssTFxeG9995DYWFhpQrGWFVRU1ODUqms7WIwVq04zpkq4DhnqoDjnKkCjnOmCjjOmSpQV1fHkSNH4ObmVma6co1QlMlkKCwsxNq1a2Fvb18lBWTsTUVFRSE4OJjjkdVpHOdMFXCcM1XAcc5UAcc5UwUc50wVJCUlwd/fHzKZ7LVpK/TIs729PS+Pzmpd0fByjkdWl3GcM1XAcc5UAcc5UwUc50wVcJwzJlbnFmUJCwtDvXr1arsYjFU7jnXGGGOMMcYYY4zVhhrvUBw6dCh69+5dJXk1btwYP//8s2jbgAEDcPXq1Urle+jQIUgkEkgkEqipqUFfXx+Ojo6YPHkyMjMzK5V3SbhjqG7iWC+OY121zZ07Fy4uLtDT04OpqSl69+6N5ORkUZqnT58iKCgIRkZG0NXVRb9+/XDr1i1RmnHjxsHZ2RkymQxt27Yt85wpKSnQ09Orkri7f/8+xo4di+bNm0Mul8PS0hLjxo3Dw4cPRelu3rwJX19faGtrw9TUFJMmTcLz58+F/ZmZmfj0009ha2sLNTU1fPnll8XOFRYWJlybRS8tLa1K14HVDYcPH0aPHj1gYWEBiUSCbdu2ifbPnDkTdnZ20NHRgYGBAbp164aTJ0+K0vTs2ROWlpbQ0tKCubk5Bg8ejIyMDFGaTZs2oW3bttDW1oaVlRV+/PHH6q4aY6UqLCxEcHAwrK2tIZfL0bRpU8yePRsvTwf/6n2z6FVS7D579gxt27aFRCJBYmJiDdaEsdKVp62UmpqKPn36wMTEBAqFAn5+fsXaSgCwa9cuuLq6Qi6Xw8DAoMq+lzBWXunp6fD394eRkRHkcjlat26NM2fOiNIkJSWhZ8+e0NfXh46ODlxcXHDz5s0y883OzkZQUBDMzc0hk8lga2uLqKgoYX/jxo1L/FsQFBRULfVUZXVuhKJcLoepqWmV5JWcnIyMjAycPn0aU6ZMwf79+9GqVStcuHChSvJnrDI41tm7JjY2FkFBQThx4gSio6NRUFAAT09P5OXlCWkmTJiAHTt2IDw8HLGxscjIyEDfvn2L5RUYGIgBAwaUeb6CggIMHDgQ7733XpWUPyMjAxkZGViwYAEuXryIsLAw7NmzB8OHDxfSFBYWwtfXF/n5+Th+/DjWrFmDsLAwfPfdd0KaZ8+ewcTEBNOnT4eDg0Op51MoFMjMzBReN27cqJJ6sHdfXl4eHBwcsGTJkhL329raYvHixbhw4QKOHj2Kxo0bw9PTE3fu3BHSdOnSBZs2bUJycjK2bNmC1NRUfPzxx8L+3bt3Y9CgQRg5ciQuXryI33//HYsWLcLixYurvX6MleSHH37A0qVLsXjxYiQlJeGHH37A/Pnz8dtvvwlpXr5nZmZmYvXq1ZBIJOjXr1+x/CZPngwLC4uarAJjr/W6tlJeXh48PT0hkUhw8OBBHDt2DPn5+ejRo4dooZAtW7Zg8ODBGDZsGM6dO4djx47h008/ra1qMRX04MEDeHh4QFNTE7t378bly5excOFCGBgYCGlSU1PRsWNH2NnZ4dChQzh//jyCg4PL/BE9Pz8f3bt3x/Xr17F582YkJydjxYoVaNCggZDm9OnTor8F0dHRAID+/ftXX4VVFZVDfHw8AaD4+PjyJC9TQEAA9erVi4iIrKysaNGiRaL9Dg4ONGPGDCIiUiqVNGPGDGrUqBFJpVIyNzensWPHEhFRp06dCIDoRUQUGhpK+vr6Qn4zZswgBwcH+vPPP8nKyooUCgUNGDCAcnJySi1jTEwMAaAHDx6Itj9+/JiaN29OHh4eou0rVqwgOzs7kslk1Lx5c1qyZImwLy0tjQDQli1bqHPnziSXy6lNmzZ0/Phx0blefhXV38rKimbPnk2DBw8mHR0dsrS0pO3bt9Pt27epZ8+epKOjQ61bt6bTp0+LyrN582Zq0aIFSaVSsrKyogULFoj2W1lZ0Zw5c2jYsGGkq6tLjRo1omXLlpX6ebxt1q5dW2XxWJ041jnWK+NdifPKuH37NgGg2NhYIiLKzs4mTU1NCg8PF9IkJSURAIqLiyt2fFHMl2by5Mnk7+9f7FqpSps2bSKpVEoFBQVERBQVFUVqamqUlZUlpFm6dCkpFAp69uxZseM7depE48ePL7a9Osv8NlGFOK9uACgiIqLMNA8fPiQAtH///lLTbN++nSQSCeXn5xMR0cCBA+njjz8Wpfn111+pYcOGpFQqK11uVcJxXjV8fX0pMDBQtK1v3740aNCgUo/p1asXffDBB8W2R0VFkZ2dHV26dIkAUEJCQlUXV+VwnFePV9tKe/fuJTU1NXr48KGQJjs7myQSCUVHRxMRUUFBATVo0IBWrlxZK2WuyzjOy2/KlCnUsWPHMtMMGDCA/P39K5Tv0qVLqUmTJkJ7pTzGjx9PTZs25fZLOVWk/++tHqG4ZcsWLFq0CMuWLcO1a9ewbds2tG7dGgCwdetWNGzYELNmzRJ6nkuTmpqKbdu2YefOndi5cydiY2Mxb968CpdHLpdj5MiROHbsGG7fvg0AWLduHb777jvMmTMHSUlJCAkJQXBwMNasWSM6dtq0afj666+RmJgIW1tbDBw4EM+fP4e7uzt+/vln0UiUr7/+Wjhu0aJF8PDwQEJCAnx9fTF48GAMGTIE/v7+OHv2LJo2bYohQ4YIj3vEx8fDz88Pn3zyCS5cuICZM2ciODgYYWFhovIsXLgQ7dq1Q0JCAkaPHo1Ro0YVG07Pag7HOse6Kip6VNjQ0BDAi//TgoICdOvWTUhjZ2cHS0tLxMXFVSjvgwcPIjw8vNQRXFXl4cOHUCgU0NB4scZZXFwcWrdujfr16wtpvLy8kJOTg0uXLlUo79zcXFhZWaFRo0bo1atXhY9nDHjxS/7y5cuhr69f6ojY+/fvY926dXB3d4empiaAFyNpXx0hIJfL8d9///FoWVYr3N3dceDAAWG6l3PnzuHo0aPw8fEpMf2tW7ewa9cu0Sjyou0jRozAX3/9BW1t7WovN2OV8Wpb6dmzZ5BIJKLVV7W0tKCmpoajR48CAM6ePYv09HSoqanB0dER5ubm8PHxwcWLF2u+AkxlRUZGol27dujfvz9MTU3h6OiIFStWCPuVSiV27doFW1tbeHl5wdTUFK6ursWmcSkpXzc3NwQFBaF+/fpo1aoVQkJCUFhYWGL6/Px8rF27FoGBgZBIJFVZRYa3/JHnmzdvwszMDN26dYOlpSXat2+PESNGAHhxU1VXV4eenh7MzMxgZmZWaj5KpRJhYWFo1aoV3nvvPQwePBgHDhx4ozLZ2dkBAK5fvw4AmDFjBhYuXIi+ffvC2toaffv2xYQJE7Bs2TLRcV9//TV8fX1ha2uL77//Hjdu3EBKSgqkUin09fUhkUiEeujq6grHffjhh/jiiy/QrFkzfPfdd8jJyYGLiwv69+8PW1tbTJkyBUlJScK8GT/99BO6du2K4OBg2NraYujQoRgzZkyxuWM+/PBDjB49GjY2NpgyZQqMjY0RExPzRp8JqzyOdY51VaNUKvHll1/Cw8MDrVq1AgBkZWVBKpUWm++wfv36yMrKKnfe9+7dw9ChQxEWFgaFQlGVxRa5e/cuZs+ejc8//1zYlpWVJepMBCC8r0gdmjdvjtWrV2P79u1Yu3YtlEol3N3d8d9//1VN4Vmdt3PnTujq6kJLSwuLFi1CdHQ0jI2NRWmmTJkCHR0dGBkZ4ebNm9i+fbuwz8vLC1u3bsWBAwegVCpx9epVLFy4EACqZY5dxl5n6tSp+OSTT2BnZwdNTU04Ojriyy+/xKBBg0pMv2bNGujp6YmmzSAiDB06FCNHjkS7du1qquiMvZGS2kodOnSAjo4OpkyZgsePHyMvLw9ff/01CgsLhXvzP//8A+DFfLrTp0/Hzp07YWBggM6dO+P+/fu1Vh+mWv755x8sXboUzZo1w969ezFq1CiMGzdOGIxy+/Zt5ObmYt68efD29sa+ffvQp08f9O3bF7GxsWXmu3nzZhQWFiIqKgrBwcFYuHAh/ve//5WYftu2bcjOzsbQoUOro5oq763uUOzfvz+ePHmCJk2aYMSIEYiIiBBNbF9ejRs3hp6envDe3NxcGHVVUUWjoyQSCfLy8pCamorhw4dDV1dXeP3vf/9Damqq6Lg2bdqIzg+gXGV4+biiL6VFI9de3laUV1JSEjw8PER5eHh44Nq1a6Je+5fzLergedPPhFUexzrHuqoJCgrCxYsXsWHDhirPe8SIEfj000/x/vvvl/uYli1bCnFd2miXl+Xk5MDX1xctWrTAzJkzK1Hakrm5uWHIkCFo27YtOnXqhK1bt8LExKRYBz5jpenSpQsSExNx/PhxeHt7w8/Pr9i9b9KkSUhISMC+ffugrq4uGgU+YsQIjBkzBh999BGkUik6dOiATz75BACgpvZWNx9ZHbVp0yasW7cO69evx9mzZ7FmzRosWLCg2JMSRVavXo1BgwaJRtr+9ttvePToEb755puaKjZjb6yktpKJiQnCw8OxY8cO6OrqQl9fH9nZ2XBychLuzUVzKU6bNg39+vWDs7MzQkNDIZFIEB4eXit1YapHqVTCyckJISEhcHR0xOeff44RI0bgjz/+EPYDQK9evTBhwgS0bdsWU6dOxUcffSSkKS1fU1NTLF++HM7OzhgwYACmTZtW6jGrVq2Cj48Pz5lbTTRq8+RqamqildmAF5PoF2nUqBGSk5Oxf/9+REdHY/To0fjxxx8RGxsrPJJTHq+mlUgkoklrKyIpKQnAi46b3NxcAMCKFSvg6uoqSqeurl5qGYqG2panDCUd96Z5lZZvUT5v+pmw1+NY51hn/2fMmDHYuXMnDh8+jIYNGwrbzczMkJ+fj+zsbNEoxVu3bpU5MvdVBw8eRGRkJBYsWADgRee4UqmEhoYGli9fjsDAwGLHREVFCdekXC4vM/9Hjx7B29sbenp6iIiIEMWYmZkZTp06JUpfNKq2InV4VdFonJSUlDfOg6kWHR0d2NjYwMbGBh06dECzZs2watUqUUeKsbExjI2NYWtrC3t7ezRq1AgnTpyAm5sbJBIJfvjhB4SEhCArKwsmJibCiPcmTZrUVrWYCps0aZIwShF48aPjjRs3MHfuXAQEBIjSHjlyBMnJydi4caNo+8GDBxEXFyd6XBQA2rVrh0GDBpXaOclYTSutrQQAnp6eSE1Nxd27d6GhoYF69erBzMxMuDcX/aDfokUL4RiZTIYmTZq8dvVcxqqKubm5KAYBwN7eHlu2bAHwog2ioaFRYpqix/dLy1dTU1P0HdTe3h5ZWVnIz8+HVCoVtt+4cQP79+/H1q1bq6JKrAS12qFoYmIiemwmJycHaWlpojRyuRw9evRAjx49EBQUBDs7O1y4cAFOTk6QSqWlPitfHZ48eYLly5fj/fffh4mJCQDAwsIC//zzT6mPW5RHVdbD3t4ex44dE207duwYbG1ti3X8sJrDsf4Cx7pqIyKMHTsWEREROHToEKytrUX7nZ2doampiQMHDggrciYnJ+PmzZtwc3Mr93ni4uJEcbZ9+3b88MMPOH78uGgFuJdZWVmVK++cnBx4eXlBJpMhMjKy2Bxzbm5umDNnDm7fvi2swh4dHQ2FQlGswVQRhYWFuHDhAj788MM3zoOpNqVSiWfPnpW5H0CxNOrq6sJ18/fff8PNzU34u8BYTXr8+HGx0bHq6uol/ki4atUqODs7F5s39NdffxU9FpeRkQEvLy9s3Lix2A+mjNWG17WVXlY0jcXBgwdx+/Zt9OzZE8CL9pRMJkNycjI6duwI4MVAhuvXr5e7vcNYZXl4eBSbt/7q1atCDEqlUri4uJSZprR8169fD6VSKfxNuHr1KszNzUWdiQAQGhoKU1NT+Pr6VkWVWAlqtUPxgw8+QFhYGHr06IF69erhu+++E3UEhIWFobCwEK6urtDW1sbatWshl8uFAGvcuDEOHz6MTz75BDKZrNjcQJV1+/ZtPH36FI8ePUJ8fDzmz5+Pu3fvinq4v//+e4wbNw76+vrw9vbGs2fPcObMGTx48AATJ04s13mKRoAdOHAADg4O0NbWfuNJor/66iu4uLhg9uzZGDBgAOLi4rB48WL8/vvvb5Qfqxoc6xDqwbGuuoKCgrB+/Xps374denp6wpyC+vr6kMvl0NfXx/DhwzFx4kQYGhpCoVBg7NixcHNzQ4cOHYR8UlJSkJubi6ysLDx58gSJiYkAXvwSL5VKYW9vLzrvmTNnoKamJsw/9KZycnLg6emJx48fY+3atcjJyUFOTg6AFz8aqKurw9PTEy1atMDgwYMxf/58ZGVlYfr06QgKChKNiCkqc25uLu7cuYPExERIpVKh03HWrFno0KEDbGxskJ2djR9//BE3btzAZ599Vqk6sLohNzdXNFo1LS0NiYmJMDQ0hJGREebMmYOePXvC3Nwcd+/exZIlS5Ceno7+/fsDAE6ePInTp0+jY8eOMDAwQGpqKoKDg9G0aVOh8/7u3bvYvHkzOnfujKdPnyI0NBTh4eFlzmvEWHXq0aMH5syZA0tLS7Rs2RIJCQn46aefio06z8nJQXh4uDDn58ssLS1F74vmcm7atGmxUWCM1YbXtZWAF50k9vb2MDExQVxcHMaPH48JEyagefPmAACFQoGRI0dixowZaNSoEaysrIQ5xov+DjBW3SZMmAB3d3eEhITAz88Pp06dwvLly7F8+XIhzaRJkzBgwAC8//776NKlC/bs2YMdO3bg0KFDQpohQ4agQYMGmDt3LgBg1KhRWLx4McaPH4+xY8fi2rVrCAkJwbhx40TnVyqVCA0NRUBAgLB4IqsGVb1s9OsMHjyY+vXrR0REDx8+pAEDBpBCoaBGjRpRWFgYOTg40IwZM4iIKCIiglxdXUmhUJCOjg516NCB9u/fL+QVFxdHbdq0IZlMRkVVCQ0NJX19fSHNjBkzyMHBQVSGRYsWkZWVValljImJIQAEgCQSCenp6ZGDgwNNmjSJMjMzi6Vft24dtW3blqRSKRkYGND7779PW7duJSKitLQ0AkAJCQlC+gcPHhAAiomJEbaNHDmSjIyMCIBQfysrK1q0aJHoXAAoIiJCeF9S/ps3b6YWLVqQpqYmWVpa0o8//ijKo6R8X/7c33Zr166tsnisThzrHOuV8a7EeUUUxdqrr9DQUCHNkydPaPTo0WRgYEDa2trUp0+fYrHYqVOnEvNJS0sr8byvXitv6uXrpaxzX79+nXx8fEgul5OxsTF99dVXVFBQ8NrP4uVr9csvvyRLS0uSSqVUv359+vDDD+ns2bOVrsPbpi7GeU0oLRYDAgLoyZMn1KdPH7KwsCCpVErm5ubUs2dPOnXqlHD8+fPnqUuXLmRoaEgymYwaN25MI0eOpP/++09Ic+fOHerQoQPp6OiQtrY2de3alU6cOFEb1X3ncZxXjZycHBo/fjxZWlqSlpYWNWnShKZNm0bPnj0TpVu2bBnJ5XLKzs5+bZ4ltS3Ym+E4rxrlaStNmTKF6tevT5qamtSsWTNauHAhKZVKUT75+fn01VdfkampKenp6VG3bt3o4sWLNVybuofjvGJ27NhBrVq1IplMRnZ2drR8+fJiaVatWkU2NjakpaVFDg4OtG3bNtH+Tp06UUBAgGjb8ePHydXVlWQyGTVp0oTmzJlDz58/F6XZu3cvAaDk5OQqr1ddV5H+PwnRKxO7leDs2bNwdnZGfHw8nJycyt1ZWRJvb2/Y2Nhg8eLFlcqHqa5169bB39+/SuKxOnGss8p4V+KcscrgOGeqgOOcqQKOc6YKOM6ZKqhI/1+NLdP34MED7Ny5E4cOHUK3bt1q6rSM1TiOdcYYY4wxxhhjjNVlNfYweWBgIE6fPo2vvvoKvXr1qqnTMlbjONYZY4wxxhhjjDFWl9VYh2JERERNnYqxWsWxzhhjjDHGGGOMsbqsxh55ZowxxhhjjDHGGGOMvfsqNEIxKioKSUlJ1VUWxsrl2LFjADgeWd3Gcc5UAcc5UwUc50wVcJwzVcBxzlRBWlpaudOWa5XnuLg4vPfeeygsLKxUwRirKmpqalAqlbVdDMaqFcc5UwUc50wVcJwzVcBxzlQBxzlTBerq6jhy5Ajc3NzKTFeuEYoymQyFhYVYu3Yt7O3tq6SAjL2pqKgoBAcHczyyOo3jnKkCjnOmCjjOmSrgOGeqgOOcqYKkpCT4+/tDJpO9Nm2FHnm2t7eHk5PTGxeMsapQNLyc45HVZRznTBVwnDNVwHHOVAHHOVMFHOeMifGiLGVo3Lgxfv75Z+G9RCLBtm3bAADXr1+HRCJBYmJirZSNsarAMc4YY4wxxhhjjLGKqrEORYlEUuZr5syZNVUU5OTkIDg4GC1btoRcLoeRkRFcXFwwf/58PHjwQEh3+vRpfP755zVWrurwcgcRq14c47WDY/zdMXfuXLi4uEBPTw+mpqbo3bs3kpOTRWmePn2KoKAgGBkZQVdXF/369cOtW7dEacaNGwdnZ2fIZDK0bdu22HmKOsNffZ04caJS5b9//z7Gjh2L5s2bQy6Xw9LSEuPGjcPDhw9F6W7evAlfX19oa2vD1NQUkyZNwvPnz4X9mZmZ+PTTT2Fraws1NTV8+eWXJZ4vOzsbQUFBMDc3h0wmg62tLaKioipVB1Y3HD58GD169ICFhUWJ98CZM2fCzs4OOjo6MDAwQLdu3XDy5ElRmp49e8LS0hJaWlowNzfH4MGDkZGRIUpDRFiwYAFsbW0hk8nQoEEDzJkzp7qrx1iJCgsLERwcDGtra8jlcjRt2hSzZ8/Gy9PBV1XsM1Zbli5dijZt2kChUEChUMDNzQ27d+8W9mdlZWHw4MEwMzODjo4OnJycsGXLFmH/oUOHSv0ucvr06dqoElNh6enp8Pf3h5GREeRyOVq3bo0zZ86I0iQlJaFnz57Q19eHjo4OXFxccPPmzVLzDAsLKxbbWlpaojS5ubkYM2YMGjZsCLlcjhYtWuCPP/6oljqqugo98lwZmZmZwr83btyI7777TvRFUldXV/g3EaGwsBAaGlVfvPv376Njx47IycnB7Nmz4ezsDH19fSQnJyM0NBTr169HUFAQAMDExKTKz/+uKigogKamZm0X463GMf5u4xivfrGxsQgKCoKLiwueP3+Ob7/9Fp6enrh8+TJ0dHQAABMmTMCuXbsQHh4OfX19jBkzBn379hVW1SsSGBiIkydP4vz586Web//+/WjZsqXw3sjIqFLlz8jIQEZGBhYsWIAWLVrgxo0bGDlyJDIyMrB582YAL77w+vr6wszMDMePH0dmZiaGDBkCTU1NhISEAACePXsGExMTTJ8+HYsWLSrxXPn5+ejevTtMTU2xefNmNGjQADdu3EC9evUqVQdWN+Tl5cHBwQGBgYHo27dvsf22trZYvHgxmjRpgidPnmDRokXw9PRESkqKcN/v0qULvv32W5ibmyM9PR1ff/01Pv74Yxw/flzIZ/z48di3bx8WLFiA1q1b4/79+7h//36N1ZOxl/3www9YunQp1qxZg5YtW+LMmTMYNmwY9PX1MW7cOABVF/uM1ZaGDRti3rx5aNasGYgIa9asQa9evZCQkICWLVtiyJAhyM7ORmRkJIyNjbF+/Xr4+fnhzJkzcHR0hLu7u+g7CQAEBwfjwIEDaNeuXS3ViqmiBw8ewMPDA126dMHu3bthYmKCa9euwcDAQEiTmpqKjh07Yvjw4fj++++hUChw6dKlYh2Er1IoFKLv2RKJRLR/4sSJOHjwINauXYvGjRtj3759GD16NCwsLNCzZ8+qraiqo3KIj48nABQfH1+e5K8VGhpK+vr6wvuYmBgCQFFRUeTk5ESampoUExNDT58+pbFjx5KJiQnJZDLy8PCgU6dOERGRUqmkpk2b0o8//ijKOyEhgQDQtWvXSjz3F198QTo6OpSenl7ifqVSKfzbysqKFi1aJLwHQBEREURElJaWRgAoISFB2H/hwgXy9vYmHR0dMjU1JX9/f7pz546wv1OnTjR27FiaNGkSGRgYUP369WnGjBnC/q+++op8fX2F94sWLSIAtHv3bmFb06ZNacWKFUREdOrUKerWrRsZGRmRQqGg999/X/R/ZGVlRQCEl5WVlbBv27Zt5OjoSDKZjKytrWnmzJlUUFAgquvvv/9OPXr0IG1tbVE5a9vatWurNB6rA8c4x3hlvQtxXlm3b98mABQbG0tERNnZ2aSpqUnh4eFCmqSkJAJAcXFxxY6fMWMGOTg4FNteUuxWl02bNpFUKhViKyoqitTU1CgrK0tIs3TpUlIoFPTs2bNix3fq1InGjx9fbPvSpUupSZMmlJ+fX21lfxuoQpxXt5fv26V5+PAhAaD9+/eXmmb79u0kkUiEmLt8+TJpaGjQlStXqrK4KonjvGr4+vpSYGCgaFvfvn1p0KBBpR7zJrHP3gzHefUxMDCglStXEhGRjo4O/fnnn6L9hoaGQtv5Vfn5+WRiYkKzZs2q9nKqAo7z8psyZQp17NixzDQDBgwgf3//CuX76vfskrRs2bJYzDs5OdG0adMqdC5VVZH+v7dqDsWpU6di3rx5SEpKQps2bTB58mRs2bIFa9aswdmzZ2FjYwMvLy/cv38fEokEgYGBCA0NFeURGhqK999/HzY2NsXyVyqV2LhxI/z9/WFhYVFiGV7t3S6v7OxsfPDBB3B0dMSZM2ewZ88e3Lp1C35+fqJ0a9asgY6ODk6ePIn58+dj1qxZiI6OBgB06tQJR48eRWFhIYAXo3mMjY1x6NAhAC+GDKempqJz584AgEePHiEgIABHjx7FiRMn0KxZM3z44Yd49OgRAAjD2kNDQ5GZmSm8P3LkCIYMGYLx48fj8uXLWLZsGcLCwoo9xjRz5kz06dMHFy5cQGBg4Bt9LkyMY5xjnP2fokeFDQ0NAQDx8fEoKChAt27dhDR2dnawtLREXFxchfPv2bMnTE1N0bFjR0RGRlZNoV/x8OFDKBQKYbRxXFwcWrdujfr16wtpvLy8kJOTg0uXLpU738jISLi5uSEoKAj169dHq1atEBISIlw7jJVXfn4+li9fDn19fTg4OJSY5v79+1i3bh3c3d2Fkdo7duxAkyZNsHPnTlhbW6Nx48b47LPPeIQiqzXu7u44cOAArl69CgA4d+4cjh49Ch8fnxLTv2nsM/a2KCwsxIYNG5CXlwc3NzcAL66DjRs34v79+1AqldiwYQOePn0qtJ1fFRkZiXv37mHYsGE1WHLGXsReu3bt0L9/f5iamsLR0RErVqwQ9iuVSuzatQu2trbw8vKCqakpXF1dyzWVVW5uLqysrNCoUSP06tWrWBvb3d0dkZGRSE9PBxEhJiYGV69ehaenZ1VXk1V1D2V5lDZ6a9u2bcK23Nxc0tTUpHXr1gnb8vPzycLCgubPn09EROnp6aSurk4nT54U9hsbG1NYWFiJ583KyiIA9NNPP4m2Ozk5kY6ODuno6NAnn3wibK/I6K3Zs2eTp6enKN9///2XAFBycjIRvRiJ8movvYuLC02ZMoWIiB48eEBqamp0+vRpUiqVZGhoSHPnziVXV1cievGLSIMGDUqsGxFRYWEh6enp0Y4dO0osc5GuXbtSSEiIaNtff/1F5ubmouO+/PLLUs9Vm96FX4Y4xv8Px/ibeRfivDIKCwvJ19eXPDw8hG3r1q0jqVRaLK2LiwtNnjy52PbSRijeuXOHFi5cSCdOnKBTp07RlClTSCKR0Pbt26u0Dnfu3CFLS0v69ttvhW0jRowodp3k5eUJI5RfVdoIxebNm5NMJqPAwEA6c+YMbdiwgQwNDWnmzJlVWofaVtfjvCaUdA8kItqxYwfp6OiQRCIhCwsLYfT7yyZPnkza2toEgDp06EB3794V9n3xxRckk8nI1dWVDh8+TDExMdS2bVvq0qVLdVanTuI4rxqFhYXC/VxDQ4MkEkmxv/VElY999mY4zqvO+fPnSUdHh9TV1UlfX5927dol7Hvw4AF5enoSANLQ0CCFQkF79+4tNS8fHx/y8fGpiWKrBI7z8pPJZCSTyeibb76hs2fP0rJly0hLS0v4HpuZmUkASFtbm3766SdKSEiguXPnkkQioUOHDpWa7/Hjx2nNmjWUkJBAhw4doo8++ogUCgX9+++/QpqnT5/SkCFDhOtEKpXSmjVrqr3OdcU7O0Lx5XkdUlNTUVBQAA8PD2GbpqYm2rdvLyzXbmFhAV9fX6xevRrAi1/Tnz17hv79+1fovBEREUhMTISXlxeePHnyRmU/d+4cYmJioKurK7zs7OyEuhRp06aN6Dhzc3Pcvn0bAFCvXj04ODjg0KFDuHDhAqRSKT7//HMkJCQgNzcXsbGx6NSpk3DsrVu3MGLECDRr1gz6+vpQKBTIzc0tcxLTorLOmjVLVNYRI0YgMzMTjx8/FtLxPBtVj2OcY5y9EBQUhIsXL2LDhg1VnrexsTEmTpwIV1dXuLi4YN68efD398ePP/5Y6jEtW7YUYqW00S4vy8nJga+vL1q0aFEtCy4plUqYmppi+fLlcHZ2xoABAzBt2jSeUJqVW5cuXZCYmIjjx4/D29sbfn5+wr24yKRJk5CQkIB9+/ZBXV0dQ4YMERa4UCqVePbsGf7880+899576Ny5M1atWoWYmJhiiykxVhM2bdqEdevWYf369Th79izWrFmDBQsWYM2aNaJ0lY19xmpb8+bNkZiYiJMnT2LUqFEICAjA5cuXAbyYDzE7Oxv79+/HmTNnMHHiRPj5+eHChQvF8vnvv/+wd+9eDB8+vKarwBiUSiWcnJwQEhICR0dHfP755xgxYoTQllUqlQCAXr16YcKECWjbti2mTp2Kjz76qMz2rpubG4YMGYK2bduiU6dO2Lp1K0xMTLBs2TIhzW+//YYTJ04gMjIS8fHxWLhwIYKCgrB///7qrbQKqrFFWcqjaFL+ivjss88wePBgLFq0CKGhoRgwYAC0tbVLTGtiYoJ69eoVawhbWloCAPT09JCdnV3hMgAvht326NEDP/zwQ7F95ubmwr9ffZxCIpEIFxMAdO7cGYcOHYJMJkOnTp1gaGgIe3t7HD16FLGxsfjqq6+EtAEBAbh37x5++eUXWFlZQSaTwc3NDfn5+a8t6/fff1/iRO4vT4D6Jv8frGwc4xzjDBgzZgx27tyJw4cPo2HDhsJ2MzMz5OfnIzs7W7T4yK1bt2BmZlapc7q6ugqP3pckKioKBQUFAAC5XF5mXo8ePYK3tzf09PQQEREhinkzMzOcOnVKlL5oleqK1MHc3ByamppQV1cXttnb2yMrKwv5+fmQSqXlzoupJh0dHdjY2MDGxgYdOnRAs2bNsGrVKnzzzTdCGmNjYxgbG8PW1hb29vZo1KgRTpw4ATc3N5ibm0NDQwO2trZCent7ewAvVjJv3rx5jdeJqbZJkyZh6tSp+OSTTwAArVu3xo0bNzB37lwEBAQI6Sob+4zVNqlUKkxt5OzsjNOnT+OXX37B5MmTsXjxYly8eFFYdM7BwQFHjhzBkiVLinXChIaGwsjIiBehYLXC3NwcLVq0EG2zt7cXViU3NjaGhoZGiWmOHj1a7vNoamrC0dERKSkpAIAnT57g22+/RUREBHx9fQG8GPCSmJiIBQsWiKZWYpX3Vo1QfFnTpk0hlUpFK3sWFBTg9OnToqD78MMPoaOjg6VLl2LPnj1lzoOmpqYGPz8/rF27FhkZGVVaXicnJ1y6dAmNGzcWGjFFr4p0WhTNMXfgwAFhLozOnTvj77//xtWrV0XzYxw7dgzjxo3Dhx9+iJYtW0Imk+Hu3bui/DQ1NYvNueXk5ITk5ORi5bSxsYGa2lsbEnUOxzjHuKohIowZMwYRERE4ePAgrK2tRfudnZ2hqamJAwcOCNuSk5Nx8+bNSn/JS0xMFHV8v8rKykqIkQYNGpSaLicnB56enpBKpYiMjCy2Cp2bmxsuXLggGg0THR0NhUJRrMFUFg8PD6SkpIg6469evQpzc3PuTGRvpGjEYVn7AQhpPDw88Pz5c9EI9KK566ysrKqxpIyV7PHjx8X+hqurq4vukyWpaOwz9rYpiuGip2zKcx0QEUJDQzFkyBCeH5TVCg8Pj2KDXK5evSq0IaRSKVxcXMpMUx6FhYW4cOGC0M4vKChAQUHBG/29YBX3Vo1QfJmOjg5GjRqFSZMmwdDQEJaWlpg/fz4eP34sGratrq6OoUOH4ptvvkGzZs1e+6UzJCQEhw4dQvv27TFr1iy0a9cOOjo6OH/+POLi4tCqVas3Km9QUBBWrFiBgQMHYvLkyTA0NERKSgo2bNiAlStXikaZlOX999/Ho0ePsHPnTsybNw/Ai86Wjz/+GObm5qKRAs2aNcNff/2Fdu3aIScnB5MmTSo2sqZx48Y4cOAAPDw8IJPJYGBggO+++w4fffQRLC0t8fHHH0NNTQ3nzp3DxYsX8b///e+N6s8qjmOcY1zVBAUFYf369di+fTv09PSQlZUFANDX14dcLoe+vj6GDx+OiRMnwtDQEAqFAmPHjoWbmxs6dOgg5JOSkoLc3FxkZWXhyZMnSExMBAC0aNECUqkUa9asgVQqhaOjIwBg69atWL16NVauXFmp8hd1Jj5+/Bhr165FTk4OcnJyALwYHayurg5PT0+0aNECgwcPxvz585GVlYXp06cjKCgIMplMyKuozLm5ubhz5w4SExMhlUqFTsdRo0Zh8eLFGD9+PMaOHYtr164hJCQE48aNq1QdWN2Qm5sr/BIPAGlpaUhMTIShoSGMjIwwZ84c9OzZE+bm5rh79y6WLFmC9PR0YbqMkydP4vTp0+jYsSMMDAyQmpqK4OBgNG3aVPgb061bNzg5OSEwMBA///wzlEolgoKC0L17d9F9mrGa0qNHD8yZMweWlpZo2bIlEhIS8NNPPwk/tObl5VVJ7DNWm7755hv4+PjA0tISjx49wvr163Ho0CHs3bsXdnZ2sLGxwRdffIEFCxbAyMgI27ZtQ3R0NHbu3CnK5+DBg0hLS8Nnn31WSzVhqm7ChAlwd3dHSEgI/Pz8cOrUKSxfvhzLly8X0kyaNAkDBgzA+++/jy5dumDPnj3YsWOHsGAnAAwZMgQNGjTA3LlzAQCzZs1Chw4dYGNjg+zsbPz444+4ceOGEOsKhQKdOnUSvjdaWVkhNjYWf/75J3766aca/QxUQlVPylgepS1Y8eDBA1G6J0+e0NixY8nY2JhkMhl5eHiUOLFyamoqARAWsnid7Oxs+uabb8jOzo5kMhnJ5XJq06YNBQcH071794R0FVmwgojo6tWr1KdPH6pXrx7J5XKys7OjL7/8kpRKJRGVPPl+r169KCAgQLTNwcGBzMzMhPf37t0jiUQiWkyDiOjs2bPUrl070tLSombNmlF4eHixMkdGRpKNjQ1paGiQlZWVsH3Pnj3k7u5OcrmcFAoFtW/fnpYvX15iXd8278JkuBzj/4dj/M28C3FeUQBKfIWGhgppnjx5QqNHjyYDAwPS1tamPn36UGZmpiifTp06lZhPWloaERGFhYWRvb09aWtrC//34eHhlS5/0XVc1rmJiK5fv04+Pj4kl8vJ2NiYvvrqKyooKHjtZ/Fy/BK9mHTa1dWVZDIZNWnShObMmUPPnz+vdD3eJnUxzmtCabEYEBBAT548oT59+pCFhQVJpVIyNzennj17iv62nD9/nrp06UKGhoYkk8mocePGNHLkSPrvv/9E50lPT6e+ffuSrq4u1a9fn4YOHSr6G8LKh+O8auTk5ND48ePJ0tKStLS0qEmTJjRt2jR69uwZEVGVxj6rOI7zqhEYGEhWVlYklUrJxMSEunbtSvv27RP2X716lfr27Uumpqakra1Nbdq0oT///LNYPgMHDiR3d/eaLLpK4DivmB07dlCrVq1IJpORnZ2d6LtYkVWrVpGNjQ1paWmRg4ODaBFTohft/pe/S3755ZdkaWlJUqmU6tevTx9++CGdPXtWdExmZiYNHTqULCwsSEtLi5o3b04LFy4UvrOyslWk/09C9PoZiM+ePQtnZ2fEx8fDycnpDbsuq8+RI0fQtWtX/Pvvv6hfv35tF4dVs3Xr1sHf3/+tjcfqwDGuelQxzpnq4ThnqoDjnKkCjnOmCjjOmSqoSP/fW/vIc3k8e/YMd+7cwcyZM9G/f3/uaGF1Dsc4Y4wxxhhjjDHG3jbv9OoEf//9N6ysrJCdnY358+fXdnEYq3Ic44wxxhhjjDHGGHvbvNMdikOHDkVhYSHi4+PLXJWTsXcVxzhjjDHGGGOMMcbeNu90hyJjjDHGGGOMMcYYY6xmVWgOxaioKCQlJVVXWRgrl2PHjgHgeGR1G8c5UwUc50wVcJwzVcBxzlQBxzlTBWlpaeVOW65VnuPi4vDee++hsLCwUgVjrKqoqalBqVTWdjEYq1Yc50wVcJwzVcBxzlQBxzlTBRznTBWoq6vjyJEjcHNzKzNduUYoymQyFBYWYu3atbC3t6+SAjL2pqKiohAcHMzxyOo0jnOmCjjOmSrgOGeqgOOcqQKOc6YKkpKS4O/vD5lM9tq0FXrk2d7eHk5OTm9cMMaqQtHwco5HVpdxnDNVwHHOVAHHOVMFHOdMFXCcMyamMouyXL9+HRKJBImJibVdFMZqFMc+Y4wxxhhjjDHGqlKNdijeuXMHo0aNgqWlJWQyGczMzODl5SVMbgoAEokE27Ztq8li1YisrCyMHTsWTZo0gUwmQ6NGjdCjRw8cOHCgtovGagDHPsc+A+bOnQsXFxfo6enB1NQUvXv3RnJysijN06dPERQUBCMjI+jq6qJfv364deuWKM24cePg7OwMmUyGtm3bFjtPUSf6q68TJ05Uqvz379/H2LFj0bx5c8jlclhaWmLcuHF4+PChKN3Nmzfh6+sLbW1tmJqaYtKkSXj+/LmwPzMzE59++ilsbW2hpqaGL7/8ssTzZWdnIygoCObm5pDJZLC1tUVUVFSl6sDqhvJcS1lZWRg8eDDMzMygo6MDJycnbNmyRdh//fp1DB8+HNbW1pDL5WjatClmzJiB/Px8Ic3MmTNLvJZ0dHRqrK5MdR0+fBg9evSAhYVFiW2kW7duYejQobCwsIC2tja8vb1x7do1UZovvvgCTZs2hVwuh4mJCXr16oUrV64UO1dYWBjatGkDLS0tmJqaIigoqDqrxli5zZs3DxKJRNRWeN39HQB69uwJS0tLaGlpwdzcHIMHD0ZGRkYNl56puvT0dPj7+8PIyAhyuRytW7fGmTNnhP1Dhw4t1sbw9vYuM8/GjRuX2DZ5+b6dmpqKPn36wMTEBAqFAn5+fsW+T7CqUaMdiv369UNCQgLWrFmDq1evIjIyEp07d8a9e/dqshg17vr163B2dsbBgwfx448/4sKFC9izZw+6dOlSqQYLEYm+pBZ5+csAeztw7HPsMyA2NhZBQUE4ceIEoqOjUVBQAE9PT+Tl5QlpJkyYgB07diA8PByxsbHIyMhA3759i+UVGBiIAQMGlHm+/fv3IzMzU3g5OztXqvwZGRnIyMjAggULcPHiRYSFhWHPnj0YPny4kKawsBC+vr7Iz8/H8ePHsWbNGoSFheG7774T0jx79gwmJiaYPn06HBwcSjxXfn4+unfvjuvXr2Pz5s1ITk7GihUr0KBBg0rVgdUN5bmWhgwZguTkZERGRuLChQvo27cv/Pz8kJCQAAC4cuUKlEolli1bhkuXLmHRokX4448/8O233wp5fP3116JrKDMzEy1atED//v1rvM5M9eTl5cHBwQFLliwpto+I0Lt3b/zzzz/Yvn07EhISYGVlhW7duomuA2dnZ4SGhiIpKQl79+4FEcHT01O00ORPP/2EadOmYerUqbh06RL2798PLy+vGqkjY2U5ffo0li1bhjZt2oi2v+7+DgBdunTBpk2bkJycjC1btiA1NRUff/xxTVeBqbAHDx7Aw8MDmpqa2L17Ny5fvoyFCxfCwMBAlM7b21vUzvj777/LzPf06dOi9NHR0QAgtE3y8vLg6ekJiUSCgwcP4tixY8jPz0ePHj14MZ3qQOUQHx9PACg+Pr48yUv04MEDAkCHDh0qNY2VlRUBEF5WVlbCvm3btpGjoyPJZDKytrammTNnUkFBgbAfAP3+++/k7e1NWlpaZG1tTeHh4cL+tLQ0AkBbtmyhzp07k1wupzZt2tDx48dFZThy5Ah17NiRtLS0qGHDhjR27FjKzc0VlXHOnDk0bNgw0tXVpUaNGtGyZcvKrLuPjw81aNBAlM/Ln8vL5UtISCj2mcXExBARUUxMDAGgqKgocnJyIk1NTYqJiaFOnTpRUFAQjR8/noyMjKhz5840bNgw8vX1FZ0rPz+fTExMaOXKlWWW9223du3aSsdjTeLY59h/E+9anL+J27dvEwCKjY0lIqLs7GzS1NQUxW9SUhIBoLi4uGLHz5gxgxwcHIptLymmqsumTZtIKpUK12RUVBSpqalRVlaWkGbp0qWkUCjo2bNnxY7v1KkTjR8/vtj2pUuXUpMmTSg/P7/ayv42UIU4rwmvXktERDo6OvTnn3+K0hkaGtKKFStKzWf+/PlkbW1d6v7ExEQCQIcPH658oVUIx3nlAaCIiAjhfXJyMgGgixcvCtsKCwvJxMSkzBg/d+4cAaCUlBQiIrp//z7J5XLav39/tZVdVXCcV61Hjx5Rs2bNKDo6ulhb4U3u79u3byeJRFLn2xXVjeO8/KZMmUIdO3YsM01AQAD16tWrUucZP348NW3alJRKJRER7d27l9TU1Ojhw4dCmuzsbJJIJBQdHV2pc6mKivT/1dgIRV1dXejq6mLbtm149uxZiWlOnz4NAAgNDUVmZqbw/siRIxgyZAjGjx+Py5cvY9myZQgLC8OcOXNExwcHB6Nfv344d+4cBg0ahE8++USYOLXItGnT8PXXXyMxMRG2trYYOHCgMNIpNTUV3t7e6NevH86fP4+NGzfi6NGjGDNmjCiPhQsXol27dkhISMDo0aMxatSoYo8aFbl//z727NmDoKCgEh8Rqlev3us/vFdMnToV8+bNQ1JSkvCL1Zo1ayCVSnHs2DH88ccf+Oyzz7Bnzx5kZmYKx+3cuROPHz9+7ageVrU49jn2WcmKHhU2NDQEAMTHx6OgoADdunUT0tjZ2cHS0hJxcXEVzr9nz54wNTVFx44dERkZWTWFfsXDhw+hUCigofFijbO4uDi0bt0a9evXF9J4eXkhJycHly5dKne+kZGRcHNzQ1BQEOrXr49WrVohJCRENKqGsSKvXksA4O7ujo0bN+L+/ftQKpXYsGEDnj59is6dO5eZz8t5vGrlypWwtbXFe++9V2VlZ+xNFLWntLS0hG1qamqQyWQ4evRoicfk5eUhNDQU1tbWaNSoEQAgOjoaSqUS6enpsLe3R8OGDeHn54d///23+ivBWBmCgoLg6+srahMVqej9/f79+1i3bh3c3d2hqalZzSVn7IXIyEi0a9cO/fv3h6mpKRwdHbFixYpi6Q4dOgRTU1M0b94co0aNqtATfPn5+Vi7di0CAwMhkUgAvPj7IJFIRCsUa2lpQU1NrdS/D6wSqrqHsiybN28mAwMD0tLSInd3d/rmm2/o3LlzojR45RdIIqKuXbtSSEiIaNtff/1F5ubmouNGjhwpSuPq6kqjRo0iov8bsfLyCKVLly4RAEpKSiIiouHDh9Pnn38uyuPIkSOkpqZGT548IaIXo7T8/f2F/UqlkkxNTWnp0qUl1vnkyZMEgLZu3Vrq5/Jy+cozSmvbtm2iYzt16kSOjo7F8mzRogX98MMPwvsePXrQ0KFDyyzHu+Bd/GWIY790HPslexfjvCIKCwvJ19eXPDw8hG3r1q0jqVRaLK2LiwtNnjy52PbSRijeuXOHFi5cSCdOnKBTp07RlClTSCKR0Pbt26u0Dnfu3CFLS0v69ttvhW0jRowgT09PUbq8vDxhhO2rShuh2Lx5c5LJZBQYGEhnzpyhDRs2kKGhIc2cObNK61Db6nqc14SSriWiF/dRT09PAkAaGhqkUCho7969peZz7do1UigUtHz58hL3P3nyhAwMDET3VlY+HOeV92obKT8/nywtLal///50//59evbsGc2bN48AFLsHL1myhHR0dAgANW/eXBidSEQ0d+5c0tTUpObNm9OePXsoLi6OunbtSs2bNy9xVDkrHcd51fn777+pVatWQhv81bZCee/vkydPJm1tbQJAHTp0oLt379ZUFeosjvPyk8lkJJPJ6JtvvqGzZ8/SsmXLSEtLi8LCwoQ0f//9N23fvp3Onz9PERERZG9vTy4uLvT8+fNynWPjxo2krq5O6enpwrbbt2+TQqGg8ePHU15eHuXm5tKYMWMIQLHvu6xkb+UIReDFPHIZGRmIjIyEt7c3Dh06BCcnJ4SFhZV53Llz5zBr1ixhpJeuri5GjBiBzMxMPH78WEjn5uYmOs7Nza3YKK2X56AwNzcHANy+fVs4T1hYmOg8Xl5eUCqVSEtLKzEPiUQCMzMzIY9XEVGZdXsT7dq1K7atpLnBPvvsM4SGhgJ4MXH17t27ERgYWOXlYa/HsV81OPbrjqCgIFy8eBEbNmyo8ryNjY0xceJEuLq6wsXFBfPmzYO/vz9+/PHHUo9p2bKlEPs+Pj6vPUdOTg58fX3RokULzJw5swpL/4JSqYSpqSmWL18OZ2dnDBgwANOmTcMff/xR5edi77bSrqXg4GBkZ2dj//79OHPmDCZOnAg/Pz9cuHChWB7p6enw9vZG//79MWLEiBLPExERgUePHiEgIKBa6sFYRWhqamLr1q24evUqDA0Noa2tjZiYGPj4+EBNTfz1ZtCgQUhISEBsbCxsbW3h5+eHp0+fAnhxry0oKMCvv/4KLy8vdOjQAX///TeuXbuGmJiY2qgaU3H//vsvxo8fj3Xr1olG4L6svPf3SZMmISEhAfv27YO6ujqGDBlSLe1zxkqiVCrh5OSEkJAQODo64vPPP8eIESNEbdlPPvkEPXv2ROvWrdG7d2/s3LkTp0+fxqFDh8p1jlWrVsHHxwcWFhbCNhMTE4SHh2PHjh3Q1dWFvr4+srOz4eTkVOzvA6s8jZo+oZaWFrp3747u3bsjODgYn332GWbMmIGhQ4eWekxubi6+//77EifmL+1GW5qXh3kXDYstmpwzNzcXX3zxBcaNG1fsOEtLyxLzKMqntAk+mzVrBolEUuKKci8rCu6Xb/IFBQUlpi3p8dGStg0ZMgRTp05FXFwcjh8/Dmtra35MqRZx7JeMY1/1jBkzBjt37sThw4fRsGFDYbuZmRny8/ORnZ0teiT+1q1bMDMzq9Q5XV1dhUmbSxIVFSXEnVwuLzOvR48ewdvbG3p6eoiIiBBdF2ZmZjh16pQofdGqchWpg7m5OTQ1NaGuri5ss7e3R1ZWFvLz8yGVSsudF6u7SruWUlNTsXjxYly8eBEtW7YEADg4OODIkSNYsmSJqDGfkZGBLl26wN3dHcuXLy/1XCtXrsRHH30kepyfsdrk7OyMxMREPHz4EPn5+TAxMYGrq2uxHx/19fWhr6+PZs2aoUOHDjAwMEBERAQGDhwo/MDaokULIb2JiQmMjY1x8+bNGq0PY8CL6V9u374NJycnYVthYSEOHz6MxYsXIzk5udz3d2NjYxgbG8PW1hb29vZo1KgRTpw4UWwgAmPVwdzcXHRvBV60ZV9dkfxlTZo0gbGxMVJSUtC1a9cy879x4wb279+PrVu3Ftvn6emJ1NRU3L17FxoaGqhXrx7MzMzQpEmTN6sMK1Wtd9G2aNFCtBqbpqZmsTminJyckJycDBsbm2Kvl3uZT5w4ITruxIkTsLe3L3dZnJyccPny5RLP86Zf3gwNDeHl5YUlS5aI6lkkOzsbwIvGCwDRvG+JiYlvdM4iRkZG6N27N0JDQxEWFoZhw4ZVKj9WtTj2swFw7KsSIsKYMWMQERGBgwcPwtraWrTf2dkZmpqaOHDggLAtOTkZN2/erHTjNzExUfjiWBIrKysh5staSTknJweenp6QSqWIjIws1rHv5uaGCxcuiEbuRkdHQ6FQFGtUlcXDwwMpKSmiDvurV6/C3NycOxPZa6+lohHsr/4Sr66uLoqp9PR0dO7cWVgJt7Rf7tPS0hATEyNa0Zyxt4W+vj5MTExw7do1nDlzBr169So1LRGBiIQ5GD08PABANB/0/fv3cffuXVhZWVVvwRkrQdeuXXHhwgUkJiYKr3bt2mHQoEFITEws9/39VUX7SpvPnbGq5uHhUWyu/atXr5Z5b/3vv/9w7969MtvsRUJDQ2FqagpfX99S0xgbG6NevXo4ePAgbt++jZ49e5a/AqxcamyE4r1799C/f38EBgaiTZs20NPTw5kzZzB//nzRH/7GjRvjwIED8PDwgEwmg4GBAb777jt89NFHsLS0xMcffww1NTWcO3cOFy9exP/+9z/h2PDwcLRr1w4dO3bEunXrcOrUKaxatarcZZwyZQo6dOiAMWPG4LPPPoOOjg4uX76M6OhoLF68+I3rvmTJEnh4eKB9+/aYNWsW2rRpg+fPnyM6OhpLly5FUlIS5HI5OnTogHnz5sHa2hq3b9/G9OnT3/icRT777DN89NFHKCws5MeUagnHPsc+eyEoKAjr16/H9u3boaenh6ysLAAvvgzK5XLo6+tj+PDhmDhxIgwNDaFQKDB27Fi4ubmhQ4cOQj4pKSnIzc1FVlYWnjx5InRAt2jRAlKpVFiox9HREQCwdetWrF69GitXrqxU+Ys6Ex8/foy1a9ciJycHOTk5AF50jKurq8PT0xMtWrTA4MGDMX/+fGRlZWH69OkICgoSTQ5dVObc3FzcuXMHiYmJkEqlQqfjqFGjsHjxYowfPx5jx47FtWvXEBISUuIoYqZ6Xnct2dnZwcbGBl988QUWLFgAIyMjbNu2DdHR0di5cyeA/+tMtLKywoIFC3Dnzh0h/1dH065evRrm5ublmg6AsaqSm5uLlJQU4X1aWhoSExNhaGgIS0tLhIeHw8TEBJaWlrhw4QLGjx+P3r17w9PTEwDwzz//YOPGjfD09ISJiQn+++8/zJs3D3K5HB9++CEAwNbWFr169cL48eOxfPlyKBQKfPPNN7Czs0OXLl1qpd5Mtenp6aFVq1aibTo6OjAyMkKrVq1QUFDw2vv7yZMncfr0aXTs2BEGBgZITU1FcHAwmjZtyqMTWY2ZMGEC3N3dERISAj8/P5w6dQrLly8XnoYoehKvX79+MDMzQ2pqKiZPngwbGxt4eXkJ+XTt2hV9+vQRLRaqVCoRGhqKgIAAYWHEl4WGhsLe3h4mJiaIi4vD+PHjMWHCBDRv3rz6K65qqnpSxtI8ffqUpk6dSk5OTqSvr0/a2trUvHlzmj59Oj1+/FhIFxkZSTY2NqShoUFWVlbC9j179pC7uzvJ5XJSKBTUvn170cThAGjJkiXUvXt3kslk1LhxY9q4caOwvzwLPxARnTp1irp37066urqko6NDbdq0oTlz5gj7raysaNGiRaK6OTg40IwZM8qsf0ZGBgUFBZGVlRVJpVJq0KAB9ezZU3Tuy5cvk5ubG8nlcmrbti3t27evxIUpHjx4IMq7tEn9iV4snGFlZUUffvhhmeV7l7xrk+Fy7HPsv4l3Lc7LA0CJr9DQUCHNkydPaPTo0WRgYEDa2trUp08fyszMFOXTqVOnEvNJS0sjIqKwsDCyt7cnbW1t4ZoJDw+vdPmL4rCscxMRXb9+nXx8fEgul5OxsTF99dVXVFBQ8NrP4uXrnojo+PHj5OrqSjKZjJo0aUJz5swp9yTV74q6GOc1oTzX0tWrV6lv375kampK2tra1KZNG/rzzz+F/aGhoaXm87LCwkJq2LChDUv0MgAAH0BJREFUaPEhVjEc52+mtHtuQEAAERH98ssv1LBhQ9LU1CRLS0uaPn26aCGV9PR08vHxIVNTU9LU1KSGDRvSp59+SleuXBGd5+HDhxQYGEj16tUjQ0ND6tOnD928ebMmq1oncJxXn1fbu6+7v58/f566dOlChoaGwneDkSNH0n///VcLpa9bOM4rZseOHdSqVSuSyWRkZ2cn+g77+PFj8vT0JBMTE9LU1CQrKysaMWIEZWVlifKwsrIq9n1z7969BICSk5NLPO+UKVOofv36pKmpSc2aNaOFCxeSUqms8vrVVRXp/5MQvX5m1rNnz8LZ2Rnx8fGi+RzeJhKJBBEREejdu3dtF+WtkpubiwYNGiA0NLTEefjeRevWrYO/v/9bHY81iWO/ZO967HOcM1XAcc5UAcc5UwUc50wVcJwzVVCR/r8aX5SF1QylUom7d+9i4cKFqFevHs8XwFQGxz5jjDHGGGOMMVa9uEOxjrp58yasra3RsGFDhIWFlTi3AGN1Ecc+Y4wxxhhjjDFWverMN+1yPLmtUho3bsyfiYrg/2cxjn3GGGOMMcYYY6x6qb0+CWOMMcYYY4wxxhhjjL1QoRGKUVFRSEpKqq6yMFYux44dA8DxyOo2jnOmCjjOmSrgOGeqgOOcqQKOc6YK0tLSyp22XKs8x8XF4b333kNhYWGlCsZYVVFTU4NSqaztYjBWrTjOmSrgOGeqgOOcqQKOc6YKOM6ZKlBXV8eRI0fg5uZWZrpyjVCUyWQoLCzE2rVrYW9vXyUFZOxNRUVFITg4mOOR1Wkc50wVcJwzVcBxzlQBxzlTBRznTBUkJSXB398fMpnstWkr9Mizvb09nJyc3rhgjFWFouHlHI+sLuM4Z6qA45ypAo5zpgo4zpkq4DhnTKzOLcrSuXNnfPnll7VdDMbeCnw9MMYYY4wxxhhjrKrVaIfi0KFD0bt3b9G2zZs3Q0tLCwsXLqySc2zduhWzZ8+uVB5Dhw6FRCKBRCKBpqYm6tevj+7du2P16tVv1XwJ169fh0QiQWJiYm0Xhb0Bvh6qFl8Pb7+5c+fCxcUFenp6MDU1Re/evZGcnCxK8/TpUwQFBcHIyAi6urro168fbt26JUozbtw4ODs7QyaToW3btiWei4iwYMEC2NraQiaToUGDBpgzZ06lyl9QUIApU6agdevW0NHRgYWFBYYMGYKMjAxRuvv372PQoEFQKBSoV68ehg8fjtzcXFEdhw4ditatW0NDQ6PYfQAQX3cvv1q2bFmpOrC64fDhw+jRowcsLCwgkUiwbds20f5bt25h6NChsLCwgLa2Nry9vXHt2jVRmqysLAwePBhmZmbQ0dGBk5MTtmzZIkrTs2dPWFpaQktLC+bm5hg8eHCxeGesupQV5+W9H589exbdu3dHvXr1YGRkhM8//1x0P37ZvXv30LBhQ0gkEmRnZ1djzRgrv3nz5kEikYgGCLzu/n3o0KES2xASiQSnT5+uhVowVZWeng5/f38YGRlBLpejdevWOHPmjLC/pPaut7d3mXk2bty4xNgOCgoqlpaI4OPjU2JbiVWNWh2huHLlSgwaNAhLly7FV199VSV5GhoaQk9Pr9L5eHt7IzMzE9evX8fu3bvRpUsXjB8/Hh999BGeP3/+xvkSUaWOZ3UXXw+srouNjUVQUBBOnDiB6OhoFBQUwNPTE3l5eUKaCRMmYMeOHQgPD0dsbCwyMjLQt2/fYnkFBgZiwIABpZ5r/PjxWLlyJRYsWIArV64gMjIS7du3r1T5Hz9+jLNnzyI4OBhnz57F1q1bkZycjJ49e4rSDRo0CJcuXUJ0dDR27tyJw4cP4/PPPxf2FxYWQi6XY9y4cejWrVuJ5/rll1+QmZkpvP79918YGhqif//+laoDqxvy8vLg4OCAJUuWFNtHROjduzf++ecfbN++HQkJCbCyskK3bt1E19qQIUOQnJyMyMhIXLhwAX379oWfnx8SEhKENF26dMGmTZuQnJyMLVu2IDU1FR9//HGN1JGxsuK8PPfjjIwMdOvWDTY2Njh58iT27NmDS5cuYejQoSWeb/jw4WjTpk11VYexCjt9+jSWLVtWLC5fd/92d3cXtSEyMzPx2WefwdraGu3atauNqjAV9ODBA3h4eEBTUxO7d+/G5cuXsXDhQhgYGIjSFX3PLHr9/fffZeZ7+vRpUfro6GgAKLGN/PPPP0MikVRdpVhxVA7x8fEEgOLj48uTvFQBAQHUq1cvIiL64YcfSEtLi7Zu3SpKs23bNnJ0dCSZTEbW1tY0c+ZMKigoICKigQMHkp+fnyh9fn4+GRkZ0Zo1a4iIqFOnTjR+/Hhhv5WVFc2ZM4eGDRtGurq61KhRI1q2bFm5y/myAwcOEABasWIFERGlpaURAEpISBDSPHjwgABQTEwMERHFxMQQAIqKiiInJyfS1NSkmJgYSklJoZ49e5KpqSnp6OhQu3btKDo6WnS+15UdgOjVqVOnMutVV6xdu7ZK4rG28fXA10NZ6kqcl+X27dsEgGJjY4mIKDs7mzQ1NSk8PFxIk5SURAAoLi6u2PEzZswgBweHYtsvX75MGhoadOXKlWore5FTp04RALpx44ZwbgB0+vRpIc3u3btJIpFQenp6seNLu75eFRERQRKJhK5fv15lZX8bqEKcVzcAFBERIbxPTk4mAHTx4kVhW2FhIZmYmAj3ayIiHR0d+vPPP0V5GRoaitK8avv27SSRSCg/P7/qKqACOM4r79U4L8mr9+Nly5aRqakpFRYWCmnOnz9PAOjatWuiY3///Xfq1KmT0LZ58OBBVVehzuM4r1qPHj2iZs2aUXR0dLH2fEXv3/n5+WRiYkKzZs2qziKrBI7z8psyZQp17Pj/2rvzGKvK+4HDX3YGEHHYhiozsiljURRMFaxBFATc6koEFRW1VsBSjRZMtUb5RbFqtQnGrYgSMVqtCJIAooC1jXVBsBoICFIoLUsEMRJ0ZmTO7w8yVy+D8A6ytMzzJPzBnXvPPSd5z5l7P3POe366y+ekfg7eldGjR2edOnXKKisr8x5fuHBhdvjhh2dr165N+h3Ct2rS/w7IGYpjxoyJcePGxYwZM+KCCy7IPf7WW2/FsGHDYvTo0bF48eJ4/PHH4+mnn85dpnbZZZfFq6++mnepwuzZs2Pr1q15y9nRgw8+GCeeeGIsXLgwRowYETfccEO1y+xSnH766dG9e/d4+eWXa/zasWPHxvjx42PJkiVx3HHHxZYtW+Kss86KN954IxYuXBgDBw6Mc889N1avXp287u+++25ERLz++uuxdu3aPVovDjz7g/2htvriiy8iYvuZtBERCxYsiIqKiryz9rp27RrFxcXx9ttvJy/31VdfjY4dO8aMGTOiQ4cOceSRR8a1114bmzZt2rsbENu3oU6dOtGiRYuIiHj77bejRYsWeWcA9OvXL+rWrRvvvPPOHr/PxIkTo1+/flFSUvJDV5mDXFlZWURENG7cOPdY3bp1o1GjRvHXv/4191jv3r3jhRdeiE2bNkVlZWU8//zz8fXXX8dpp5220+Vu2rQppkyZEr17944GDRrs022APbHj8bisrCwaNmwYdet++3WnoKAgIiJvX1i8eHHcfffdMXny5LznwoE0cuTIOPvss3d6JUNNj9/Tp0+PjRs3xtVXX72P1xq+NX369DjxxBPjkksuiTZt2sQJJ5wQTz75ZLXnzZ8/P9q0aRNHH3103HDDDbFx48bk9ygvL49nn302hg8fnncm4tatW2Po0KHxyCOPRFFR0V7ZHnZuv//WnDlzZvzud7+LadOmxRlnnJH3s7vuuivGjh0bV155ZXTs2DH69+8f48aNi8cffzwiIgYMGBBNmzaNqVOn5l7z3HPPxXnnnbfLyzrPOuusGDFiRHTu3DnGjBkTrVq1innz5u3R+nft2jX++c9/1vh1d999d/Tv3z86deoUhYWF0b1797j++uujW7du0aVLlxg3blx06tQppk+fnrzurVu3joiIli1bRlFRUe5LOf877A/2h9qqsrIyfvWrX8Upp5wS3bp1i4jtcwI1bNgw92WwStu2bWPdunXJy/70009j1apV8eKLL8bkyZPj6aefjgULFuz1SzW//vrrGDNmTAwZMiSaN28eEdu3oU2bNnnPq1+/fhQWFtZoG77rP//5T8ycOTOuvfbaH7zOHPyqIvxtt90Wn3/+eZSXl8d9990Xa9asibVr1+ae96c//SkqKiqiZcuW0ahRo7j++utj6tSp0blz57zljRkzJpo2bRotW7aM1atXx7Rp0/b3JsFu7ex4fPrpp8e6devi/vvvj/Ly8vj8889j7NixERG5faGsrCyGDBkS999/fxQXFx+w9Yfvev755+ODDz6Ie++9d6c/Tz1+V5k4cWIMGDAgjjjiiH252pDn008/jUcffTS6dOkSs2fPjhtuuCF++ctfxjPPPJN7zsCBA2Py5MnxxhtvxH333RdvvvlmDBo0KLZt25b0Hq+88kps3ry52lQWN910U/Tu3Tt+9rOf7c1NYif2e1A87rjj4sgjj4w777yz2qTIH374Ydx9993RrFmz3L/rrrsu1q5dG1u3bo369evH4MGDY8qUKRGxfW6VadOmxWWXXbbb96xSp06dKCoqig0bNuzR+mdZtkfX4e84X8WWLVvilltuidLS0mjRokU0a9YslixZUu2MrL257vz3sT9sZ3+ofUaOHBkff/xxPP/883t92ZWVlVFWVhaTJ0+OU089NU477bSYOHFizJs373vPxh00aFBuP0u58UlFRUUMHjw4siyLRx99dG9vQp5nnnkmWrRosdObt8COGjRoEC+//HIsW7YsCgsLo0mTJjFv3rwYNGhQ3tlXd9xxR2zevDlef/31eP/99+Pmm2+OwYMHx0cffZS3vFtvvTUWLlwYr732WtSrVy+GDRsWWZbt782C7/V9x+Mf//jH8cwzz8SDDz4YTZo0iaKioujQoUO0bds2ty/cdtttUVpaGpdffvmBWn3I869//StGjx4dU6ZMyTvT/LtSj98REWvWrInZs2fHNddcs69XHfJUVlZGjx494p577okTTjghfv7zn8d1110Xjz32WO45l156aZx33nlx7LHHxvnnnx8zZsyI9957L+bPn5/0HhMnToxBgwbFj370o9xj06dPj7lz58bDDz+8l7eInam/v9/w8MMPj5deein69u0bAwcOjJkzZ+bOptqyZUvcddddO52Av+qAetlll0WfPn1iw4YNMWfOnCgoKNjtnYB2vDSnTp06e3x32iVLlkSHDh0iInIfRr77wbqiomKnr2vatGne/2+55ZaYM2dOPPDAA9G5c+coKCiIiy++OMrLy/fZuvPfx/6wnf2hdhk1alTuZiXf/Wt5UVFRlJeXx+bNm/POUly/fn2NLldo165d1K9fP4466qjcY6WlpRERsXr16jj66KOrveaPf/xjfPXVVxFRfZztqOrL66pVq2Lu3Lm5s2GqtmHHyP3NN9/Epk2b9uiSiyzL4qmnnoorrrgiGjZsWOPXUzv17NkzFi1aFF988UWUl5dH69at46STTsr9MWfFihUxYcKE+Pjjj3MBvXv37vHWW2/FI488kvdhv1WrVtGqVas46qijorS0NNq3bx9///vfo1evXgdk2+C7dnU8jogYOnRoDB06NNavXx9NmzaNOnXqxO9///vo2LFjRETMnTs3Pvroo3jppZci4tvPMK1atYrf/OY3cdddd+3fDaLWW7BgQWzYsCF69OiRe2zbtm3xl7/8JSZMmBBLly5NPn5HREyaNClatmxZ7QZysK+1a9cujjnmmLzHSktL8+5IvqOOHTtGq1atYvny5dWu3tvRqlWr4vXXX682zdXcuXNjxYoV1a54uuiii+LUU09NjpWk2e9BMSKipKQk3nzzzVxEmTVrVhxyyCHRo0ePWLp06feerh2xfc6I9u3bxwsvvBAzZ86MSy65ZL/N5VP1oeOmm26KiG8vsVy7dm2ccMIJERGxaNGipGX97W9/i6uuuio3192WLVtqfOlo1ZfL1FOC+e9kf7A/1BZZlsWNN94YU6dOjfnz5+didJWePXtGgwYN4o033oiLLrooIiKWLl0aq1evrlG8OOWUU+Kbb76JFStWRKdOnSIiYtmyZRER3zsH4eGHH5607Kovr5988knMmzcvWrZsmffzXr16xebNm2PBggXRs2fPiNi+r1RWVsZJJ52UvA1V3nzzzVi+fLkzC9gjhx56aEREfPLJJ/H+++/HuHHjImL73EIRUW2+uHr16u3yjzRVP6uapxEOpN0dj7+rbdu2ERHx1FNPRePGjaN///4REfHnP/8598ekiO13Dx0+fHi89dZbud8fsD+dccYZ1c40vPrqq6Nr164xZsyYGh2/syyLSZMmxbBhw8x9y353yimnVLsyaNmyZbucD3zNmjWxcePGaNeu3W6XP2nSpGjTpk2cffbZeY+PHTu22jRBxx57bDz00ENx7rnn1mALSHFAgmJERPv27WP+/PnRt2/fGDBgQMyaNSt++9vfxjnnnBPFxcVx8cUXR926dePDDz+Mjz/+OP7v//4v99qhQ4fGY489FsuWLdvjud92p6ysLNatWxfbtm2L9evXx6xZs+Lee++Nc845J4YNGxYR2yd2Pvnkk2P8+PHRoUOH2LBhQ9x+++1Jy+/SpUu8/PLLce6550adOnXijjvuqPGZVm3atImCgoKYNWtWHHHEEdG4cePclwf+t9gf7A+1wciRI+O5556LadOmxSGHHJKbU/DQQw+NgoKCOPTQQ+Oaa66Jm2++OQoLC6N58+Zx4403Rq9eveLkk0/OLWf58uWxZcuWWLduXXz11Ve5cH3MMcdEw4YNo1+/ftGjR48YPnx4PPzww1FZWRkjR46M/v375521WFMVFRVx8cUXxwcffBAzZsyIbdu25bahsLAwGjZsGKWlpTFw4MDcJR0VFRUxatSouPTSS/Mux1i8eHGUl5fHpk2b4ssvv8xtw/HHH5/3nhMnToyTTjopN88kRGz/o8vy5ctz/1+5cmUsWrQoCgsLo7i4OF588cVo3bp1FBcXx0cffRSjR4+O888/P84888yI2D7PYufOneP666+PBx54IFq2bBmvvPJKzJkzJ2bMmBEREe+8806899578dOf/jQOO+ywWLFiRdxxxx3RqVMnZyeyX+xqnLdr1263x+OIiAkTJkTv3r2jWbNmMWfOnLj11ltj/PjxuTNXdoyGn332WUREbgoW2N8OOeSQar/zq+ax7datW1RUVOz2+F1l7ty5sXLlSnMwc0BUzWN4zz33xODBg+Pdd9+NJ554Ip544omI+PZqvIsuuiiKiopixYoV8etf/zo6d+4cAwYMyC3njDPOiAsuuCBGjRqVe6yysjImTZoUV155ZdSvn5+0ioqKdnpVUHFxcbWTGdgL9vZto3dlZ7cFX7NmTdalS5fs5JNPzr744ots1qxZWe/evbOCgoKsefPm2U9+8pPsiSeeyHvN4sWLs4jISkpKqt0evE+fPtno0aNz/y8pKckeeuihvOd07949u/POO3e5nhGRRURWv379rHXr1lm/fv2yp556Ktu2bVu1denVq1dWUFCQHX/88dlrr72WRUQ2b968LMuybN68eVlEZJ9//nne61auXJn17ds3KygoyNq3b59NmDBhj9b9ySefzNq3b5/VrVs369Onz/du08Hk2Wef3Svj8UCzP3zL/lDdwTLOv6tqHO34b9KkSbnnfPXVV9mIESOyww47LGvSpEl2wQUXZGvXrs1bTp8+fXa6nJUrV+ae8+9//zu78MILs2bNmmVt27bNrrrqqmzjxo0/aP1Xrlz5vdtQNcazLMs2btyYDRkyJGvWrFnWvHnz7Oqrr86+/PLLvGWVlJTsdDnftXnz5qygoKDaPn8wORjH+f5QdSzd8d+VV16ZZVmW/eEPf8iOOOKIrEGDBllxcXF2++23Z2VlZXnLWLZsWXbhhRdmbdq0yZo0aZIdd9xx2eTJk3M//8c//pH17ds3KywszBo1apQdeeSR2S9+8YtszZo1+3NTDwrG+Z7Z1ThPPR5fccUVWWFhYdawYcNqY3xX77nj5xR2zzjfd3b8TLy743eVIUOGZL17996Pa3rwM85r5tVXX826deuWNWrUKOvatWveZ9qtW7dmZ555Zta6deusQYMGWUlJSXbddddl69aty1tGSUlJte+qs2fPziIiW7p0adJ6REQ2derUH7o5tUZN+l+dLNv9zNoffPBB9OzZMxYsWJA3nwMcCFOmTInLL7/ceOSgZpxTGxjn1AbGObWBcU5tYJxTG9Sk/+33uzwDAAAAAP+7BEUAAAAAIJmgCAAAAAAkExQBAAAAgGT1d/+Uby1ZsmRfrQckW7lyZUQYjxzcjHNqA+Oc2sA4pzYwzqkNjHNqg5qM76S7PK9evTpKS0tj69atP2jFYG+pV69ebNu27UCvBuxTxjm1gXFObWCcUxsY59QGxjm1QZMmTWLJkiVRXFy8y+clBcWI7VHxs88+2ysrBz9UWVlZNGrU6ECvBuxTxjm1gXFObWCcUxsY59QGxjm1QatWrXYbEyNqEBQBAAAAANyUBQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGSCIgAAAACQTFAEAAAAAJIJigAAAABAMkERAAAAAEgmKAIAAAAAyQRFAAAAACCZoAgAAAAAJBMUAQAAAIBkgiIAAAAAkExQBAAAAACSCYoAAAAAQDJBEQAAAABIJigCAAAAAMkERQAAAAAgmaAIAAAAACQTFAEAAACAZIIiAAAAAJBMUAQAAAAAkgmKAAAAAEAyQREAAAAASCYoAgAAAADJBEUAAAAAIJmgCAAAAAAkExQBAAAAgGT/D05+lXb3hon7AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sample data for the top 10 high-volume shooters\n",
        "data = {\n",
        "    'Player': [\n",
        "        'Devin Booker', 'P.J. Tucker', 'Jovo Stanojevic', 'Guna Ra',\n",
        "        'Rimantas Kaukenas', 'Brian Qvale', 'Justin Dentmon',\n",
        "        'Troy Gillenwater', 'Stephen Curry', 'Kevin Durant'\n",
        "    ],\n",
        "    'Season': [\n",
        "        '2017 - 2018', '2011 - 2012', '2003 - 2004', '2016 - 2017',\n",
        "        '2003 - 2004', '2015 - 2016', '2014 - 2015',\n",
        "        '2015 - 2016', '2015 - 2016', '2016 - 2017'\n",
        "    ],\n",
        "    'PTS': [\n",
        "        1477, 1428, 1354, 1729,\n",
        "        1471, 1365, 1339,\n",
        "        1336, 2827, 1983\n",
        "    ],\n",
        "    'FGA': [\n",
        "        894, 866, 834, 1134,\n",
        "        868, 902, 874,\n",
        "        833, 1936, 1294\n",
        "    ],\n",
        "    'FTA': [\n",
        "        433, 418, 384, 337,\n",
        "        520, 271, 296,\n",
        "        387, 483, 487\n",
        "    ],\n",
        "    'TS%': [\n",
        "        68.09, 68.01, 67.50, 67.42,\n",
        "        67.06, 66.83, 66.67,\n",
        "        66.58, 65.79, 65.74\n",
        "    ]\n",
        "}\n",
        "\n",
        "# Create a DataFrame from the data\n",
        "df_top_shooters = pd.DataFrame(data)\n",
        "\n",
        "# Plotting the table\n",
        "fig, ax = plt.subplots(figsize=(12, 8))  # Set the figure size\n",
        "\n",
        "# Hide the axes\n",
        "ax.xaxis.set_visible(False)\n",
        "ax.yaxis.set_visible(False)\n",
        "ax.set_frame_on(False)\n",
        "\n",
        "# Create the table\n",
        "table = ax.table(cellText=df_top_shooters.values,\n",
        "                 colLabels=df_top_shooters.columns,\n",
        "                 rowLabels=df_top_shooters['Player'],\n",
        "                 cellLoc='center',\n",
        "                 loc='center')\n",
        "\n",
        "# Adjust the font size and the layout\n",
        "table.auto_set_font_size(False)\n",
        "table.set_fontsize(10)\n",
        "table.scale(1.2, 1.2)\n",
        "\n",
        "# Title for the plot\n",
        "plt.title('Top 10 High-Volume Shooters by TS%', fontsize=16)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "# Answer: After calculating TS%, the most efficient high-volume shooters that emerge are:\n",
        "\n",
        "# 1. Devin Booker\n",
        "# 2. P.J. Tucker\n",
        "# 3. Jovo Stanojevic\n",
        "# 4. Guna Ra\n",
        "# 5. Rimantas Kaukenas\n",
        "# 6. Brian Qvale\n",
        "# 7. Justin Dentmon\n",
        "# 8. Troy Gillenwater\n",
        "# 9. Stephen Curry\n",
        "# 10. Kevin Durant\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRY68798bnra"
      },
      "source": [
        "**Question:** How does Stephen Curry compare to these top performers in terms of TS%?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9iVd_6ERbnrb"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "### Stephen Curry’s TS% Performance\n",
        "\n",
        "- **Player**: Stephen Curry\n",
        "- **Season**: 2015 - 2016\n",
        "- **TS%**: 65.79%\n",
        "\n",
        "### Comparison with Top Performers\n",
        "\n",
        "Here’s a summary of the TS% values for the top 10 high-volume shooters:\n",
        "\n",
        "| Player              | Season      | TS%     |\n",
        "|---------------------|-------------|---------|\n",
        "| Devin Booker        | 2017 - 2018 | 68.09%  |\n",
        "| P.J. Tucker         | 2011 - 2012 | 68.01%  |\n",
        "| Jovo Stanojevic     | 2003 - 2004 | 67.50%  |\n",
        "| Guna Ra             | 2016 - 2017 | 67.42%  |\n",
        "| Rimantas Kaukenas   | 2003 - 2004 | 67.06%  |\n",
        "| Brian Qvale         | 2015 - 2016 | 66.83%  |\n",
        "| Justin Dentmon      | 2014 - 2015 | 66.67%  |\n",
        "| Troy Gillenwater    | 2015 - 2016 | 66.58%  |\n",
        "| Stephen Curry       | 2015 - 2016 | 65.79%  |\n",
        "| Kevin Durant        | 2016 - 2017 | 65.74%  |\n",
        "\n",
        "### Analysis\n",
        "\n",
        "- **Stephen Curry’s Position**: Curry ranks 9th among the top 10 high-volume shooters based on TS%.\n",
        "- **Comparison**:\n",
        "  - **Highest TS%**: Devin Booker leads with a TS% of 68.09%.\n",
        "  - **Lowest TS% in the Top 10**: Kevin Durant has the lowest TS% in the top 10 at 65.74%.\n",
        "\n",
        "### Insights\n",
        "\n",
        "- **High Efficiency**: Stephen Curry’s TS% of 65.79% demonstrates that he is a highly efficient shooter, particularly notable given the volume of shots he takes.\n",
        "- **Close Competition**: His TS% is close to the top performers, indicating that Curry's shooting efficiency is very competitive, though slightly behind the very best.\n",
        "\n",
        "### Summary\n",
        "\n",
        "While Stephen Curry does not top the list in TS%, he is among the elite shooters in terms of efficiency. His performance is just behind the top values, highlighting his status as one of the most effective high-volume shooters in the league.\n",
        "\"\"\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lekaQfdPbnrb"
      },
      "source": [
        "* Create two new DataFrames from the original DataFrame. Name one of them BC (Before Curry) and AC. Steph started his career on 2009-2010 season. So 1999-2000 up to 2008-2009 should in BC and the rest in AC (again `loc` is a very useful command!)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INvWE8p8bnrb",
        "outputId": "9b6c538a-7ac7-4981-ae57-d9d2c184e4d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BC (Before Curry) DataFrame:\n",
            "  League       Season           Stage            Player Team  GP     MIN  FGM  \\\n",
            "0    NBA  1999 - 2000  Regular_Season  Shaquille O'Neal  LAL  79  3163.0  956   \n",
            "1    NBA  1999 - 2000  Regular_Season      Vince Carter  TOR  82  3126.0  788   \n",
            "2    NBA  1999 - 2000  Regular_Season       Karl Malone  UTA  82  2947.0  752   \n",
            "3    NBA  1999 - 2000  Regular_Season     Allen Iverson  PHI  70  2853.0  729   \n",
            "4    NBA  1999 - 2000  Regular_Season       Gary Payton  SEA  82  3425.0  747   \n",
            "\n",
            "    FGA  3PM  ...    birth_date  height  height_cm  weight  weight_kg  \\\n",
            "0  1665    0  ...   Mar 6, 1972     7-1      216.0   325.0      147.0   \n",
            "1  1696   95  ...  Jan 26, 1977     6-6      198.0   220.0      100.0   \n",
            "2  1476    2  ...  Jul 24, 1963     6-9      206.0   265.0      120.0   \n",
            "3  1733   89  ...   Jun 7, 1975     6-0      183.0   165.0       75.0   \n",
            "4  1666  177  ...  Jul 23, 1968     6-4      193.0   180.0       82.0   \n",
            "\n",
            "     nationality                  high_school  draft_round  draft_pick  \\\n",
            "0  United States  Robert G. Cole High School           1.0         1.0   \n",
            "1  United States         Mainland High School          1.0         5.0   \n",
            "2  United States      Summerfield High School          1.0        13.0   \n",
            "3  United States          Bethel High School           1.0         1.0   \n",
            "4  United States          Skyline High School          1.0         2.0   \n",
            "\n",
            "              draft_team  \n",
            "0          Orlando Magic  \n",
            "1  Golden State Warriors  \n",
            "2              Utah Jazz  \n",
            "3    Philadelphia Sixers  \n",
            "4    Seattle SuperSonics  \n",
            "\n",
            "[5 rows x 34 columns]\n",
            "\n",
            "AC (After Curry) DataFrame:\n",
            "              League       Season          Stage            Player Team  GP  \\\n",
            "9732  Australian-NBL  2009 - 2010  International     Derrick Allen  FRA  48   \n",
            "9733  Australian-NBL  2009 - 2010  International     Predrag Suput  BRO  45   \n",
            "9734  Australian-NBL  2009 - 2010  International        Jeff Gibbs  EIS  44   \n",
            "9735  Australian-NBL  2009 - 2010  International  Taylor Rochestie  BGG  39   \n",
            "9736  Australian-NBL  2009 - 2010  International    Chris Copeland  TRI  33   \n",
            "\n",
            "         MIN  FGM  FGA  3PM  ...    birth_date  height  height_cm  weight  \\\n",
            "9732  1437.7  252  488   12  ...  Jul 17, 1980     6-8      203.0   238.0   \n",
            "9733  1183.6  243  490   69  ...   Jun 1, 1977     6-7      201.0   244.0   \n",
            "9734  1432.5  203  380   14  ...   Aug 4, 1980     6-2      188.0   242.0   \n",
            "9735  1071.7  205  451   71  ...   Jul 1, 1985     6-1      185.0   194.0   \n",
            "9736  1069.2  189  399   61  ...  Mar 17, 1984     6-8      203.0   235.0   \n",
            "\n",
            "      weight_kg                 nationality                high_school  \\\n",
            "9732      108.0               United States   Gadsden City High School   \n",
            "9733      111.0                      Serbia                        NaN   \n",
            "9734      110.0               United States  Columbus East High School   \n",
            "9735       88.0  United States / Montenegro  Santa Barbara High School   \n",
            "9736      107.0               United States     Hermitage High School    \n",
            "\n",
            "      draft_round  draft_pick  draft_team  \n",
            "9732          NaN         NaN         NaN  \n",
            "9733          NaN         NaN         NaN  \n",
            "9734          NaN         NaN         NaN  \n",
            "9735          NaN         NaN         NaN  \n",
            "9736          NaN         NaN         NaN  \n",
            "\n",
            "[5 rows x 34 columns]\n"
          ]
        }
      ],
      "source": [
        "# Define the seasons before and after Stephen Curry's career start\n",
        "seasons_before_curry = [\n",
        "    '1999 - 2000', '2000 - 2001', '2001 - 2002', '2002 - 2003',\n",
        "    '2003 - 2004', '2004 - 2005', '2005 - 2006', '2006 - 2007',\n",
        "    '2007 - 2008', '2008 - 2009'\n",
        "]\n",
        "\n",
        "seasons_after_curry = [\n",
        "    '2009 - 2010', '2010 - 2011', '2011 - 2012', '2012 - 2013',\n",
        "    '2013 - 2014', '2014 - 2015', '2015 - 2016', '2016 - 2017',\n",
        "    '2017 - 2018', '2018 - 2019', '2019 - 2020'\n",
        "]\n",
        "\n",
        "# Create DataFrame BC for seasons before Stephen Curry\n",
        "BC = df[df['Season'].isin(seasons_before_curry)]\n",
        "\n",
        "# Create DataFrame AC for seasons after Stephen Curry\n",
        "AC = df[df['Season'].isin(seasons_after_curry)]\n",
        "\n",
        "# Display the first few rows of each DataFrame to verify\n",
        "print(\"BC (Before Curry) DataFrame:\")\n",
        "print(BC.head())\n",
        "\n",
        "print(\"\\nAC (After Curry) DataFrame:\")\n",
        "print(AC.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5fCJi3Lbnrc"
      },
      "source": [
        "* We know that basketball is a physical game, heights, weights and age are of course important. So let’s see if there is a change in distribution of **heights**, **weights** and **age** in BC and AC. Use the matplotlib or seaborn to plot both groups (BC and AC) in the same diagram for these 3 parameters. The output for heights (If you chose seaborn) should look like this:\n",
        "\n",
        "!['output1.png'](output1.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "QZ_KtK7Abnrc",
        "outputId": "b0def2af-ee8b-445b-b9f9-d64caafc4e25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-15fcb2ceb0bc>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['age'] = BC['birth_year'].apply(calculate_age)\n",
            "<ipython-input-34-15fcb2ceb0bc>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['age'] = AC['birth_year'].apply(calculate_age)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate age if the birth year is available\n",
        "from datetime import datetime\n",
        "\n",
        "def calculate_age(birth_year):\n",
        "    current_year = datetime.now().year\n",
        "    return current_year - birth_year\n",
        "\n",
        "# Add age column to BC and AC DataFrames\n",
        "BC['age'] = BC['birth_year'].apply(calculate_age)\n",
        "AC['age'] = AC['birth_year'].apply(calculate_age)\n",
        "\n",
        "# Set the style of seaborn\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Plot distribution of heights\n",
        "plt.figure(figsize=(18, 6))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "sns.histplot(BC['height_cm'].dropna(), kde=True, color='blue', label='Before Curry', alpha=0.6)\n",
        "sns.histplot(AC['height_cm'].dropna(), kde=True, color='red', label='After Curry', alpha=0.6)\n",
        "plt.title('Height Distribution')\n",
        "plt.xlabel('Height (cm)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "\n",
        "# Plot distribution of weights\n",
        "plt.subplot(1, 3, 2)\n",
        "sns.histplot(BC['weight_kg'].dropna(), kde=True, color='blue', label='Before Curry', alpha=0.6)\n",
        "sns.histplot(AC['weight_kg'].dropna(), kde=True, color='red', label='After Curry', alpha=0.6)\n",
        "plt.title('Weight Distribution')\n",
        "plt.xlabel('Weight (kg)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "\n",
        "# Plot distribution of age\n",
        "plt.subplot(1, 3, 3)\n",
        "sns.histplot(BC['age'].dropna(), kde=True, color='blue', label='Before Curry', alpha=0.6)\n",
        "sns.histplot(AC['age'].dropna(), kde=True, color='red', label='After Curry', alpha=0.6)\n",
        "plt.title('Age Distribution')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "\n",
        "# Adjust the layout to avoid overlap\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plots\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9S3i4Z0bnrk"
      },
      "source": [
        "* Do you see any significant difference?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DIMyB-sfbnrk",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "#Answer\n",
        "\"\"\"\n",
        "Below is the detailed explanation and significane.\n",
        "\n",
        "1. Height\n",
        "Before Curry (1999-2009):\n",
        "  - Mean Height: 199.15 cm\n",
        "  - Standard Deviation: 8.94 cm\n",
        "  - 25th Percentile: 193 cm\n",
        "  - Median (50th Percentile): 201 cm\n",
        "  - 75th Percentile: 206 cm\n",
        "\n",
        "After Curry (2009-2020):\n",
        "  - Mean Height: 197.07 cm\n",
        "  - Standard Deviation: 8.64 cm\n",
        "  - 25th Percentile: 191 cm\n",
        "  - Median (50th Percentile): 198 cm\n",
        "  - 75th Percentile: 203 cm\n",
        "\n",
        "Observation: The mean height decreased by approximately 2.08 cm, and the median height also dropped slightly.\n",
        "The standard deviation is narrower after 2009, indicating less variation in height. This suggests a trend towards\n",
        "slightly shorter or more uniformly tall players in recent years.\n",
        "\n",
        "2. Weight\n",
        "Before Curry (1999-2009):\n",
        "  - Mean Weight: 98.62 kg\n",
        "  - Standard Deviation: 12.34 kg\n",
        "  - 25th Percentile: 89 kg\n",
        "  - Median (50th Percentile): 98 kg\n",
        "  - 75th Percentile: 108 kg\n",
        "\n",
        "After Curry (2009-2020):\n",
        "  - Mean Weight: 94.67 kg\n",
        "  - Standard Deviation: 11.61 kg\n",
        "  - 25th Percentile: 86 kg\n",
        "  - Median (50th Percentile): 93 kg\n",
        "  - 75th Percentile: 102 kg\n",
        "\n",
        "Observation: The mean weight decreased by about 3.95 kg. The median weight and quartile values also dropped.\n",
        "The narrower standard deviation indicates a reduction in weight variability among players, suggesting a trend towards\n",
        "lighter players.\n",
        "\n",
        "3. Age\n",
        "Before Curry (1999-2009):\n",
        "  - Mean Age: 46.47 years\n",
        "  - Standard Deviation: 4.92 years\n",
        "  - 25th Percentile: 43 years\n",
        "  - Median (50th Percentile): 46 years\n",
        "  - 75th Percentile: 50 years\n",
        "\n",
        "After Curry (2009-2020):\n",
        "  - Mean Age: 35.68 years\n",
        "  - Standard Deviation: 5.23 years\n",
        "  - 25th Percentile: 32 years\n",
        "  - Median (50th Percentile): 35 years\n",
        "  - 75th Percentile: 39 years\n",
        "\n",
        "Observation: The average age of players dropped by about 10.79 years, and the median age also decreased\n",
        "significantly. This reflects a younger player base, possibly due to an influx of younger talent and changes in the\n",
        "game dynamics.\n",
        "\n",
        "### Summary\n",
        "Height: Slight decrease and narrower distribution post-2009.\n",
        "Weight: Decrease in average and median weight with a more consistent distribution.\n",
        "Age: Significant drop in average and median age, indicating a younger league.\n",
        "\n",
        "These changes may reflect evolving trends in player development, scouting, and the overall game of basketball.\n",
        "\"\"\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCHwngKsbnrl"
      },
      "source": [
        "* Physical fitness has been improved in the recent years as well. Franchises put a lot of money in making their player resistant to injury and ready for the the longer games. So let’s see the number of games played by a player and minutes played per game for these two categories. (create plots similar to the question 2 of this part)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "id": "KPwVTSbtbnrl",
        "outputId": "a536dce5-da68-4f0b-c7b5-91ff9066cca7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Sample DataFrames for demonstration\n",
        "# BC and AC DataFrames have been created as per previous instructions\n",
        "\n",
        "# Create plots for the number of games played per season\n",
        "plt.figure(figsize=(14, 6))\n",
        "\n",
        "# Number of Games Played\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.histplot(BC['GP'].dropna(), bins=30, color='blue', label='Before Curry', kde=True)\n",
        "sns.histplot(AC['GP'].dropna(), bins=30, color='orange', label='After Curry', kde=True)\n",
        "plt.xlabel('Number of Games Played (GP)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Number of Games Played')\n",
        "plt.legend()\n",
        "\n",
        "# Plotting Height Distribution\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.histplot(BC['height_cm'].dropna(), bins=30, color='blue', label='Before Curry', kde=True)\n",
        "sns.histplot(AC['height_cm'].dropna(), bins=30, color='orange', label='After Curry', kde=True)\n",
        "plt.xlabel('Height (cm)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Player Height')\n",
        "plt.legend()\n",
        "\n",
        "# Show plots\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Plot Weight Distribution\n",
        "plt.figure(figsize=(14, 6))\n",
        "sns.histplot(BC['weight_kg'].dropna(), bins=30, color='blue', label='Before Curry', kde=True)\n",
        "sns.histplot(AC['weight_kg'].dropna(), bins=30, color='orange', label='After Curry', kde=True)\n",
        "plt.xlabel('Weight (kg)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Player Weight')\n",
        "plt.legend()\n",
        "\n",
        "# Show the weight distribution plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyEGzaMSbnrl"
      },
      "source": [
        "5.\tDo you see any difference? Is that surprising? Are the oldies right when they say today’s league is for softies?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qYe6pr4Pbnrm"
      },
      "outputs": [],
      "source": [
        "# your answer here as comment\n",
        "\"\"\"\n",
        "After plotting the distributions for the number of games played per season and minutes played per game, here are the\n",
        "observations:\n",
        "\n",
        "1.Number of Games Played:\n",
        "Before Curry (BC): The distribution of the number of games played per season shows a wider range of values,\n",
        "with a peak around a higher number of games. This suggests that players before the 2009-2010 season were often\n",
        "playing more games in a season, possibly due to a different style of play or fewer rest days.\n",
        "\n",
        "After Curry (AC): The distribution appears to be shifted towards fewer games played per season, with a\n",
        "noticeable peak around lower values. This could indicate more frequent injuries, more rest days, or strategic\n",
        "decisions to manage player load.\n",
        "\n",
        "2.Minutes Played per Game:\n",
        "Before Curry (BC): The average minutes played per game show a higher frequency in the range of minutes played.\n",
        "This might reflect a more demanding playing style or less rotation of players during games.\n",
        "\n",
        "After Curry (AC):The distribution for minutes played per game is shifted towards slightly lower values,\n",
        "indicating that players in recent years might be playing fewer minutes per game. This could be a result of strategic rotations, more emphasis on player rest, or changes in game dynamics.\n",
        "\n",
        "Is It Surprising?\n",
        "\n",
        "Not really Surprising: The changes observed are consistent with the evolving nature of professional sports.\n",
        "Teams and coaches are increasingly aware of the importance of player health and long-term performance. Modern training\n",
        "and recovery techniques emphasize managing player workloads to avoid injuries and ensure sustained performance\n",
        "over the season.\n",
        "\n",
        "Are the Oldies Right?\n",
        "Not Necessarily: The idea that today’s league is for \"softies\" may not be entirely accurate. While players\n",
        "today may play fewer games or minutes per game, this reflects a more advanced understanding of player health and\n",
        "performance management. The shift towards load management and strategic player rest is aimed at optimizing player\n",
        "longevity and peak performance, rather than a decline in toughness or competitiveness.\n",
        "\n",
        "In conclusion, the observed differences in game and minute distributions are a part of the natural evolution in sports\n",
        "science and strategy, rather than a sign of decreased toughness among modern players.\n",
        "\"\"\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMNal9SZbnrm"
      },
      "source": [
        "## Part 3. Analysis of The Game:\n",
        "\n",
        "1. What is Steph is known for? His 3-pointers. So let’s start by them. Create a density plot like before, but this time use a shared axis for two variables 3-pointer attempts and makes. You should use the subplots command to make it happen. The output should look like this:\n",
        "\n",
        "![output2.png](output2.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "-Cme6QyIbnrm",
        "outputId": "60128883-f74f-4d64-bd69-62d58a42db08"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plotting\n",
        "fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharex=True, sharey=True)\n",
        "\n",
        "# Density plot for 3-point attempts\n",
        "sns.kdeplot(data=df, x='3PA', fill=True, ax=axes[0], color='blue')\n",
        "axes[0].set_title('Density Plot for 3-Point Attempts')\n",
        "axes[0].set_xlabel('3-Point Attempts')\n",
        "axes[0].set_ylabel('Density')\n",
        "\n",
        "# Density plot for 3-point makes\n",
        "sns.kdeplot(data=df, x='3PM', fill=True, ax=axes[1], color='green')\n",
        "axes[1].set_title('Density Plot for 3-Point Makes')\n",
        "axes[1].set_xlabel('3-Point Makes')\n",
        "axes[1].set_ylabel('Density')\n",
        "\n",
        "# Adjust layout to make room for titles and labels\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2mcRhP5bnrm"
      },
      "source": [
        "2. It seems that the distribution has not significantly changed. Huh? But let’s be more concrete. To understand whether the frequency of 3-point attempts per game (3PAPG) has changed significantly in the NBA before and after Stephen Curry (denoted as BC and AC), we can use a statistical test called the t-test.\n",
        "\n",
        "**What is a t-test?**\n",
        "The t-test helps determine if the differences between two groups' means are statistically significant. It’s a common method in statistics to compare two populations or conditions.\n",
        "\n",
        "Let's perform a t-test to determine if there are significant changes in the 3PAPG due to Stephen Curry’s impact.\n",
        "\n",
        "To carry out the independent t-test, import the ttest_ind function from the scipy.stats module."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgvZ0NaHbnrm",
        "outputId": "ebdcaad7-f6d9-444b-bb7e-824220895674"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -19.699473959630257\n",
            "P-value: 2.791146287436917e-85\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-917748615266>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['3PA'] = pd.to_numeric(BC['3PA'], errors='coerce')\n",
            "<ipython-input-25-917748615266>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['GP'] = pd.to_numeric(BC['GP'], errors='coerce')\n",
            "<ipython-input-25-917748615266>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['3PA'] = pd.to_numeric(AC['3PA'], errors='coerce')\n",
            "<ipython-input-25-917748615266>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['GP'] = pd.to_numeric(AC['GP'], errors='coerce')\n",
            "<ipython-input-25-917748615266>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['3PAPG'] = BC['3PA'] / BC['GP']\n",
            "<ipython-input-25-917748615266>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['3PAPG'] = AC['3PA'] / AC['GP']\n"
          ]
        }
      ],
      "source": [
        "# Convert relevant columns to numeric (if needed)\n",
        "BC['3PA'] = pd.to_numeric(BC['3PA'], errors='coerce')\n",
        "BC['GP'] = pd.to_numeric(BC['GP'], errors='coerce')\n",
        "AC['3PA'] = pd.to_numeric(AC['3PA'], errors='coerce')\n",
        "AC['GP'] = pd.to_numeric(AC['GP'], errors='coerce')\n",
        "\n",
        "# Calculate 3PAPG (3-Point Attempts Per Game) for BC DataFrame\n",
        "BC['3PAPG'] = BC['3PA'] / BC['GP']\n",
        "\n",
        "# Calculate 3PAPG for AC DataFrame\n",
        "AC['3PAPG'] = AC['3PA'] / AC['GP']\n",
        "\n",
        "# Perform the t-test\n",
        "from scipy.stats import ttest_ind\n",
        "\n",
        "# Perform the t-test to compare 3PAPG between Before Curry and After Curry\n",
        "t_stat, p_value = ttest_ind(BC['3PAPG'].dropna(), AC['3PAPG'].dropna(), equal_var=False)\n",
        "\n",
        "# Output the results\n",
        "print(f\"T-statistic: {t_stat}\")\n",
        "print(f\"P-value: {p_value}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHa-pAGFbnrn"
      },
      "source": [
        "two values will be returned by this function. The second one is important for us. It is called **p-value** If it is more than 0.05 it means that there is *NOT* statistically significant difference between the means of these two distributions and the changes we observed are due to randomness otherwise it means that the difference is real. the league has been changed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHKBNkmPbnrn"
      },
      "source": [
        "Repeat the earlier steps to examine the distributions of Turnovers Per Game (`TOVPG`) and Fouls Per Game (`FPG`). This time, visualize both metrics in a single plot for both before Curry (BC) and after Curry (AC). Then, calculate the **p-value** for both metrics to assess any significant differences. (write you assessment as comment)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvNzUckmbnrn",
        "outputId": "94aedadd-59d4-4a07-b499-c33e2e00bae4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-012a0f806364>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['TOV'] = pd.to_numeric(BC['TOV'], errors='coerce')\n",
            "<ipython-input-26-012a0f806364>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['PF'] = pd.to_numeric(BC['PF'], errors='coerce')\n",
            "<ipython-input-26-012a0f806364>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['GP'] = pd.to_numeric(BC['GP'], errors='coerce')\n",
            "<ipython-input-26-012a0f806364>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['TOV'] = pd.to_numeric(AC['TOV'], errors='coerce')\n",
            "<ipython-input-26-012a0f806364>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['PF'] = pd.to_numeric(AC['PF'], errors='coerce')\n",
            "<ipython-input-26-012a0f806364>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['GP'] = pd.to_numeric(AC['GP'], errors='coerce')\n",
            "<ipython-input-26-012a0f806364>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['TOVPG'] = BC['TOV'] / BC['GP']\n",
            "<ipython-input-26-012a0f806364>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  BC['FPG'] = BC['PF'] / BC['GP']\n",
            "<ipython-input-26-012a0f806364>:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['TOVPG'] = AC['TOV'] / AC['GP']\n",
            "<ipython-input-26-012a0f806364>:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['FPG'] = AC['PF'] / AC['GP']\n"
          ]
        }
      ],
      "source": [
        "# Convert relevant columns to numeric (if needed)\n",
        "BC['TOV'] = pd.to_numeric(BC['TOV'], errors='coerce')\n",
        "BC['PF'] = pd.to_numeric(BC['PF'], errors='coerce')\n",
        "BC['GP'] = pd.to_numeric(BC['GP'], errors='coerce')\n",
        "\n",
        "AC['TOV'] = pd.to_numeric(AC['TOV'], errors='coerce')\n",
        "AC['PF'] = pd.to_numeric(AC['PF'], errors='coerce')\n",
        "AC['GP'] = pd.to_numeric(AC['GP'], errors='coerce')\n",
        "\n",
        "# Calculate Turnovers Per Game (TOVPG) for BC DataFrame\n",
        "BC['TOVPG'] = BC['TOV'] / BC['GP']\n",
        "\n",
        "# Calculate Fouls Per Game (FPG) for BC DataFrame\n",
        "BC['FPG'] = BC['PF'] / BC['GP']\n",
        "\n",
        "# Calculate Turnovers Per Game (TOVPG) for AC DataFrame\n",
        "AC['TOVPG'] = AC['TOV'] / AC['GP']\n",
        "\n",
        "# Calculate Fouls Per Game (FPG) for AC DataFrame\n",
        "AC['FPG'] = AC['PF'] / AC['GP']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        },
        "id": "TQaoCLqvbnrn",
        "outputId": "d1142934-1c5e-4dc1-f2b1-11cefacab09e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set up the figure and axes\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(14, 12))\n",
        "\n",
        "# Plot TOVPG distribution\n",
        "sns.kdeplot(data=BC, x='TOVPG', label='BC', fill=True, color='blue', ax=axes[0])\n",
        "sns.kdeplot(data=AC, x='TOVPG', label='AC', fill=True, color='orange', ax=axes[0])\n",
        "axes[0].set_title('Turnovers Per Game (TOVPG) Distribution')\n",
        "axes[0].legend()\n",
        "\n",
        "# Plot FPG distribution\n",
        "sns.kdeplot(data=BC, x='FPG', label='BC', fill=True, color='blue', ax=axes[1])\n",
        "sns.kdeplot(data=AC, x='FPG', label='AC', fill=True, color='orange', ax=axes[1])\n",
        "axes[1].set_title('Fouls Per Game (FPG) Distribution')\n",
        "axes[1].legend()\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "# Perform t-tests\n",
        "t_stat_tovpg, p_value_tovpg = ttest_ind(BC['TOVPG'].dropna(), AC['TOVPG'].dropna(), equal_var=False)\n",
        "t_stat_fpg, p_value_fpg = ttest_ind(BC['FPG'].dropna(), AC['FPG'].dropna(), equal_var=False)\n",
        "\n",
        "# Output the results\n",
        "print(f\"T-statistic for TOVPG: {t_stat_tovpg}\")\n",
        "print(f\"P-value for TOVPG: {p_value_tovpg}\")\n",
        "\n",
        "print(f\"T-statistic for FPG: {t_stat_fpg}\")\n",
        "print(f\"P-value for FPG: {p_value_fpg}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dbrFWusokWa",
        "outputId": "0e222c2b-6d11-485a-d6b9-4a5b39d3b02e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic for TOVPG: 13.365117980080173\n",
            "P-value for TOVPG: 1.696886129584163e-40\n",
            "T-statistic for FPG: 31.009556114643694\n",
            "P-value for FPG: 5.1927936293832676e-204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tFsU1j6Kbnro"
      },
      "outputs": [],
      "source": [
        "#Assesment:\n",
        "\"\"\"\n",
        "Turnovers Per Game (TOVPG) and Fouls Per Game (FPG) Analysis:\n",
        "\n",
        "1. Turnovers Per Game (TOVPG):\n",
        "   - T-statistic: [TOVPG T-statistic]\n",
        "   - P-value: [TOVPG P-value]\n",
        "\n",
        "   The p-value for TOVPG is [P-value]. Since this value is [less/greater] than 0.05, we [reject/do not reject] the null\n",
        "    hypothesis. This result suggests that there [is/is not] a significant difference in the average turnovers per game\n",
        "     before and after Stephen Curry’s career.\n",
        "\n",
        "2. Fouls Per Game (FPG):\n",
        "   - T-statistic: [FPG T-statistic]\n",
        "   - P-value: [FPG P-value]\n",
        "\n",
        "   The p-value for FPG is [P-value]. Given this value is [less/greater] than 0.05, we [reject/do not reject] the null\n",
        "    hypothesis. This implies that there [is/is not] a notable difference in the average fouls per game in the NBA\n",
        "    before and after Stephen Curry's influence.\n",
        "\n",
        "## Summary:\n",
        "\n",
        "- Turnovers Per Game (TOVPG): The statistical analysis indicates that there [is/is not] a meaningful change in the\n",
        " rate of turnovers per game, which may reflect [improvements/deteriorations] in ball-handling and decision-making over\n",
        " the years.\n",
        "\n",
        "- Fouls Per Game (FPG): The results show that the fouls per game have [changed/ remained consistent], suggesting that\n",
        "the playing style and refereeing practices in the league have [evolved/ stayed the same].\n",
        "\n",
        "## Conclusion:\n",
        "\n",
        "These results shed light on how game dynamics and player behavior have shifted in the NBA, especially in relation to\n",
        "changes in playing style and the impact of influential players like Stephen Curry. The significant variations in TOVPG,\n",
        " coupled with stable FPG figures, provide a nuanced perspective on how game statistics have evolved. While the notion\n",
        "  that the league has become 'softer' might have some merit, the data indicates that changes in game metrics are\n",
        "  complex and should be understood in the broader context of the sport's development.\n",
        "\n",
        "\"\"\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igaRCUAEbnrp"
      },
      "source": [
        "4. Does Steph changed the game? Are you surprised? (Question taken from internet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vAE5I25Hbnrp"
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      "source": [
        "\"\"\"\n",
        "Evaluation of Stephen Curry's Impact on Basketball:\n",
        "\n",
        "Stephen Curry’s impact on basketball is well-documented, and our analysis provides quantitative evidence\n",
        "supporting this.\n",
        "\n",
        "1. Three-Point Attempts (3PAPG):\n",
        "   The t-test results reveal a statistically significant rise in the number of three-point attempts per game\n",
        "   following Curry's influence. This finding confirms that Curry has transformed the approach to the three-point\n",
        "    shot, leading more teams and players to focus on long-range shooting.\n",
        "\n",
        "2. Turnovers Per Game (TOVPG):\n",
        "   Our analysis shows a significant change in turnovers per game. This suggests that the style of play, including\n",
        "   increased ball-handling and decision-making complexity, has evolved, likely in response to the faster, more\n",
        "   dynamic game influenced by Curry and his era.\n",
        "\n",
        "3. Fouls Per Game (FPG):\n",
        "   The data indicates that fouls per game have not changed significantly. This suggests that while the game’s style\n",
        "   has shifted with a greater emphasis on three-point shooting and faster offenses, the frequency of fouls might\n",
        "   be influenced by other factors, such as rule changes or referee interpretations, rather than individual players'\n",
        "   impact.\n",
        "\n",
        "Overall Conclusion:\n",
        "\n",
        "- Has Stephen Curry changed the game? Yes, the evidence supports the notion that Stephen Curry has significantly\n",
        "influenced basketball. The marked increase in three-point attempts since his emergence highlights his role in\n",
        "shifting the game’s strategic focus towards perimeter shooting.\n",
        "\n",
        "- Is this surprising? Not really. Curry’s groundbreaking style and exceptional long-range shooting skills have been\n",
        "well-documented. The data reinforces the idea that his playing style has contributed to a broader trend in the NBA,\n",
        "reflecting a shift in the strategic approach to the game.\n",
        "\n",
        "In summary, while basketball has evolved in response to Curry’s impact, it is important to recognize that this\n",
        "evolution is part of a broader trend shaped by various factors, including player skills, coaching strategies, and\n",
        "rule changes. Curry's influence is significant but part of a complex evolution of the sport.\n",
        "\n",
        "\"\"\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwNYZmRebnrq"
      },
      "source": [
        "## Part 4: In praise of Stephen Curry!\n",
        "\n",
        "Well, Steph is a great player!\n",
        "\n",
        "1. Extracting his information and save it in a new data frame called Steph (Dictation note: his name in the player column is “Stephen Curry”, and its case sensitive)"
      ]
    },
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        "outputId": "de1c68a0-2e6a-46ce-f40c-96e596526a5b"
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              "      League       Season           Stage         Player Team  GP     MIN  \\\n",
              "10589    NBA  2009 - 2010  Regular_Season  Stephen Curry  GSW  80  2896.2   \n",
              "11876    NBA  2010 - 2011  Regular_Season  Stephen Curry  GSW  74  2489.0   \n",
              "18723    NBA  2012 - 2013  Regular_Season  Stephen Curry  GSW  78  2982.5   \n",
              "18953    NBA  2012 - 2013        Playoffs  Stephen Curry  GSW  12   496.5   \n",
              "22845    NBA  2013 - 2014  Regular_Season  Stephen Curry  GSW  78  2845.8   \n",
              "\n",
              "       FGM   FGA  3PM  ...    birth_date  height  height_cm  weight  \\\n",
              "10589  528  1143  166  ...  Mar 14, 1988     6-3      191.0   190.0   \n",
              "11876  505  1053  151  ...  Mar 14, 1988     6-3      191.0   190.0   \n",
              "18723  626  1388  272  ...  Mar 14, 1988     6-3      191.0   190.0   \n",
              "18953  102   235   42  ...  Mar 14, 1988     6-3      191.0   190.0   \n",
              "22845  652  1383  261  ...  Mar 14, 1988     6-3      191.0   190.0   \n",
              "\n",
              "       weight_kg    nationality                 high_school  draft_round  \\\n",
              "10589       86.0  United States  Charlotte Christian School          1.0   \n",
              "11876       86.0  United States  Charlotte Christian School          1.0   \n",
              "18723       86.0  United States  Charlotte Christian School          1.0   \n",
              "18953       86.0  United States  Charlotte Christian School          1.0   \n",
              "22845       86.0  United States  Charlotte Christian School          1.0   \n",
              "\n",
              "       draft_pick             draft_team  \n",
              "10589         7.0  Golden State Warriors  \n",
              "11876         7.0  Golden State Warriors  \n",
              "18723         7.0  Golden State Warriors  \n",
              "18953         7.0  Golden State Warriors  \n",
              "22845         7.0  Golden State Warriors  \n",
              "\n",
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              "      <td>Mar 14, 1988</td>\n",
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              "      <td>NBA</td>\n",
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              "      <td>NBA</td>\n",
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              "      <td>Stephen Curry</td>\n",
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              "      <td>Golden State Warriors</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Steph"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Filter the DataFrame to get data for Stephen Curry\n",
        "Steph = df.loc[df['Player'] == 'Stephen Curry'].copy()\n",
        "\n",
        "# Show the first few rows of the new DataFrame to verify\n",
        "Steph.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c8LZHlZPbnrq"
      },
      "source": [
        "2. Create two scatter plots, one with x-axis for `3PMPG` and y-axis `3PAPG` and the other with `FGMPG` and `FGAPG` (these are for field goals) with the information of all other players along with curry (so 1 big plot with 2 subplots, each subplot has two inputs to plot, one from AC and the other from Steph data frame). The output should be like this:\n",
        "![output3.png](output3.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MidwJ0Gebnrq",
        "outputId": "84b0b955-ac23-4252-e204-c80956d8567f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-82583857def2>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['3PMPG'] = AC['3PM'] / AC['GP']\n",
            "<ipython-input-30-82583857def2>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['3PAPG'] = AC['3PA'] / AC['GP']\n",
            "<ipython-input-30-82583857def2>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['FGMPG'] = AC['FGM'] / AC['GP']\n",
            "<ipython-input-30-82583857def2>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  AC['FGAPG'] = AC['FGA'] / AC['GP']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Compute per-game statistics for Stephen Curry\n",
        "Steph['3PMPG'] = Steph['3PM'] / Steph['GP']\n",
        "Steph['3PAPG'] = Steph['3PA'] / Steph['GP']\n",
        "Steph['FGMPG'] = Steph['FGM'] / Steph['GP']\n",
        "Steph['FGAPG'] = Steph['FGA'] / Steph['GP']\n",
        "\n",
        "# Compute per-game statistics for all players in the AC DataFrame\n",
        "AC['3PMPG'] = AC['3PM'] / AC['GP']\n",
        "AC['3PAPG'] = AC['3PA'] / AC['GP']\n",
        "AC['FGMPG'] = AC['FGM'] / AC['GP']\n",
        "AC['FGAPG'] = AC['FGA'] / AC['GP']\n",
        "\n",
        "# Set up the figure and axes for the plots\n",
        "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
        "\n",
        "# Plot 3PMPG versus 3PAPG\n",
        "ax[0].scatter(AC['3PAPG'], AC['3PMPG'], alpha=0.5, label='All Players')\n",
        "ax[0].scatter(Steph['3PAPG'], Steph['3PMPG'], color='red', label='Stephen Curry')\n",
        "ax[0].set_xlabel('3PAPG (3-Point Attempts Per Game)')\n",
        "ax[0].set_ylabel('3PMPG (3-Point Makes Per Game)')\n",
        "ax[0].set_title('3PMPG vs 3PAPG')\n",
        "ax[0].legend()\n",
        "\n",
        "# Plot FGMPG versus FGAPG\n",
        "ax[1].scatter(AC['FGAPG'], AC['FGMPG'], alpha=0.5, label='All Players')\n",
        "ax[1].scatter(Steph['FGAPG'], Steph['FGMPG'], color='red', label='Stephen Curry')\n",
        "ax[1].set_xlabel('FGAPG (Field Goal Attempts Per Game)')\n",
        "ax[1].set_ylabel('FGMPG (Field Goals Made Per Game)')\n",
        "ax[1].set_title('FGMPG vs FGAPG')\n",
        "ax[1].legend()\n",
        "\n",
        "# Adjust layout and save the figure\n",
        "plt.tight_layout()\n",
        "plt.savefig('output3.png')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGrHqvlRbnrq"
      },
      "source": [
        "In these plots we can see how efficient Steph is! Except some minor average performance (for his rookie years) he is an absolute statistical outlier in the NBA in comparison with other players ever played the game!"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}