Bubble_Dedection_for_the_Crypto_Market.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqxGUvXOeo6I"
      },
      "source": [
        "# Dedecting Bubbles In Crypto\n",
        "In today worlds we all became an inverstor and we try to learn protect our money's value. At this time some market has what we call bubble by\n",
        "other various investor to manipulate markets. As we know, we can track multiple behaivor of markets to understand how price can be move in the\n",
        "future but understand market manipulation by other investors is a hard topic to handle for regular investors like us. The mathematic is full of\n",
        "magic and some great minds make this kind of market mainpulation more visible with the power of math.\n",
        "\n",
        "I am going to cover a great way to track this kind of behaviour here with a python module called lppls aka Log **Periodic Power Law Singularity**.\n",
        "\n",
        "###some main outputs of lppls:\n",
        "- Expected Log Price Colon : expected log price at the date of the termination of the bubble\n",
        "- Critical Time Colon ('tc'): critical time (date of termination of the bubble and transition in a new regime)\n",
        "- A Colon(a) : log price at the peak when the end of the bubble is reached at Critical Time\n",
        "- B Colon : amplitude of the power law acceleration\n",
        "- C Colon : amplitude of the log-periodic oscillations\n",
        "- m Colon : degree of the super exponential growth\n",
        "- omega Colon : scaling ratio of the temporal hierarchy of oscillations\n",
        "phi Colon : time scale of the oscillations\n",
        "\n",
        "Let's start"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hx5pBh0afk6O"
      },
      "source": [
        "Let's first install required modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0Sy2Kkodc2A",
        "outputId": "9dd8bcc8-34b4-4091-923b-597a4b83f19e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.6)\n",
            "Collecting lppls\n",
            "  Downloading lppls-0.6.5-py3-none-any.whl (45 kB)\n",
            "\u001b[K     |████████████████████████████████| 45 kB 3.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (2.23.0)\n",
            "Collecting ipython-autotime\n",
            "  Downloading ipython_autotime-0.3.1-py2.py3-none-any.whl (6.8 kB)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.1)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Collecting cma\n",
            "  Downloading cma-3.2.2-py2.py3-none-any.whl (249 kB)\n",
            "\u001b[K     |████████████████████████████████| 249 kB 14.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from lppls) (3.2.2)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests) (3.0.4)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from ipython-autotime) (5.5.0)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.8.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.7.5)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (2.6.1)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (5.1.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (1.0.18)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.8.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (57.4.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (0.2.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lppls) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lppls) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lppls) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->lppls) (4.1.1)\n",
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            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from xarray->lppls) (4.12.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->xarray->lppls) (3.8.1)\n",
            "Installing collected packages: cma, lppls, ipython-autotime\n",
            "Successfully installed cma-3.2.2 ipython-autotime-0.3.1 lppls-0.6.5\n",
            "time: 201 µs (started: 2022-07-27 20:37:21 +00:00)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas numpy lppls requests ipython-autotime\n",
        "%load_ext autotime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dr1-Xr-SftW9"
      },
      "source": [
        "And import them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAm9a1ckaLEw",
        "outputId": "dc886d03-3929-49bc-b402-d24a8ad2c175"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The autotime extension is already loaded. To reload it, use:\n",
            "  %reload_ext autotime\n",
            "time: 2.26 s (started: 2022-07-27 20:37:21 +00:00)\n"
          ]
        }
      ],
      "source": [
        "from lppls import lppls, data_loader\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime as dt\n",
        "import requests\n",
        "import json\n",
        "import warnings\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "#pd.options.mode.chained_assignment = None\n",
        "#pd.set_option('display.max_columns', None)\n",
        "#pd.options.mode.chained_assignment = None\n",
        "%load_ext autotime \n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQDXYGcxgAXP"
      },
      "source": [
        "I am am going to mine hourly data of 8 populer crypto from binance which is populer, free and has fast response time "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "vQW-g6nOeXE9",
        "outputId": "ea8861cc-cc5a-41bb-8b36-6d28bc0fc533"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "                     ADAUSDT   BTCUSDT  DOGEUSDT  DOTUSDT  ETHUSDT  LTCUSDT  \\\n",
              "index                                                                         \n",
              "2022-07-13 00:00:00   0.4193  19428.23   0.06002     6.34  1042.85    47.50   \n",
              "2022-07-18 04:00:00   0.4602  21300.29   0.06505     7.21  1413.09    57.51   \n",
              "2022-07-09 16:00:00   0.4818  21693.15   0.06990     7.26  1222.32    52.47   \n",
              "2022-07-15 17:00:00   0.4361  20786.11   0.06259     6.78  1221.58    51.39   \n",
              "2022-07-15 12:00:00   0.4422  20986.53   0.06385     6.91  1227.61    51.46   \n",
              "\n",
              "                     SOLUSDT  XRPUSDT  \n",
              "index                                  \n",
              "2022-07-13 00:00:00    32.77   0.3128  \n",
              "2022-07-18 04:00:00    40.91   0.3575  \n",
              "2022-07-09 16:00:00    38.30   0.3448  \n",
              "2022-07-15 17:00:00    37.13   0.3282  \n",
              "2022-07-15 12:00:00    38.12   0.3365  "
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              "      <th>ADAUSDT</th>\n",
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              "      <th>2022-07-13 00:00:00</th>\n",
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              "      <th>2022-07-18 04:00:00</th>\n",
              "      <td>0.4602</td>\n",
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          "metadata": {},
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        {
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          "text": [
            "time: 2.04 s (started: 2022-07-27 20:37:23 +00:00)\n"
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      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "cryptos=[{'name': 'Bitcoin', 'symbol': 'BTCUSDT', 'usym': '₿', 'gecko': 'bitcoin'},\n",
        "         {'name': 'Ethereum', 'symbol': 'ETHUSDT', 'usym': 'Ξ', 'gecko': 'ethereum'},\n",
        "         {'name': 'Cardano', 'symbol': 'ADAUSDT', 'usym': '₳', 'gecko': 'cardano'},\n",
        "         {'name': 'XRP', 'symbol': 'XRPUSDT', 'usym': '✕', 'gecko': 'ripple'},\n",
        "         {'name': 'Solana', 'symbol': 'SOLUSDT', 'usym': '◎', 'gecko': 'solana'},\n",
        "         {'name': 'Polkadot', 'symbol': 'DOTUSDT', 'usym': '●', 'gecko': 'polkadot'},\n",
        "         {'name': 'Dogecoin', 'symbol': 'DOGEUSDT', 'usym': 'Ð', 'gecko': 'dogecoin'},\n",
        "         {'name': 'Litecoin', 'symbol': 'LTCUSDT', 'usym': 'Ł', 'gecko': 'litecoin'},\n",
        "        {'name': 'Litecoin', 'symbol': 'LTCUSDT', 'usym': 'Ł', 'gecko': 'litecoin'},\n",
        "         ]\n",
        "\n",
        "\n",
        "symbols= [i['symbol'] for i in cryptos]\n",
        "\n",
        "#main function to get data as ohlc\n",
        "def get_bars (symbol, interval='1d'):\n",
        "    root_url = 'https://api.binance.com/api/v1/klines'\n",
        "    url = root_url + '?symbol=' + symbol + '&interval=' + interval\n",
        "    data = json.loads (requests.get (url).text)\n",
        "    df = pd.DataFrame (data)\n",
        "    df.columns = ['open_time',\n",
        "                  'open', 'high', 'low', 'close', 'volume',\n",
        "                  'close_time', 'qav', 'num_trades',\n",
        "                  'buyer_seller_volume', 'taker_quote_vol', 'ignore']\n",
        "    df.index = [dt.datetime.fromtimestamp (x / 1000.0) for x in df.open_time]\n",
        "    df['close'] = pd.to_numeric (df['close'])\n",
        "    df['open'] = pd.to_numeric (df['open'])\n",
        "    df['high'] = pd.to_numeric (df['high'])\n",
        "    df['low'] = pd.to_numeric (df['low'])\n",
        "    df['volume'] = pd.to_numeric (df['volume'])\n",
        "    df['close'] = pd.to_numeric (df['close'])\n",
        "    df['buyer_seller_volume'] = pd.to_numeric (df['buyer_seller_volume'])\n",
        "    df['num_trades'] = pd.to_numeric (df['num_trades'])\n",
        "    df['symbol'] = symbol\n",
        "    #df_.loc[:, 'name'] = symbol\n",
        "    return df\n",
        "#this function will transform data to close values of each hour for each crypto\n",
        "def return_close_values():\n",
        "    listi = []\n",
        "    for i in symbols:\n",
        "        data = get_bars (i, interval='1h')\n",
        "        listi.append (data)\n",
        "    dfi = pd.concat ([pd.DataFrame (x) for x in listi]).apply (pd.Series)\n",
        "    lis = dfi[['close', 'symbol']].reset_index()\n",
        "    data2 = lis.pivot_table(index='index', columns='symbol', values='close').reset_index ().rename_axis (None,axis=1).set_index ('index').dropna ()\n",
        "    data2.index = pd.to_datetime (data2.index)\n",
        "    return data2\n",
        "data = return_close_values()#data\n",
        "data.sample(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uf0IkS9igpA6"
      },
      "source": [
        "As you can see, we mined the data. \n",
        "Now below, I copy some codes from example in [lppls](https://github.com/Boulder-Investment-Technologies/lppls) github page and functionalized them for fitting above data.\n",
        "\n",
        "-  ***bubble_finder***:  this function will return lppls results as a dataframe\n",
        "- ***return_lppls_df***: this function will apply above function to all data and return concaded dataset\n",
        "- ***plot_pos_confidence_indicators***: this function copied from [lppls](https://github.com/Boulder-Investment-Technologies/lppls) to plot all results separetly to understand results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HUTMORbheRw",
        "outputId": "39ec42bd-cc69-47dc-bbb5-7b292ed7b1d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 25.8 ms (started: 2022-07-27 20:37:25 +00:00)\n"
          ]
        }
      ],
      "source": [
        "def bubble_finder(data):\n",
        "    time_ord = [pd.Timestamp.toordinal(t1) for t1 in data.index]\n",
        "    # create list of observation data\n",
        "    price = np.log(data.values)\n",
        "\n",
        "    # create observations array (expected format for LPPLS observations)\n",
        "    observations = np.array([time_ord, price])\n",
        "\n",
        "    # set the max number for searches to perform before giving-up\n",
        "    MAX_SEARCHES = 25\n",
        "   \n",
        "    lppls_model = lppls.LPPLS(observations=observations)\n",
        "\n",
        "    res = lppls_model.mp_compute_nested_fits(\n",
        "        max_searches=5, workers=8\n",
        "        # filter_conditions_config={} # not implemented in 0.6.x\n",
        "    )\n",
        "    res_df = lppls_model.compute_indicators(res)\n",
        "    res_df['ordinal_time'] = res_df['time']\n",
        "    res_df['symbol'] = data.name\n",
        "    #res_df['date2'] =data.index\n",
        "    #res_df['time'] = [pd.Timestamp.fromordinal(int(t1)) for t1 in res_df['time']]\n",
        "    #res_df.set_index('time', inplace=True)\n",
        "  \n",
        "    return res_df\n",
        "\n",
        "\n",
        "def plot_pos_confidence_indicators(res_df):\n",
        "    #https://github.com/Boulder-Investment-Technologies/avoiding_bubbles/blob/main/utils/plotting.py\n",
        "    fig, (ax1) = plt.subplots(nrows=1, ncols=1, sharex=True, figsize=(12, 6))\n",
        "    ts = res_df.index\n",
        "    # plot pos bubbles\n",
        "    ax1_0 = ax1.twinx()\n",
        "    ax1.plot(ts, res_df['price'], color='black', linewidth=0.75)\n",
        "    # ax1_0.plot(compatible_date, pos_lst, label='pos bubbles', color='gray', alpha=0.5)\n",
        "    ax1_0.plot(ts, res_df['pos_conf'], label='bubble indicator (pos)', color='red', alpha=0.5)\n",
        "    # set grids\n",
        "    ax1.grid(which='major', axis='both')\n",
        "    # set labels\n",
        "    ax1.set_ylabel('ln(p)')\n",
        "    ax1_0.set_ylabel('bubble indicator (pos)')\n",
        "    ax1_0.legend(loc=2)\n",
        "    plt.title('Positive LPPLS Conf Indicator')\n",
        "    plt.fill_between(ts, res_df['pos_conf'], color='red', alpha=0.5)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res_btc = bubble_finder(data['BTCUSDT'])\n",
        "plot_pos_confidence_indicators(res_btc.reset_index())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "tr3NNX1EYmNH",
        "outputId": "d8790ab7-65b5-4c51-b8e8-1ad7fcb3866e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 85/85 [01:49<00:00,  1.29s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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11QHPuWPHDurXr19kmy/Ofv36cejQIQ4cOMCyZcsK477gggvYt28fhw4dom/fvtx1110888wzHDhwoDD20047LSLjPYwxHtm7F1S9jsIYY04I0WypeBl4DvAfHXsvkKKqU0TkXvf1Pf6FRORUYCLQE1BglYh8oKr7ga5+x60C3vMr+qaqjo/oFXg0ELf47EG+10lJSYXdl4pPWVpSmZK2+6gqo0eP5m9/+1vQ8SUnJxfWk5iYSF5eXon1ByMrK4vf//73rFy5kiZNmvDwww+HNSVr9erVA26vVq1a0O9XIPfeey/Dhg3j448/pm/fvnz66aecddZZZGVlUa1atZDjNMbEgUOHeLdPH5ZWq0ZGz56cddZZhY+WLVsWfnlgjDEmOFFrqVDVpUB6sc2XA6+4z18BrghQ9CJggaqmu4nEAqDIp3sRORM4DfgsokHHiS1btvD5558D8Nprr3HeeecBzriBVatWAfDuu+8WKfP++++TlZXFvn37SE1NpVevXoDT/WnTpk0UFBTw5ptvFtblM2jQIN555x12794NOOMKfv7557Di7tu3L2+88QZAiWMNBg8ezIsvvliYiKSnpxd+4K9Xrx6ZmZlFBnXXrFmzcAxD27Zt2bx5Mxs2bADgX//6F/379y8zrnbt2hWW8XnzzTcBWLZsGbVq1aJWrVqcf/75hXGnpqZSr149TjnlFH766Sc6derEPffcQ69evfjhhx8A+PHHH0OeqcsYEx/2/vwzNbKz+ef113P//ffTrl071q1bx5QpU7jwwgs599xz+fHHH70O0xhjKoxYD9Q+XVV3uM93AqcHOKYR8Ivf663uNn/X4LRM+LdbDxeRNBF5R0SaRCxiD7Rt25Zp06bRrl079u/fz+9+9zsAJk6cyO23307Pnj1JTEwsUqZz584MHDiQ3r1789BDD9GwYUMAevXqxfjx42nXrh0tWrTgyiuvLFKuffv2TJ48mSFDhtC5c2cGDx7Mjh07CMfTTz/NtGnT6NSpE9u2bQt4zJgxY2jatCmdO3emS5cuvPbaa9SuXZtbbrmFjh07ctFFFxUmRAA33ngjt956K127dkVVmT17NldddRWdOnUiISGBW2+9tcy4Lr744iLdpACqVq1Kt27duPXWW5k5cybgTM27atUqOnfuzL333ssrrzj571NPPVU4sDw5OZmLL74YcLps+cauGGMqlqUpKTRt1IjEnBxat27Nr371K+6++25mzZpFamoqf/zjH/n000+9DtMYYyoM0Sj2JxWR5sCHqtrRfX1AVWv77d+vqnWKlfkzUFVVJ7uvHwKOqupUv2O+A36jqqvc13WBTFXNFpFxwNWqekEJMY0FxgIkJSX1WLBgQZH9tWrVonXr1uW78CDk5+cflxiY6Ln22muZNGkSrVu35pJLLmHy5Ml07969XHUOHTqU119/nTp1jh8atGHDBg4ePFiu+kuTmZlZuA6IMeGo7PfQ7AceYGJCAgnNmrF5zPHD8LZv387s2bN54IEHPIiuYqjs95Apv8p2Dw0cOPCIqgbuq30CiHWn0V0i0kBVd4hIA2B3gGO2AQP8XjcGUn0vRKQLkORLKABUdZ/f8TOAv5cUgKq+BLwEUL16dfWflhWc2ZBq1qwZ5OWELyMjIybnMY6pU6eya9cuatasSWJiItWrVy/X+79nzx7uvvtumjZtGnC/ryUkWlJTUyl+7xoTisp8DxUUFPDi4cO0bN8eGjSgeYD3QVWZNm1apX2PglGZ7yETGXYPnVhi3f3pA2C0+3w08H6AYz4FhohIHXd2qCHuNp9RwOv+BdwExecy4PuIRWxOCG3btqVfv36A859Yz549y1Vf/fr1ueKKQEOCjDHxbvXq1XRu1w7y8iA3N+AxIsKpp57Kvn37Au43xhhTVDSnlH0d+BxoKyJbReRmYAowWETWAxe6rxGRniIyA0BV04G/AivcxyR3m89IiiUVwG0islZEvgFuA26M1nUZY4yp2D755BPO69271KQC4JxzzimcntsYY0zpojn70yhVbaCqyaraWFVnquo+VR2kqm1U9UJfsqCqK1V1jF/ZWara2n3MLlZvS1X9odi2+1S1g6p2UdWBxfeHEXt5iptKzu4fY+Lb4sWL6dm5s5NQ5OSUeNw555zD//73vxhGZowxFVesuz/FvapVq7Jv3z77YGjCoqrs27evcNVtY0x82b9/P8nJyVRLTCyzpaJXr16sWLEihtEZY0zFZav7FNO4cWO2bt3Knj17onqerKws++B5gqpatSqNGzf2OgxjTAApKSkMHjzYSSby80tNKmrXrs3BgwdR1bAW9jTGmMrEkopikpOTadGiRdTPk5qaGtXZgYwxxhzvk08+4c4774RffimzpQKgdevWbNiwgTZt2sQoQmOMqZis+5MxxphKQVVZu3Yt7du3d5KJvDznUQobV2GMMcGxpMIYY0ylsHbtWjp27Oh0ZcrJCaqlwpIKY4wJjiUVxhhjKoVPPvmEoUOHOi98YyrKaKno1KkTaWlpMYjOGGMqNksqjDHGVAoLFy5k0KBBzgtf96f8/FLLJCcnk5iYSFZWVgwiNMaYisuSCmOMMSe8zMxM8vLyqF27trMhJ6fM2Z98unfvzldffRXlCI0xpmKzpMIYY8wJLzU1lYEDBx7bkJ0NqmV2fwIbV2GMMcGwpMIYY0zc+uWXX3jiiSdYuHBhueopMp4C4OhREHFaK8pY7NSSCmOMKZslFcYYY+LKgQMHmDlzJhdddBG33XYbDRs25N133+Wyyy7ju+++C6vO1atXF10bKCvLSSqgzHEVTZo04ZdffgnrvMYYU1nY4nfGGGM8l52dzbx585g7dy7p6emMGDGC1157jbp16wIwatQo1q5dy7333kujRo145JFHOO2004Kqe8OGDbRq1YqEBL/v0bKznZ+qTlKRVPKfQxGhXr167Nmzh/r164d9jcYYcyKzpMIYY4znfv3rX9O7d28ef/xxWrZsGfCYDh068MEHH7BgwQKGDx/OJZdcwh133EG1atVKrfu4rk9wrKXCl1SUwdcF6tJLLw36mowxpjKx7k/GGGM8l5mZyUMPPVRiQuFv8ODBpKamUr9+fQYOHMgbb7yBljIuYv78+QwZMuTYhoICZ/Yn1ZCTihPNxx9/zMaNG70OwxhzArCkwhhjTIWTmJjImDFjWLhwIV999RWXXHIJP/zww3HHZWVlcfDgwaLdlnJzncTCJ4ikomfPnqxcuTISoceNgoICHn74YUaPHs2RI0e8DscYU8FZUmGMMcZTR48eLbMLU0lq1KjB448/zhNPPMHtt9/Offfdx+HDhwv3L1u2jPPPP79oId9q2r7uT0FMK3vKKaeQmZlJgX8yUsEtXryYfv36cddddzFu3LhSW3uMMaYsllQYY4zx1O7du4MedF2S9u3b88knn9CpUycuuOAC3nvvPVSVTz/99PjxFL6WioQE52cQLRUAbdu25ccffyxXnPFkxowZjBkzhiuvvJJGjRoxbdo0r0MyxlRgllQYY4zx1K5duzj99NPLXY+IcO211zJ//nyWLl3KZZddxuLFi+ndu3fRA30tFYmJQY+pAGdcxRdffFHuOOPB3r172bt3L2eddRYAkydP5uOPP2b58uUeR2aMqagsqTDGGOOpSLRU+KtVqxZPPfUUkydPZuzYsSQVny42JyfspOJEGaz96quvcsMNNxS+TkpK4pVXXuFPf/oTO3fu9DAyY0xFZUmFMcYYT0WqpaK4Ll26MHbs2ON3+Hd/UnVeB6FDhw5hL74XT1SVd999lxEjRhTZXr9+fZ555hl+85vfkBvke2KMiRwRGSoi60Rkg4jcG2B/PxFZLSJ5IjKi2L58EfnafXzgt72FiPzPrfNNEakSrfgtqTDGGOOpSLdUlCk31xmcnZTkDNbOyQmqWGJiIlWqVKnwMyUtX76c7t27Bxwcf/bZZ3PVVVcxYcIEDyIzpvISkURgGnAx0B4YJSLtix22BbgReC1AFUdVtav7uMxv++PAk6raGtgP3Bzx4F2WVBhjjPHUrl27vEkqkpOPvQ5S9+7dWb16dZQCi43p06dzyy23lLj/lltuISMjgzfeeCOGURlT6Z0NbFDVjaqaA7wBXO5/gKpuVtU0IKhp6EREgAuAd9xNrwBXRC7koiypMMYY46ndu3dHpftTiXJznUdystP9KciWCqj44yr279/Pli1b6Ny5c4nHiAjPPfccL7zwAmvXro1hdMac8JJEZKXfw79/ZiPgF7/XW91twarq1vmFiPgSh7rAAVX1zZsdap0hsaTCGGOMp3bv3l10cbpoy8kp2lJRiZKKuXPnct1115V5XNWqVZk0aRJz5syJQVTGVBp5qtrT7/FSBOtupqo9gWuBp0SkVQTrDoolFcYYYzyVm5tLlSpRGzsY6IRhd39q1KgR27dvj1JgocvMzGT8+PF07tyZLVu2lHqsqvLGG29wzTXXBFV3x44dT4iB6cZUENuAJn6vG7vbgqKq29yfG4FUoBuwD6gtIr4p8EKqM1SWVBhjjKlcfEmFL5EJcaajM844Iy6mXU1JSWHw4MEMHDiQ1157jauvvprdu3eXePyKFSto3749NWrUCKr+unXrsm/fvkiFa4wp3QqgjTtbUxXgGuCDMsoAICJ1ROQk93k9oC/wnaoqsBjwzRQ1Gng/4pG7LKkwxhjjmfz8fBISYvynyDelbBhjKsD7LlCHDh1i3LhxvPzyy3z44YcMHz6cjh078uSTTzJixAgOHjwYsFxZA7QDqVatGkePHo1E2MaYUrjjHsYDnwLfA2+p6loRmSQilwGISC8R2QpcBbwoIr5BT+2AlSLyDU4SMUVVfc2M9wB3icgGnDEWM6N1DZZUGGOM8czevXupV69ebE/qSyoSE4+9DsF5553HokWLohBY2T755BOGDBnCsGHD+Ne//kXdunUL9/Xu3ZuHHnqIq6666rhEICMjgx9++IGePXuGdL6zzjqLdevWRSR2Y0zpVPVjVT1TVVup6qPutr+o6gfu8xWq2lhVq6tqXVXt4G7/r6p2UtUu7s+ZfnVuVNWzVbW1ql6lqtnRij9qSYWIzBKR3SLyrd+2U0VkgYisd3/WKaHsaPeY9SIy2m97qrsoiG9xj9Pc7Se5C3pscBf4aB6t6zLGGBM5MZ/5CZyWCdWwkwpfS0VBQVCzOkZEZmYmN910E2+99Rbz5s3jsssuC3jc4MGDGTt2LNdee22RBex8YymcGSaD1759e5sByhgTlGi2VLwMDC227V4gRVXbACnu6yJE5FRgInAOzpy9E4slH9f5Le7h6zx6M7DfXdjjSZyFPowxxsS5mK9RAU4Soeosfgchd39KSEjgnHPO4fPPP49CcIG99957tG3bllmzZlGnTsDv4wqNGDGCSy65hDFjxhQmPsHO+lRc+/btbbC2MSYoUUsqVHUpkF5s8+U4C29AyQtwXAQsUNV0Vd0PLOD45KQ4/3rfAQZJqF/HGGOMiTlPWip83Z98SUV26L0BRo4cydtvvx3hwEqWlpbGgAEDgj7+lltuoUOHDtx555188803NGvWjNq1a4d83g4dOlhSYYwJSqzHVJyuqjvc5zuBQH9Jylr8Y7bb9ekhv8ShsIw70OUgzmAUY4wxccyzlgpfUiESVlLRp08fvvjii5h1gfr222/p0KFDSGUmTJhA1apVufLKK0MeoO1Tr1499uzZE1ZZY0zlklT2IdGhqioiGmKx61R1m4jUBN4FfgO8GkoF7uqFYwGSkpJITU0NMYTIyMzM9OzcpuKz+8eUV7zcQytXriQ5OTmmsTRIS+P0/fs5tG0bdTIz2bF2LdvDOH+jRo2YNm0anTp1inyQxWzfvp2VK1eGXG7o0KEkJSWRm5sb9nt85MgR5s+ff9xaIvFyD5mKy+6hE0usk4pdItJAVXeISAMg0ITa24ABfq8b4yzi4b+wR4aIvIYz5uJVji0YstVd4KMWzoIfx3FXL3wJoHr16hpKc3IkpaamhtSUbYw/u39MecXLPfTqq68ydOhQWrduHbuTpqVB3brUbt4cNmygZtOmnBnGe5GcnMzbb7/NH//4x4iH6G/Pnj20bt067N/XwIEDy3X+Pn36cMYZZ9C5c+ci2+PlHjIVl91DJ6WSVbYAACAASURBVJZYd3/6AGfhDSh5AY5PgSHuQh51gCHApyKS5C7ogYgkA5cCvpml/OsdASxyF/wwxhgTxzwZU5GVBQkJzkMk5IHaPrHqArVmzZqYtIaUxGaAMsYEI5pTyr4OfA60FZGtInIzMAUYLCLrgQvd14hITxGZAaCq6cBfcVYWXAFMcredhJNcpAFf47ROTHdPNxOo6y7scRcBZpUyxhgTfzIyMoJe4TlisrKc6WRFypVUxGoWqDVr1hzXShBLNgOUMSYYUev+pKqjStg1KMCxK4Exfq9nAbOKHXMY6FHCubJwVhc0xhhTwcR8sr7s7KItFSGuU+HPNwtU3759IxhgUWlpaVx88cVRq78sHTp04LnnnvPs/MaYisFW1DbGGOMJT3qpqh7r/lTOlgqITReon376iVatWkWt/rLUr1+fXbt2eXZ+Y0zFYEmFMcYYT2RkZFCzZs3YnjQ/H/LyjrVUQLmSimh3gcrPzwcg0bf6twdEhCpVqpAdxtS7xpjKw5IKY4wxnvB04bsItVRAdBfC27hxo6etFD5t27Zl/fr1XodhjIljllQYY4zxhGcL3+XnH0sqEhKclotyCKULVGZmZkh1p6WleTrzk4/NAGWMKYslFcYYYzzhWUtFfr6TUIAzC1Q5WyqC7QK1fPlyGjVqxIEDB4Ku2+vpZH1sBihjTFksqTDGGOMJT1oqcnKc7k++pEKk3C0VUHYXqG+++YYJEyZw880389lnnwVdb1pamqfTyfp06NDBkgpjTKksqTDGGOOJuGmpKMeUsj6ldYHasGED48aN480332TEiBGkpqYGXe+ePXuoX79+ueMrr9NPP52dO3d6HYYxJo5ZUmGMMcYTno+pgIiMqXCqCdwFatu2bVx//fW8+uqrNG7cmF69erFixYqg6jx8+DDVq1cvd2yRICIkJyeTU86uYsaYE5clFcYYYzzhWUtFXh4kuWu/JiREpKUCju8ClZ6ezsiRI3nhhRc488wzAUhOTqZGjRqkp6eXWd/atWvp0KFDRGKLhDZt2rBhwwavwzDGxClLKowxxnhi79691K1bN7Ynzc11HsnJzuvExIi0VEDRLlCZmZmMGDGCKVOm0K1btyLH9e/fP6hxFfEynsLHZoAyxpTGkgpjjDGeKCgoiP2ibjk5x7dURCip8HWBWrJkCSNHjuRPf/oT559//nHHDRgwgMWLF5dZX7zM/ORjg7WNMaWxpMIYY0zl4ev+FIWWCnC6QP3617/m+uuvZ9iwYQGP6dGjB6tWrSqzru+++4727dtHLLbysmlljTGlSfI6AGOMMZVPTk4Oyb4P9rFUPKmIYEsFOF2gPvzwQ/r27VviMUlJSdSqVYu9e/dSr169gMeoKllZWVStWjVisZVXgwYN2L59u9dhGGPilLVUGGOMibndu3fHfuYnOJZU+LpdJSQ461aoRqT6hISEUhMKn/79+7N06dIS9+/YsYOGDRtGJKZIERGSkpLIjdDAdmPMicWSCmOMMTHnycxP4IypUC06pkLVmWY2hgYOHFjquIp4G0/h07p1a3766SevwzDGxCFLKowxxsScJ2tUgNNSoVp0nQoPkopu3brx1Vdflbg/3mZ+8inPDFDbtm3jiSeeoE+fPnz//fcRjswY4zVLKowxxsScZy0VvqTC1/1JxJOkIjExkbp167J79+6A++O1pSLUGaDS09OZPn06Q4YM4Q9/+AONGjVi1KhRfPHFF1GM0hjjBUsqjDHGxJynLRUFBUXHVKhGdLB2sPr378+SJUsC7tu8eTPNmjWLcURlC3YGqI8//pgrr7ySq6++moKCAl5//XX+85//cM0113DuuefyzTffxCBaY0wsWVJhjDEm5jwdqJ2f73n3Jyh5XEVubi6JiYkkJMTfn+hGjRqxdevWUo9JS0vjqaee4umnn2bBggWMGzeuyCKHHTp04Ntvv412qDGxZs0aNEKD/I2p6OLvfyxjjDEnPM+6P2VnH9/9CTxJKrp06RLwG/sff/yRtm3bxjyeYIgIiYmJ5JXQslNQUMCdd97J888/T9OmTQMeU61aNbKysir8h/E9e/ZwwQUX8Mgjj3gdignStm3b+Oc//+l1GCcsSyqMMcbEnGfdn7KznUTCl0z4ppT1IKlISEjgtNNOY+fOnUW2x+t4Cp9WrVqxcePGgPtmzZpFv379aN26dal1NGvWjC1btkQjvJh5+eWXefrpp0lPT+fRRx/1OhwThK1btx73781EjiUVxhhjYi4rK4tq1ap5ceJjrRTg2UBtnwEDBpCamlpkW1paWlwnFSXNALV3715mzpzJPffcU2YdXbp04euvv45GeDFRUFDAv//9b4YPH87TTz/N1q1b+cc//uF1WKYMu3bt8qaFtJKwpMIYY0zlUTyp8HBMBQQeVxHvLRUlzQA1YcIE/vrXvwa1CnjXrl0r9GDtRYsW0bdvX0466SREhGnTprFu3Tqefvppr0MzpbCkIrosqTDGGBNTBQUFiK/7UaxlZx8bpA3HukF5tEp0x44dWbNmTZFtBw4coE6dOp7EE4xAM0AtW7aMrKwsLrzwwqDqiKeWivnz5/Paa6+FVOall15i7Nixha8TEhJ48cUXWb16Nc8//3ykQzQR4lm3y0rCkgpjjDExtX//fu8+NGdlFU0qfM9zcjwJJyEhgQYNGrBt2zYADh48yCmnnOJJLMFq0qRJkfEQubm53HvvvTzxxBNB13H66aeXuEZHLKkqkydP5qmnniIjIyOoMjt27CAjI4M2bdoU2Z6YmMisWbNYtmwZ06dPj0a4ppw8myCikrCkwhhjTEx59odd1UkeirdU+LZ7ZODAgYXjKuK96xM4M0AlJCSQ73YZe+qpp7j66qtp0KBBSPXUrFmTQ4cORSPEoM2bN49zzjmH22+/nalTpwZVZtasWdx8880B9yUmJvLKK6+wYMECXn755QhGaiLBuj9FlyUVxhhjYsrThe/y848fUwGeJxW+cRVr1qyhc+fOnsUSrJYtW7Jz5062bNnChx9+yO9///uQ6+jSpQtpaWlRiC44qsoTTzzBXXfdxahRo1i0aFGZMwPl5+fzf//3f1x++eUlHpOcnMycOXOYM2cOGzZsiHTYphz27NlDvXr1vA6jRCIyVETWicgGEbk3wP5+IrJaRPJEZITf9q4i8rmIrBWRNBG52m/fyyKySUS+dh9doxW/JRXGGGNiyrOWCl9S4T+eIw6SCv/ZlOJ95ief9u3bs2nTJu666y6mTp1Kon+iFiSvx1UsWbKEtm3b0qBBAxISEnjooYeYNGlSqWU+/fRTBg0aRHJycqnHValShaFDhwa1+riJnfz8fJKSkrwOIyARSQSmARcD7YFRItK+2GFbgBuB4oOAjgA3qGoHYCjwlIjU9tt/t6p2dR9R+0cXtaRCRGaJyG4R+dZv26kiskBE1rs/A3aqFZHR7jHrRWS0u+1kEflIRH5wM7EpfsffKCJ7/LKwMdG6LmNMADk5ThcSY4LgWUtFTo6zJoV/UuHxQG0nBCkcp7Bu3bq4XfjOX4cOHXj77bc57bTT6NWrV1h1eD0D1N///ncmTJhQ+HrIkCFs3ryZH3/8scQy06dP55Zbbgmq/tatW1tLhQnF2cAGVd2oqjnAG0CRJjFV3ayqaUBBse0/qup69/l2YDdQPzZhHxPNloqXcbIlf/cCKaraBkhxXxchIqcCE4FzcN7giX7Jx1RVPQvoBvQVkYv9ir7pl4XNiOylGGNKlJsLjz8OS5d6HYmpIDxvqQg0UNvDpAKOdYHKzc2lSpUqnsYSjA4dOvDLL7/w2GOPhV1HmzZtSv0AH01ffvklp512Gs2bNy+yffLkyTz44IMBy/zyyy/k5+cfV6YkrVq1sqQijuTm5pbZwuSxRsAvfq+3uttCIiJnA1WAn/w2P+p2i3pSRE4qX5gli1pSoapLgfRimy8HXnGfvwJcEaDoRcACVU1X1f3AAmCoqh5R1cVu3TnAaqBxVII3xgQvKwt27ICUFK8jMRWEp2Mq4rClApyk4pVXXqFZs2aexhGsZs2aMXv2bGrXrl32wSXwdUPJy8uLVFhBmzJlCvfdd99x27t3706VKlX44osvjts3c+ZMxowJviNEy5YtS1x53MTe7t2742E62SQRWen3GFt2keCJSAPgX8BvVdXXmnEfcBbQCzgVKHt1yjDFumPZ6aq6w32+Ewj0VVWZmZrbT+xXgP8qM8NFpB/wI3CnqvrX4V92LDAWnP/Qiq9kGiuZmZmendtUfPF0/yQdOkTLrVvJ27ePjXESkymbl/fQd999x/r168scFBtpVXfupNnWrSQeOcKRzZsBSDp4kFoZGWxevZp9Xqzw7VJVvvnmG9q0aRM3/7bLkpiYWO5Ya9asyZw5c4L+9j8SNm7cyP79+9mxYwc7duw4bv+wYcMYP348//jHPwrXU8nPz+ett97i/PPPD+mad+zYUWF+n16I5f9D69evJycnx+vfR56q9ixh3zagid/rxu62oIjIKcBHwAOqWpgV+33uzhaR2cCfQws5eJ6NVlFVFZGQO2GLSBLwOvCMqvq+Avg/4HVVzRaRcTitIBeUcN6XgJcAqlevrgMGDAgn/HJLTU3Fq3Obii+u7p/du+HDDyE5mabxEpMpk5f3UGJiIpdeemnsF8Bbvx4++shZAM/3ITY9HTZtolPbtuDx/TtkyBAuv/zy+Pm3XYZI3ENr167lpJNOiuk1T58+nSeffJKuXUueBOfLL7/k8OHDXHrppQC8//77XH311QwaNCikc9WtW5e+ffvGe7cbz8Ty/6GsrCx69OgRz/++VgBtRKQFTjJxDXBtMAVFpArwb+BVVX2n2L4GqrpDnP9wrwC+DVRHJMR69qddbtOMr4km0Mo3ZWVqLwHrVfUp3wZV3aeq2e7LGUCPiEZtjClZXp7TpSQry+tITAXiyYraOTnO/eo/+0tCgufrVPg8++yzXHTRRV6HEVOxngFqw4YNZGZmlppQADzwwAP87W9/K1yLY8aMGSWuTVGa5s2b8/PPP4cVq4mseF+jQlXzgPHAp8D3wFuqulZEJonIZQAi0ktEtgJXAS+KyFq3+EigH3BjgKlj54rIGmANUA+YHK1riHVS8QEw2n0+Gng/wDGfAkNEpI47QHuIuw0RmQzUAu7wL+BLVFyX4fwyjDGx4Esqjh71OhJTQahXM4Xl5jr3q/+3xgkJzriK7OySy8VIvXr1wpqatSLr3LlzTNeqmDJlCvfee9wcMcepV68ew4YN45VXXmHTpk0kJyfTuHHowzhtBqj44dlYrhCo6seqeqaqtlLVR91tf1HVD9znK1S1sapWV9W67hSyqOocVU32m7CocOpYVb1AVTupakdVvV5VM6MVf9S6P4nI68AAoJ6bVU0EpgBvicjNwM84mRUi0hO4VVXHqGq6iPwVpxkIYJK7rTHwAPADsNr9lus5d6an29wsLg9ncPiN0bouY0wxeXnOjDrWUmGCcPjwYU4++WRvTp6b6zz8WypE4iapqIxOOeUUMjIyYnKuX375ha1bt9KnT5+gjr/jjjsYOHAgaWlpQU8jW5zNABU/du/ezcCBA70O44QWtaRCVUeVsOu4DomquhIY4/d6FjCr2DFbgYDt5ap6H87odmNMrPmm6YyD7iMm/nk2nSyU3lJh969nTj/9dHbu3MkZZ5wR1fP84x//4O677w76+JNPPplbbrmFxx57jCeeeCKsc7Zu3TrgTFIm9uK9+9OJwFbUNsaUT16e8/B4Sk5TMXg6rWOgpMI3tsOSCs906dIl6ovg7dq1izVr1nDBBQHncCnRb3/7W+bNmxd2tzRrqYgfFaH7U0VnSYUxpnx83Z8sqTBB8LSlwreitv8HRGup8FwsVtZ+8sknufPOO0OeICAxMbFcK5zXrl2bgwcPhl3eRE52djZVq1b1OowTmiUVxpjysZYKEwJPvy3MzXVmevJPKnxjKiyp8Ey0Z4Dat28fy5cv51e/+lXUzlEaESmcRcqYE5klFcaY8vG1VPh+GlMKz8dUFE8qrKXCc02bNmXLli1Rq//pp5/m9ttv92YaY6BJkyZs2xb0GmYmCgoKCkhIsI+80WbvsDGmfHzJhC+xMKYUnrdU5Oc7iYSPJRWeExGqVq3K0ShMS33gwAFSUlL49a9/HfG6g2XTynpv37591K1b1+swTniWVBhjysf3Qa2gwJIKUybPx1QE6v4E1n3PYx07dmTt2rVlHxiiZ599lvHjx3v6LXWrVq346aefPDu/sUHasWJJhTGmfHxjKqylwgRh9+7d1K9f35uT5+Y6ya//B0wbUxEXojGuIiMjg3nz5jFy5MiI1hsqa6nwnqdfZlQillQYY8rHl1BYS4UJQl5eHsn+U7rGUna201JRPKlISLCWCo9FYwao559/nnHjxnm+SrklFd6zNSpiI2qL3xljKgkbU2EqiuzsY2Mo/CUmWlLhsfbt20e0+9Phw4f597//zWeffRaxOsNVr1499uzZ43UYldquXbto1qyZ12Gc8KylwhhTPv6zPtkHM1OKvLw8b781zsoqOp7CJyHBEmKPnXTSSeTk5FBQUBCR+l588UXGjBnjXauYH9+sU6rqcSSVl42piA1LKowx5ZOX53R9sn7ppgx79+6lXr163gVw9GjRrk8+1v0pLrRo0YLNmzeXu56jR4/y1ltvccMNN5Q/qAg544wz2LVrl9dhVFo2piI2LKkwxpSPb/YnEeebYGNK4Pkf9uzsklsqLKnwXKTGVcyYMYMbbriBKlWqRCCqyLAZoLxlYypiw5IKY0z5+Lo/iTgf2owpgaddEFSPjakoLjHRuj/FgUjMAJWdnc3cuXO56aabIhRVZNhgbW9lZGRQo0YNr8M44VlSYYwpH/+WCksqTCk8bakoKHDu1ZK6P1lS4bkuXbqUu6Xi5ZdfZtSoUVStWjVCUUWGJRXe82pF9crEkgpjTPn4uo1Y9ydTBs9X0y6+RoWPdX+KC/Xr12fv3r1hl8/NzWX27NnccsstEYwqMqz7k3dsgHxwRKSPiEwTkTQR2SMiW0TkYxH5g4jUCqYOSyqMMeXjG5ytakmFKdXu3bu9SypycpwWtZK6P/lmMDOeql27NgcOHAir7L/+9S+GDx/OySefHOGoyq9hw4Zs27bN6zAqpUOHDlGrVlCfiSstEZkHjAE+BYYCDYD2wINAVeB9EbmsrHpsnQpjTPlkZ0NSktN9JNTZnw4dggMHoGnT6MRm4oqngyV9LRWBukDYmIq44esC1b9//6DLZGdnM2vWLGbNmsXixYujGF34EhIS7Btzj9h0skH5jaoWbybMBFa7jydEpMyp+6ylwhhTPv4z6oQ6puK//4UpU6yFo5LwtKXCN/YnUEuFiJNwRGiNBBO+888/nwcffJA5c+aQkZFR6rFHjx7lmWeeoX///uTm5rJ06dK4Hoxbp04d0tPTvQ6j0vF81rkKwJdQiEh1EUlwn58pIpeJSLL/MaWxpMIYUz45OU5LBYSeHGRlwaZNYH9oY+7IkSMxP6enM7D4kopAEhKc7nuWVHhu6NChvPnmm+zdu5fLLruMkSNH8t5773H06NHCYw4fPswTTzzBgAEDSE5OJjU1ldtuu41q1ap5GHnZWrdubeMqPGDTyYZkKVBVRBoB84HfAC8HW9iSCmNM+eTkOC0V4QzUzs11WjdskGxY5s+fz6uvvhrSKsTbt29n5MiR3HXXXVGMrGSezcDiG1MR6Py+pMK6QMWFhg0bcscdd7B48WKmTJnCDz/8wODBgxk9ejQTJ05k0KBB1K5dm88++4zf/e53cTfTU0lsBihvWFIRElHVI8CvgedV9SqgQ7CFLakwxpRPbu6xpCLUb7/z8pzyft9CmuDNmzePlJQUBg4cSGpqaqnH5ufn8/zzzzNy5EjuuOMOqlevXuTb32hTVW+ndMzNde635OTj9/mSChusHXdatmzJ/fffz7Jly5gwYQJdu3Zl2bJl3HzzzXG1uF0wbAYob9iYipCIiPQBrgM+crcFWDE0MEsqjDHhKyhwPqj5kopQP6T6PuhZUhGW3bt38+ijj/LGG28wZ84crrzySn744YfjjktLS2Pw4MEcPHiQRYsWce6559KqVauIrF4crO+//55WrVrF7HzH8d1rSQHmJxGxpKIC6NChA1deeSVJgX6HFYC1VHjDxlSE5A7gPuDfqrpWRFoCQc9+UDH/ZRpj4kNenpNYJCWF1/0pJ8epwwZqh8U38LlKlSrMmDGDtLQ0/vSnP9GiRQsmTpxI9erVeeSRR/j222+ZPn16kQ/1bdu2ZdWqVfTu3TsmsaakpHDhhRfG5FwB5eY6j0DTjVpLhYmBpk2bsmXLFq/DqHSs+1PwVHUJsEREaohIDVXdCNwWbHlrqTDGhM9/QbFwVtT29XO3pCIsOTk5RbqAdO7cmQ8//JBhw4ZxxRVXMGDAALp06cKHH354XCvBmWeeyapVq2IWa0pKCoMGDYrZ+Y5jLRXGY0lJSeTZuJ2YS09Pp06dOl6HUSGISCcR+QpYC3wnIqtEJOgxFdZSYYwJX16e80HM1/0p1KQiK8tJSiypiBgR4eKLL2bw4MHk5uaWOCNOo0aNWL9+fUxiysvLY+/evd5+W+hrFbMxFcZD1atXJzMzM66nvj3RqCoJgaaSNoG8CNylqosBRGQAMB04N5jC9i4bY8Ln6/4UblKRnW1JRZjy8/NL/UOZlJRU6hSbCQkJnHTSSSEP1j5y5EhIs00BrFy5kl69eoVUJuJ8U8paS4XxkE0ra+JcdV9CAaCqqUD1YAtbUmGMCZ8vqRBxEotQk4PsbOv+FKZ9+/ZRr16ZC5yWyrd6cSjuvvtu5syZE1KZhQsXejueApykQvXYQo3+bEpZEyM2A1RsHT16NO7XL4kzG0XkIRFp7j4eBDYGWziqSYWIzBKR3SLyrd+2U0VkgYisd38G7OgmIqPdY9aLyGi/7T1EZI2IbBCRZ8SdozDYeo0xEVQ8qQinpUI19KloTURWp+7Ro0fI4ypWrFjBe++9F1KZJUuW0K9fv5DKRJxv/E+gpMLXUmHrpZgosxmgYsumkw3ZTUB94D33Ud/dFpRot1S8DAwttu1eIEVV2wAp7usiRORUYCJwDnA2MNEvSXgBuAVo4z589ZdZrzEmwoonFTk5wZdVdY4vKLApZcMQiRlNevbsGVJSsWvXLho2bEhGRgaHDh0Kqszhw4cpKCigZs2a4YYZGaUlFb5uZKHcv8aEIdikQlVjEM2Jz2Z+Co2q7lfV24CBQH9VvV1V9wdbPqpJhaouBdKLbb4ceMV9/gpwRYCiFwELVDXdvZgFwFARaQCcoqpfqPMv7lW/8sHUa4yJJF8/9YSE0JOK/HwnKbGWirBEoqWidevWIQ3WXrp0Kf369ePSSy/lo48+KrsAsGzZMs4///xwQ4ycnBznXgs0DsWSChMjLVq0YNOmTaUes3TpUvr06UOWdQstN1ujIjQi0ktE1gDfAGtE5BsR6RFseS/GVJyuqjvc5zuBQL/tRsAvfq+3utsauc+Lbw+2XmNMJOXlHVv8LjExtO4jvlYOSyrCEomkItTB2kuWLGHAgAEMHz486C5QcTGeAsru/gSWVJioO+mkk8gupZtobm4u999/PyNGjOCee+6JYWQnJmupCNlM4Peq2lxVmwN/AGYHW9jTKWVVVUUk4m18pdUrImOBseDMjpKamhrp0wclMzPTs3Obii9e7p/qmzbRdMcONDGR5IwMyM9nTZBxJR45QsstW6hz5Ah71q1jUxxcT0Xy5ZdfkpCQEPZ94LuHTj31VGbPnk379u3LLLNkyRKuvPJKDh06xIYNG5g3b16ZgyDnz5/PkCFDPL9fz0hL44z0dA5s2XJca0WVffuomZnJ+v/9j0OHD3sUYcUTL/8PVTRHjhxh/vz5RdaY8Xn77bfp2rUrPXv2ZP78+Tz22GOce25Qs3lWSNG+h7744gvOPPNMu0+Dl6+qn/leqOoyEQl6BgsvkopdItJAVXe43Zl2BzhmGzDA73VjINXd3rjY9m0h1IuqvgS8BFC9enUdMGBAoMOiLjU1Fa/ObSq+uLl/ateGjz6C6tVh3z7IzAw+rvR0+OADOPlkmp12Gs3i4XoqkDlz5jB06FDatGkTVnnfPbR9+3b2799f5u9t7969tGjRonABu1GjRnH48GEuvvjiUss0adKEwYMHhxVjRH3/PdSpQ+2WLY/fV7UqbN1K906doH//2MdWQcXN/0MVTI8ePWjatClnnXVWke07duwgLS2NxYsXk5SURI8ePRgyZAijR4+mUaNGJdRWsUX7HnrvvfcYMmQIXbt2jdo5TjBLRORF4HVAgauBVBHpDqCqq0sr7EX3pw8A32xOo4H3AxzzKTBEROq4A7SHAJ+63ZsOiUhvd9anG/zKB1OvMSaS/Ls/JSUdGyMRbFnfatyhzhplItasH+xgbd94Cp/hw4fz7rvvllpm0aJF3q6i7e/o0cDjKeDYdpv9ycRAq1atAg7WnjBhAo8++ihJ7loqNWvW5Nlnn+Xmm28m39ZQCYuNqQhZF+BMnMmSHgbaAd2AJ4CpZRWO9pSyrwOfA21FZKuI3AxMAQaLyHrgQvc1ItJTRGYAqGo68FdghfuY5G4D+D0wA9gA/ATMc7cHrNcYE0W+FbWTk50PZgUFwS8g5hvkLWKzP4Xh0KFDEZlRKdjZaFJTU+nv9y1+q1at2LZtW6mDSRcuXBg/SUV2duDxFGBjKkxMBfo3t3TpUoDjpl7u2bMngwcP5vHHH49ZfCeSPXv2UL9+fa/DCJqIDBWRde6yCYFmR+0nIqtFJE9ERhTbF9JSDIGo6sBSHheUFX+0Z38apaoNVDVZVRur6kxV3aeqg1S1jape6EsWVHWlqo7xKztLVVu7j9l+21eqakdVbaWq491ZoCipXmNMFPmvUhzqAmK+gbMi9mEuTKX8bQhasIO1v/rqK7p161Zk20UXB/XJ8QAAIABJREFUXcT8+fNLLPPtt9/SsWPHcscYEVlZpbdU2DoVJkaKr6qdl5fH/fffz9///veAx995553897//5fPPP49ViCeMvLy8wpafeCciicA04GKgPTBKRIoPdtsC3Ai8VqxsOEsx+Je/XkRKzAlEpJWInFfWNdiK2saY8Pl3fwo1qfCVFbHuTyGK9Bz2nTt3LnVl7fT0dGrVqnXcH+fhw4fzzjvvBCyzceNGmjdvTkJJH+RjrbSWCuv+ZGKoZcuWRVoqpk2bxvDhw2nQoEHA4xMSEpg5cyZ33XUXBw4ciFWYJvbOBjao6kZVzQHewFkuoZCqblbVNKCgWNlwlmLwVxf4yl20+g8iMlJEbhCRSSKyBPg7sKusC4iT/+2NMRWSbwyFb0rZgoLQWiry88NbibuSO3z4cEQXkytrZe2lS5cGXGvirLPOYuPGjeQEaGlKSUmJn65Pqs49VlKC41tR2+5DEwM1atTgsDvL2M6dO3nrrbcYP358qWVOP/10HnnkEW699VZbGC9Iubm58dhKkSQiK/0eY/32lbScQjDCWYqhkKo+DXTHGaBdHxjkvt4G/EZVh6tqmYsaWVJhjAmfL6lISAi9C0lurlM+Odm6P4Vo165d5V6jwl9ZSYVvfYpABg0aREpKynHbU1JS4mN9Cii6SGMgCQnWDc/EVFJSEnl5edxzzz089thjJCcnl1lmyJAhNG3alFmzZsUgwoovTsdT5KlqT7/HS14H5KOq+aq6QFUfVtVxqnqHqr6oqluCrcOSCmNM+HytEr6kIpSWCl/3pypVrNtJiCKx8J2/Nm3alDpYe9WqVXTv3j3gvhEjRhzXBaqgoIAtW7bQrFmziMVYLr7xO6W1VFg3PBNDTZs2Ze7cueTn5xeZAKEskydP5r333mPq1KkUFBTvAWP8VcCF77YBTfxe+y+bEG7Z0pZiiDhLKowx4cvNLX9LhS+psCb9oEX6j2Vpg7X3799PjRo1SvwmtWPHjvzwww/k+v3e09LS6Ny5c8TiKzf/mcYCsZYKE2OtW7fmvvvuK3FwdkmqVKnCBx98QEFBARdffDE///xzlCKs+CrgdLIrgDYi0kJEqgDX4CyXEIxwlmKIOEsqjDHh819rwpdUBPvBzPdBr0qV0Fo4TMRbKsAZrJ2Wlnbc9mXLlgUcT+EjIvTv358lS5YUbourrk9QdvcnX0uFJRUmRs4991wmTZpEw4YNQy6bmJjIhAkTePzxx7nuuuuYM2eOjbMIoKK1VKhqHjAeJ0H4HnhLVde6g6UvAxCRXiKyFbgKeFFE1rplw1mKoQgRSRCRkeW5BksqjDHhK55UQPBdSPzHVNh0niGJxh/LHj16sHLlyuO2L1mypMzuGcW7QC1atIiBAwdGNL5y8Z++OJBQ711jyumCCy5gzJgxZR9Yiq5du7Jw4UJWrVrFtddeS3q6zaTvL9Jjz2JBVT9W1TPdZRMedbf9RVU/cJ+vcJdoqK6qdVW1g1/ZkJZiCHDuAmBCeeK3pMIYE77iA7Uh+IXs/GeOsqQiJNFoqShpsPaKFSvo2bNnqWW7detGWloa+fn55OTkkJmZSd26dSMaX7nk5Fj3J3NCqlq1Kk8++SRjxoxh2LBhLFy40OuQ4kZFa6mIEwtF5M8i0kRETvU9gi0cd3NtGWMqEP8BsL6kopQVlgOWTUpyflpSEbRo/LFs06YN69cXnTHw4MGDVKtWjSpVqpRaVkTo27cvy5YtQ0To3bt3RGMrN+v+ZE5wgwYNonv37gwfPpxGjRrRrl07r0PyXAUcUxEPrnZ//sFvmwItgylsSYUxJnw5Oce+5Q2n+xMcSypsTEXQ9u7dG/GWAP/B2tWqVQNg+fLlnHdemYuoAk4XqLlz51K7du34Gk8BRbvaBWItFeYEUKdOHa6//no+//xzSyqomN2fvKaqLcpT3ro/GWPC579KcagDtX3jMZKSQitnKCgoiMqiTl26dCkyWDs1NbXE9SmK69WrFytWrGDp0qVBJyIxk5vrPEp6z3zdoqy1zFRwvn+HBrKysgq/IDHBEZFkkf/P3pmHSVFd//s93T0L+xYUFAUEFJAgRAVBkc0oMRh34gJoXFAQcYlJ1GgiJt/8JEaNQaPRgCKKChIUEQUVEEFRVBBcQBE3dh2W2Xq6p2fu74/qYnp6eqnu6WWW8z5PPdNdVbfqNH2ZuZ86m0wRkReC22QRid9EJYiKCkVRksfnq1qQJeOpCBUVTsOmlLRVegnPq1i7di0nnniio7Eul4uBAwficrnq3h9yvz++qBBRUaHUe3r16sVnn32WbTOU+ssjwPHAv4Pb8cF9jtDwJ0VRkscOf4Iqj0UiosKYqoWeigpHlJeXp8VLAZaomDZtGgBFRUXk5uaSl5fnePwVV1xRNxc0oT1RIiFizV8VFUo9x+Px4PF4KCsrIz8/P9vmZI3KykokWmEGJRYnGmOOC3m/TEQ+djpYPRWKoiRPqKhItPpTqKcCVFQ45IcffkhbnHBosvbq1as5+eSTExrft29fLrroonSYVjvsRO1YYszlUlGhNAiOO+44Pv7Y8TqwQbJ3717atnVctEipokJEutlvROQooMLpYBUViqIkT3l5dVEh4lxU2KFTtofD6bhGTjrKydqEJms76U9Rb1BRoTQiNK9Cy8nWgt8By0VkhYi8BSwDbnE6WEWFoijJYSdX10ZUuFxVokI9FY5I9x9LO1l7zZo1DBw4MG33ySi2V8yea5FQUaE0EAYMGKCiQkVFsqwCegBTgOuBY4DVTgerqFAUJTkqKqrX/rdFhVNxYFeOsqtGqahwRDo9FWDlVaxcuRKPx9NwYrLtymLR+lSA5lQoDYajjjqKrVu3ZtuMrKI9KpLmXWOMzxizIbj5gHedDlZRoShKctglYUOrPyUqKpJpmtfISfcTuOOPP55///vfDB48OG33yDhOPRXaK0VpAIgIzZs3p7CwMNumZA3tUZEYItJBRI4HmohIfxH5WXAbBjR1eh0VFYqiJEc0UeG0+lN4+JPmVDgi3Z6KHj16UFBQ0HDyKaCq0lgsT4WKCqUBEV4eurGh4U8JcwbwD6ATcD9wX3C7Gbjd6UW0pKyiKMmRSk+FCJSWps/WBkS63foul4urrrqKk046KW33yDh2orbmVCiNBDtZe/jw4dk2JSto+FNiGGNmAbNE5HxjzPxkr6OiQlGU5AgXFSLWwsyJp6Ky0hqvoiJhdu/eTfv27dN6j/vvvz+t1884fn98T4XbrSF4SoPhxBNP5Omnn862GVlDPRXJYYyZLyK/BI4F8kP23+1kvIY/KYqSHJFEhdvtTFTYT47tMS6XigqHlJaW0qxZs2ybUb/weqvEazTcbg1/UhoMhx12GDt37sy2GVmjsLCQFi1aZNuMeoeIPAr8GqvykwAXAp2djnfkqRCRQ4CTgcMAL/AJ8IExpjJRgxVFaSDYya+hT39drqpKO07HJho2pSiJUlYW20sB1hysrKw5pxWlntK+fXt++OGHtHs26yraUTspBhtj+orIBmPMVBG5D3jV6eCYvzlFZLiILAFeAX4BdAR6A3cAG0Vkqoi0rIXxiqLUVwIBy9sQitvtTFSEejlUVDjGGKN/KJPBLl8cC7fbCpEKn9OKUk9prE3wjDHZNqE+Y1dMKRWRw4ByrLW/I+J5Ks4ErjbGfBd+QEQ8wGjg50DSSR2KotRTbFERuljzeJx7KuweF4k2zWvE7N+/n9atW2fbjPqHU0+FLSpycjJjl6KkkRNPPJHVq1dz5plnZtuUjFJUVETLlvq8O0kWiUhr4F7gI8AA/3U6OKaoMMb8LsaxAPCi0xspitLAiCQqnHoq7PAnEWvzeJyXom3EpLucbIPFiaiwmzCqp0JpIJxwwgk8+OCD2TYj42iPiuQxxvwl+HK+iCwC8o0xB5yOdxQ4KiLtRGS6iHwkIh+KyIMi0i4ZgxVFaSAEAtbmCXk2kWj4U2g3bhUVcdGKJklQUWHNt3jhT6GeCkVpALRp04b9+/c3unAg/T2ZPCJyXdBTQbCbtktEJjkd7zQb7TlgD3A+cAHwA/B8grYqitKQsD0VoaLC43FWQae83DrPzg/weDSnwgHqqUiC0FC7WKinQmmAdO7cme++qxHB3qDRHhW14mpjzH77jTFmH3C108FORUVHY8xfjDFfB7e/Akl/YyJyg4h8IiKfisiNEY63EZEFIrJBRN4XkT7B/ceIyPqQrdAeLyJ3icj2kGONK4hQUTJNpIZidlfiyjiF4cLHOs3FaOSoqEiCSFXKIqGeCqUB0hiTtdesWUPv3r2zbUZ9xS0h1UBExA3kOh3sVFQsFZGLRMQV3MYASxI01DawD5bqGQAcB4wWke5hp90OrDfG9AXGAw8CGGM2G2P6GWP6AccDpcCCkHEP2MeNMYuTsU9RFIdEC3+qrIy/MLPH2gmxTsOmGjnq1k+C0J4osVBPhdIAaWyioqSkhFWrVjFy5Mhsm1JfeQ14XkRGishI4NngPkc4FRVXA3MAX3B7DrhGRIpEpDBBg3sB7xljSoPJ3m8B54Wd0xtYBmCM2QR0EZHwv6Qjga+MMd8meH9FUVJBpERte2EWLwTKDn+yBYl6KhzRaD0Vs2fDa47/rlXH73cmKmxPhTbAUxoQ/fv3Z926ddk2I2PMnj2bsWPH4tJeM8nyB2A5MDG4vQn83ulgR//qxpgWxhiXMSYnuLmC+1oYYxKt2/UJMCSY/N0Uq2ztEWHnfExQaIjIAKxufp3CzrkIS0GFMjkYMjVTRNokaJeiKIkQS1SUl8cea4uK0PCn8nJrrBKVRuup2LIFnnsuubFOw5+cCmJFqUc0bdoUr9dLZbyQ1AaAMYbZs2czfvz4bJtSbzHGVBpjHjHGXBDc/mOMcey+jVlSVkS6GGO+iXFcgMONMdsSMPhzEZkGLAVKgPVAuMH3AA+KyHpgI7Au9BwRyQV+BdwWMuYR4C9YNXX/AtwHXBHB5gnABACPx8OKFSucmp5SiouLs3Zvpf5TF+ZPmw8/5Ii9eynZuZOKQsth2eTHH2ly4AAbVqwgEKNOeMuNGzly9258QHkgQLO9e8ndt4/1y5Zh4lXpacRs3bqVjz/+OCVP4erCHHLKUZs3k79jB58lYW/+jh103r4dt9dL6TffRD0vb/dumhcV8emqVZR9/XXyxjYi6tMcasy0atWK2bNn07lz52ybUoNUzqG1a9fSqVMnPvjgg5RcrzEhInONMWNEZCPWOroawXSEuMRrfneviLiAl4APsao+5QPdgeFYIUh/BhyLiqBxM4AZACLyt/DxxphC4DfB4wJ8DWwNOeUXwEfGmN0hYw6+FpHHgUVR7v0Y8BhAs2bNzLBhwxIxPWWsWLGCbN1bqf/UiflTWgpt2tC2c2do3tzaV14OBw5wykknQawwHRF45RU48kho1w5KSsDvZ+jgwdCkSWbsr4e0atWKESNGpORadWIOOSEQgAULYP9+DknG3s2bYfFi6zpdukQ/z+WC3bs5qX9/0CRPR9SbOdTI+eKLLxCROvldpXIO3X///fzrX/+iS6z/50o0bgj+HF2bi8R83GWMuRC4EzgGeBh4G1iIlWOxGRhhjHk90ZuKyCHBn0dihTnNCTveOuiNALgKWBkUGjYXExb6JCKhbcTPxQqzUhQlXUQKK0kkp6Kysir8yWnYlNL4sHMiysqSS6IuL7e2eB4wex7rHFQaGI0hWXvz5s3k5eWpoEgSY8zO4M9vI21OrxPPU4Ex5jPgj7WwNRLzg83zyoHrjDH7ReTa4P0exUrmniUiBvgUuNIeKCLNgJ8D14Rd8+8i0g/LbfNNhOOKoqSS8AZ2YL2urHSWU2FM1ULPrhql8exRKSsrIz8/P9tmZB5bVPj9VoPEpk0THx8IxB9nJ3JrwQClgdGnTx8++aRhP2edPn06119/fbbNqLeISBERwp5snOZPxxUVITccDHQJHWOMecrp+FCMMUMi7Hs05PW7wNFRxpYANbp5G2PGJWOLoihJEghYwiBUVLjd1uIsXiO78LHqqYhLo6385PNViQq/P3FREV4UIBrqqVAaKDnB0t1+v5/cXMctB+oN+/fvZ8OGDUyfPj3bptRbjDEtAETkL8BOYDYgwKVAxxhDq+Eo209EZgP/AE4BTgxuJyRmsqIoDYpIosJ+HU9UhIdOOfVw1FGMMXzxxRf4fL603aPRiopQT0UyXgRbVNg9UaKhngqlAfPTn/60wXorZsyYwZVXXonEKxutOOFXxph/G2OKjDGFxphHgLOdDnbqqTgB6G2M1ntUFCVINFFhjPV0ORaRwp+cjKtD7N69mzfeeIPXX3+dzZs307p1awYMGMDUqVPTdr9GWU7WDl8qL09ufjgVFeqpUBowAwYM4P333+dnP/tZtk1JKYFAgHnz5vHWW29l25SGQomIXIrVj85g5TCXOB3stC7hJ0CHxG1TFKXB4vdbQiD06ZC9MIu3+AvPx3Dq4cgyq1at4pZbbmHIkCFMmDCBvXv3cuutt/LOO++waNEiXn/9dQ4cOJCWezdqT4Xt2Spx/LetivCiANHQEDylAdNQk7UXLlzIqFGjyMvLy7YpDYVLgDHA7uB2YXCfI5x6Kn4CfCYi72N11AbAGPMr53YqitKg8PmshVgyoiJS+BNYZWrrIFu3buV3v/sdbdu25bLLLuP//b//dzBO2cbtdjNp0iQefvhhbr/99pTbsHv3bo477riUX7fO4/NVzZfi4sTH2+FM8USFhj8pDZgePXrw5ZdfZtuMlPPoo4/y1FNJpfcqEQj2pnMc7hSOU1FxV7I3UGpSrk/ClIaA31+zS7HT8Cf7uD3eXvDVsfCn0tJS7rnnHlavXs29994bN3Tgoosu4pRTTmHKlCk0t3t3pIhG76kwJjnRmUhHbft8RWlguFwumjRpQklJCc2aNcu2OSlh3bp1dOjQgQ4dNJAmVYhIe6y2EV2oXpipRjPpSDgKfzLGvBVpS8ZgBc4880y8Xm+2zVCU2hFNVED8MCa/v/qTY3tcHfl/YYzhhRdeYMSIEfTo0YM33njDUSyyx+Ph6quv5j//+U/KbWrUORXl5dYcSTb8KTR/Jxoi1nnqqVAaKP3792fdunXZNiNlPPjgg9xwww3xT6xHiMgoEdksIltE5NYIx/NE5Png8fdEpEtw/6Uisj5kqwy2WUBEVgSvaR+L9XTqJaAV8AbwSsjmiJiiQkSKRKQwwlYkIoWxxirROeecc3jjjTeybYai1A6/v3roEzgXFWVlkRO860BOxaeffsovfvEL3nvvPZYuXcq4ceMSqioybtw45s2bl/IHB3v27KF9+/YpvWa9wBYVIsl7KioqnOVUiKioUBosP//5z3n++eezbUZK2L17N9u3b+f444/PtikpQ0TcWI2mfwH0Bi4Wkd5hp10J7DPGdAceAKYBGGOeMcb0M8b0A8YBXxtj1oeMu9Q+bozZE8OMpsaYPxhj5hpj5tub088Qr6N2C2NMywhbC6eNMJSajB8/niVLlqDFtJR6TW08FXY+ho294MuyqFiwYAHXX389//znP7n33ntp2TLxX3O5ubmMHz+eGTNmpNQ2v9/fOJMRfT4rsd/lgqKixMf7/c7Cn0SsrY6F4ClKqhgxYgRbt27l888/z7YptWL//v3ceeedXHNNg+txPADYYozZaozxY1VgCs9vOBuYFXz9AjBSaj71ujg4NhkWiciZSY51XP1JSSEtWrSgV69eLF++PNumKEry+Hw1n/7aT3vjPaUPFxV1IPxp/vz5PP744yxatIiePXvW6lpXXHEFTz/9dFr7VjQabE+F251corbPZ81JJzkVKiqUBoyIMG3aNP7whz9k25SEqays5M033+TSSy/lvPPOY8CAAZx77rnZNivVHA58H/J+W3BfxHOMMQHgADUbQv8aeDZs3xPB0Kc7I4iQUG7AEhbeZCKTVFRkiXPOOYeHHnoo22YoSnLYpTcjeSqcdNSOJCqSDW9JAXPnzuXJJ59k/vz5NE20Y3ME8vPzueiii5g1a1b8kx1QWVmJK96iuKFiexrc7uQ8FeGhdtHQ8CelEdCnTx86derEa6+9lm1THPHtt98ydepUBg8ezJIlS7jjjjtYtmwZV111Fe54IY11E4+IfBCyTUjlxUVkIFBqjAntdHipMeanwJDgNi7a+GAkkssY0ySZyCSn1Z+UFHP44YdTUVHB119/TdeuXbNtjqIkht1nIhy3O76osJNhw8OfsiQqnn32WZ599lnmzZtHfn5+yq47YcIERowYwW9+85sa5WcTpaCggHbtwh9GNRLs3J1kPRVlZfHzKaAqP0g9FUoDZ+rUqZxzzjmcdtppeDx1bxlYWlrKggULeOaZZ8jPz+fyyy/n9ttvr/Xv0TpCwBhzQpRj24EjQt53Cu6LdM42EfFgJVUXhBy/iDAvhTFme/BnkYjMwQqzqlaHV0R6GmM2iUjEiiTGmI9ifqogjfTRV91g8uTJPPzww9k2Q1ESJ7x5nY3LZW2xREWkEp9Ow6ZSzNNPP83cuXNTLigAmjZtyrnnnsucOXNqfa1GW04WquZSbUSFeioU5SDt27fn/PPPT0uVumQxxvD2229z5ZVXcsYZZ7B7926eeOIJ/ve///GrX/2qoQiKeKwFeohIVxHJxRIIC8POWQhcFnx9AbDMBBN0RcSF1bjuYD6FiHhE5CfB1znAaKyG1uHcHPx5X4TtH04/QN2TqI2I0047jbvuuqtB1Y1WGgmxREU8T0WksVkQFbNmzWLhwoU8//zz5ObmpuUekyZN4owzzmDs2LG1ctU32nKyUOVp8HiS82Ql4qkQ0T4VSqNg8uTJDB06lEsuuYQ2bdpkzY5du3Zx99138+qrrzJw4EAmT55Mv379Eqq411AwxgREZDKwBHADM40xn4rI3cAHxpiFwAxgtohsAfZiCQ+bU4HvjTFbQ/blAUuCgsKNVSr28Qj3nhD8Obw2n0E9FVlERBg7dixPP/10tk1RlMSwhUGkkrLxRIVd4jMUt9samyFRMXPmTF555RWee+65tAkKsIoyjBo1innz5tXqOo3aU1Faas2NnJzERYXdiFE9FYpSjdzcXG677TbuvvvurNnw8ccfc/fdd3Pcccfx1ltv8c9//pP+/fs3SkFhY4xZbIw52hjTzRjzf8F9fwoKCowxZcaYC40x3Y0xA0IFhDFmhTHmpLDrlRhjjjfG9DXGHGuMucEYE/YHOHWoqMgy48aN46mnntLyskr9wg5hCv/lb1fZibUwixX+lIF49rVr1/Liiy/yzDPPZMSlPmXKFB566CEqI+WgOKRReyq83ipPRaKiM5r4jYR6KpRGxllnncWmTZv44osvsnL/p556igkTJnD22Wen9eGOkjlUVGSZ5s2bM3DgQJYtW5ZtUxTFObEWa253bHEQKfzJFiMZ6FOxcOFCrr/++ozF6LZu3ZqhQ4fy0ksvJX2NRu2p8HotQZGTk7iocNr4DqrmsooKpZGQzRKzFRUVrF69mr59+2b83kr6UFFRB7juuuu0vKxSv4hW/QmsBVw8T0V4+BNYC8cMiIq3336bIUOGpP0+odx44421+j/eaD0VlZVVidbJzI9ocy0SdoUpDX9SGhF9+/blkEMO4fXXX8/ofVesWMGpp57aeEtl11HEYqyI/Cn4/kgRGeB0vH6bdYBu3bphjGHr1q3xT1aUukAgYC3WkvFURAudysCCrqCggGbNmqW80lM82rdvT7du3VizZk1S4xutp8IWBS4X5OZa8yORUNHQHhdOcLnUU6E0Ov7yl78wdepUAoFAxu75zDPPcOmll2bsfopj/g0MwurKDVAEOC5TqqKijqDlZZV6hS0qIi3W4omDQMDawsOPPJ6051S88cYbnHbaaWm9RzRuvPFG/vnPfyY19sCBA7Rs6bj/UMPB76+aZ263JRASEZ62KHGa+KmiQmmEHHLIIZx99tnceuutLFq0iFWrVvHJJ5+wbds2iouLU57zWVZWxhdffKGhT3WTgcaY64AyAGPMPsBxwouWlK0jjBw5kj//+c8UFxfTvHnzbJujKLGxRUWkxklud+yFWXm5NT5ckMTzcKSApUuX8tvf/jat94hG7969KS4u5ptvvqFLly4Jj2+UFVF8vipPRaioyMtzNj6aVywaLpc1NxWlkXHDDTcwZ84cNm3axP79+6ttxcXFlJWV8eqrr6ak/OyiRYsYPXp04/ydVvcpFxE3YPe+aA84rjKioqKOICKMGzeO2bNnM3HixGyboyixsYVBkyY1j7ndsRNq7bHhC8NkqvskgDGGzz77jF69eqXtHvGYMmUK06dP57777suaDfUKv9+aK3a+QzKeikDAWUlZiC+IFaWBkpuby+WXXx71+KJFi7jjjjtSElHx7LPPcv/999f6Okpa+BewADhERP4Pq8HeHU4Ha/hTHWLs2LHMnj1by8sqdR87hKk2nopI4U9pXNB99tln9O7dO6tPx37+85+zZs0aCgsLHY9p1N5LO/xJxJofiYoKW5REmqeRUE+FokRk9OjR7Nq1iw8++KBW19m7dy+FhYV07tw5RZYpqcQY8wzwe+D/ATuBc4wxjhstqaioQzRv3pzBgwdnvAqDoiRMvJyKioro1aGiCRJ7nNNqPQmydOlSzjjjjLRc2ykiwhVXXMHMmTMdj2m0SdpQJQpCcyoSaYBXXm5tKioUpdbcf//93HLLLVTU4nf0Cy+8wAUXXJBCq5RUICJt7Q3YAzwLzAF2B/c5QkVFHWPy5MlMnz4922YoSmzi5VQYE93rEK13gMsVe1wteeONNxg5cmRarp0Il156Kc8995zjP8yNtpwsWDkV5eVW5Sd7vpSUOB9vixKnPUncbhUVihKFzp07M2rUKB577LGkrzFv3jwuvPDCFFqlpIgPgQ+CP8M3x+4pFRV1jC5dutCkSRM+//zzbJuiKNGJJSpscRBcnPl8vurdpG1REb7Qs59Ep2FR5/V6KS0tpV2dPLzuAAAgAElEQVS7dim/dqLk5+czatQoXnzxRUfnN3pPhd9vzZVkREWoKHGCeioUJSY333wzs2fPZs+ePQmP/e6772jevDlt2zp+8K1kCGNMV2PMUcGf4dtRTq+joqIOcsMNN/Dggw9m2wxFiY7d/C5S+FOYqBg3bhzTpk2rPtaYmsmzafRUZKPhXSwmTpzII4884ujcRu2p8Put+RAqKhIJfyorS0xUqKdCUWKSm5vLX//6V37/+98nPPbZZ5/l4osvjn+iklVE5DwRuV9E7hORcxIZq6KiDjJ48GA2bdpEQUFBtk1RlMiUl0cWBmDtC3ocli9fTn5+PgsXLqSoqCj2WHtcGkTF0qVLOf3001N+3WQ59NBD6dy5M++//37ccxu9pyIQqAp/MiYxT0VZmTWnnIY/2XMwWj6QoiiMGDGCQCDA22+/ndC4l19+mbPOOitNVimpQET+DVwLbAQ+Aa4VEW1+V58REa655hr+85//ZNsURYlMNG8DHPQ4BMrKuPPOO7n33nuZOHEiDz30kHXcFhWR+lSEeDhSyXvvvcfAgQNTft3acNNNN/HAAw/EPa9Riwqfr0oU2POjuNj5eFtUJJKobUzaigUoSkPh3nvv5bbbbqPc4UOgjRs30r17d5pEKkOu1CVGAGcYY54wxjwBnBnc54isiAoRuUFEPhGRT0XkxgjH24jIAhHZICLvi0ifkGPfiMhGEVkvIh+E7G8rIq+LyJfBn7Xv0JJFLrjgAl566SX8iZRPVJRMYYc/xRAVT82cyfnnn8+hhx7KJZdcUuWtiBX+VFmZ8gZ427dvp3379uQ4fVqdIfr06UNhYSHfffddzPMaffiTHWbndltzJFFRES33JxJ2uWEVFYoSk44dOzJmzBj+9a9/OTr/mWee4dJLL02zVUoK2AIcGfL+iOA+R2RcVAQFwtXAAOA4YLSIdA877XZgvTGmLzAeCE8wGG6M6WeMOSFk363Am8aYHsCbwff1lpycHM4991zmzXNcHlhRMkccT0W538/iBQuYPHkyAB6Pp8pbYXc5jiQqIOUN8F5//fU6FfoUyvXXX1/lwQlj06ZN3HjjjWzbto2f/OQnGbasjhAqKjwea9Fvh9E5IVFRoZ4KRXHMpEmTeOmll9i2bVvM8yorK1mxYgUjRjh+4K1kGBF5WUQWAi2Az0VkhYisAD4P7nNENjwVvYD3jDGlxpgA8BZwXtg5vYFlAMaYTUAXEYn3qO5sYFbw9SwgoeSSusiECRN47LHHtBmeUveIIyp27tjBTZMmVfMO2N4Kb2Fh5CRv+31ZWUpNXbJkSdb7U0TjjDPOYPXq1RQHn76Xl5czb948zjjjDO666y7OOeccVq1ahcfporihEclTkUDjQEpKrHFOGx6qp0JRHOPxeJg2bRq//e1vY563atUqBg0ahDtSYQ+lrvAP4D7gT8AvgD8HN/u1I7Lxl+oT4P9EpB3gxYrXCq+B+zGW0HhbRAYAnYFOwG7AAEtFxAD/McbYBZMPNcbsDL7eBUQUISIyAZgA1n+IFStWpOpzJURxcbGje7du3Zrp06fTt2/f9Bul1Buczp90cejGjXTYt48D339fQxx4v/wSt89HnjE1bBw2bBjz5szhV/v3s//bb6st9nILCmhRXMyX77yTUMfpWFRUVLBx40a+/fZbvv3225RcM9UMGjSIiRMnAvDhhx8yePBgrrnmmoNlF99666203Dfbc8gJh65fT4e9ezmwbRsYQ9viYvZv2sRWh3YfuWEDrYuKKPzmG0fnN/nhB5ocOMCGt94i0MLxw7lGS32YQ0r6ycvLo3///gwcOJBTTz2Vww47rNrx++67j9GjR0ecKzqH6gbGmIN/aESkA1Y0kQHWGmN2Ob1OxkWFMeZzEZkGLAVKgPVA+GOhe4AHRWQ9Vgb6upBzTjHGbBeRQ4DXRWSTMWZl2D1MUHREuv9jwGMAzZo1M8OGDUvRJ0uMFStW4OTeHTt25I9//CNTpkxJv1FKvcHp/EkbX38NLVvS5qijqgkDYwyLli7l9M6dyfvpT+Gkk6oNO+WUU/hnjx40P+wwWnftWv2aTZrAtm38rFcvSNFn++CDDxg2bFh2/63iMHDgQCZNmsR5553Hk08+mbGneVmfQ07YvNmaZ/Zc+eILWrZrx5FO7V6yBNq3p22XLs7O9/uhqIhTTjoJ2rdPxuJGRb2YQ0raGTZsGMXFxSxevJj58+ezc+dOfvGLX3D++efTuXNnCgoKmDBhAhLBY6hzqG4hIldheSuWAQJMF5G7jTEznYzPik/dGDMDmAEgIn8DtoUdLwR+EzwuwNfA1uCx7cGfe0RkAZaaWonVSryjMWaniHTEajNe7znmmGPw+/1s3bqVo45y3H9EUdKLXUAg7I/Ehg0bOLRjR/KiJFx7PB6GnXQS299/n87hB+3qPikMf6rLoU82TZo04Yknnsi2GXUTr7d6+FJOjvOcm4qKqvFO0ZwKRUmK5s2bM2bMGMaMGUNpaSlLlixh6tSpbNy4kTFjxkQUFEqd5HdAf2NMAUAwqugdwJGoyFb1p0OCP4/ECnOaE3a8tYjY3YquAlYaYwpFpJmItAie0ww4HSucCmAhcFnw9WXAS+n9FJljypQpTJ8+PdtmKEoVfn+NfAqfz8fatWvpf/zx1o4o4qB/797s/vFHfOGiw75eCkXF8uXLGT58eMqup2SYcFHg8TjvU+HzWeIgUt5PNFRUKEqtadq0Keeeey7PPPMMa9eu5Q9/+EO2TVKcUwCEVsMoCu5zRLay/+YH1U85cJ0xZr+IXAtgjHkUK5l7VjCE6VPgyuC4Q4EFQcXrAeYYY14LHrsHmCsiVwLfAmMy9mnSzMiRI7nrrrsoLCykZcuW2TZHUawFW9hibeXKlQwaNIicvDxrYRalNKw7EODwI45g7dq1nHLKKVUH7AVdikRFYWEhIkLz5s1Tcj0lC5SWVhcViXgqysqsggKJeCpEDjZuVBSl9uTl5WXbBMUBInJz8OUW4D0ReQkrp+JsYIPT62Qr/GlIhH2Phrx+Fzg6wjlbscrQRrpmATAyhWbWGUSEK664ghkzZnDTTTdl2xylkTJ37lxEhEMOOYRe27fTuqKCHGMQEX788Ud2797NaaedBvv3WwOi9Zvw+eh42GG8+tlnDBgwgNzcoFPSru5TWpoSe5cvX554CcOKCti6FXr0SIkNSi0wJrKnwqnotEVFop4KSEtXd0VRlDqMXZniq+Bmk1DUTyOtU1j/uOSSSxg6dChTpkzB5XKxa9cuPvnkk4PbV199xc9+9jPuv//+bJuqNEDWrVvHE088wejRo/nkk08oWrWKw7/9lo+/+w5jDD6fj3PPPdeKm7UXZpFERdCD4fJ4OOGEE3j//fervBUul/WkOEWiYsmSJVx11VWJDdq0Cf79b5g4Efr0iX++kj4qKqzFfagoyM11Hv5UVlZzfDxErE2bjiqK0ogwxkxNxXVUVNQT8vPzGT16NIMGDSIvL4+OHTvSp08f+vTpwy9/+Uu6devG0KFD8fv9VU9+FSVFPP744/zpT39i0KBB1g6/Hz76iP72+1BscRApTKWiwgovEaFv377MnDmzylsRa1wSrFu3jn79+iU2qLgYdu6EL75QUZFt/P6aOREejyUUAoH4De1sUZFIjw/1VCiK0ogRkeVYYU/VMMY4cvurqKhH3HHHHfz+97+PGqN46qmn8vbbbzNyZIOMAlOyRElJCRs2bOCk0PKwsZ4AxxIH5eUHF4oul4sTTjiB5cuXc/rppyN2lZ8UeCq++uorjjrqKFyJPKUGy2avF/burbUNSi2xE61Dw5/sCmF+vzNR4fNBs2bO7ylSdX1FUZTGxy0hr/OB8wHHSWZZqf6kJIeIxEx6+tWvfsXChQszaJHSGJg7dy6//vWvq0oC2oms0UoE2qIiUux7ebk1PrjYP+6448jNzWXBggWUV1REH5cgr7zySnKlZL1ea0GpoiL7RPJUuN3W/HGy6Pf5rPMSSRS176WiQlGURogx5sOQbbUx5mZgmNPxKioaEAMGDOD999/HmIh9/xQlKWbPns3YsWOrdgQC1YRBDWJ5KsLGigjDhw+nZ8+ePPPss/jLy2stKvx+P3PmzOH8889PfLDXay1G9+2rlQ1KCrBFRah4tUVFtCIAoZSVqahQFEVJABFpG7L9RETOAFo5Ha/hTw0Il8tFnz592LhxI3379s22OUoDYMOGDXTq1Ik2bdpU7bSFQbKeigg9AHr37k3btm3Z/PTTtO/cmQ61sHnWrFmMGTOGZomEvdjYC9EDB2phgZISfL6aHrFERUVlpVWG1in2vTSnQlGUxsmHWDkVghX29DVVbR3iop6KBoaGQCmp5PHHH2fChAnVdzr1VERa+IWFP4XSoUMHjjn2WDa8/z5PPvlkUvb6/X5mzJjBtddem9R4SkstUVFYmNx4JXX4/dZ8CS08YedUOKkAZYuKRBO1jVFRoShKo8QY09UYc1TwZw9jzOnGmFVOx6uoaGCMHDmSN954I9tmKA2A0tJSPvroI04++eTqB0KSrSMSS1QEAjVDWkLIzc9n5CmnsGbNGm6++WYCCTYhe/LJJ/n1r39N06ZNExp3kKIiayGqoiL72KIi1NNgJ207FRUVFYmJCvVUKIrSiBGRfBG5WUT+JyLzReRGEcl3Ol5FRQOjadOmtGnThh07dmTbFCVL+JyEhjjghRde4IILLqhK0LaxPRXRsHtVRPNUxBIkHg/u8nIeffRRevTowYUXXkhFhHCpSNheimuuucbR+REpKrJ+Ou2FoKSPSKLCFghOvh+fz5qniXTU1pKyiqI0bp4CjgWmAw8FX892OlhFRQPkrLPO4uWXX862GUqWuPDCC1mzZk2trzNr1izGjx9f80C8nAqwFn/RREWsHgNu98FxEydOZPjw4fz5z392ZO8TTzzBRRddlLyXwhhLVLjdVsJ2LOGkpB+fr3bhTyUl1rmJNr8DTdRWFKWx0scYc6UxZnlwuxpLWDhCRUUD5Je//CWLFi3KthlKltixYwf/+te/anWNTz/9lA4dOtCuXbuaB52IihBxUI14osJubhbk+uuvZ8uWLSxevDimvX6/n5kzZyafS2FdxNpELG9KCkrbKrXA7685V2yvg5NeJsXFlpcj1jwNJ1Y3eEVRlIbPRyJysCmViAwEPnA6WKs/NUAOPfRQSkpKKC4upnnz5tk2R8kwTZs2Ze/evezYsYPDDjssqWv897//5eqrr458ME5eBGAt/iI97Q0E4ouKkKfQIsJjjz3GqFGjOPbYY+ncuXPEYTNnzuTiiy+mSZMm0W2Kh9drCRpbVHi9kKzXQ6k9fn/NRGtbVBQXxx9fXJxY6BNY3320fCBFUZQGiohsxKr6lAO8IyLfBd93BjY5vY56KhooZ5xxBkuXLs22GUqG8Xq95OfnM3HiRB599NGkrlFWVsaaNWsYOnRo5BPsvIhYCbBud+S4dCfhT3aFqCAtW7bkP//5D5dffjn+CELF5/PxxBNP1C6XAizPRCBg9TWorNS8imzj91vhS+EdtV2uqtyXaBhjeTMSSdKG2EUGFEVRGi6jgbOAUUBXYChW07uuwC+cXkRFRQNFS8s2TmzvxOjRo1m6dGlSSdvz58/n/PPPr5mgbWN7G2I9BY4lKioqovcOsPsQhFV9+ulPf8rll1/OLbfcUmPIzJkzueSSS2rnpYAqT4Vtm/aqyC52onW4p0IkfnUuOx8jkXwKqBIVmlOhKEoWEJFRIrJZRLaIyK0RjueJyPPB4++JSJfg/i4i4hWR9cHt0ZAxx4vIxuCYf0mEP+7GmG9jbU7tV1HRQOnZsyebN292XDlHaRhs376dww8/HLfbzYUXXshzzz2X8DVmzZrFZZddFv0EO/wpnqciWvhTRUV0QRKj+s5ll11GaWkpzz///MF9Pp+PJ598smYvjWSwu2nn51tPuvfvr/01leSxO2pH8lTE8yLZjfOSCX+y760oipJBRMQNPIzlGegNXCwivcNOuxLYZ4zpDjwATAs59pUxpl9wC00wfAS4GugR3Eal6zOoqGigiAiDBw/m3XffzbYpSgbZsWMHhx9+OABXXHEFM2bMwBjjePzmzZtp27Yt7du3j36SE0+Fx2OdE35vO7QplqiorIxa0nP69Ok8/PDDbN68GYAZM2Zw6aWX1t5LAVWiolkzy271VGSXSCVhbU9FPFFhh7IlKirUU6EoSvYYAGwxxmw1xviB54Czw845G5gVfP0CMDKS58FGRDoCLY0xa4y1GHgKOCf1pluoqGjAnH322bz00kvZNkPJILanAqBNmzb06dOHd955x/H4xx9/PHqCto0TT4UtDsI9ZeXlNePkQ7FLhkZpetekSRNmzpzJVVddxb59+5g1a1ZqvBRQJSqaNlVRURcoK6tKnLZxuWok80cdGwgkVvkJqu6nfSoURUkPHhH5IGQL/QN2OPB9yPttwX1EOscYEwAOAHaZxq4isk5E3hKRISHnb4tzzZShoqIBM3jwYFavXp1tM5QMEioqACZPnsz06dMdjfX5fKxevZrhw4fHPtGpqIgkDmzvRaxu3DE8FQDdu3fnpptu4sQTT2Ts2LHk5ztu9hkbr9eyr2lTy46CgtRcV0mOkpLIc8zjiV9StqzMmkPJJmqrp0JRlPQQMMacELI9lqLr7gSONMb0B24G5ohIyxRd2zFaUrYB4/F46N69O5s3b+aYY47JtjlKBggXFb1796awsJDvv/+eI444Iuo4YwzXXXcd1157La54ya1Oqj9FExW2pyKeqIizqDvvvPMoKyvjvPPOi21rItgN7/LzLY/J3r2pu7aSOF5vZI+Wx2Mdi0VZmTWHQhvnOcH2VKioUBQl82wHQv9Qdwrui3TONhHxAK2AgmBokw/AGPOhiHwFHB08v1Oca6YM9VQ0cM4++2ytAtWI2L17N4cccki1fZMmTeKRRx6JOe62226jc+fOsRO0bezmd7Hi1W1xEElUxBpr73fQeO6SSy5JnZcCrIVqRYUlKlwuTdTONtFERU5O/Pnh81lbsqJCw58URck8a4EeItJVRHKBi4DwBdxCwP5DfQGwzBhjRKR9MNEbETkKKyF7qzFmJ1AoIicFcy/GA2mLi1dR0cA5/fTTee2117JthpIhKisrcYctxM4880yWLVuGN8rT3XvvvRev18sdd9zh7CbxQpggem6ELUhieSog/pPodBDapyInJ37ZUiV9VFZaoiDSPHEiKmxPRV5eYvcVse6pngpFUTJMMEdiMrAE+ByYa4z5VETuFpFfBU+bAbQTkS1YYU522dlTgQ0ish4rgftaY4ztbp8E/BfYAnwFvJquz6DhTw2cFi1akJ+fzw8//BC7oo9S74lW5cnlcnHxxRfz7LPPcsUVV1Q7NnPmTDZs2MCsWbOi96UIxxYKsURFtNwIu6FZPFHhwFORcoqLq2zLzVVRkU0ilZO1ycmpqgwVbR6VlVnjE/VUgHXNKIUCFEVR0okxZjGwOGzfn0JelwEXRhg3H5gf5ZofAH1Sa2lk1FPRCBg9ejSvvPLKwffGGD7//HMee+wxxo0bx/HHH8+aNWuyaKGSCgoKCvjJT34S8djll1/OE088UU14vPjii7z44ovMnDkzfh5FKPG8DVCVUxEuKny+mhV9QrEXkdnwVBQWWgtWEesJd3Fx5m1QLPx+a55FmmO2FyxWiFJZWc3GeU5xuTT8SVEUJQnUU9EIOOussxg/fjwFBQWsWrWKH374gZ49ezJkyBCmTp1Kbm4uF198McuXL8eTzB9hpU4QnqQdSqtWrejXrx8rV65k6NChrFixgoceeoiFCxeSE627dTSceiqMqRlGUlYWPxfDmOx5Kuz5n5cH+/ZZtiRallSpPbanItK/vcdTlcwfLbzJ9lQkKyrUU6EoipIwuoJsBHTq1IlzzjmHww8/nLFjx3LooYfWOOe8885j+vTp3HTTTVmwUEkFsUQFWOVl//jHP9KiRQvuvPNOFi5cSNOmTRO/kZ1sHU9UgOWZCMXvT25cuqmosMqU2ovQ3FzL1vLy5EJolNphi4pongo756JFi8jjayMq3G71VCiKoiSBhj81EqZMmcL5558fUVAAXH/99fzvf/9j27ZtEY9H4vXXX2fq1KmpMlGpJfFExTHHHIPP52PSpEnMnTuXNm3aJHcjp+FPUNPjUFbmLME70+FPXq+1kLS9KB6PtSjNRhiWYgmGeKIiVjK1XR440Y7aoJ4KRVGUJFFPhQJYPS3+/ve/c/PNNzN37ty452/evJm77rortSU9lVqxfft2hgwZEvOcf/zjH+Tn59OxY8fkbxSv1wREFxVOPBUi2REVgUB1UVFZaXkvWrXKrC1KlZcokiiwRUWsOWI3zksmdE1FhaIoSlJkxVMhIjeIyCci8qmI3BjheBsRWSAiG0TkfRHpE9x/hIgsF5HPgmNvCBlzl4hsF5H1we3MTH6mhsCgQYNo3bo1r74au9rYvn37+M1vfsOsWbMo1zCBOkM8TwVY3orOnTvX7kZ+f+xka4gsKuwci3ieCpH4HZNTjd2B2bYtJ8eyt6gos3YoFraoiJTvYwuNkpLo44uKkgt9sq+vokJRFCVhMi4qggLhamAAcBwwWkS6h512O7DeGNMXq1HHg8H9AeC3xpjewEnAdSLSO2TcA8aYfsFtMUrC3HPPPUydOjVqT4NAIMDYsWP561//Svfu3WnevDnFWiWnTuBEVKSEeMIAIidq2yVm43kqXK7Miwqvt/rn8ngs+7UBXnZwIiqiCT5jLMGRTOgTqKdCURQlSbLhqegFvGeMKQ02+ngLOC/snN7AMgBjzCagi4gcaozZaYz5KLi/CKs5SAZWUY2Htm3bMnHiRP72t79FPP7b3/6W0aNHM2LECAC6d+/Oli1bMmmiEoXi4mJaREtcTSVORQVUT7h2muCdrfAnn8/qpg1Vi1kVFdnB57PmS6TqTrZYiCY8AwFrvHoqFEVRMko2RMUnwBARaSciTYEzgSPCzvmYoNAQkQFAZ6BT6Aki0gXoD7wXsntyMGRqpogkmYWqjB8/nvfee4/NmzdX2//4448TCASYOHHiwX0qKuoOjpvX1RafL/5T4EiiwkmCtx3+lG1RYXsqtAFedojnqTAmuqiIleTtBLtxY2VlcuMVRVEaKRlP1DbGfC4i04ClQAmwHqgIO+0e4MFgu/GNwLrQc0SkOVbnwBuNMfZf/UeAvwAm+PM+oHr7YGvsBGACWMnJK1asSNlnS4Ti4uKs3dsJF198MePHj+eee+5BRFi/fj1z5szhb3/7WzW7vV4vS5Ysidp0TUkP4fPH7/dTUlKSkTnV5YsvaLlvH4XffBP1HHdJCa2Li/l+/Xr2BCuOeYqK6Pr99+QUFFAabWxlJW2Lijjw5Zd8lcH/H63XreOIXbvwulwEystxe720Li5m+7vvsquBzu26/Duozdq1dPrxR0qbN6ciLMzJXVxM6+Jivlu7lh8iJNHnHDhA1+++I2ffPkpizNFoNCsoIPfAAdYvW4bRvj0xqctzSKkf6BxqWGTlN6YxZgYwA0BE/gZsCzteCPwmeFyAr4Gtwfc5WILiGWPM/0LG7LZfi8jjwKIo934MeAygWbNmZtiwYan6WAmxYsUKsnVvJwwbNozNmzezc+dOBg0axG233carr75Ku3btqp13+OGHc88999Tpz9IQCZ8/X3/9Nb17987M9/Daa7BnD227dIl+TnExbNlC786d6W3btGcPvPyy9aQ52lhj4IsvaNm+PUdkck7ZVZ66doWWLa3E7S1b6Nm+PT0b6Nyu07+DiouhTRvadekCzZvXPPbVVxzbqRNEsn/bNmue5eTQPtYcjXXv8nKGnnJKledKiUidnkNKvUDnUMMiK6JCRA4xxuwRkSOxwpxOCjveGig1xviBq4CVxpjCoMCYAXxujLk/bExHY8zO4NtzscKslFpw5513MnLkSB5++GFmzpxZQ1AAdOnShW+SeBqopJaMJWnbydfxQq0i5UbY4U+xxopYoiPTze/svgb2k2mPx/oM+/Zl1g7FwueL3mfCDpGLlqgdXskrUewiAxXhDnRFURQlFtny7c4XkXZAOXCdMWa/iFwLYIx5FCuZe5aIGOBT4MrguJOBccDGYGgUwO3BSk9/F5F+WOFP3wDXZOzTNFCaNWvGtGnTqKiooFevXhHPycnJ0bKydYCMiYrKSmfx6pFERXm5NTaeIHG7a/a3SDd2B2Y7ht/ttrYDBzJrh2Lh98cWFS6X5VGIRFmZNT5SPoYTRCxRocnaiqIoCZGt8KcaHbqCYsJ+/S5wdIRzVgERVyTGmHGptFGxGDp0aNxzmjVrRnFxMc3DwxSUjLF9+3a6du2a/hs5SbaGKlERKg5sURFvrMeTeU9Faallm+2pEIHcXE3UzhZ+v/V9xPJUREvULiuz5k+yTQvtRG31VCiKoiREVprfKQ2L7t2789VXX2XbjEbNjh07MuOpsMvCOg1/Ci8pGwjEL/Xpdlfvb5EJioqqFqs2eXna/C5b2GI0kgB1uazvKtp34/NZ8ydSOVon2PdUUaEoipIQKiqUWqNlZbNPxsKfnORFQGRREQg4ExUeT3ZERbhdeXmxuzYr6cNuXhdpnolY31W0ssN2TkVubnL3tsOfVFQoiqIkhIoKpdb06NGDL7/8MttmNGp27drFocHSrWnFqagQsYRFePhTXfRUGGOJivAY/Nxca+Gqi8vM4/XGDpOLJyoqK5PPqdBEbUVRlKRQUaHUGvVUZJ+Kigo8maipn4ioCK/iZOdUOPFUlJdbC7tMYIfLhMfv5+RY9mY6aVyx/s1jNVjMyYkvKpL9/6CJ2oqiKEmhokKpNV26dOHrr7/OthmNFpOpxTc4T9QG65xQj4PT8Ce327pHphZ1Xq91r/BFrMdj2ZHp7t6NHWOsf/NY8ySdosKe21rVTlEUJSFUVCi1Jjc3V8vKZpG9e/fStm3bzNzMqaeilcEAACAASURBVKcCaoYxOQ1/ssNPMjWnvN7IfQ1sT4XmVWSWQCB+n4mcHMvDFElQRxOJTrHndqYrkCmKotRzVFQoKaFZs2aU6OIrK2QsSRuqhIETT0UkUWGMM09FJsNPojVL83isBeb+/ZmxQ7Gwy8nGy6kIBCLnPZSWWmNr2/xOH5QoiqIkhIoKJSV069ZNy8omyGuvvca7775b6+tkrJwsVC3knDwFDhcVgYC1WHPS46KyMrOeCr+/ZrWgnBzLXhUVmcUWFbG8YbbwjJTQX1ycfOgTVM3PTFcgUxRFqeeoqFBSglaASpxHHnmE+++/v9bXyainwmleBNQsDWv3uHAiKjId/lRWVrOvgf0Ztat2ZvH54gtXt9s6J1KIUm1FhS1mVFQoiqIkhIoKJSVoBajE8Hq9HDhwgP3791NQUFCra2VcVDj1VNghKjZ2+FO8sXaidqYWdV6vtTht0qT6fttToaIis/j91rxJxlNhJ9Ynm08BmqitKIqSJCoqlJTQvXt39VQkwPLlyxkxYgQXX3wxzz33XK2utX37dg477LAUWRYHW1Q4eRLsclUvDZtI+JMxmUuUtXtRhIc/eTyWLbUUfUqCOMmpiOapKCtz7kmLhi1mVFQoiqIkhIoKJSV07dpVy8omwKJFixg9ejQXXHAB8+bNq9W1spKo7WTRFp5wnUj4E2SuP4QtKsI/ky0q9u7NjB2Khd9vzZVYzevsJPrw4hA+X3wvRzzUU6EoipIUKiqUlKBlZZ1jjGHdunX079+fli1bcsQRR/Dpp58mfb3CwkJatmyZQgtjkEj4k+1xsEWF/QTaSfgTZK4/hC0qwhexOTnWZ9BE7czi88UXFbZgLS6uvt/2VCRb+Qmqmt/p7zNFUZSEUFGhpIymTZtqWVkHbNiwgb59+yLBp6mXXXYZs2bNqtU1pTZPZhOhtqLC5Yr/FDnTnoqyssieChFrYVtYmBk7FAu/39riiQqo6amwRUUqcio0UVtRFCUhVFQoKaNbt25s3bo122bUeezQJ5vhw4ezcuVKAkn0ZfD7/eSG5wKkEzsvIhFRYT/x9fmcd+KGzIkK+2l3pM+Ul6eiItPY4U+x5rXtqSgtrb6/rMwar9WfFEVRMo6KCiVlaFlZZ7z55puMHDny4Hu3281pp53G66+/nvC1du7cSceOHVNpXmycJltDVb8JWyyVlTlvmmdM5kRFYWH0RWheXs0QGyW92GFysYSBLQCjiYrw8sCJYM9R7aitKIqSECoqlJShZWXjs2fPHpo1a0bTpk2r7R8/fnxSIVAZTdKGxEVFaPhTXfVUFBVFD7XJzbUWrnYFKyX9+HzWv7eT8KdwL1JZmTW+Nt47O0RPPRWKoigJoaJCSRnqqYjP4sWLOfPMM2vsP/roo/nhhx/Yt29fQtfLaDlZqAplchr+FNoZ2+933okbaj6FTgeBgJWoHe2peG6udY4uMDOHk4R+t9uaX0VF1ffbSd618VSIWJt6KhRFURJCRYWSMrp06aJlZeMQnk8Ryq9//Wuef/75hK6XFU+Fk7KwUDOMxOdzVurTflKciepPXm/sxF6Px1rgZqoSlWKJinh5O263NUciVX+qrIzt5YiHeioURVGSQkWFkjLy8vLw6x/iqPj9fnbv3s0RRxwR8fiYMWOYO3duQtesF6LCXuiVlzsfJ5IZT4XXG9uunBxLVGTCFsXCiafC7iESSVQ4bc4YDdtTob/LFEVREkJFhZJSmjRpQqkuwCKycuVKhg4dGvV469at6dChA5s2bXJ8zTqdU2EvCu0yn07FiP0UOpOeimh2eTzW5z1wIP22KBa2CHXiqYhUUra2okJLyiqKoiSFigolpXTr1o2vvvoq22bUSV5++eWooU82ifas2LVrFx06dKitac6xQ1OchjFBVZx7RYVzT4XLlRlREa8EqR1Go6Iic3i98fuZiFjfWaTqT5WVqfFUaE6FoigZRkRGichmEdkiIrdGOJ4nIs8Hj78nIl2C+38uIh+KyMbgzxEhY1YEr7k+uB2SLvtVVCgppUePHloBKgLGGN5//31OPPHEmOeddtppLF++nIqKCkfXraioIKc28eOJUl5eteiKR7ioqKysmzkVZWXRE3vtxal21c4cXq+zhP5IosKu1FWbjtr2/NOO2oqiZBARcQMPA78AegMXi0jvsNOuBPYZY7oDDwDTgvt/BM4yxvwUuAyYHTbuUmNMv+C2J12fQUWFklK6d++uFaAisGnTJnr27Ik7zmLJ7XYzfPhw3nzzzbjXNMZgMl3qtKzMebdiu6Ssz5d4LoZIZkrKer2WpyI/P/LxnBzrM2gDvMzhVFTk5NQUFcXFltioTYd5TdRWFCU7DAC2GGO2GmP8wHPA2WHnnA3Y4QwvACNFRIwx64wxO4L7PwWaiEgtyuAlh4oKJaWopyIysao+heM0BGr//v20bt26tqYlht/v/ClwpPAnJ4s9EWtRmSlR4fPF9lSIQIKlfpVaUFrqXFSEz5GSEueiNxYiVf1VFEVRUodHRD4I2SaEHDsc+D7k/bbgPiKdY4wJAAeAdmHnnA98ZIwJjeF8Ihj6dKdIbZ66xKYWgaeKUpOuXbuydevWbJtR51i6dCnz5893dG7Pnj3ZsWMHBw4coFWrVlHPy3iSNliiwunvI9tTUVZWFf7kFLc7MzHtXm/sEqQ5OdbnKChIvy2K9V04bZLo8VSv/mRMlaeiNohY91dPhaIoqSdgjDkhXRcXkWOxQqJOD9l9qTFmu4i0AOYD44Cn0nF/9VQoKUXLytZk7969eDweWrZs6XiMk/KyWRMViXgq7NyIRBK1wVoY1gVRYZcuVU9FZnBSTtYmJ8c63xarfr81z1LhqXC51FOhKEqm2Q6E1pzvFNwX8RwR8QCtgILg+07AAmC8MeZgxRxjzPbgzyJgDlaYVVpQUaGknPz8fLzaLOwgr732GqNGjUpozCWXXMKsWbOojPF0v16JikDA2pw+Rc6kqIhll8tlHdPqT5nBFhVOPRWVlVUeBbt0capEhSZqK4qSWdYCPUSkq4jkAhcBC8POWYiViA1wAbDMGGNEpDXwCnCrMWa1fbKIeETkJ8HXOcBo4JN0fYCsiAoRuUFEPhGRT0XkxgjH24jIAhHZICLvi0ifkGMRy20Fv4T3gvufD34hShbQsrLVSSSfwqZVq1accsopvPLKK1HP2bFjR2ZFhTHOG9hB9YTr8vLEFnxud2bCT0pLY5cgFYHcXE3UzhQ+n3NPhdttzUl7nthja1P5KfTa6qlQFCWDBHMkJgNLgM+BucaYT0XkbhH5VfC0GUA7EdkC3AzY6+DJQHfgT2GlY/OAJSKyAViP5el4PF2fIeOiIigQrsZyvxwHjBaR7mGn3Q6sN8b0BcYDDwbHxiq3NQ14IFhmax9W2S0lC8RL1jbGMHnyZLZvD/fqNTwCgQDffvst3bp1S3jsTTfdxAMPPBC1wlOd7qYNkUWF0/K3Hk9mREVRUVUjtWjk5dXs3KykB9tT4QS3O7KnIhWiQj0ViqJkAWPMYmPM0caYbsaY/wvu+5MxZmHwdZkx5kJjTHdjzABjzNbg/r8aY5qFlI3tZ4zZY4wpMcYcb4zpa4w51hhzgzHG4S/ZxMmGp6IX8J4xpjSoyt4Czgs7pzewDMAYswnoIiKHEqXcVjCTfQRWeS2wym2dk/6PokQiXlnZZ555hjfeeCPmU/iGwurVqzn55JOTGnvooYfSq1cv3nrrrYjHsyIqnFZwguqiwh6bSPhTebn1JDqdFBXFtykvr2bnZiU9+P3OhUEkUZGIJy3etVVUKIqiJEQ2qj99AvyfiLQDvMCZwAdh53yMJTTeFpEBQGeshJVI5bYGYpXT2h8UKfb+iKutYPmuCQAej4cVK1ak4CMlTnFxcdbunW727dvHypUrIzZ6+/HHH7nnnnv405/+xL///W+OPvroLFiYOR599FEGDRqU9Hc9ZMgQbr31Vu65555q+4uLi/n2229Zt24daawOVw13aSlHff89uQUFlHzzTfwBFRW0LS5m/5df8uN773Hk7t34KioodxBW0mzvXnL27ePjZcswqYiRj4QxHPX55zQrLKQoxudpduAAuQUFrH/zzfTZkgXq4u+gJt99R+ft25FAAG+cOZb3ww80Lyzk02XLKOvUiWZffcWR339PpceDz8n8jEGL/fvBGDbWsX+fukZdnENK/ULnUMMi46LCGPO5iEwDlgIlWDFe4a6Ye4AHRWQ9sBFYF+GcZO//GPAYQLNmzcywYcNScdmEWbFiBdm6d7opKyvj6aefrvH5jDGce+65/Pe//+Wkk046uODOi9YjoJ5jjOG2227j6aefxlOLMpeLFy+mRYsWHH/88Qf3rVixglatWjF8+PBUmOqMfftgoZUz1r5Ll/jnGwNffEHLQw7hyJ/+FFq1gi5doF14Se0IlJSA38/Qk0+O3piutni9sGABeL20i/V5ysrA52PowIHQvHl6bMkCdfJ30IYN8MorVh5LvDnm8cCuXZzUpw/06wdNm0Lr1tC2LRxxROyx8dizB3y+uvfvU8eok3NIqVfoHGpYZCVR2xgzIxjjdSpW/sMXYccLjTG/Mcb0w8qpaA9sJXq5rQKgdbC8Vuh+JQvk5+fji1C5Z/bs2Rx99NGcdNJJAAwdOpSVK1dm2ryM8dFHH3HcccfVSlAAET0VgUCg1tdNmERzKux6/6EdtRPtxp3OEBS71G08mzweK3RLK5qlH7/f2pzk3tjfmx2a5vNZY1PxkMLlcp7boSiKogDZq/50SPDnkVhhTnPCjrcOqd50FbDSGFNIlHJbxspkXY5VXgusclsvpf+TKNHIy8urVlZ2x44dPProo9x9990H95155pksXrw4oevu2bPHcRO5bDNv3jzGjBlT6+v07t2byspKNm3adHBfQUEBHTt2rPW1E8IWBomEW9lN7Oz8iESSvCsr0y8qnMTv26JCk7XTj91rItdB8T5bVJSWWj/t3B0nY51cOxBIf06PoihKAyJbfSrmi8hnwMvAdcaY/SJyrYhcGzzeC/hERDZjVXq6AaKX2wqO+QNwc7DMVjussltKlujWrdvBztrGGCZNmsQDDzxAfkgoy4ABA3jvvfcSuu6sWbP4+9//nlJb04ExhrfeeotTTz01Jdf7wx/+wLRp0w6+LygoyHyPCrsrdiKiwu5MbIuKRErKpttTYSf2xvs8OTnWOfv3p88WxcJO1HbiqfB4qrpog/V9xioPnAi2qFVRoSiK4phsJGpjjBkSYd+jIa/fBSJm8BpjFgM1Hm8Hy2qlrUugkhh2Wdljjz2Wp556ip49ezJw4MBq57jdbrp27cqWLVvo3j28qnBNjDG89NJLeDwevF4vTZo0SZf5tebDDz+kX79+KQtRGjBgAHfddRffffcdRx55JD/88EPmRYVdwSkR7H4T9lPfRD0V6ewV4PVaXpR44TL24lVFRfrx+ax/ayf/b8LDn8rKEqswFgt7nqaq74WiKEojQH9bKmnBLiu7fft2HnvsMe66666I5yUSAvXxxx9zzDHHcOqpp7J27doUWhubAwcOJFydYu7cuSkJfQrllltu4R//+AdgVdE67LDDUnr9uNiiIpEKSHa/CdvLkYioMMZaKKYLr9e6fjxxmpNj2aJdtdOP3afCafM7kfR4KkSs71wb4CmKojhGRYWSFnr06MGXX37JxIkTa4Q9hTJq1Chee+01R9d86qmnGD9+PCeffDKrV6+OPyBFzJ07l7Fjx0ZMPo+EMYa33347ZaFPNsOHD2fjxo3s2bOHH3/8MXueikQWbbanwhYViYQ/QfpFhZPEXvvzqqhIP36/8zA5W1QUFVnv7e7oqSj7a4taTdZWFEVxjIoKJS0cddRRPPfcc/Tp04cBA6JHpbVr146ysjJK4jQXCwQCvPPOOwwZMoRBgwbxzjvvpNrkqCxYsICxY8cyZ86c+CcDH3zwAf3798ed4p4GIsKNN97Igw8+mD1REQgktmizm4gl46mA9FZc8nqtRWM8UZGTY9lTUJA+WxQLv9+5t8Httr6XUFERrzu6U1RUKIqiJIyKCiUt5Ofnc8011/DnP/857rkjRoxg+fLlMc95/fXXOe2003C5XLRr1459+/ZRWVmZKnOjUlBQQEVFBbfffjv//e9/Hd0zHaFPNmeddRYrVqzg+++/z071p4oKZ0m0NraosLseO13wZcpT4cTz4vFYi8x9+9Jni2Lh8yUW/uRyVYU/FRenxksB1jytrFRRoSiKkgAqKpS08fe//91RYzsneRVPPfUU48aNO/i+Z8+ebN68udY2xmPhwoWcc845tGzZkiFDhsS10w59GjKkRi2ClOByuZg4cSJ79uwhNxWlMxOhvDzxnAq3u6rHg8vlXFTYnoq6JCo0UTv92N+3k3niclmbXVK2uDg1+RT2tUFFhaIoSgKoqFCyTr9+/Vi3bh0mSvnGwsJCdu3axTHHHHNw3+DBgzOSV/G///2Pc889F4ApU6bw4IMPxjx/7dq1nHDCCSkPfQrloosu4qKLLkrb9aNihz8lsnCzqzh5vYmJkUwkapeWOitfKmL1PtCcivSTSAiTiDUX7X4jPl9qPRUa/qQoipIQKiqUrONyuejVqxefffZZxOMvvPACF1xwQbV9J598ctrzKvbv309ZWRkdOnQA4LDDDuPII49kzZo1UcfMnTuXCy+8MK12eTyetIVXxSTZRG2o8lQkOi6doqKkpKrrdzzy8qpi95X04fUmNr9ycqzv0W58l0pPhYoKRVGUhFBRodQJ/n979x9kVXnfcfz93b27QBZFBIuo4C/UgOngTwKLqUL8gcVBR2nQdkcno3VwrCZOaaP9o2lTbZtMNFUnbceJiRGTkAjWaJAq/oijQhFFHFypigZ/VVYQkB+7e3fZ/faPcw5cVljuueece+9ePq8Zhj3nPOfsc9iHs+d7n+/zPP2lQC1YsIA5c+bste/kk0/m7bffzrROjz/+OLNmzdprX+G0rn25Oy+++CLnnHNOpvWqmGhF7VJ6HOIGFeVIf9q2rfjxIYMGaUXtcmhvj9dOcrmgjURBRRqDtGFPT0WWiy+KiNQYBRVSFS644AKWLl36hf3vv/8+TU1NjBw5cq/9ZsaIESPYuHFjZnVatGgRl19++V77xo8fT09Pzz4Dmpdffpmzzz4709Snioq7gB3snf4U9zyzPQubZWH79uI/2R40KLgHrbCcnSjdLU47aWgIzsnng/aZ1kJ1UTCsoEJEpGgKKqQqDBs2DAgWmiv00EMP7TVAu1BzczPLly/PpD7btm1j+/bt+5y2dd68edx5551f2J/lrE9VIVoILG5wUMrLYpRXHw3CTVt3d/AiWmxQ0dCwJ29fshGN2YkbVOTze8ZVpNVTEdUhmrVMREQOSEGFVI0LLriAp59+eve2u7N48WJmzpy5z/JZLoK3ePFiLrnkkv1+33feeYe2trbd+3p7e3nppZeYOnVqJvWpCnHXmoCgbE9P/Be+qKciq3UqOjqC+ym2VymX2zOLlWQjzmrakVwuaFs7dhS3kGGxoraqoEJEpGgKKqRq9B1XsXLlSiZOnLjfaWnPPPNMXnnllUzqsmjRIq644or9Hr/55pu59957d2+vWLGCSZMmUZdW+kU1isZUlDI2oru7tPSnrHoqok+24wYVWdVHSkthqq8P2uSOHcH5aQUVhe1WRESKUsNvQDLQTJgwgdbW1t0LzD344INcffXV+y0/ZMgQent7yaeckrJjxw42bdrE2LFj91tm1qxZPPPMM+wIB+8+/PDDtZ36BKWPqTAr7WXRLLt0o6inotjek4aG4N41rWx2op6KOD1aUVCxfXtwflprt6inQkQkNgUVUjXMjNNPP53Vq1fT1dXFq6++yuTJk/s954wzzmDVqlWp1mPJkiX7TbmK1NXVce211+5eZXv58uU0NzenWo+qk6SnopT0p7q67NKNOjuDF8Zix1RE5bQAXnaioCLu7E/uQQ9Sd3d6QYV6KkREYlNQIVVl5syZPPHEEyxZsoSLL74YO8CLaBbjKhYuXNhv6lOkpaWFBQsW8MILL9R+6hOU3lMRzaITJ6iI1o/IakrZjo6gF2Tw4OLKR1PPqqciO6WmP/X0BNMD9/YWP0XwgURtVUGFiEjRavwtSAaaadOm8eyzzzJ//nxaWloOWL65uTnVRfDa29vZsGEDJ5xwwgHLDh48mEsvvZTrrruu9lOfYE8qUjl6KiD4FDrL9KfOzuKDiugTcfVUZKerK3iJjxMYRGNitm6NvzBjfzT7k4hIbAoqpKo0NTUxePBgtm7dWtSL/ZFHHklbWxue0voBTz75JDNmzCi6/Ny5cxk5ciRTpkxJ5ftXta6u+OsAFKaRxA0q6uuzH1NR7MDehoag/lu2ZFMfSRZUfP550FORVlChxe9ERGJL6Qkskp45c+bQGCM3+sQTT+Tdd99l3Lhxib/3woUL+e53v1t0+eHDh2e2VkbVyedLCyqil7O4iwJmHVTESZfJ5YJ72bw5m/rInqAizrgI9VSIiFQNBRVSda655ppY5adOncqyZcsSBxWdnZ188MEHnHzyyYmuU7NK6amIXvp27Yqf757LZfdS19ER7yU0CirUU5GdfL60oMI9GFMRjcNJgwZqi4jEpvQnGfCam5tTGay9dOlSLrzwwhRqVKOSpD+5x/8UuRxBRbGBTn19UFYDtbMTzf5USvpTnNXRixGl6mkFdRGRoimokAHv1FNPpbW1NfF1Fi5cyOzZs1OoUY2Ku4Ad7Fmnore3tPSnrIKKnTvj16mxMVgPQbLR1RX/ZxKV7exMN6iI2q3Sn0REiqagQga8uro6Dj30ULYmmJmnq6uLdevWMX78+BRrVmPy+fiBQWFQUUr6U09P8Cdt27cH148zeHzQIAUVWerqCnq04rSxKC2toyN+2+yPWbaLL4qI1CAFFVITpkyZkmjA9FNPPcX555+fYo1qTG9vadPCFgYVcT9JjgZ579oV77xiREFFHI2NQQ+HZCOfL62nwiz9oEI9FSIisSmokJqQZBG8jRs3cvvttzN37tyUa1VDotW0kwQVpfRyZDGtZ29vEByUElREMxRJ+qIxFXGDirq60nrR+hP1VCioEBEpmoIKqQmTJk3i5Zdfjn1ed3c3LS0t3HXXXYwePTqDmtWIpEEFxH+Jr68PvmfaL/GdnaVNcdvQELz0ZrXK98EuSU9F3JXeDyS6ln7WIiJFU1AhNWHo0KHk83m6Y76Azps3jyuuuILm5uaMalYjoqCi1IHapbz01dXtSbtKU0dHcM1Sg4qOjnTrI4EohSlO4GoW/FxKCXj7E7Vb9UqJiBRNQYXUjIkTJ/L6668XXf6BBx4gn89z/fXXZ1irGpG0p6KUoCJagyDtF7toNe249cnlgn8DDdbORnt7aSlM0YD+tKeUVVAhIhKLggqpGc3NzSxbtqyositXrmT+/Pncc889GdeqRnR3l9ZTUbggWSljKnp705+BJwoq4tYnmi0qwSxj0o/29tJSmKKgYtCg9OqigdoiIrFVJKgws2+Z2Rtm1mpm397H8WFm9riZvR6W+Wa4f5qZrS7402lml4XHHjCzPxQcO63c9yWVVexg7ba2Nm666SYeeughGuOs3nsw27Wr9Kldo7ERpS6cl3Zee2dnEKjEfQltaAh6TrQAXjY6O0vrqWhoCNpnmkFF1FORxcxjIiI1quxBhZl9BfhLYBIwEbjEzMb1KXYj8Ka7TwTOA+40s0Z3f87dT3P304DpQDvwVMF5fxMdd/fVmd+MVJUxY8bQ3d3N7NmzefTRR+nax6eMXV1dtLS0cPfdd2tgdhylpj/BnjSmUtKfIP0xDB0dwQts3JfQXC64D/VUpK+np/QZnKKxLmkGFRC0V/VUiEgZmdkMM3vLzNaZ2a37OD7IzH4dHl9hZscVHLst3P+WmV1U7DXTVImeivHACndvd/ddwPPA5X3KOHCImRkwFNgM9P3IaDawxN3bs66wDByPPPIIP/jBD1izZg3nnnsuN9xwA8uWLcPdAbjlllu46qqr+OpXv1rhmg4wSYKKaCxCtfRUdHQEL7CDB8c7L1q8b8uWdOsjpU0nG4mCvbiLKx5Ifb16KkSkbMysHvgxcDEwAbjKzCb0KXYtsMXdxwE/Ar4fnjsBuBI4FZgB/LuZ1Rd5zfTuIXrZKhczGw/8FpgCdADPAK+4+00FZQ4BHgO+DBwCzHH3xX2u8yxwl7v/Ltx+ILxmPrzmre7+hWRsM7seuB4gl8uduXTp0rRvsV+NmzczYtky8u3tDEr7kzXZmzsb2tpYu3YtbW1tHHbYYTQ2NjJ9+vRK1yyxfD5f1vZT19XFoWvXQm8vHWPHxjr30NZWGjdvZtPXvhbrvIYtWzj0zTfZPGkSnUceGevc/uR27mT4qlW0H3ssu5qaij6vLp9n+KpV7DjhBLZNyOyZXDblbkP9sd5ehr73Hg2ffcbOcX07rvv3pfXraVq/ns2TJ9MTN1Dsx2GrV+O5HJ+ee25q16w11dSGZGCqSBsy45NZs8r7PUPTpk1rd/d9/uIxsynAP7j7ReH2bQDu/i8FZZ4Myyw3sxywATgCuLWwbFQuPK3fa6YpxekyiuPua83s+wRpSzuB1UDfZO2Lwv3TgROBpWb2grtvAzCz0cAfA08WnHMbwT9uI3Af8B3ge/v4/veFx2lqavLzzjsvtXsrSlsbPP88G9es4YiRI8v7vQ9CpwDnHn88vccey+YtWzh8+HDq1q+vdLUS27hpU/nbz+GHQ1NT/E+EjzoKjj2WoXHHrwwfDmPHckhnJ6T9Mxs3jsOHDIn3yXhDA4wZwyGDBjFabSh9jY1w4okcEbedjBoFw4bR1NSU7gJ4Rx8NZgyvgZ91VqquDcmAU5E2ZMYpJ50U/B8vv5yZvVKwfV/4XgpwNPBhwbGPgL5pFbvLuPsuM/scGBHu/58+50Y3eKBrpqbsQQWAu98P3A9gZv9McJOF7fA5UwAABa9JREFUvgn8qwfdKOvM7A8EvRbR6mbfAP7L3XfP9+fun4Rf5s3sZ8C8DG+hdKNGwd130/r731P2gOYgVgfU0q8+tR9JSm1IklIbkqQOwja0y93PqnQlslKp2Z/+KPx7LMF4il/2KfIB8PWwzCiCD5zfKzh+FfCrPtccHf5twGXAG1nUXUREREQkZR8DYwq2jwn37bNMmP40DPisn3OLuWZqKtJTASwysxFAN3Cju281s7kA7v6fwD8BD5jZGsCA77j7JoBwpPsYggHehX5hZkeE5VcDc8txIyIiIiIiCa0ETjKz4wle/K8E/rxPmceAa4DlBBMWPevubmaPAb80s7uAo4CTCLJ7rIhrpqZS6U9fGLEZBhPR1/8HXLifc9ezJ0+scP/AH30rIiIiIgedcIzEXxGMF64HfururWb2PYIJjR4jGDow38zWEcyMemV4bquZ/QZ4k2C21BvdvQdgX9fM6h4q1VMhIiIiIiIhd38CeKLPvr8v+LoT+LP9nHsHcEcx18xKRcZUiIiIiIhI7VBQISIiIiIiiSioEBERERGRRBRUiIiIiIhIIgoqREREREQkEQUVIiIiIiKSiIIKERERERFJREGFiIiIiIgkoqBCREREREQSMXevdB0qxsx6gY4KffscwVLqIqVQ+5Gk1IYkKbUhSepga0ND3L1mP9A/qIOKSjKzV9z9rErXQwYmtR9JSm1IklIbkqTUhmpLzUZLIiIiIiJSHgoqREREREQkEQUVlXNfpSsgA5rajySlNiRJqQ1JUmpDNURjKkREREREJBH1VIiIiIiISCIKKsrMzGaY2Vtmts7Mbq10faT6mdkYM3vOzN40s1Yz+1a4/3AzW2pm74R/D690XaW6mVm9mb1mZr8Lt483sxXh8+jXZtZY6TpK9TKzw8xsoZn9r5mtNbMpeg5JHGZ2S/h77A0z+5WZDdZzqHYoqCgjM6sHfgxcDEwArjKzCZWtlQwAu4C/dvcJwGTgxrDd3Ao84+4nAc+E2yL9+RawtmD7+8CP3H0csAW4tiK1koHibuC/3f3LwESCtqTnkBTFzI4GbgbOcvevAPXAleg5VDMUVJTXJGCdu7/n7l3AAuDSCtdJqpy7f+Luq8KvtxP8Ij+aoO38PCz2c+CyytRQBgIzOwaYCfwk3DZgOrAwLKI2JPtlZsOAPwHuB3D3Lnffip5DEk8OGGJmOeBLwCfoOVQzFFSU19HAhwXbH4X7RIpiZscBpwMrgFHu/kl4aAMwqkLVkoHh34C/BXrD7RHAVnePVrPV80j6czywEfhZmEL3EzNrQs8hKZK7fwz8EPiAIJj4HHgVPYdqhoIKkQHCzIYCi4Bvu/u2wmMeTOOmqdxkn8zsEuBTd3+10nWRASsHnAH8h7ufDuykT6qTnkPSn3C8zaUEAepRQBMwo6KVklQpqCivj4ExBdvHhPtE+mVmDQQBxS/c/ZFwd5uZjQ6PjwY+rVT9pOpNBWaZ2XqCtMvpBPnxh4VpCKDnkfTvI+Ajd18Rbi8kCDL0HJJinQ/8wd03uns38AjBs0nPoRqhoKK8VgInhTMdNBIMUHqswnWSKhfmvt8PrHX3uwoOPQZcE359DfDbctdNBgZ3v83dj3H34wieO8+6+18AzwGzw2JqQ7Jf7r4B+NDMTgl3fR14Ez2HpHgfAJPN7Evh77WoDek5VCO0+F2ZmdmfEuQ21wM/dfc7KlwlqXJmdg7wArCGPfnwf0cwruI3wFjgfeAb7r65IpWUAcPMzgPmufslZnYCQc/F4cBrQIu75ytZP6leZnYawUD/RuA94JsEH07qOSRFMbN/BOYQzGr4GnAdwRgKPYdqgIIKERERERFJROlPIiIiIiKSiIIKERERERFJREGFiIiIiIgkoqBCREREREQSUVAhIiIiIiKJKKgQEREREZFEFFSIiIiIiEgiCipERERERCSR/wfKgmLdyEPYHgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1min 49s (started: 2022-07-27 20:37:25 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res_dog = bubble_finder(data['DOGEUSDT'])\n",
        "plot_pos_confidence_indicators(res_dog.reset_index())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "eX6Yja8rnwaM",
        "outputId": "30a58541-fbd3-4e4a-ef40-dcfca6bfbcc0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 85/85 [01:28<00:00,  1.04s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1min 29s (started: 2022-07-27 20:39:15 +00:00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res_eth = bubble_finder(data['ETHUSDT'])\n",
        "plot_pos_confidence_indicators(res_eth.reset_index())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "HJjbN6bZn0tS",
        "outputId": "3a4eb035-cc82-4f2a-d632-3bba8f7cdc4e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 85/85 [01:32<00:00,  1.08s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1min 32s (started: 2022-07-27 20:40:45 +00:00)\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Bubble Dedection for the Crypto Market.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM7sk4S7nuvzrgq4QWOU8Co"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}