Stocks.ipynb

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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.75-py2.py3-none-any.whl (28 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.9.1)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.4.4)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.1.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.6.15)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed requests-2.28.1 yfinance-0.1.75\n"
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        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "import plotly.graph_objs as go"
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        "IBM_data = yf.download(\"IBM\", start=\"2015-01-01\", end=\"2022-01-01\")"
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              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
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              "                            Adj Close   Volume  \n",
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              "                             Open        High         Low       Close  \\\n",
              "Date                                                                    \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1763 entries, 2015-01-02 00:00:00-05:00 to 2021-12-31 00:00:00-05:00\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Open       1763 non-null   float64\n",
            " 1   High       1763 non-null   float64\n",
            " 2   Low        1763 non-null   float64\n",
            " 3   Close      1763 non-null   float64\n",
            " 4   Adj Close  1763 non-null   float64\n",
            " 5   Volume     1763 non-null   int64  \n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 96.4 KB\n"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc1d12a9290>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": 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EiCSyzocgiNEPd98VzfJd+i0TSXD9o5uMx5WmoNQyRDAaD252GG9NZH2j5rTLZa8/unIuvnDONADA9hZNpZOyfFd/M95SjuOwCgmvZbjwzLI5iM9HKCglCKJgkWQFFkFAmUMb6oYblALaYL9sRg0AoFQ/H2VKM09QoYksQeQbfNKt1ZTqEkiqKSVSpNTk6ZC8zVA44hAtYXhAd7zfBwCYVlNs7Buu9JzXlIoRQWmp7gicbj+K7gIJSkm+SxBEwSLpjnlnNGgOfWc2xHfqSwV+Y+nVzRiIzEFtJAgi/+C/W0EAyXeJIZEVFZf9fi1W7D5ubGMMSfUUHwpeUxrrXuINyBjU28/854N2ADAku+bHfEHFF5TxqUc2YntLX8L3DGVKtdcvnFKB6TXFhmqAMqWxoaCUIIiCRZI1o6Mr50/Ay/91Dq5ZOCkt551VWwrGgA9a+tNyPiI+5uyKJCt4cOVB4wZMEMTohGelRMaMuroPWvpw54s7ScZLROEOSNh7bAD/89wHxrbiNJkIcvddJUYkeLjLHbXNHJTyBRW//p09eMKFd4/04GMPrU/4npE1pf/66tl461vngxn5XsqUxoKCUoIgChZJ0YyOGGM4dXJ5WlZdAaDcacXJdWXYfLg7Lecj4sNlUACwq30Av37jAD772OYcXhFBEENhdt+16RP77zy/A09tPoqdbYmzTMTYxbwIyfuMjhTD6EhVcfVD63H/G/uNfYc6NZdes/OuzVS7yh+39nrwlSe3Ymtzj7Gv2+WP+55BObQoA2gZX0Fg0BOnaTc6okwpQRDEKCcoq7AK6QlEI1kyvRpbm3vhC5I7bCYxS/74v/WutgF0DsafEBAEkVvM7ruRdXVUWkpEwrOY5vGeB6UN1VqNZ7lzeJ4Q/OsXkBR80NKHB1Y2QlFUdLv8+O9ntgEAbjlvunF8nTlA1bOmf1jZiNd3H8eL29uNfQdNbWcikRUVAgtlaTk8U5pu+W6PJ6CbOua3VRAFpQRBFCyS3hImE5w/exz8koKNTZQtzSSSyejIEwgZS+1uJ+k0QYxWzO67s2pLsWhKBb536RwAwKAvmMtLI0YhPBg11306dfnunVecjD9/7gycVl8xrHMLerayw7SQuampGy29XuM5D3jn1JWitiwUlHL57u72AQCaBJ2z99hAwr8n1tyDx6jp7lPa2uvFuBJ72tRguYKCUoIgChZJUaMs2dPFWdOq4LAKeGdfR0bOT2iY5bs/fXmP8Zjq0ghi9GJ2360rd+BfX1uGj8yrBQAMkms5EUEsE6JiPVPqsIr48NzaYZ+bZ+qP6e66APDOgU5IppT94mlVOHVSOR789MKw1yaSEPNANRaPrD4U8x41VHua4aAoKjY0dmHxtPQYOeYSCkoJgihYuPtuJnBYRZx9Ug3WNnZhT/sAbn9mG7U8yABmOdeRbo/xONW+cQRBZA+z+y6Ht9KiTCkRSSxn5qI0SVF5prRzMBSU/t+WFrT1aZnSuRPKMLmyCC//9zmYMb407LXx5LAzx5ckDErjo11LOlvC7G4fQLc7gPNnj0vbOXMFBaUEQRQkqqpCVlTDkj0TzJ1QhqZONz728Hr854N2tPR4hn4RkRJSnOCTMqUEMXox3HdNSpUyvZXWAGVKiQhiOeNWjaCvuBkelHa5NDOgmhI7ej1BfP2f2wEAv79+QdzXFscISu/66FxcPLcWe48N4In1h6P2J2pjlgnh1uoDmlrr3Jn5H5Tmd0UsQRBEHLj7nTVDmVIAaNCbbPMAqddDGYB0E6+3IQWlBDF6kZVw91EAsFsE2CwCBrw0ThLhRI7zy2ZU48cfnZeWc/OFEe6WW1duR5fJObfEET8UMmdK133vQviCCmaML8GrO48BAO56eQ9uOrshrJbzW89pwe7XL5oZdT5+nJLGTOmaA104dVI5akrsaTtnrqBMKUEQBQmfFGXK6AgArpw/Iex5Iot4YniYa0rN+CkoJYhRi2Jy3+UwxlDhtKKPFu+ICGSTod3Ecgf+etOZqExTe5NKPeN6sMMFUWAYX+oI2x8rG8pxWEPzh8mVRZgxvgQAMG9imbF9fWN3WO/sl3SH3lieQ4bRURprSg92DGL+5PL0nTCHUFBKEERBEuTNqzNkdARodaVmekw3JiI9BJXw4PORG08HQJlSghjNxMqUAkBFkRV9XhoniXDMVRobvn9R1L11JIwvc6C+yolBn4QKpxU/uSo8A+tM8F7x3GynVBUZj2/862Z89amtAMJrRWN5TKS7JYysqOjzBlFdAFlSgIJSgiAKFG9A62mZyaA0EjI6Sj9yRKaUm6WQ0RFBjF7M7rtmKpw2ypQSUfDWX/d/6rSMnH9xQzUAoLLYhvqqUMbTIjCj7Us87r56Hl746tKwbYwxfPWCk4znR7o0P4lmkxlfMIbKhxmZ0vREpb2eAFQVqE5TVjnXUFBKEERB8tj6w2AMODPDNumzakuMx4kMDojUOdzlhjsQborCpVYk3yWI0YsSw30XAMqLrOinmlIiAn7v5GZY6WbxtEoAISkvv48kk5H97NIGnD41eh5x9YKJxuOTxmv+Eh+0hvqYJmoJk66ZQrdu3lRVIEEpGR0RBFFwBCQFj68/go8tmIR5EzNba/Hv25ah2xXAub9ahTjlj8Qw2NzUjese3RQmkwIAmyjAJgok3yWIUYwcw30XACqcVuyioJSIwJB7Z8iYcPE0LVNaUaQFb6EeqMPPzZmNhXj2n2dMAeCyU+qiXsPlwOnKlHa7NR+L6hIKSgmCIEYl3qCMgKTglEmZL/4vslkgF2k3mFi29sTwWLW/EwBwNKLNjs3CEJAVrGvsBDAnB1dGEMRQJKwpJfkuEYFhTJihcpuG6iJMqSoyFjnrK4sAdMNuGX7tamVRKBDc3T6AHa19cAckOKwC9t19WczXpNvoiPtYVBdTTSlBEMSohNd22jLYDsaMkAGb97HOxkNdMbfz+p9dbcNpXE4QRDaI5b4LAOVOK7xBGb6gnIvLIkYpUpxFjHTBGMNLty3Ddy6ZDQA4faom5z3W7x32OUWBYZreFg7Qyk1cfimsjUzUdaTZ6KjQ5LsUlBIEkfcc7nLjcVMTax6UDmVgkC64RE2moDQt9HuD2NnWbzyfOT5Ut2sVBZw/S2sSni4JFEEQ6SVeprRczy5Rr1LCjPF9yaAxYWWxzaghXaQHpSMNDld9+wLjsV9S4PFLKLIlCEqNmtL03Lt4ppTXyuY7FJQSBJHXyIqKC3/9Dn7y8h5jgA5K2oBvs2RniDMypSTfTQubm7qhqMDCKRWYVVuCj58+2dhnFQUsm6HVB7n8UrxTEASRQ4yFwYgxuMKpTZ77KCglTIT6imdH3TTdlOFMF73uAFx+GUW2RC1mtP+naz3VG5ThsAoZ7ceeTaimlCCIvOZgx6Dx+HCXG1XFNgRkTRqW9Uwpee+khQ2HuuGwCvjnLUsgMIZ/bGo29tlEAeX6xLbfG0RphtwaCYIYPtyIzBYxBlfoGR2qKyXMhDKlWVpIFhhuWjo1rf09+7xBeALJyXfTpfLxBuS09nTNNYURWhMEUfDEG8QVUyB4pMsNAAjomdJsBaVccUQ1pelh8+EenNlQBbtFhFUUwlaVi+yiEZRmamL70KpGNNyxnBx+CWKYBCQFjAFWMbpPKQBqC0OEkema0lj85OpTcPtFM0d8nnd0Ce+gLwi3X0JRgqBUSHNLGF9QhpOCUoIgiOzykd+uwe3PbIva7jH1sTzSrQWlhtGRJTs3OMYYGKOgNB2oqoqj3W6juTkAnFZfgdPqK7D89nNgFQWjx5w3Q2YpD61qBAAEKPVNpAFFUSGNse+SX1JgEwWjBQYnlCkN5OKyiFFKNmpKM0VDTTEaqosw4JXgDsgosSeS76a31McnKZQpJQiCyCYdgz4c7HDhPx+0R+3zBEKByaFOFwKSYgQT2cqUAtoKr0w1pSNmwKfd2CeWO41tp0+txEu3LTN6znJJYCqZzL+sbcJv3tgfJgWOh6Q3nM2/6RExGvnS37fgvF+tGlPGXH5JiVnTz6WNVA9OmMl2TWm6KXVYQ5nSREZH+v/TmSktpKCUakoJghj1uP2hwDNyEOaZ0vGldry68zje2f+GEahG1jNlEkFg5L6bBjoGfACA8WXxa334ZDfZTGa/J4ifLd9rPL9xydSEx/PzUuabSAcr93UAANYc7DKcowudgKzAHiMo5UEHLeARZiS9DkfIonw3nZQ5Lej3akFpoppS/vel69bi042OCoXC+UsIgihY/FIoKL31ya1h7V94ADp3YlnYcyDa+TGTiIyl7UYzlvHr2c9Eq83WFDOlG5vCe54mK52ieTORDkod2nf56c1DZ+kLhYCkwG6JzuBYdCOboEw/LiIEXwC05KF8FwCmVBWhscOFQb+U0H0XafSfuPuVPVh7sAuOGL+zfIWCUoIgRj2+YCj4WH2gEz95eQ963QG09HiMIHRihTPqdVnNlDJa/U8HPCiNNEgxwzMwyQalaw+GB6VJt6Ogj5MYIR2DPgz6JP2xP8dXkz3iyXdDTuVjq8aWSAwvmcjHmlIAOLOhCgM+CaoKw/MgFun88/66Tluc39rcm76T5hgKSgmCGPX4YxjaXPr7Nfjy37fgaI8HVpGhrswBILw3aTZrSgWBakrTgWFSleCz459xMEn5buRNu9uVXHBA8l1ipCz++dvG421H+3DTY+/m8GqyR0CSY/6GeSZMorGSMJHPRkcAsHhalfG4OGGfUt3oKA33lgnl2pzn6gUTR3yu0QIFpQRBjGpUVY2Z2Tox4Me+44P41/ttWDK92pDMfOasKZg7QZPyFidwwUs3osDGRBDTMejDD17cCV+GnG95oJlIep2qfDfSVKXLlZzz51j4PInsME8vL1h9oDPHV5IdAnEypYLASFVCRCHnuXx3cmWR8ThRptQwOkrD1z8oK/jE6ZPxy4/PH/nJRgkUlBIEMar59/Y23PrkVgBaa5Cl06vxuaVTMX+y5sTa5fLj4pNrw1oP/Omzp+Pxz58ZdqPINCIbG0HpuoNdeHrzUexu78/I+YNJOCcPZXTk9kv4+8YjhttpZPDa7U42U5rUYQQRhayo+O2bBwAATquIi+aMz/EVZZeAHDsoBbS6UqopJczke6YUAMaVauZ8ieW7utFRGt7P7ZdRWWSFkMf/ZpGQ+y5BEKOa9Y3dxuPffHI+ZowvBQD88vV92NGqBUYXnTweb+w+AUBbgayvKkJ9VfYCUkCT5YyFVoR9Hi1rfbw/M/VxAUm7XSeqKbUNUVN69yt78M/3WjCtphgN1cVRtXzdSWZKx1ILDyK9bDzUjd+/fRDTa4rxwA0Lsampe+gXFRB9niBq9ZKKSESBUU0pEQavKeVGWPkIjw0TZkrTZHQkKyq8QTmhIWA+kr+fPkEQY4LqEpvx2Ozm+F8XzjAeZzMjGg9RSF9D7NEMl1If11u3pJukakrFxJnSll4PAC3T+em/bIraH6+mVFVV3P/GfuP5GPg4iQzh1ltVPXDDQpwyqTzhRLUQ6Rz0Y3xp7LZOFpFRTSkRBs+U5nFMCqaLcxPXlGr/H+l6p1cvn8lmiVI2GFujJEEQeUefO1RPapaDFdst2P2TS4zJTa7bm4lsbPQp7fdoWcYTGQ5KE8p3h6gp5Y7MImPo0bOi1yychMmVTjyx/gi63LEzpV2uAB5Y2Wg8V8l+lxgmSoQcMZuma7lGVlR0ufyGnDESi8CMzBgxNtBKKppx87KGsD7jnFBNaf7+TpLKlIL3KR3Z99+j+yQ4KVNKEASRPXj937yJZagssoXtK7ZbUO60AgCuOm0iTptcji+eMy3r1whoBh5joaa0n2dK+zMclCYwOhIEBovA4rrvevWg1BOQjLDSJgr4n4/MxoQKR9xMaeTnR8kcYrjwSTYPSsdQTIoedwCKirhBqSgIlCkdY6zYfRy/fH0f7nl1b8z9hVBTyn0tnDGC7tAx2v9HOlXgrdPsWezFng0K668hCKLgaOpy45J5tVh++7lxjTMAoLrEjpf+65ys15JyBMZIvpsGAvLQNaWAdjM29681w6VN3qBsZGR4kFBdbI9bU+qPOF+hfZ5NnS5yPc0ShhyR8aB07Ey3OvUa7nElsYNSq0g1pWMNHqj9fWMzJFnBva/tw7F+r7HYjKYAACAASURBVLE/3/uUAkBDjTb3sFsTLKimyegomTKXfKSw/poxTFBWcNFv3sGK3cdzfSkEMWI8AQlBWYGiqGju9mD6uJJcX9KQiALDWFCkcaOjjMl3peRutk6bGLctjcfIlMpG3SkPMKtLbOiOI9/1S+HnK6TEd3ufFx/6zWr86vV9ub6UMYESkSmtilB5FDIdg9rYML4sXqaUakrHGub6/2fePYpHVh/C0l+sxLPvHQUQWjTM45gUD96wCH+4YSEmlDvjHsP/vJGqqrh7daKF+nxkyL+GMfYYY6yDMbbLtO0+xtg+xtgOxtiLjLEK077vM8YaGWP7GWOXZOrCiXB6PQEc6nTj1ie34pxfroxqFk8Q+cTcH63AF/+2BQFZgayoKHWM/roJgRVeZi0WZvluJtxpk6kpBbSglMt0I+HbNx4KOZ7ySXB1sQ2Hu9w40uWOel1k5rWQ5NhHezTzpzf3nMjxlYwNQm6i2jR02YxqOK0iqooLPzgNZUpju+9STenYw9wC6HCXx3j8vRd2AgBkRYFFYGGt3fKNymIbPnraxITHpEu+y/0UCq1WPZm/5gkAl0ZsexPAKaqqzgdwAMD3AYAxNhfA9QDm6a95mDFWWNZQoxS3PzQ5a+314ndvHUjTeaW42QiCyAS9ehZrzYHOvJKoaG0OCn+i1ecJQGBaTQsPUNNJ0kGpVTRkumZUVTW2t/eF5GF8JZ5f89ef3R712shMaSEFpS16UFo4f9Hohn93eA9Bxhg+fvqkXF5S1ujUa7ZrSmMH4GNlrCRCmOv/uTu6eZ+kqAXVbzMePOge6YJuwLhPFta/2ZAzPVVV1wDoidj2hqqqkv50E4DJ+uOrAfxTVVW/qqqHATQCWJzG6yV0Hlt3GK/saDeeu3xS2P506PJVVcW8H6/AJx7ZMOJzEUSy7GrvNx5HZhtGMwIbG0ZHnoCMar1WLBNBabI1pZFB6eoDnfje8zvgDsjGhHd3+4Cxn9v084lPrEkBN4+4cckU7Zjh/hGjEJ4p5cZgRGbhc3DRlPmxiWJcx+h8p9vlx069b3THgB8ldkvcHopWUYBENaVjCnNQeqjDFbavscMFRVHz4j6fDhhLY01pgcl306GJ+wKAZ/XHk6AFqZxWfVsUjLFbANwCAFOmTEnDZYwdVFXFT1/ZA0Az7bBZBMMJc3KlE629XiPbNBJ26DeYXW0DQxxJEOljZ5v2vasosiKoT1wseZAp3Xd8EPuOD0JW1Lw2axgKVQUcupFDPPfbkRCUFVjFoWVcTpto1I4CwE2PvQsAWDazxthmDlrvvGIuAOAHl5+Mf73fhnNmhI7j8EwpN+XIhDw5V/CglIKB7GDUyJmGLptFSFtQ+s93j6LIbsFVQ8gFs8UnHtmoyeLvvQKdrvg9SgHKlI5FzPLdpojSiZ1t/ZAK/L5pRmBsxPLdfFKRpcKIglLG2J0AJABPpfpaVVUfBfAoAJxxxhk0OqXAMVMrhhv+HN4Y/pEbT8dj6w9jc1NP5MtS5p96Afq8iWUjPhdBJMtufRFEVlQjU5pPEpUBbxCVBVw3pqgq7BYtaAtIGagplZSk6mScVjHMsMhh1dx4/7bhCAC9xtd0eTxDWFOiLeTFmhPzmlIelBbSvJkHpZ44dbhEejH6lJoWVxxWwaiTT3YC3tjhwvSa4ihp4x3/0mrxRktQelgPNLwBGW6/lLBXo9bOqYB+XMSQxFvALLKJ2NM+AEUdO0Epw/BLQ1RVxU9e3oNiu3aPGos1pTFhjH0ewJUAPqOGlpPbANSbDpusbyPSiNsvxd1X6rCgpsSOLpcfbr8EaQSZjEZdYlFko7JgIjtIsoI9x7SgdNAnGRPofBp4PQVeg60FpSPLlAYkJW6fUy1TOvTnXWSzhBkd8bsQN3mbXKnZ81tFhrXfvTDstdY4PU6f3qwtxDlsPCgtnInz0W4tKPVRUJoVeCbQYkqV8sWOZH0aGjsGcfH9q/HAyoNh20djBp/H3r2eAOQh6gMtokCZ0jFGMI5CoLLIhkGfBGkMyXcFxoYt3+31BPHEhiN4aNUhAPk1N0qGYf01jLFLAXwXwFWqqporlv8D4HrGmJ0xNg3ATADvjvwyCTP+BPKfErsFU6qK4JcUzPvxCnzuseH/8w94teCX7h1ENhj0BTHnh6/jcJcb1XqmkbcWyAf57nmzxgEAPAkWjQoBFaGG3YEUg9KOAR8CkoLvPv8Blvzi7ZiBYUBWk7rROqwh+a4vKMMvKZhdW2rsry7RvkNLT6qJ6l1rtQgJA+qpVcUAgEJRuu5o7TOyyoW+aDJaMPqUmr7KTltqQWnHgGYYZHaRBjJTyz1SuBKhxx2AqgKJxC1WkRVsbS0RGz7elpmc9JdOr4ZVZEb7t7GSKQUb/oInV7xwCq2mNJmWMM8A2AhgNmOslTH2RQAPAigF8CZjbDtj7BEAUFV1N4DnAOwB8DqA21RVpTtgmol0iDRT4rDg5Akhue2GiJtZKgz4tBsfrWgS2aC522O07TijoRJAqLWANQ9uVjef3QAAcBdwJkpVVagqDPluvNXvWHgDMhbf8zbuenk3/r1dM2mLNTkPygpsSci1nTbBeD3vnbpkepWxn/eFrIvRK9EqCjHlg7KiYnFDFSz6++d7prTfG8ShTheuenA9AM1zIF4bHSK98JpS80Tbof9uYrlGx8IaR5HQ3peZHsEjoVg3Ner3BoeUJ8dzziYKl4CshtU/3nn5yXjmliWw6KZXWqa0sAKseDBg2E5HLZFBaR4s2KfCkDWlqqreEGPzXxMc/3MAPx/JRRGJiZcptYkC7BYRc+pKY+5PFb4aOxqlQkT6OdzlxruHu3HdmbkxHmszte84s6EKK3afwImB/MmUcpn7PzY1Y05dKRzWwpO986HAbk09U8przt7eG+qTGStbEpQVYzKeiCKbxciUuvTs9NTqYmM/z9zUlUX3SrSJsTOlPklGid0CIY975Zm59HdrwjwIThpXgtZeb8GbcY0GjEypuaY0xUwpr0uNXEA5PuCNdXhO4XX/vqAMWVUT/oa0364n7n6i8JB0Azs+VteVa+OyVRTw6s7jAID6KmfOri+bjES+G9lOR8wjv41kGP0zPSKKeEFpiS6LKLZbUFEUsv0fTlAZlBVjwidTUDom+NhD6/G9F3aOqA55JBwzBaXzJpYDCEnI88HoiBt7PL+1Ff+3tTXHV5MZeOaQr86mYlZyqFOrUa8qDmUuY41lydaUOvRsi6qqhqNsrSkA5QEzb19jxiKG15S6/RLa+7zwBxXYLQJ4vJbvmdJjEXW7i6dpmWTqPZ15DKMjU/DPa0q9geTGWP77yIdMKV849AWHlmI6rKJhKkaMDfhiIxfeTazQxmqzKqalZ/QttmQCxkLjg5nd7f1DKhMj/42cBbb4TUFpHuKPM5iXmrT69ZWhGqpENajxMDs0FkpdFZEYnhl35agmsr3fB6vIsP1HHzbqAfm15EMxv3kh6If/3oXXdh7L4dVkBn67tFu5+27yg0NTp5YprTD1yYw1NgWk5GpK+c241xNEUHcBtpsyrHwM498lM1ZRMJydAeCXr+/D2feuxNEeD+xW0cjyFFLlwj3XnGrcI0g6mXkM+W6E+y6gZeSTgS8e7Ds+aCzqAMAL74cWvWJNbnMBN6nhmdJEQWmRTYQnUNi190Q4AVmT556rt+yaotft54MKKt3EypSuOdCJKx5YZ3S9iEefJ+Q4X1tmR1WBOf2PvW9DARBLMnf5qXVoMEnXppiMPdr7Ul99Mq+k53u2gEiNQV/2JgvH+r341CMb0eXyo63Pi0kVTlQU2YyAg9c154Mr3+TKIrzw1bPRUK399v6y7nCOryj98LFgOO67fFLdZzJpiVUfn2xNKZdLL7r7TWNMNMt+TxqnjYfTaoqjXmsVhbBx9FV9AcHll2C3CIaTaD6PfZHBit0imDJ1FJRmGllRwRjCXGhT/fc3L9q8vkuTOB7v92Hb0T5je7IBbqbhC0k+SYaiJJbvOm1UUzrWCEjauP6Xm87Auu9diHF6H1uzCuqea07N1eVllVgtYVbu6wAQ8keIhycgGwvgsUpT8p0R9SklcoM/xmD+wPULw5rNn1ZfjuX6ROtDv1mNZ29ZgrOmVyf9HuabZj5PzIjUyWZQ+pe1h/HukR68sLUVx/q8mFih1ZTwekwu382X1dTTp1bi6S8vwcceWo/W3sKrmeJDgWMYNaVNXVpQuldv+QPEVn0kK981y5a45NxsiPXtS2bjI/PqDCm4GavIcLzfh3te3QtRYIZxE6CZOPGxNJ/r6SMn/bwOONY+Iv3IihqWJQVC41qy//58cdgqMmw72gdvQMaO1r6wY1p7vZilu0639XlRU2IL+z5nC9HIlCpDZkqdunxXGaJ1DFE4BGQFDqsIu0U02nUBod7QV502EZ8+Kzd+FlmHhe6lnF1t/QDCFVex8AZkzKkrxbkzx+HK+RMydYU5Iz9mekQYfPW02Cbi8ZvPxNcvmgmLKITdBL6wbJpRPwQA1z26KaX34DdNUWAFJWEjomnuduMHL+40ng/6stdugH9jVWh1UhPKtaCUt05w+bVrySeHuYkVTnz53Ok4MeBHl8uf68tJK6GaUt19N8mgVFVVQ75rZiQ1pU5T/2Re22rOlNotIs5sqIp6HaBldXa29ePRNU344zuH4DZJCR1Wc03pkJcxaokKSi2ikV32UKY048hqdMCVaksY/vtYelIN3tp7Aif/6HW8uUczCvu87vb9kd+uQVBWsLW5F8vuXYk7XtgZ73QZJczoSMGQmVKAFkfGEv6gHLN9yU49GCs0GWoiIn8bqqoa/w5D1Vq7AxKKbRbcduGMMGO/QiF/ZnqEAb9RbbjjIlw4ezy++eFZUcdYRAE/unLusN/jxW1tADSJ3GipWSEyw5t7TuDpzaE6htue3oaj3dnN8gUkBR2DPsP8wKHfvHjW1pIHRkdm5k7U2jKd/YuV6BgYfaYkwyXSfTfZljD93iA8Ac3ZFgitBseS7wZkNSn3XXOmlAfHFoHh2VuW4GcfOyXhayONs8ySKbslVFOax4nSKImo3SKEMnUUlGYcJUGmNOWg1KRyWt/YBQA4ZVJIAfD4+sP4+B83AIChkMo2/DfjD8q60VH8Y0NGaWRYMVbwS0pYzT+HG/uMmSwpdKMj082lY9Bv/NYTtXwEtLHbvCBbaFBQmmdIsoL3m3tRWWQ13HbjccqkcjT+/DJ8dsnUsIbFQ+ELynh0TRMAoMRuIflugcOzJhPLHbhi/gR0ufzY1d4f89iGO5bjzhfTtxLPB+IPWvqgqKFaaC7X3d2uST2HkrSMNniv4ICs4JvPbc/x1aQPPhbwfovJmqhxt9CTJ5SGvb61N1Tv7vJLWL7jGDoGfChO4qZrvjFzky6rKOCs6dW4ccnUhK9NlIktlJrSyCwUY6aaxiCZzGQaWYmuhU+1prTXHYAoMMMcBtAWbUSBodqUWdrZFpLE50pyzoMLnzS0fJfvox7oYwe/JCeUlXP3+rGAwFjYgqdZnTZUptQTkA3FSyFCQWmecfkDa7F85zF8aE5tUn3mLKKAyiIrBv0SXtnRjlN+vAIdg4kzN2ZjpCKbmNcSNiI2v3htL1bphfXugASbRcCG71+EH1x+MgDga0+9jz3tA2Gv4ROIpzYndodLBe7euuFQNwAYtVGR5FtBv1mK1OvOnhw60/CxoMiQISYXlPLeaoumVurn0U70mzf2G8f8+KXduO3p93Gs34fLTx26VsYclPa4NUfCZF2aEx03vsxuct/Nz8Hv/aO9hsqA3ye0yYzFeExkFllRouW7xqJAcr+btj4v6socxiIXAHS5/JAVNez739gRcuZNpU1TOpH0wcHtl4Y0OuL/LtRubuzgl5SwuvZISsZQUBppdBSQQo+HypS6A5IxjhciFJTmGQdOaDefD80Zn/RrypxWqCrw7HstcPklrDuoyX9W7e9Awx3LcSJCXthmCkqL7RZazSww3j3cgz+tbsJ/P7MNgDaJ4DeEqqJQMLWtpTfsdZmoNeUTGZ7VmTG+xNj3vUvn4OOLJuN31y0IM/HKF/bdfSkA4BxTliPf4VkYUWCwiULSNWG72vohCgwfmVsLAIahFa8hBhBmDHXpKXVDntMs3w21Dkrue5LouEkVzryW7x7qdOHahzfgdv33fYUe4NeWOVBX7oBNFLDlSG+iUxQce9oH0NKTvZIEVVXxt43NRgafw+WLycp3uSO5KDC8dNuysH3mbInZPGxieW4W8Hiv4GP9vqEzpXzRh9S7Y4ZAHPkuJxl1TKHAGMJawpgNA+O1fAS0ccVLmVJiNMFrMS6cMy7p15TpfQF5Xz63X8I9r+7FzY+/B0DrgWaGZ0pPnVSOqdXFee1ASUTzyOpDADRDF1VV4fGHBjnz6nukudBQVuXDQTLNSiZVOMMkPF+94CT85lOn4WMLJ6X9fbOBwyqi1G4J64eZ7/D1KYFpdaXJTq6buz2YVOHEgvpKfGHZNPz6k/Nx7swa7Gzrx82PvwtPQDJ6aF67cFLK7rtew6V05JnSyZVFeS3f5Z8JX1z8+TWnYMU3zsOC+gqUO6247NQ6/Ov91jFVV3r5A2tx7q9WZe39trf0xdwuCAx2S/K/m7ZeLyZVags3p9VX4N5rQy0zzN9/zsUnj0d5UW4MY/g419brjek8bIb//ChTOnbQakrjB1P54rCfDhhjYV4t5trqRGNDQFYgKWpBS53HzregQJg7sQznzqxJKX0/rkTrB8X7Aw74JKNmFEDUzaOt1wuBAf/62tkosop04ygwuPV4lyuAQ51uw80tktZeb5iUu9fDJZLpy1pKpoF5Vm1JgiPzE6tFKCgzD75AJQhMb+sgDyk3AjR5bXWJDaLA8KOPzsWM8aVGELpqfyeW7zgGl18LTO/75GlJXYt5tbhJ74GaalA6qcKJ6RF9TLVMqfY4H4c+s3xzfKkdpQ4rZteFZPGfPL0eAz4JG5u6cnF5Y4InNzXH3Zdsj05JVnB8wIdJFSE1wfWLp2D1dy7A6984FzNrS/HMl5fg4pM19YHTKsIiCDkzJuSKqsZOFwa8wYStXgx5PKmwxgx+Kbb77s8+dgrOPin5doWFQG2ZPUyRGJCSC0r5QmKsBalCgYLSPENSkmuXYIY3Ke7Ua0nN5iJAqO0Gp63Ph9oyB6yiAIFawhQULr+EjkE/LtLl37998wBW7D6BIntokHvxa2cDAH7/9kGcfe9KY/uq/Z0AENZjbKTIpgm0WbpbKFhFVlBBKR8LGGNwWEXsau/H7P99Ha/vOp7wdd3uQJgxCxCqAwWAP64+hK3Nvbj+zPqkauUBwGEKSlfs1tpkJOvSzBdWlkyvxoSKcLmj0xbqU5rPmVIgtJBkpqFG+/12DhZWu6J4rD7QmdX36/ME8MqOYzh3Zg2e/tJZUfv5Ys5Q7Dk2AFlRDak7Z2p1MebUaTWmS0+qNszhiu0iBCF331m+wCgrKgZ8EhL9jMnoaOzhD8aW7964ZCqe/vKSHFxR7pg1vhQ72/qNMdgs3+12R4/ZHO4FQPJdYtQgyWqUo99Q1OomMV0u7cve3B3eL3DAF+7E2K7XsQCaTI9WMwuHrzy5FQAwZ0IpnFbRaB9QXWw3jlk4pRIL6iuM57va+rGjtQ9PbjwCQFvlSxdm+S5fPCkkLIIQdsMZCU+sP2xkuXMFz5QyaJPrXbrr5+3/3JZQ5t/j9qMyQlY4aBp3mjrdmF5TglvPPynpaymKsVqcaqa01GExxkczuaop3XCoK6y2djjwLFx1sQ3fv+zkqP38c+jNgBx/tNHS48FNj72b1fc8cMKFgKTgi+dMw9kzouvJHVYxKaOjqx5cDwCGfDce3Dym1GGFwFjOlE1BWQmrC0zKfTcPF32I1Hly4xF0DPoLWnaaCp9f1gB/UMHX/6nV/fNMaX2VE5uauuO+zqP30y4q4H9HCkrzjGQby5upLrbBYXI9M5siAOGTQ0CrReKrs6LA8jJbQMRmnd7jLiApYRKyuvLwgPDfty3DjUumwGkVceUf1uGqB9ej1xOE0yom3QYkGczy3VMnVSQ4Mj+xWYSU3DA9AQmrD3TiQ795By09Hqiqil+9vg8/e2UPfvLKHjzzbvqcj4dDqKaUIWhaUAhIStwgx61n5+urwjPsv79+Ae65JlQj9+CnF6KmJPmFCYso4PNnN4RtS1Zazhf24gel2v+zPfZ9+s+bceGv30npNQ13LDcmN4DWJxIA/v7FxfjCOdOiji+yibCJQkZqxEcbjZ2uoQ9KM316dtq80GfGYRVTqucdynmc1/6X2C3a/ToHi8jH+31o7/NiqUmGmdB9dxTId69+aD0u//3anL3/WOJfet/7686sz/GVjA7mT67AjUumYMuRXsiKagSlTquIoKxia3NPzNcZmVKS7xKjBUlRk5aocQSBYWpVqG6KTx4/etpEWASGble4jKvL5TeyVgIj+W4hEtkSIlaN8oRyZ1jgOrHcgWUzqsPqH0aKrKhYUF+B5befEzahKRSsIoOUQqb0yj+sw02PvYumTjcuvn81/vNBOx5+5xD+su4wVFX7beYSVfcMFFjIJfDW86cDAI5GuJsO+oK48Nfv4NLfr4GqIqytBQDMGF8a1jC9bhiuoR+ZV2s8vuyUuqRr7Xn2utRhCZv0nzdLM5ALtYRJ+ZJGTCqLGDw7/dL2dmMbb9PjiDNxYYyhoshqBE+FyBPrD2NP+wCOdoe+k9lqK8WD/Xi9lZ1JGoSV6waFJ40rTngcr9NzWIWc3a9f3NYGRQX+60MzjW2jPVP6QUsf9kQs0BMh3H4pLRL/O1/ciW1H+3DB7HFxW76NRaaPK0FAVnCs34t/6DXonz9bW0Tkfb0jcfv1oNReuEFp4eaAC5SgpMAipL6WMLW6CPtPhLvs/uGGhTi3pRctETWmfkkxCqkZyXcLgvY+L57f2opxpXZ0Dvoxb2I5nrj5TLy+6zja+ry4KSLjBCCqBnD+5AqIAoubKW3p8aDT5ceiKZVJX1dQVmATBcybWJ7S35MvWMXkjY5UVUVTZ0ha75cU/OBfO8OOyXUdoDlT+scbFxljxZ9WN+F4vw8wLYQf7nLjcJf299y0dCounJ3YMXw4ferMqpFvfXhW0q/jgVupw4pKPXiYWO7AXz53Rthxo10lMuCVorbxgCeRGYbTllxdYz7S0uPBXS/vMZ4zBpw5tQqZ7irFVUx9Xi3YjxeUOqwi1jV24YtPvIe/fv7MuOebVlOMMqd1SFdSXqdnt4iafDfL92tVVfH81hacMbUSp00OjeOJfjt80YdqSkcvV/5hHQ53uXHk3itGdB7e13xqVfq8KAqB8Xrip9sVwObDWmaUG9K5/dHjOgAcH9Dm6qkoivINCkrzjKCiwmZJ/e7aoDtMnjKpDLvaBrBshpaVqq8sCqthkmQFsqIaNzqRkXy3EPjpy3vw+m7NjKbUbsENi+vBGMMFs+P3uz11cnigeOv50/Hkxua4bquX/m4N3AEZTfdcntB50YysqMNaZMkXLKKAQJKZr+bu6FpCd0RGuzPHmVK+QMWYtkgBAEf0wDPyRsp7hz78mUW4XO+VGYv5k8uxo7V/WL1ozdmYSHlwInhAVmK3YFqNZrDlDoTcIUd7n9I+TwAMDI9vOGxse35rK1p7PUZwHy9TCmjy5WCBBgTn3xfe+kVVNQOsTBqO+YIyzvzZWzhv9jhMqSqCRWBxF1n4YsHb+zoSnlNSlKT8I2xGUCpAzIHR0f4TgzjU6cbPr5kGxhj+94qT8bPlexMqavjvlvqUjl74guJIMPsMTKlOnPEfa/BxwLw4WKUnAlxxgtJdbQNwWIUox/hCgoLSPEOSh5cp5Q59br+MTd+/CCV6O4b6yqKwmyPPgnHzBEHInXECkT7Mco9Bv5RUAHByXUhuyVdLn9vSgj53EM9tacEnT59snMcvyUYA1dTlTtpJV1JUOKwZTmHkEJvIEExS7pzI4IDTOeiHqqpY39iNB1YexFNfOivlGvORwIcC8/eHm1dwEwYOz+JNrU4cLD5369KEDcMTYTWNhYmCsEjM2cRZtSW4dtEko7UGAPDTZrNHc7LvFZQVLPjpm5hVW4KWnpDK5dv/9wEAYEF9BUrtloSZZ6sopCQrzydixdqiwOANZu6z7HEHMOiXsHzHMSyaUoGKImvcMdaRpHNmsqaGfAHZZuHy3ezer9870gsAOG+mpoTgEvpE3gO57lNKvdezgzm4okxpOHwc8JiCUi7Zj/R54exq68fJE8oKuqdr4f5lBYokp15TCgAN+ipVt8uPunKHMWGpr3Kiy+U3jBeMoFRvckw1pYWBudfdnLrk6joEgeHrF83E1y8K1QnZLSIG/RK++/wObG3uNbav3h9qu/D+0V4ky3DcpPMJqyiEOQwnIl5903cumW089gUVuPwSvvXcdrx7uAcz73wNGw5lr9+kuaaUw8cSlz88qzvo02rryhyxZYwch1VEeRyp41AMZywEQvJd3v7l/k8tCMvm5qKmVIrxZr6gjNuefj+sNpLXHx044YrZ73J7Sx8unDM+Zk9AjiYrL8yBPTKLYLMIGTcAMk8i3z/ah4oIp2kzyfYYTNY/QhRCmVJBYIi31vDE+sO488WdaQ/IOgZ8YCx0j+HtKhLJw3Mt3/UNcxGMSA1z26+hFifHGg59jv3suy3GtnKnFUU2MWamVFFU7GkfwCkFWurEoaA0zwgOo08pAEzTzRKuXTQ5bDvvOdnY4cLW5l4j22E3ZGxUU1oIeAMyimwitv7vxXjuK0uTft03PzwL3zTV6pknuuZvhc+0Kr4tlaBUUQt61c9qku+eGPBhwU/fwP7jgzGPdfklTNDNfr587jQU2USU2C342gUnocRuMfY1d3vQYaotfXPPiQz/FSHMNaUczWAlWr7LW00NFZSOhOEuaPBgzuxKbiYX7rtSjCDxg5Y+LN9xDN98brux7bWInrCRtd8AjO9KPDItZ80Vqqqivd+LL54z453jFwAAIABJREFUDW988zwAgMMiQGQsZtCfLgZ84U7GFc743/lkg9JkSxv456jVlMb/zt718h48tfloWmSZZgZ9EkrsFqNkg2d74kkQAZN8N0cZy35v4TtPjwZ4G0IgtfKKsYBTX7zhZVUvfHUpRIGhpsSOJzc1443d4eN8S68Hg34J8yaWRZ2rkCjc2WAB8u9tbfAFk6sziWRShRMrvnEefnjl3LDtvPXLT17ejY//cQPO+aVWj2PId6mmtCDwBmU4rSKqS+wjChLMza/Nk+gB/SY/p64UO1qT76UpJ1k3la+MK7WjqcOFPk8Ab+w5gT5PEH/beASqquLEQLjDnscvo9Rhwb67L8X3LzsZ7915Md698yIwxrDrJ5fgF9dq7VOu/MO6sNdF9v/MJHwsMCsTGWMotlkw4AviigfW4qXtmv1/e58XDquAUkfmqkSGu6AxV7+xjyuJF7xlf9IcjJFR52UWW5t7DTVLe1+4Md2rXz8XD316Udi2WG1uzFgFIWYQnO/0eYLwBRVMrHAa41xNqR2ikFkDID7+XaCbeTkTSHQXT6tK6pxBObmxkbcAsluFuB4Q5uxoPGfP4TLgC4bdU7gJS487fuAn5jhTGrmIQGSGZ9/TTI6+cv5JKZVXjAXMC6JnTK3E6VO1ceFvX1iMyiJrmKO6oqho0heTphR4cE9BaR7xjWe11fLhTsRm15VG2bTzFfUtzeHZLUO+K5B8txDwBuSEE6VkMQel5hpCLl+bXVeK3e0D8AXlpNw9JVlN2Dog3/nSudPgCkj489omQ3EgMM2R8Kx73sYBkyO2OyChyGaBwypCEBiK7ZawFifxJIGVMTJlmUI1gtLwz6zMacXzW1uxu30A97y6FwBw4MQgZtWWJm16NRz4pD1Vj6QfXTkXL922DFPiSMq4BDGydVKqLN9xDH/bcCSpY2VTkCjJCvySHBY4Lr7nLciKiuP9vrDAprbMgSvmT8DzugKipsSGaxZOSvhehZopbdMD9onlDtSW2fGNi2fisZvOzHhQytuszUyiln5mbeiYRFJaWUlubAyV3HD5bvQ5zS7Nx/q9UftHwqBPClt4qizWAtRELYcEw+goR0EpZUozjqqqeG5LKwDgxiVThjh67GFWTMw1ZT+n1RTjtMkVYd0yfvSfXbj58fcAAPYCD+4pKM1DrGmc5NWVO4wJnbllg1m+C5CEN9/xBmVjoj0S+GIFED5hH/AFwxwn5/zwdcz54etDBqaSombVqCfbzKkrw9Lp1Vh9oNPIYAiM4cmNWl3gwRMuvHu4B0FZMSTW8TBLAl/46lLccp7WHzSbjbRVQ74bvv3cmTXG9+HUSVrNy/7jg5g5PrN96fh3J9XvkMMq4rT6irj7uQuiuSYqVVRVxW1Pv48f/2f3kMf2e4JhmdKFd7+JLz6xJUxyOuiT0OXyQ1JUnH1SNWrL7LjvE/ON/Wc0VOH9H34Ym75/0ZALFVZRQFBR8eW/b8H8u1YM468bXRzt9kCSFRzr17KAEyqcYIzhGxfPQkNNccaD0p2tfXBaRaO1VSIjuWLTQlOiawrKyZU2cFnknLoyTdkU45ydrlB2lP8bjZSfvbIHp/54Bd7ccyIsKOWZ0gUJfl+57lNK8t3M02ZSdBRyC5PhYs4cRy5mza4rxeEut9Hp4B+bjhr7kpX/5yuFOxssYKwJDCxSPpcoGP2SKopsxs2CBx8iy23tB5EePAE5LYOZ3SQ58ZqCUpe+Wt4XcbOP128L0Bx7j/Z44raYKRTqK4vQMeA3FAdPbT5qrII+uKoRn/rTRvzw37uwpbk3LDMaSbkpKD19ahU+u2QqgOxO7GLVlALhteqDPgm97gA6Bv2YXZecC/NwMcarNC9sOKwiim3iiILS/3wQkl8lOk9Ljwen/fQNPLH+iLFt0CdhXWNXlEPu9pY+AFo5xuYfXIxPnlEftr+q2JZUIGMVGSRZwZt7Thi1v/nKsX4vzrtvFe5bsd/IAk6MqKm1ZNhFft/xQcybWGYsyiVaNza7Iieqc022tOGyU+rw4tfOxrWLJmmGTjFO2TkY+v6lK1O6sakbg/r4bjbcclhFvPWt83D/pxbEfW2ujY5Gu3z3N2/sT8kwMN2MtIdxrzuAL/1tCwDg/k+dRtLdGJhVZ5HtcmaML4GsqDjSFd0mLp4PQqFQ2H/dKOKtPSfS1qw8kYnCcOB1pcWmtiFOW6glDJC7FU0iPfR6AgkdIZPFPEUyy3d5zWpkEX4i+eOK3ZpBz6s7j8c9phAYX2ZHl8tvyCXNE7G9uuPuP9/THPgSZUrL9N89b13CnTmzqWIIZXvDt58xtRLTdTO1HncAv3/7IABgVm1mM6U8Nk7kNDtcKottIwpK71ux33icKBDgtcW8yTyHsehJ+8q9Wvuu0hGaR1kEoWDku116wLX2YBeO9ftg0c1CzAgCS3sNrScg4S9rm7CnfQCtvV7UVxUZ5m+JQsliU1Ca6DNI1n2XMYaFUyrBGIPAYgff3NyoyCamtaaUGUqq8O0zxpcmLBcxMqU5k++O7oWYP6xsxLUPb8ja+/mCMtYeDDno93lGFrTf8+pe7NMN/a46beKIzlWoMMaw6tsX4KdXz8N5M2vC9nGlTqyMfqEH+BSUZoEdrX340t+34GfL98Q9ZtX+Dvzitb1JnS8dtYFmeFBaYrcaNwnuyjvam8gTydE56Me40pFLaMw3K09QRnufFw13LMfGQ92wW0Xcet5JYbLIWC0rOLI+kzltcmFbnI8vc0BRtc9gKBLVkIkCw7rvXYgHP71Qe67/NjPpKhpJSDERfp2CwLDyfy7Atz48Cwc7XHhCr6OcU5dZp0Auhbx5WUPaz11ityTM9CfC5ZfQ2us1jG8STfICek1g5ATEpktszaxr1Nr/lI3QPMpqKRyjI96miDGgxxVAVbEtqo7ZIqTfsO/zj72Hny3fi4feacTxAR8mVzqxcIo29n1Zl9bHwhbHLC6S4bTLiueWv/ZgJyZVOLFsRg2Om+S7qqpi1p2v4S9rm1J6H0DLNjZEZHiSJedGR6bfGpUmAX985xA++9d3ccOjm9Bwx3J0uYa+VyVLIbvrj5RpNcX43NKGKLk/X7hyB6LvPxSUEiOGmyAksmK/+fH38KfV8W8M5smRua4vHYzTV5WLbaKxQsMlvblojUCkF0VR0eVKT1Daa5pcv77rON7eq2U72/q8sOv9AEtNmQBvgkxpt24X//jNi0d8XaOZWv3fvbU3JMX5weVzsPa7F6K62IbKIiu+cv5JABIHpYC2WMRvSrloqxCvppRz5fwJYc/rhmhNMlKcNhFN91yO2y6ckfZz262iYSKTiI2HusOkukDIIZf3lGvqdMV9fTz5rF9ScN+KfQCAl25bhqXTq406rZFmSq0Ci+n2m49wpQVjQJ83gIoYPW9FQWsJ09rrwftHNSfjax5ej3UHu9A9jAl4jzuAd4/0AAAaT7ggKyomVzpRU2LHkXuvwNkn1QxxBo1EmVJ5GO2yxDjBd1ufF9PHFaOuzIH9JwYNE6ITA34EZAU/Wx5/QfzFba14bktL1Fg+4JVwil4//rmlU1O+TgC45cmtaLhjeZjqJhuYF4BG2+8g3X1kk4EvDmxs6gYQ7fCeKlUl2TPfK0S4xD/WoijJd4kR0drrQXO3FoyyhKIejUCMSdCGxi7M+3HIjGLJ9OQs5ZOlRh9Aulx+vHTbMjz1pbOMlRt+UwxKFJTmK66AhKCsxuxnmCrXLgq5eu5o7cf7R/uM59wV7tNnhZz2Esl3O11+2EQBlTEmkYUEb89x4EQoMLl03gTUVxXh0c+dgcdvXoz6Kk2tkEpmhE/sspnxCgWlsa9z+rhQDWm2rOsFgSU0lhkudosAv6S5SCeS8d7w5024/ZltYZPJtl49KJ2kZYp/+FJ8s6PBiPq2hz+zCN/+iNYbeFebJu+2iAwNNaGsFHc4HS4WkRXEmK6qKh5ZfQgA0NzlwYrdJ1DhjB7nTgz40Tnoxzm/XIVrH96AtQc7se1oH27862ac+6tVUbW7Q9HSE1pg4vXhXF2UDL/8uNbeKTITbiY4jHZZWgu36MCmvc+HSRVOI6v8j02a0RpfKC+OUF8d7/fh/aO96PcG8c1nP8B3n9+BR9c0weWX8KOXduHVnccw6AuioboIR+69AtcvTs1dNXLx7Q8rG1N6/Ugx15SONsVALrLH1XGCyOGWRfC5xo8/OneII4lY8DKemEFpmpNSow0KSjPMZb9bix8lmJAA4YFo5AQFAN47Eip4v+ujc9NSG2jm8lO17MaiqZWoryrCshmhVV6+KuMrcDOaQsalZ2LS0S/ylEnlOHLvFcZzPvkGQoX7l586Af++bRmAxIYJXYMBVJfYMhJQjCZ4ttCslKjQg4rTp1ZiQX2FMRFJpT1OLjKlRk1pgjvHPddoE24Vo2uylypaUKrgv5/ZhkV3vxkzq2We/Dd3hwKVVp4pnRSSpncMxK7lc0VMPGyiECV7tggCptWEgp4J5c4U/pJoLKIAaZRliIYDD9oBGKY7RfboSdu+YwNhz7e1hBbTPAE5qYy4mViyukkVyX8m3C06XjCsKCpUVfvcU0EwjAlD2/ySjC6XHxPKnfjvD80EEFpc4mNSZOb90t+vwbUPbwhbjPntWwdwxQNr8feNzfjaU+9DUTHsnteRCeBEippMYK4pHXVBaQ4ypbHGtroyRxJplMREGrERycEzpYM+KcrwKpMt1kYDFJRmmMEkapLMk9VYUi6z+UnJCGVbsZg+rgT77r40ZkE6X5XhwUWsoJkY3fBJb4k9/d8dLmEDwmsduNPvoF/ChsaumKu/3W7/mLCKH1dij5K7miXOADB/slaLdsHs8Umf18iU5qCmNJHqg7eEyfeYx24R4Q9qDrVAyPmWI8kKHl0TKrn4oDW0v63XC6vIMLHcieW3nwMAWH2gE7HYd2wwzJFVFBkunluLd759gbHNIrKw+r2R9va1iQKCo2wynoiWHg/ufmVPVBD3xp5ok7Rk5vS72vrDnqcalHr82v3wO5fMNrZNqEheqm4okOJ8BlxSmozRkRke7JkXqngN6cQKB2rLHCiyiYZL+uEuTb0RmRHjNdCRtYXmhRcg3BE8FSKVFtmWrJod3wOjzPArF5VS/HvIy0gA4KoFE4d9Lfx8qWb6CQ3uwv/I6ibD8GrxtCrccdmcXF5WVqCgNMNMrhx69fRgR6hJbqygz2q6MZXEWAVOBw6rGDNjxU2V2vq8WLW/A6fe9Qa2mAIRYvQzqC90lKQhU5oIm2n5m09Wbn9mGz79l83YfLg76vgul9+QjhcygqlNw4fn1uKahZOifmsL6iuw6yeX4MNza5M+by4cLPlbJUpux8pU5SN2qwCfFOody90kAW0S/fKOdvzitX3Gtj2mbFzHoA/jSx0QBIa5E8owrtSONQe7ot5DVlS8ve8ELpwz3hjnrXp2zFyPaxEYptUMz1QmFhaBpSxZzRWqquK6P23EX9cdDvsMAGDF7uigNFbNVeTi8NqIzyLVtlQ8U3rJvDpjWypeD1ZjQSn2Z8Czd6kuPrAYBkK8DpkbGlY4rejzBHG832dkmuMZeh3RF8zvvfbUmPvLnMO7p4x0UWWkmBcD0tUVIV3kQr7LF2V4b9nzZ42DRWDDVrvw728h9yDPJDaLgBK7JWxR6AvLpoUtGhQq9I3JMOYV2Hhp90MdoUxpvzeIzU2hCfyX/rYFd70ccu3NRLYrEfwG/+k/b8YafaV/a3Pu+mcRqRPKlGY2WDjSHfoe15aFZ0BjybO6BgNjIlMKAJfqk9f7PjEfv70udv++EntqE7xsOliqqqpLvHhLmPiTSr44ke/maHaLgKZOt1EXfUAPiHrcAUz7/qv45rMfGMdOKHcYE3hAM/HidVqMMSysr4iSkAJAc7cbXa4Azp1ZY0yU+YKOuY+dRRRQr9fozk5Dmx1LHmVK/7SmCe16tu/WJ7ca33eXX8KBEy5868OzcN6sccbx1THGlEiJNIdnHmJ5OSSCj2fFdhFvfPM8PP2ls1J6Pe/3HE+2yucNjhRr+iIl/Q+tasRzerspHpTarSJeeL8VS37xtmFs0+0O4Kv/2Bp1vpX7OsAY8KGTx8ecEA9XvhuVKR3WWYaP+fMedUFpjuS7VpHBZtE+F8a0/4Z7awnKCgSW+8WHfCZywaoqDZ4g+QAFpRnG5ZNw09KpEAUWV6Li8oeyoz9fvhfXPboJu9r64QvKeEt3N+VkOtsVibmoulevL0k0ISVyh6yo+N7zO3DwRHg2gdeUZmpB461vnQ8AaOwIGflEZgIja1ZUVdXku2lwBM4Hfnf9Arz29XPTWg+eTfnuva/tw8w7XzMCmURjQKV+87zuzPyuJ4rMfB3Qf1fmOmpA+xxOmVQeVobR4w6EGYtNG1eM5m5P1AJCs26YM92UBTUHsxyrwOCwinjqS2fhHykGQLGwifnjvvve4R5M1LPGbX1erNfb4nCzoZPGleDPnzsdl8yrhd0i4HuXREvcrj8z2ojHJgqo182JUq8p1QKZIpsFs2pLcfaM5Nx2OeNKtL/nmoc34G96+yQzPFBKtf2DaKop3d3ej/tW7Me/t2vO0BP0f8N4tc2v7YrOOr+26zguO6UO40sdhiO/mbJhyncz0Vc4FcyS3URty3KBmoOfZVBSwrKaDNoYP1xZdfD/2TvvMDeqc/9/z4y6drXF27yu696wwRgb25gOweESuOkJIUCAEEK4aTe5ECCkkPxIbvpNuTcJIYQAIQUChN4ChGZMde997e1ddcrvj5kzGkmjLq2k1ft5Hh5Lo5F0bEZzznve9/1+FYWsYPIkvte5MU9xu0qBrpoiIskKAhEZk2qcOHlWY1Il0mAkehfi5UnBiIy7XjmQcG6xs13xOE2TIp/cyu0mTmjs6h7BfRsP4dp73ow5/s7hQa2/LYuep3T88uLlAIBzF7ViTksNLjq+HT/9qHUGEADCphvsY5uO4qF3OhGR1arJlLrsIhZOLqxnJ2MsqS9hofntv/YBALr0BW2qDfAapw07bj0Pnz9rbtHHVUx4K0V7nQsfPHEqdnaNQFVVjIRiWyx2f2c9ZjV5sd8UdPaOhtDojV7bk30uhGXFsOLg8MDKrFRstSPONyDWzmkqiLWTTRQqwnv6oXc68cz2biNLDMCwoeKbYNMbPXDaRPzfJSuw49b1qLNQ8/7ORUuw/dvn4cQZDcaxRz+/zgiOQpFse0p1UaUcPcNbTJUkP31mV8xr33t8O8784T8BZO9JzvcxZEXFb1/cZxxv9DqMAJeXHPO+f7MA3qA/jAffPmI8P3NBC25YvzBhzFet68A5i1qxoC23rL2jxAFLWFIMxeHxFllKRyEzpfduOIgXd1n3spuJyAoctug9gTFNNSDXqUWSVaNEnciN+64+Gf/7ieXGhlydhar4RISC0iLCxQTqPXaMhmS8cWAgpjSXY9XPsrNrFN95VPMOu/+za4zj412+a5UQycXXjSg+vCTJvAutqioe33wMa+c05e1taIYr7P7Px08AAPzkoyfgwuOnxJzzt2tW48wFmnBPRB/bM9u6cM3db+Lzf3obAKqip7SY2ARhXDKlfAF7mGcJ06w3nDbrHvVKYqOuev5/l6zA4nYfBvwRPPROJ8Z0kZu7r1yFp790KhhjmNXsRVhS0DkYQERW0DUcxBTTJhAPlOKF7A70+eG2i2iudRpWX1bZsUJnHbIV0CkFYyEJtzy4GUCsOuzjW45BVVU8va0LDR47Fk5OHxgJeqb5b9esQaPXgRvfuxBzWmqMEulse0p7RkPwuWw598yZs+i2uCqqX/1zj7FRna0nuVG+q6jYeCCq/WAuX/7u+4/DM18+zSgPPM/UF7unZ8zwS79gWTt+d9lJxoZAS230er7x/EX4zSdX5HxdxmdKx3uDJCIrRpa33DbZC9mOccP9m3DJ7RvSnheWVdhNG1UM0UqNXLKlkqzAXuJseKVz4oxGnLdkMv545Sp8/qy5VbNWoqumiPBy1waPA3v0Xd2fPbsr4Tyr0iG+G7xsah1O0JvPAa2HZTyx6rXpTeHZR5QOIyg1LRS2HR3BwX5/jCBHoTh+Wn3KRdOJMxpx60VLAGj9EYP+MK64c2PMOc1VkiktFqLAxqV3k2dsDg9omb1qKOG/5YJFmNtSg8XtPqxfotlm7ekZM9ot2uvdmNOiBUQdTZo/697eMXQOBqCowFRTdo/33g0FYrOsB/r8mN7oAWMMd1y2Eq/ecJblWOwFDiLtWVqNlIK7Xj2AAV0F1nyNdw2H0DUcwnPbu3H2wtasA6M3bz4HV506CwBMQWl0njvY509ogYinczBg9GjmgnnM3SMhdNzwKB7ddDThvGwzpeaeUrPtiXked9lFzG6uMUrxv3TuPPz56tUAtHXH1qPD+PSpswwvVY5V+W6uJASl49xVGpEV4zdZbj2lxVAinnn9I9jbM5r09YiswCEKWKavNa84pcNISOQynLCsZm1nRFgzq7kGXzxnXsVv8mYKXTVFhHt8NXodxmRxeCCAv2w8FFNyF4ooCSInz+3oRkutE3+/dm3Mxeh1jG9PqbncidM7QpnSciAYkWN6NXkWyzzh79EnouXTE/8/jgc8kxCWVUMF2Ey19JQWC1Fg4+Kzxyux9vSM6c8n/gS5/rjJeOpLp0EQGNrqXHDZBYQiMkb1TKn5ns2Vcff1jGLbUS2gmd1cY7zOxYvig9JD/X4jE+V2iDGKuwBw0fGaTVehVSwLHeQWgz9tOGiUx3YOBvCptR1Guehr+/owHJRifGBzgd8rL7sjmk361J2v45wfvwC/hRcpp3MwmFdQCiSKwDy9tSvhnGyFjszqu8nEnTg8yGytdWH59Ho4RMGwOLp87UzDlsI431e4e3WprULCkmIoB49HplRV1YyD32IJHT27vTvpa2FJEzpqrnVi/23nY82cJuMen8to7t1wMMFOiCAygYLSNMiKimvveRP3v3k46/ce0/uvGr0O48Z3oM+Pr/z1Xdz92gEjMA1JckJQqqjAqlmTEnZHxts41y4K+OzpUdW9yXUu9FGmtGQoimrseC64+XF89NevGq994T6tJNZhyl5yqf/xFsji8MVvRFIwbGF3NKlKFOWKxXhkSo8MBtA1rC0wNuzTSgKrsV3IaRMRjMgm4bDob6qpxoFapw3feHgrPvPHN+C0CVgyJdpDzIPSYVNQqqoqDvb7MWNSNKMazw8+tAwbbjyr4EFpuYuQBCMy9vf5ccHSdtR77LjilA58/YJF+PUlKwAA/9ItXWbmaZPDSyV51nAoEDF6VXmW1syLu3owFIhgKBBBvUXvajb44u7JEUVNKN3MVehob+9Y2jLQB65di3uvOhmCwGATBcxs8kBSVLjsAibXJQbc8UFqPsSva8a7fDcsq0amNBAuvrLQ/z6/FwtuftyonktFsdTUnSmuJU19N/aewP8PZTu/kJc9kQ9pZybG2O8YY92Msc2mYx9ijG1hjCmMsRVx59/AGNvNGNvBGHtPMQY9nogCw7929eLNg9nZoPjDEn7y9C6017nQ0eTFf50Xqwh484NbcNvjmsddSFJixAY4Kzsacx94AVncHt2NXjjZRztgJeR3L+3DmT983hAv4PY85pIfc/muYQczzhl2jt0wiVeMcrJpjdEFT0MB1WirEVFgSb0O82EsJEGSFVx371tYe9uzAIBPrp5R8O+pJGwCw52vHMC9Gw7CIQoxfpi8r5SzLK603WeRKR3wRxCIyJiSIuNmE4WYXr5CUe6Z0mvv1sTammudePvr5+JDKzQl56Za7X6x6cgQABgiILnCM631Hjv+8W4nln3zSeO1eB/XIX8El9y+Acu++SSGAxHUZmnhFE98j//D73Tiqa2xCrjZbkbwXmHzZmUyptS7sXr2JOM5VyJOZfMyvdGDj61MVDKuNCKyYgT849H+8MBbWlKja8Ra+dhMsYbDALy8uzehPLh/LGwdlOZYvnt0KP3fkSCSkckd7/cAzos7thnA+wG8YD7IGFsE4KMAFuvv+SVjrOKd1Nvr3egczO6H9r/P78W+3jH84MPL4LKLuOKUDtx+aUz8jod0NdtgRLYOSmdGg9L7Pn0yvv+BpTmMPn/OXdxqPF7QVotBfyTB4oMoPoqi4q5XNUVms3iBqqroNpVUm/0N/SY/vVLAJzpJUY0d1EtO1oKbOS014575n2iIAkOhf4qqqmLxLU/gkts34OF3Oo3j33zfYlx9mtaLNx7iSuXGmF7OebDfj3qPPSHb02HK2pnv3YB1+S7f3CuEmm62mPu9yjFAfUYvNYwPGDwOG7wO0VCpz/ffzmUX8YHlU+G2i/jnjliV0ngfV3Olx0hIyls4zmrO5xUJvEyZB+GZsnrWpPQnJaHF50o6Ls4LXz0D/+/9xyV9PVdK4VPK58li9HDGw8BFg9Kfa86UFjLr+Kt/7sHHf/saOm541Pjc53Z0Y/m3n8LzO3tiNtmAaDY726C9czCQ/iSCSELaoFRV1RcA9Mcd26aq6g6L0y8E8CdVVUOqqu4DsBvAyoKMtIS017my/qHt7h7BrGYv1syO+pedtbA15hw+oQYiCmosJjhzRmnVrEn4cIl8/+yigOvXL8DKjkajj6afSnjHnRd39+JAnz/h+EhIwrajw8Zz86Q2FpLgtAklK9fjC95Bf9hQHj1lTjNWdTSmtJAhMqNnJIT7Xj+Y9+cEI7KxOONli6+YlMJ/88kVYIzhq+9ZgN9dtgKryqSKYzwxJ6StMvzmUtJlJnE6QNsocohCTGDDe/NLYYtk7mcs5/0FKxFA3ofuEAUj2M+HBo8dR4eCMfdQINFbeTCunDdV8JYJVhlJXtly+dqZ2P2d9Vlnyac1eoxe0ROm12PdXG39Ed+/akWr3jOaybmFZjzLd/f1jiEQkTGg2zONx/WfTdbR3FP64NudKc5MTXyw3WXyp+XZTH7NR2Q1oVQ8V9kA/tk/15X5CSIbCr1SnQLgkOn5Yf1YRdNe7866JGHkh11EAAAgAElEQVQ0JKct72mpdaJ/LIxtR4djslscd5b9JMXkM6fNxp+vXm3IUlMJ7/jztzcOWy4YekdCRuYAiBWkGA1J8OZZZpYPfLf1Ny/uw8u66f2MSR7cd/XqmLJwIncU1VolO1M6BwNYcPPjuO/1Q7jjpX34/cv7Y15/46azcc4ibUNNFBjOXNBaNUqAZswZAysvzHZTH97MuD5Rxhh8bjuGTIENVzEvhdR/2BRwjUf5Yq5YVeTwYLC51lmQ6/AM3bZqT5w6abyAWLxIVb731e9ZVD5xMTiXXcx5I3FOiyawNbelBrdfehK+/8GleOqLp6Z9Hw9gJ/qG80v6PNSv/xazzZR2DwfRPZy4HnzgrcO4+7VEb3kzmagMm8djrlTJlvhqFnMPNN8QazRtrsUHpYbQUZa3h87BAAQWazVEEJlSMrUDxtinGWMbGWMbe3rSm/uWksn1LgwFIoZoTCaMhaS04jJ1HjuODmkZ2KUWKoLluPDju/q9o2EM+SMxKsJEcdmwrz+mLPCE6Vo2pm8sjO1HhzG5zgWbwFDjtOFQvx+PbjqKvT1jZSMm9ObBAdQ4bSUNkicaV5zSAQA42D+W82c8t0Mrlbz/zSP45sNb8bNnYm2rJpFtD4BoBmPhZB/OXdSa8LpZOXdaY6J4kaQo+NPrUeX1UmZKzZsYqjo+JYzZwDOgK2YmZuS5An2hlLt5EMe9QTmRuF7twUA45j35Kv9OtxC44nZD2QocmeF/n1qXHQ6bgA+vmIZZJiXoZJw4oxG/+Phy/OLjy3P+7kqAKzp/bb2m85HtEmbld5/Byu8+k3D8i/e9gxsf2Gz5W2JZBHjmfZj47Hw2pNpU6dGTCmbl4fjESK5CR52DQbTUuspeTI0oTwp91RwBYK4xnaofS0BV1V+rqrpCVdUVzc3NBR5GYeFCFDyAzISxkGRp3/Kri6M3fFlRjfKkJVO1Cc4qY1pO8AXUzX/fjGXfehK/e2lfiUdUHYyGJBwbDmLdvCbccflJePcb5+JHH9bKX3d1jWL7sREsnOyDIDAoKnD9/e/is3e/iVf29uFMPRNQajqHggX1uiOA9y/XClG2dA6nOTM5vDUhTH3iKeFrs1svWoIr181KeH3tnCZcta4D91y1yjKo4BtKXL28dzQEm8AKUoKaLfEZyDKLSTG/rRazm71437L2hNe46nGhPI6TCftE4qoPDvVrv5MXv3oGnv7SaTg+rkS7EHBl53yqpHhvcypLm2Scv3QyVuXRl5o743cB8g2Zej1LWOhKgWe2JVqv8AKnTJR1+Tluu5iXXU38/dzcI905GMQ9rx3ENx/eahxLminN4Lu+dN/b+M4j2mcdHQpgcn3hxdmI6qDQEdBDAD7KGHMyxjoAzAWwIc17yh4uj34kC7Gj0ZCUYPMCwCiDAzR/Uu5d5bKJuOfKVXj2P0/HA59dg3uvOjnPUReHSXqp2cF+rbfxr28cxi+e223psUYUjiO6B+m0Bg/OmN8Cn8uOmZM8aPU58dLuXuzpGcXc1hqITLMIMe+Kzs5gl3w8CEsKjptKJbuFZEGbD7UuG17d25/+5CQcG9J2zbnPLQB8+Zx5AEpTWlqucE/LZKJhosBw4/mLYnQEzPANBN7b1TcaRqPXURKxr/hy73Ir4fWHJcycZG33wistCiUQ5bILlmJP8eWPO7tGMLnOZZkFz5cvnq393nj5rtuRe1DapgsWcdGkcmX7t6P6meN5+fFgrZAJgJAUDR739o4mvM6L3jIRiOS/Ra9TzNjb1IwkK1BVFX/ZeCjhNbvI4LAJ+NFTO/C1BzbFvJYodBQ7nlTc/9YR/OZFLUFxdCgY08pAENmQiSXMvQBeATCfMXaYMXYFY+zfGWOHAawG8Ahj7AkAUFV1C4A/A9gK4HEA16qqWnxn4iLTru/6HM1Q7OjPGw/h8EDAskzRXNIQlGSE9JIhl13AmjlNmFLvxgnTG2Kk2suJGqfNKH8BgO3HRvDfT+zAlX/YWMJRTXx4lr7dtAPJGMPqWZPwyKajiMgqptS7ITBtp5XbrwBAR3N+Xn758trXzjIel0vWdqIgCgwrZzbi3g0HMejPrRfs2LB2bfE+8eOm1OHq02bjpvMXlu3mWCk4aWYDgFjl2mxoNYIFLSjtHQ2VpHQX0EqQAWCpvklUbl0Y/pAMT5Iyf754LlRQyhgzsqUCA+78lKbNGB9A7Dg2gnmttQX5znhOnKFdW4P6ZqLVhna2n8U3QcoVc2ZuXINSia+5CmcJc8ykOcIV781w9d14RWcr+Hg8DlvWQakkK5hz42PouOFR3PrINgDA1afOwgr9mnDZRDTXOC3HYbawArIrOTbTPRyM0bUgiGzIRH33Y6qqTlZV1a6q6lRVVW9XVfUB/bFTVdVWVVXfYzr/O6qqzlZVdb6qqo8Vd/jjA1fA6xoOZdRD+dW/vgsAaEjSy3fN6bMBaJlSvsMWf0MoVxhjuP3Sk0o9jIrDSojGrHiaDj45ue2xixXz5kVLrUsv31URlhR8ZMU0PPof64wJqVSYF3ynzSvvUv1KZHG7FmB85o9v5PT+Y0NBLNKDFEBbzDpsAq5cNwtzi7QIr0R+8fHl+Ob7FmN2jps8PCjdsL8f/9rVqwWlJSpnP3VeM1654Uyct0QTIym3TOlYWII3SbaQ30sL2SvPS6jXzW02xF/MPXmSrGB3zyjmtxXn9+Bza/d1rgibj7Jvi8+F/bedj39bmlj6XK6Mp8VcfKa0EBsyR0wJC6uglBdDZOIpzct3bQLDcFDCzOsfSRDZSoZVua/DJmDBZP26ZdHNnHgxtgT1Xf3PbPrNhwIRjIXlshLpJCqL8m5gLBMcNgECA3789E6c8+Pn057PVewuWzPT8vX/Om8BTpzRgFf29uHOlzW1tvjSiXKmMcliIN5snND4+1tHMO+mx7CvNypG0zsawoKbH09QOk0G7z12xJUcnWHKPLb6nBAFBkVREZJkOGwCFrX7Si6Y5bILYAxYMaPR6OMhCseHVmht/JnswnM6BwOGV13XcAirZjXiriu0DJE5QCWi1HscuHTNzJx/T5paLPB/z+/FJ25/De8cHoK9hD69k+vcOStsFht/SIbHQpMBiGZ5C1n2XKsHpQ0eO2wiz2pF57MD/X6EJaVomVLueTowFo55Xi1YBXLFIiJpF7vTzn1Kc/sc80az2ceeC2IeHvDjwl+8hJ6RkFELm1n5rvan2eHgp0/vSnJ2LFY9qyNBCWfMbzEe8+qMGabyeFFgCRsh/OcV/+9zoG8Ml9+xwdKB4T0/fgFA4jqFIDKFrpwM4ZnMPT3pVS69DhvmtdYkDd4AoE//QXMvwHzU9sabBgs7BADw59GUP5F5cZcmQX/LQ1uMY1zq/Y0DAxl9Bp8A4/tgzD52C9p8EBmDrGdKy2ViYIzhgqXtuDTJJg2RH9MaPVg9axIYtF720QxUwj/2m1dx4wOb8fzOHoyGJLT5XFg3txk7bj2vREInEx+7KCSU65r7eEuBkEXf2Hjw+OZjeGzTUS1TmqR391OndODbFy3Bh06cWrDv9ekL8nqPw+gvjZgW+Hu6tT7BuS3F6c/nHqHcIzif8t1K4vylkwHkv3YY9IfxxJZjGZ0blmWIAjPs1XK99s1uDFwsrqXWaWRNf/viPrxzaBAPvHXYyDpmU75r7mm++7UDGYkk8c83e9yPhSSsnRPtc+fX99KpdZjV5MW1Z8zGn68+GZ9YNSPms/jmW/y/zwd+9TKe29GDdw4NJnz/Mb01oVzWHkTlUR13vgJg3hxXVdVyt/y9P30RbXUuKGqiEXE88a+Xu+qumaYaJy5bMxPvPW4yrr//XZw0oxH3bTwEf0hOqmRYzfA24hd29hjB4iPvHgUQ3fVPBy85srrZ33H5SdjdNQq3QwRjDLKinV9OE8PPPkZG2sVkxiQPHtt8DMu/9RTCsoL/+dgJuCBOubR/LIzl334KP/rwMhwZCOBAnx8P6Zsj3M6kUtoIKpVWnxM9IyEwBly6eiYuPL60JZZCkoVnqTCXoCfLlNpFAZecPMPytVzh85bXKRo9w+bKH66YXKg+1ng8DhsEpmXJapw2Sz/qichPP3I8Dvf74c/Cbs+Ka/6oKc1vuPGsmI1aKyKyCocomKoEcrv2R0OS0aJ1ZCCA5lonukdC6N7Rg0F/2CjV3dU1aighx9u0WMGDT3P2OCQpkBU17XXB37tubjPuee0gAOBL586Dyy5iQVstRoKSUSJ+xoIWfPnc+Uk/y8iUmo6pqoreUe398RZKZhxkB0PkCF05GRIylWokq+/fenQYz27vRjAiw5VmceeMC0orKVMqCAzfeN9irOxoxLNfPh1r5miZlbEcJOirgb7RqABNICwjEJaxUc+QJtv9fPfwIAKmSYlnSq1u9mfMb8FVp2oWFaIAyIqCiKxW1EYHkR+rZ0/CUCBibF5cd+9bCefs01Uhb3lwS4KyKFfsJIoLt/yoc9vxjfctxgnTS9vvHc2GlHQYliTLlBYDPv96HDbY9fsmL7XsHgnihvs1pdKGArcfmNt2uAhitWRJAe3v3FTjzLt8l7fGZJJN5BvD0Z7JzL/HHMBypWQA6BwKoL0+mp085XvP4ahe0vuXNw5jZ5d27zWX7z61tQt3WFjq8Q2iS1fPsDyeCv75S6fUweey4fT5zYZ7xD+uOwUvfPUMfOvCJfjMabNx/NQ0lkYWG1Z8cwaA0f5h1bZVLZsqROGhVWsOHB1KbQ3z6t5+o18hGas6Yk3BPXlIwJcavqMdGMe+kEpiwKSKOhaWYrxdrSbRoUAE7/v5S/jCfdHAIpykpzQekTFjB7OcMqVEcbGyz4jPAHTrFhEjFlkJniklisv+Ps1Ka9CfmXBJsYn2jZU+Ko3vt0uWKS0GvKTRbReNPt+xkDaf/UHXfQDys2qx4rUbzsYbN50NQBO2AfITOapEPE5bXn6cACDr128mmythWYHdlCnNZkPG7EH60u5e45o9MhjAlHoXXrr+TABaFvWZ7Yl+peZr/Ko/bIzxCeVwLaTzl7bjqnUd0eMZ/Eb5esJhE/DNCxfj8rXR99tEAaLAMK+1FtevX5C2J9t42fS15nYD3iZi5W9djptcRGVAq9YcOJYmKAXSl8F99T3Rsolrz5hdcjGafOAqiZn0slUj5iy7Pyzh+Z09xnOriSakT9D/3NGDnz+7CwNj4YyDUsaYMcFTCU31YGVWHr95Fv+cW5wAUWVYorj86uLlpR5CDLkszIvF9qMjxmOvQxxX1XAubmQTGRq8DjTXOvGsHlRYWbsVijqPHZP0PmOxWoNSu2iUt+YK31TJRGwxLClw2oSsfDg5tz2+3Xj8nUe34dFNR6GqKjoHA2ivc2NKfWp/zkx6SnmALQrAjecvwo3vXaiPM/34eMmwTRTw7ydMzUvtntvYKDFBqd94PKxniq2cBcphk4uoTGjVmgNPbu3CLQ9uxvZjw0nPOdCXWhDJ7Fda6aVzXClwOEPZ8mqDT4KAtvvutAlYNq0eNt2+BUDMpMwznSFJwQ+e3IkfPLkDYVkBY9Hd9GSIAjPsY6h8t3po8jpx5oIW/PBDy/DtCxcDAHbr4ix3vXoAu7pG0BOnlvjbT0atnSqpfaCSOSmuQqbUlJPQ0cYD/cbjm/5tEWY2jZ+/Mu8jjcgq7KKAsxe2YnPnEFRVRc04lRHb9TVBtSnvuh1i3lVWPEOYibptICLDaReiPpxZfE+8DZE/LOPOl/cjGFGM8t2/fmZ10veHpfR/T/5b5OPjGc1MSpMlk51MvvCPWH3bM7j6Ls2HnmdK7SLDu4cHsadnFD9+amfe30UQnOrakisQ927QGsjvevUAHvv8qZbeZbv0BWEmtFR4UFqvq/GWS0lauRGSFDR4HDg2HIQ/LGMkKMHnskHQRYm2dA7h/J/9C7+8eDnee9xkBOMmLoEx0+5u5kEple9WD4LA8LvLtCCzZySEmx/cgpf29OKprV2469UDmN7owaqORngdIlx2EecubkWdx46zFrTEWBURxaXc+gWTKWyWgo0mJfJi+YEmg5fv8kzbwsm1uHfDQXQNh8atjLhaM6V2kSX0uGcLD9jCUvLP2dk1grcPDWI4EEG9O+qdnWlWT1VV7O4exfuWtWNn1wi2HxtBKCLjG3oJLhfBWjGzET6XzcgkmgnqWcVU3ynr2VSRB6VZlNhzIaVCBKXM+F7giS1dALRMaZ3bjktXz8DPnt2Nf+6wtkgsg9sJUaFU192vwCiqtgBs87lQpwdmK2c2YtWsRpwwPU0TuYlKz5RyBTpz7yQRJSTJRlD6/M4ejAQjaK93QRC0xeCbBzVp9X/t7tWC0rj+mjq3HaMhKaNyXIFFVfsoKK1Ommq03+P/Pb/XOCYrKnpGQ5jVXIOHrzvFOH77ZSclvJ8oHuVWvVAuPqWv7OnDI+8exdyWGhw3pQ7HTakb1+/n/sn8nrmgTVNF33ZsGHoStegbCv1FVvgtV+yikFGGMxX8+k31ORf8z78QkhQsmeIzFHoFxjLekOkeCaFvLIzl0+vx/Q8uxYKbH8f3Ht9hvD690WM8bvA6MByU8JnTZmNxu88QnuNzu1m5Nl5Vl7f78OoVMZdMqViIoDTxMw4PBDCt0Y1PnDwDv/znnqSbCeWwyUVUJuU1Q1Yg/++xbVj2rSfRNRyEXWRYMbMBXz53Ps5c0JrxZ1S6yIjXIcIhCuinoNSSkMl0fW/PKEaCEmqdds1TVFER1INItz4JxUutj4Yk3eIlfRmZENNTSiWZ1QhjDMvjNsVWzGxAz0jICFiJ0sAXeuUSnJZD+e7/Pr8HH/vNqwCAi1dNx48+crxRyjpeXHFKB77ynvm4WPdq5Jna7UdHjOzTn69OXpZZCHjQcVmV+TlrQamaVx8i78NMFZTyYO9Qf8CUKWU40OfHzOsfwUu7e1N+x9ajWrvWwsk+4/cbiMg4aWYD2utcWDYtes/lc3lTjSMmWOVz81hMu07sJjQ/h39GNgrZPNPPy9HzwSqsPTwQwJR6N1p8LrxncVvS95L6LpEr5TEzVgD8J/bFs+fFHN/Sqd2ouodDmv9VDouN+D6FSoMxhlqXzbA7yJaQJOPb/9iKrZ3Je3QrFUlWMOiPoNXnxLKpdXhyaxe6R0Jo8DogCAz9Y2FjguKTULwS4XAwgkBYhtuR/toSBQa/rhqZyfnExOSvn1mDH35oGV786hlYONmHsZCM3tFQ1WVhypE/X70az3z5tFIPA0DphY4e23QUtz0WFY9ZMbM0Pbcuu4hrz5hjzN91bjum1Lux/diwkQ1q8Ba31/OqdR246fyFmGGhpD2Rcdii/by5ohhBafLP4PPrUCBiZMbBgLf0SqV79LasZHCBy2mNnpgs4lAggkXtsZl9nqlsrnVixqRoUBrSN5zNPbTxQSl/zu2CxCxK7AvbU5r4GV3DQaOy7wqTMrBZ4OnytTONzR2CyBZatWYI7/Not1C5BKIiDbns8NomgEqqw5Z7Cc7TW7tx+7/24SdPT7yG+f9+Uivv6RkJYW9PtHfvhOn1GAlKeOCtI/jJ07sARO0G4iep7uEQRoIRw+A9FQJjRpBL4jXViyAwfODEqZjW6EGNU8RoKILe0TAFpWXAyo5GTG3wpD9xHDAUSEsUlf5rdy98Lht23roeL11/JpaMc9luKhZOrtUzpYXLPqXixvMX4cp1s4r6HeUI7+fNp4SX26ik+gyzVsMlugeowKJ+uCNpNtV5e1K8V23faDhBDOvfT5gKUWBY0OZDvceBe65aBbddNOZ2sy9r/Ca0EZTq64Fsqhlko3y3AJnSuJg0oOthcA2U5dMbDHVfc+B9ywWLC26dRFQPlR8NjRN1erkHNyKOh/tNlUtZ1nhjFwVLafBMGAxoN/s3Dw6kObPyeH6HZv8yGIgY/pCXnDwDZy1oSTjXJjCoqopD/ZrsekeTF7OavdjfN4bhgJSRAIYoMIyFKCglongcNhzqD0BWVDTXUFBKRCl1T+loSEKD1wGHTUhrpzHezGutxZ6eUaOdwl6APj0iEb6Rn09Qyr0yU30Gtxh66HNr0aErOwuMGd8/Ekwt1Djoj8BpExICrr6xcIJt0BWndGDXreuNMvA1s5vQXOs0BaWJavvR53pQauNBaeY9pfzvX4jy2fhMac+Ipt7eYtrYbNSr/HiJMrWHEPlSnRFUDvCSmnSBQTblu3/69Mm456pVeY2rXLCLLOfyG17K0jsaTjsxVAqf+v3r+MvGQ0Yfi7m0+XNnzrHcyYzICl7c1YtbH9kGm8Dwt2vW4LzFbTg8EMCG/f0ZWQUILFqK56aglIAm0HJkUJPyL5cMHVEe8ORfqXpKR4KZbbaVgqkNHkiKavj7Up9cceBBYTjHoLR7JOq/nCoobfW5MKvZi6VTo72fzPQeXsabjIGxcEyW9Pr1C4zHkyw2+4S468VlF4wA1Fy+OxSI1eIIRGSIAjM2QfjnZPIT5YFrITZQ4jOlPHnAEzRANHvMg1PyuybypTxngzLkxx85Hve/eRhLp6YuL8qmfPfkWZPyHVbZ4LCJRgCWLeZSlrcPDWLd3NwNn8uBgbEwnt3ejWe3dxuB4cLJPsPyIL78hxOWFGM38m/XrEGj1xGzA7s/A+sO80RIQSkBRBcMgNYPRRAchtJZwoQkGc9u78a81ppx/+5MaKvTAo3DA1rlyniLL1ULDjG/ntJNh4eMx+EUnxGRFdjjSrAFxmLWLaqqJrVdG/BHDPs7AGj1RQPR+a3pLYzcdhFdI0F85q43cJxpHcnnfE4wosBlsn/LpnyX/xsWYgMl/t/hfT9/CQBiMsW8uoH/u0yn+YXIEwpKM6TR64jp92ivc6FzKJhwXqWLFuWKQ2Q5l9+Yleh2do1WfFC67WhUsCkkyfjqefPxqbUdWNnRiL++cTghm377pStwzR/fREhW4NdLdybrvcvmwPJ9y9rTfrdomkiofJcAgCZ9F7/eY8es5uoSUSFSY/SUliBR+oa+SbezK3NP7/GEVxW8sqcPQGHEY4hE7Da9pzTHTe13TUFpqs+QZDXBKoUxxLQdhWUFziQq94P+2Eypy3Te/Lb0GysL2ny4b+MhAMDjW44Zx3tGYzOlt/9rX8zzbCxhopnS4qjvArFrkpvOX4TzlrTh5FmT0DMSwjWnz8n7e4nqhrb+cmD7t8/D3z+31vK146dl7k86kchH6CgQlo0SruFA5ZfvbjUFpZecPAOfPX0OXHYRFyxrx52fWplwvssuwmHTenIDeoDODdudugLf+0+YguvOmpv2u82ZVcqUEkDULuG8xW2U7SFiiPaNjX9UWmzfz3yZ21KDKfVuQwuAyneLQ749pZuODMGnrx9SfUZEURPuf4LAYoJSc1ltPAP+cIwCM5+bAWSkmHz6/NjN9vctawdjWqZUVlR855GteHprFwDAY8pGZmUJoxSvp5Rj3ux2O0Ssm9sMuyjgxvMXxVTlEEQu0AolB1x2MWkWqqVKa+rzETryh2XUOm2oddrw02d24St/eafAoxtfth8bMR63JRHGMuOwCUZQ6o/zLOUTQ6Zm2E2m3hbzpElUL+89rg1LpvjwhTg7K4Lg2b987DhyhS+yf33JieP+3ZnAGINP75+zCSxpWSeRHzxQ/NSdr8cIAGWCqqp49/AgTtRFjCKygrGQhE69h96MJCsJvZYMiCnfHUsRlA76TVYyQExGNZPNvjVzmmKef3jFNEzyOtAzEsKLu3rwmxf34co/bAQA/PcHlxnnZWMJwzf1C9Gnnexy95CyLlFEaNWaI/G9CdWOZoCde6bU7RCNHem/vHE4LyPtUqIoKl7c1WM8d2cQGDptAhx6UB+IyHDYhJx3OptqtUmTsepVgiZiWdDmwz+uW4e2uurcMCOSUwjl01zhi+xcvL3HC34PzXRTkMge3lN6qD+AJ7d0ZfXeYERB72gY89t8ALSe0q8/uAVrbnsWXcOx7VURWUmw9dF6SqOBqD+UPCgeCkRQbxL54T6iszNsiahz27HM1Eu6evYkNNU40TMSStgUmtMSLQfOpqd00K8FpfXu/DOWyZYgZPdCFJPynQ3KHCt1s4tXTS/BSMoDh03IWehoKBCJUXQDgH0ZiPqUI+8cHkTXcFS4wJ7BgsthE+BxihgLSwiE5bx2Irnlh6omChUQBEGY4QFhKYJSvvGYrEywHOCBB21CFw9zllHNsoycB5Q+d7R890Cftnb4/uM7Ys6NyGrCfMxYrGtAskypJCuQFDWmJWbp1Hp8+Zx5+P3liS05yfi4aY0oCgzNtU70joYwGoptW5rZFBUMYllYwgz4I/A6xAJt9Fj/LqktiCgmdKfNEatM1rcuXFKCkZQHjjwypQO6gMDZC1vQppc/v7avv5DDGzdG9Z1WrijJksoFRHHaRDR5negbDcMfluGxuOln8jlAbPkuQRBEKgw7Dql05bvlHJTyEk3KlBaPXOxL3jo4AFVVjY3wWr0/OSIpaNZ9NF/Z0xvzHklRYI9bt8Vfesk0Lfj3mFti7KKA686am5Wi+UdOik1cNOuZ0r44sSNzabCYhSXMoD8cU2KcD8kypSSgSBQTCkpzxCoLVc1CCJrQUfYLG39YwpbOYXidNvz20pPwyg1nYpLXgbfTeIaVK2MhvSdUFyrKZL3lsAlo9DrQPxY2Spk5k/WSy0xVUykoJQgiUxxc+bQU5bsKz5SO+1dnDC/ftfKhJApDJtVEZp7b0Y1//+XLuOvVA4aOBZ9vI7JiZDuDcZVbESlRfTf+2ktWoRXUVfGTKfPmyqQaB44MBmIUhOPhY8wkUzoYiLWtyQfzGveTq2fg0f9Yh198fDkFpURRKW/5O6JisIssJ6Gjv71xGADwzDatl4SLS3BrlEqDCzVk0kvKcdoENNY48Nq+EKY2uGOC0tPnt+DuK1dl7GnLe0oJgiDS4RC1e02uInX5wNfY5dxm4NQX4IvbfSUeycTFkaUi+AE9cNzdPYo1s7V50dBlkFVDwT4Yt4aIKEqCIJG51cZtF7Gza+vjs54AACAASURBVARW8EypqwDigbdetAQdTdom84E+zQP3oXc60eZz4YvnzEVHU6y9DPcez6SndCDOtiYfzAH7xatmYH5bLRbR74AoMpQpzZO1czILFiY6HocN/rCE/31+D772wKaE14MRGY9uOpogYCTqvTrfNJU+C6w0Zu6FgKvneh2Z7/d4HTYcP7UeA/4Int/ZA4899r1r5zRlnIWnTClBEJliL2GmNNpTOu5fnTFc+IaC0uKRrU0VL8gSBYZgRLtuHTbB2Bh/fb/mfxuSFIyGJDyl26yEIkrKXstF7T7sSuKZW8hM6SdOnoG1uhLvletmGccbvQ585KTpWNnRGHO+kIElzBNbjuEbD23BkD+CugJlSpdPb8AHlk/F+iVtmDEp8xJlgsgHCkrz4J2vn4vfXXZSqYdRFtQ4bRgNSbjtse2457WDCcHn71/ej8/e/SYeeqcz5vhIUOvhWL+kzTgmCswo7ao0eKaU95lkIs3usgu4YFk76tx2KGp+6naF2iUlCGLiY/SUlkR9V/tTKOOodHePFqQsaa9LcyaRK+ae0ky0E2Tdi9MmMOO6ddoE2G0Cnt3eZTpPxVf+8g6u+sNG/OTpnegbCyXdtP38WXMxr7UWO7tHLJX/jZ7SAitFr+xoNISDJtVYz92ZWMJcfdcb+P3L+9E7GkJDgYLSBq8DP/zwMvzqEydSyS4xblBQmgd1HjucNhHvP2FKgjFyteF12mJ28j53z1sxgeWYvuMcvxM5FIjAJrAYxVmBsYz6J8oR3lP6X+ctwK0XLcF7l0xO+x7GGNwO0QjM81Hfrea+ZoIgssNhWMKUQuio/DOlPSNaeSeVLRaPbDOlEu9FFhhCER4sirCLQkw5LhD1DP/J07sQjCiGOn08XzxnHua11mDQH0HPaCjh9Wj5buGDM69T+8xkAbNhCWOxJnrw7SNY+Z2njefDQYk2pomKhoLSAvCjjxyflSz4RKQmLiP4yKajGPBHFeX6x7THCWp3wQh8bntMX5EosAou35XgtotwO0R84uQZWWUBuI9ktpO0FSTbThBEOng5Y1hSxn0jkN/jy7mn9M5PrcS1Z8wumKIpkUi22UcenNmEqMeoQ+8pDehltpetmQkgsVc6lebCTL3P81C/H4P+MAZN65do+W7hl8wevdWn0Ws9NsMSxmJN9M2Ht6J7JDaIjrfXI4hKgoSOiILAJdnNSIqK53Z04/I7XjeOxZeJDQWkhJuoKFRwpjQsGzuf2TJJn5TMZt65sOHGs7IWjyAIovrgG2A/f3YXvvbAJmz6xrmodY3PolatAEuYk2Y24qSZjelPJHIm10ypyKLiik69pxTQNmQXTdYy2/HrjeYaV9LP5WuYsZCM47/1FABg/23nA7C2hCkU/PJPWr6bwhKm3mM3Nvw5lCklKhlauRIFwWsKSmucUXl2c0AKJO5cDgci8MVlWRljKEE1WUHwhyRj5zNb6vTJJF8lzJZaF+3sEwSRFr6Q7xwKAoj6LI8HlVC+SxSfbC1heKZUFIQYVVwe3E6ucxnBoz/ueuYeplbweZvrQpjhir7FKN8N6OKITd7U5btWG/VWAWihLGEIohRQppQoCDWmoLTObcdoSMIp33su4bz4gGsooJXvmhGZdf9EJeAPyzn3hJ44owFr50zCTf+2qMCjIgiCSCQ+SzWeXRNKBWRKieJjF7P7/8/LWEUhmsF0iKKxjrCLgqGSG18anioo5RVOXBcCAD5z1xtYNavRWN/4ilBFwIPNZJnSVJYwVv92hfZSJYjxhDKlREGosciUWnH3awdx8W9fNZ7znlIzlVy+m01Q+tbN5+CNm842nk+pd+PuK0/G7OaaFO8iCIIoDE6bENPnL41jiUq0p3TcvpIoQ+xC5svQ+14/iD9tOARAy5SGTWW1t1ywCF6HiDMXthjBmln4TxQY6lP0W/JM6VAgYhx7fMsx/OCJHUYFQaq1Ta7Y9LEm6ykVUqjvWm0irZpF5eZE5UKZUqIgmIWO0hlMv7S7z3isle/GThQCq1yho7GwlPHE1ZBkEiIIghgPGGOocdowEtQW3eNpDcOrYShTWt1kIwb4X3+LeqALLKq/4LQJWDq1Hpu/+R4AwAu7egHEBphNNY6U38UzpYcG/DHHV8xsNH4f3mIEpXpQHr85zzEsYSx+muZV0nP/eTqaahwFEUokiFJBVy9REMyBmDPDvgtVVTGcROioUoPS0aBEyrcEQVQM5k1ByWrlWySofJfIlqkNbuOxoprKd/W+VMYYGGOwWQSfySxXOC6bCMaAg32xQWlEVjAakuC0Ccb3FJKffewEnDG/GdN1b/N4+M/DSn3XTEeTd9xEygiiWFBQShQEc1CaiRjAo5uOIhhREJYV+Nyxu4+VWr778u5e7OoexdKpZLROEERl0DUcNB5HpPEv36XEDsFRkfr6kxUV5y/VvL8VVTXKd+PV5q2C0nSqtILA4LQJ2Nw5FHM8JCkYDkRQ6ypOYeGJMxpwx+Urk2Y4+aaNahWUVt4yiSBSQtMBURDMJbsui93EV284K+b5Z+9+E51DAQCJvlpCBarvyoqKmx7cjBmTPLhy3axSD4cgCCIjJNMGYGRcM6Xl71NKjC/pqseDEdlYL4QiMl7Z0wdRYLDFB6WWAkDpl7vBiIKu4ajvZ6PXgZAk4+1Dg5jXWpvB36DwiIbQUeJr6bKnBFFpUFBKFATzwsIqU9pWl+gPdtYPnweQqGgnVKD6budgAHt7xnDlKR1FkY0nCIIoBlPqoyWRkTztqLKhEnxKifEl3bwfjCjwOrQy2589uxuv7O2zrKoSTeJJs5q9AGI3XzLF57LhUH8A24+N4NR5zVm/vxDwADtiEbHnax9HEOUGBaVEwTFnTQWmqcymwqqndNORISy55QnrkpUyJBDRBBfIH5QgiErikf84Bb/4+HIAuS3cc4V8Sol4UmX+VFVFUJLhsotprYvM5bvc/zOXlqApDW5DLGnd3Kas318IeIY3FEkMQIeDkYRjBFHJUFBKFAx+8zRnClt9LkNl9oJl7Zbvi+/14DvnoyEJu7tHizHUguPXDbBz9SglCIIoBfUeBybXa5UsVtmYYkFCR0Q8qQLHsKxAVTPTrDCX7542vxmzm734z/fMz2osz3z5NEyt18SHapw2LGzzZfX+QsH/vkFJTnhtYCw83sMhiKJCQSlRMNx6QGaeNMwB5/987AT86uLlCe9r8cWq4pm9xZ7f2VPoYRYFf1iTjHdTUEoQRIXBvSIj5FNKlJBUqvtBPVOYSW+ozVS+2+pz4Zkvn47jp9VnNZbZzTWG2q7HIWZlXVNIkmVKJVnBsG5VQxAThbS/bsbY7xhj3YyxzaZjjYyxpxhju/Q/G/TjjDH2M8bYbsbYu4yxxAiEmLC4bHpQajNPCLEBp5UX16Q4v06hAoPSgJEpJetfgiAqC55ZksYxU6oa5bsUlRIaqTKlIb1FJqNMqWkNUeNMfv7jX1iHp790WtLXe0Y00aOe0VDSc4qNkSmNxGZKKSAlJiKZZEp/D+C8uGPXA3hGVdW5AJ7RnwPAegBz9f8+DeBXhRkmUQnwLKHZp/R7H1gac86JMxpw9sLWmGPxynmiaZGyt2es0MMsClS+SxBEpcLtKMJUvkuUkJRBaZwnaSrM1VZeZ/KN4gVtPsxpqUn6OreoKaW0BQ+wf/jUTmw6HLWrGfBrpbsL2mqNnnCCqHTS/rpVVX0BQH/c4QsB3Kk/vhPARabjf1A1XgVQzxibXKjBEuWNXd9t54uM2c1etPhiVXdddhG/vXQF2vTj3/vAcQmfY66SqQS/UlVVcd29bwEA3KS8SxBEhWE3MqUkdESUjlTlu3wtYOVBGo/Z8zNVUJoMvk/ylSz7UIuB2dng7cODxuNBPSi9fv0Cw7uVICqdXHtKW1VVPao/PgaAp76mADhkOu+wfiwBxtinGWMbGWMbe3oqo0STSA2fCPjE4rAlD9C4EbVVKY55XZSNGuRoqDTlLL9/eb/xuMFL6rsEQVQWTltyMZViwW/t5FNKcFIl6iXdQze+ssoKMaZ8N/ug9P5r1ujvTWw3KiV2099rYExT3o0XiiSISiZvoSNVawzJentVVdVfq6q6QlXVFc3NpfF/IgoLnyymNWqKdRevmp70XH4jtcosmvua5AzN3LcdHcaSW57Aw+90ZjzeQvH3t44A0P4uuUyABEEQpaTR6wBjQPdw5r1zf33jMGZe/0jOthQqZUqJOFJlSrkIlz2jTGn0nI4mb9bjOGF6A4Dyq3wyb7wPBigoJSYeuQalXbwsV/+zWz9+BMA003lT9WNEFeDQJ4LmGif233Y+PnHyjKTnfuuixThnUatx8zcjmcp0Ms2Ubu0cBgA8u707zZmFh/e6NNc605xJEARRfjhsAppqnDg2FMz4PV+7fxMAoH80N1sKRSGhIyKWVO06/DUxg6DUfI49g8xqMspNTd8sbsTLd+u95ZXNJYh8yPXX+hCAS/XHlwJ40HT8k7oK78kAhkxlvsQEh8uwp9rt5Cxo8+E3n1xhGcjxTKnHIUJWVMiKihvufxfbjg4n/TyuAP/u4UFjB3684CJH46lcSRAEUUjafC50jWQelHJRJDnH+y0JHRHxpApKuYduJkGm2RImG758zjysX9JmPDdnXEvJy9efCQAY1YPSYETGrY9sAwDUUnUWMYHIxBLmXgCvAJjPGDvMGLsCwG0AzmGM7QJwtv4cAB4FsBfAbgC/AfDZooyaKEvstsIoOPLsqMdhgySrODYcxL0bDmH9T19M+h6+sNnTM4Y7Xtqf1/dnCw9GszXnJgiCKBdqnDZj0ZsObpUBRIOFbDF8Ssktvep5+HOnAEjdB2ZUUGUQKGZyjhXXnTUXv/rEicbzcul3bq93Y3KdCyN6qfygP1oyXy5jJIhCkHaLRVXVjyV56SyLc1UA1+Y7KKIycRRIwZEvcjxOEd0jCoYD6XuWzB5ef3ztAD51SkfS8zLxOcsUVVXROxbGp0+dhfcvn1qwzyUIghhPvE4bjgwGMjr37UNRFdCIlGumlMp3CY3jptZpD1Jk3fm6IpPy3UwUeiuNWpcNI/qm0eYjQ2nOJojKhPYoiYLBy2ryLWPlk4/XYYOiAr0ZGFePhqJB6d6eMYQsVCRf3tOLBTc/jjcODOQ1PjO9o2GEJQVT6t0F+0yCIIjxpsYpYixDBXPzRuFND242+kMzZXf3KPb1+gHE+lIT1YvA0mVKMy/fZYzhP8+dh39cd0re4/ri2fPw20+uyPtz8qXGaTOEjq78w8YSj4YgigMFpUTBuGH9Qpw+vxmnzc9PTfmoLrYxuU7zMr3k9g1p3xOfTd3SOYyhQASPb462NG85ovWkPvDW4bzGZ2ZPzygArbyGIAiiUvE6bQlB6ZbOIUurLS7uBgDvHBpETwYbh2ZuuP9d3LvhIICoJyRR3TDGUupR8M3qTLOgnztzLpZMqct7XJ8/ey7OXtSa/sQiU+uyG+W7nGtOn12i0RBEcaCglCgY0yd58PvLV8LjyK/x/pYLFuHMBS1YMNmX8Xs2HuiPeT7oD+OL972Nz/zxTRwe0Hbk6zyaSp25Hypf7nhpHwCgo8lTsM8kCIIYb7ymTAygBaTn/+xf+METOxLOja9EyTaw7B+LKvZS+S4BAAwpq3dNqvzRZesnV8/APVetKvLIyoMalw0jcRtEJ1q4FxBEJUNBKVF2nLu4Db+77CTLkrD4Y6qq4lC/H6/tjQ1KhwMSdndrWUzubxbQVXKzrDRLiayocNtFzGmpLdyHEgRBjDNehw0hSTHaL25/Udtw67ZQ5N3ZNRrzPFsdgTFTu8UEbP8jckBgLHX5rn5dmkWMvnXhEqyZ3VTkkZUHPlNPKYeLSxLERIGuaKJsMXuUXn3qLADAgD+6w373awfQccOjWPf95yApKv7wqZVGD8lQIGLIyx/q9+Pk7z6Dl/f0AkgMbPNhJChh6dT8S4QIgiBKidepCcCN6Zt3/F4bthAy4qW3nFRWHlaYy4QpU0oAAFhqOzm+HigXm5bxhqtjmy3vHHl4sBJEOUJXNFG2yLqwwQ3rF+Acvafj9f1RkaIntnTFnL9m9iTMb9MylsOmoPTpbV04Nhw0zo8UMCgdDUmodZFPGEEQlU2N7nfIA0YenMb3i1oFoNnYwqiqirFwNCilmJQAtPLdVKlSLnQk5uhBWunUuuwIROSY0ncHZUqJCQZd0UTZwndGRYFhbqsWbB7q9xuvj4UkzGr2AgDee1wbbKIAuyjA4xC1TKm+o7i3ZyzmczNVmMyE0ZBkLOYIgiAqFW98UKr/2RvXgx+WEgNQKYuNvkBEhqJqFhc1Thv5LBIA0pfvRkxCR7dcsAgnTK8fn4GVCXyd8fr+aKvSRLS+IaobWk0TZYtslOsI8LlscNqEmP6m0aCEuS01+MGHlmFJe7SE1ueyYzgYMcp0O+O89zLxPc2U0aCEGsqUEgRR4fDy3dG4oLRnJARVVY3g0TIozaKndNCv3X+vX78AF6+akdeYiYkDY6lba8zrgcvXduDytdZe5BMVXpH12r5oUFpAeQyCKAsoU0qULXz3XRAYGGNornWi27RrPxqS4HXasHx6Q0wZS53bjqFAxHh/31gYDR477rlyFWqdNuzvG0MwkuhjmgsjIQk1TntBPosgCKJUeB08U6rdG3n5blhWMGTayAvJifdOXlqZiu6RIG64/13c9/ohAMD0RlIsJ6IwpPEplXn5bnVmB42g1CTqqKaSKyaICoSCUqJskeN8yVpqnTF2LqMhCbUWpbM+tw2D/mimdCgQgdsuYs2cJvzww8sQkVVsPjKU9/hCkoywpFBPKUEQFY9RvhuOZkpbfU4AwJV3bsTPn90FAAhFEgPQSAaZ0pd29+LeDYfw02e0z5nVXFOQcRMTA4GxlJYwbx4chNMmVO18W+vSNr+3Hh02jk3yOks1HIIoChSUEmWLuacUAFpqXUamVFVVI1Maz+v7B/Davv4YTy+XQytNWz5D8/V648BAwvuyZVSXZ6eeUoIgKp14oaNgRDaymRsPDOAHT+4EoGVOORevmg4A2No5hG2mxbIVfaNRgRZRYJhS7y7c4InKJ4367sF+P5ZPb4DLLo7joMoHczB+xSkdePpLp2L6JKo2ICYWFJQSZcu5izXF3ROmaYIGLT4nuoe1ntJBv6au21STuFN4/LREAQS3PpE11Tgxc5KnMEFpiIJSgiAmBmahI0lWoKjA1IbYRe9wMGL0lP7q4uW4YFk7AODmB7dg/U9fREhK3hbRawpKLzy+vdDDJyqcdEW5YUmBy169S1bzOqPBYydvdGJCUr2/cKLsec/iNuz+znpDebe5xonhoIRgREaXLnjU6nMlvO+r581POOY27a4un9GANw4M5N2PwY2sSeiIIIhKJyp0JBvZ0Pi+z81Hhoyg1GETEjwjdx4bTfr5fSZrGZ+L+vCJWASBpZyTw5JS1RYotabfjNNWndliYuJTvb9woiKwmcyhW/T+pp6RELqGQzHHzHgciUGi2xG9iZ/cMQl9Y2E8va0753Gt+u7TuPaeNwHAsq+VIAiiknDbRQhMy5TywLPObY+xndh8ZAghU1Bqi/OM3N8Xa79lpm8sDP5RPjcFpUQsDEAqZ6GQJMNRxcGYuXzXWcUZY2JiQ1c2UTG01GpZ0b+9edhQg2zwOBLO8zgSJy5zH8pFJ0yBTWDYeKA/4bxM6RoO4UCf5pnqtvg+giCISoIxBq/DhrGwFJMNba6NbvxtOzoSfU0UEpRQu4aDSEbfaAgnzmhAS60T81pJ5IiIhTEGNYX+blhS4KziTKn5717N/w7ExIaubKJimFyvBaU/eXqXIcbBS87MuC2EEBa3+4zHDpuAGZM8ONDrz2kcz+2IzbBSKQ1BEBMBr9OGsZAUkw29/dKT8P7lUzCvtQajIQkB3U7LaRdhN1WyOGxCjGVXPL2jYUxr9OC1r52Ff1tKPaVELAJDSvXdsFzd5bvcJxigNQcxcaneXzhRcSxo82FWsxduu2gEpValulaZ0ktXz4x53upzoWc0+QIqGSPBCC6/4/WYY9U8URIEMXHwOkWMmXpKnTYBi9p9+NGHj4fbLiIsKdjbo/WNzmj0wKb3lApM6/nvTRGUDvjDaPQ4YhbXBBGFJZTvdo8EsfnIEIb8EYQkBQ6R5lqAMqXExIWa4YiK4sJlU/Djp3diWC/ftQpAzYHqty9aArvA0OB1xJ0jYtAfiX9rWv7+dmfCMZogCIKYCHidNoyaekrN9zaHTUBYUrC7exTNtU40eB3o1Tf2pjS4UeO0G20V8ciKCn9YJlE4IinaXkU0Kn1hZw8++bsNAIBFk31VX75rhnpKiYkKzRBERcGb/buGQ7r6Y+LN2WUXcP5xk/GBE6fgzAWtlp/jdtgQjCS3L0jGQ28fSThGmVKCICYCXoctRujIEReUBiMKjg4FDY9R3m/6pXPm4b7XD2E4aB2UjoXJPotITXz57qYjQ8bjrboHLs21GlS+S0xUaIYgKgojKB0JwptEYIgxhl9cvDzl57jtAvzh7IPSXd2Jlge0e0sQxETA67ThyGAg2lMqRu+xDlHAcEDCUCCCOc2aUFG9x4H9t50PAHh88zHs67VW341qANCSg7CGgUFJY9NGc60G/TsQExW6somKgnt1vbynD3V52Ap4HDZDsCNThvwRy5Jf2r0lCGIiUOMUk2ZKbaKATUeGcKBvDG11if7Qde7k5btRDQDK8BDWsLhMqVXrMc21GvTvQExU6MomKgqeKQ1LCs5bMjnnz3HZRQTCMkaSlJtZwYPYT586K8Y0nsQXCIKYCHD1Xd7a4DL1rv1TVx2PyGrWQeloSPs8Kt8lkiEwFmMII1hEpTTXalD5LjFRoV84UVGYDaTNNi/Z4nGICMsKjvvGk/jLxkMZvUdStOzBnOYafPnc+cZxG02UBEFMALjQ0UhICy55ZQoQGyS0+ayD0mBEQUhKrEDhwnRUvkukwly+a6XR7KBgDACV7xITF7qyiYrCvEjqaPLm/DlmL9Ov/PVdvLy7N+179JgUosBw6tzmnL+bIAiiHHHaBIRlBSNBrdzWvAloFpVrTRKUArDMlr66tw+iwLCgrbbQQyYmCIzBLL5L5bspIPVdYqJCVzZRUZgXSTPzCEp97tgd+ye3dqV9D8+U2kSGhZNpcUUQxMTCJghQVRi98+b7rVmt3Kp816cHpcMWQekz27qxcmYj6j2OhNcIAsisfJcyhBpUvktMVOgXTlQU5kVSPv1JDXGLo0x2YGXd2VtgDIwxzMojKCYIgig37DYtEOgfC8NhE2IWv5ISDRmSle8CiZnSQ/1+7OgawdmLrO25CALQMqPp1HcpU6pBwTkxUaEGD6KiKNQOYYM3Nig1Cxclgy/KbIJ27iP/sQ6juqokQRBEpWMXtMXugD8Mnyt2efDxVdNxz2sHAQBuCxXdZEHp0aEgAGB+K1WXEMlhiFfftRA6omAMAAWlxMSFglKiIuHm7bkSb01gEzLPlIp6UOp2iJaLM4IgiErEJkYzpeb+fQC49cIluOn8hVCSJLN4ULrt6AjOXBDNikpytO2BIJIRX75rdbU4SVQQAIkrEhMXurKJiuOFr5yBRz+/Lq/PmN9ai0tXzzCeZ1O+S4srgiAmInyxO+APx7RKAIAgMHgctqRtEzwo/e8ndhg+p0C0wiSTahSiiokr3yWhI4KoPugXTlQc0yd5jAVQrthEAd+8cAnWzpkEAFDT9LIA0cWVlQADQRBEpWPXq0AGxiIJQWk6fKZ78s6uEeOxIRCXQTUKUb0wIEZ919KnlIJSgpjQ0C+cqGpuv/QkAEAwoqQ505QppcUVQRATEJ4p7RsLodaZ3caf2TImZMqURmSqMCHSo5Xvps6UkuosQUxsaHVNVDUuuwiPQ7Q0fI8nvqeUIAhiIsFLbIMRJetMKQB8/wNLASC2fFemzTwiPYxFvcC155Qpjefmf1uE9x7XVuphEETRIKEjoupx2oSYnf1kUE8pQRATGXPgGC90lAmzWzSbrIhs7ikloSMiPQyxmVIrqj0oveKUDlxxSkeph0EQRaO6f+EEAa0Xqm8snPY8vriiTClBEBMRc+Doc2e/Z81LeM1B6U1/36y9RplSIgWMxVrCKBYyzw5SnSWICQ39womqZ2GbD1s7h9OeZ5TvktARQRATEPOi323Pvn/PKigdCWpezpQpJVLBGIuxG5KtgtIqz5QSxESHfuFE1bNkig/7escwHIykPI96SgmCmMiYA0c5A0XyeHhQytsh/GHJ8rMJIh7t6ohec4rF9eekoJQgJjT0CyeqniVT6gAgbbaUekoJgpjImHtKZTn7oJQHDVxx99hQ0HiNyneJVERkBc/t6DGeW2ZKqXyXICY09Asnqh4elG4+MhRzPCIr+MMr+zEU0DKokmEJQ0EpQRATD7tpw621zpXD+2PLd81BqUibeUQKdnWPQlZUYx7mManPpAIt0NxLEBOavIJSxtjnGWObGWNbGGNf0I81MsaeYozt0v9sKMxQCaI4NNU4MbnOlRCUvnFgAF9/cAvWfe9ZhCTZ2Lm1MvUmCIKodDyOaADwweVTs34/D2rDkoJdXSN4aU9v9DXKlBIZMODXRAd5+e4pc5tKORyCIMaRnC1hGGNLAFwFYCWAMIDHGWP/APBpAM+oqnobY+x6ANcD+K9CDJYgisXi9jpsiSvf5bv8w0EJ7xwaMmVKaXFFEMTEY+HkWvzy4uVY2dGYU1bKbotmSs/58Qsxr1HbA5EJfPOXNoEJovrIZ3W9EMBrqqr6VVWVADwP4P0ALgRwp37OnQAuym+IBFF8JnkdGA1JMce6hqOlZ8GIbEjUUxkaQRATEcYY3nvcZDTVOHN6P+/5C8uJvs/U9kBkAs+Q8qCUUVBKEFVDPkHpZgDrGGOTGGMeAO8FMA1Aq6qqR/VzjgFozXOMBFF0RJEZmVCO2bs0JCnUU0oQBJECo6dUShSpoeCCyISjQ0EoigpFVUFTxilpxgAAFMVJREFULUFUFzmX76qquo0x9j0ATwIYA/A2ADnuHJUxZinhxxj7NLRSX0yfPj3XYRBEQbAJLEHtLxiJXs4hSYakaLv/ZAlDEASRiCgw2ASGoBS9d05v9OBgv7+EoyIqiRsf2Iyjg0EoqkpzLUFUGXk1x6mqeruqqieqqnoqgAEAOwF0McYmA4D+Z3eS9/5aVdUVqqquaG5uzmcYBJE3osAgxZWchSLR52FJQVj33iMDb4IgCGvcDhHBiIxalw2XrZmJh687BU984dRSD4uoIJ7e1gVZVSEwBjUHv1yCICqTfNV3W/Q/p0PrJ70HwEMALtVPuRTAg/l8B0GMByKzyJRKsuG7F5IUwxCeDLwJgiCscdtFBMIyAmEZHoeIOrcd89tqSz0sooKQFRWKogel+rFLV88o6ZgIgig+OZfv6vyNMTYJQATAtaqqDjLGbgPwZ8bYFQAOAPhwvoMkiGJj1VMaiijwue3oGQkhFJGNoJQMvAmCIKxxO0SMBCVIigq3XSz1cIgKRFZVRGRV02/Qp+UVMxtLOyiCIIpOXkGpqqrrLI71ATgrn88liPHGqqc0JMnwuWxaUKqX7zpEgQQ7CIIgkuC2i+jXReLcDgpKieyRFRU7jo2go9lrHKNplyAmPpTyIQgAoiBAUtSY/pWgnikFEA1KqXSXIAgiKW4HBaVEfkiyioP9fsxproEK6ikliGqBVtgEgajNizlZGpJkeB02iAJDSJIRlmUKSgmCIFLgcYiGnRaV7xK5IOuWMDaRYcmUOgBAe727xKMiCKLY5NtTShATAi49LykKREFbSIUkBQ0eAU6bgFBEy5SSyBFBEERy3HYRA34tKPVQppTIAVlVIQAQGMNnTp2NU+c2G8EpQRATF1phEwSimVJZUbFxfz/W3vYstnQOw2nXg1Iq3yUIgkhLjdNm9Oe7KFNK5ICWKQUYYxAERgEpQVQJtMImCEQzpbKiYsP+fhwZDAAAVBVw2kSEJE19l5R3CYIgklPvcRiPPQ4qxiKyR5IVqKpK4kYEUWXQCpsgEBuUBiOKcZwxwGkXEKZMKUEQRFoaTEEp9ZQSuaCo2n8CBaUEUVXQCpsgEC3flRQVwYgc8xov3x0NSVSORhAEkYIGr9147HbQEoPIHknRMqUCpUoJoqqgGYMgoFnCADxTGg1KGRicNhGBiIytncNY0FZbqiESBEGUPebyXTeV7xI5wHtKKSgliOqCglKCQGymNBA2ZUqZlind0jmMkZCEE6Y3lGiEBEEQ5U+Dx5QppcoSIgck3RKGYlKCqC4oKCUIRHtKD/b5EZRMPaUAHDYBPSMhAMDy6fWlGB5BEERFQD2lRL6oqvYfZUoJorqgoJQgAKj6nx/7zat4+J1OtNe5MLelBtedOdfwJq332NHR5C3dIAmCIMqcBm80KHXZaYlB5IaiqiR0RBBVBs0YBAFgUDd750iKiqe+dBrmt9XCadN2+0+YVg9GO7cEQRBJMZfv0v2SyJS7r1wV81whoSOCqDooKCUIAIP+SMxzbv4OAHY9U7qo3TeuYyIIgqg0qGSXyIW1c5pinisqbWoQRLVBQSlBAHA7YhdSoqluiD/yOklJkiAIIhUUSBCFQCWhI4KoOmiVTRAArlzXgVafC7OavXj/L1821HgBGBMjL+MlCIIgCKJ4aJYwpR4FQRDjCWVKCQJawPnBE6eiVs+GimLibOiw0c+FIAiCIIqNSj2lBFF1UKaUIEy49H6omZOiKrt8WnSKFJQSBEGk42/XrEHYZK1FEJkgMC1DClBPKUFUIxSUEoSJaY0e/PSjx+PUuc3GMT4xilRLRBAEkZYTZzSUeghEBWITBITl6GYGTbkEUV1QUEoQcVx4/JRSD4EgCIIgqgpRYIAcfU7luwRRXVA9IkEQBEEQBFFSbHGpUcqUEkR1QUEpQWSImv4UgiAIgiByIF5gkHpKCaK6oKCUINJA0yJBEARBFBebELskpfJdgqguKCglCIIgCIIgSkp8+S7FpARRXVBQShBpuHLdLDR47Dh1XlOph0IQBEEQE5J4hXvqKSWI6oLUdwkiDYvafXjr6+eWehgEQRAEMWGxifFBKUWlBFFNUKaUIAiCIAiCKCnNNc6Y5yR0RBDVBQWlBEEQBEEQREn55SeWY+nUOuM5le8SRHVBQSlBEARBEARRUlpqXbh87UzjOZXvEkR1QUEpQRAEQRAEUXJEky0MZUoJorqgoJQgCIIgCIIoOWZbGOopJYjqgoJSgiAIgiAIouSYg1Iq3yWI6oKCUoIgCIIgCKLkmG1hKCYliOqCglKCIAiCIAii5FBPKUFULxSUEgRBEARBECWHekoJonqhoJQgCIIgCIIoOSL1lBJE1UJBKUEQBEEQBFFyYoWOSjgQgiDGHQpKCYIgCIIgiJJDmVKCqF4oKCUIgiAIgiBKjs0kdEQxKUFUFxSUEgRBEARBECXHbAlDmVKCqC7yCkoZY19kjG1hjG1m/7+9Ow+yrCzvOP79ycCwjQPCKLIoSwQJYgQmiBLCREQ0YEUTMGYRTCyNGtzAaMVSMCWapKKIlEvEGEGNRAMGE0xpjCEuEJFF1EJLJDChQKIzIgIq28yTP87bSTP2DNN9L/d0n/5+qqa659x7mufOj3P7PO9533OT85NsnWSvJJcnuT7Jx5NsNa5iJUmSNExLnL4rLVpzbkqT7Aa8ElhZVU8AtgCeD/wl8M6q+gXgR8CLxlGoJEmShmurJX5OqbRYjTp9dwmwTZIlwLbArcDTgAva4+cBzxnxvyFJkqSBe8wjtv2/771QKi0uc25Kq+oW4O3ATXTN6I+Bq4Dbq+r+9rSbgd1m2j/JS5JcmeTKNWvWzLUMSZIkDUASfv3AXQBYe9e9PVcjaZJGmb67I/AbwF7ArsB2wDM3d/+qOqeqVlbVyhUrVsy1DEmSJA3EaccdwFP32Ykj9/XcUFpMloyw79OBG6tqDUCSTwKHAzskWdKulu4O3DJ6mZIkSRq6XZZvzcdefFjfZUiasFHWlN4EHJZk2yQBjgK+BVwCHN+ecxLwqdFKlCRJkiQN1ShrSi+nu6HR1cA32886B3g9cEqS64GdgA+OoU5JkiRJ0gCNMn2XqjodOH2DzTcAh47ycyVJkiRJi8OoHwkjSZIkSdKc2ZRKkiRJknpjUypJkiRJ6o1NqSRJkiSpNzalkiRJkqTe2JRKkiRJknpjUypJkiRJ6o1NqSRJkiSpN6mqvmsgyRrgv/uuYxN2Btb2XYTGxjyHx0yHxTyHxTyHxTyHxTyHZb7n+diqWjHTA/OiKZ3vklxZVSv7rkPjYZ7DY6bDYp7DYp7DYp7DYp7DspDzdPquJEmSJKk3NqWSJEmSpN7YlG6ec/ouQGNlnsNjpsNinsNinsNinsNinsOyYPN0TakkSZIkqTdeKZUkSZIk9camVJIkSZLUG5tSSZI0Z0nSdw2SpIXNprRJsrx99d9kAJLs0r56sjQASQ5IsnXfdWg8khyeZJ++69DYbNN3ARqfJFu0r/7+HAgzHZah9iqDfFGbK8nDkjw8ycXA2QBVtb7nsjSCJAcl+TzwFoDyTl4LWpInJvkycAawU9/1aDRJDk7yr8C/A8v7rkejSXJYkguB9yR5xtSJrxamJE9J8gHgNUmW+ftz4WsDgOcBb0zyCDNduJIcmuSVMNxeZVE3pS3UO4Etgd2S/DYMdwRiyNJ5J/Bh4LyqenHfNWks3ghcUFXPrapbwJHehSjJlkneT3er+rOBzwKr2mO+3y5ASVYB7wU+CXwH+H1gxz5r0twlORJ4N92A0a7AG5Ic029VGkWSvemO0UuAxwJvSXJsv1VpLpK8GvhHusGFZ7VtgxsE9GQAHg+sBc4Cfq+NDq73xHdhaaN/2wNfq6oPAyTZxxPehanNYtgbuKuqzmrbjk6yA+A0pIVnKfAF4Iiquhi4ENg/yZKhjvguAgcCV1TV3wEfoRvcvavfkjSCg4FLq+p8uplGjwKeP7UURgvSLwPfrqpzgVOBa4DjkuzRa1WaixuA44CXAX8KUFXrhnYetKhO2NtUo33b91NBXg/cC9zY/pyU5DFOcZj/pufZnAo8OcmbklwK/BVwbpJD+qlQszE9z9aorAWOSHJskouA19JdZfuT9hyP0Xlsg+PzJ1X1sar6Wfv7EmBdVd3vwNHCMMP77ZeAE5KcBlwNPBp4b5ITeilQszJDntcBOyR5dFX9iG6AYSvgOb0UqFlL8uwkJyc5rG26AtgjyR4t00uB24Hf7K1IbZYZsvw08I329a6paby0QfqhWBQnA0l2SPJp4HPA85JsN+2EdiVwR1VdC1wLnA68r003WxT/PgvNTHkCVNUdwHuA4+lGkn4HuBX4rSQr+qpXm/YgeX6IbtT+b6vqGOBvgMOmvVFrntnY+22bYj/1nvoF4LlJdvRK6fw2Q57bA1TVNcAzgT2Bl1fVKrqT3mcm2b+ncvUgNpYnXVN6B3BeWye8B/A1uhlIzkyZx5I8Osk/A6+jm0L/oSTHVNUNwH8Cz2tP/Q7wLeAR8caB89ImslwHrK+qu4F3AC9KsnNV3d9nveO2WJqu7ejWML2iff+r0x67CViW5ON0/xNcBVxXVfd5sjRvbTTPqjobWFVVX6yqe4CL6AYeftpHodosmzo+L6Y76Z1aq3Yl8H3gngnWp9mZMc/qrG+N6er2nCP7KlKbbcM8j5h6oKq+CqygyxO69YjLgJ9MtkTNwsaOz+8CpwB/DvxDVT2XbqB+VXvcmSnz10rgS1V1RFW9BXgXMHVfjS8BByY5tDU2twCHt+ZG88+GWZ4FvBQecAz+B/AVumOYJIf2UOdDYrBNaZITkxyZ5OHtBinnAJ8A7qab4rlre+qOdL9U/wc4iG6+9n6O9M4vs8iTNk1lyiHAzcC6iRasTdqMPHcDqKpv0E3XPTnJznQ3U3kC8MOeStcMNvf4TJI22Le07Xr31PY+6tbMZpHnUuAy4I/brkfR3SXbE9555EHyPHQqz6q6t6ouqaq/b7seAnymn6q1KS3TVe0Y/Dzduu4pPwS+276/nO6K9zvbVfEDgJuSbDvRgrVRD5LlbcC32/MeBt1aUrpPJHh9kh8DBw/ld2iGNPjVQtkF+BiwHvgvupHAV1XV2vacw+mmMlxZVR9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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Check for null values\n",
        "IBM_data.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0PL3jypjbFM",
        "outputId": "e860f3f8-61fa-4dab-c7a6-9a4d07cc3f3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Open         False\n",
              "High         False\n",
              "Low          False\n",
              "Close        False\n",
              "Adj Close    False\n",
              "Volume       False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Set up column for strategy 1\n",
        "strategy1 = np.where(IBM_data['Adj Close'].shift(-1) > IBM_data['Adj Close'], 1, -1)\n",
        "IBM_data['Strategy1'] = strategy1\n",
        "\n",
        "IBM_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "iYkP03bvjc-l",
        "outputId": "4e22ad83-afc4-4617-9078-b06558bc487c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2015-01-02 00:00:00-05:00  154.216064  156.128113  153.919693  154.933075   \n",
              "2015-01-05 00:00:00-05:00  154.177826  154.177826  152.189301  152.495224   \n",
              "2015-01-06 00:00:00-05:00  152.648178  152.925430  148.346085  149.206497   \n",
              "2015-01-07 00:00:00-05:00  150.286804  150.286804  147.256210  148.231354   \n",
              "2015-01-08 00:00:00-05:00  149.369019  152.045883  148.709366  151.453156   \n",
              "\n",
              "                            Adj Close   Volume  Strategy1  \n",
              "Date                                                       \n",
              "2015-01-02 00:00:00-05:00  110.782318  5779673         -1  \n",
              "2015-01-05 00:00:00-05:00  109.039169  5104898         -1  \n",
              "2015-01-06 00:00:00-05:00  106.687645  6429448         -1  \n",
              "2015-01-07 00:00:00-05:00  105.990341  4918083          1  \n",
              "2015-01-08 00:00:00-05:00  108.294060  4431693          1  "
            ],
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              "      <th>2015-01-02 00:00:00-05:00</th>\n",
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              "      <th>2015-01-08 00:00:00-05:00</th>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f740a5f-c5fa-4b6b-81ea-55db1332b068 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f740a5f-c5fa-4b6b-81ea-55db1332b068');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Identifying Variables\n",
        "##important to use double brackets\n",
        "\n",
        "xfeature = IBM_data[['Adj Close']]\n",
        "yfeature = IBM_data['Strategy1']\n",
        "\n",
        "xfeature.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "U0JszlIpjeer",
        "outputId": "1abf0c67-d40e-4470-ebe1-7e52e8184f35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Adj Close\n",
              "Date                                 \n",
              "2015-01-02 00:00:00-05:00  110.782318\n",
              "2015-01-05 00:00:00-05:00  109.039169\n",
              "2015-01-06 00:00:00-05:00  106.687645\n",
              "2015-01-07 00:00:00-05:00  105.990341\n",
              "2015-01-08 00:00:00-05:00  108.294060"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d43c3abd-ea99-4568-b89f-e70f840baa07\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02 00:00:00-05:00</th>\n",
              "      <td>110.782318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05 00:00:00-05:00</th>\n",
              "      <td>109.039169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06 00:00:00-05:00</th>\n",
              "      <td>106.687645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07 00:00:00-05:00</th>\n",
              "      <td>105.990341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-08 00:00:00-05:00</th>\n",
              "      <td>108.294060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d43c3abd-ea99-4568-b89f-e70f840baa07')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d43c3abd-ea99-4568-b89f-e70f840baa07 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d43c3abd-ea99-4568-b89f-e70f840baa07');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Scaling"
      ],
      "metadata": {
        "id": "vkhjVo7GjhT7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "57U61gf7jgEx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(xfeature, yfeature, train_size = .8, random_state = 50)"
      ],
      "metadata": {
        "id": "y1T27BX8jnYQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Pre-feature scaling description\n",
        "x_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "d3uyKiObjqHr",
        "outputId": "9fe5d44e-e0c3-4a78-c580-f27caceb8d25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Adj Close\n",
              "count  1410.000000\n",
              "mean    112.345099\n",
              "std       9.533904\n",
              "min      79.847000\n",
              "25%     107.222914\n",
              "50%     112.234531\n",
              "75%     117.461323\n",
              "max     135.368500"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55f84e85-8d6c-4337-b6d9-e54c7dfe810c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1410.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>112.345099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.533904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>79.847000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>107.222914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>112.234531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>117.461323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>135.368500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55f84e85-8d6c-4337-b6d9-e54c7dfe810c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-55f84e85-8d6c-4337-b6d9-e54c7dfe810c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-55f84e85-8d6c-4337-b6d9-e54c7dfe810c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "scaler.fit(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MuLROBkjrxy",
        "outputId": "c3811175-e3cd-42f3-eb85-12b784ac18f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.data_min_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVMqU_fRjtcH",
        "outputId": "608bc014-a094-45b9-ecec-7800b5277135"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([79.84700012])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.feature_range"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMiw9_APjvC7",
        "outputId": "f0ef3f87-5e74-4ecd-b100-02d4404bd665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.transform(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVVjwTf1jwbe",
        "outputId": "89e75f21-48e5-4d2c-851f-026217c17058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.69329797],\n",
              "       [0.4811381 ],\n",
              "       [0.60750244],\n",
              "       ...,\n",
              "       [0.6720775 ],\n",
              "       [0.5156162 ],\n",
              "       [0.88652627]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "S1listofmodels = []\n",
        "S1listofaccuracies = []\n",
        "\n",
        "S2listofmodels = []\n",
        "S2listofaccuracies = []"
      ],
      "metadata": {
        "id": "iuEy-VpqjxqR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Strategy #1"
      ],
      "metadata": {
        "id": "703X5j6oj0ir"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN"
      ],
      "metadata": {
        "id": "nSV6-aj7j5ZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "knn.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5CCC0GpjzQO",
        "outputId": "19374a4c-125c-4697-f2f5-efffa2316473"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(x_test)"
      ],
      "metadata": {
        "id": "FHxl7mmTj8VF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "S1listofmodels.append(\"KNN\")\n",
        "S1listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVyjTPzgj99O",
        "outputId": "4862a75d-f7d5-407e-f57d-2e693d10b03c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.5014164305949008\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()\n",
        "##https://www.stackvidhya.com/plot-confusion-matrix-in-python-and-why/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "iyhDkMCxj_Zs",
        "outputId": "f43aaa86-d02a-4c31-c72c-b7671e788506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest"
      ],
      "metadata": {
        "id": "-J1AdKr-kC-W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_clf = RandomForestClassifier(n_estimators = 100)\n",
        "rf_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hu2eY2zvkBzm",
        "outputId": "23a3d3e8-ac47-414e-c379-3d8e81d6e7b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf_clf.predict(x_test)\n",
        "print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "S1listofmodels.append(\"Random Forest\")\n",
        "S1listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9zVuYyYkIK8",
        "outputId": "8b25d4ff-8853-4fb3-f8ef-28163ae8915c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.5042492917847026\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "cDeEJpidkKr7",
        "outputId": "21d0d0ca-cc15-43c9-e6c0-9b085fee0ea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gradient Boosting"
      ],
      "metadata": {
        "id": "Olb3cUPMkMy1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]\n",
        "for learning_rate in lr_list:\n",
        "    gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=1, max_depth=2, random_state=0)\n",
        "    gb_clf.fit(x_train, y_train)\n",
        "    y_pred = gb_clf.predict(x_test)\n",
        "\n",
        "    print(\"Learning rate: \", learning_rate)\n",
        "    print(\"Accuracy score: {0:.3f}\\n\".format(gb_clf.score(x_test, y_test)))\n",
        "\n",
        "S1listofmodels.append(\"Gradient Boosting\")\n",
        "S1listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))\n",
        "#https://stackabuse.com/gradient-boosting-classifiers-in-python-with-scikit-learn/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "if9byIomkQGn",
        "outputId": "c99a52b9-db80-4449-ffed-e812857752d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate:  0.05\n",
            "Accuracy score: 0.510\n",
            "\n",
            "Learning rate:  0.075\n",
            "Accuracy score: 0.501\n",
            "\n",
            "Learning rate:  0.1\n",
            "Accuracy score: 0.507\n",
            "\n",
            "Learning rate:  0.25\n",
            "Accuracy score: 0.535\n",
            "\n",
            "Learning rate:  0.5\n",
            "Accuracy score: 0.535\n",
            "\n",
            "Learning rate:  0.75\n",
            "Accuracy score: 0.516\n",
            "\n",
            "Learning rate:  1\n",
            "Accuracy score: 0.516\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate = .05, max_features=1, max_depth=2, random_state=0)\n",
        "gb_clf.fit(x_train, y_train)\n",
        "y_pred = gb_clf.predict(x_test)\n",
        "print(\"Accuracy score: {0:.3f}\\n\".format(gb_clf.score(x_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zmBS2w5kSji",
        "outputId": "708d6233-5a9f-4ac7-a204-31e8830ccb4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score: 0.510\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "qc91UHaokWYn",
        "outputId": "c876bfa1-cbdd-4d3b-d0ae-2a1abdb867a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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/eOmyS/dtGPPkEwBcOOhX9D+0H0Ov/NvS/dw47Nql263xKikRL/77HO694qSlab8/bX/eumcQr991PqcfuXuV5R64agDTn/zjMuUAevbozAu3nsNLt/2S0TcMpFP7NgCcdsRujBvxK+77+yms1qwUgF226chlv6hqroNVl0qU99LYFPtk57XAPEnbAOcAHwK3FLnORql58+Zcd+Nw7rjnfm6/6z5eeP453n7zjWXy/P2vl3NAn36MuPcBTvnZ6Vxz1RUAdN9hR26/+z5uv/s+rr1xOC1atGSnnXflg/cnsvrqqzPi3gcYP/5tvp0zhy+/mMH4t96i5969GuIwrQ4G9t+DiR/NWLr+kz49aP/9ddjm8EvZ7ohLufux16ss97dbn+Kk3922XPpV5x/Kib/9Nzsd81fuHPUag05K3gP9e29Pj6P+wktvfcQ+O/8AgEEn7cOf//l4EY4qu3zVSvXKIiJIpi8aGhFDSZ7Fu8qRxBprrAlAWVkZZWWLl3tDfDR5Et133BFIgvczTz253H5GP/4Yu+y2Oy1atqRZs2YsXLiQ8vJyysrKKCkt4bqhV/Oz0wcW/4BspWzUbm1677Ylwx94aWnagEN35U83Pk7yJwNfzPq2yrJjxn7AnLnLXzMQwFprtgBgrVYtmf7FbAAkWK1ZCWu0aM7isnKO2n97HnvxPWbNnlfgo8o2B/LqzZH0a+BY4OF0ZozVilxno7VkyRKOPvxg9um5GzvuvAtb/2ibZbZ37rIFTz2R9JKeGv04c+fO5euvZy2T57FHH2G//f8PgI6dNmPdddfl2CMPZY8992LKJ59QHuVs0XWr+jkgW2GXn3MQF1z1EOXlsTSt40bf47B9tuW5m3/B/VeewmYd2tRpn6dffCf3/f0UJj00mKP3356/3DwagGvveo6nh59Fh/XX5cU3P+K4Pjtw3V3PFfR4mgIH8uodSXK54UkR8RnJbBeXV5c5dx684Teu0CxKjVppaSm3330fjzz+FOPfeZtJH7y/zPazf3ker706lqOPOITXxo2jXbvvU1pSunT7l1/MYNKk99l5l92Wpv3y/N9w+933cezxJ3LdNVdx2hln8c9h1zHo3F9w3z131duxWf72360rM2Z9y+vvTV0mffXmzVi4aDG7Hf83ht//Etf/tn+d9nvm0Xty8Nk3sPmBQ7j1wbFcenYyBn7Ho6+y87FX8NPBt3Hm0XvyjzufZb9dt+T2S47nsl/0a5SBqUFk+DryogbyiPgsIq6IiGfT9U8iotox8ogYFhHdI6L7iScPqC5b5rVeay2699iBF59ftlfUtl07Lv/b1dx+1384/ednLc1b4fFR/2WvvXvRbLXlv9SMeWo0W3Tdinnz5jJ16hQu+cvfGP3EYyyYP7+4B2N1tvM2HTlw961474ELueVPP6Fnj87cNOQYps34mvufehuAB556m607b5D3PtussyY/7LwhY8d/AsA9j7/OTj/adJk8G7RZi+5dO/Dg0+9w1jE9OfY3t/D1nPns1aNzwY4ty0pKSvJeaiOpt6SJkiZJGlTF9lMlvS3pDUnPSeqapm8qaX6a/oak6/Jqe52PNg+S5kiaXcUyR9LsYtTZ2M2aOZM5s5NDX7BgAS+/+CKbduy4TJ6vZ82ivLwcgOE33kDfgw9ZZvuoRx9mv/0PWG7fZYsXc8e/b+H4E09i4cKFVHSwypcsYfHixUU4GlsZg4c+zOYHDmGLfhdz3G9uZczYD/jp4Nt48Ol32HP7zQHYvdtmTPrki7z3OWvOfNZq1YLNN24LwN47/oCJH89YJs/gU/fnouv/C0DL1VcjAsojWKPFKjvauQwp/6Xm/agUGArsD3QFjqoI1Dluj4gfRsS2wGUkkzFX+DAitk2XU/Npe1GuI4+IVfKEZk2+/PILfnfhrylfsoTy8nL22a83u++5F9cNvYotu27NnnvtzbixrzD0qiuQxHbdunP+Bd9NKPLptGl8/vlndOveY7l93zXidg7sexAtWrakc5cfsGD+Ao48pC+77r7HMj16a9z+8q/RDL/oWM48ek/mzlvIaRcnQ2PdtmzPyYfswul/TNafGDaQLpu2o1XL1Zn00GBOvfhOnnhpImf88W7uuPQEysuDr+fM42cXjVi67226bATAGxOTeXzvHPUa40b8iqmff80Vtyx/Un1VVMAhph2ASRExOd3vCJILPiZUZIiI3A7tmqR3v68oVZwhLyZJ7YAWFesR8UltZeYsLC9+wyxz2u12bkM3wRqh+WOvWOko3OW8/+Ydc96/rHe19Uk6DOgdESen6z8BdoyIgZXynUFyWXZzYO+I+EDSpsB44H1gNnBhxdB0TYp9Q1BfSR8AHwFPAx8DjxazTjOzFVGXq1ZyL8xIlzqf1IuIoRGxGXA+cGGaPJ3ksd/bkQT52yXV+rW62M9auYjk9vwnImI7SXuRXIpoZtao1GVkJSKGAdVdWjcN6JCz3j5Nq84IkpsniYiFpA8WjIhXJX0IdAHG1dSeYl9+uDgivgJKJJVExFNA9yLXaWZWZ6WlynupxVigs6SOkpoD/YGRuRnSSXYqHAB8kKa3TU+WIqkT0BmYXFuFxe6Rfy2pFfAMcJukGcDcItdpZlZnhTrZGRFlkgYCo4BS4KaIGC9pCDAuIkYCAyX1IpmrYRZwfFp8D2CIpMVAOXBqRMysrc6iBHJJG6cnNPsB84FfAMcAawNDilGnmdnKKOR9URHxCPBIpbTBOa/PqqbcvcC9da2vWD3y+4FuETFX0r0RcShwc5HqMjNbaVm+w7VYgTz3J9KpSHWYmRWMA/nyoprXZmaNUobjeNEC+TbprfgCWubcli8gIsK3G5pZo1LSCCeMyFexbtEvrT2XmVnj4aEVM7OMy3AcdyA3MwP3yM3MMi/DcdyB3MwMfLLTzCzzPLRiZpZxGY7jDuRmZuAeuZlZ5mU4jjuQm5lBtnvktU4sIekySWtJWk3SaElfSPIsP2bWpJSUKO+lsclnhqB90xmfDySZc3Nz4FfFbJSZWX2ry5ydjU0+gbxi+OUA4O6I+KaI7TEzaxBS/kvt+1JvSRMlTZI0qIrtp0p6W9Ibkp6T1DVn26/TchMl7ZdP2/MZI39I0nskM/2cJqktsCCfnZuZZUWhetrpnJtDgX2AqcBYSSMjYkJOttsj4ro0f1/gCqB3GtD7A1sBGwJPSOoSEUtqqrPWHnlEDAJ2AbpHxGJgHskUbmZmTUYBe+Q7AJMiYnJELAJGUClmpsPVFdbku3kb+gEjImJhRHwETEr3V6N8TnauAZwOXJsmbQh0r62cmVmWlJYo76UWGwFTctanpmnLkHSGpA+By4Cf16VsZfmMkQ8HFpH0ygGmARfnUc7MLDPqcrJT0gBJ43KWAXWtLyKGRsRmwPnAhSvT9nzGyDeLiCMlHZVWPk+N8bStmdlKqMtVhRExDBhWzeZpQIec9fZpWnVG8N2IR13LAvn1yBdJakk6hiNpM2BhHuXMzDKjgJcfjgU6S+ooqTnJycuRlerqnLN6APBB+nok0F/S6pI6Ap2BV2qrMJ8e+e+A/wIdJN0G7AqckEc5M7PMKNQ4Q0SUSRoIjAJKgZsiYrykIcC4iBgJDJTUC1gMzAKOT8uOl3QXMAEoA86o7YoVyCOQR8Tjkl4DdiKZPPmsiPhyxQ7RzKxxEoUbMY6IR4BHKqUNznl9Vg1l/wj8sS711RrIJe2RvpyT/t9VEhHxTF0qMjNrzPK4GqXRymdoJfd2/BYk1zS+CuxdlBaZmTWALF/Ckc/QSp/cdUkdgL8XrUVmZg2gJMORfEUeYzsV2LLQDTEza0gZjuN5jZFfzXe3j5YA2wKvFbNRZmb1Lcu3x+TTIx+X87oMuCMini9Se8zMGkSG43heY+Q310dDzMwaUmmGI3m1gVzS23w3pLLMJiAi4kdFa5WZWT1rqkMrB9ZbK8zMGliGLyOvPpBHxP/qsyFmZg0pyz3yfJ5HvpOksZK+lbRI0hJJs2srZ2aWJYWc6q2+5XPVyjUkT++6m2RCieOALsVslJlZfcvyLfr5PMaWiJgElEbEkogYDvQubrPMzOpXAR9jW+/y6ZHPS5+p+4aky4Dp5PkBYGaWFY0vPOev2oAsqUf68idpvoHAXJLZKw4tftPMzOpPiZT30tjU1CMfJqkVyTREd0TEBOAP9dMsM7P61Qjjc96q7ZFHxHYk15KXAfdIelPSIEmb1lPbzMzqTSHHyCX1ljRR0iRJg6rYfo6kCZLekjRa0iY525ZIeiNdRlYuW5Uax7ojYmJE/CEiupJcrbI2MFqSn7ViZk1KaYnyXmoiqRQYCuwPdAWOktS1UrbXge7pHfL3AJflbJsfEdumS9982p7XSUtJJUA74PvAmsCMfMqZmWVFAa8j3wGYFBGTI2IRyfB0v9wMEfFURMxLV18C2q9M22u8akXS7sBRwEHA22mDfhER36xMpflYrdQXxlgVyhY1dAusiarLZYWSBgADcpKGRcSw9PVGwJScbVOBHWvY3UnAoznrLSSNIxnWviQi7q+tPTU9NGsK8D+S4P37iHAv3MyarLp0HdOgPazWjLWQdCzJjZZ75iRvEhHTJHUCnpT0dkR8WNN+auqR7+bnrZjZqqKAN/pMI7lMu0L7NK1yfb2AC4A9I2JhRXpETEv/nyxpDLAdUGMgr+mqFQdxM1tllCj/pRZjgc6SOqY3U/YHlrn6RNJ2wPVA39zRDknrSlo9fd0G2BWYUFuFKzJnp5lZk1OoZ61ERJmkgcAooBS4KSLGSxoCjIuIkcDlQCvg7vSbwCfpFSpbAtdLKifpaF+S3sNTIwdyMzMK+zzyiHgEeKRS2uCc172qKfcC8MO61lfTyc7cSZerqvDnda3MzKyxyvKdnTX1yMfVsM3MrElpjM9QyVdNMwR50mUzW2Vk+c6VWsfIJbUFzie51bRFRXpE7F3EdpmZ1aumPrHEbcC7QEeSpx9+THJ5jZlZk5Hlqd7yCeTfi4h/Aosj4umI+Cng3riZNSkFvI683uVz+eHi9P/pkg4APgXWK16TzMzqX5M82ZnjYklrA78ErgbWAn5R1FaZmdWzDMfx2gN5RDyUvvwG2Ku4zTEzaxiNccgkX/lctTKcKm4MSsfKzcyahNIMd8nzGVp5KOd1C+BgknFyM7Mmo0n3yCPi3tx1SXcAzxWtRWZmDaCAj7Gtdyvy0KzOJNO+mZk1GU26Ry5pDsuOkX9GcqenmVmTkeEOeV5DK63royFmZg0py9eR13pnp6TR+aSZmWVZaUn+S20k9ZY0UdIkSYOq2H6OpAmS3pI0WtImOduOl/RBuhyfT9treh55C2ANoI2kdYGKj6u1SGaJNjNrMkooTI9cUikwFNgHmAqMlTSy0kw/rwPdI2KepNOAy4AjJa0H/I5kQuYAXk3Lzqq57dX7GfAqsEX6f8XyAHDNihygmVljVcCHZu0ATIqIyRGxCBgB9MvNEBFPRcS8dPUlkgmaAfYDHo+ImWnwfhzoXVuFNT2P/ErgSklnRsTVtTbdzCzDCnjVykbAlJz1qcCONeQ/CXi0hrK1joDk8/TDcknrVKykszyfnkc5M7PMKJHyXiQNkDQuZxmwInVKOpZkGOXylWp7HnlOiYivK1bS7v4pK1OpmVljU1qivJeIGBYR3XOWYTm7mgZ0yFlvn6YtQ1Iv4AKgb0QsrEvZyvIJ5KXKueUpHchvnkc5M7PMKOAY+Vigs6SOkpoD/YGRy9al7YDrSYL4jJxNo4B905GPdYF907Qa5XNn53+BOyVdn67/LE0zM2syCjVnZ0SUSRpIEoBLgZsiYrykIcC4iBhJMpTSCrg77Sd/EhF9I2KmpIv4bha2IRExs7Y68wnk5wMDgNPS9ceBG+pyYGZmjV0hn7USEY8Aj1RKG5zzulcNZW8CbqpLfbV+CEVEeURcFxGHRcRhwASSCSbMzJoM1WFpbPJ6aFY6nnMUcATwEfCfYjbKzKy+ZfkW/Zru7OxCEryPAr4E7gQUEZ4lyMyanKb69MP3gGeBAyNiEoAkz9VpZk1Slp9HXtMY+SHAdOApSTdI+jGNc3jIzGylldRhaWyqbVNE3B8R/UmetfIUcDbQTtK1kvatrwaamdUHJXds5rU0NvlctTI3Im6PiD4kdxm9jqRSLz8AABLfSURBVCeWMLMmpslftVIhvT1/WLqYmTUZjbGnna8VmbPTzKzJKXUgNzPLtuyGcQdyMzOgiU++bGa2KijUVG8NwYHczAz3yM3MMq9JPmvFzGxV4qEVM7OMy3CHvHiPDVDiWEmD0/WNJe1QrPrMzFZGAad6Q1JvSRMlTZI0qIrte0h6TVKZpMMqbVsi6Y10GVm5bFWK2SP/B1AO7A0MAeYA9wI9ilinmdkKUYGGVtJ5jYcC+wBTgbGSRkbEhJxsnwAnAOdWsYv5EbFtXeosZiDfMSK6SXodktv704lIzcwanQI+j3wHYFJETAaQNALoRzK7GgAR8XG6rbwQFRbziYyL00+mAJDUlqSHbmbW6JRIeS+SBkgal7MMyNnVRsCUnPWpaVq+WqT7fEnSQfkUKGaP/CrgPpJH3/4ROAy4sIj1NWqfTZ/OBb8+j5lffQUShx1+BMf85Phl8syZM4ffnP8rPpv+KWVLlnD8iT/loIMP5dNPp/GLnw8kystZXFbGUcccyxFHHsWiRYs4a+BpfP755xzZ/yiOPOoYAIb87rccfmR/tuy6VUMcquWppEQ8f9t5fDrjGw496zqe+OfZtFqzBQDt1mvNuHc+5ohzlp/n/Jg+OzLo5P0AuOTGUdz24MsAPHDN6azfdi2alZby/Osfcvaf76S8PLj45/3Yd9euvPX+VE7+7a0A9P+/HrRZZ02uuX1MvRxrFtRlaCUiivnwwE0iYpqkTsCTkt6OiA9rKlC0QB4Rt0l6FaiYkOKgiHi3WPU1dqXNSjn3vEFs2XUr5s79lv6HH8pOO+/KZptvvjTPnXfcRqfNNuPqf1zHzJkz6XdAbw44oA9t27Tl1tvvpHnz5sybO5dDD+pDz732ZsI777Bdt+05ecCpHH9sEsgnvvceS8qXOIhnwMCj92LiR5/TOg3evU76+9Jtd/zlZB4c89ZyZdZdaw0uGLA/ux5zGRHBC7efz8Nj3uLrOfM59vybmDN3wdLyh+7TjVHPj2fbLTuww5F/5h+Dj2arzTfkwylfcFzfneg7cGj9HGhGFHBoZRrQIWe9fZqWl4iYlv4/WdIYYDugxkBezKtWNgbmAQ8CI4G5adoqqW3bdkuD65prtqJTp07MmPH5MnkkMW/uXCKCefPmsvbaa1ParBmrNW9O8+bJ6YVFixdRXp6MUDVbrRkLFiygrKyMiABg6NV/54wzz6rHI7MVsVG7dei921YMv++F5ba1XrMFe/bowoNPLR/I99llS0a/9B6zZs/j6znzGf3Se+y7a1eApUG8WbMSVmtWSkRQXh6s1qwUgDVaNGdx2RLOPu7HXDviacrKPNKZS3X4V4uxQGdJHdPzgv1JYmDtbZDWlbR6+roNsCs5Y+vVKeYY+cPAQ+n/o4HJwKNFrC8zpk2bynvvvssPf7TNMun9jz6GyZM/pFfP3TnsoL6c9+sLKClJfkWfTZ/OYQf3Yb8f9+TEk06hXbvvs9POu/LptGkce9QRHH3MTxjz5Gi27LoV7dp9vyEOy+rg8l8dygVX3k95eSy3rc9eP2LMKxOXBuZcG7Zdh6mfz1q6Pm3G12zYdp2l6yOHnsEnoy/h23kL+c8Tr/PtvIWMem48L40YxGdffsPsb+fTY+tNq+ztr+oKdflhRJQBA4FRwLvAXRExXtIQSX2TutRD0lTgcOB6SePT4lsC4yS9STIz2yWVrnapUjGHVn6Yuy6pG3B6serLinlz5/LLs3/Orwb9hlatWi2z7YXnnmOLLbbkxuG3MOWTT/jZKSfSbfvutGrVivU32IB77nuQGTM+5+wzz2Cffffje23acMnlfwVg8eLFnDbgJK685h9cfumf+Wz6dPr07UfPvX/cEIdpNdh/962ZMXMOr787hd2377zc9iN6b8+/7ntxhfbd94yhrN68Gf/60wn07PEDnnz5Pa64+QmuuPkJAP4x+GguuvYhTjh4Z3rttCVvfzCNS28ctVLH01QU8nnkEfEI8EiltME5r8eSDLlULvcC8MPK6bWpt3lEI+I1YMea8uSeCf7nDU1vEqLFixdzztk/5/8O6EOvfZaf9vSB+//Dj/fZF0lsvMkmbLRRez6aPHmZPO3afZ/NO3fmtVfHLZN+14jb6dP3IN56801at27NZX/9G7fcPLyox2MrZudtO3Hgnj/kvYf/wC2XnEjPHl246eLjAPjeOmvSfatNefTZd6os++kXX9P+++suXd+o3Tp8+sXXy+RZuKiMB8e8RZ+ey8aDbX7QHgne/3gGh/TqxrHn30Sn9m3ZbOO2BT7CbMryVG/FHCM/J2c5V9LtwKc1lYmIYRHRPSK6n3TKgJqyZk5E8PvBF9CpUyeOO+HEKvOsv8EGvPxS0hP76ssv+fjjj2jfoT2ff/YZCxYkX7Nnf/MNr7/2Gpt27Li03OxvvuGZp8fQp99BLFgwf+kEsRVlrHEZfPVINu/9W7Y44HccN2g4Y8a+z08vvAWAg3ttx6PPvsPCRWVVln38hXfptfMWrNO6Jeu0bkmvnbfg8RfeZc2WzVm/zVoAlJaWsP9uWzHx42XPwQw+/UCG/ONhVmtWSmlpEo7Ko5w1Wvj2DiDTkbyYlx+2znldRjJWfm8R62vUXn/tVR4a+QCdu3ThiEP6AXDm2ecwfXry2XbEkUcx4NTT+e0Fv+bQg/oQEZx9zrmsu+56vPjC8/z18ksQIgiOP+GndO7yg6X7vv7aoZw84FRKSkrYZdfdGXHH7Rx6UB8OP7J/gxyrrbjD99uevwx/bJm0bl035uTDduP0Ibcza/Y8/nzDf3nu3+cB8Kdh/2XW7Hm0W6819/z9ZzRfrRklJeKZcR9wwz3PLd1Hn54/4rUJnzD9i28AeGviNMbe9Rve+WAab7+f9wUVTVqh7uxsCKq42qGgO01uBLo0Iqq6/TQvC8oofMMs89btMbChm2CN0PzXr1npKPzK5G/yjjk7dFq7UUX9gvfIJTWLiDJJuxZ632ZmxdKoInMdFWNo5RWgG1Dx5K67gbkVGyPiP0Wo08xspSjDz7Et5hh5C+ArkqcfBskHXgAO5GbW6GQ4jhclkLeTdA7wDt8F8Aoe9zazRinDcbwogbwUaEXVPxcHcjNrnDIcyYsRyKdHxJAi7NfMrGiyfPlhMQJ5dn8aZrbK8hj5svxwDzPLHAfyHBExs9D7NDMrNg+tmJllnHvkZmYZl+E4Xn+PsTUza9QK+PRDSb0lTZQ0SdKgKrbvIek1SWWSDqu07XhJH6TL8ZXLVsU9cjMzoKRAYyvpQwOHAvsAU4GxkkZWmunnE+AE4NxKZdcDfgd0J7nv5tW07Cxq4B65mRkF7ZDvAEyKiMkRsQgYAfTLzRARH0fEW0DliVP3Ax6PiJlp8H4c6F1bhQ7kZmZQyEi+ETAlZ31qmpaPFSrrQG5mRnL5Yd7/cqalTJcGndLMY+RmZtTt8sOIGAZUN7HwNKBDznr7NC0f04CelcqOqa2Qe+RmZhR0jHws0FlSR0nNgf7AyDybMQrYV9K6ktYF9k3TauRAbmYGSyctz2epSUSUAQNJAvC7wF0RMV7SEEl907p6SJoKHA5cL2l8WnYmcBHJh8FYYEg+d8sXZc7OQvCcnVYVz9lpVSnEnJ2TZszPO+Zs3q5lo7p/yGPkZmZk+85OB3IzM8h0JHcgNzPDTz80M8s8P/3QzCzjHMjNzDLOQytmZhnnHrmZWcZlOI47kJuZgXvkZmaZV9ut942ZA7mZGR5aMTPLvAx3yB3IzczAlx+amWVfduO4A7mZGWQ6jjuQm5kBlGR4kNwzBJmZQUHnepPUW9JESZMkDapi++qS7ky3vyxp0zR9U0nzJb2RLtfl03T3yM3MKNzQiqRSYCiwDzAVGCtpZERMyMl2EjArIjaX1B+4FDgy3fZhRGxblzrdIzczI7n8MN+lFjsAkyJickQsAkYA/Srl6QfcnL6+B/ixVuKOJAdyMzOSyw/z/VeLjYApOetT07Qq86STNX8DfC/d1lHS65KelrR7Pm330IqZGXW7IUjSAGBATtKwiBhWgGZMBzaOiK8kbQ/cL2mriJhdUyEHcjMz6hbI06BdXeCeBnTIWW+fplWVZ6qkZsDawFcREcDCtI5XJX0IdAHG1dQeD62YmVHQoZWxQGdJHSU1B/oDIyvlGQkcn74+DHgyIkJS2/RkKZI6AZ2BybVV6B65mRmFe9ZKRJRJGgiMAkqBmyJivKQhwLiIGAn8E7hV0iRgJkmwB9gDGCJpMVAOnBoRM2tte9KTb3wWlNE4G2YNat0eAxu6CdYIzX/9mpUOw3MWlOcdc1q3KGlUdw+5R25mBpm+R9+B3MyMbN+i70BuZkamO+QO5GZmQKYjuQO5mRnZnlii0V61Yt+RNKBAd41ZE+L3hVXwDUHZMKD2LLYK8vvCAAdyM7PMcyA3M8s4B/Js8DioVcXvCwN8stPMLPPcIzczyzgHcjOzjPMNQQ1E0hLg7ZykgyLi42ryfhsRreqlYdagJH0PGJ2urg8sAb5I13dI54A0W4bHyBtIXYKzA/mqSdLvgW8j4i85ac3SOR7NlvLQSiMhqZWk0ZJek/S2pMqzbiNpA0nPSHpD0jsVE7NK2lfSi2nZuyU56Dchkv4l6TpJLwOXSfq9pHNztr8jadP09bGSXknfI9dXzDZjTZsDecNpmf6xvSHpPmABcHBEdAP2Av4qLfdczaOBURGxLbAN8IakNsCFQK+07DjgnPo7DKsn7YFdIqLa362kLYEjgV3T98gS4Jh6ap81II+RN5z56R8bAJJWA/4kaQ+SKZ42Ar4PfJZTZixwU5r3/oh4Q9KeQFfg+TTuNwderKdjsPpzd0QsqSXPj4HtgbHpe6ElMKPYDbOG50DeeBwDtAW2j4jFkj4GWuRmiIhn0kB/APAvSVcAs4DHI+Ko+m6w1au5Oa/LWPbbdMX7RMDNEfHremuVNQoeWmk81gZmpEF8L2CTyhkkbQJ8HhE3ADcC3YCXgF0lbZ7mWVNSl3pst9W/j0l+90jqBnRM00cDh0lql25bL33PWBPnHnnjcRvwoKS3Sca536siT0/gV+kM298Cx0XEF5JOAO6QtHqa70Lg/eI32RrIvcBxksYDL5P+riNigqQLgccklQCLgTOA/zVYS61e+PJDM7OM89CKmVnGOZCbmWWcA7mZWcY5kJuZZZwDuZlZxjmQm5llnAO5mVnGOZCbmWWcA7mZWcY5kJuZZZwDuZlZxjmQm5llnAO5mVnGOZCbmWWcA7mZWcY5kJuZZZwDuS1D0hJJb0h6R9LdktZYiX39S9Jh6esbJXWtIW9PSbusQB0fS2pTKW24pJ9VSjtI0qP5tNUsaxzIrbL5EbFtRGwNLAJOzd0oaYWmB4yIkyNiQg1ZegJ1DuTVuAPoXymtf5pu1uQ4kFtNngU2T3vLz0oaCUyQVCrpckljJb1V0ftV4hpJEyU9AbSr2JGkMZK6p697S3pN0puSRkvalOQD4xfpt4HdJbWVdG9ax1hJu6ZlvyfpMUnjJd1IMnN8ZaOBLSRtkJZZE+gF3C9pcLq/dyQNk7Rc+dxevqTuksZU7EfSTZJekfS6pH5p+lZp2hvpz6NzAX72ZnlzILcqpT3v/YG306RuwFkR0QU4CfgmInoAPYBTJHUEDgZ+AHQFjqOKHraktsANwKERsQ1weER8DFwH/C39NvAscGW63gM4FLgx3cXvgOciYivgPmDjynVExBKSCYqPSJP6AGMiYjZwTUT0SL9xtAQOrMOP5QLgyYjYAdgLuDz9kDgVuDIitgW6A1PrsE+zlbZCX5OtSWsp6Y309bPAP0kC8isR8VGavi/wo5wx5bWBzsAewB1pIP1U0pNV7H8n4JmKfUXEzGra0QvomtNhXktSq7SOQ9KyD0uaVU35O4C/kHwg9AduTdP3knQesAawHjAeeLCafVS2L9BX0rnpeguSD5IXgQsktQf+ExEf5Lk/s4JwILfK5qc9y6XSYDo3Nwk4MyJGVcr3fwVsRwmwU0QsqKIt+XgB2EDSNiQfRP0ltQD+AXSPiCmSfk8SjCsr47tvq7nbRfJNYmKl/O9Kehk4AHhE0s8ioqoPMbOi8NCKrYhRwGmSVgOQ1CUdYngGODIdQ9+AZPihspeAPdKhGCStl6bPAVrn5HsMOLNiRVLFh8szwNFp2v7AulU1MCICuBO4GXg0/UCoCMpfpr376q5S+RjYPn19aKXjPrNiXF3Sdun/nYDJEXEV8ADwo2r2a1YUDuS2Im4EJgCvSXoHuJ7k2919wAfptltIhhyWERFfAAOA/0h6kyTYQjK8cXDFyU7g50D39OThBL67euYPJB8E40mGWD6poZ13ANuk/xMRX5OMz79DEpTHVlPuD8CVksYBS3LSLwJWA95K678oTT8CeCcdkto6PXazeqOk42JmZlnlHrmZWcY5kJuZZZwDuZlZxjmQm5llnAO5mVnGOZCbmWWcA7mZWcY5kJuZZdz/A7W2TQZ2YV45AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM"
      ],
      "metadata": {
        "id": "1DsYJzn7kZRP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "\n",
        "svm_clf = svm.SVC()\n",
        "svm_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cuMJUDzkX-M",
        "outputId": "119a16cd-20dd-42c9-d907-5f94a7ee1066"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svm_clf.predict(x_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "S1listofmodels.append(\"SVM\")\n",
        "S1listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wi-6h4p4kcMT",
        "outputId": "6ad0beb8-92d7-4c65-a300-af7fcedaf48b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.5127478753541076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "UO4JJQ-dkeoX",
        "outputId": "78e6a591-17c7-49de-de9b-34914f2ff832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XG Boost"
      ],
      "metadata": {
        "id": "6bQF5rL6khHu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb_clf = XGBClassifier()\n",
        "xgb_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OmcrqGRTkk4v",
        "outputId": "30aff9b3-05c4-49b4-844e-23d8bf88b86a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = xgb_clf.predict(x_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "S1listofmodels.append(\"XGBoost\")\n",
        "S1listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTK7RUzVklzS",
        "outputId": "5eb3391a-be61-4cd7-a01c-da8f5b62f6e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.5354107648725213\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "_Dlp03vhkniN",
        "outputId": "23646ab0-e676-4cec-fbca-ebb80efb908b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Strategy #2"
      ],
      "metadata": {
        "id": "HEl3UQY6kp5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create 1 days simple moving average column\n",
        "IBM_data['50_SMA'] = IBM_data['Close'].rolling(window = 50, min_periods = 1).mean()\n",
        "# create 50 days simple moving average column\n",
        "IBM_data['200_SMA'] = IBM_data['Close'].rolling(window = 200, min_periods = 1).mean()\n",
        "# display first few rows\n",
        "IBM_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "AHjJDRld3EE3",
        "outputId": "4316c707-777b-4bb9-9b84-cb3d0765d594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2015-01-02 00:00:00-05:00  154.216064  156.128113  153.919693  154.933075   \n",
              "2015-01-05 00:00:00-05:00  154.177826  154.177826  152.189301  152.495224   \n",
              "2015-01-06 00:00:00-05:00  152.648178  152.925430  148.346085  149.206497   \n",
              "2015-01-07 00:00:00-05:00  150.286804  150.286804  147.256210  148.231354   \n",
              "2015-01-08 00:00:00-05:00  149.369019  152.045883  148.709366  151.453156   \n",
              "\n",
              "                            Adj Close   Volume  Strategy1      50_SMA  \\\n",
              "Date                                                                    \n",
              "2015-01-02 00:00:00-05:00  110.782318  5779673         -1  154.933075   \n",
              "2015-01-05 00:00:00-05:00  109.039169  5104898         -1  153.714149   \n",
              "2015-01-06 00:00:00-05:00  106.687645  6429448         -1  152.211599   \n",
              "2015-01-07 00:00:00-05:00  105.990341  4918083          1  151.216537   \n",
              "2015-01-08 00:00:00-05:00  108.294060  4431693          1  151.263861   \n",
              "\n",
              "                              200_SMA  \n",
              "Date                                   \n",
              "2015-01-02 00:00:00-05:00  154.933075  \n",
              "2015-01-05 00:00:00-05:00  153.714149  \n",
              "2015-01-06 00:00:00-05:00  152.211599  \n",
              "2015-01-07 00:00:00-05:00  151.216537  \n",
              "2015-01-08 00:00:00-05:00  151.263861  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d6f06d4-965e-4942-91ce-ae1b809f7a0b\">\n",
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              "      <th>Open</th>\n",
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              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02 00:00:00-05:00</th>\n",
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              "      <td>-1</td>\n",
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              "      <th>2015-01-06 00:00:00-05:00</th>\n",
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              "      <td>6429448</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "IBM_data['Signal'] = 0.0\n",
        "IBM_data['Signal'] = np.where(IBM_data['50_SMA'] > IBM_data['200_SMA'], 1.0, 0.0)"
      ],
      "metadata": {
        "id": "-2bI7-fm60-J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IBM_data['Position'] = IBM_data['Signal'].diff()\n",
        "# display first few rows\n",
        "IBM_data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "aU7xgX4S7DZQ",
        "outputId": "6d4cce74-7012-4bbc-f582-953effa3c910"
      },
      "execution_count": null,
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        {
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              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2015-01-02 00:00:00-05:00  154.216064  156.128113  153.919693  154.933075   \n",
              "2015-01-05 00:00:00-05:00  154.177826  154.177826  152.189301  152.495224   \n",
              "\n",
              "                            Adj Close   Volume  Strategy1      50_SMA  \\\n",
              "Date                                                                    \n",
              "2015-01-02 00:00:00-05:00  110.782318  5779673         -1  154.933075   \n",
              "2015-01-05 00:00:00-05:00  109.039169  5104898         -1  153.714149   \n",
              "\n",
              "                              200_SMA  Signal  Position  \n",
              "Date                                                     \n",
              "2015-01-02 00:00:00-05:00  154.933075     0.0       NaN  \n",
              "2015-01-05 00:00:00-05:00  153.714149     0.0       0.0  "
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          },
          "metadata": {},
          "execution_count": 41
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (20,10))\n",
        "# plot close price, short-term and long-term moving averages \n",
        "IBM_data['Close'].plot(color = 'k', label= 'Close Price') \n",
        "IBM_data['50_SMA'].plot(color = 'b',label = '50-day SMA')\n",
        "IBM_data['200_SMA'].plot(color = 'g', label = '200-day SMA')\n",
        "# plot ‘buy’ signals\n",
        "plt.plot(IBM_data[IBM_data['Position'] == 1].index, \n",
        "         IBM_data['50_SMA'][IBM_data['Position'] == 1], \n",
        "         '^', markersize = 15, color = 'g', label = 'buy')\n",
        "# plot ‘sell’ signals\n",
        "plt.plot(IBM_data[IBM_data['Position'] == -1].index, \n",
        "         IBM_data['200_SMA'][IBM_data['Position'] == -1], \n",
        "         'v', markersize = 15, color = 'r', label = 'sell')\n",
        "plt.ylabel('Price in $', fontsize = 15 )\n",
        "plt.xlabel('Date', fontsize = 15 )\n",
        "plt.title('IBM_data', fontsize = 20)\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "VkPM0Dss65gR",
        "outputId": "cb24431c-51f0-4608-d665-d69be74e1eb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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gAQD169cnLi4uz7mpqalMmDCB77//nuuuu46RI0c6j7Vp04a1a9eSkpLCxo0bmTJlCosWLeL+++9nzpw5zJw5k3379pGamkpYWJjLdcePH89NN93EwoUL6d+/P+PGjSMkJMR5fNSoUbz88svcdttt7Nixg/Hjx7N27Vrn8fnz5/PnP/+ZevXqMXz4cKZMmVLsr11xqHJJRETKTO5w6auvvspz/MKFC2RmZl7JIYmIiIiIuFi3bh1bt27lm2++YdasWaxZsyZPH2MMxpg87Xv27KF58+aEhITkWQspISGBESNG0L17d5566innH1tHjBjB0qVLycjI4IMPPuDee+/Nc93w8HCio6N55plnOHfuHF27dmX37t3O4x07diQmJob58+dzyy23uJwbFxfH/v376dWrF61atcLLy4uoqKiSfnmKRJVLIiJSZhzhUlBQEDt27KBWrVqcP3+e7OxsACZNmsTEiRPZuHEjTZo0Kc+hioiIiEg5u1yFUVlxTD+rW7cuQ4cOZdOmTfTp04d69eoRGxtLgwYNiI2NpW7dugAMGjSIuLg4IiIieOyxxwq87tSpU+nbty9z587l7NmzREZGAlC1alUGDhzIkiVL+PTTT9myZUu+5wcEBDBs2DCGDRuGh4cHX3/9NW3btnUev/322/njH//I6tWrnRvpAHz66afEx8fTvHlzwP6D7vz585k+fXqpvk6FUeWSiIiUGUe49M033xAYGEjjxo1d5o4fOHCA2NhYli9fXl5DFBEREZFKLCkpyfk7a1JSEitWrKBDhw6AHd589NFHAHz00UcMGTIEgG+//ZZt27bx3nvv0aZNG2JiYjh48CBgT0dzSEhIcP7uO2fOHJf7PvDAAzz++ON07dqVmjVr5hnX+vXrnbvTpaens2vXLpo1a+bSZ/z48bzwwguEhoa6tM+fP5/ly5cTExNDTEwMW7ZsKfN1lxQuiYhImUlISMAYQ1hYGKtWreLtt992+UfR09MTgNOnT5fXEEVERESkEouLi6NXr16EhYXRrVs3br31VgYPHgzYVfYrV64kJCSEVatWMWnSpDzn+/r6Mnv2bG699VY6d+7srG4C+NOf/sTkyZPp1atXnqUgunTpQmBgIPfdd1++4zp48CA33ngjoaGhdOrUiYiICIYPH+7Sp3Hjxjz++OMubTExMRw+fJgePXo425o3b0716tXZuHFj8b44xaBpcSIiUmZiY2MJCgqiSpUqdO3aFYBmzZqxbt06WrduzcyZMxkxYoTCJREREREpFy1atMizU5tD7dq1+e677y57jcGDB7Nnz5487ddffz379u3j4sWLVKtWjWnTpjmPnThxguzsbG666aZ8rzl27FjGjh2b77HExMQ8bZGRkc5pd8ePH89zfOvWrZf9PEpDlUsiIlJmTpw4QcOGDV3a6tSpA8Bdd93F4MGDqVu3rsIlEREREak05s6dS/fu3Zk+fToeHtdGLKPKJRERKTP5hUsXLlwAoF69eoC92PepU6eu+NhERERERMpDYVVJFdW1EZGJiMhVJyUlhb179zp3qXCYPHkyPXv2ZNSoUQAEBgY6F1EUEREREZGKR5VLIiJSJr7++muSkpIYOnSoS3tISAjr1q1zfuzn56fKJRERERGRCkyVSyIiUiYWLFhA3bp1ufHGGwvt5+fnR2pq6hUalYiIiIiIuJvCJRERKbGsrCyGDh3Kjz/+6NKemprK0qVLufPOO6lSpfAiWV9fX1JSUspymCIiIiIiUoYULomISInt2rWLL774ggceeMDZFhsbS/v27UlNTSU8PPyy1/Dz81O4JCIiIiLl4ujRo/Tt25d27drRvn17/v73vzuPnTt3joEDBxISEsLAgQOJj48HwLIsHn/8ca677jo6duzI1q1bi3SvgIAAt4x5+vTptG/fno4dOxIeHs7GjRsBiIyMpGnTpliW5ex7xx135LnvzJkz8fX1JSEhwS3jAYVLIiJSCo5/SFu0aOFs+8c//kF0dDRgL9Z9OQqXRERERKS8VKlShTfeeINdu3bx008/MWvWLHbt2gXAjBkz6N+/P/v376d///7MmDEDgG+++Yb9+/ezf/9+Zs+ezSOPPHLFxrthwwaWLl3K1q1b2bFjB6tWraJJkybO4zVq1GD9+vUAnD9/ntjY2DzXmD9/Pl27dmXx4sVuG5fCJRERKbEzZ84Arn+FMcY4n1erVu2y11C4JCIiIiLlpUGDBnTu3Bmwf3dt27Ytx48fB2DJkiWMGzcOgHHjxvHFF18428eOHYsxhh49ehQY4hw6dIjrr7+eHj168PzzzzvbExMT6d+/P507dyY0NJQlS5YA8Oc//5mZM2c6+z333HMulVRgzxKoU6cOPj4+ANSpU4eGDRs6j48aNYoFCxYAsHjxYoYNG+Zy/sGDB0lMTGTatGnMnz+/BF+x/Gm3OBERKTHHQtzp6enOttzhUlEql3x9fcnKyuKXX36hU6dO7h+kiIiIiFQITy5/km0nt7n1muH1w5k5eOblOwIxMTH88ssvdO/eHYC4uDgaNGgAQP369YmLiwPg+PHjLtVCjRs35vjx486+Dk888QSPPPIIQ4cOZe7cuc52X19fPv/8cwIDAzlz5gw9evTg9ttvZ/z48QwbNownn3yS7OxsFixYwKZNm1yuedNNN/Hyyy/TqlUrBgwYwMiRI1020Onfvz8TJkwgKyuLBQsWMHv2bF555RXn8QULFjBq1Ch69+7N3r17iYuLo169ekX6+hRGlUsiIlJijnApOTnZ2VbcyiUvLy8A51+MRERERESutMTERIYPH87MmTPz/QOpMcbl99yiWL9+PXfffTcAv/vd75ztlmUxZcoUOnbsyIABAzh+/DhxcXEEBwdTu3ZtfvnlF1asWEGnTp2oXbu2yzUDAgLYsmULs2fPJigoiJEjRzJnzhzncU9PT3r16sWCBQtISUkhODjY5fz58+czatQoPDw8GD58OJ999lmxPqeCqHJJRERKzDGd7cKFC8624lYunTt3rsj3W7cOOnSAGjWKMUgRERERqRCKWmHkbhkZGQwfPpzRo0e7TCOrV68esbGxNGjQgNjYWOrWrQtAo0aNOHr0qLPfsWPHaNSoEc899xzLli0DYNs2uwIrv0Bq3rx5nD59mi1btuDl5UVwcLDzj7YPPPAAc+bM4eTJk4wfPz7f8Xp6ehIZGUlkZCShoaF89NFH3Hvvvc7jo0aNYujQobz44osu5+3cuZP9+/czcOBAwJ590Lx5cx577LFifsXyUuWSiIiUmOMfwfPnzzvbihsunTp1yvl8+/bt+c79XrMGbrgBeveGnj0hLa00oxYRERERsVmWxf3330/btm35wx/+4HLs9ttv56OPPgLgo48+YsiQIc72uXPnYlkWP/30E9WrV6dBgwZMnz6dbdu2OYOlnj17Otc/mjdvnvO6CQkJ1K1bFy8vL3744QcOHz7sPDZ06FCWL1/Ozz//zKBBg/KMd+/evezfv9/58bZt22jWrJlLn969ezN58mRn1ZTD/PnzefHFF4mJiSEmJoYTJ05w4sQJl/uXlMIlEREpMUfl0pEjR8jKygKKPy3uhhtucD4PDw/nnnvucTmelAR33w3HjkGfPrBrF9x/P+RMeRcRERERKbH169fz8ccf8/333xMeHk54eDhff/01AJMmTWLlypWEhISwatUqJk2aBMAtt9xCixYtuO6665gwYQJvv/12vtf++9//zqxZs+jRo4dzkXCA0aNHs3nzZkJDQ5k7dy5t2rRxHvP29qZv377cddddeHp65rlmYmIi48aNo127dnTs2JFdu3blqVAyxvDHP/6ROnXquLQvWLCAoUOHurQNHTrUGYCVhqbFiYhIieVe0DsmJoaWLVu6hEve3t6XvcaECROoXbs2d955p7PNsizndf7v/+DECVi/3q5emjIF/vpX+OEH2LQJGjVy8yclIiIiIpVGr169sCwr32O1a9fmu+++y9NujGHWrFmXvXbz5s3ZsGEDFy9epFq1akybNg2wd3jbsGFDvudkZ2fz008/FbgWUpcuXfjf//6X77HVq1fn256YmAhAdHR0nmNvvvnm5T6NIlHlkoiIlJgjXAK7RBdcd44rCmMM/fr1c2lLS0sjOxv++187SLrrLjtYAnj1VTtUunAB+vWDl16CI0dK93mIiIiIiJS3Xbt2cd1119G/f39CQkLKezjFonBJRERKLCUlhebNmwOwZ88ewA6GiqtmzZouW7cmJibyxhswejS0bGlXL+XWuTMsXgy+vvDiizB4MGRnl/jTEBEREREpd+3atSM6Opo33nijvIdSbAqXRESkxFJTU2nUqBF16tRxhku5q5mKo3Xr1s7nx48n8dprcPPNsH07NG2at//Agfax2bNh926IiirRbUVEREREpJQULomISImlpqbi5+dHmzZt8oRLVaoUb1m/Rs7Fkzx49dUA4uNh+nTwuMy/VI5NNH74oVi3ExEREZEKKi4xjhvn3MjxC8cv31muCIVLIiJSYikpKfj6+jrDpR07dhCVU0J09OjRYl3LDpeqAxv59NPaTJwInTpd/rymTaFFC4VLIiIiIpXF6xteZ92RdUz+bnJ5D0VyKFwSEZESS09Px9vbm1atWnH69GluvvlmNm3axMiRI6lfv36xrmVvlfowEEGrVq9wzz0bi3xu377w44+QlVW88YuIiIhIxXIh7QLv/PwO2VY2C3ctZO+ZveU9JEHhkoiIlEJmZiZeXl7Url0bgBMnTvDwww+zYMGCYl9r6NC78PP7E8Z8x759f2bJki+KfG6/fnD+PHzzTbFvKyIiIiIVyDs/v4OFBUB6VjpPr3i61NeMiYmhQ4cOpb5OZaZwSURESiwzM5MqVapQo0YNZ1utWrVKdK2ff25GSkotvvyyH9WrVyc5ObnI5w4dCu3awYQJcOZMiW4vIiIiIle5tMw0ZqyfQXKG/XtilpXF94e+Z/OJzeU8MlG4JCIiJZZfuJT7eVEdOACTJ0OrVnDLLQZ/f3+SkpKKfL6fH8ybBydPwty5xb69iIiIiFQAH+/4mMysTJe21MxUJn4zsdTXzszMZPTo0bRt25Y777yT5ORkgoODOZPzl8vNmzcTGRlJdnY2ISEhnD59GoDs7Gyuu+4658eVlcIlEREpMUe4VL16dWdbccOljAx7WltSEnz8sb07nL+/P4cOHeLChQtFvk54uL24988/F+v2IiIiIlIBZGVn8eLqF0nMSHRpt7DYGbeTVdGrSnX9vXv38vvf/57du3cTGBjI22+/nW8/Dw8PxowZw7x58wBYtWoVYWFhBAUFler+FZ3CJRERKTF3VC6tWAFHj8J770G3bnbb+fPn+f7773nwwQeLda2ICNisqmgRERGRa86SvUtISEvI91hSRhKPff0Y2VZ2ia/fpEkTevbsCcCYMWNYt25dgX3Hjx/P3Jxy+Q8++ID77ruvxPe9VihcErfLzi75N7SIVCzuqFz67DOoXh1uvvm3NkdZ8SeffMK8efP49ttv2b9/P+PGjWPnzp0FXisiwp5iFx9fvM9DRERERK5elmXx/PfPk5ieWGCfYxeOsWjXohLfwxiT5+MqVao439+mpqY6jzVp0oR69erx/fffs2nTJm7O/YtsJaVwSdzq3XffxdPT0zkvVUSubY5wKfci3uHh4UU+PyMDvvwSbr8dvL3z7zNmzBgGDx7M8OHDmTt3Ll9++WWB14uIsB+3bi3yEERERETkKrc6ZjVHEo4U2icpI4knv32SjKyMEt3jyJEjbNiwAYD//ve/9OrVi+DgYLZs2QLAokWuwdUDDzzAmDFjGDFiBJ6eniW657VE4ZK41SuvvALA2bNnXdrT0tLYtWtXeQxJRMqQI1zy8PDgxRdf5M477yzWfPMffrCrjIYPz/947r8gOSqWEhML/otVly72o6bGiYiIiFw7nv/heZIyLr/ZS0JqAu//8n6J7tG6dWtmzZpF27ZtiY+P55FHHuGFF17giSeeICIiIk+AdPvtt5OYmKgpcTmqlPcA5Npy/PhxAD777DOWLVvG999/j5+fH08//TSzZs3i+PHjNGzYsJxHKSLu4giXAF544YVinXvhgr1DXPXqcNNN+fexLMv5vH379sTGxhYaLqGY8sMAACAASURBVNWqBcHBqlwSERERuVb8EvsL205uK1LfpIwkpnw3hbFhY6nqVbXI9wgODmbPnj152nv37s2+ffvyPWf79u2EhYXRpk2bIt/nWqbKJSkTU6dO5aeffuLQoUMAzvLCmJgYRowYUejiaCJScWRkZDjDpeJ69FHYvh3mzwc/P9dj999/f57+Dz/8MAEBAYWGSwBhYbBjR4mGJCIiIiJXmRdWv0BqZurlO+ZIy0pj5k8zy3BEMGPGDIYPH85rr71WpvepSBQuSZk6deoUAIGBgQAsW7aMhQsXMn78+PIcloi4QXZ2NpZl4eXlVexz09Lg88/hgQdcF/J2eO+990hJSeHDDz/kmWeeITY2lkcffbRI4VLXrrB3L+zeXexhiYiIiMhV5OC5g6yMXlmsXeCSM5J5de2rxKeU3Q4vkyZN4vDhw/Tq1avM7lHRaFqclKlLw6VPPvkEAO+CVu51g+zsbDw8lJuKlLXMzEyAElUurVsHSUlw660F9/H19eXee+91aStKuPTggzBjBrz4IuT8yBERERGRCqhKRDfOHU0l0Rsu+EC8L5zzg9NVIS4ATvvbbfF+ro8pAZlM++El3rilbCuY5DcKl6RMxcXFAb+FSwcPHgQgISGhzO5Zo0YNbr/9dv7zn/+U2T1EpHTh0tdf27vD9e1bvPOqVavGxYsXC+0TFARPPAHTp8Nrr0GLFsUenoiIiIhcBaLqZDEoBoKS7f8ulWEg3RMyPcDK2QfGMxt8stKImfsuHFO4dKWovEPKlGPXOMeb0ICAAADi48umRDE6OpqLFy8yb968Mrm+iPympOGSZcGSJdCvH+T8SCiyolQuAQwbZj9qYW8RERGRiuvW99dSxdevwONeFvhnQvV0qJFm/1ctA7y9fWl11yNXcKSicEnKjLe3NwcOHODUqVMkJSXRqVMnDh8+zLPPPktSUhJpaWluv+fChQsB6Nixo9uvLSKuShoubd0KBw/CkCHFv2fNmjWd020L06YNGANRUcW/h4iIiIhcJUJD7f+Ky9MTpk51/3ikQAqXpMz4+Pgwb948Hn74YRITE/H396dWrVo0a9YMKJvqJUe41LRpU7dfW0RclSRc2rcPbrsN6tT5rbqoOMLDw4mNjeXEiROF9qtaFVq2hF9/Lf49REREROQqMnUqVKtW9P5Vq8KUKVCzZpkM595773W+74yMjGTz5s1lcp+KRuGSlBnHot07d+4kKSkJf39/AGrVqgXAuXPn3Hq/9PR0fv75Z8Be1FtEylZJwqU33oCLF+HHH6Fu3eLfs2vXrgDO7/XCtG+vcElERESkwrvlluKtpeDjA08+WXbjkXwpXJIy4+PjA8ChQ4eIj493rrfkCJfOnDnj1vtduHDB+VzhkkjZK0m4tHq1vYh3u3Ylu2enTp3w9PRk06ZNl+3bvr1dKVUGM3BFRERE5Erx8IDJkyGnWKFQ/v7w6qt29VIxJCUlceuttxIWFkaHDh345JNP2LJlCzfeeCNdunRh0KBBxMbGlvATqBwULonbZGVluXzsqFzKysri4MGDzsolx7S4DRs2MGDAAI4dO+aW++feQUrhkkjZK264dOKEHfZERpb8nn5+foSHh7Nu3brL9u3QAbKy7HuKiIiISAV23332rjCXU7063H9/sS+/fPlyGjZsyPbt24mKimLw4MFMnDiRhQsXsmXLFsaPH89zzz1XgoFXHgqXxG3S09NdPvby8nI+z87OdoZLzZs3x9PTk9dff53vvvuOKVOmuOX+CpdErixHuJT7e70wP/5oP5YmXAK48cYb2bhxI6mpqYX2a9/eftTUOBEREZEKLiAAxo+Hwn7vDAiAmTML71OA0NBQVq5cybPPPsvatWs5evQoUVFRDBw4kPDwcKZNm+a2oohrlcIlcZtLw6VLK5kc0+K8vLxo0aIFCQkJAPzqpnd+CpdErqyMjAyg6JVLX30FgYEQHl66+954442kpaWxcePGQvu1bm1vFKId40RERESuAU8/bf9yV5BGjWD48BJdulWrVmzdupXQ0FCef/55Fi1aRPv27dm2bRvbtm1j586drFixooQDrxwULonbXBouOd54OvjnmiPbqlUr53GrKOWNReBYc8nLy0vhksgVUJxpcS++CPPn239wKux3gqLo3bs3xhhWr15daD8fHwgJUbgkIiIick0IDrZL4I3Je8zfH/75T3t9phI4ceIEVatWZcyYMTzzzDNs3LiR06dPs2HDBsB+b+uuoohrVdFXYRW5jNzh0qBBg9i+fbvL8YBcK/y3atWKZcuWAWDy++FQAo7KpRo1aihcErkCihourV4NL70E994Lr79e+vvWrFmTsLAwfsyZZxcXF4e/v7/LzxiHnj1hwQK4cMGumhIRERGRCuy552DtWkhK+q3NGAgNhQEDSnzZnTt38swzz+Dh4YGXlxfvvPMOVapU4fHHHychIYHMzEyefPJJ2jvWXZA8FC6J2zjCpZdeeolnnnmGJk2aAHbFUlJSUp7KJQd3h0vVq1dXuCRyBSQnJwOXD5dy/uDD3/5W+qolh4iICL788ksA6tevT7du3fKdJvfQQ/D++/Dxx/Doo+65t4iIiIiUk5497elvuXds8fWFt94q1WUHDRrEoEGD8rSvWbMmT9ucOXOczy9XSV+ZaFqcuE1azn7frVq1ws/Pz/nGs3PnzkDeaXHu5rhfQECAwiWRK+DDDz/E19fX+T1ekO3b7SrmGjXcd+/mzZtz6tQpoqOjAdi0aVO+/bp2hYgIePdd991bRERERMqJMTB1qr14N9h/uezXz/6FT8qVwiVxmzNnzgBQrVo1AFJSUgDo1asXkHdanMPldnwqKsd1/P39FS6JlLGYmBg+/vhjJkyYQL169Qrtu20bhIW59/7NmjUD7Aqmy7njDti5E3LyZxERERGpyO66CxyV897e8Oab5TseARQuiRutXbsWgG7durm033bbbVSvXt0lUGrYsCFVq1YFfguhSssRLvn5+SlcEilj3377LZmZmUycOLHQfklJdtVyaXeIu9QNN9wAQHx8PGD/TClIy5b2Y06Rk4iIiIhUZN7e8MQT9uLdd94JZTArRopP4ZK4xfTp05kyZQrt2rUjKCjI5VjXrl05f/68y+JnHh4ehISEAHDq1CnefPPNPLvLFVdqaipVqlTB29tb4ZJIGXOEuXXq1Cm0X1QUWJb7w6XmzZtz4403Oj92TMvNT4sW9qPCJREREZGrU7F3EH/0UejdG157rWwGVMmVZEd3hUviFs8//zyAy5s9By8vr3zPcUxnSUxM5Omnn2bevHmlGkNqaiq+vr54eHgoXBIpY44F/Av6/nZwbBrp7mlxALVq1XI+j4+PL/D7XpVLIiIiIlcvX19fzp49W7xAIyjI3pK4UaMyG1dlZVkWZ8+exdfXt1jnabc4cavu3bs7n2/ZsoW9e/cW2Pett97CGMN7770HwH333ceYMWMK3XkqIwPi4uyfIZduMqdwSeTKcVQaXi5c2rYNAgPtBb3dzREu1a5dm7Nnz5KZmYm3t3c+/ewxKFwSERERufo0btyYY8eOcfr06fIeSplxvFetKHx9fWncuHGxzlG4JG41ePBg5/POnTsXuouUn58f58+fd2m7ePEiNWvWzNM3MxMmTYK334aUFGjaFAYMgHvvtashQeGSSFk5efIkNWvWxMfHx9lWnHApLCxvGOwOjp8VERERfPvtt2RlZeXbzxh7atzBg+4fg4iIiIiUjpeXF82bNy/vYZSp1atX06lTp/IeRpnStDhxi9DQUIYOHXrZXaMu9corr/Dyyy87K54uXryYp09GBkyZAm+8Ya/X9uSTEBoKn30GffrA4sVw6NAhVqxYoXBJxM0sy6JBgwaMGDHCpT09PR1PT088PAr+Z2TlStiwAfr3L5uxNWvWDD8/P+cU28K+71u0UOWSiIiIiEhZUeWSuEVmZmah09kK0qZNG6ZOnUrr1q0ZOXJknnDpyBE7QDp8GO67Dz744LdjiYnQsyf86U9w+HBbMjPT6NChA8YYhUsibvKvf/0LgK+++sqlPSMjo9CqpbQ0uP9+aNPG/h4tCxMmTOC2225j0aJFwOXDpWXLIDvb3lhEyl9ycrJz11ARERERqdj0K7a4RUZGRonCJYdq1aoBduVSdnY26enpZGbC2LFw9iwsWQL//rfrOQEBMHWqPdUlM3MEYM+7UeWSiPt8+umn+bZnZGTku76Rw9q1cPQozJgBfn5lMzYfHx+Cg4Od1VMFTYsDe1HvtDQ4caJsxiLFs3TpUvz9/dm8eXN5D0VERERE3EDhkrhFSSuXHAICAgA7XLr77rvx8WnBmDHZ/PgjzJoFt98Onp55z7vjDsc24x8D2zh7NpPk5PpkZZXRu1kRAexpcYVVLn37LXh7l92UuNw8c344FBYqO6bxHzpU9uORy3v//fcBOKiFsERERESuCQqXxC0yMzMvu7BvYXJXLn366VJgE4sWwZ//bFcv7dmzh48//jjPeVWq2BUS8CTQntjY3Sxb9g+OH3+jxGMRkfylpKQ4n19uWty330KvXnaFYVlzVC4pXKo4HDuJBgUFlfNIRERERMQdFC6JW5S2cil3uAQPAg15442dvPSSfbxdu3aMHTs233ODgjKAvwNjqVNnJU2abCI5+Ua9iRS5jNjYWIwxLF26tMA+udfEOXz4sPN5YdPi4uJg50646Sb3jbUwRZkW16yZvWvcgQNXZkxSuDNnzgCFB4IiIiIiUnEoXBK3KG24FBgYCMD+/QAvAKsICTnB5s2bee2117AsC7Cn4lwqLi4OgMceq8XPP4fQtet/AYv33ivxcEQqBcdC2I5Fu/NjjHHuxhYTE+NsL2xa3Pff249XYkocFG1anI8PdOsGCxdCzo8TKUeOn+mFBYIiIiIiUnEoXBK3KG24VKtWLeA5Xn31d4An8DCJiYl07dqVKVOmOPslJSXlOffkyZMADBw4kODgYKpVO4+f32o++sjeGUpE8hcbGwv8Fs7kJy0tjbZt2wJw4MAB546OBU2Ly86GuXOhZk3o1KkMBp2PolQuATz0EOzeDWvWXIlRSWEULomIiIhcW65ouGSM+cAYc8oYE3VJ+0RjzB5jzK/GmL/map9sjDlgjNlrjBl0JccqxVPacCk62hN4nqZNf6Vq1e7AwXyDpMTExDxtjjfI9evXB+w3mn5+Szl+HFasKPGQRK55aWlpgGtFUm6ZmZlkZmbSokULvLy8mDhxIoGBgbz77rt89tln+U6LmzIFli+310srJLNyq6KsuQQwciTUqJF350m58hQuiYiIiFxbrnTl0hxgcO4GY0xfYAgQZllWe+D1nPZ2wCigfc45bxtjrtBbFSmu0oRL2dlw333g4ZFKhw6z8Pa2w6L8wiVH1URujsqlBg0aAPYbTR+fpbRuDePGaetxkYI4wqWEhAQOHTrEvn37XI47FvAOCAigadOmzvannnoKIE/lUloa/OMfcM898MQTZTlyV0WZFgdQtSr07Qtbt16JUUlhFC6JiIiIXFuuaLhkWdYa4NwlzY8AMyzLSsvpcyqnfQiwwLKsNMuyDgEHgG5XbLBSLCUNlzIy7GBp/Xpo2XIWycn7nG9Yi1u5VK9ePcBRxZDE4sUQHw9vvlnsYYlUCo5w6fz587Ro0YLWrVu7HE9OTgbsRb3r1KnjbM+9a1xu69dDSgqMGmUvnn2lFHVaHEBICBw8CMo0ytel4ZKlhbBEREREKrSrYc2lVkBvY8xGY8yPxpiuOe2NgKO5+h3LaZOrjGVZJQ6X/vIXe32Wl1+G9u23EBcX56xCKGq4dPLkSWrXru2couPh4UF2djbt2sGAAfD551rAVyQ/jnDpwoULzrYDBw443+g7QqSqVaticqVFDRs2BPJ+j65YAVWqQGRkWY46r6JWLgFcdx2kp8OxY2U9KilM7nApKSkJDw8PZs6cWc6jEhEREZGSKvkiOe5TBagF9AC6Ap8aY1oU5wLGmAex96+nXr16rF692t1jLBeJiYkV4nNx/OX56NGjxRqvZcHbb3cnIiKF3r138Msv9lbnjpBq165dec7ZsGGDs0rBYefOnQQGBjrvHRsbS1paGqtXr6Zt2wZ8801rPvzwZ1q0yBtWXcsqyutHys+RI0cA16qRadOm8f333zNkyBDatWvn7OcIoDp27MjgwYP561//ypkzZ1xeY4sXd6Fduyy2bNl25T4JYM+ePYD98+H48eOF9k1MrAGEs3DhNrp0OX8FRif5/SzKyMgA7J/fjn9DnnrqKcLCwlyCTBHQv2dSenoNSWnpNSSlVRleQ1dDuHQMWGzZ7242GWOygTrAcaBJrn6Nc9rysCxrNjAbICIiwoq80n82LyOrV6+mInwuqampAISEhBRrvAcPQmwsPP+8H5GRkfz88898/vnnzuO513+58847WbhwIc2aNctzj6ysLFq0aOFsX7hwIZ6enkRGRtKmDfztb3DsWFfGjy/xp1ghVZTXj5Sf/CpFPvroI8CuZnJUCk6YMIHly5cD8MQTT9C4cWP++te/kpaW5nyNnT4N+/fDtGlc8dddXFwcABEREc5ArCAtW8If/gABAeFXvMKqssrvZ5HjjwStW7emY8eOLsf0c0supX/PpLT0GpLS0mtISqsyvIauhmlxXwB9AYwxrQBv4AzwJTDKGONjjGkOhACbym2UUqDMzEyAYk+L++47+7FfP/uxSZMmLsd37NgBwDfffMO7774LwJkzZ/JcJzk5mYCAAOfHjmlxAPXrw/XXwxdfFGtoIpWCY1rcpTw9PVm3bh2rVq2iWbNmzsXyAfz8/GjWrBkAffr0cbYvXWo/3nRT2Y23IMWZFteoEfj4wIEDZT0qKcyl0+IcFi5cWF5DEhEREZFSuKKVS8aY+UAkUMcYcwx4AfgA+MAYEwWkA+Nyqph+NcZ8CuwCMoFHLcvSEqxXIUe4dOnOUYWJj4dZs6BxY3CsIZw7XOrYsaMzXOrbty/e3t5UqVLFWaGQW1paGj4+Ps6Pc4dLACNGwFNPwX//a+9iJVLZpaenM2TIENasWUOHDh2IjIwkKiqK3/3udxw5coR//etfREdHEx0dzR133OFyrp+fH61bt+bgwYM0amQvg3fmDEyeDGFh0Lnzlf98HFUwRQmXPDzs6iWFS+WroHApv7X2REREROTqd0XDJcuy7i7g0JgC+k8HppfdiMQdilu5lJwMgwbBnj12RZFjeY3c4dJtt93mDJccwVHdunU5depUnuulp6c7F/OGvOHSo4/C4sXwwANwww0QHFysT0/kmnP8+HHnNLd69erx1ltvuRx/6aWXnM87deoE4FwHx8/PD4AWLX5bGu/ll+HcOfj2W8gpIrqiirNbHNiLeitcKl+OcCktLc1ZmQr2z3MRERERqXiuhmlxUsEFBQUBRQ+XliyBn3+2d4m7+ebf2h07UAFUr16dCRMm8OCDDzrbCgqX0tLSCg2XvLzgww/tLdK//LLIn5bINevcuXPO57mr/hz69+/vfB4eHu5yzBEu5fbDD9C/v125VB6KMy0OICTEXvOtiN2lDDjCpZkzZ/LJJ58AdoBZ0FRNEREREbm6KVySUsm9y1RRw6XVqyEwEIYPd22vUqUK/v7+AHh7ezN79myXv2g3bNiQY/nsH56enl7otDiwp8G0aPHbOk8ilVl8fLzzuWPXrtwWL17sfB4YGOhy7NLdGi9ehF9/he7d3TzIYihJ5VJKir2hgJQPx78djl0IAWrWrKnKJREREZEK6mrYLU4qMMdOcVC8cKlPH8ive7169YiOjnapRHIIDg5mw4YNedovNy3OoX9/+OQTyMzM/94ilUXucGn79u15jgcGBnLkyBEsyyI6Ohr4bVpc7kAZYOtWsCzo1q0MB3wZRVpzqXNn2L0bAgIY6xVIBDXwG1IbWgdBvXoQFAQ1a+b/X40a5TPf7xrm+H8VGBjIyZMnAahRo4bCJREREZEKSm+xpVRyL75alHDp2DHYtw8eeij/4443sHXq1MlzLDg4mPj4eBISEqhevbqz/XLT4hwGDIB//xs2b4YePS47VJFrVu5pcflNNYXf1kBzhEsFfX9vytnDszzDpSJNi2vbFnbsgDNnqMoZIgC25PxnXwS8ve3k2VGdlZkJ6en2ufmEcFJyjv9XuXf6rFWrlsIlERERkQpK0+KkVJKTk53PPYvwl/2VK+3HgQPzP+64xvXXX5/nWNOmTQE4evSoS3tRpsUB9O1rP2pqnFR2jsql3/3ud871bi5nzpw5PPLII3m+N7dsgWbNIJ88+Iop0rS4SZPs8KggWVn2XLmLFyEhwf4vKckOnXKtQSXu4aiAyz0ts3r16lpzSURERKSCUuWSlEruyqVL12bJz4oVUL8+dOiQ//FFixaxbNkyl53jHBo0aABAXFwcGzdupGvXroSEhJCdnV2kyqWgIHvB4c8/h2ef1dQ4qbwOHz5MzZo1mTt3bpHPCQ4O5u23387TvmULdOniztEVX+7KpTPJZ/jp2E8cij+En5cfgT6BdG/UnaYdOmBCQ38rtSr6xWHq1DIYdeXmCJdSUlIA+Oabb/jnP//J+fPny3NYIiIiIlJCentdicTHx+Pp6VmkEKiocodLtWrVKrTvmjWwcCHcfz/kzH7Lo0OHDnQoIHmqX78+YL8xnjBhAoGBgSQkJADkqVyyLAvLspzT7ByefBLuuw8eecSeIidSGUVHR9OyZctSXychAQ4cgHvvLf2YSsUAYTB241iOrD6Sb5em1Zvy1YSH6Lh7t12dVBRVq8KUKfa6S+JWjnDJsW5f586d8fb21rQ4ERERkQpK4VIlUqtWLTw9PcnMzLxs35MnTxIUFHTZqW65w6XatWsX2C8tDUaOtHds+8tfij7m3Bzh0saNG7EsyxksAXkql4B8w6V777WXXfnb3+C55yA4uGRjEanIDh06RHh4eKmv88479uMNN5T6UiW27eQ2Xtr1EgwFP08/ZvSfwfVNrqdNnTakZaZxMvEkm09sZubGmXQ6+hynPX0oPAbPxcfHTqSlzDgql7y9vRUuiYiIiFRgWnOpkinKVt1xcXE0aNCAl1566bJ9i1q59OOPcPIkvPEG5FqLu1iqVauGn5+fc8e4v+RKqfILlwpa3HfCBPvx229LNg6Rimb//v1MmzbNWS2SlJTksih+SWzZAs8/D3fdBZGRbhhkCfzf+v+j07udWH9mPayGmR1m8myvZ+nTrA91/evSpHoTujbqyiNdH2Hrg1t5utczvNUvgESvIlzc3x9efdWuXhK3yr3joKNyycvLCx8fH4VLIiIiIhWUKpckj3379gGwcuVKXn755UL75g6XatSoUWC/r78GX1/o16/k4zLGUL9+fXbu3AnAiBEjeO6558jMzMwzLQ7yCZdytiJvExBAjGcgmc/WgEW17cWYtBW5XMP69evHsWPHeOihhwgKCiIjI6NIuzsW5uOPwcsL3n234GmuZenAuQNM/WEqt7e+nd83/D2DXxgMzxbc39/bn78O/Ctc/2ey6taBjMssHF29uj2HV9wu9x85Lq1c0oLeIiIiIhWTwiXJ49ixYwDUrVu30H7//Oc/2bNnj/Pjgt6sZmfDkiX2bm2lLQKoX78+hw4dAuwtrKtUqUJmZmbRKpdytiI3Z87QjDOQAKzMdVxbkcs1yvE9ffbsWYKCgsjMzMTLqyjlOwVbuRJ697Zz1yvJsiz+tPJPvLP5HXyq+PDOre9wbLf9+RWlMpOAADzvn2CnYrl2Ksstq6ofHn/7G6aUXyNxtXTpUkaMGMH777/vbHP8nK5SpYqmxYmIiIhUYJoWVwns3LmTX3/9tcj9HeFNUFBQof0mTpzIrFmzADh48GCB/davh5gYuPvuIg+hQI51lwD8/f2db5BzVy4lm2Twzydc0lbkUsmdOXMGoNSVSydOwK5dMHCgu0ZWdG9ueJPXN7zOra1u5cd7f6RhtYYuu8UVydNPF1qJuN8vhb6J/+TDXz5k3ZF1pGcp8HCHzz77jNTUVDZdsmOfl5cXxhhNixMRERGpwFS5VAk89thjnD59usj9o6OjgcLfqF16rKAqp5Mn4Zln7OVLhg4t8hAK5AiXjDH4+fk5F+z28fFh9+ndPLLsEX5M/RGegVbvtCKiUQSdG3SmS4MuRDSPoJ62IpdKJjEx0fncES6VtnJp1Sr78UqGS+lZ6Xzwywc8u+pZhrUdxoLhC5zf/45qxSJVLoG9kn9kpL3wWq71fwCyq/qx58Xx7ItfzPgvxwNQzbsaofVCCa0byujQ0bSs1ZIavjXwq+KXZ9MAKZhjvbyoqCiXdsdr0dPTkwsXLhAfH09N7dAnIiIiUqEoXKoE4uPj2b17d5H7O6qQcr8pvVRycrLLx1ULmO82ZAhERcFHH0FAQJGHkK+jCUc5X+88dAAPbw/+vfXfXGh3Afxh+tHpbH1nK9V8qjHYezDLv1rODU/cwM4zO/ly75dYWBgMfxvYn4m7/PFITLr8De1PTFuRi4svvviCwYMHs3btWowxDBgwoLyHVKhdu3Y5nztC5tJULp06BTNmQP360LGjW4Z4WZZlcddnd7Fk7xJ6Ne3Fh0M+dAl1il25BPZ2kWvX2tWJDsbg0TGMOx77J7dlzyTmfAxRp6JYeXAlu8/sZt7Oeby75V1ndy8PL2r41qCGbw1qV61NXf+61K1al2Y1mhFeP5xbQm7Bw6hAGODUqVPs378fgO+++w4vLy8ycqYlOqY1X7hwAYB58+bx2GOPlc9ARURERKREFC5VApeGRJZlFfrXdkflUmHhUu6FvP38/JyVA7nt2WMXCb3xBtx5Z3FHnTOW+Gje3/o+m05s4rvo77Cw4E7IIouHlj4Eg4FsSDWpPNXjKf7U80/M+/c8lm9YzuyvZ1OjRg0upl1ke9x2Fu9ezDM//YMRZNGwqAPQVuSSS1RUFEOHDmXs2LHMnTsXcN356mqUu0rk3LlzWJZFVlZWiSqXz4q6RAAAIABJREFUkpPtaqWYGHuR/ny+7cvEh9s+ZMneJbza71Um9ZqU5+fX5XaIzFfPntCoEeRsYADYuw689RYAVTyqcF2t67iu1nXc0eYOAC6mXWTFwRWcST5DQloC51PPE58ST3xqPGdTzhJzPoaNxzYSlxQHQO+mvXmwy4Pc3vp2An0CS/EVqPj+97//uXw8cuRIvvrqKxISEpyvxcmTJzN79uxC/+0RERERkauTwqVK4NJf1FNTU/Hz88u3b3p6OkePHs33vNxyVy75+/vn2+f55+1sZvTo4o33VNIppq2Zxqbjm/4/e+cd3lTZhvFf0j0pUMoolD0KlE1RmbIFERSQoYCigohMWYJsBVFA4BMRBUWGrKIge2+QvaHILNCW0kJbuts0+f44nDRJkzZpU5rC+7uuXs045z1v2qz3PvdzP5wKO4VSoaR6sepMaDqBQhGFGD10NKgh9G4ovj6+kAbnU89rF5jywlNeaHo4edDErwlN/JowqMEggmPH4LVwE66p2YgCohW5wACVSgXA/v3783km5nPlyhWcnZ1JTU0lNjZW+xhy4lzasQMuXoQNG6SqsrwmLT2NJWeXMHbPWJqXbc7YJmONCuMWl8WB1OJu4kTSPhmEQ3I8aqUdypYtoUEDk7t4OHnQtXrXbIdOSE1g7ZW1jN0zlj5/98GvkB9L31pKy/ItX1on09GjR3F0dKRZs2bs2bOH4cOHs23bNiDDuVS2bFkcHByIiYnJz6kKBAKBQCAQCHKAEJdeAhISEnB2diY5ORmQhCFT4lJISIjWiREXF2d0m+joaL0v/8ZK4s6dkxagU6dC8eLZzzFZlcy0g9PYemMr16Ouk65Jp1nZZoxrPI7BgYMp5SF5jU6ePAlSbAylPEqxdcNWjhw5oueckktkjC00KxetTOVpK9AsLg6piZnu10O0IhcYILdNlwXYgsD169epUqUKoaGheuJSTpxLu3eDhwd06mTtWRpn0NZBLD23lIalGvJHlz9MCjM5KosDePdd7D8fAsmQonYkeeJcrFEA6+boRv+6/elXux9H7h3hk82f0GZFG7ycvfi84eeMbjz6pXMyHT16lAYNGvD222/j5+dH/fr1tf83+bmoUCgoVKgQsbGx+TlVgUAgEAgEAkEOeDlPob5EpKenk5iYSPfu3bW3GeYl6XLv3j0AvL29SUlJASA8PFx7v0ajoW7duvTr1097m6G4dPkydOwoxRR99lnW81OpVWwM3kjzZc2ZeWQmJdxL8Hng51z89CJ7++7lm1bfaIUlgMqVK+vt36FDB2bMmKF3m3wW3GTXIXd3FP37Q1aLa3d3mDcv620ELzwajUbveaRbDmrrxMTE0L9/f/bt20fFihUpVKgQMTEx2pybnDiXdu2SHEvP42Wx+tJqlp5byrjG4zjx8QnKepU1uW2OyuIAHB1RDB+GRqEkSNGNiSuq5GbKmbBT2tG8XHPODTzH751/p3WF1nx9+GuKzCpCvcX12Bi80ebLKq1BamoqZ86coXHjxnz22WcsXboUyPi/6QqdQlwSCAQCgUAgKJgIcekFRxaSatWqxapVq/RuM4bsbipcuDCpqakcOXIEX19fbRBrVFQUISEhXLp0SbuP4SJ12DBIT5eycr29Tc9NpVbRfX133l77Nree3GLDuxvY+f5OZredjX8xf6P7mNNByNnZWe+xGCWbVuT4+kLX7MtfBC82w4cPx8nJSeuCy+q1Y2scPXqU33//naSkJK24lBvn0s2bcPs2tG2bF7PNICktiU+3fEq/jf1oXKYx01tOz7YjW47K4mQGD0bRrCl7Ws7k4MGczDh73Bzd+KDOB6zvvp7Tn5xmbOOxJKuSeXvt21RcUJHhO4aTmFZwnluWcv36dVJTU2ncuLHe7fL/TT4hAODl5SXK4gQCgUAgEAgKIEJcesGRc5Pc3d212UhZLZBlt5KbmxtpaWlcvXoVjUajdS9dv3490z52OiJNcDDs2ydlYNeoYfwYSWlJTDkwhcBfA9kYvJHv23xP+BfhvOP/jlmPKSQkJMuyJCcnJ73HYhS5FbmRRWuio4K9I98mnRffUSAwjUqlYsGCBUCGo8/Ya0cWa2yNhw8fai9XqFBBKy7l1Lm0fbv0+403rDZFo0zcP5HFZxbzcb2P+bvH39grs5+n/B509+5dyw9YrBgcOIBvoC/BwWDK8Ggt6peqzzetvuHioIss6bSE2iVqs+DEAhotacS6K+t4mvI0byeQD8ifG40aNdK7Xf5ceU8nmE84lwQCgUAgEAgKJkJcslFuhoRx9kZorseRy3jc3Ny05WtZiUtyCZC7uztpaWmEhYUBGVkzx48fB6BZs2bafXTFpbVrJb2mf3/Tcxq7ZyxTD07F2d6Znzv+zKjXRuFgZ76Lws/Pj9KlS5u83yxxCaRW5AYlfRqFguCSjrQO/5Yv935p9pwELx5btmzRXv7vWUcxY2Vxtloqp1vOqisu5cS5pNHAunVQuTJUrGj1qWo5dv8Yc4/PZWD9gfzU8SeKuRUzaz/ZATN58uQcH7tmTVCp9JvH5SX2Sns+qvcRf/f4mx3v7+BRwiN6BPWg5k81OXrvaJ4cMzEx0fLSQSsQFRWFk5MTxQ0C+EaOHMnMmTMZN26c9jY3Nzft541AIBAIBAKBoOAgxCUbpfLc1xh/6Ndcj/P0qXQW3MPDQysubd682eT2hs6l0FBJ4EpKSmL58uWMGTMGgH379jFr1ixAX1zatg0aNTId4n3g7gH+d/J/DA0cyrGPjjGwwcDcPUAjGIpLly5dYsSIEZmzTeRW5DqoHZ2ps/4Qn9T7hNnHZrP1v61Wn5+gYLBmzRrtc/v8+fOAcWH2yJEjNpmboysulS1bFnd3dxITE3PkXPrhBzhyBEaMsPo0tcz7dx7tV7bHr5Af37f53qJ9dQP9c0pAgPRbp+L3udG2YlsejHjA3r57cbRzpOnvTWnwSwMOhRyy2jFUKhVly5blt99+s9qY5vLkyRNKliyZqbxxzpw5jBs3Tu92R0dH03l5AoFAIBAIBAKbRYhLNopDtB+phXPfkUrOrihcuLBWXJo1a5bJs9e6zqXU1FQ959LGjRu129nZ2VGmTBntZYAbN+DUKejQIfO4Go2Gb498y1ur36JSkUrMaDUj80ZWQs5cOnHiBCCFfs+bN0+vTAjQtiLH3R0AFXZcLtYSZcNA5rWfR+0StXlz9ZvU/rk2225sy7P5CmyTyMhIXnnlFWrWrMnevXsB4+LSm2++yY4dO5739LJFFpd69+5NpUqVcHZ2JikpyWLnUlqagq++kjrEffpp3sz1VOgpRuwcQWO/xuztuxcPJw+L9reGuFS1KtjbSw0J8gMHOwdalm/J2YFnmf76dKKTo+n4Z0dOPDhhlfFjYmKIioricj48wMePH1OyZEmztnVwcBDikkAgEAgEAkEBRIhLNkqh5OJoCt8lKjZ3Ia+yuOTl5aXX1c1UZpHs9jFWFnfu3DmqVq2qFW1cXFwASVx6+FAK+i1cGN5/P/O4m65v4su9X9KiXAt2vLcDN0e3XD2urJCdS8OHDwcygr2N5ni8+660ogTSlY5M85oLgKuDK7ve38W3rb4lLT2Nruu6svvWbpt0qAjyhri4ODw8PGjUqFEm59Kff/5J165dtUH3WWWApaWl4eXlxcqVK/N+0jqEh4fTqlUrVq1ahb29Pc7OziQnJ1vsXLp9252kJOjTx2hEmVX4+vDXFHYuzLpu66hYxPK6O0vDyY3h6CgJTPnhXNLF08mTCc0mcOTDIxR3K07T35vSfFlzdt7cmatx5c+CTCL7cyAmJiZTSZwphHNJIBAIBAKBoGAixCUbpayjDyjVbD6Ru7PM0dHRgL5zCeDatWtGtzfMXJLL4h48eMDdu3fp378/gYGBQIZDSKlU8tNPcO8e7NgB5cvrjxmTHMPQ7UOp6VOTv3r8laPFoyXI4pKMLAg9efIk88aOjlJ7O6WSi1W6sf1WFWRTVzG3YoxtMpaDHxyktGdp2q5sS/NlzYlOis7T+Qvyn9DQUE6dOoW7uzs1a9YkMjKS9957j2nTpuHu7k6vXr0ICgrSujGWLVtGSEiI0bEeP35MbGwsI0eOfJ4PgfDwcD23iIuLi564ZK4gExwsuYgaNrT+HAEuP7rMP9f/YVijYRY7lmSKFStGly5dsu0qlx0BAXDyJGQX1/Y8KOlRkoMfHGTEKyMIfRpKl7VdclUml5/iUlpamvbzIjuEuCQQCAQCgUBQMBHiko1Su4QPAHsunc3VOKacS5cuXWLGjBmZwoh1M5dUKhWRkZEAHDt2DIB69eppt5UXp0qlHevXQ/PmmRegu27tovFvjQmLC2PpW0vN6vyUWwzFJTl3yqi4BDB4MDRtyo3+M0lKgjt39O8u5laMMwPOML/9fE6EnqDdynY8SniUF1MX2AgdntV2KpVKqlatCkhuJQAfHx/tdq6uriiVSo4fP56pzbqM3LFRdvrlFVevXkWhULB//35th8dSpUpp75fL4ix1LgUHe+DtDWXLWn/OR+8dpf3K9ng4ejCk0ZBcjVW/fn00Go328eWEDz+EiAhYsiRXU7Eavp6+zGozi38//pfSnqVpvqw57Ve2Jy4lzuKx5M+CiIgIa08zW9LS0nB0dDRrW0dHx1z9DwUCgUAgEAgE+YMQl2yURlV9IcWds6HncjVOTEwMSqUSd3d3PXFpzJgxTJgwgenTp+ttr+tc0uXoUal7Ud26dbW3paenA5CY6E9wMHTvrn/sO9F36LKmC2npafzV4y8CfQNz9VjMRfcM+cWLF7ULFZPi0rNW5BWaSuHexiJJPJ08GdpoKEHdgzj38By+c30ZvWu0KJN7QZHL3JKSkvA1CH3XLe9RKBTa54Ds8jNELsfMa3HpwIEDAKxdu5bo6GhSU1P1nEvOzs5oNBptaZ+5zqXr1z1o2ND6JXEpqhR6buiJk70TBz44QBGXIrkaz5xumNnRpg00awZffw22ZJ7xdvXm+EfH+fr1r9lzew/vBr2LSq2yaAz5eZgfziWVSmX28y2/nEtRUVHZdxgVCAQCgUAgEJhEiEs2im+pkhBRnvtpOS+LU6vV7Ny5k/Lly6NUKo2WJZw5c4Z9+/Zpr6empmJvb5/J/RMfH0/ZsmUpWrSo9jZZXLp3ryceHtC7d8b2Go2Gz7d/jp3Sjr199/JW1bdy/DgsRXfuFy5c0F42KS49o2ZNaQF9Lgs9r1PVTpwfeJ7eAb2ZfXw23x39LtfzFdgeshCUlJSk5/4ByQWoS3YCo7yoN7csKLfIriUgU1kcZDipzHEuxcVBSIhbnpTELb+wnAdPH/Bzx5+pV7Je9jtkg5ublOMWFBSkfYyWolDAoEHw8CFcvZrrKVkVb1dvJjSbwE8df2LHzR00/LUhG65uMHt/2bkUExOjzaF7XhQEcalYsWK8+eabz/24AoFAIBAIBC8KQlyyUUqUKAFPPElyMp7jYg5r167l1KlTTJ48GZBcFjt27KBw4cLabfbs2UOrVq2011NSUnB0dDS6ENAtiQNwdCwErCUiohmffw6FCkm3q9QqJh+YzLYb25jWYhplCpXJ8WPICbrikhxIDtmLS+7uUK0anDmT9fg1fGrwe+ff6VmzJ+P2jqPLmi7ceHwjV3MW2Bay8JKYmKgnqAImc31Mlf08L+eSLPYCRsUlWdyKi5NKqrJb7CcnS25EjQbatbPuXFVqFTOPzCTQN5DWFVpbZUzZufTxxx+zePHiHI9Tp470++JFa8zK+gyoP4BlnZeRmp5Kt/Xd+PHkj2btJ4tLAI8ePd+yXpVKZVFZXH5lLu3ZsydfjisQCAQCgUDwIiDEJRulePHikGKH2iEh+42NkJqaypdffkndunV57733tLe3a9eO9u3bZ7mfk5OT0YWAbkkcwMKFr6JQdGfq1FR0q+tG7BjB9EPT6R3QO9c5KjlBV1y6d+8eIC3ssxOXAOrXz15cAlAqlCzrvIyvmn7FwZCDtFnRhofxz7/cRJA32NnZAZJgoVAomDRpEt988w0VKlRg0qRJetvWrl0bMC06yYv6vBaX5PB+tVptlriUnXPp889h504YNeo6r71mvXlGxEfw1uq3uBNzhwlNJ+Q6hFtG/p8BbNy4UZu1ZimVKoGzs+2KSwD96vTj7ICzdK7amSHbh9B2RVsuPLyQ5T663TKfd2mcpc4ltVqtJ5bmNaK8WSAQCAQCgSD3CHHJRvHx8YEUBTjGoVZb/sX35s2bhISEMGzYMJRK/X9zVmf1s3IulddpA3fqFPz9t4Lp0xVMmuSIvK4LfRrKL2d/4eO6H7PqnVXPJcDbEN1sqXv37uHm5oavr69Z4lKDBhAenjnU2xhO9k5Mbzmd3X12E5kYSbPfmzH72GyepuRsUSvIf2rUqMHHH39M5cqVAfjjjz8AmDp1KuPHj+fWrVs0atRIb5/NmzcDULp0aZo2bcqmTZv07g8ODgbAwyNnndDM5fHjx4AkIsiZUbolfbK4JYf0ZyV2padDUBB88AF06GBdIWLYjmHsv7ufBe0X0KlKJ6uNW6dOHXx8fPD39+fIkSNUrlwZtdz60QLs7aFGDdsWl0B6/1nffT3ftPyGCxEX6PBnhywFbl3nki2LS/J2OXEv3bt3j4YNGzJo0CC9kyrZIQLEBQKBQCAQCHKPEJdsFEdHR+zU9qBMJybe8nwMuQucYUkPZL3IlZ1Lspvgo48+0l4u9KzuLTQURo+WysiGGBiT5hyfQ7o6nfFNx1s8Z2vh4ODA2rVrAWmx4e3tTZEiRcwSlzp3BicnGG/B9BuUasDGHhtxsHNg9O7R1PipBj+e/JHLj3KelyXIH65evcrSpUtJSEigZcuWmfKWjFGmTBk++ugjbt26xZEjRxg8eLD2vrS0NJYvXw6YH6CdU+TX/OPHj7l16xYlSpTQ5hBBhnNp3rx5AFSqVMnkWOfOQWysFHBtTW4+ucn6q+sZ1mgYQxoNsZprCaB69epERETQpUsXQCr9CgnJWVlxrVq2Ly4BONg5ML7peHb32U10UjQ9gnqYFLdjYmK0jtTn2TFOo9FYXBYHOROX/vnnH06fPs3PP/+s7e5oDvlVhicQCAQCgUDwIiHEJRvGUSMtRsOfWN52Wu6YpOviyQ6NRsOyZctwcHAgKSkJkNwNcsmAl5cXKhW89hr8+y/Mng2enhn777uzj8VnFtM7oDflC5c3dojnhpwrdf/+fYoWLUrRokW1zo6sKFdOEpbWrIFjx8w/XpuKbbjy2RX+/ehfiroUZcj2IQQsCuCTfz4R5XIFkKNHj1pUVtWjRw8CA6VuiIGBgahUKhITE9m2bZvWJSIvYENCYPVq2LBBumwtVCqpe1hUVBS3b9+mYsWKevfLIc4REREUL148UzC5LnLG/+uvW29+t57cYsDmATgoHRjWaJj1BjZgzJgxtG4t5Thdu3YtR2PUqgUREdJPQaBW8Vos7LCQQyGH8PvBj0MhhzJtExsbq31OPE/nklzeZklZHORM8Dl0SP9xm1vuJrrECQQCgUAgEOQeIS7ZMC520pfsyFjLOx/JLgZd50JWqNVqbWaLQqHQE5dkvLy82LYN7t2DFStg4MCM/VddXEWr5a0o4V6CKS2mWDxfayOLS9HR0RY5lwCGD5d+P+vsbhGNSjfi3MBz3Bl2h1GvjuK3879Rck5J3ln7DikqsYApSJw+fdrsbdu0acOJEycICAhAo9HQq1cv3NzcmDFjBiVLlqR+/fokJ6fw559QvbrUWbFbN6hQARYulEKzc4ssLsnOpQoVKujd36JFC1q1asXQoUOZOHGiyXFSUmDtWqhdG3Qim3LFiQcn8F/oz4nQE/zQ7gdKelhpYCN4eXlpnYtXc9jyrVYt6felS9aaVd7zYd0POf3JaYq7F6dHUA8i4vWVsZiYGHx8fChSpMhzFZdkkchSccnSUjWNRpNJXJLzxbJDFpes6aQTCAQCgUAgeNkQ4pIN4+YgBVM/is1751JqaqrkbHADxYcKIpKkhYmuuOTqWohx46TA286dM/aNiI9g1O5RNPJtxJXPrlChcAXD4Z87devWlULRwWJxydMT/PzgypWcHVuhUFDOqxzft/2ey4MuM67xOP4O/pu31rxFcFRwzgYV5DlyPs/kyZNRKpUMMaz5NAMvLy+io6MJCgoC4OTJk/Tv3x9Hx+KcOjWD996DevWk0PgzZ6B5cyk4u0kTOH48d/OXHSKhoaE8ePAgk3OpZMmS7Nmzh/nz5+uV7ukSGQktW8LZsxkiqzX47th3eDh5cHPITQY1HGS9gU1QpEgRfHx8cuxcCgiQfheE0jhd6peqT1D3IGKTY3lz9ZscuHtAe19sbCyenp74+Phoc7eeB7JIlNdlcTdu3MhU7hcVFWXWvpYKYAKBQCAQCASCzAhxyYZxVkgp2VFPLReXsnMuLVy4UO96SkqK9AX7NbiZdpNdql2ArrhUn2HDSnLtGsyZA46O8DD+IV8f+pr6v9TnacpTfuzwI872zhbPNS+ws7Oje/fugLRYKVKkCDExMWZ3IKpRAy5bITLJv5g/M1vPZOlbSzlw9wD+C/3pv6k/ao3lQcOCvEUuG3NxcUGlUrFgwQKLxyhcuLDWASgzdepUwsPfJyamDvPnSyVn9epJPzt3wuLFUnlc48bQqRMcPZqz+cvOJZBcHIbikjlMngynT0vOpQ8+yNk8DAmJCWFj8EYG1BuQp44lQ/z9/XMsLhUrJrm2Dh+28qSeAwHFA1j+9nJCYkJ4/Y/XWXdlHSCJjw4ODjg7Oz/XMjBZXMrrsrjjeupsFaAJkZHZl0JDhnNJiEsCgUAgEAgEOUeISzaM67MWbINHjmDRokUW7ZuduPTZZ59pnT0gfbl+HP8YGoIGDTedbkJR6Ny5M/36fQ/8y+HD9kyaBLWb3ePXM78SsCiAifsnUrloZfb13UeDUg1y9kDzCFlcevToEUWKFAH0OyZlRc2aEBwMOuv1XNG/bn/uDLvDF69+we/nf2fg5oHEJJs3F8HzQbcUNKflMV5eXplKsezs7Hj8+BU8PE4xdCjorl8dHGDAALh6FcaNk7owtm8Pt29bfmyVwZPVUnFJo4F//oE334R337X8+KZYeGohChR81vAz6w1qBv7+/hw/fhyFQpGjYO/+/WHjRti/Pw8ml8d0q96NkOEhvFbmNT7c9CGnQk+hVqtRKBQ4ODg81+5oOS2Ls1RcyhB1A4GLwGG6d/cnNjb7fYW4JBAIBAKBQJB7hLhkw7gpnwWxuDoyffp0i/Y1pyxO7h4F0pfrFcErtNdVGhUdF3Tk5k1/tm37Ak9POy5cTcKp9Qyq/FiZAVsGUNK9JJcGXWJ/v/00Kt3I2CHylSZNmjBp0iTmzJmjFZfMLY2rUQNSU+HmTevNp5RHKb5v8z1jG49lybkllJhdgp9P/2y9Awhyha5zKaeULVs2k8gTFQVxceVxcTlpcj9PT/D2nsvcuYexs4P33pOyjyzB0JVnmLmUHefOSZ0gO3Wy7Lim0Gg0TN4/mTnH59C9RnfKFCpjnYHNRLcMa8qUKRbvP2GClIk1apQVJ/UccXFwYcO7GyjiUoRGSxrxIPABGjvNcxeXcloWJ78ezUUSoyoD64CHwDAePHDDnH+9LGTZ29tbdEyBQCAQCAQCQQZCXLJhCsknUV3szf5iDtKi7vr160DW4pKTk5P2clxSHL9d/w2eHSZdk87O//bx1tcLULcfSpFJ1aiwxJ0J+ybQuWpnLg26xPlPz1PTp6bFj+t5oVQqmTp1KtWqVbNYXKr57GHlNHfJFAqFgm9bf8vJj0/yevnXGbR1ENMOTiNZZdlCSmB9ZOeSruhqKY0aZYis//vf/9i9ezfbtknXHRxMtx/UaDR88cUXvPdeMxYvlrox+vhIwfnmYihq+fj4WDT3DRvAzg46drRoN5Ns+W8L0w5No3dAb3558xfrDGoBuplZfn5+Fu/v4gJ9+kiiW7zlPRVsghLuJTj9yWmGvzKcp+Wesr3ydkKqh/DYybxyMWtgaVmc/Jklvx5Ncf/+fcLDw7XXL10qA1wCCgHdgAXUr3+NX3+FbIbSOpcs+ZwVCAQCgUAgEOgjxCUbppAToFZCiXQcnLJ2U6hUKvr378/NmzdZvnw5S5YsAaSSHFPoOjSC/gtCpdFfnKpIgg7DSKy6lBolKzOlxRT29NnDuu7rqOlTE6Wi4Dx9LBWX/P1BobBO7pIxGvo25J+e/9A7oDeTD0ym4oKKIuw7nzHWIdFSAgMDtZcHDx7Mkyet+eQT8PJ6gFJpuvvc06dPtZd79IDdu6FaNRgyBB49Mu/YKpUKLy8v7XVLSvs0Gli3TgrzLlbM7N1Mz0WtYsyeMVQtWpXf3voNDyeP3A9qIZUqVWL+/PmA5UKbTP360t/mwgVrzuz5Uty9OHPbzcV3ry+FUgoRXjKc8/XPs+z8MjTWaFOYDZaWxcmvv+zEJT8/P0qVKgVImWUbNnQGbuLu3hCQXmuVK58kISH70kZRFicQCAQCgUCQewqOOvAS4uToAEo11DnK7dfcSU0zHUZ96tQpfv/9d/r06cNNM2q57oRHo2raGKcBr8CAqkw/NYukdIMv8wpwsnNifff1bOm9hUnNJ9GqQqvcPqx8wVJxydVVKomxtnNJFwc7B1a+vZI9ffaQlp7G22vfJi7F8vB2gXWwhrhUTEeZOXpUQa9eEBgIb701h+Tkp7z77rscO5bZwTRhwgTt5cjISFq3hj/+gIQEmDjRvGOnp6fr5aiZi1otdYa7eVMqx8stao2aWUdmERwVzLetv8XBLv8W7H369AEsb2svU7++9PvMGWvNKP9wCXfh9dDXaXWhFe6P3flw04eUnVeWDVc35OlxLS2Lc3Z2ARqwYkVpGjeWOheapgZDh0KdOunP3EldcHbOyLJzczuLoyOsWSOJhKYQZXECgUAgEAgEuUeISzaMvb09hPkDoC53mnYzvja5rVzKk5SURJkyUraJLI+5AAAgAElEQVTJm2++mWm7y3ciaPf1TCrOr8IV75/QOCWCXQoqZaLRcVPSU/hi1xcFvrtZ0aJFAXj82PxykIAAybGQlyf3FQoFrSq0Ym23tfz3+D9ar2jNvjv78u6AApNYI3MJ4OjRoxw8eJHevaF8edi6FQoVSicyMpL169dz8ODBTPvodm+88MwmU60aDB4Mv/4Kc+dm/zxUqVRaccuSjLagIFiwQBKY+vY1ezejaDQa3l77Nl/t/4oOlTvQuWrn3A2YS2QnSk7FpVKloESJF0Nc0mg0KJVK3JRulDtSjiWdllDcvTjvBr3LL2d+yTMXk6Vlcbt3lwBOsXJldY4dkwS+SpWk4Ps+faBDB1kE3QVcZvFiqFjxNvA6cJOqVasC0nt+TEw4gwdL5aXjxhk/3rfffsvu3bstmqNAIBAIBAKBIDNCXLJh7O3tYeVDmOkBV17lQOpswh4bd7bIZ1wvXLhAWFgYAEuXLtXbJiQihvr/a8Gu9PG4ppTnj8an2PrWHFDezXIeD54+yPOz23mNXC40bNgw1GrzhLI2beC//+C77/JyZhKvl3+dFW+vICwujLYr2nIq9FTeH1SgR/yzYB1THRbNxdPzNYYNCyA8HFavlsK6dfPNjAUV+/v7ay+fP39ee/nrr6FzZ/jiC8l9kRUqlQonJyc0Gg1fffWV2fNdvhzKlIE5c6RS0NywMXgj/1z/h8nNJ7O51+Ycd92zFrkVlwDq1XsxxCW9bnEpaXxU7yMOfnCQ1hVaM3DLQAIWBXAv9p7Vj2tpWVxQUBHgChMnbuLBA5g1C9zd4a+/4MgRCA+Hffs0QFFgBmFh8PHHewEpMP/vv/9m9erV+Pv7ExUVxZw5khg1fz5GO8d9+eWXzJ0716I5CgQCgUAgEAgyI8QlG8be3h4SoyElDk7Eg2M8s/7aZnRb3TDfrVu3ApmDiTv9MJFUjxv8UHs/8T+cpG/rBgSnBINn1vNISEtg+M7hpKU/vw5D1kY3eyomJiaLLTMYNAh69pTOeG8z/me3Kr0DenPx04uU9CjJ+3+/T0yyefMUWIeoqCgAvL29czxGUhK0bw9hYVJAdsOG0u26JUHGxCWFQsE777xD6dKl9cQleVFdtiysWpX1sdPT0y0u64mMhB07oFcvUOby0yAuJY4h24dQq3gtJjSdYBOZbNYQl+rXh2vXpBLFgozsXHJ0dNT+PVwdXNnSawt/dPmDOzF3GLlzpNWPa0lZ3JMncO6cExBE8eIP8PWFMWPg/Hmp6+KdO1LA+tmzD4H6wASKFtXPFytWrBg9e/bE29ubx48fo1BIDsCUFNi0KevjlyxZMucPVCAQCAQCgeAlJ/+//QtMoncW9f5lSPZiw7UgklNVmbbVXTzJLcl13RIHLtzmkt1vVE7sy/AuLbS3/3Yno0NcVsQmx7L03NLsNywAxMWZl2ukUMDvv0ulMcuX5/GknlHYpTAr3l7BrSe3KDevHHOPz30+BxYQGRkJ5E5c+u03CA2FtWvhrbcybs/OuRQXF4eHhwc1a9bk2rVrevcpFNC9O+zaBdHRpo+tUqmyDPA3xrp1kJ4O779v0W6Z2H9nPwGLAgiLC2Pxm4vzNWdJF6VSiVKpzLW4pFbDxYtWnFg+oOtckt1EIGW/9a3dl/FNxrPh2gb23t5r1eNaUhZ36BCo1Qpgd5aB3oblzUojymjRokW1gvErr0juvL//1t/G0MVaqFChbOcoEAgEAoFAIDCOEJdsGD0XgkaDz9PmhBYKwuUbJyp80UdvW93Fgtx5Sj5T/MfuU7y+rjZo7JnZeYR2u3Ph5wiODQYzKlcS0hIYv3c8iWnGs5kKAsuWLQMg1lhthAmcnaFZMzCSwZxntCjXgsMfHubVMq/yxa4vWHhyYYHPvCoIREVF4eDggKdnNlY+E6Slwfffw2uvQfPm+veZKy55e3sbDZ1/911p/KycFyqVyiLn0n//wY8/StliAQFm75aJhNQE3v/7fRzsHNjdZzevlH4l54PlAQ4ODrkSlxo0kH5v326lCeUTsnPJ1N/ji9e+oJxXOfpt7Mfm65utdlxLyuL27wcXFw1wKktxSRaNZIyJS97e3kRFRaHRaFAooHVrOHAAdI2rup+bwHPpnicQCAQCgUDwoiLEJRvGcKE40Lcp7nvb4xwRyB2XDXrd43QXCxERETg5OWlLBSZs/w5FuiPH3r9E1yYZq8jJByaTkp5i9nxS0lOY9++8nD6cfEduW63b9t0cXn0V7t+Xfp4Xr5Z5lY09NtKmQhs+3/45/gv9OXLvyPObwEtIZGQk3t7eOcoJevpUChwOCYGxYzNnF2VVFqfRaLTiUuHChbl79y4HDx7k0qVL2kV0gwZQrlzWuUuWlMXdvQt16kguq69N9wkwi9nHZhMWF8YfXf6wyW6SuRWXfH2ha1cp++fqVStO7DkjO5d0y+J0cbZ3Zn339RR2KUznNZ05HXbaKsc1tyxOrYbdu+G11xTY26vNdi6ZEoS8vb1JS0vTZqkNHCiVNvbqJbn1IPNr0dw8PoFAIBAIBAJBZoS4ZMMYnukNWrmU+MM7SP5XCQ5JLN+bEfqsu1iIj4/XOiXO3won1H0j9ZQf8Gp1P+02t57cYvft3RY5YhLTEplxeAbRSVnU5tgwcsmDJc4lkJwoAMePW3tGWeNk78SO93ewtttaVGoVXdd1JSI+4vlO4iUiOjqawoUL52jfvn2l0slx48BIk8YsnUvJycmkp6fj4eGhDZ5v0aIFtWrVok2bNoAkVn3wAezcCTNmGJ+DJWVx33wjLeYvXNAv37MEtUbNn5f+5Ltj39G9endeK/NazgbKYwzLwHLCwoXg5pZ7IS4/0XUumfp7NCjVgKP9j+Lj5sOQ7UOs4pg017n0889StlWfPlLHRnOdS8nJyXz88ceZtpE7hMrbNmokOfV27JAchgApKfonV4S4JBAIBAKBQJBzhLhkwxi6EOQsltJJCRBfjE/+fQ3XEfU4df1BpjPRTk5O/L7rJK3+1xfsVHzzzkC9+785/A0qdebspuxIV6cz/ZD5bc5tCbncadasWXh6epq9kKhdG1xcnm9pnIxSoeTdGu+yqecmYpJj+GzbZzn6vwmyJz4+Hg8PD4v3O3JEKlf7+muYOdN4MHZW4pKcAebh4ZFp0a8b7j1hAvTuLf0+bcRUYm5Z3PXrUpbYwIFQvny2mxtFo9EwfMdw3vvrPSoUrsDstrNzNtBzILfOJYDixaFdOykTqKBWTmXnXJLxdPJkVutZ/PvgX745lP3nRLVq1fjpp59M3m+OcykuTnL8tW4tCbUuLi4kJpouwdZ1LoWGhhrdRs5OmzNnjva2AQPg9ddh2TLp/6grLpUpU0aUxQkEAj2KFy/OJ598kt/TEAgEggKDEJdsGFMLRR93O0rtrEwj1RiSPC8ydOXCTIvSxAp16H+8EdEup+no+D3tGlTRu/9M+JkciRTJ6ckcuHvA4v1sAVlcOnz4MHFxcWaXxzk4SBk6f/6pn9fxPKnpU5OpLaby17W/8F/oz60nt/JnIi8w8fHxuLu7W7SPRiOJPSVKwLBhprfLzrkE0oLa0FVXoUIF7WV7e8lB4+ICS5ZkPoY5ZXG//gpt20pjTJiQ5aZGSU1PZc6xObRa3or/nfwfQwKHcOHTC/gV8st+53zCGuISQOPGUhnhvXtWmFQ+oFars3UuyfSp3YdOVTox6cAk2q1sp+dg0mg0LF++nPj4eNLT07l+/TqDBw82OZY5gd6nTkF8PIwaJbn0XF1dzXYu3bx50+g2sri0cOFCvdt79ZIE1sOHM157lSpVwtvbWziXBAKBluTkZB49esQSYx+4AoFAIDCKEJdsGFMLRXd3d5ziw5na9HW4W5uTySuJjksCe0dKdugOjZuR0OYgntFNeTDqHlu+HJVpjAufXkAzWYNmsoYdr+yAKcAUWF5hufZ2Uz9nB57N08edVxh2AoqJiWHZsmUsWLAg231nzIDHj2HixLyaXfaMbTyWjT028ijhEYO3DRZn2a1MfHw8bm5uZm+fnCy5LA4dkp4Xrq6mt80qc0l2Tzg5OdGvXz+9+3TFJQAvL6lz3KpVUtt2XbIri/v5Z8m5Ubo0/PMP+Phk9egyo9FoGLB5AKN2j+JRwiOmvz6dee3noVTY9seItcSlJk2k30eP5nqofEEKtpa6xanVam1XUWMoFUo29dzEvHbz2Hdnn17XysuXL9OvXz+CgoLM6rxpTlncyZPSbzk83VhZnFqt1r7n6YpLF0208ZPL4gx57z1JXF2/PuO1N3PmTJRKpRCXBAKBFsPOrQKBQCDIHtteFbzk6H4Z37dvn/ayu7s7KpWK9u3bw3ln1O4PGPrvYvi8GOGB66HNIYguzdkxGylVNPsyH92Fb7Fixaz7IGwIV1dXva5CAQEBfPjhhwwbNgyVKmsXV926UubNkiWQy/iWHKNQKOhcrTPTWkxj562dbLqeReswgcUkJCRY5FyaPx9WroSpU+HTT7Pe1phzKT09nZEjR3Lp0iXtNq+88gr//POPdltjYtGoUZLLY+xY0F1/Z1UWFx8vOZVatoSDB6XSIEt48PQBn275lD8u/MGU5lO4/Nllvmr2lc0LS2A9cSkgADw8Cq64JDuX5Pf77P4mCoWCoY2G0qlKJ0bvHk3XdV0JfRrKjRs3AKk0TXbaZRWCb05Z3MGDUL06yHqQMXGpWbNmdOrUSXtsmdM6NaLOzs7ay6Y+y1xdpdfBtm2QnJwh7CoUCiHYCwQCLXLnVktOOgkEAsHLju2vDF5idBekumdh3dzcuC+3Lrt4jCpRn5BQ/BB4PKK7Yi7M8YFFIVQsVcSs4+iKWC+yuKRQKPTazMtdhMC8M1RvvCG5Vc6dy5Ppmc3gwMHU9KlJv439mH1stlgQWQlLyuJUKvjpJ0mkmTTJeM6SLsbEpUuXLvHDDz/Qq1cvvW3kUG/AaGlQQAAMHiwJnY0aZYidpsri1Gr44gvJ6fT111J5nSWEJ4VT86eaLDm3hKGBQ5nUfJJlA+Qz1hKX7OzglVekjK2CiG6gN2QvLoH0nvlXj7+Y/vp0tt/YTo+gHtoytOjoaK24pCvqGJJdWVxamlSipit4GopLd+/e5ejRo2zdupXY2FiioqIICJA6n+qKS+Hh4drLhQsXJjAwUFsep0uHDnD7Nhw54gJIrz3hXBIIBLrI7wfZnXwUCAQCQQZCXLJhdIWQIkUyhCLDBfCcdm8x2fVH+KkUc/t3h7hHoDZd8mCI7hllH0trZQoYun9TXczJX5K7xuVHsLcu9kp7NvbYSBO/JozePZrlF5bn74ReECwRl7ZskbJ3hgwxb2z5Nebo6MjFixcJCQnh4cOHANryJHmBrnuW1FTuzI8/Shlgly7BL7/AkiVLePToUSanU1qaVLr3yy8wZgy8+qp58wVJjLj86DLTr01Hg4bLgy4z/435WbpUbBFriUsg5S5dugQWNpy0CeRAb1nENOyUZgp7pT1fNfuKBW8s4Oj9o+wK2wWYLy5lVxZ3+jQkJECLFhm3GQZ6R0RkdMncuXMnjx8/pnr16iiVSu7cuaO9T1eYBahXr56eW1Xm/fehZk2YMKEqUA1nZ2chLgkEAj3kz+asSogFAoFAoI8Ql2wY3YwgXXHJ0KK7bNkynFRP4XEIDg4ObN++nS1btph9nJfFuQSZc5caNmwIkGVnIplSpaBcOdsoi6lYpCKbe22mednmDN0xlHuxBTRl2EZQq9UkJiaaZX9PS4O5c6FMGXhWpZMt1atXp27dujRr1gyAcuXKcfv2bUBaSEOGc6lcuXLa/bIKNe7ZUyrvmToVPvlkJJA5p+2bb6R8phkz4NtvzZsrgFqjptv6bgQsCuB63HV+7fQr/sX8zR/AhrCmuNS6tRTiPmWKdP3ixYuULl1azzFjq8jOJVkIMsz+yo4P63xI3RJ1OeJ6BIpZ7lwyVRZ34ID0W1dcMgz01g2637RpE1FRUfj4+OCqE3Q2bdq0TGPb29sbdR14esLOnZCergAGibI4gUCQCSEuCQQCgeUIccmG0V3oygtQyPiSXqdOHQA2bNjA+PHjtfe1b9+ejh07mn0cXXEpq0XCi4Chc6l48eJA1ot4XRo3lsQlW1iDKBVKfuv8G+nqdBr/1pigq0H5PSWbJioqisDAQEJCQjLdl5SUhEajyda5lJYGHTtKZTzjx5tfYlaiRAnOnj1L9erVtbfJgoS8oJXFpSJFiqDRaOjVq1eWAoBCAd99B1K28X3ga2JjfQkNlcr21qyBOXOkAPAvv5S2Nwe1Rs3MwzP569pfjG8ynvWvrufdGu+at7MNkltxKTU1lXLlyvHzzz/TuDEMHQrz5kGnTj+ze/duQkNDuXr1qhVnnDfIzqWcikt2Sjt+6vgTqYpUGATBBPPgwQMAPZHHkKzK4s6dk4Ta+vVBt3rNxcWFCxcuaJstxDxr0xkYGMi2bduIjY3F09NTe9zGjRsz0Ui3BVPiEkgnC1xcDgJdcXQUziWBQKCPLCppNBpu3rzJDz/8kM8zEggEAttHiEs2jK6dX7cURXYnlCpVKtM+WXXkMUVWQasvGp6ennh7ezN8+HAgw6lljnMJpNK4hw/h7t28mqFlVChcgT1991DcrTg9gnqw+9bu/J6SzbJq1SpOnTpl9Aui3HGqfPnyWY6xbx/s3i2JC9mFeBtDVzCWQ4kTEhIA/VwmMB5qbEj9+rB9uwbYC0xg2bLRlC4tdZXr1UvqCDdzpvnz23t7LxXmV+Cr/V/R1b8rX7f8miKO5mW32Sq5EZdGjBhB3bp1CQkJYdCgQWg0GkaMCAWesGWLPd999x0AkZGRVpxx3iA7l+QTFeYK6rrUL14fxQIFPIIrfldYvloqyfX19TW5T2pqKkqlMlM5pUYjCZ8uLpIQqos8x2HDhgEZ4lKfPn20l11cXLTiUokSJYweO7v/fbFi+wBfrl4NQKlUCueSQCDQois2d+jQgZEjR2pDvgUCgUBgHCEuFQD8/fXLUWRxyVh+UE7EpZzsU1Dp1q0bgwYN0nYd6ty5M2CZcwlsozRO5pXSr3DggwNUL1adTqs70X5lew7cPZDf07I55OwXY2Lq7t27USgUtGzZMssxtmyRuk0NHJizOeg6A9cYrKhzIi4BNGmSAHQDxlGu3Ey+/14K/Z4zB27cgIoVzZtbYloiH276EAc7B9Z0XcPqrqsLXL6SMZydnc0Wjw2ZN2+eniupZMmSXLhwGjgEtODRo0dAwRCXcutcAggJCUEdr4btkOqSynG740DGa8sYaWlpRj9jbt6EW7ckB2ClSvr36Tp1IUNcaqFTO2eOuJSVcwmgaNH9eHpeZ+BAJRcvLiAsrJXJbQUCwcuFbjmc3AAmOjo6v6YjEAgEBQIhLtk4T58+5ezZs3q3yeKSh4dHpu2Fcylr+vfvz7Rp02jZsiVqtZrXnqV0m7v4rFkTSpSARYtsozROxt3Rne3vbWdA/QFcjbxKp9WduJNwJ/sdXyKyChbes2cP9erV0+vKaIhGA5s3S7k71qgeNfySakxcSkhI4NatWzRu3JhLly4ZHUc6k6oBZlG9+hFGjYLjx2HkyOy72Oky68gs7j+9z29v/UaPmj1wsHsxRGdfX19CQ0Mt3k+3m6RMREQEu3btAg4AlQA/QCq5tHUMnUs5EZdu3boFQHW36nAJaA5en3qRpDItgqalpRntYrhnj/S7lRE9x5i4ZG9vr+eQkkO4QRL9jJGduJSSkkjDht/TtSvExtYhOHgs90R8nUAgQF9ckt+TCsJ7vUAgEOQnQlyycTw8PDLlIMlf1A3zYeR2ypbyMjmXdFEoFNovDOaKS3Z2MH261DFu3bq8nJ3llPYszYI3FnD8o+N4OHow6Owg+vzdh4j4iOx3fgkw5VyKi4vj+PHjtGnTJsv9r1yBkBB4882czyGrshvD17mvry8pKSlUqlSJY8eOcfz4caP7yTb9kSNHsny55Z0D09XpfH/0e2YdnUXPmj1pWrapxWPYMn5+fjx8+DBLd40xdFvcA/Ts2ROQupXBZiAdGAwUTOdSTsri7t+/D0CtWrXgH3A74UZMiRgeVHiQaduI+AiaL2vOE9WTTF0MAfbuBT+/zK4lyJzhlJiYiKurq15DBhcXF22Xz6zK4jQajclA3pSUFAoXjuWPP+DVV3uiUGjo3RtWrjT++AUCwcuD7vuGXCInxCWBQCDIGiEuFSAOHDjA6tWrtV/UDRfJ1apVy9G4srhk6Jx4GchJ/siHH0KVKrBkSV7NKnf4evpy6MNDtCvRjvVX1jNsx7D8npJNIIsLhs/zs2fPolKptJ3cTCE3YLQgKz8ThuKSn5+f9rLhvKpUqaJ33Zj7AzKymzp37pyl88oUP536iTF7xtC6Qmvmt59v8f62TpkyZdBoNBa7l06cOKF3XXY53rp1i/79WwBrgJHAGh48SNFuFxcXZ7XudNbEsFtcXFwcffv2zfQ4s0IOoff394c0KHK5COWfludx1cfcfHJTb9vZx2dz5N4Rjrkcy3QC48EDKb+sVSvjQfO6ziWVSqUtrdM9eeLi4qItl8vKuSSPYYyUlBTt687VNQo/v1+4dQv69IGlS7P6SwgEghcd3cylu8+CNoW4JBAIBFkjxKUCRPPmzbVnz4FMLqWaNWvmaFz5i/yoUaNyPrkCip2dHY6OjhZlstjZQaNGEBychxPLJZWKVGJE5RGMaTyGtVfWcjL0ZH5PKd8JCwsDMouysvtBDnc3xtOnsHgxNGwodZnKKbriUt++fbl8+bL2uqG4VLlyZb3rpgQL2blUpIjlwdvJqmS+Pfotzco2Y3Ovzfi4+Vg8hq0jC3iy68Zc/v33X73rFSpU0F6uWrUqMAT4AejE3r0TSXmmL3l6etKjR49czDhvkJ1L8vv91q1bWbFiBb169TJ7jPDwcIoWLartsqnRaGj0tBGKdAVd13XlVOgpAJ6mPOWnUz+h1qi57XIbO58M59KmTVC9OiQnwwcfGD+Orrgki3WGApWzs7P2tVumTBmj4xgTlzQaDfPnz+fevXskJydrxTalUkmpUiu5fx/atoUBA6AANAEUCAR5hDHHoyxoCwQCgcA4QlwqgMhnU6wlLjk7O5Oamsr06dNzPbeCiKurq8UlItWqSWff4+LyaFJWYvRroynmWozhO4bzJOnl6HISERGBt7e3niMjNTVVWzJmuEiVu7XpdnIzZMQIuHcPctuJOCAgQHu5S5cuerlphuJSuXLl9K6bEpdk51JOxKUlZ5cQFhfG5OaTX4jwbmPIwsM9C8N0goODado0o0RQt5PgO++8A0QDY/D1HUliYjl+/jlj4fH333/j7Oys/d/YAobOpWXLlgFw584dbbfE7AgLC6NkyZJaITYlJYWYBzFogjSExYTx+h+v8+DpAxadWqTNdFJr1CjfkD6r1GopC6xsWbh8GUyZBXXFpadPnxoVl3S3MSUuyfvoikthYWEMHz6ccuXK6TmXFAoFGo0Ge3tYtUoK7u/RQ+oOKhAIXj6MiUu26EoVCAQCW0KISwUQU+KS7sLVUhwcHF7YxWV2uLu7E2ehSiRXIP73Xx5MyIp4OHkwp+0cToSeoOqPVbkT/WKGfEdGRjJlyhTS09PZv38/jx8/ZubMmdr75SBiyOxcyk5cio6GP/6Azz/P6BaYU7p160aDBg0ACAwMBODKlSvMmjUr07wMF9OGX2ojIyO5fPlyjp1Lm69vZuL+iTTxa8Lr5V63aN+ChCw8yM6l9PR0s8SUhIQEKukEAsndOWvXrk2lSpWoU6cOAC1aJODgcIThw6Fnz4ySiZSUFM6fP2+1x5FbDJ1LGo3mmQMLvY54WREcHEzFihW1rrqoqChq1KgB/8GowqNI16QzYscIvj36LWq7ZyUldvDY8zGnw06zfz/cvg1ffgk6RrBMGHMuyS4k+bezs7P2NaObxaSLvK3uayflmcVMo9GQkpKi51ySP1u9vSWH1Z07pt1VLyoJCQmMHDlS6woTCF5WjIlLlmb3CQQCwcuGEJcKINZ2Lr3sFCpUiNjYWIv2kcUlWy6Nk+lTuw9nBpwhPjWeifsn5vd08oT58+czdepUli9fri1x1P1i+J+OCmgo4sjbG4YIy+zcCenpkovBGuzbt4+TJ09qO19Vr16dMWPGGN22R48etG/fHsgsLo0dO5a2bdvy5MkTXF1dMwWCZ8W1yGt0WduF8l7lWd5l+QstLLu5uVG0aFGtc2ny5MnUrl1bryTRGImJibi4uLB7924uXLiAr68v3333Hf/88w8Ae/fuZf/+/ZQrVxaVqiODBmnYubMS0Fc7RuHChfPscVmKoXMJ0Aqw5ri6EhMTuXHjBrVr16ZixYraMceOHQuAW5obXzb5kqBrQSSm6ZcZq1AxZPsQliyBwoXhnXeyPpapzCXI+Ju6uLhw6dIltm3bZvL5a6wsTvd1lJycrHUu6YpLAC1bSi6r3btfLvfS0qVL+eGHH/j+++/zeyoCQb4inEsCgUBgOUJcKoDIX4ANv1DrhgMLzMfLy8viOvqKFaXspYIgLgHUKVGH4Y2G8+elPzn/0HbcFNZAo9GwevVqAPr378+dO5I7S/dL4PXr17WXDV832TmXtm2DokWlnC1r4OHhQcOGDc3ads2aNWzatAnIeDwqlYopU6awdetWwsPDefjwocWupZlHZuJk58TO93dSvnD57Hco4JQpU0brXDp27BgAD7NRDOQOZa1bt6ZWrVooFApGjx6tfZ8tUqQILVq0wMvLC43mKaVLzwIOA/8D6gEY7ZKWH8hZXwqFAnd3d5ydnXn77bfp0qULhQoVMiuP6t69e6jVaqpUqYKrqytfffUV+/fv1zq6Tpw4QZn7ZXBQOpCarn92X4OGiw8vEXR2D336QHY6qK64lJ6eblRccnR0pEqVKrzxxhsmx5H3Wb9+PZUqVUKhUGidSyC9lgzL4nTp2VMq5QsKynq+LxLy3yc6OjqfZyIQ5C+6YrOMcIS9g/4AACAASURBVC4JBAJB1ghxqQAifwE2dC69yO6DvCQnziUnJ6mso6CISwBjm4zFy9mLXht6sePmjvyejtU4ffo0t2/f1l6Xs5Z0F0e64pLhF8aEhAS9Fu26pKfD9u3Qvr0kJuYH8gJZFpe2b9/O1KlTefToESCVKlnSJe5a5DVWXlzJ54GfU8zNdIj5i4Sfn5/WnSO/f8rCz/jx4zO5NNRqNUlJSSbdbLrIJVkTJnwJ9MbZWQ2cATYSE6PJatfnhu5nhoODAzExMfz1118oFApKlCihfS5lhVx+KT/Xpk+fTosWLXBycsLBwYGVK1fSf0Z/7DHe1TBRlUB6u8/5dFDmBZshuu7C9PR0VCqV9nUg5z2Zk5MnO5eGDh2qLY01FBWNlcXJVK8OAQHwTLt+KYiMjAQQZXGClx7ZuTRz5kyio6NRKBTCuSQQCATZIMSlAoixsrhr167l13QKPDlxLoFUGleQxCUvZy9Wd11NiiqFN1a9wZrLa/J7SlZhtcHK78CBA4B+y2DdsjjDBWRiYiJubm5GxdnTpyEqCjp0sOKELUShUGBnZ6f9Umvohrl27ZrZzqXDIYfpuq4rbo5ujGlsvBTvRUTXuWT4/jlz5sxMZYlyGLUl4hLAq6+W4fjxeGAS0JFJk8qiY5TJNwzdrrrh8W5ublr3XlbI4eTGhEytMNsSktSmRR9nnwdcTt+Q7bF0P9sMnUurVq1iwIAB2uyyrJDFJV0ePHigd91UWRwA9epx5poLG48VI82vItSvL7WSe+89qWZu5kz4+WdYuxZ27YJTp+DmTXj8WFKmCyAhISEAFp9wEQheNGRxacCAAXh5eeHh4SGcSwKBQJANxk8xCmwaY+KSYfivwHxy4lwCSVyS83hspPolW9pVakfw58HU/KkmP578kZ41e+b3lHJNUFAQjRs35ujRo0CGw0e3U1d2ziVjIoJGA3PnSv/bdu3yYubmY29vr31chjkQiYmJZjmXwuLCeGPVGxRzK8b67uvxdvXOk7naIiVLliQmJobk5GTt30+tVmd6LsTFxeHq6pptDpcuXl5e2styyd3w4XHMmzeCQ4f+R9mysGABvPuutR6N5Zhyu4L54lJWwfFxcXFQDvC0A0yLKknpCQzbMYwu1brgYGf6M0t3noaZS2XLlmXx4sXZzheMfy6aEpeMlcXh74/9hYsUIwruR4Fh9aCdHTg6gr09yHNWqSA1Ffz94cIFs+ZpS8jikqUdVAWCFw35s0I+oePo6CicSwKBQJANwrlUABHiknUpVKhQjp1Lqalw967155SXONo58nG9jzl6/yjXIp+f423nzp1Gz/rFx8ezZcuWHI0ZHh7O/fv3n7WGl/D29qZUqVLExsaiUqmIjo4mMjKSUaNGAZnFmfj4eKN5S3PmwLp1MG2alLmUnzg4OGhDiY11NjTHuTT1wFTS1Gns7buX9pXaW32Otkzx4sUBqeRHfv+Mj4/Xc4EdPHgQT09PFi1aZJG4JDuX5GMAdOjQAfiR2bMvU6YM9OkDN25Y69FYjqmcPrCOcwmAli7gmL1b52H8Q4btGMaTpCcmt8nKuWQJxpxLhvlScr6TUefSuHEonPQbAOiRng5JSRAXB7Gx0k9CgiQ6tWpl8XxtgbvPPtDk14BA8LJi+F3bwcFBOJcEAoEgG4S4VAAR4pJ1cXJyQqVSGQ1vzIoaNaTfzyJ+tKxcuZLZs2dbaXZ5wwd1PsBB6cDgbYN5mpL32RpXr16lffv29O7dO9N9bdu2pVOnTnpOI3M5d+4cgLZEplu3bkRGRvLll18CkttCLonz9/cHMl4/K1as4MGDBxw+fFh7n4xGI7lNWreW2qbnNw4ODtozprK4pPscy865FJMcw4qLK+hbqy8VCmfRA/4FxcfHB4CIiAitO2Xz5s162yxatAiABQsW8OqrrwLmiUtyPpDu/0AWrRo0eMzmzeDgAJMn5/JB5IKsnEvu7u5miUuRkZHY2dlpA7x1canQD0oAZsT+adCw6PQiin5XlFqLarH8wnIeJehnPlWvXl17Oa/FpVKlSgGS8JbpMyAgQPqxFDs7mFjwOnMmJSURERGhvSwQvMwI55JAIBBYjhCXCiDGxCXD9uoC85H/dpZ+aWjYEMqWhd9/z7gtKSmJPn36MHr0aIvFqueJj5sPv3T6hcP3DtN7Q2bBx9rIC5YNGzZkEpGOHz8O6LcLNxe5hKNy5cokJCRo85fKlCkDwM2bN7UlcbriUlRUFH379qVnz57cu3eP9u31nTznz8P9+9C7N9hCTr4xcemTTz7R3v/++++b3FetUTN+73iSVEl82uDTvJ2ojSKLS48ePdK+Ln/77Te9bdavXw/AjRs3CAsLA8wTlypXroyfnx8LFizQ3iYvRtLT0ylRAoYMkUKhe/WSzC3Pm+ycS/Hx8dmOcePGDSpWrJhJoAoOhuRXHoO9+eFSTnZOtKnQhsS0RPpt7EeJ2SVo8lsTBm4eyB/n/8CtsBtHjhwBciYuPUl6wtb/trI5ajO0AZoBjYAACE4JBh/AE3CEQsULERwVzEPPhyR5GhFUJk4EDw+zj42rK4wfD8+62hUk5NB7EM4lgcBQXBLOJYFAIMgekblUAAl4dia1WrVq2tuEcynn6Hbj0g26zQ6lEvr3lxwJd+5A+fL6HXbu3r1LhQq26xL5oM4H3I25y9SDU7kbc5dyXuXy7Fi6gtKSJUsYO3YskCE6QeZyNXMIDQ3Fzs4OHx8fvRKnwMBAQOoc9/jxY+zt7alUqZL2ODdv3gTQ5jSVK1dOb9yNG6X/75tvWjylPEFXXJKFAHd3d+39uk4PQ0bvGs2i04sY8coI6peqn7cTtVHkkjVdcUn3+dazZ0/WrJEC7nXLo8wRl9zd3bUip4yuuATSe4RCAbNmQcmSUpbX88TYCQmZrMriUlJSWLduHR07duTs2bPazx5dRk6/habCHlCaL6anpKdw7P4x7g67y93Yu2y7sY1/rv9D0LUgfjn7C59t+4zqHtWhEaSkpZglLj2Mf8iW/7Zw+N5h1l9ZT5LqmVDUCL1vOiGEQIuM669sfEW6UFb6qbu4Lo18GzEkcAg1fGpIaf7u7lLpmzk4OcHw4eb+KWwK+Xlcvnx54VwSvPQI55JAIBBYjhCXCiD9+vWjXr161KpVS3ubEJdyjmGrd0v44ANp4RgUBKNH65/tPX/+vE2LSwD96/Zn2sFpLDu/jCktpuTZceQwYNAXlHbu3Km9nBOnV1hYGCVKlMjUQa1kyZKUKVOGEydO4OjoiK+vr1Y4VKvV3L59W2972ekks3EjNGkCz7qe5zuGziU3NzeUSiUtWrTQ61ZmyMG7B5n771w+a/AZc9rOeV7TtTmMlcUBTJw4kYoVK1KpUiXWrFmDl5cXy5cv56233gKgcA7dJ4bikrMzzJgBERGwcKGkPfj55eYRWUZOA73Xrl1Lv379AMn1NHXqVL37r1+H7QnfoLRTYemrN12d/n/2zju+ifr/489Luje0QBkte4rMQtlLtiJLLENxgAMVQZZ8FREVEZThRPwJKgIiUJYylT0EASl7Q4EWWtrS0j3T+/1xvSNJkzZp00Xv+XjwILmVd9Lk7j6vz+v9fjPn0BwW9llIQLUAZnadiSiKHLp9iLXn17Lj0g7oB2+ee5Pkx5KpoKlAbGos7g7u6EQdpyNPc+LuCf6L+I8z985wKvIUOlGHt7M3w5sO56UWLxF7JZZBvQdJHm1HwBVwy/nfETSuGj6f+zlV3avyf1/9H+fvn6fy0MqsOLOCH/77gUY+jRjQYACzpkzAeeYnCPmlD7q6Sn/oHFEyKzuLWw9ucTX2KncT7yIgoNVoqV+xPo9XeRw3B7e8j1fMyOJS48aNOX78eAlHo6JSssj3JKpzSUVFRcVyVHGpDCIIgoGwBKq4VBgKIy75+0sChFysV3+29/Tp0waFpksj/p7+9Krbi59P/cwHXT5Aqymatneyc8nDw8NAgNu7d6/yuCDOpcjISKpWrWpyXdu2bTl27BgNGzakcuXKysA6OztbEbiCgoJYu3Yt/noj/StX4MyZ4neX5IW9vT1Xr14lKSmJ2NhYRfTQ//xMsfDoQqq4VmF+7/kmU6LKC66urri4uBAVFWXwPevVqxedO3dGFEUCAwNp0qQJAwYMUNZbUijdFMbiksysWbBqlZQ1tWJF8aVc5pcWl5aWRlhYGLt37+bFF19U1smiVIMGDfjtt99o3fqh8+3CBakmmWbIf2QL1qe0punS2Hdzn8EyQRDoXLMznWt2JqRqCK2ea4X7S+6E1w1nn3Yf3p/nri1WyaUSzX2b879O/yOoaRCPVXpMeZ9H7h7J+QDg4/99TEhICBs3blT2rVajGpM7TAZga+pW7py7w86NO7mfcp9lIcvYf2s/8/+Zz+8aL65kpuKUz3uKd4KPapwnY9tb3Ii7we7Q3WToTA9GBQTaVG9DR7+OVHevTnWP6lR3r46Piw+Odo44aB2w19jjoHVQ/tlp7Irsd7xlyxZeffVVtFotdevW5cCBA0XyOioqZQX5/C3/5lTnkoqKikr+qOJSGWfGjBnMnj07l3NDxXLkmksFnZGqVethxzh9cenUqVOFjKx4GNNyDEHBQSw8spApHaYUyeAlNjYWZ2dnKlasaPAZXdVroVUQ51JqaqrZ1KXAwEDWr19PVlYWjz/+uPIbyc7OVm4av/rqK1599VVFREhIkFrGu7vDsGFWh1NkZGZmcvToUV5++WWSk5MVJ05enL13lq1XtjKlwxSc7Z2LIcrSTZUqVYiKijL4/nl5eQHS4GHfvn25CkDbWlzy84PJkyVzi709LFv2sIN9UZKXc0kWVuX/Bw8erLjh5Ppehw4dopKRjW/8eMjKgtOvn6ZpU2nZ9OnTmTdvHpcvX6ZBgwbKtvv27aNbt25WxazVauECvOX6Fm/MeoOOIzoybMwwEjMS0WXraFalGQHVAqjhUcPsOUs/zblx48ZEREQYrNf/HemnQ3q7eDOt4zSmdZxGSEQI84/MZ2OHvxhyMAZHMxp4kgO81VvHn+dWYKexo6JzRcYFjKNZlWbUq1gPf09/BATSdelcirnEyYiTbLmyhR/++4GUTMvqG7nYu1DNvRr+nv4EVA3gldavUK9iPYv2NUdYWBh3795VUpV1Oh3u7u6kpKQgimK5FqUfJdLT0wkODiYoKMhkoXuV3Oh0OoN7a9W5pKKiopI/6hWmjPPJJ5/wySeflHQYZZrCOJdAqrV04oT0WB64ent7lxlxaXCjwQxqNIhpu6bhZOfE+MDxNn+NlJQUXF1dcXZ2VpxLISEhSs0jKJi4lJGRkae4BNLg6YknnlAG1jqdTike7u3tTY8ePZR9vvxSci3t2AE1algdTpEhtwcPCQmhQoUKuQb6xhy6fYg+K/vg4+LDuIBxxRBh6ady5cqEh4cbCJr6aW9OTrl9KaY6o1mCOXEJYPZsSZT5/HN44QWwUnMpEHk5l5588kkDYSUuLk4Rl+Qacsafw8mTsGeP9B5kYQmk69HIkSMNhKWCIn+GEyZMgEy4+sdVJvw4wapj6De6cHd3x9fX12C9XIsLpM9GP2VSpmXVlqwasgpa3URs3Bh0aSZfy612Q1asvGCRWtjAuwFPN3yaWd1mIYoi8enx3Em4w93Eu9xPvU+GLsPgX6Yuk3RdOrGpsdxJvMO12GssOrqI/zv5f2wevpkuNbtY+pHkQhYVn3rqKS5cuABItcays7PJzMxUm4U8IsgprlevXmXWrFklHU6ZwFhckp1Lly9fZvbs2Xz//fcGtQ9VVB5lvLy8GDp0KMuWLSvpUFRKOaq4pFLuKay41LIlrF0LMTEPxaU2bdqwY8cOUlJSLCoKXJLYa+1Z/+x6ui/vzoIjC3ijzRs2T49LT0/H0dERFxcXRVzas2cPAM2aNePMmTMFSovLyMhQ3CfGtG/fngoVKhAXF0elSpUM0uIyMjIQBCGX42/PHmjVCnr3tjqUYqFp06aEhITQsGHDPLd7f8/7VHSuyLGxx6jqbjptsLxRo0YN1q9fb7DM3HdHxpTTxxLyEpcEQarT9v338OuvxSMu5eVcqly5Ms2bNyckJASABw8eKOsSEhJwcHDI1ehg/nzJ3ffqq4bHsre3z5WyXVDkz1A+L6enW96NTkZfGPHw8DAQk8C8c8kktWohdOsGO3eCsQjl6grfflsgG5ogCHg5eeHl5CUVELeQmw9u0m9VP5749QkCqwfyduDbDGsyzCqnkf77DQ8PB+CXX35R0phTUlJUcekRQb7uzps3jxkzZqjuJQsw5VwKCQlRmukMGjSIoUOHllR4KirFhk6nIz4+np9++kkVl1TypRgM+SoqpZvCikvt20v/Hz36UFyqVq0aYNg9rjSjETS81eYtbsXfYuf1nfnvYCXp6ek4ODjg4uKifEby/5MmTQIK7lwyN/ixt7enZs2aADRs2DCXuOTg4GAwEEtPl/6GXbtaHUaxodFoiIqKyjMt7mj4UQ7cOsDk9pNVYUmP2rVr51rm6upqctupU6fSsmXLAr9WXuISSPWehw2DdesgvxrRtiAv5xJIQqxMXFyc8jgxMTGXa2nXLklMf+01yKOWfKExFn7j4+OtPoa1zqV8z0Hvv68U69bbER5/XCpAVYzU8qrF4ZcPM7n9ZGJTYwkKDiJwaSDv7HiH/+7+Z9ExjNO4AwMDeeGFF5QJEbVj3KNDTEwMAGlpafz5558lHE3ZIDs720CQd3BwUD5HwKruwioqZZmoqCjlcUHu1VXKF6q4pFLuKWzNpTZtQKuFI0ce3ozLg/9ES9tXlwIGNhpIFdcqfH/ie5sfOyMjA0dHR4O0uKSkJBwcHHB2luoB2VpcAnjsMckJ0Lx5c5Pikj5Hj0oCU2kWl44ePUpqaqpZ4UMUReYemksFpwqMbTW2mKMr3eiLS5MnT6ZTp05mxZbPP/+ckydPFvi1ZFeAnH5pihdegKQk0KsvXWTk5VwCmD17Np06dQIMxaWEhATc3d2V5yEh8NRT8NhjMH16EQZMbnHp+eeft/oY+YlLxs4lU2lxBnTsCNWrGy5zcoJvvrE6NltQ0bkic3vO5ey4s3zX/zsEQWDJf0sI+DGAaguq0WFZB0asH8H0XdNZcmIJO67tICw+THmf+s0V4OFgWT4nG69XKbvoiyKhoaElGEnZwdi55ObmhrOzM+PGSanmZen+TkWlMLz++uvKY7lMg4qKOVRxSaXcU1jnkosLtGgB//yTW1wKDg7m/Pnztgm0iHHQOjCm5Ri2XtnKhosbbHpsfeeSvrjk5uZmUAsJoH///syYMcOi4+YnLn333XcsX76c1q1bIwgCgiCg0+kUsUufjRvB0RG6dy/IOyxaIiMjadiwIXfv3gWgqwkFLEOXwcgNI9l8eTMTAieUujbnJY1+p7P58+dz8ODBInut/JxLAJ06Qb16MG3aw5ptRUV+zqUKFSqwcuVKwDAtTr/+EkjpcI6OUvqod+7GbTZFP21nxIgR/Pzzz1Yfwzgtzlhc6t+/v/I437Q4kFxKH3wAOXVWstCS1aUHBARYHZst0Wq0vNHmDf4d+y+RkyNZ3H8xfev1xcnOiWN3jrHwyELGbR1Hv1X98P/SH98FvgwPHs6CfxeAP5DztZDPiapz6dEiKiqKFStWULt2bbRaLXfu3MlT+FaRMBaX5syZw6FDh5T7k4K4KVVUyiJ//PGH8ris1JNVKTlUcUml3FNYcQmgQwc4dgyioqQZYVlceu+992iqX/G2lDOt4zTa1WjHsHXD+Df8X5sdV9+5JA9YEhMTcXd3N+jiBrB9+3Y+/fRTi4+bl7jk6enJ6NGjlUG1PIA03i8+HoKDoW9fqZZMaaNKlSrUqVMHgDp16uDn55drm5VnVvL7ud+Z1XUWM7pYJs6VJwJyBIBp06YV+WtZIi5pNA8Fzaeflop8FxX5OZfgYf0pfefS9evXFcfXrVuwZg2MGVP0whIYOpcaNmxYoI6o+gKyu7u7QRpcUlISjRs3Vp5blBYHUjvJHOErAwcmZi/MVYKpJPF08mRcm3H8NPAn9rywh+tvXyf1/VTC3wnnwIsH+K7/d/Ss05N/wv5h3n/z4GVgOvAaXGlwha+OfsW+pH3wGJy7d460LNMFzFXKDkFBQUrH1goVKrBw4UIGDhxY0mGVeozFpTp16tCqVatcDQ9UVB51AgMDqVWrFhqNRhWXVPJFFZdUyj22EJeeew6ys2H+/IFAxVyFY8sKnk6e7HhuB45aR1acWWGz48oFvZ2dnbl27RqxsbG5nEt5DeyMu3zJZGZmKn8/SzAlLp07B82bQ2SkVEemtCK7CUy1dBdFkS+PfsnjlR9nZteZNi/I/iig1WrR6XTMnTu3WF4L8haXQOq0tmgRRETA338XXTz5OZdAEl80Go3iXMrKyuL69etK8fgFCyTjzjvvFF2c+ugP6mrVqlWgY+gLyHZ2dgYdAY3PGxalxUkHhQkTQKPhavNn+O7vBly6VKDwig2tRkt1j+p0rtmZN9q8waohq7j9zm329d0HwVDhVgVIhvCK4UzcOZHFtxfDMBhxaAS+831Zd35dSb8FlUJw8eJFQBJbK1asCMC2bdtKMqQygXHNJRkXFxe0Wq0qLqmUGxITE2nVqhWNGjVSxSWVfFHFJZVyT2FrLgG0bQvbt0N0dEXgNapWNSykfO/evcKEWKx4OHrQt15fNlzcQLZom8J9spgjO7p2796tOJf00+LMCUx+fn4m25vn51zSZ+9eyMo6x6VLDcnIyECnC+DTT6UaS5mZcPAg9OtXwDdYDMjFp02JS7tu7OJs1FkmtZ9kVbeo8oZGoymWz8dScQmgf3+oWBFW2E7LzYUlziWNRoOnp6fiXAoNDSUzMxM/vyaMHi2VFXr+eTBhmisS9MUluTC/teQlPBt3y7IoLU7mzTehc2c8v/sMkNIEyyJ2WXZwDnpl9YKV0G5fO2KmxvBntz9hCUyrO41GPo14NvhZHlv8GL+c+qWkQ1axElEUqZ5TJ2zp0qVUqFChhCMqOxg7l2QEQcDDw0NNi1MpNyQlJeHu7k7jxo25cuVKSYejUspRxSWVco8tnEsgtRSvUeMqgvAqbm6GHZb++8+y7j2lhcGNBhORFGFx16H8kJ1Lr+VYg5KSkhTnkn5aXLKVrbMsEZfi4qRaMQMGgCg2YOPGF9iyZT5hYeuYMQNq1ZKEJb2GWaUS2blkXG/pdvxtpu2aRhXXKoxoOqIkQlMxwhpxycEBgoJg0yYoqvqwljiXQKq9JDuXLl++DDjw9deD+e03qVHad98VTXymsIVzyZSYdvjwYaZMmZJrncVpcQCVKsG+fdTsUB1//7IrLsn172rUqAFAanIq3i7e+Hn5QSQEugRy4KUDLO6/GAetAy9tfokjYUdKMmQVK7h8+TKNGzfm5MmTjB07llatWinXEZX8MScugZRyr1+fTkXlUUa+X/f39ycsLMwyl69KuUUVl1TKPbYSlwDq1t2NKNbi7NmqtGjRgtWrVyMIQpkTl/rW64uAwI5rO2xyvJSUFBwcHBT3zZUrV7h58ya+vr4GaXH5zQRGREQwatQokpKSANPikihKxgJvb6hbF+rXh6lToVUrcHFpSuPGJ0hKqo6Lyznu34f//oOcckalmt69ezN69Gj8/f2VZXGpcbT5sQ3XYq+x5KklONqprZFLA9aISyCl1aamwrJlRROPJc4lkOouyc6lEyfCgR+5fNmNtWth9mzIaSJWLOgP6qobd2grBB06dOCLL77ItdzitDg9BAGeeEJyRVr4py5VyPXv5M9XFptkASIlJQUHrQPj2ozj0EuHqOxamem7p5OelV4yAatYRXBwMJcvX+bLL7/ks88kl52xY0/FPHmJSxUrVjSoT6ei8igjZxr4+/uTkpJCbGxsSYekUopRxSWVco8txSUvr71otTHMnWvHkSMhDB8+nAYNGpQ5camSayUCqgWw7sI6kjKSCnWsVatWcf78eSIjIxVxae7cuURERBAQEKAMeNu3b8+hQ4fyPNYrr7zCb7/9xh9//IEoimRmZhqISzodfPwxLF4sFVlv107q/nbggPTPzi6MJ55YS6NG39KkySRyyk+UCQYPHszy5csNln3xzxdEJUex94W9DGo0qIQiUzHGWnGpfXvo3VsSQQ8csH081jqX4uNh/vwgYBTTpsGQIbaPKT/0B8HW1FUrKFalxenRo4fkjjx92vYxLVq0qEhTEIydS7Jz1DlHRdTvFufq4Mrs7rM5cOsA7Za1Iy5VHViXduSOcOPHj8fHxwdQxSVryM7OzlNcUgfYKuWBjIwMMjMzcXNzU64V4eHhJRyVSmlGFZdUyj22qLkE0o345s1rqVp1EYcPw5NPSmJHw4YNCQ0NtUWoxcrEdhM5H32ewKWB3Hpwq8DH+fXXXwG4du2aMmgByYkzaNCgXK1+8+LChQuAVHxYFgPlv19kJDRsCLNmwfDh8McfsGoVrFsHnTtL+2u1WgQhDV/f9bi4lO3Z97D4ML48+iUjmo4goFrJtkJXMcRacUkQpE5sNWpIne5tjaXOJR8fH27fDmPkyAckJ3sCXZg3z/bxWEJBusMVBqvS4vTo0UP6f/du28aTnp7OpEmT6NSpk20PDKSlpZGWlmaRc0mfV1q/wvpn13Mq8hRLTiyxeVwqtkU+/+j/7ov7d1WW0el0Zs+ZFSpUUJ1LKuUCOVPA3d1d7ZSoYhGquKRS7rGVc2nz5s0AhIfPYfFiqQ7H5s3STKGlg8zSxMjHR7LzuZ3cfHCTj/Z/VOjjGRdT3rFjBzVr1jS4eTt79iyA2boQskiXmpqqiIEODg5kZ8Pbb8PNm7B2LaxcKQ3YTcVg3C2uLLLx4kY6/dyJbDGbz574rKTDUTHCWnEJwMsLRo6Ew4clJ4wtsdS5VLfuM4SH/x/btnkBH1GvXpRtA7GC4h4EFyQtDqBaNSnlduFCuHPHdvHI352icEdUrlwZf39/IiMjxzTWzgAAIABJREFUAahXrx4Avr6+AHh4eODh4UFISEiufYc0HkLPOj359vi3ZOoK7/ZVKTpMpXWpziXLyes+QXUuqZQXZBHV09MTDw+pnmxiURWIVHkkUMUllXKPo6NUpyYtLa1Qx5FvQipUqMCrr0r1fj79FARBW6AZ8dJAzzo9efaxZwm+EExqZmr+O+Rw48YNNmzYYLDMeKZDHuiamhmsVq2awXM/oxZViYmJirh05kxTHntMcijNmQPDhoG5cemjIC4dvHWQZ4OfpYJTBXY+t5OaXgXrpKVSdBREXIKHbsedO20bjyXOpZQU+OmnwUA74CPgY9atK7kW9LYSl1asWMHhw4fz3c7Ozq7A7tVff4WkJBg0CNJtZIiUJzuK4tqRmJhIdHQ0165do0qVKlSpUoXVq1ezY4dUY8/Ozo5nnnmG9evXm2yy8E67d7ibeJfgC8E2j03FdpgSl1TnkuXIjUhMITuX1MLGKo869+/fB8Db2xt3d3dAFZdU8kYVl1TKPXIdoOTkZDp27KjM4pri/PnzXLt2zeQ6+WR76NAhtFr48EM4eRKuXh2ETld228M/3+x5EjMS+ePyHxbv06FDB4YOHUq63kgr3cyoy/hmt2XLlrkGecaD9ISEhJxt2rFiRV/s7aUUuKlT845Lo9Gg0+nKpLgkiiIHbh1g2Lph1KlQh/0v7qdrra7576hS7MjuALnmiaUEBkLVqrB6deFjEEWRJ598ks2bN1vkXPrpJ4iM1AJPA7MAlDotJUF+KXyW8txzz9GhQ4d8t6tQoQLp6ekGdYYs5bHHYOlSOHECtm4tSJS5kb87RTl4vXbtGnXr1gVg+PDhSj0NgOeff56kpCQ2bdqUa7++9fpS26s2cw7NITZVdW+UVlTnUuHIS1yqWLEimZmZVne4VVEpSURRZN26dVZlaqjikoq1qOKSSrlHX1z6559/uH79utltmzZtSv369U2uk505lStXBmDUKKlI75kzI7lzZ5GNoy4+utbsip+HH1P/nsp/dy0rTH7v3j0A/v33XyW1Yvz48Sa31R9EfvHFFwQEBOS68BkLUw/FpZm4u6dy5IiUUpRP1g9areQiS09PL1PiUlZ2FqM2jKLrL13RiTo2Bm3E08mzpMNSMUNBnUtarXTe2LYNIiIKF0Nqairbtm1j0KBB+TqXMjNhwQKpCP7kyW2V5eWpbXnFnOr+BU11GTIE3NxsV3vJWmGyIJw4ccLsZEqXLl3w9fVl27ZtudZpBA3f9v+WK/ev0GtFL7LFsunMfdRRnUuFIz/nEqDWXVIpU2zevJlnn31W6R5pCaq4pGItqrikUu6xs7PD0dHRYAYqv9nrBw8e5Fomn2zlk69GAzt2QP36f5Oc3J2kwjVdKzG0Gi0bgzYC8NTqp8jQWZ460q1bN+Lj4zlz5gxff/01AGPGjOGdd95RttEf8A4ZMgQHB4dcziXjlMXo6AwWLHAC+tG//xVy9MF8kdPi0tLSDIqLl2YiEiN4fuPzrD63mg+7fsjNCTdpUqlJSYelkgeCICAIQoFqrY0dC3Z2UopVYSbF9UWSvJxL165BmzZSvbL33pMEXhlVXLIce3vo0gV27bJNPMUhLqWkpJgVlzQaDf7+/sTExJhc379+f77t9y0nI05y4u6JogxTpYCYEpfefvttgDJz/StJ8nMuQdHURFNRKSqio6MBuH37tsX76ItLbm5ugCouqeSNKi6pqCC5l/TFpZYtW+ZqAa1f+8KcuOTg4GBwMyIIULNmCGDPoUO2j7u4aF2tNT889QORSZFsupQ7TUIf/YuOKIp8+umnPP7448qypUuXsnDhQuW5/s2vg4ODSXHJ0LmkYe3al/j668rAboYNs7yKrr645OTkZPF+JcUPJ37Ab5Efv5/7nc+e+IxZ3Wbh6mChkqZSomi12gKJSw0bwu+/w7FjUpHogqI/6MnLufTuuxAaCsHBUs0nfQHK3MDqUcTb2xso3GDxiSfgyhWwRZfmohSX5KKsQJ5p4Pl1xBraZChaQcvmS5ttGp+KbcjKysqVBhcYGMhLL71UoimvZQXVuaTyqGJNuvXp06dxdXXFy8sLOzs7nJ2dVXFJJU9UcUlFhYfiUtWqVQG4fPkyR48eNdhG/ybCVOHXhIQExbWkT5Uq14EM9uyxbczFTe+6vanlVYsf/vshz+2uXr1q8Hzo0KF5bq8/4DUlLul0OoOBlo/PZ0RH12PKlItAT3x8LBdb5JpLpV1cSs9KZ+GRhby+9XV61+3NpTcvMb3T9JIOS8UKsrKyWLp0aYH2HThQcsH89hsUtOSOKeeSsbgUHw9//im5pUz9TPPrLvcoITsR5FnagvDEE9L/8+ZJhdkLg7maGNnZ2YwdO9ZkJzdLSU1NZfLkySxbtoyBAwea3S6/jlgVnSvSpWYXNl3Oe8JBpWQw5VwC6TprrgaiykNU55JKeScxMZG1a9cyfPhw5Vzi5uZGUllNxVApFlRxSUWFh+JScnIyTz/9NGBYL0UURaWOEJi+8U9MTDQpLjk6ZuHoeIT16ws+UCwNaDVaXmn1CntC93A55rLZ7S5fNlyXn4hjLC7Z29uTkZGhzKzIN8GvvPIKCxYsISHhdVxc9tOpk+QsM/WZm30POTWXSrO4dC32GnW+rsPkvyYzoMEANgZtpKFPw5IOS6UAREdHF9iBMnw4XLoEZ88W7LUtSYvbsUOqtzR4cMFeozho0aJFsbyOfB4pzE1zs2bw6qvw7bcwZkzh4jH3vbl//z7Lli2jS5cu+R5j+PDhucR9nU5HZmYm7u7uvPzyy3mmPrq7V+L+ffs8X2NQo0FciL7A1ftX89xOpfgxJy45OjoWuDNieUJ1Lqk8alg7YbRmzRqSk5MZo3dBc3Z2LnR3bZVHG1VcUlHhobiUkpKCp6dUKFm+uT958iQ9e/bkmWeeUbY3JS4lJCQYpBvIaLVanJw2c+MGHDlSRG+gmHi55cs42TkxZO0Qbsebztk2FpfyS60xlRYniiI6nY7o6GieffZZABo2bM7u3a+RkeFBSsp0dub0a7dGXJKdS+np6aVSXErNTOWZtc+QmpnK9lHb2TR8E4525Sc16VGiU6dOAISFhRVo/6FDpQLfv/9esNe/e/eu8lgeABk7lzZvBh8faN++YK9R1Ny+fZuDBw8Wy2vJ56nCODoEAX74Af73P1i+HA4fLng85sQlWXS3RARbs2YNGzZsMFgmv7+8au7cuwejR8Mvv3zOgwfn+fjjbMxpEQMbSs6nX079km88KsWL6lwqHKpzSeVRQxaXLE2L2759O7Vr16Zdu3bKMmdn5wJ1VVUpP6jikooKkrgUFxdHVlaWgbgkiiKtW7dmz549XLx4Udne1KyfOeeSRqPBwWELPj4wYgTculV076Oo8XXzZdvIbYQnhDP2j7Emtzlw4AD+/v7Kc2ucS46OjkoXt4yMDA4dOsTWrVsZPnw4d+48z7ZtMHDgVuAo33//PWC9uJSSkmJRXMXNntA99FrRi9P3TrNyyEr61uuLRlBP0WWVTz75BIAbN24UaP/KlaU0q1WrwExN5TzRF2WmT5dSKvVnLR88kLrSPfWUJGKVRvz8/JQCokWNLcQlmRkzoEoVmD4dsgvYSM2cuKS/PDQ01OrjyoMCc+c/UZSEzXXroEWLS8BuPvxQQ/36khhpTE2vmgxtPJQ5h+aw8EghioSp2Jy8nEvp6elW1V0pj+QlLrm6umJnZ6c6l1TKFNY6lxISEvD19TXYz8nJSXUuqeRJsY5cBEH4SRCEKEEQzplYN1kQBFEQBJ+c54IgCF8LgnBNEIQzgiC0Ks5YVcoXrq6uREVFARiIS+ZuHMylxZlyLmk0GkQxjr/+gthY+PBDGwZeAnSv3Z1pHabx942/c6XHhYeHs3fvXrp166Ysy8+5pC8uabVaZUY9JSWFyMhIABYsWMgff3jQsyd06nTRYP9HQVw6cfcEfVb24eaDm/zfU/9H//r9SzoklULSqFEjAM4WNK8NmDQJIiOldKt//7Vsn6ysLHQ6HXv37lVmG//N2Vn+rWVnw3PPSd3oxo3LfYzffvvNoOh+ecCW4pKLC3zyCRw6BK1aJbB8+T9WH8OcuKR/7alTpw6nTp2y6rjyoMDc+e/33yXH1TffwMiR+4DerFuXiJubVJvLlFi2euhq+tfvz6x9s0jJTLEqHpWiw5y45OzsTHZ2ttm6XioSeYlLgiDkW5NMRaW0Ya2gnJycjKtRO2YnJydlkiIzM5Pdu3cDcPz4cbPdRVXKF8U9Lf4L0Nd4oSAIfkBvQD/Pph9QP+ffq8D3xRCfSjnFlLj09ttvM2TIEJPbW1PQW67z07Il9OoFxZTlUaS80voVHLQOLD6+2GB5fHw8AE899ZSyzNTNrT7G62W7eVxcHJGRkWg0GsLDK3P9uuT8Mh4UWdMuXavVljpxKTE9kZHrR1LVrSpnx53lldavlHRIKjbA19cXPz8/RdgpCH36SF3jBEFKtcoPnU5Hs2bNsLOzIzo6mtdee42YmBjFDSjXXtq6Vfq3cCG0bZv7OCNGjOCdd94pcNxlEX1x6cqVK4wcOdKgg6i1jB0Ly5bB6dM6XnyxCVZ0fgYMRaRbt27x4osvEhMTk0t02rt3r8n9jx8/bvB8586dhIeHK4MC47S4lBTp/DpyJDz+OLz4Ikqnsc6dU5g+XXLQnT6d+7XstfZMaT+FxIxEtXNcKSIvcQlQroUqpklPT1fOnabIr5uiikppQ64la6nIlJKSkuseW665dOTIESZOnEjPnj3ZuHEjbdu25fnnn7d5zCplj2IVl0RRPACYkvkXAdMA/W/7QOBXUeIo4CUIQtViCFOlHOLq6qq01tRX6ffv329y++3bt3Ps2DGDZXmlxcmDuo4d4cYNyY1QlqnsWplhTYbx86mfiU6OVpbLN6vWCD7GdWD0axlERERQqVIl9u+XbpCffPLhjXG1atXYunWrVTZfrVar1CopDeLSV0e/otZXtbgWe40Vg1dQwblCSYekYkMCAwMLJS4BNG8OL78M+/dDdLTpbRYsWECjRo04efKkQfpu9+7d8fb2VoQiuQ7T+vXg5QWvvVao0B4p5EFkeno6CxYsYPXq1SxfvrzAxxME6NbtBtAWcGPRIsvax128eJEbN24YiEhPPPEEy5cv5+jRo7nEJf3aF2FhYbz22mukpKQQFBSkLD9//jx9+/alfv36nDlzBsh9jl60SHItffAB/PMP2NmBvb1UzDsrK0vphLdrl+m4u9bqip+HH7+e+dWi96lS9JgTl+S/vVo3xTyiKJKRkZGn81p1LqmUNeSJcUvFpbycSx06dGDxYmmC+csvvwTgwYMHNoxWpaxiV9IBCIIwELgjiuJpo0FidUC/Emp4zrIIE8d4FcndRJUqVdi3b1+RxVucJCUlPTLvpbQjO27AsjoWX3zxBQcPHuSzzz5TlsXFxREfH5/rb3b37l0yMjLYt28fzs7uQGt++OE8XbuaGSnaiKL+/nSx78LazLU0+boJnzf7nNqutZUUjStXrijb5RfDnTt3DLaVP//9+/dz6dIlnJ2d2bw5hurVXbh48ZhSw8bNzQ0XFxer3qMoikqB5Rs3bpTo72tN2BqW3FhCQIUARjUYhXhTZN/NkovHHOp5qOB4e3sTGhrKhg0bFNG0IPj7u5GdHcDnn1/mySdzXQKZMmUKAMHBwcqyJk2aEBoaSmhoKN26dSM8PJyaNWuya9d+NmzoQPv29/nnn0sFjskaysp3yN7enqtXryqTATt37qRJkyYFPt6cOXOAa8Bavv02iIiI27zySiharfkb++7duwMYpCVev34dgGPHjhFtpDBevHhR+Ww//fRTdu3aRaVKlbilV9yvadOmgJQSt2zZMpycnHB2dmbfvn0kJ2vZudOXpUtr06HDA3r0OMeJE4ave/DgQXx9falZsw3BwWm0aWM61bOLVxdWX1vN+p3r8Xb0tvhzsoSy8h0qTURGRpKWlpbrc7t58yYgud6qVatW/IGVENZ8h+RB+N27d83uk52dTXh4uPq9LEeU9fPQhQsXAOncYMn7iIuLIyEhwWDb5ORkJdNDRp5sd3d3L9OfT3FQ1r9DFiGKYrH+A2oB53IeuwD/Ap45z28CPjmPtwCd9PbbDQTkd/zWrVuLjwp79+4t6RDKDdOmTRORnHPiypUrlcd5/evWrZuyv06nEwFx5syZuY49adIk0dXVVRRFUczIEEUvL1F8+eWif0/F8f0JiQgRved5i/1W9hNFURS3bdsmAuLRo0eVzyk/QkNDDba9dOmSCIirVq0S+/btKwYEtBF9fETxxRel7YODg0VADAgIsDreIUOGKK+1ceNGq/e3BVuvbBWbLm4qMgvx2XXPipm6zBKJw1LU81DB2b9/v8W/g7zIzhbF2rVFsXVrUYyNzc61Xn6NsWPHihqNRkxLSzN7rF27RBFEsTi//mXlO+Th4SFOnDhRHD16tAiIPXr0KPCxLl26JAqCILZu3VoEH7F373siiOLKlXnvJ/8t//rrr1zXnB9//FE8deqUwbJJkyaJoiiK586dEwVBEAGxcePGIiD269cv1zF69+4ttmzZUhRFUczKEsXOnaXvQ0CAKIaGGsayYsUKERCvXLkiiqIojhsnim5uohgZaTr2yzGXRc1HGvGJ5U+IyRnJBf7sTFFWvkOliYEDB4rNmjXLtXzNmjUiIJ47d64Eoio5rPkOxcTEiIC4aNEis9sMHDhQbNGihQ0iUykrlPXz0Jw5c5RrQZ8+ffLd3tPTU3z77bcNlo0YMUKsUKGCyXHRuHHjiir0R4ay/h2SAU6IZrSYkm5FVBeoDZwWBOEmUAM4KQiCL3AH8NPbtkbOMhUVm6Nv+8yvALWMvqVcrs1hqqC3XHMJwN4e+vaV6p0UtItQaaKFbwsmt5/M9mvbOXH3hNl6HnlhnBZXoYKUGnb//n1SU1OJjR1NTAz07o3VxzbG2/vhbHpJpMVtvbKVgb8PJFvM5pt+37By8ErsNCVuIFUpIlq3bq08LkzxXEGAOXOkejd+frfp3r0PAH/99ZfiQgA4dOgQtWrVyvMctmkTODs//D2pPMTR0ZGwsDD+/PNPAC5fvpzPHuY5duwYoijmpCTGMG7cUWrXhtWrLdtfTt81Xmb8PZLPub/99htarRYnJycuXrxI06ZNqV69eq5jnD9/Hl9fX7Kz4d13pRqAP/0Ex49DrVqG28ppcfJrjhsHOp3UTc7U17mBdwN+Hvgze0L38N7u9yx7oypFhlpzqeDcu3cPkLIhzGFnZ2e28L6KSmlEv17szp07893eVFqcs7OzWmtMJU9KVFwSRfGsKIqVRVGsJYpiLaTUt1aiKEYCfwCjc7rGtQPiRVHMnQ+gomID9E+echFTY3x8fAye69+YJSQkAKY7l2k0GqWIHsDgwXDvHgwYAGZqsZYp3mz7Jl5OXnyw9wMSk6W6VYWpueTj40PVqlXZs2cPsbFVCA19nUGDYPhwaX1hRKGSEpeik6MZunYoT//+NC18W3BkzBHeavsW9lr7YotBpfhxdXXlmWeeAchlI7eW4cPhl19SSU6uyb591RBFkT59+lC7dm1lm0uXLtGgQQOzx4iMhFWrJIHbip9oucHR0ZH169crN8537twxKfJYQnh4OADNmzcHID4+jqeegj17wJJSN3K9p0qVKinLEhMTlcHs8uXLqVixonIdCgsLo1q1atSsWROQ0iI//PBDXnzxRYPunXfu3MHZuTnNmsGCBfDmm1LxblMYi0uPPw5Llkjd5HL0t1yMbj6a4U2H8+vpX0nLUttVlyT5iUtqzSXzyOKSr6+v2W3s7OzUjnsqZYpZs2ZZvG1ISAhZWVm5JnTzunfWH+uolF+KVVwSBGE1cARoKAhCuCAIY/LYfBtwA6lgwY/AG8UQoko5xRJxqWfPngaiSUpKinJjLxcDz6+gN8CwYVKXpl27oEcP2LjRJm+hxPBw9GBG5xnsuLaDmbdngrP1Hdz00Wg0BAUFsW3bNm7dCkQUNSxZIrk3wHJnmSn0BcLiEpeyxWyCgoPYemUr0zpM46/n/sLDMbfDTeXR5LnnngOkGgeFpVmz68Ap4F1u3QozuU1e4tJbb0ldwebMKXQojyT655bKlSsDhvXjjImJiWHt2rUm1925c4eKFSsq7qG4uDj695eEJTN9IgxcEJs3S13Xpk2bRmBgIIIgcOjQIbZv3w5IDQ0qVaqkCAR37tyhRo0aSg0dPz8/atSowc8//8x77xm6iC5ffoGwMFixAr7++uG51RhjcQmkbnI+PrA5j6ZwL7V4ibi0OP68bEaBUikWSqqgd0pKitUtz0sb8vladS6pPCqY6n6anUcKxdSpUwHDuqiQd/aAKi6pQPF3ixshimJVURTtRVGsIYriMqP1tURRjMl5LIqi+KYoinVFUXxcFMUTxRmrSvlCX1wydTMG8NVXX3Hy5Enl+fXr13F1dWXbtm2Kcym/tDiQbuTfeQeuXQN3d5g/H8r4fRiT2k9i7TNrCc8Kh16Fcy4BjBw5koyMDBISOuHtfQ39+ztZ/LOmS5xMcTuXNlzcQOefO7P35l6+6/8dn/X8TO0IV86QZ77v3r3LgQMHcrWIt4Z79yKBuUAjFi+OUZZXr16dtm3bAubFpXv3pC5xU6dCo0YFDuGRJiZG+kydnJz4/fffAckNZo5Ro0YRFBRkUDxbJjw8nBo1auDp6QnAO++8Q8eOOuzszItL8iTFE3JrNqB///4cPXoUkNIgP/74Y0A6D7q4uCgTHOHh4VSvXl0Z1Pv5Pawq0KtXLyV9smXLFzh/vhFvvw3PPQcmTr8K8rlWfwBtZwedO8OhQ+b361G7BzU8avDzqZ/Nb6RS5JSEc+nBgwdUqVJFEUfLKpY6l1RxSaWsYNwMAqQmD+aQm1lMnjzZYLnpe+eaQAibNr3PjBlSx9FHofSHSsEo6ZpLKiqlAkvT4vTFCZkdO3ZY5VyS8fOTHAT//AMzZxY08tKBIAgMe2wYnTSdoBUcjjzM2bNnOX36dL77mhKXAgICqFo1AGiLv/85m8VZnOLSoduHGLp2KFHJUXzV9ytebvlykb6eSumkUaNGaLVa/v33X7p27Urbtm1z6vBYT0REBBAMnGXBghbAOEDqMCaLGDVq1DC5r9wBTK21ZB65a+jhw4dp3749Go0mz7pLFy9eBFC6ZOojiz3657erV08TECDVvdIrfaEgX0eaNWumLJPdQ8ZOEDs7O5ydnRWBICYmhkqVKtGyZUtAEqX0qVmzJocPR+Do+DMVK0oTHPmh71w6dOiQIhh07Ag3bkCEmUIFWo2WF5q/wM7rO1l1ZlWZd7FYQmhoKJMnT87TCVDcFJW4FBYWZlC7RZ8bN26QlJSkdHUtq0RGRmJvb6/UgDSFvb29Ki6plBnkyROQOl6D5DLctGkT//77b67t09LS8PX1pX79+gbLje+dpevEDKAFkM3cudI1okYNmDABHjyw9TtRKe2o4pKKCpY5lzQajXKzrU9UVBQ9e/YEzItLYNp++uabMGYMzJ4NeqaoMkv7jPZwH55e+zRfXPuCxo81zncfU5+3IAjY2Y0CoFEjw7bXhRmoFFdanC5bx/jt46nhUYNTr53i7cC3C+S0Uin7eHp60rJlS44cOaIs+/LLLws0KJFSNXRAJ3x9LwDzAR+mTJnCmDFSlrksLhhz/LjkUjGzWkWP5s2b4+TkhFar5eOPPzZbd0k+d5kTl2ShT66ftGfPHt57Dy5dgs8/z308WVzST8WRJzsWLFjAkCFDDJa7uLiQmpqKKIokJCTg6enJ7NmzuX79eq4Bwblz0KOHLyEhAl9/DRUr5v856ItLnTt3ZtCgQYA0cACp9pI5JrabSLsa7Xhu43Osu7Au/xcr4wwcOJCFCxcWqgi8rSmKgt6pqan4+/vz6quvmlwv1xor68XC7927R5UqVfK8bqs1l1TKErJz6YcffqBizgXg3LlzDB48mL59++baPjEx0eSYxjgtrnbtxnh6jsXdPZg+fWYSEwO//Qbt28PixTB6dNnPzlCxDlVcUlEhb+fShx9+mOMYAAcHh1z7rlmzRnlsLi0O4ODBg6SnpxusEwSYNw+0Wsu7CJVmMlMycVnrwoTACfx6+lfe3v42GTrTM5wyppxL8fEQGzsUOEG1aokG65o3b06bNm345ptvrI6vOJxLyRnJTP17KqciT7Gw90JcHVzz30nlkaZWrVqcOXPGYFlYmOmaSXkRGRmJi4sLXl4akpNfAZwICvqH5s2bExQUhE6nw9/f3+S+J05A48bg5laQd1A+GDZsGF27dlXO2Z07dwZM18tKSkpSalGEhIQYrMvIyODevXuKuDR69GgaNWrEnj17GDBA6rb22WdgrC/K35HHHntMWSZfjyZNmmTgeLO3t8fZ2Znk5GTS0tLIysrCw8MDFxcX6tSpkyveWbPAwQGuXIFRoyz7PEzVXAJJoLSzy3tCxMfFhwMvHqCBdwMWHV1k2QuWYWJjY4HCdTO1NUXhXJJFo+DgYJPr5d+EqfouZYnIyMg86y2BmhanUraQnUvdu3dXSlfI1xR7e3vGjh3Lm2++qaR5mxOXDO+d7bl37zvi48HT8090Oh1eXjBihJSGP3++1Pxh0aN/CVDRo1DikiAIHQVBeFoQhLzPwCoqpZy8nEs1a9ZU8u5NOZf0ycu51K1bN8aNG5drvbe3NNj44Qcw0p7KHCkpKbiJbizss5CpHaay5L8lVP6iMrMPzDa7j7G4FJFwj5ofdiVZmw28n+tm3dnZmWPHjhEYGGh1fEXtXErLSqPjTx1ZdHQRLzR/gWeaPGPz11Ape1StWtXAkg5SzTZriYyMxNfXl5kzZxIffxTYxNattcgxvJgUakGaNTx+HNq0sfolyxVr165l3759yvPXX38dINekwO+//467u7siupw9a+iulOu1VK1aVVnWvXt3Dh48SGZmJk8/LRVWv3rV8PW3b9/Iun8aAAAgAElEQVSOt7e3ImqB4WSHnPooL2/QoAGXLl1SXs/U5MbFi/Dhh9KN/sSJYEZ7NIk5ccnRER57DP77L+/9tRotb7V5i6PhR/lk/ydki6UnZczWyK6z0kRRFPSWfwvmHDuyc6msi0v37t3Ls94SqOKSStlCdi5VqlRJOQfIrtvo6GiWLVvG4sWLWblyJWCpc+k54uM7sXgxuLsfz1XQe/x4GDgQJk+WJtJLA40aNVKu7SpFg0XikiAIbwmCMMNo2VrgILAJuCIIQjfbh6eiUjy46U3nGzuX9GevtFotDg4OSrtnY0zl5+sP+DZs2GByv+efh8REOHjQqrBLHSkpKcqFZ17PeWwduZWutbrywd4PWHhkocl9jG9+p22aT7znIXyCngP+sulMsJeXl/L3KApx6d2/3+X0vdNseHYDvwz6RU2FUwFMF7gvjLg0ceLEHIfSXJKS7OndG8wZoe7dg59/hqgoCAiw+iXLNfI5Qi56mpmZSWJiIhMnTlS26du3L7du3TIYZMpCYqVKlZRlXbt2JSkpiXPnztGihbRMvySdKIr89ddf9OnTx+B6pH+e0heP7Ozs6NatG+np6ezduzfXeoC//oIWLeCTT6BbN5g0ybr3b05cAujZE/bsgRwtwSzj2oxj1OOjmLlvJt2Xdycls2ynS5li1qxZSlOP0tQtqSicS/JvwVzNJdm5VNbT4lTnksqjRFZWFlFRUdjZ2eHp6WlwT6I/aQEPRaj8nEtNmjShbdtZVKuWzeuvS/fyxuc/jQaCg2H4cPjf/+CLL6R0ajOnj2Lh8uXL/PDDDyUXQDnAUufSBECZdhUEoQ8wGBgE1AD2ILWwUVEpk+TlXJJbUoNUCyg9PZ358+ebPI4pZ5P+8eLj403WDOreXZoN3rrV6tBLFampqcpFSxAE+tfvz4ZnNzC08VAm/zWZwKWBHLxlqKDpi28J6QmsCf0eNNnEVj0C3qbdYAVFo9EoAqB+2/HCEp4Qzptb3+TrY18zIXACgxsPttmxVco+HXOK1Bw4cIDTp0/j6OjItWvX8t0vJSXFoIaLLC4JgsDx48f58MP+rFql49w5qXabPqII338PdepI63x84KmnbPq2Hnnkc4Q8oO7duzceHh6KUwigX79+6HQ6xbEBD8Ul/TTchg0bApKo2KgR2NuDfqmmuLg47t27R0BAgME1Q//G31hccnBoCizm009bAjv55JP+PPYY9O8PTzwBAwZIqZB37sDeveDlZd37N9UtTuatt6RuQN99l88xNHasGLyCJU8u4cCtA/xw4tG6qY+Li+Ojjz5SnpcmscGcuKTVarG3ty+UuGSORyEtLjs7m6ioqHydS/b29mrNJZVSjyiK2NvbM2/ePHx8fBAEwWDM065dO+Wxn58fUVFR6HQ6oqOj83QueXpW4uJFf/r10yAIpsUlkFKoly6FVq1g2jTo1Elyvu7aBZGR0Vy7ds2i+6GCoNPpWLNmjRJXeWguURrIU1wSBKGLIAhdkXoMOuY87wK8AFwAHgD1gB1AU3m9IAhWGK9VVEqevMQlU7NX1apVs/jYxqkqoaGhJl4fnnwSvvlGchmUVVJSUnK5NLQaLb8N/Y3Pe35OVHIUA1YP4GL0RWW97O6pVq0a3x//nuzsnJO/RsRtqBsjRoywaYw+Pj44OjrazFWUmJ5IrxW9WBayjBdbvMjcnqrOrmLIgAEDSE5OpnPnzjRr1ozatWtb5FxasmQJzZo1U2YSIyMjlVSrypUrM2vWLEaO1PLRR/D331IHsLlzYexYqFkT3ngDunSRZgpDQ6VlKpYjz9CuXr2azMxMg5Q5maZNmwIY3BzL4pJ+Gm7t2rUBqZuWgwM0aQL6DXpu3rwJkMsVq3/9cHNzByTB59IlF4YOrQk8T0SEJ+COhwc0bAjXrkFSErzwgvS90MvOs4q8nEu1asHgwfB//5d/sVZBEHgt4DV61O7BvMPzHin3knFXtLLgXAJpgFgQd1F+4lJZTYu7deuWIhrfv38fnU6npsWpPBLoi8iym1Z/crVxY6nxjqenJ9WrVyc6OpoFCxYQHh7OUyZmpOTrYnZ2FxITpbQ3kK5V5s5/rq5w5AicOQO//y7VG+zVC6pWtad+/cvUr7+H1avB1qfPDRs2MHz4cHbv3g2UvfNSWSU/51J3oFvOdk1znncHegB39Z43Ahxytu0O1CqKYFVUioq8CnrrO5dkCiMuHTbTYufnn6FrV3j9dSiF5RsswpS4BOCgdWBqx6nse2EfjnaO9F3Vl59CfiIrOwsnJye+//57du/fzdzDc9FppBvebLLR+eu4mXHTpjF6e3vbLCUuLSuNlza/xNX7V9nx3A5+HvgzTnZF14VOpeyi/7uoV6+eWXEpPT2dBQsWkJ6ezu3bt8nIyGDQoEG4ubkRGxtrcsDzxhvw7LPw5ZeS9XzDBqm+0q+/Sm7IDh3UQt4FQT5PfPfddyxduhRfX1+DLllxcXGKuHTu3Dll+f379wFD55KnpydeXl7cvn0bgGeegX37HrqX5CKqtWrVMohBFOHQIelG3NvbDsgERAYN8sPZWaBatT5otc2BDixdGsaGDVLR7n//lYQfvcw8q8lLXALJcRsbCybqnZtkVtdZ3Eu+xxeHv3hkZpCNf8dlRVySOw1ai764ZEqcKqtpcbVq1VLOrbLIZElanCiKJjsBq6iUFuRmA/BwwkNfXJLHMw4ODlSuXJldu3bx7rvvMmTIEJOTu7Jz6cGDzri4SCnSIE3M5/VbsLeHxx+HoCA4f17qKAfBQB1gGCNHQrt2sG2b7dLmtmzZAsDdu3cB6ZotYy61V6Xw5CkuiaL4kSiKHwG3gfs5j1cDFYE5euuDgUhRFD/OWXagyCNXUbEh+oKS/s1YvXr1TKZPVbViKlj/eK6urpw4ccLkdh4eMHOmdFLdvt3iw5cq9GsumaKmV022jNiCi70LY/4Yw/Dg4aRkpvD6669zKOEQWTrDWcC0rDTGbx9v0xhtJS4du3OMWl/WYv3F9cztOZdutboVPjiVckHdunW5fv26yQH2rl27mDJlCtu2bVNEin/++UeZcTMlLjk5wZo1kiidnAz370sFnJ9/Xqp5oFIw9M8Tv//+O0lJSbi5uTF48GDef/99vLy8qFy5Mr6+vgbdAO/du4dGo1HaPctUqFCBBw8eAJIg6Okp3UzPm/dQXDJ0Ls3DwwM6d4azZyUnEiwGDjJrVjwnTkD9+vZKMWljYaqw5CcuyU3pTJhxTdK5ZmcGNRrErP2zGPvHWFuEWCJ88MEHVK9eHXjo1JFreJQWJ0taWhrnz58369B1dnYutLj01ltvGazbsmWL8l0syw4BeQBq/Ps1Jq+0URWVkiQ7O5v58+cTHx9vIC6Zci7J3UVFUVQ6nILknDZ1/pCui7W5fbsdvXqBfMtvLi3OFC4uUkc5eAVoAlTk11+zuHNHyuKoUyei0AKTTqdje85gKioqCkC5/oLpLrAqtsHS287vgGmCIFwGTgAnRFHUL5zSFzhm6+BUVEoCfaHJuAuQjKOjI6dPn2bOnDnKsvXr15vcVt+55OHhkeeMXqdOUurK999bG3XpQL/mkjnaVG/DhTcusLD3QtZfXE+9r+sxbss4Ptz7IUmZSQbbioicvXeWXTd22SzGxo0bm23XbinRydEMDx6OVqNlz+g9TOkwxUbRqZQH6tSpQ3JyspLupo88w3bq1ClFXNInr1QNNzfppk2tI28b9G/ADxw4oIhLGzZsYPbshx0w69evb+BgCQsLo1q1arlcsJ6ensTHxwNQsaLUwa97d5g+HX78sT3Ozm3JzKzIzJkAx4FpdO8udRK9cUNKm4Y3gS6MG5eOv//DgUGFChVyFWYtLPmJSzmZfhhlhuXJumHrmBA4gZ9O/cTGixsLG2Kxk5WVxezZs7l79y6TJk1i8uTJODo6KhNOpcW59N577wGw1Uwhx4KKS/r7yK4AmQEDBiiPy7K4JItFxr9fY1RxSaW0snv3bqZOncqbb76Zr3NJntDIzs5WapJ++umnBg0p9ElLc0Xq56Vl1qyHy60Rl0zRq1cMf/11FZjAnTtVWbu2wIcC4Pjx48o9luxG1BeX5HstFdtjkbgkiuICYBhSbaU5QB+jTdyAr20bmopKyaDvNMrL4dKsWTODmeLBg00XcZbFJUEQsLOzy/Pkq9VKM9r79kkz1WUNc2lxxgiCwDvt32H36N108OvAkv+WEJUSZXLb5Mxk3tr2ls3aWM+ePZv9+/cXeP9dN3bRa0UvIpIi2Bi0ke61u9skLpXyg5wupW/RlpFn01asWMH27dtznYPyqwOiYjtMnf/dTOQX1q5d26CW3u3bt00K2PriEkD9+lIK4+TJcOFCIKmp/1K1qsDs2dCyZUvGjctk/Xp49VVJNNRHHtjK4pJc08mW5CcuyZc/S51LIBX4/rTHpwRUCyAoOIjQOCt2LgUsWrQo1+MqVaoo9w2lQVwSRZE1a9YA5mskFda5FBQURHR0tDJY03/fbm5uZS4tTh/5vRiXNDAmv9+HikpJIQueMTEx+TqX5LQ4Z2dnJkyYwLPPPmuQ/m3M5s2+QHXGjdusdD6FwotL0dHR/O9/k4BvgOusXFngQwFw7JjkeXFzc2PhwoUMGzaMpKSHE9iquFR0WGyYF0VxvSiKE0RRnCuKYqLRunfUVDiVR4X8Zqv00Vf2zdnP5RsUOzs7tFptvrNcY8ZIA4knn5REprKEpeKSTI/aPVg3bB3ezt5kZZv/XMITwll/wbQzzFrs7e3zTN3Li59CfqLXil5Ep0Sz9pm1tK3e1iYxqZQv5K5fcvtyfSIiIoCHhf/79DGcy1HFpeLDGnEpPDxcqeFw8+ZN/Pz8cm1nLC6BlFIwfz40afIUzZp9wfvvw3//wcmTWhYvtsdEA1Lg4XVKFpWKQlzKz5nh5CQVC7dGXAJwdXBlU9AmNIKGfqv6cT7qfGFDLTZM1Urz8fEpVS6WY8eOKQMncy7dwhb0ljtgrl+/ngULFijOgHbt2hEUFMT169eZPn26sl96ejojR460qJFBSaLT6ZS6MebqVcmUpr+5ioo+svCZkZFhMIklj1kcHByUZVWrVmXixIls2bKFSpUqsWbNGoNmFMbs2VMBjeYo/foZOpusFZeM6zNt2rSJLVu2IAig0Wxk924RE/NvFiNfj2VnVnBwsFLzEFRxqShRqzGoqBiR3w2FPuZso6aOJ4tL+Z18vb2lFp0ODlKNjVIwEWoRISEh3L17l7p161q1376b+0jLyrsDTXJmMhN3TiRTV/wzhJm6TL499i3dfunGmD/G0KVmF66Ov8qAhgPy31lFxQRy+pKx0AAPxSWZBg0asHz5cuW5qQYDKkWDvrjUpUsXwPT1oXbt2oiiyO3bt4mLiyM0NJRmzZrl2s6UuCQTGXmEjh1Dc1xL5mNat24dTZs2VZpQyM4l+X9bYokzo3Zt68UlgOoe1dn53E4epD1g1IZRZabAtyk3i7Ozc6lyLv3444+4uLhw/Phxjh8/bnKbwhb07tChAwBjx45lypQpihg+fvx4pX35vHnziIuL4+DBg8yYMYPVq1fzxhtvFOQtFRtxcXHK31AVl1TKKvK5OysrK9+0OEEQWLRoES30bUhmuH0bzpyxY86ctvSUK3nnYMn4RhRFPv/8czZt2kR6errBuqNHjwLSOSU7ey1ZWQJz5+bfjdQc8nVLro8HkoAlo4pLRYcqLqmoGGGNcykvdV9G37lkaeva9u3hs8+kE/mOHRaHU6LMnDkTLy8vXn/9dav2m7F3BsmZ+ddniE+LZ1nIsoKGZzWJ6YmsPb+WwKWBjN8+nvup95nTYw67R+/Gxd5yd5aKijGyuCQ7l+7fv6/YtSMiIgxEDTc3N55//nnluf6Mo0rRon8DHhgYCEhpBsbI6dH9+/dX0gnk7fUxJy4lJiYSGxtrVMzbNM888wxnz55VBr4NGzbE2dnZooGBtVgiLtWpY13NJX261urK7B6zOX3vNAdulQ3ze3h4OLVr16Zfv368//77QOkTl3bv3k3//v0JCAgwK0YXNi3O39/foPiv7Ajw8PAw6L57/fp1unTpwvz584HSL8SkpKSo4pJKmUf/3J1fWpw1/PGH9P+gQbnlA0vEpbt37/Luu+8yePDgXOKSXOO2cePGwHGGD0/m889h8eIChar8LvXFpR16AypVXCo6VHFJRcUIrVbL7du3DeyT5jBlMTXGOC3O0pvPgQOl4t5vvgknT1q0S4lx7NgxtmzZwtSpU/Hy8rJ4v5CIEE5FnrJo2+TMZN7b/R4pmUVfy+F67HWaL2lOUHAQEUkRBA8L5uy4s/yv8/+w01guPqqomEJOi4uPjycmJgYfHx/atGkDSOJSt27dlG1HjRplNuVWpWjRvwF/7733eOONN0yK53JK2tWrVwkODkYQBOXvqY+npycJCQm5XDphYWGA+RSmvKhYsSK3bt1i+PDhVu+bH/LgOT/nUng4FLTszKjHR+Ht7M2io4vy37gUcOfOHRo1asS2bdsUQc/Z2Vn5rEqDuJSQkECVKlXy3Kag4lJMTAyCIODp6UmrVq2U5XK3Q3d3d4PUeOM0uLxalZcG0tLSrBaX1JpLKqUN+Xd29OhRbuip//KEeH71xEyRni41l2jUCBo2zL3ekvHNhQsXlMeyUP2N1KmCsLAwXF1dFdF6+vQbdO0qdVMtyGlV/l3KNaX08fPzU8WlIkQVl1RUjNBqtfj5+ZmsmWGMg4MDFy9e5N9//83zeGCdc0k6NgQHQ1wctG5NTgeh0onc7nP8+PFW7ffhvg/zTYnTJ12XzpdHv7TqNawhIT2BhUcW0vnnziSkJ7B91HbC3gljaJOhRfaaKuUP/bQ4WZi4dOkSoigSGRlJs2bNiIiIICsrS0kzPXHiBAcOlA13x6OCvqjn5eXFd999Z7I9efXq1ZWZYpBmXmUBUR9PT0+ys7MNiorCwzbJBa2nValSpQINFvJDbkKRn7iUnS25bAuCs70zrwe8zh+X/+CfsH8KGGnxcefOHWUmXP5cnJyclOt8SbtYRFEkIeH/2Tvv8Ciq9g3fk00nlST0jvRepXekCH6iVCtFFESKoKKiwk8+PxVpClJFpShFFBGlgyi9hxIQCC2BQCBAEtKzyf7+OJnJ7mY7CRA893XlymZ2ZvZsspnynOd93kStNM0aropL0dHRlChRAk9PT9q3z21mcenSJcCyc8nY4fSwi0upqakOi0vGpUcSycOE8WdylVHbNUeiPKwxaRKcPAlffGH5eUfEpdOnT2uPVUdVkSJFtPNq8eLFtVzD1NRkRo2C6GjIucVwCr1ej7u7u0VxybzDqyR/keKSRGKGM2VxANWrV7dZkmDLuWQvZ6JxY3HR/tJLMHky/PSTU0O7byQnJ+Pt7W33gtaY87fPs+XCFqe6wKVkpvC/nf/jTuo9pPxZ4XbqbTot6cS4zeMo5V+Kvwb+RdfHukqnkiTfCQgIQKfT8csvv5gIDfHx8WRkZFCiRAlKlChhcnPTqFEjWrdu/SCGK7GDTqczuYC1VBIH1rO21HbJjpRZ3288PDzsikvgWu6SyqjHR1EhqAIdFndgx6Udru+ogMnMzOTGjRuauKQKvx06dHhoyuLS09PR6/UOiUuuBHpHRUVpE299+/bVSjnnz58PCOeSubhkzIP+/djDGXFJlsVJHlasfSZdPcdkZsKXX8Lzz0OPHpbXcda5dCSnJMPLy0sTvUqUKKE5htPT0+nZUzSNmDJFOKecG3MmHh4ePPbYY3mea9OmDRcuXNBcw5L8xSVxSVEUnaIovuZf+T04ieRB4EygtyOYZy6pB99nnnlGy+qwRWAgLFgALVpA//4weDBYaDL1QElNTXW6A9snOz+x2SHOGlnZWUz+e7LT21kjOiGaCdsm0GRhE47FHuOXvr9w6NVD1CpWK99eQyIxRp1N27lzpzZjV69ePW0mz5I7RvJgWLhwIYMHD7a7XkhIiPa4UaNGFtexJy7dy6xyQWHPuaTmiLuauwRQrEgxDgw9QKhvKFP3THV9RwXMtWvXMBgMmrjUtGlTIiMjee211x6asri7d0UzZ0vOOWNcDfSOjo7WyjdLlSql3SCqBAQEmHxezp8/T1JSklbq+7AHt7tSFifFJcnDhrXjkIe19qN2CA+H1FQR12ENZ8UlNUvS29tbO/cVL15cixnJyMjAwwPeew927hQi019/OT5m1blkKcvwmWeeAWDmzIKrhPg347C4pChKgKIosxVFiQHSgbsWviSSQo+zziV7mHeLUy9E1qxZQ1RUlFYSYQsvL/jjDxg1CpYsgSFDXO+gUBC4Ii4dvnbYJXEpLSuNjZHOp5yfiTvDmI1jGLR2EEN/G0q779tRbXY1Kn9VmSl7plDCrwSbXthErxq9nN63ROIso0ePpnr16nz33Xf06dOH9PR0rWWwFJceHl555RUWLbLfSMD4b2atc5sqLsXHx5ssV0PCjQWqhwUPDw8tG8MSpUuDhwecO3dvrxPqG8oLdV9gY+RGbiTbPyc+CK5cuQJgUuZVuXJlFEV5oGVxKSkpHDhwAMgVlxwti3NG7FE7IhpHBhQtWpTffvtNK+kMCAjQbmDd3Nw0cal58+Y88cQTD1x8s0dqaqpWumev1NS43btE8jBhfhzq1KkTe/fudXl/u3eL7zlNIi1iLC7duXPHYvOK06dP07lzZ5NlxuKSuXMJ4I03RGOjokVh4EAwqyq3irFzaciQIVoDBoA6depQtmxZk7BzSf7hzF30fKAH8A1wCpBHU8kjyf1yLqlcvnzZofbiQUEwYwYUKwbvvw9//w1t2+brUF3GFXHp2LBj2uMNG6B7dzE70aqV5fUv3LnArP2z+OHED1y9e5VZ+2fxZNUnqRhUMU/gscFg4FjsMc7eOsvhmMP88s8vRN6OxEvnRbEixcjMzqRsQFnql6hPz6o9Gdl0JOWD7Hdqkkjyi3HjxjFu3DhACM3JycnSuVSIMRaGrAVz23IuBQUFuTyrXJB4eHjYdLjodOI8tHw5fPKJyAp0lRfrvsjnuz9n+YnljG422vUd5SPTp08nPT2d9957j6tXrwKm3YdUHmRZ3MKFCxkzZgxTpkzRbtwcEZdA3MAZd6e0xZ07d0hJScmTR9mzZ09OnjzJoUOH8Pb2ZtCgQZQtW5Z9+/YxadIkbTw6ne6hzFwy/ps5Uxbn5eUDTGb27FDefBNq1YICiD6TSJzGXFyaOHEizZo1c3l/u3aJBkMWDn0axuJS7969MRgMbN++XXv++vXrxMXF0b17d/z8/FizZg0gyuLURkDFixfPIy4pCnTpAt9/D23awFtvwddfi3OPLVTnkk6n45tvvmHTpk0mz7u5uT2Ux6NHAWfEpS7AmwaD4ZuCGoxE8jBQUOKSTqczcS6pmIe7WmLs2LH88MMPxMbGMmYMTJ8OEyfC+vXg+xAUpKakpJh0iHEWdda7ShXr61QKrsSMrjN4s/mb9Fvdj1EbRzFq4yjKBpTlsaKPEegdSMWgivh5+rEzaqdJdkenSp14vfHrDKgzgBJ+roXmSiQFRZEiRUhOTtacS8HBwQ94RBJncURcUi+gx44dS/fu3bXlcXFxD2VJHAhxyV42z7hx0K0brFgh8gFdpVaxWjQs2ZDFxxYz6vFRD0WXRFUAticuPciyOLWsctKkSVpJpqPiUmpqqsPikq2uhiEhIXTp0gUQ1zpdunTRxgXg5+eHm5vbQ+lcMr4mc6YsbseOisATfPcdfPcdNGkiWrW7mMsvkeQbxp/pey1F1eth2zbIqSSzivH/95kzZ7h69SpnzpyhWrVq6PV6SpYsCYicWrU8zsPDg+rVq2ultLVq1dLEJXNHYKtWMGaMmGTfvx+WLoXata2PR3UuqZjfoyiK8tCX6RZWnBGXkoErBTUQieRhIb8vaM3L4tLNUukcEZdmzMht0+zjA//9LwwbBi1bwr59omzuQeKKc8mYc+fA31+4suxRLrAcewbv4Z+4f9h+cTs7o3Zy9e5Vzt06x8bIjaTp00Rr6y4z6FixI5WCK1HEs4j9HUskDwhVXJLOpcLLRx99RGRkJO+//77VG/uqVasC5HEC3bx586EM8wZx8b9xo+0y5C5dhGtj4cJ7E5cAhjYcyvA/hvPprk95v/X797azfCYmJgYvLy+L/58PsiwuOTkZEJM8v/76K2C/xFI9X6ekpBAcHMyFC3DzJpw4IdwJnTqJckdjlixZAmDiXMrOhn/+EY6dSpVMnWuqmAriGPewOpeM/2bG/5u2xKVDh2D27ErAXv773yxu327F9OkisuD334XbQiJ5UKif6WXLllld5/Tp0w5NCu/fD/HxYgLBFqpzKTs7m9jYWAAWLVrElClTTNy6FStW1P7PBg8eTOnSpXn//fepUqUKzzzzDJcvXwbIc68EMG0aNG0qIkIGDoQDB6y7BVXnkopxowEQYpgUlwoGZ8SlacDriqJsNhicaO8kkRQShgwZ4lC2hrOoYpW3tzeKorB7924uGKWfOiIuqRgMBhRF4bXXhMj08suweTP07Jnvw3aKexWXDh2CunUdvyBTFIUaYTWoEVaDEU1H5Hle/T1JJIWBIkWKkJ6eTkxMDIqiPJTZOxLbFC9ePI/t3hydTseIESP48ccfTZbfvHnToeYOD4LY2Fi7ziVFgQ4dhHvDYLi3G+tXG73K7ujdTNg+gbrF69KjqpXWRA+AW7duERISYvHc8iDL4hISEggKCiI5OZlvvhHFBZbcVcaoN5Xx8fGcOlWaJ54wfb5aNdiyBVQdKTMzk+nTpwPCuRQRIc7b338PO3aIddzcch1sfn5oDgQQJaGFwbmUmpqqBQpbE5cMBujXDwIDs0lO7k2lSlOZMAHKlIGxY+Hnn6F37/sydInEIupnukmTJlbXqV69ut39GAxC0PHwEIKzLVRxKS4uDr1ej5ubGytWrMgjLpUrV4527dqxbNky3n33XQBq1KjBRx99BJNuHPQAACAASURBVJCnLM4YRRGNjTIyxP3Pb7/B009bHo8jzqWHUex+FHCmOrg0UA84oyjKAkVRpph9fV5AY5RI7gsLFiwokGDGqKgoQHSWUR+3bNlSe94Zccn4In/AABFwt2JFPg30HrgXcSktDQ4fFi6s/EIKS5LChJ+fHyBmEkuUKPFQZu9I8ocSJUpw584dkwvnuLi4h9a5pJ5z2rRpY7PZRa1aImg15xTnMm6KG4ueWkTtYrV5/Y/Xych6eOI979y5Y7Vk1VpZXFpaGuHh4QU6roSEBEqXLk3btm1JTU1Fp9PZLbNUS1Ree+0CXbuKZYsWwfnzMHu26P5XuTJ88YV4zvjaaOXKYtSpI5wD4eHi5nPxYtGi/I8/RFenDRvyikuOdJN6EDhbFnflivj9vPZaEhCj/Y+MHAn168Po0Q9fR1/Jvwv1M3yvDYpmz4Y1a+B//xO5r7ZQnYnXrl0DoH79+kRHR3P37l0TccnHx4fBgweTkpJicVLFlrik8vzzQvh+6y04ftzyOubOJXNxSTqXCg5nxKXeQDbC7dQZ6GPhSyIptLi5uRXITV2/fv14/vnn+e9//6stu379uvZYtbQ7wujRo7ULIQ8PePZZodzbmVguUFJSUjh48KDdzirWOHIEMjOhefN8HphEUkhQb1iPHTtmNa9H8migdtVSywYMBgM3b958aDOXVIoWLWpzlrdWLfE9IuLeX8tT58knHT4hOjGarRe23vsOXWTx4sUmP9sSl1QhIjExkc2bN2strvv27UuDBg2IiIhg586dBTLOhIQEAgMD6dChAyBuouydjzt16oSfX2X27evKk0/C7dsweLAobRsxAk6fFo7od96B556DnTsNQCAwgjFj3HjqKVEOd/26cOu89BKsXSuCf0uVEmX7Ol1ullNAQMBDG6Cr5r2A+PvZE5fUplstWohJLFVccneH+fMhJgYWLCjAAUskdlDvE+5VXPrmG9EhLid6zibGziWAFjmt5dq2bauV/E+bNg0QE8DWJqStZS4lJSVpk/E6nWgikZwMjz8uxmmONeeSOpEjnUsFh8N3gwaDoaKdL8u9dyWSfznFihVj2bJlBAcHW5y1c8a5tGjRIpP8iz59xGzxn3/my1BdYurUqQCsX7/epe1/+03Y6W21OJVIHmXUi53IyEjKl5ddCx9lVHFJnWC4e/cumZmZD61zScXf3/++iUsAXR/rSpB3EEuPL82fHbrAAiOFYNu2bezYscOuuPTmm2/SpUsX3nzzTbKzs1m37nfgGWrX/os2bY5x9Kj12XhXiY+PJzAwUPtsOTpJ5uX1FllZOqZNA/O3VbkyrFolGof88gt06+YHxAOzadAAliwRpXPmeY8tW4rA3ago2LKluLa8sDiXoqOj7YpL+/aBtzc0aSLevPEEYdOm4uvnnwtwwBKJHfJDXLp1S7iCund3rNRZ/f9Wg/yb58wYHz16lEOHDgFCaLKHNedSsWLFTPIMW7aEY8egdWsYOlS4LI0PL+bOpZCQED777DMOHDgASOdSQSKbZkok9xHjC6uePXvi4eHhlLgEsGfPHu1xy5ZCwd+9O9+G6BSxsbFMnDgRgF69ejm9fUICzJ0rRDJHwrwlkkcRY2HhySeffIAjkRQ0qgAQHh7O22+/rXUge9idS2rpprWL8eBg4Vg5eTJ/Xs9T58ng+oNZcXIFU3ZPyZ+dOkFcXJzJubZTTuCIPXEplwq8+eZd4AjwMzAAGESLFp4sXSqyTPKD7OxsLl68SOnSpU0CtO2RlAR37vQHfuXSpS0W19HpYNIkuHEDpk5NAFYzbtwvHD4MAQHW992tGzRoAIsWlQTEid0h51LDhiJMMixMqFuNGsETT4gamLFj4dNPYd48WLlShE0ePAiRkeIu+B5EK2Nx6fLly9oYrbm/9u6Fxo0hIEA4s8wzydq3F6X+aWkuD0kiuSfUz/S9dL9WjZYO6EHaaxk7l5o1a6Y9d+vWLUCIzPZQBSFzcUkNAT9//ry2rFgxUYrbo4dwWb74Yu765s4lRVEYP348FStW1H6+n86lb775hgkTJty313uQ2JQ0FUXpDuwyGAyJOY9tYjAYXLMuSCT/ElRxqUmTJixbtoyKFSs6LS4ZW+t9faFdO3G9NWKE6PKSlJRkcvAtSK5cyW0gOX/+fKe3X7BAZBOMH5+fo5JIChfG4tIz9vr9Sgo1qrg0ZswYUlNTNednYXAuge1mCbVq5Z9zCeDzzp8TkxTD+K3j+bzO57SjXf7t3A7WOuTZy1wSNAb+ZvZsL0APvAj8CIRSt240L73kyaZNIqfoHu79ADh79iy3b9+mefPmmrhkryQuOlqU1GdnBwFTmDOnJJ07d7a6fkAA9Ot3l7fe6kP16gvtuhgUBWbOhK5d3YF9QCPHnEs1agirRFyc+DJHpxPt6Nzdc1tE6fUi3bdGDWFjcAFzccmScyk2KZa+q/syr/OPHDlSmtGjxc2pr69vHnGpUSNR6n/6tBDZJJL7TX44l3bsUB16jq0fGhpKQkIC58+fR1EUExe2OokSYEuVzkFRFLy8vMjIyODo0aNMnjyZzz77THu+du3aJl0dPTxEBcQHH4hsqAEDREmvXq9Hp3MnKkocTs6dExEijRqJQ0lmZnn0+nsrG3SGoUOHArnlgo8y9pxLvwPVjR6vy/lu6WtdAY1RInlkUC9avvjiCwICAvDz83Mqcwlg37593L17V/t5zhwxQ/b66+LnoUOHUr9+fadFK1dQ7a/g2EnDnJ07xQ2JvACT/JtRXSuBgYGaQ0TyaFIsx6JpPqP8sDuX1DbO9krjTp0S7enzA3c3d777z3fUDKvJrMhZ+V7CULlyZV5XT5xm/PHHHxaXlypVyuJy8ffUAU8Ci4HbfPXVOqAJsAwRWXqDH3+MYdIk+OEHEf7cuTNMmCCcRK5w5swZAOrUqaOJS/YaWrz/vvg7wbPAfofcBM7erLZpA4sXxwEVgX74+PjYdy69+y7ZHp7Wn8/KgtRUuHtX2J4TEkToik4HHTs6NC5LqO+tQoUKREVFaS4L4//RqXunsitqF50/e4/sbOG2BiyKS1WqiO/3aY5PIsnDvQZ6JyTA77+LuArz0ldrtGnThuzsbH799VeKFi2KTqejTJkyQG7GoHoesYeXlxfp6em88MILrFmzhi1bct2VaRYsgYoixKU6deCpp0Szo507V3LgwGbKlxeCUv/+IleuXj2oXRsiIzcQHj7IsTeXj5w7d+6+v+b9xp64VBEIN3pcKee7pS+ZuSSR2EE94Ks1xX5+fk6LQHq9nv3792s/V60qHOPr1kFsrOg4BXDp0qX8GbQNYmJitMdejp6BjDh3ToxfIvk3ExQUxIIFCzhure2J5JHB09OTkJAQ7bjfunVrILd718OKepNiSxyoXVvc+1+8mH+v6+3uzTst3uFK6hX2RO+xv4GDHD9+nAsXLjB37tw8z+n1ejZt2sQTTzyR57nixYvnWQaQmqoDtiLmWsOAIUyY8LL2fI0aNXL2nc7EiSKAtkQJiI8X1V6jRzs3/tTUVLKzs7UOtOXLl9dEIlviUkaGCN4eMADgFwCHyunU0GtnblbbtlWACGAAiqLYdS5FuNXhcHodh/evodPBhx86v10O6nurXLkyer1ec0loIe3picw9OJdsQzZXA1czYfoZzc3h7u5u4nwS+xHfpbgkeVDci3MpPl5Uo16+LLqxOUqzZs3w9PTk0qVLmhNXFelVccnRrtJeXl7s37+fU0IFZ86cOSbPG4fwq/j4iIiQzz8XTQi8vK5QtGg4X38t8uPCw0U+7ZdfwtKloCipXL/eKN9KlG2RnZ2Nm5sbr7/+OvXr1y/4F3zA2BSXDAbDZYPBkGH02ObX/RmyRFJ4UQ/4zohLxu4gFXVmbd68ebz++uv07SsyHNauhUqVhM57P8QlteWoK2RliXa+6iyfRPJvZujQobJT3L8EtTQOYMOGDURFRVG2bNkHOCL7qIKFI6He+a2RPlvzWbzdvBmzaQzXk67b38AOZ86c0TqrAZw0C4qKjIzkzp07PP/880SY1flZuzkSJXBtgKFAKWCT1n570aJFTJ48GciddR8yBLZsEbFBb70F334rluXcg1nFYDAwefJkfH19WbhwIVFRUXh5eREWFqaJS/Xq1bO6/b59wvhjHO1WEM4lAG9vL4Rrqw2HDmHXufT++zDN90Oyi/hbXScPvr5iQyvlio6gvrfKqiqUgyouzT04FwM5d6C6DA6FjDNZJysryyQvyr9+ZcLdG9FjVsHnRUkklrgXcWnGDDh0CFavFvlpjuLj46PlLKniklpOfePGDby9vR3uKu3r68vevXu1Dm+qyKRiflxW8fcX2UuzZ0NWVgsaNfovr78unIb16okYkVGj4IUXoHTpKaSlhZAzH1+gxMbGkp2dTS31JPmIIwO9JZL7iDpr5+kprN9FihSxKy5Zal+slsUNHz6cuXPnUqeOmC37+efc4FXj0rmC4l7EpagoMYsqxSWJRPJvwrjjjbe390MvLEFujo+t0rT69SE0FL77Ln9f28/Tjwk1JnDq5im6/dCNbMO91d2tXbuWW7duMXDgQECUlBk7atSJmUqVKlGzZk0Td5Ma7G3MypXwxRc6YCPwDSJnKZfg4GC8vUX4s3lILcDHH4tW30uXgr2+GLt27eKjjz4ChPsqKiqKcuXKoSgKISEhbNy4kVWrVlndftMmYfTp0CH3xtORUhX1ZtXRTnSgTqJ9DcTRrx/8+eerJCS8bbJOUhKcOQPvvityU2q93R23ACdKg728YMwYx9e3gDqBV716dZPlOp2OdH06n+3+jJTMnNI3tyy2X9zOoZhD2jpZWVki8ykzU4S7XLhAPf0Ral3dAj/+KO7WP/xQiExDh0LfvqIesn59KFlSCFMSST5yL+LS5s2i4+F//uP867Zr1w6wLC456lqC3MmMF154weLzBw8etLn9nj17SElJsZqdBxAYKCpAvvzS4WG5THR0NMC/ZgJRiksSyX3EUlmcvcylv//+O89BOTEx0eRnRRHK/LZtcO5cOwCTwLuCQi2LmzZtmtPbqmXHsixOIpH8m9i3b5/2+F66+dwP+vTpQ+nSpTVxyZbzxNtbNJZYt458nw1uFdqKhT0XEn49nJ9P3Vuf98TERNzc3GjVqpW2TG1PDSLUGdACaYcNG6a1qC9atKjJvq5dE7PgjRsrzJqVyNatW02eb968OU2aNLHaXhvE723qVPjkE9GJLDLS+th37doFiFK2+Ph4TVxS6dKli80yt82boXlzEdJ99OhRgDxlXZZw5WZVTKLdBfrh7w/nzzchJeVNxowRRp4aNYTToHp1UcoyeLCBfgOuwHvvgSPZLEWKiATfHHeDq6xcuRI/Pz+ee+45k+WKorD0+FL0Waa/nzR9GiM3jCRNn0ZmiUwuel9kz/NtyPa0IbwVUF6URGIJ9f/VUaeQSkKCMNVZ0NAdQhWX1AxB44kUXyf+T9V7HHV/Kt9++y1BQUEcOnTI5vbx8fF2X8PLK5ZKlX5mwQIxMV+QqOXLUlySSCT5jjNlcXq9noMHD/LXX3/RvHlztm/fzt69ewHLrqR33xUnhH37BgNvWQy9y2+uXbtGp06dGDt2rNPbnj0rvkvnkkQi+TfRvbvd5rsPDatWreLKlSsOiUsgxCUvL1Hmld/0q9WPGqE1mPTXJLKyXSslWrRoEZ988gkBAQEmHfrWrcvtSXPq1Cm8vb1NwrvLlClj8UZt4ULRsOy77+CNN/rToUMHPvjgA+35PXv2UKZMGc25ZOu83K+f+L5ypfXxR0ZGUrJkSapVq0ZcXFwecckWp07B4cMiTwWgZs2agOX8EnNcyVzKzX7aTng4DB8+Gg+PNcyeLSrZ9HqYNEl0ltt/IJtb5XpRbWp5Qm+8TXKG/UYnST7uxD//rMPjsURqaiqrV6/m2WefzZOnlZWdxaQdk0jKNL1GM2Dg4NWD+H/qz7Xu1/i75N+0PDiMA0VduOa6x7woicQSWVlZ6HQ6u+H+5vz1l9BBXdU7mzVrRkBAABUqVADEvY4a6u2MuKQeb0JCQrRKj4iICF588UUaN27MggULmDFjhtXtVXFq2bJlVtdRFIWqVRdTpYo4jhckqrhUGFzK+YEUlySS+4h5WZw1cWno0KF4eHjQtGlTwsPDadOmDe3bt6dZs2b4+vrmcS4ZDAa6dGlG6dKvEhZ2BBhLcnLeGdL8JiYmxuUg2nPnwM9PhJpKJBLJv4Xly5c/6CE4jaPiUliYyF4yizHKF3RuOia1m8Spm6cYuWEkqZnOuXOTkpJ4+21RlmUuLv3222+AOJeuW7eODh062HWVpaXB11+L/CJ1kkRRFCZPnkzNmjXpp6pFYNO5pFKuHLRsCcuWiQorSyQnJ+Pv7098fDybN28mJibGIXEpKQmefVb8fV55RSxzc3PDzc3NIXHJlbI4czw9FXx9BxEXB0ePioDdiROheMcV/Ofv0qxlLbjDE5WfIbx7Q/Tu1m9Rkj0VBrVPIGRGCWp8XYNXfnuFb49+y+bzmzl18xSJ6YlWtzVm3bp1JCYm8uKLL+Z5bu2ZtSSkJ1jcLsuQhb+nPyX/LkmnC53Y/tJ2oke+RJKXEzfz+ZAXJZFYQq/Xu1QSt3WriA5r3ty11/Xx8eHkyZMmE85q0wpnyuLU403RokW5fv06N2/epGbNmri7u9MkJ01/7NixFs9HaWlpDBAdC3j88cetvoabmxuKoueJJ2DXLuvH3Pzg5s2beHh4ONQ84VHAtR6FEonEJczL4ixlLhkMBhYtWmSyrKPRNEJAQEAe51J6ejr79+9n//791K1bgps3P+b06We5dQtCQgrinYhxXrt2zWprZnucPStK4pycWJFIJJJCjXGpQGHBkUBvlWrVRHlXQdC7Zm/GPD6GmftnkpyZzOKnFzu87cKFC7lz5472c4jRyTEiIoLbt29z7do1Ll68yPjx4+3u7+uv4cYNkZdkjnngrCPiEoj4oD59RDTPd9/lPT8mJSVRpEgRbYIKcsv3bPHzz/DPP7Bxo4j5UfHw8HBKXHLlhrVatWqAuJnLysoiKEjEDZ2/fZ65u+cybe80mpVphtcOLy6vv8wrvV+h5cxKsKkG6C27gXwrVmX89CXUPPcH4bHhrD61mkVHTa+b/Dz9KBtQlrrF69KoZCPc7rhRJ6UOIb65f/cffviB0qVLa+U34eHhWjenD7Z/QFKG9UzMjKwMirgXITA5kPYV28PbbTHM3AzXHQydz4e8KInEEq6KS9u2QevW4qPpKubunNatW7N8+XKnMlqNxaVgM/G1cePG2uM7d+6YHMcBjhw5oj1WM2gtoSgK2dnZtG0rjuVHjoANLeqeuHXrFiEhIU47yQorTn3yFEWpC0wAGgNlgOYGg+GIoiifALsMBsOGAhijRPLIoF6YqxeaAQEBJCUlaRZWEKq7wWBgxIgRfP311wC0aNFC24e/v38e55KaEQFw/vwsoD0XL75EiRLw66+mnWHyi1u3bpGZmemyc+nsWbR2vhKJRPJvoTBeYDoS6K1SvTqsWAEpKfcch5N3HIobM7rOwFPnyRd7vuCt5m9Rp7j99vUZGRlMnz4dLy8v0tPTSUxMzNMlLTIyUutKZJ71Yc6UKTB+vCgxs7MqkHvOV4NdrdG7tygVmzQJevQQPxuTnJyMn58fy5cv18pNHHEu7dkDgYEiR9oYR8UlV8riAC5evKjlVOl0Ou0aaO7BuYxYPwIDBoY0GMKcJ+fQaGYjyIbbt2+LxPF27UQCudlnzuBbBGX2bBqXaUrjMk0B0GfriU6I5kriFa4kXuHq3atcSbzC5YTL7Luyj5URotbwreNvUSusFi3KtqBusbpsObGFl3q+pF1/aeHmFSAqIcrme0vOTCa9QTqZZ3J+f25uKO+/LzKj7GRp5ldelERiCVfEpa1bRems6mzML1TnkqXO19YwFpfMaWJ049C3b19Onz6t5b+CaaahLXFpycmTVEhLw+NgGJEEUOTZIKgZIuydxYuL78HBlr+CgkRJq4PcunXL4nt5VHH4k6coSjfgN2APsASYaPR0OjASkOKSROIA6qxjaGgoBoOB27dvawF4asB3lRyffZcuXUyyHiw5l04bpacmJ8cDHahe/Xn0+mVMmgTdu+e/Q+hsTmiSK86ljAy4dEl06ZVIJBLJw42jZXEgxCWDQZQ+16tXMOMZ32o88w/P54M/P2Bt/7V21z969ChXrlxh3LhxTJs2jfj4eEqWLMn8+fOpVKkSnTt35ty5c5w/fx43NzcqVqxodV9JSaLDW8+eol23I+dWNXPpzTffZIwdt8oHH8BPPwmN4j//AeNKtOTkZEJCQihdujR16tThxIkTlHCgtnz3blHqYh4bVdDOJTV7BcRnKLNoJsN/H868w/N4ssqTzO4+mwpBYh11HLdu3RIbTJgAO3eaCDXZKMSG1aGkWeKwu5s7FYMrUjHY8t8tLiWOxZsWow/Ts/3SdlafWs3CIwvhZfhW+Zb1M9YT6huKv84fegAVhHhkj2yPbC4GX8xdMGiQCMC0R2AgDBlifz2JxAX0er1TzSLu3oXBg4XrdNiw/B2Lmu3mDG+88QazZs0iICAgz3OqqA6wffv2PM8bi0u2cp4u+fjwWEoKbrfjqEwcXEV8qeh04OkJ7u65B069XtzA1KgBx445/H5U59KN5BsOb1OYcSZz6VPge4PB0Bb4xOy5cKB+vo1KInlEmTNnDiEhIdpBXxWU4uLitHXUMjl/f3+io6NZu9b0wll1LhnPIKt20759+2rL3NyOMnYsHDoERo1w8o3PPvsMEO2aneXMGcjOlmHeEolEUhhwVlwCcZwvKIr6FOXtFm/z25nfePLHJzl5w3bIU0KCyM7p1asXoaGhjB49GoBXX31VcwZHRUVx4cIFypUrZ1J2Zs7q1ULvGD9e3Hs4gpcTdSY6nXBGRUaKrmpZRtnlalkcwIoVK3jttde0sjNrxMdDRAQYGaA1nBWX7iVz6Yb7DTIHZrLo6CKGNRrGmn5rNGEpJSWF8+fPA3Dp0iWxQcuWULq0yT4y3Lz5T9Qspk7NY2iySahvKI2CGzG+1Xg2vbCJW+/cYvnjy2Et9CrViw4VO1DKvxTJ+mSoC4Ta3SUA2e7ZnC59mpTMFLHAz0/cpdv6Pfn5iRTze/hdSiS2cNa5tHEjREfDnDkicyk/cXNz44cffmDz5s0ObzNz5kxSU1MtNlFQFIUzZicX4/shY3HJVre8H8qWJdNWN7187PAYdzuOmzVvUn5meU4lnnJq28KIM+JSdUDtYWF+SE8E/j1+L4nERYYPH24iJKmBosbLVOeSn58fZcqUyXNRqjqXUlNzw0zVHIlRo0ZpZWoJCQk895xoczxzphBz8pPMzEwCAgJo2LCh09suXiwmAzp0yN8xSSQSiST/cUZcqlJFuHn++adgx/ROy3f4X4f/se/KPtp9345L8ZcsrmcwGLSJl4CAAGJjY5k5c6b2vK+vL35+fty8eZMrV67Y7eizaZPQPCyJNdYwL8GzR7duwnE8caIoH1fNysnJyZq4VLNmTebNm2f3JlLNv2rZMu9zBV0Wp5KVncUfHn9AMkS9GcXcHnPx0OWKK8ePH9cErKVLl4qFiiI6qamlLTod7p07ULZXY95+G+bNc2koObtW8M3whaPwToN3+P7p7/n9ud/ZP3Q/XAKcuF7KVrKZuS/388S4cbZLZkqXFunqEkkBkZWV5dT/6v79ImepVauCGc9zzz1HZ/OaXBu4ublpbk9LmLs11S6c165ds1t6rHLRz48LahmsMzjR4XHdmXV0X9qdiM4R/FPyH3pV70Vpn9L2NyzkOCMu3QCsWRRqAbaLkyUSSR5Uccm4FlkVl4pYOeipziXjIHBVXAoJCeHYsWO88847xMTE4O6ezJtvivyLzp3txwA4Q3JyMo0aNXI6PyQpCb75RmRJlH70j7ESiUSSh5EjR2rumcKAM4HePj5QvnzBi0seOg/ea/0e+4bsQ5+tp+uyrvz6z6951ktJSdGcS35+fhZns4sVK8aNGze4ceNGnpb05hw9Co0bO1dq7uPjw7Bhw0w61NlCUeC330So99GjMH26WK5mLjnDrl3ifshSWO39CPTOzMrkoz8/4qZyEzZB8SJ5f79XrlwBoE+fPly9elW7WaRvXzETBeDpiftX01m9Gho1gvnznR6KCepnwrj05lLCJaiIU3dH2bps/rfzf9xJzQmLr1BB5EVZ+IAYihSB2bPz1idKJPmIs86lffugYUPHnZgPGn9/f5Puc7dv3wbQ3I9NmzallR2lzM3NjaXly4MzDTYc6PAYlxLHG+vfoOqsqjy14ikORR+CKJhUfRI/PvsjgR7OTTQURpw5uq0APlYUxfivZVAUpSowHvghX0cmkfwLsFUWZ01cCggIyCMuqQdWb29vwsLCaN68OQaDgZMnT/LJJ8Lqun276IiQXyQlJTl9kQsiYDwhAUaOzL+xSCQSSWHiq6++MnHPPOw4E+gNojSuIMvijKkSUoVf+v2CPltPr5W9+OX0LybPG2cUWuvUZywuFStWzOprJSWJZhQuGHbx8fHJFU0cQKeDgQPhqafgyy+Fe8m4LM5Rdu2CBg1EhrQ596MsbsDPA/jfrv9Ri1pwyvJnSHUbNGvWDCA3oNfTE0aPFmJM795QtSqKIirPjh0TwpurqI1RjF1ln+z8xLk7oxyysrOY/Pfk3AUTJuQJ684Gbj1WCszyoiSS/MYZcSkzEw4fhpx/vUKBoigm7iU1h0k91s+cOZOdO3fa3cfe4OBcZ6QjWOnwmJmVybLjyxjw8wCqzqrK/MPzqRlWk9ndZtPjQg8CNgfwXq/3HH+dQo4zh9APgUPAX+S6lNYCJ4HjwP/yd2gSyaOP2kLTWlmcJSyJS6pzSbWRqq10w8PDURQYPlx0tZk2TXTwyQ9cucgF0Y3CwwOaNs2fcUgkEomkYHGmLA6EuPTPndjPjQAAIABJREFUP/lfjm2NDhU7cHrEaeqXqM+I9SNyXSRg0l3Vlrh09epVbt++bdG5FJsUS9vv27J1/1UMBiHWOIvaqc5ZJkyAO3dg0CA96eneBAUFObztDz/A33+LznOWKGjn0tFrR/n59M9MaD2B/rr+gCjZMefKlSv4+vpSp04d7WeNESNEf/RPP9UWDRggdKfvv3dqOCaoziVjcenwtcPgeA6yRlpWGjsu7chdYCEvKt1DYWCHxNx8JomkgHAm0Pv4cUhLs+xsfJipYiG0VRWXLAWBm+Pm5iaqX997z7Lybkamjxd7X3+KH8//yvITy/lq/1eM3zKeLsu6UOHLCry45kV2Xt5Jh4odCH8tnF/7/8rwxsNZ//t6unbtajPH71HD4bOEwWBIB3ooitIR6IiIu7sNbDMYDFsKaHwSySONt7e3lvWgYs+5lJ2dTWZmJl8b2ZDMxaXy5ctTpEgRwsPDtXUmTIC2beHHH/On1agr9nwQHYQqVsx1ukskEonk4cYVcSklBc6fv3+NGzx0Hix6ahFNFzal89LOjGgygoH1B5o4l6wFawcGBmolFZacS1P3TmVX1C6unX4PD48lLk2OeHt7k5mZSXZ2ts2gWXOaNhXd6SZN0gG/U6rUBYe2u3kThg4Vusx7VibNCzpzacL2CQR6BfJWi7eYs28OYPkzdOXKFcqUKaM5EEzEpbAw2LHDZP3gYHj6aVi6VFzP5GhSDjF8+HCCgoK4ePEinp6eJp+JY8OOaSWg164ZKFlSuMZGjbK8r44dO5Kens6uXbtMn8jJi0ofMhyvjCQMOh0prZrwR+A+Wn/Xmq+7f02zMoXIKiIpVDjjXNq/X3wvTM4lEA0N1q1bx8svv6wtU4/11iYRVLIN2Vwud5m7Xnd5t+wZPtZnYE/6ueGeThu3xeh/Wawt89R5UiO0Bi3KtuClui/xZNUncVNyj+2HDx8mNjaWnj17Ov8GCzFO394ZDIZtwLYCGItE8q8kLCzMxLmkCkXBVmp6VUV+wYIFebZRxSVFUahcuTLHjFpltm4Njz0mLpSeegpsOP8dwlXnUmSkGIdEIpFICgfOiktdu4qyrnnzhGP2ftGwZEMW9FzAlN1TGPzbYFafXk0v/16gkLcVjRF+fn6aq8jcuZSYnsjcg3PJNmRzzmM1L46YQPHitju0WUIVMdLT07W8kISEBM6dO0fjxo1tbvvhh5CQ8A/TprXi4MFiDBxo//V+/VU0O/ryS+tZKgVVFheXEse7W99lQ+QGpj0xjSDvIM1JYcm5FB0dbV1cssJHH8HOneKm+Ngxx64r9Ho984ySwC2JfBEREVy7dg11bq6+jV7YOp3O4vsBRF7Uq6L+3+DuSci8xfyQfojxW8fz7KpnOfPGGfw8nZ+gk0js4Uyg9759ULw4lCtXwIPKZ4KDg00y7DIyMjSXqi1xaeflnUzZM4ULFS/gmebJ16cXU6peJq8dAi8rpze9rzduX07hzNNPos/Wk5WdRbEixSjqU9Rm7uxff/0FQNeuXV14h4UXh6dOFEXpryjK21aee0tRlL6WnpNIJLYJDQ01EZdu3LihLbfEuHHjAKhcubK2TM1cMp6Be+yxxzh+/Lh24aMoomtcZCQ0bw5qpIErGAwGl5xLBoNwLt2vmWyJRCKR3DvOBHqDCPTu3x8WLBAlXfeTwQ0Gc3rEaV4Oe5ktkVsYemQovAZUEkKRpcwf43OZuXNp7sG5GFRlyi2D6/XGuTQudfLHOHfp448/pkmTJuxX7QM2qF37IPAbc+ZUpUUL23lDGRmiK2vlyrbFkYIoizMYDDz/y/MsObaEMY+PYWRTIbDYEijVLn3+/v4EBAQ4JC7VqiU64aWlieBzR1BvPlvktPqzNJaaNWvSsWNHTVyqV8/6/myKS56eRD0zmizcuNlO5EU9V+c5VvVeRczdGD7d+anl7SQSK/zyyy8EBgaauDEt4ahzKTUVtmwRnS+d7M3zUGDcNTs2Ntauc2l31G46Le3Enug9VL5cmQY7GnD3vbu89sM/eHhYdrUCuJctT8mXR1ApuBJVQ6pSI6wGIb4hdhsaxcTEUKRIEYcbOTwqOJO59C5gLYkwBfj3JFVJJPlIaGioSVnczZs3KVq0qNUTg4+PD0WLFjXZ5s6dO3h7e5sc6CpXrkxycrJm9Qd48knYtg0uXBB2cldJT08nKyvLaXEpNlZ0rJPOJYlEIik8OBvoDSKHOSkJfv+9oEZlHUVRWDxiMZlTMxkSNgS8gZcg8LNAgj4PosePPRi5fiRLjy3lZvJNk3OZsXMpXZ/OZ7s/y83J0WWx6+p2DsUccnpMxs4llX9yWuoZl7BbIyHhDvAskyencPEi9Osnbg5BTNzExMDGjTB2rGhYtns3vPmm7ZtGR8UldcyO5IYsO76Mzec3M6PLDGZ0nYGHTridrDmXsrKyiImJ0VxLZcqUcUhcAiFiPvEELFsGOfqXTeLj4wFo6EAi++HDooQ/0EZzJ5viEpA5dAQ7ac2Bp3OFpOZlm/NcneeYvm86f1780/6gJZIcvv32WxITE/nOjprqqLg0Zw5cv24xo7pQULJkSe3xnTt3uHv3Lt7e3nneuz5bz5rTa+ixvAcVgipw9o2zVL5aWXOzej1WDbf27S0fLO+hw2NsbKzd7qOPIs78pqogwrstcTrneYlE4iTmZXH2utWAEJiMQ0qTkpK0WVGVx3IUnN27d5ssb9ECqlUTF56uYi8XyhqRkerYXH9tiUQikdxfnC2LA6hbV1yrG81v3H/uQmO3xvA1zO80ny86f0G/Wv24FH+JJceX8NKvL1FsajGmMhVaAh6m4tLS40vRZ5mqFmn6NEZucL7dqbG4dOvWLXbs2KH9Pq0JPCkpKfTt25eoqKicc76e8eM9WLZMuIDbtoVevUSZe+nS0K2bKINr1gw2bBBZ2LZQG4TYQ3UIGLf/NsdgMPDt0W8ZtXEUzcs0Z3iT4SbPm3+G9Ho9HTp0YN68eWRlZdkVl15++WWmWaixHD4coqJg0SK7b0ML8a5Ro4bN9TIyRBC6nW7m6HQ6m/8TJeqE0Z4dRKaahnv/t/1/CfUNpcOSDny26zP7A5dIgHo5Nrovv/ySrKwswsPD+eSTT/Ks54i4pNfDZ59Bly7Qpk2BDLfAadGiBZ9//jmQKy6Zh3lnZGXQ/YfuPLPqGYr6FGXTC5sI8Q3Bzc3NdLLEYodHRQS6udjh8d8qLjmTuZQClLHyXFnA+RYYEokkT1lcXFycXQulpQs8c3GpUqVKPPbYY8ydO5dBgwaZPNeqFaxZIzr5uCDG2+1oZ41z58R3WRYnkUgkhQdXxCUvLyhb9sGIS+o5CoRAQyb0r9Pf5MbDYDCw98peDlw9wDc7viGicwR0gM4rO9OuQjvKB5bng+0fkJSZZLJvAwZOxJ5g64WtdKrk+E2HcVlc7969OXToEM2bNwesi0vr1q3jp59+4qeffmLEiBH4+Pjg4eFBx47ixnDxYoiIEK7kRo1ECVfduuBoQ7kSJUrwxx9/oCgK27Zto0OHDhbXU8Ula4HoAF/s+YLxW8fTpFQTlvRaYhJsC3mdS2vWrOHPP//kzz+Fe8dYXDpx4oTJtgaDgSVLlgC50QAqPXuKTMlRo8T1zNCh1t+v6lyqWrUqkFseZ4xeL+KSbtyAPn2s70t9T7acS8HB4O0NV6+aLq8YXJGzb5zlxTUv8uGfHxLsHcyzNZ8l1Dd/y2cMBgOjRo3i5ZdftpvrJXn4ycjIAODChQscPnyYx3NavPn6+tK0aVNatmwJONYtLjIS4uLguecKdswFTacc4efOnTskJiaalMTps/UM+30YWy5sYVa3WQxrPAx3NyF9KIpiej5TOzyePastynDzxnvWLJfHFhsbq030/5tw5rZyK/ChoigmlgpFUcKACcDm/ByYRPJvITQ0lOTkZK7mXH0kJSXZbaOpiks1a9bUTiDm4pK7uzudO3c2KYtTadUKbt+Gk9a8iHa4F+eSu7uwskskEomkcOCKuAQi8+dBiEvXrl3THqekiJI280kZRVFoUbYFY5qN4YMKH8C3wF7QuemYtncar69/ndtpty3uPzkzmcFrB/PnxT/5YvcXDFk7hI5LOtJkYRPafd+OXit7MfvAbA7FHCIjS9wQGjuXDh0SZXVqxqI1ccm41P2PP/4wuTYYPx5OnRL3Qt9/DyNHCgeCo8ISCHFJnb2fPXt2nuejoqKYOHEiycnJeUrvjTkUc4gPtn/AMzWeYf8r+3msaN4bKvPP0MyZM4HcfMmyZcsCYtLq2rVrrFixQtvWOAbAHEURk2Xt28Orr0LLluOoUqUK06dP19ZR36PqXAoNDeXw4cP88ccfefa3aBGsXQszZgjhyhb2xCVFEfer5uISgI+HD9889Q01w2oy7I9hlJpWitWnVtt+QSeJj49n9uzZbNy4MV/3K3kwqOISYDIpPXbsWFoZ2ewcCfRWr/9r187fMd5v1OZH8fHx3L17VxOXNp/fTPmZ5fku/Ds+avMRbzR9QxOWgLzOpZwOj+RMmmcpOrZmdyCuguui7L/VueSMuDQe8APOK4ryk6IoXymK8hNwHvAB3imIAUokjzrqxVq3bt0AcSHsa2bNNEe9SK5du7b2uESJEnnWK1u2LLdv3zaZxQVhgwVxQeoKqrjkinOpYkUhMEkkEomkcOBsoLdKpUoPRlw6aTRzkpKSgru7u81OZ35+fhAFbIXdg3dz9927FgUSY6ITo+mwpAPvbH2HDZEbSMlMIdQ3FAMGjsceZ+SGkTRZ2ISwL8Lo9kM3lt1cBj6mpeqxsbGAdXHJ+PcdHR1td+LJWYyvGyz9fkaOHMnHH3/M1q1bLTqms7KzmPjnRDov7UwJvxIs6LHAqgBl7Fw6f/48e/bsAXJvksPCwgA0N9eyZcu0bc+ptmcLGAwGVqz4mtmzb1K+vIE9e8YRGZnE3LlzOXr0KHq9nurVqzNo0CDNuRQYGEjDhg0JMlLi9Hr4+Wch0tWrJzLD7GFPXAKoUQMOWYnoCvIO4sirRzjwygGalG7C8788z/HY4/Zf2EHU9+tIrpbk4cdYXLpjo1OCI2VxJ08KPcVOhehDj/o/rJbF+QX4seLkCp5a/hRB3kGs7b+WSe0m5dkuj3MJhGUx5/emeHoyluls2eLauPR6PXFxcVJcsoXBYIgC6gGzEWVw3XK+zwIaGgyG6AIZoUTyiNMlR+lRZ1odEZfUA2Lt2rU1oeeFF17Is165nN6i0dGm/54lS8LAgWJm7scfnR+zq2VxkZEyb0kikUgKG/fiXLpxA+w0N8p31O5r5cuXJzU11e45VS2TUtl7ZS/X7l6zsnYuIT4hXB59mZhxMewdspcNz2/gr4F/ETkykvOjzrOq9yr61OzDjeQb/H7jdxgBi04ugpzhqOdvazf/qtMGhChT0OJSenq6yVjUv3dERIRFcWnC9gl8/PfHtK/Qns0vbibEN8Tqaxl/hlTHljHq9US/fv3w9/c3yZ6MVAMbyfsZPHv2LG+88QavvdaflStTgWBgH5GRZWnYsCFffPEFZ8+e5dKlS5rYEmRm74qJEZ/V3r2henXYvt2x7lmOiEtPPCGufayJrDo3HU1KN+HXfr8S7B1MpyWdmLF3hlPh+daQ4tKjRUZGhlaloHaJNub69euA4+LSY4+BjRi1QkFgYCA6nY7rsde5EHKB/W32M+DnAdQvUZ+/B/7NU9Wesih453EuAXh6ClXZzQ369OZGYFW2b3dtXHFxcRgMBiku2cNgMNw0GAzvGQyGZgaDoUrO9wkGgyHO/tYSicQSDRo0YNiwYVqr35SUFJuhmQAXL14EhLik8pyFwumQEHGhp15gGDN/PjRtCu+/LzrNOIMrZXGZmcK5JMUliUQiKVy40i0OcmfFzSJ0ChxVXFIUxaEJmyo5QYADBw4E4IM/PyA5M9nGFoI0fRrrI9fnWa4oCpWCK9GnVh++eeobDr96mEOvHsI305fDxQ7DSKB67vrWbv7Nz93GpTD5gXG3JQ8PD4KDg6lbt26e9bKyskyuS1IzU/l056d8vvtzhjUaxs99f6Z6aPU82xlj7FwyFs1U1OsJRVGoWLGiyc2zsXPJfFv12unKlSuUKHET6Ii4//4ZKM2ZM2fybBsQEEByMhw4IDrovvSSyJ9ZvBj27IGiRW2+FZP3ZE9cUp3imzbZ3ldYkTA2vrCRusXrMnbzWJafXO7YIGwgxaVHi4yMDE10NS79VVHLbR0RlyIiCn9JHECqPpWQ50OYlT2L6LrRBKcHs/zZ5fz58p82xW6LziUQXRBat8bts0/p3l101nagmWceVFeqFJckEskDISwsjPj4eAwGg0MXwrdu3QKEuNSgQQM6duyo1R0bo85wqGGcxnh6imPo5cuQcx3uMK6Uxf32m2hL/cQTzr2WRCKRSB4srjqXcvJl+euv/B6RdbKysjh48CAgbsYcmbBRFIXMzEy+/fZbjl47Svh1x+4mkjOTeX/b+6Rkpthdt16Jejx+/HHq760P8UB/YAzQHatCllr6oigKw4cP56233nJoXI5i7FzS6XSkpqbyzz//aMtiYmK0x+rvMDMrkxbftuD97e/Ts2pPvur2ldVSOGOMP0OWOtQZBxAXLVrUYXFJdVJnZGTkiG97+eCDw+h0nsAObt4MBWoDA/nrr1p4ek6hdWsdJUvC44+LRlDbtsH06UJkcsaQ7Yi4VKUKVKhgX1wCqF+iPhtf2EizMs14dd2rrIpYRZo+zfEBmaF+fqS49GiQkZGBr68vAQEBeULv3dzcTMQlW4HeaWlisrdWrQIdboGSlZ3F7qjdNF/UnJsVb5J2LQ3vDd48Ff8U/Wv3x8fD9jHfonMJICwMduyA0qX58ksIDYUBA5yfhFfv09RJ/n8TNsUlRVEOKIpSM+fxwZyfrX7dnyFLJI8eqpiUmprqkLikUqlSJQ4fPswWK0XB6sVgWprli5P//Ed09DHKzbRJUlISe/bs0S7mnHEuLVgAZcqIVskSiUQiKTy4Ki6FhUHNmvdXXDp9+jRJSUn4+fmRnp7u8DnV3d0dRVGYuGOiUzf06VnpzNw306F1AwMD0V/VwzfAOuAq0BiWeC9h7sG5eUrxrl69SmBgIAkJCcyZM4c33njD4XE5gvGs+lULqdPGeY3q9cRX+78i/Ho4S3stZW3/tXjorGdZGWPPuWSMKi7p9Xq+++47Tp06pT1n7tgwLi1UnV1t2xZj3boiwGOsXz8VOAF8x59/9iYj423S0kSXrDVrxGfzn3/gtdcceht53pM9cUlRoGNH2LXLsX26u7nzS99fKB1Qmn6r+9F4QWNup1oOlreHdC49WmRkZODp6UlQUBDHjh3TlgcHB1OjRg1NXLIX6H3mDGRlFV7nUro+nfaL29Pqu1ZEJ0bzcbWPMSwzkLY/jQB/x0qHrTqXjAgLg4kTxfHByADpEGojCWfjQx4F7DmXIoBUo8f2viQSiQuoF77x8fFkZ2fbvRD+/fff+fTTT9HpdCiKYnXW0JZzCSAwELp3h5UrRdmaPZ577jlatmxJVFQU4PhB8+pV2LwZXnkF7HRHlUgkEslDhquB3iC6eO3cCRbMKvlKdnY2n3/+OXXq1AGgTZs2ZGRkkJqaate5pHL+9nm2XNhCtsHx95mSmcL/dv6PO6nWA3ZVgoKCxA1/FnAY+An8f/NHj57X179OvXn1TAKdT506RbNmzUzaa+cnxhNEp0+f1h5v2LABENcOFSpUAIS4dPbWWSb9NYkeVXvwQt0XHHIsqRgLlK+++qrNdUNDQ4mIiCA4OJjBgwdz8uRJatasCUCLFi20MHCAuzmBXnq9njVr1uDl5UX16tXp1g0aNnwNGAi8DFShXr0u1KjRgiNHYN48ePpp0WGvWjWH34YJqrh04sQJmyWL1aqJDr0WEgosUtK/JCeHn2RV71Wcu32Op1c87ZA7zhwpLj1aqOJSQECASZZqUFAQZcqU0boq2iuLK8yd4i7eucjg3wazM2onM7vM5OLoi4zuMVo7Fjl6rLTqXDKjY0fx3dnsJVVcctQs8ChhU1wyGAyDDAbDxZzHA3N+tvp1f4YskTx6qAcf9eLE3sHoySef5N1337W7X/WC2pq4BELwuX4dhgyBFDvXLvv27QNyL1hU8coe6qRju3YOrS6RSCSShwhXnUsAL74ozi2uNI9whvfee8/kvFirVi2tLM7RC/xPdn6CPlvv9GtnZWcx+e/JdtdTXUiqi+fbb7/F95ov/WP7c+TVI3jqPGmysAlPr3iaqPgoTp8+TY371M7p8uXL2uPu3bsD4tqhVatWuLm5ca3yNWrNEXU0X3b90un9q9cLxiKWNdQMLNWVBNCoUSPt8S4jG5AqLsXGxjJ//nxeeuklQkNDc/aTACwGlgCRHDu2GbMs73tCFZfq1q1LWFiYVRe5mjXpTOdED50HfWr1YcnTS9gZtZPac2qz/4pzGQZSXHq0UMUlcwGlZs2aeHp6at3k7IlLJ06Ah4co2SxMTP5rMpW+qsSPJ37kozYfMbrZaIK8g/D396dixYqA4+KSI84lEB2uy5WT4pIzOJS5pCiKt6Io6YqiPF3QA5JI/o04Ky45inoxZ60sDkSZ2sSJsGwZ/N//2d6fav9WrfKenp4OjePSJfE959gvkUgkkkLEvYhLTZtC/frCKZIPDbAssn79eqZMmcKwYcMoVaoUtWvXxtvbm/T0dOLi4hx2Lh2+dtglcSktK40dl3bYXS8wMJC7d++SlZXFpEmTGDRoEB4eHugz9TQo2YA9Q/YwoskItl3cRuMFjUktl0rl6pWdHk9+kZKSgnsJd8o9X45z5c/Rs2pPTo84TaXgSk7vq3Pnzvj4+PDBBx/YXbdoTqJ2//79tWUNGzbUHqsOjYyMDGbPng2I7riVKlXiww8/1NZTM6Xc3d155513cHNzo3fv3k6P3Ro6nU4LFAf4/PPPLa5XOedPaNT0zmH61e7Hny//SZYhixfXvEhGVob9jXKQ4tKjhbFzCURe68KFC1m2bJnD4lJWFvz0EzRrJrJXCwt7o/cyccdEelXvxcXRF/m/9qY3LOr9iKMdNRVFcci5pCjQoYOIYXLm9CfFJTsYDIY04Abg/BlXIpHYRT34nD171uTne8UR55KiwKRJueVx1o61W7du1QI2ExMTcXd312447HHxIri7Q6lSTg1fIpFIJA8BHh4iV8eVm1RFEc7YY8fgwoX8Hplg/fr1BAQEMHPmTM6fP8+hQ4fw8vICICIigm4Ohv0dG3YMw0QDhokGEscaYJKByTqDtszW15HXjtjdf5CRbUYNevXw8NB+r+UCyzG9y3T2DdmHW5YbDIAJtybw44kf86U1vTNcv3ud5E7JfO/3PZcqX6Jzxc6s6L2CMgFlXNpfYGAgNWvW1IKIt27danXdAQMGMG7cOKZNm6YtMxaX1A5wy5Yt0zoDRkREcP78ecqWLautp4pL3t7edOvWjTt37jB27FiXxm8JnU5nMZzcnEo5WpwzziVj2lVox7wn53Hu9jm+PvC1w9up4pIqOkgKN+np6SbOpXLlyvHKK68QFBSUR1yyFui9YYM4DudzfFuBcuTaEV7+9WXKBZZj8dOLqRBUIc86BVUWB0JcunXLua6nUlxyjPnAKEVRHEvuk0gkDqMefBYtWoSnpycd1SLfe8QRcUmld2/ROe7wYcvPd+7cWXucmJjosGsJRBhe5cpCYJJIJBJJ4UI93rvqgGjQQHzPmT/JdxITEylatCheXl54e3vj5eWluay6du3KqFGjnN5nRE6SaN26+TfOwMBA7bHqzjEWl1RqFavFKPdRsAyqhlTl+V+ep8qsKkzaMYnUTPvnc2fIU3ZXCRgJJaeXhPrQ0q0lEa9HsPHFjXjq7s3qUL16de1xw4YNNaGpffv2Juv5+PgwdepUSpYsqS1r3Lgxffr0ITQ0VBOXVq1aBUC3bt0sNhhRt09PTwccdzU4inmgt7WbVT8/KF7cdXEJoFuVbnSp3IWP//6Y7Rcdq9GR3eIeLcydS+XKldOe8/T0JCkpiUGDBnH16lWrzqW5c6F0aejV674M+Z5ZfmI5jRY0IjY5lsVPL8bfy7J4pE5253dZHIjcQICNGx1aHcgVlxx1zT5KOCMuBSF6eV5SFGWJoihfKIoyxejLshdUIpHYRb0oioiIoGrVqiYXVPeCI2VxKk89JcSf1avzPmeekeCsuBQRIToGSSQSiaTwoR7vXXVAqNkeRh3l85XExMQ8woEaeNujRw+ngqdV1FnqnHzwfMFRcQkg8nQkJZJKsG/oPhb0WEDVkKr831//R9kZZXnyxyf5Pvx7l0r4zDEOx6Y00B/IhsmtJ8M8eNbvWWqG1cRNceaWwTLVjJKz/f39qV27NgaDge1WAk2M/26+vr6sWrWKoUOHcuHCBWJjY9m2bRvjx49n/fr1Fv/GqnOpoMQVc3eILSdE5cr3Ji4BzOo2i2DvYDou6cjPp362u74si3u0MM9cMnbpeXp6cv36db7//nsAq+LSgQOiUsGjEFhFYpNieWPDGzQv05yoMVG0rdDW6rrOikvOOJfKlBGZsZ984pj7Njs7m+TkZDw9Pa06yB5lnDlTPAukAxlAa6A30MfsSyKRuIDqXEpNTaV8+fL5tl93d3fc3d0JDw+nQYMGXL9+3eq6RYtC587www+QM8mnsXLlSpOfnRGXkpJEzkB+XqBLJBKJ5P6hlsW5Ki6FhUFAwP0Vl2JiYgC0bmfOcuKEcJzk4ynZpCwuODgYsCEuRUZStWpVdG46hjYayvrn1/P3wL/pVqUb526dY9DaQZSbUY4eP/Zg5cmVXE286lLpXFBQEPghruJfAVKAxTDo/9k77/goqvX/v2fTCyG0JCRA6DV0AgLSiyDFghRRuIheBQS+1wY/roooylWxgIogKEW4wkWkCyICAWliQEqQTuihpUBC6ibz+2Myk9nNbrItBXLer1deu5ly5szuzu45n/m6HyfyAAAgAElEQVQ8z9PgObjh2jvveudSQQmHC6JBgwYYjUa++OILjEYjgwdbn36o4lJRYc/E0RXiUr1K9Tg+7jiNKjdi2s5phVY1FOLSg4UqLqnXpLm4pOfo0aOYc/u28ldMNQKcIik9idHrR5OSmcJ3A7+jvHf5ArdXxWVLDkZr29uTQ3DxYjAYwJao2t69e/Pxxx+XSdcSgM3f7LIsi1S8AkERoY/J1dtcXYG3tzerV68GYOrUqcydO9fqgGjCBOWOxqBBsHp1XrK/DRs2mGxnj7h04ICSBO+hhxw/B4FAIBCUHM6GxUmS4l4qCnFJlmUuXbpk4ooBmDlzJgEBAQ6HmR87ppTqtjG1oE3onUv+/v6AdXHp9u3bNGnSxGRZp/BOdArvhCzLbDy9kR9ifmDf5X0M+0lJfB1ePpyPe33M4MaDrbq1ZFnmbsZdEtISmL5rOvuv7IdXclfuBvYD9/KqsLkyZ4j5e2QLX3zxhYlIpLaxadMmDAYDLdWYSwu4ygVuDXsEsrp1YelSSEsDZ+acPh4+THl4CiPXjuTn0z8zoMEAq9sKcenBQhWXJk6ciJeXF88884y2znxM/vDDD+fbXw1CKO3i0rXka0QuiCQuOY5ZfWbRqErhHVa/72x1qdrjXALlJsPIkfDtt8oN+NyUfhbZtm0bAHfu3LG5/QeJQn8yJUnykSRpkCRJr0mSNFySpODi6JhAUJbQ2zhd6VwC07uOCxYsYPp06+WS+/aFOXPg55+V5N6gDETPnDljcjfAHnFJddwLcUkgEAjuT5wNi4OiE5f+97//ce7cOc6Z2UIaNWrEDz/8oIWH20NaGhw65Np8S2AqLqmijSVxKS0tjZs3b2qhc+ZIksSABgNYPmg5ZyeeZdeoXXzZ90sq+VZi6KqhhHwawuMrHmfZ0WVsO7+N4zePc/j6YVbErKDlNy0J/CiQ2l/UZnnMchpWbqgISnPgvS7vgVIMVktU7cq77/UcqH0+YcIEE3eS2sbhw4cJCAgosLCImjS9qDAfBxUWFgdKgRNnGRYxjJqBNXlz+5tcunPJ6nZCXHqwSE9Px9vbm6pVqzJt2jQT8VT/WTxz5gwffPBBvv1VcUlnICyVvLLlFRLSEtj/wn4mtrMtX94nn3xCWFiYzQK2vc4lgJ49ld+GffsK3k6d09kaovegUaC4JElSbeA48CMwE1gGnJIkqXcx9E0gKDPoB5yudi6Zhwrs3r27wO3HjIGgINiyRfk/MTGR5ORkIiIitG2Sk5NtEpeysmDDBuUuSW4EgEAgEAjuM5wNiwPFuXHhAri6cJUa/qEmeXYFP/wAd+/C8OEuaxIwDYtTb9hYEpd8fX2Jj4+3SRxxN7jTKbwT49uO58ALB/hu4Hf0r9efXRd3MWLNCHou7UnE3AhaftOSp396mpTMFN7v9j4f9fyIsxPOsnroatgKJEDTpk21Cm2quORK55IrhCo1cTuYjp0s4ebmxrvvvsvOnTudPq4lvMzsCwWJS2rFOFdUTPRw8+DzRz7nbMJZIr6O4OTtk/m2MRqNpKSkAEJcelBQxSVLeOiSKIWHh1sUXU+cAF9fcPE0w6VEX4tm5fGVTO44mbZhbW3er2fPnly5csXmsDh7nUsAXbqAmxusWWN9G1mWSU1NZfz48S79TbqfKMzP+TGQg5Jj6SBQC/gapXKcCJMTCFyEfvDmaueSfjALhdu4DQbo2FEJZwO4ePEikF+k8igkG6Aswz//qbSzcKGdnRYIBAJBqcFVzqWcHMW54UB0lFXUO9VdulhP9moPsgxffKG4ljp3dkmTGtacS9bCJ6w5l6zhZnBjdMvRjG45mq8e/YpLdy5xPeU6cSlxuBvcCS0XSmRoJB5uln+//fz8tDGCWmnM1Xffx4wZo4UEOoIkSYSGhhIbG5tvfGOJqVOnAhAVFeXwMa1hT2ET1bl06hT07+/8sR9v+Dgx42KIXBDJP9b+g93P7TZ5X1XXEghx6UGhIHFJ/1m0Nj4/eVL57nVlqK8rOZdwjlFrR1HRpyKvtrchuZETOOJcKl8ennkG5s+H118HXcorjfT0dLKzswkNDS3ysNzSSmEfr/bAW7Is75FlOV2W5RPAS0ANSZLK5ismEBQB+hhhVzuXzAdxhYlCAK1aKeELd++i3fkaPXo0zZs317YpbFC1ejUsWQLvvgvPPedAxwUCgUBQKnA25xIUfcW4BQsWuKSdc+fg6FF48UUlV5Qr0f9uquJSkyZNiI6O5sKFC/m2t1dc0uPj4UODyg3oUrMLwyKG8VTjp+hQvYNVYQksi0vOCEGWmDt3LjNnznSqDXXSVphzqaixx7lUpYqSw2vxYkXAdAW1K9RmXr95HLh6gCdXPsnqE6u1CoJCXHqwkGWZjIyMQsN8//3vf1tdd+JE6c23lGHMoPv33bmWfI3/PfU/ArwCCt/JCRxxLgFMnaq4l3r0gKtX869Xc9WV1ZA4KFxcqgqYGzjPARJQtCUYBIIyiquVbnWAqGJLAspWrZTHI0cgOzsbgODgYHbs2KFtU5i49MsvEBgIb75pZ4cFAoFAUKpwRVhcUYlL6t1ne1wkBaHmJWnTxiXNWUUNWxk5ciSyLBMdHQ2YChRBQUFF2wkzfH19tfdaFSdK4yRJdXir45OSwh5xSZLgjTcgJgY2bXJdHwY3Gcy7Xd9l96XdDFo5iPbftWfpkaUcvnIYUIq6CHHp/icjt4yzNXFJXW8t9PTePbh4sfSKS/Oi53HpziX+99T/6Fm7Z5EfzxHnEigOxF9/hStX4L338q8X4pINCb0BF+nrAoHAFuwpbWsLt27dMvnfVucSwMGDeYM3Nzc3ypcvr90pLGggL8uwdSt07aoo/AKBQCC4f3FFWFylSsoNh7NnXdUrBXWCUFBiZ3s4mZu+xpWhewXRqFEjDAYDMTExgJIrRyUsLKx4OpGLv79/kTuXXMFrr70GQGhoaIn2w15B8+mnoWpVWLTItf2Y2mUqt964xfJBy7l05xIj145k8M7BMAmyxmVx7pFzLPxrIYlpiYU3JiiVpKenA9bFJXW9ueCpoqb/KY3JvBPSEnh357v0qt2rWIQlUMQlR5xLAB06wOOPK7mXdF/XgBCXwDZxaYskSTfVPyAud/k2/fLcdQKBwEGqVatGhSLIep2ammryvy3OpZAQ5e+vv0zFJYPBQIcOHYCCB1Vnzyp3SHqL1P8CgUBw3+OKsDhJgiZN4PffXRcWBEUjLoWEKEJYceDj40OdOnU0cUn/GlerVq14OpFLWFhYkedccgWtW7fm+PHjfPXVVyXaD2sTeWt4eEDLlkropatxN7gzLGIYV1+9yrGxx3i17qtwAvzi/SAbnl//PFVmVmHKb1PIMGa4vgNlnBdffJGFRZhgVBWPrDmTCnM2HTmiPJY251LUhSjafduOOxl3+LT3pyZpQooSg8HgkHNJ5amn4NYt2LXLdLmaSqQ0ivLFRWG/xO8Cs4E5Zn/WlgsEAgc5d+4ccXFxhW9oJ5s3b+Zf//qX9r8tziVQBkB79kBamvLlqzqqOnbsCOR9gVri55+VRyEuCQQCwf2Pm5sbkiQ55VwCGD0ajh2D7dtd1DGKRlwq7rv7ERERFp1LlStXLtZ++Pj45BOXXFktzpU0bty42MMGzbEnLE6lVi0lqX1R4W5wJyIogh4VesAGaHSyEb7/9SXqH1E82+xZPtzzIe2+bceaEwWUvCpFTJw40W4RryRYsGABzz//fJG1b6tzydJ6WYZ585SQrtLkXDodf5qBywciIfHLM7/QNLhpsR3bGecSQJ8+SuW9VatMlxf2PpUFCrQwyLL8bnF1RCAo67gqX4Q57du3p3379syaNQuwzbkEyiRg8GD4+ONmgEHb7+GHHwbQ8kOYc+GCksS7ffu86igCgUAguH+RJAkPDw8yMzNZuXIl9evXp0WLFna3M3w4TJkCn32mJER1Ba4Ul2RZybk0dKjTTVnliy++4Pr16ybLIiIiWLduHenp6ZpzaeDAgcV2F1+PXlzy9/d3mWj3IGI+brNlslqzJty5A0lJReuOU4XgwMBA0u6l0aVmF7rU7MKA+gP49/Z/8+TKJ/n12V/pVadX0XXCBXz55Zcl3YVSgTPi0p49SuXmOXNKR6oKWZY5GHeQUWtH4enmyW8jf6NGedcWMyoMRxN6q/j6Qr9+SvGizz8HVf9Ur7v7QRAtKsQvhkBQRhg3bhyAzTbQp56CTz6BvXtDgfGacykyMhKwHh4xcaJSbnrZMuf7LBAIBILSgaenJ5mZmQwdOpSWLVs61Ia3NwwbBjt2uC40Tv1Nc4UQc+sWJCYW7d39CRMm8MEHH5gsi4iIICcnh1OnTmnOpT59+hRdJ8xYsWIFK1asAPLczYmJiaUyJK404cgEslYt5dFCcUCXok5yK1SoQGZmpva5GtR4EIdfOkyDSg14aeNLpGWlFW1HygDFkVi+MHHp6aefBvKiC/QsXQoBATBqVJF1z2ayc7IZs3EMkQsiOZtwluWDlhe7sASOJ/TW8/zzcOOGcjNCnRKp4YlCXBIIBA88X331FbVr19a++GzhtdegVq1EYJgmLvn6+vL666+zzIJ6lJAAmzfDSy9B7dqu6rlAIBAIShpVXHKW+vUhLQ1cFQXuSueSmsy7uPOSNGnSBICYmBjtxo2tLmNXMHToUIbm2rXU4966datI8kA+SDgSFlezpvJYlKFxkDfJDcy1R6Wl5YlIPh4+zO03l9ikWEauHcnlO5eLtjMPOPfu3SvyY6jvnzVx6dFHH0WWZWpbGHwfPaoU6inpCNfdl3bT9799mX9oPq+3f51rr10rMeecs84lgEcegS++gHXr8m6oC3FJiEsCQZlBkiR8fHzsEpcAOnW6ALRn797y2rKZM2fyzDPP5Nv2hx+UygnDhzvZWYFAIBCUKjw8PIiPj3e6HTVc2lVV44pCXCruvCT16tXDw8ODmJgYzWFia35EVyPEJdtxxrlU1OKSPiwO8hd36VarG+92fZf1p9bTYWEHktKTirZDTuKsEFCUqBXCipLTp08D9ufyycmBmBhoWnzpjCzy7aFv6bSoEweuHmBuv7nM7D2Tij4VS6w/rnAuAYwfr1QW/e475X/1uiuqVCf3A0JcEgjKEF5eXnaLS927nwbOM3ZsOE88AWbjE42EBCWeu3VrcCAVh0AgEAhKMV5eXty4ccPpdlq2VCrHRUU53yfIm3S6Slzy9YViLtKGp6cnDRo0KDHnkh71uKmpqZowIbCMm1kCG1sEkAoVwM8Prlwpql4p6MPiIL+4BDC1y1R2jdpFXHIcL218idupt4u2U05QHKFnjlLU4lJSUhKjcmPawsPD7dr30iVISYGIiCLomI3subSHcT+Po3ed3lx77Rpj2owpuc7k4grnEii/Zc8/r+S1OnFCOJdAiEsCQZnC09PTbnHJzS0LaM/48fGsWwdhYbBli7JOlpUB0pgxEBysDMynTHF9vwUCgUBQsvj7+3P5svPhM8HBSsGHtWtd0Clc61zau1cJiSuJHNZ169YlNjZWcy6VtLgECOdSITgSJipJinhZ1OKSeVicJXEJoF21drzd+W1WHl9J1U+rMmv/rKLtmIO4IiS3qFCrJ/v4+BRJ+6dOnQKgc+fO1FKtbzZy7JjyWBLOpeSMZPr90I+HFz1M9fLVWT5oOb4epaP6ZEHOpdjYWAYPHmz1mjFn1Cjw9FQq8glxSYhLAkGZwhHnknK36CavvHKHjRuVu259+iiTg5AQqF4dFiyAF19UqlEMGlQ0fRcIBAJByREQEMDFixcB58ssDxgAf/2lJNB2FleJSwcOwB9/wMiRzvfJEUJDQ4mLi9OcSyUVFqc/rhCXCqZiRdOwHludENWqgQt02gIpLCxOz9QuUzky5gg9a/dk8m+TOXn7ZNF2zgGsFZEpDajOJT8/P5e3vXHjRl555RUA5s2bZ/f+MTHKY25at2JDlmVe2PACW85u4e3Ob7P7ud0lGgZnTkHOpSFDhrBq1SoOHjxoU1tVqsCQIcpc6MKFAECExQkEgjJChQoVuH3bPtuzakV2c3Pj0UcV2+fEibB/PzRurCSzO3ZMCYnLLSQnEAgEggeMgIAAbYIXFBTkVFvqROf8eWd75TpxafZspaLSc8853ydHCA0NJSEhQXNBlAbnUrXijg+8z6hbty6HDx/W/rdVXKpevfjC4lRxqaCk05Ik0Sy4GYsfW4yvhy9jNo4hw2jfjciipjSLS4mJiQCUL1++kC3tZ8CAAezbtw9Jkux2LYEyPg8PV77bioub924ycfNEVh5fyfvd3+e9bu9RtVzV4uuADRTkXIqOjgbsu4nyxhs38PfPYvHi/oCHcC4JBIKyQaNGjThz5oxdP9LmFn0vL2UQfvs2bN8OEyYoIpNAIBAIHlwCdLMTSxWJ7EFNG3LpklPNAK4TlzZvhqeegnLlnO+TI4SGhgJwMjereEkn9AZ44YUXSqQP9xPNmze3e59q1ZRqibnDqyIhMzMTSZLwzS0RZsu4L9g/mM96f8bOizup/1V9/or7q+g6aCelOSwuISEBKFqnX40aNRxyjMbEFG++pcS0RB769iG+jv6aF1u9yKSOk4rv4HYgSZJFMdiouyjtmSt99tlkjMbxxMdXBNoL55JAICgbNGrUCKPRyFk7yvTonUt6KlVScgcIBAKB4MGnnE51cdZFoIpL58451QzgGnEpIQESE4s/dERPt27dkCSJxYsXAyXnXFJFxLFjx+YL+xIUjK3Vp6pVg+xscEF+fKtkZGTg6empXRe2uqqea/kcW57dQnZONkNWDSE5o+grodlCaXYuqVU0i1JcsjeRN0BWlpILtTjyLcWnxvP5vs/ptbQXV5OvsuMfO/hmwDcYpNIpNRgMBovX619/5Qmq9nzmTpw4QWLiCsAI9HRJDsD7lbJ75gJBGaRxrsXoxIkTNu9jTVwSCAQCQdnh+vXr2nNnJ3rly0Pz5rBqlbO9co24pN5vqVvX+f44Ss2aNQkKCuLmzZtAyTmX6tWrR0xMDHPmzCmR49/P2OquUaMNL1wo2r7oxSV7yq73rtObHwb9wPnE8zy58kn2Xd5XVN20mdIiLn344YcsWbKEe/fusXfvXiBPXHI2F11BVK5c2e59fv9dEZiaNSuCDunIys7i0R8e5dVfX+VW6i2+f/x7Ood3LtqDOok159Lvv/+uPbfHLXfu3DngLnAAeMT5Dt7HCHFJIChDNGzYEIC///7b5n2EuCQQCASCW7nZtxs0aOCSid7zz8PBg3DkiHPtuEJcyo1Eo3595/riLF5eXiWecwmgSZMmSMKabDdpaWk2bRcZqVSX+u9/i64vc+bMITk5WXsf7RGXADqHd2bWI7P4/eLvdFjYgf/8/p+i6KbNlJawuClTpjBq1CgGDx5Mx44duXnzpiYuuaK0vR79e1apUiU794VJk5T8Xo8/7tJu5eOD3z/gwNUDLB+0nIv/usjQiKFFe0AXYM25dPXqVe25rb9zSUlJ2mcA1gBtsWOa9cAhxCWBoAzh5+dHeHi4cC4JBAKBwC4WLVrEu+++S+PGjV0iLg0frkywc6PAHEadIDgjhhw/rvSlJJ1LoDgf1MpTJeVcEjhOQUmz9QQFwYgRsGiRayommpOZmaldo/aGxemZ0G4Ctyfd5pmmz/Dv7f+m/w/92XBqg0v7aiulxbmksnnzZgCCg4PZt09xdtkr4BWGmssJ8nKy2crGjYp4P2MG+Pi4tFsadzPu8uKGF5m+azojmo1gWMSwojlQEWDNuaSvqG3rZ+6cSXz3YiCTadOKNqdaaUaISwJBGaNRo0ZWxSVZlvPdHRLikkAgEAgiIiKYOnUqHh4eLpnoVaoE7drBoUPOteMqcalBAyhBsxCgiEulwbkksA+1upSt4hLAq69Cenrh7qWZM2eyY8cOu/qjTpADAwMdCovT4+/pz+t1XmdAuQH8df0vBq4YyPJjyx1qyxlKm7ikRx1Tu9q5pOZHDQoK4rXXXrNr3927FcF8yBCXdsmE8ZvGs/Cvhbwc+TJf9/u66A5UBBgMBpeJS6Z5bG8D7/HjjzBypJOdvE8R4pJAUMZo3LgxJ0+etDjQWLhwIV5eXia2UCEuCQQCgUDFVeISKIm9L150ro2cnBynk6ceP16yybxVvL29td9m4Vy6f2jdujWTJ0+2S1xq3BhCQ+GvQgqyTZo0ie7du9vVH/Um4bvvvutwWJyeli1asuG1DZyfeJ4u4V0YtW4Uy44uw5hTfNaM0hIWZ4mGDRvSpk0blzuX2rdvD8BPP/1kUlDBFg4dUhJ5F1XRsvWn1rP06FLe7PQmX/T9An9P/6I5UBGhXhfmApMj4pI+HyHAihVNmToVli+HH390sqP3IUJcEgjKGI0aNSItLY2LFkb0y5crd6OO6JJgCHFJIBAIBCquFpeuXHEufMBZcSklRUmsXBrEJS8vL+25cC7dX/j5+ZGVlWXXtdG0KRw96vq+qBNkLy8vp8LizDHIBlYPXU29ivUYsWYEtWfXZu6fczkTf6bQ9o1Go1PiS2lxLqkiT7du3bRlb731Fu7u7i51Lt3QlRK0NyROlhVxqXVrl3VHI92YztiNYxm2ahjNg5vzZuc3XX+QYkC9LtQ5jkpmZqb23WvrZ051m6oJ3du0acOUKdC+PQwdCt27w76Sz4lfbAhxSSAoY6gV4ywl9Q4MDARME9oZc0f9YqArEAgEAleLS9nZcO2a4204Ky6pUeK5P40lir7aVFGWNRe4Hj8/P8C+0LimTZXPnzVx1VGxQnX5OFotzhrJyclU9KnIkTFHWDdsHdXLV2fcpnHU/6o+kQsiiboQRY5s+TgeHh706tXL4WOXFnEpMzOTSZMmMXnyZG3Z8OHDkSTJpc6l3bt3a8/tFZdiYyExEVq1cll3NCZtncS8g/MYGjGUdcPW4elWRNaoIkZ9TS9dumSyPCMjA39/xYVlq1suOTkZT09PLXSxUqVKeHvDpk0wbZrijO3XT3EyudjcVioR4pJAUMYICQkB4Pbt2/nWqXdNz5w5oy1TVX1nww4EAoFAcP/janEJnAuNk2XZqd+n48eVx9LgXFLFpUqVKlG1atUS7o3AHhwRl5o1g4wMOH3a8npHrzO9c8kVYXEqd+/eBcDN4MbABgPZ/dxuov8ZzVd9v+LGvRt0W9KNoJlBzP1zrkVhbPv27XYfU3XNl4awODUvqZeXFz65WbIrVaqEJElWc/g4wvbt25k9ezY+Pj5kZGSYiM62cOCA8ti2rUu6o7Ht/Da+PPAlE9tOZNFjiwgPDHftAYqRiIgIAP4yi0vNyMjQ3Gn2OJf8/f157733SEhI0G7UBwbC1KlK/quaNZUiFtHRFV13EqUUMVsUCMoY6g+1uRUU0EppntaNdLKzs0VInEAgEAiA0icuOeNckmVYsABCQqBOHcf74CrUSWTz5s2dSlAuKH4cEZdUZ4kqBpiTlpbmUF8sOZdcIXyo4pKKJEm0Dm3Ny21f5sTLJ1jy+BJaVm3JuE3jGLFmBFEXopw+pmdu0iBLN0SLm+zsbGRZxtPTUxOX1NfXlc6lHj168Pvvv9OuXTvt/O3hjz+UCnG5+olLeGv7Wzyy7BHqVazHjB4zXNdwCdGsWTOqVq3KhAkTTBJy651L9ohL5cqVw2AwWHSc1qsHf/4Ja9dCZGSChRYeLIS4JBCUMQoSlxITEwEhLgkEAoHAMq4Ul2rUUB5LSlzauBH27lVCF0pD5LeaxLtZs2Yl3BOBvahigz4hcGE0aqRUTVy0SKkcZ066pYU2YCnnkjPCh9qGubikx9/Tn5HNR7Ll2S1M6jCJn078RLcl3Xj555fJMNr+mpjTsGFDAJYtW+ZwG65C/d7z9PTUhGBVBHalc0nl4Ycfdmi/P/5QhEtX1QT49dyvfPD7BwxpMoTdo3fj5+nnmoZLEF9fX7Zu3UpycjIzZuSJZY6KS+o+1nBzg8ceg7Jwz0CISwJBGaMgcUkdyJw7d05bL8QlgUAgEKi4Ulzy8YGgoJITl+bPVwSu0aMdP74ruXLlCqA4lwT3F6rDxJ7wLYMBPvkEdu2CQYPA/LJyVFxS++CqsDj13AoSl1QMkoGPen1EwqQE3ujwBl9Hf02ljyvBIMC+gmcmbN26lXPnzjnegAXsTTKu5iDVh8WpuDrnEjj2PbBiBezfD126uKYPmdmZTNw8kboV67LosUUE+QW5puFSQJMmTYiMjDTJQVtU4lJZQohLAkEZoyBxSb3blZmZqSW5E+KSQCAQCFS8vLwwGo0Wf0McITy85MSl6GhlEuaqO/zOcurUKUA4l+5HHBGXAEaNgtmzleS/v/1mus5Z55KrwuLUfJzJycn51smyzIwZM4iJiTFZ7uPhw8e9PmbbyG2MaDYCGgLjYfCPg5n751yrib/Nyc7OJjIyEoAfXVzX3cPDg6efftrm7YvbuVS7dm27tr94EUaMgE6d4E0XFHE7E3+Gx1c8zqn4U8x6ZBZe7l6F73Sf0aBBA+179+rVq0RHR2vXjD0JvYW4lIcQlwSCMoZa9c2auFQjN05BjUEW4pJAIBAIVIKClDvXN2/edEl7JSUuXbsG168XTbluRwkLCwPyqroK7h/UkEZHEk+PGqU8Hjxoury0hMWp52bJuXTnzh3efPNNHnroIYv7dq/Vnbn958LXwEk4eO0g4zaN46mVT7Hv8r5CBZmcnByqVauGt7c3CQmuz1ezcuVKm7fVi0t6UQlKh3Np3Tql8uDCheDr69yxE9MS6fF9D/Zc3sPMXjPpV7+fcw2WUsLCwkhKSiIrK4s3cxW5bdu2IUmS3TmXBApCXBIIyhiqUGS0UPs2IyNDq1CjDiKEuCQQCAQCFVUAuXr1qkvaCw+HS5eU5NqOkJOT41Dy60OHlMfSJC5t3LiRHTt22F0dSlDyOOpcAggIgNq14dgx0+WuSOhd1GFxaijnvXv3Cu5vIrAGzk08x6e9P2XD6Q10WNiB7t935/jN41Z3U3u33LEAACAASURBVMegvr6+Dr8ehSFJEv/+978L3U4fFhcSEsKwYcNYs2YN4FrnUoUKFRg/frzdY+8NG5Q8Xs4UJ8jMzmRFzAoGLB9AXEocv434jdc7vO54g6Uc31wVLjU11eR3xJ7wbxEWZ4oQlwSCMkZBYXGZmZkEBAQAeXe+hLgkEAgEApXQ0FAArl275pL2wsMhLQ1u3XJsf0edSwcPKslVW7Rw7LhFQdWqVenatWtJd0PgAM6IS6CIS7Gxpstc6VxyRvgoyLmkiksA27dvt7i/frwpSRKvtn+VG6/f4Ku+X/FX3F9EzI2g4VcN2Xd5n8V93dzc8PHxITU11eFzMMf89fjPf/5T6D560c7NzY3ly5fTrl07wLXOpaysLO01t5W7d2HnThgwwPHjGnOMDFo5iKd/eprT8af5pv83RIZFOt7gfYBeXFLDP0GIS84gxCWBoIxRWM4l1dqpDk6MRqMWSicQCASCsk1ISAgA169fd0l74eHKo6Ohcc6ISw0bgpgTCFyBs+JSrVpw4YLpMmcTeutzLjkqfBw7doyLuRfnnTt38rWjF5cs5WQCy075ij4Vebnty5war+TzycrJovv33VlyeAlJ6Unadjk5OUXiXLLUJ1v3Ud9rPa50LmVlZVk8RkFs2aIkhHdUXLp17xaj141m4+mNzO4zm7jX4hjdspRUOihC9OKSmtOrVq1aeHp62lz5UYhLpghxSSAoY9grLgnnkkAgEAhUVHdrSkqKS9pT04rs3OnY/s6IS6UpJE5wf+MKcenWLdBfVvHx8Q61pXcuORsWN3XqVO357NmzadCggfb/wYMHmTlzpva/te+EgoScYP9g/u+h/2P/8/tpWLkho9aNotbsWuyI3QEoY1CDweBy55Ij75PqZNE7XFQMBoPLnEuZmZl2O5eWL4eKFcFK6qsC+e38b9SYVYOlR5cyrcs0JrabiJuhbIz7VXEpOjpay+m1d+9eQkJCiIuLK3T/7OxsUlNTRc4lHcUqLkmStFCSpJuSJMXols2UJOmkJElHJUlaI0lSoG7dFEmSzkqSdEqSpEeKs68CwYOKtYTeOTk5ZGVlibA4gUAgEFjFz88PcJ24FB6uiDyOFoJyRFy6fl1J6N2qlWPHFAjMUcUlW0NpzKlVS3nUO/gO5SYGU5Po24qlanGuEj7UYi85OTm0adOG06dPa2KLI+KSShW/Khx44QA7R+0ktFwojyx7hHE/jyO1Qipubm5IksS6deuYOHGiw6+xHvM23N3dOXToEPPmzdOqJavIssy2bds0QcqSuOSqsLjs7GxkWbZLXPrmG1izBsaPB3sDDc4nnmfIj0OoW7Eux8cd552u79jZ4/sb9fds8+bNAHz88ceEhIRQo0aNfJ8DS9y7dw9AOJd0FLdzaTHQx2zZViBCluVmwGlgCoAkSY2BYUCT3H2+liRJzHAFAidRBxrm4pL6o2kuLmVmZlr8IRUIBAJB2cPNzQ1vb2+XiUsAgwfDgQOOhcY5Ii4tXKg8duxo//EEAks461yqWVN51OddOphbPs5e0eLOnTsAlC9f3umcS8ePH6dbt24my7Kzs5kzZ472f/369QFTcenevXtER0cDtoegebh50Dm8M3tG7+Gpxk/x/ZHvudH/BruCdnHE8wj4wpdffmnilnIU8/fJ29ubrl27MnbsWMLDw7l8+bK27vDhw/Ts2ZOdufZKHx+ffO25KixOH9JoCxkZ8Prr0LMn6ExmhSLLMvOi5/HwwocBWDdsHY2rlL0qlapzacmSJfj6+vLqq68CULlyZaKjo/nzzz8L3F/9zAtxKY9iFZdkWd4FJJgt+1WWZfVbZz9QLff5Y8AKWZYzZFmOBc4CbYutswLBA4ybm1u+H3v1B021dqr/Z2Zm2h37LRAIBIIHF39/f5eKS089pTxu2GD/vvaKS+fOwfTpyjHbilGlwEW4IiwO8vIu5eTkaM4lW9tcvXo1W7ZsITExES8vL3x8fJxyLqWkpHD27Fm6devGli1b6NWrF6AIR1FRUdp2lSpVwtPT0+Q7YcSIEURGRpKUlGR3fqNA70B+GPQDV1+9iu8JX2543YB+wETgYfjP4v+QmJho9/nosSQu6as06nNJ3cqtNnDixAnAsrjkKueS6qiy1bm0a5cSSvl//wf2BBnM2j+LsT+PpU7FOvw64ldqV6jtSHfve1RxCaBFixZapMbAgQMBhLjkAKUtS+9o4H+5z8NQxCaVK7nL8iFJ0ovAiwDBwcEmX3j3MykpKQ/MuQiKn4I+PwaDgdjYWJP1SUlKAsVr164hSRKnT58mKiqKuLg4MjIyxGexjCK+hwTOIj5DDx7u7u6cO3fOofc1Li6OGzdu0EJXpk2Wwdf3YXbsuE5ExNl8+xT0GYqLiyMzM9PmvixcWJPMzHCGDNlHVJRjQoDg/qOov4fUSmrHjx936Dg5OeDu3pm9e68QEXGeS5cukZycTGBgIKmpqTa1OWjQIAD69euHv78/O3fu1PLInDx50u5+HT9+HFmWMRgMeHp6EhERwdatW/nyyy9NxI+0tDQ8PDzYtm2bdoytW7cCsGnTJpNt7e2D129edEzvyMYDG6En0BNSSGH418OZ3HGyXW3pMc+nI0kSdevW1YSkbdu2aQ7+ffuUKnbnzp0D4OjRo9qYWSUhIYHk5GSnP2Oq6+zixYs2tbVwYW3c3avh5rabqKjCxa17xnv8HPcz887Po3PlzrxT8x1STqcQddq5ft+vxOqsglWqVNFe8/LlywOKa62g90EVHC9cuGDT+1UWxkOlRlySJOlNwAj81959ZVmeD8wHaNOmjfyglHGNiooSJWkFDlPQ58fd3Z2wsDCT9epdmoiICLy8vAgJCaFr166UK1cOWZbFZ7GMIr6HBM4iPkMPHpUqVcLf39+h91VNMGweQlK7NmRnV6Nr12r59inoM/Ttt9/i6+trc18+/BAiImDw4A529Vtwf1PU30OqgyE8PNzh41SrBgZDDbp2rcF//6tMhzp37szGjRvtalM/hlPFknr16nH58mWqV69uc1snT54EFBdSjRo1tHHiW2+9pYUPAdSoUYM//viD6Oho6tSpQ/Xq1bXru3z58tSoUUPb1t7Xxs3NjRrVa8AGlBliINATtkRs4aWQl3i84eN2tady6tQpk/+9vb0JCQmhUqVKxMfHU6lSJa2vF3LtZKoDq2vXribnBIowcffuXac/Y6ro1bhxY5vaev116NAB+vbtXOi2SelJtPu2HafjT9OjVg/WP70eXw/fQvd7kKlcubL2vG3bttprLssy7u7uVK5cucD3Qc1BNnjwYOrVq1fo8crCeKhUVIuTJGkU0B94Rs4bbVwFqus2q5a7TCAQOIm7u3u+nEv66iJeXl4mOZdEWJxAIBAIVBwNiysoJ0mNGmBD/tR82BMWJ8tKbqd27ew/jkBQEM6GxQGEhcHV3JnOwYMH8fHxoXnz5uTk5Fis8GuNkydPUrFiRQCTsLiRI0fmy59UEKdOncLPz4/q1ZXpmD70Jzk5WXuuJkUGeOeddzhx4oQW3tW/f3/aFXDBbd++XRNvLKFWixs6dKiyIAlYC7V9ajNo5SBe2vAS2Tm2vzYq5gm9U1NTSU9Pp0aNGkiSxPXr17V1+nOFos25pPbLlnF3QgIcOgQ9ehTebo6cw4g1IzifeJ7Nz2xm64itZV5YAmjYsCG1ayshgf3799eWS5JE+fLlNSeZNQ4cOEBgYCB169Yt0n7eT5S4uCRJUh9gEjBQlmV9jcn1wDBJkrwkSaoF1AMOlEQfBYIHDTc3N22gcvfuXTp06KB9MQpxSSAQCAQF4e/vr1XJUZEkidGjRxe4nz5Jrjnh4UWf0PvsWUhMFLmWBK5HDf1avHgxPzpY+rBePTh6FIxGpTR68+bNtZww9lRI+/vvv6mZmyFcdQo6kg/o9u3bVKlSRWtDL6okJycTFBTEgAEDmD59On/88QcAixYtonHjxiZ5ltLS0iy2L8syPXr0oHXr1lb7oFYsXrFiRd5CI0ytNZUxrccw/9B8Fh9ebPe5mYuAiYmJpKSk4O/vT1BQENevX2f//v088cQTTJw40WRbe3IuRUXB8uVw5oxt/froo4+AwnMuZWTA998rgnlh4tLey3tpPb81G09vZNYjs+hTt4/2npZ11BBvo9FIs2bNTNYFBARo4a7W+PPPP4mMjBSvp45iFZckSVoO7AMaSJJ0RZKk54GvgHLAVkmSDkuSNA9AluXjwErgb+AX4GVZlu2XpgUCQT704tL58+e1eHLILy5lZWUJcUkgEAgEGn5+fhadS4sWLSpwP/1vjTk1aih34u01ROXk5Ng8sN++XXl8+GH7jiEQFIYkSXh4eHDmzBmGDBniUBsDByrXwK5dcObMGZo0aeKQI2r27NksWLAAwKlqcWp4mIo6LgRFXAoLC2P9+vVUq1aNtm3b5rmLsC5m6fuh5i1S80JZQhWXQEkkfuCA4jPwM/jx1aNf0aF6B97c/ibJGclW27DEyJEjgbwKyTk5OcTGxmrhcVeuXKF3795ssFBloDDnUna2IhI+/TR06wbDh0OjRvCvfxUuMn399dcA2jlbYs8exeX2yitKOHFBYvnt1Ns8+b8nSUxLZPFjixkXOa7gDpRRLL3ehTmXUlNTOXbsGG3F3QoTirta3NOyLFeVZdlDluVqsix/J8tyXVmWq8uy3CL3b4xu+w9kWa4jy3IDWZY3F2dfBYIHGX21uPT0dJN1fn5+eHp6mjiXbK1aIRAIBIIHH/OwOFtDdvbu3Wt1XXi48mhvaJytzqW4OPjoI2Uy1qCBfccQCGzB2RtxjzwCPj7w00/K2Ewdj4F94tLTTz+Nl5cXgFPV4szFpeDgYO15cnKyVl1YpWPHjoW2qXc05VVk8+LwYTDLkQ2YXt++vr6akys7OxtJkvj8kc+5ce8GI9eO5MrdK/kb0DFkyBC6dOnC1q1bOX78OAArVqxg3bp1Wn98fHwICQnhyJEjJCcnM2DAAJM2PD09LQoRqnPp4EEld1bz5sr7OH06HDsGL7wAX3wB9esrYtDNmwW/TtYEt2PHoH9/qFgR1q2DI0fA2hA9R85hzMYxJKQlsG7YOv7R4h/CYWMH5cuXZ+/evSxbtszi+sOHD5OdnU1kZGQx96x0U+JhcQKBoPjRO5fMxSV/f3+8vLy0gYwIixMIBAKBHvOwOPMQOWscPHhQe24uSKn5ce0NjbNFXEpNhb59lQndihUg5leCosDZG3G+vsrndM0aSE/PwMvLS2vTFnGpYsWKjB8/nipVqmjLLIXFnT9/3qb+JCQkaLmbANq1a0erVq2oWrUqt2/fpkKFCibbjxs3Dm9v7wLb1I85FXHpOeAOLVsq7p4rZvqQ3rkE+cWytmFtmdF9BpvObKLDdx1IzUrFGj/++CO7du2id+/e2jI/Pz+TfDmqc+lqbvKrpk2bausCAgIsupYUPEhNbcGoUUqo2pIlcP48vPWWUkBg3jzl/88+U1xNY8cq4Y/W0Od8UlmzBlq2BIMB1q9XnG66NFgmXEi6QNsFbfnpxE+83/19moc0t34wgUWmTZtGaGgoo0aNyjdXAjSBsnlz8drqEeKSQFAG0Sf01tucQfmhFTmXBAKBQGAN87A4W8Ulff6K6Ohok3VF6VzasEG5w790KYibzIKiwpmxUmxsLPHx8XTurLjs0tP98Pb2tsu5ZMlpbiksbtWqVTb1ydy5BNC+fXtSUlI4ffo0jRo1Mlnn5uZmkp9o4MCB+Spj6fMvXbqUCnyGp2cMX3+thMQ2a6aEe6ndNReX1Od6sWxKpylseXYLl+9eZtb+WTadm4qHhwcNGzYkJCQEUMSlqlWrauv14lJ4eLhVcWnPnle4fHkFMTEwcyaMHKk4mPTUrKmc27RpsHo11KkDhw/nrdcnDh8/frz2PCcHjh+HyZMV59OpU9CwofVzypFzeG7dc5xJOMPSJ5byRoc3Cn8hBPno2rUrU6dOJTs7WxOSVFatWqUtCwwMLInulVqEuCQQlEEKci6pNmzhXBIIBAKBJVTnkjrBs7VynP5mxkMPPaS5AwCqVgV3d9udS6dPn6ZHjx7Ex8cXKi5t2gSVKil3+gWCosKZsVLt2rWpW7cu1bU62dXw8vLS2rQloXdWVpZVcUnvFLRFDM7OziYpKSmfuOTt7U1ycjJGo5EmTZrk20+//dtvv50vuXlaWhrp6bByJcye3Q7wp3Ll/8fYsfDbb0py6lmzYP585Xyys7Px8fFBlpUk1kajATDkcz52rdmVxxo8xn92/4fLdywXDvDz8+OZZ57J1x+DwcBjjz2mLVOFJoAWLVoAyuvYtm1b2rRpk6/dO3fg6tWW+PuvIyYGRoyweHiNyZMVcSk1FTp2VM5VlvMKHixfvpyQkBCysxWRysNDcT9dvQqzZ0PlytbbzszO5INdHxB1IYpPe3/Ks82eFaFwTqC6ko4cOaItMxqNDB48mNmzZwNooZoCBSEuCQRlkMLEJQ8PD20gI8QlgUAgEOjx9/dHlmXNhWCri8ncKat3Y7i5KXf6bXUu7d+/n+3bt7N3794CxaWcHNi8WclnU0COXIHAaZwdKyUlJencLtXtdi5ZKsCiCgv6/W0RgxMTE5Fl2aK4pJKT05aZMxUxpWFD5TrTu36Cg4PN9n+CV18tT2AgDB0KZ8+GAP/Gy+scAO3aKaJTz57w+utw7FgG4MnOnT2oXx+8vaFJk7pAKmfOVMScmb1mYswx0mhOIxb9lb+4QFZWFtWrVzcJY0pNVcLonnjiCQB2795tIi7VrVuXSZMm8ccff/Dss89aTPCtpJIz4O+/Egt6GwsWLODLL7/U/pckeOIJ2L0bWreGl16CNm3g6NE4AKpXr8GyZcq6pUvhscdg7lw4dw569crfvspv538j7LMwpkZNZVCjQTzf8nnrGwtsok6dOvj5+ZmIS7dv39aeu7m5iby0ZghxSSAog+jFJUthcUJcEggEAoE1/HMTfahCkl5QmjNnjtX9zH9vzKlRw3bnkjpBLiws7uBBuHULHn3UtnYFAkdxxVjJmnOpMHEpOzubnJwcq84l/bVni3MpPj4eIJ+4lBcW1p9//KM+kyYpjqOLFxVn4NatnVCnl0FBQTrXzERgNVu3+jNwoJIz6M03PwZmmly/kgQLFigV1yIj/YAkfvutPbVqwXvvwRtvJAKZREXl5UlSqVepHodePETr0NaM+XkMx2+ahjIZjUY8PDzo3LmztuyRRx4BoFu3bgAMHTpUyyUVERGBJEl89NFHFh1LKrt2gSQZ8fT8y+L6F1980SRcUKVBA+W1W7BACXt79dVWwCg+/LAFI0Yor8HixUpi8DFjQKd5mXDr3i3e3/U+ff/blxD/ENYPW8/yQcuFY8kFGAwGmjRpYhIWd1OXjd3X11e8zmYIcUkgKIMUVC3O19dXiEsCgUAgsIqfnx+QJ/DonRD6nCrmZGRkmIQQGM0y2oaH2y8uAQWKS5s3KxPW3DmkQFBkuGKsFBwMHh4y5s6ljIwM5s2bZ7U0ujpms0VcssW5ZE1cysioBHwH/EijRnDjhpIjKi4Ohg2DpUtrAkromVqxrl+/Z4D/B+xg48Y/WLkSBgyA9HQlx5D590DNmhAdDYMHpwA+9Oz5N7/+Cm+/DRMmpAA/ER1dC0tfNY2qNOLHwT8S4BXAyLUjScnME6FzcnJwd3cnIiICgOHDh2uvl1ol+YMPPqBdu3b06dOHn376yerrcyPlBl0Wd2HttqvMmgVVq54AbMs9pxIbG8vLL/+TkSMzWbkSrl8PABaxebMP06creeL+8Y+CCxBEX4um4ZyGvL3jbR6t9yh7Ru9hQIMBeLgJN42rqFKlComJidr/t27d0p6LkLj8CHFJICiDuLm5abHd5uKSavEU4pJAIBAILKE6l9RJqt4JoU5Kzdm3bx+JiYkm5cvN88jUqKHkFbGl6rot4tLdu0pYSdu2BecpEQhcgSvGSgYDBAcbgeom1eI2bdrE2LFjCQwMtCjgWhOXVFdF3ljvSX7//R9MnarkCgIl34/5NWdJXIqLg9mzRwDDCAnZzC+/QFCQsi4wEL7/Hho0yMFgeIc6db6kcWNl/c8/LwOqAtPJzMzru/q9Yel8GjeGqVMvA+X55z9jtOVKQu/vSUvzxJr2E+QXxIIBCzgUd4hGcxpx7MYxk9dnwIABAIwaNcpkP09PTyRJonz58mzevJn69etbPgDwyb5P2H1xN0PmT6FGDejS5VuTJOMqSUlJVtv417/+xbfffsvWrVsZOBCeeOIVQkO7cOeOxFtvKZ8FS6RlpTFr/yyG/DiEhxc+TIBXAEfGHGHdsHUEeAVYPZ7AMQICAkySrevFJevVA8suQlwSCMogMTEx/PHHHxw6dEgbcPTp00dbr4pLsixbjOEXCAQCQdnFPCxO74rQhwzo6dChA4CJuGTuWGjXTsmRtGVL4X2wRVwaORJiY+GDDwpvTyBwFkfHSubXQVBQJlDNxLl0+vRpbb2+kpiKKp6Y98HUuVQP+JErVzoxfboiCDVrpghAXl5Qrhx8/bWynzqZDghQxIrbt2H4cLh3zwfoyJgxR6lRw7QPkgTjxxvIyanDuXPjMRqVULnp02Hu3FPADhMhSb2GrbkdlXxId00m8Mr5RFG58h2++grMXjqNxxs+zp7Re0jNSmVq1FTtNXZ3d6dq1arIskyvghIYFcDdjLvM/XMuOeSQVW8Vny45ha/vPZOKfCqxsbFW21G/R1Uh7/btI9SpI5NrDNVIyUzhdPxprt69ypwDc2g4pyGvbHmF6GvRPN30aQ68cIBmwc0cOhdB4ZQrV86k0qlwLhWMe0l3QCAQlBxr167Vqh2sW7dOu8Olikvql6mf+S+dQCAQCMos5mFxaj6YsLAwbty4YdO+kH9S3acPVKkCS5YoYTMFkZKSQoUKFUhJSbGY8+L8eVi3Dt59V6lAJRAUNY6KS+a5yKpUyUBxLt3V2rxy5Yq2fuHChbzzzjvU0Kk7tjmXxgFGWrR4kqee2sTKlUoIWtOmSkLuTZuUSmZDh+YJPqqw89RTsH8/LFiQQ8OGX9O2bVuL5/LPf8KxY0rCat09S44fV651vZCkz9mWnZ2d60rKQ022rZ/AK9vI9OnzJ8uW9WTcOKXamiU6VO/A2DZjmfH7DM48dMbi6+MIc/+ci0yukOSWybzzr+Hn7se92vf44egPuBvcMcpG7mbcZcefO+ARIAt+O/cbkWGRBHgFIEmS5gqLj49HlmVup94mpG4I3x36jtUnV3PpziUu37nMnQzTUMj21dqz+LHFdKvVzelzERROQECAibhknnNJYIoQlwSCMsz06dO15/pBkSouHTt2DIDGjRsXe98EAoFAUDoxD4tTJ8fVq1e36lxScXfPG3qah8V5eCjuiLlzISEBKuYvCKWRnJxM5cqVadGihcUbINu2KY9DhhR6OgKBS3CVuFS5chpQDU/Pc1qbV69eNdlm586djNDVvFcFXkviicFgID09AxgCrCM9/SJTpsCUKabbPfmkUvJ+/nwICFBc7d7e3vz5J+zcCR9/DM895w60t3ouXl7wzTf5l6silSXnUk5ODvHx8QSpMXa5mAtc6rkAREb+TXBwTz79FF57TUmObYmxbcby4e4P+e7Id4Dp948jZBgz+HDPh6RmpeZ2KJttsdvICs4iu2o2z6x5xmR7CQlaAe7Qa5nilHKT3KjoU5GcijkwGj678xkffPwB8f3j+Zu/2b5hO/Uq1qNxlcZ0Ce9C9YDqhJYL5U7GHZoGNaVzeGeRRLoYCQgIIC0tDaPRiLu7u4lzSUR25EeISwKBIB+quKSW3tSXbRUIBAJB2cY8LE6d2FavXp3t27cXuK8+dMTcuQSKuDR7Nvzyi/LcGikpKfj7+7Ny5UqL6/fuVVxQ1iadAoGrcZW4VLFiChBGerq/1ua1a9dMtjHPl2ktLA4U91JSkj8QSoUKMcTGxlqsstikCXTqpDgHX3hBaf/6dV9eeklJNP7SSw6dHpD3nXHlyhXq1avHt99+y4kTJ7T1169fzycuWXcuKYLUyy/Dp5/Cr79av87DAsIY2GAgS48vhRrOO5eWHl2KMdv0eyvdmI4kSQSuDiTpWhIY4K+DfxFUPoj3/v0e33z1DXjACx+/QMO2DYlPiyc+NZ5d0buIz45HypG48+cduAadWnRi1tuzaBnSUghIpQQ1lDshIYFDhw6ZuHPN3XYCkXNJIBBYQC8uVaxYkbCwsJLukkAgEAhKCdbC4qpXr058fLxF0UhFPym2tF3r1lChglKiuyBu3bpFhQoVqFy5MpUtZOveuxfaty+40pJA4EpcJS6VL69cV0lJAZoYkmmWcds8T5G1sDgASfLnzz8nAtk0bx5PWlqa1VxAzz8Pp07B7NnDgPW0aOHH33+rbiZHzk6hSpUqhIeH88UXX3D27FmeffZZYmNjefzxxwFFXDJnxYoVgGXnUnZ2NrVqKUUAfv+94GP/p8d/CPQKhFFwJedKwRsXQLaczbSoaaRk5a+2Z5ANyG4y3AZuQk3/moSWC+VKbO7xsmDdjHW81uE1ZvSYwTcDvqHfvX6wBJ68+yTGn4ywDxplN6JV1VZCWCpFqHnHPv30U/r27cvatWvp0qUL33//PZ9//nkJ9670IcQlgaAM8+mnn1pc7uHhQWZmJkeOHKF58+biR04gEAgEGtbC4tQcMPqwAXP0JZ3Nw+IA3Nyge3dFXDLPj3vlyhXWrVuH0WjkwIED1KlTx+Ixbt+G06chN4e4QFAsOCoumbuQKlRQcuzcuuWHl5eXxX3sEZeMxhe5c6cFMJwuXRQh9u+//7bY7siRipCUkuIDtGHKFCV/2cCB9pxRfiRJom/fvlrYrJpD6p///CeQJy5lZmZq3xGrVq0CICQkRGtHFZfUymwPPwy7d+f/rtDToHID1vRdA+mw5u4acuT8Vd1sYc/tPfnyH6lkG7JJ7pQMucNl9f2JjY3VqSnlnQAAIABJREFUCuZ07drVZB/1fdd/D1p7vwUlh3rzQv08giKWjhgxgtatW5dUt0otQlwSCMogP//8M+vXr6dfv34W13t6epKRkcGxY8dESJxAIBAITFDDVMzD4qpVqwbA5cuX+c2K9SghIUF7bs3h1LEjXL6siER65syZw+OPP86LL76ILMsEBwdb3H//fuWxvfXUMAKBy3Ek5EqW5XzOpcBARVy5edOP8PBwnnjiiXz7paenYzQa+euvv4A8McPb29vCUboAfwMrtTHd8ePHLfZHkpSk3M8/PwNf33p88IFEaKjdp2WRvn37mvzfvn17OnfuDOSJS0OHDqVixYq88cYbSJLEW2+9Rfny5bV91DCk7OxsQBGX4uIUAawgfA2+EAXH048zbNUwrqfkd0oVhCzLfHfhO1Iy87uWVHL8c6CR8lwVji5dukSDBg2IjIw0SQqt30bvShNhVqWP8PBwAM7rPmTmIZyCPIS4JBCUQR599FEGDBig3X02x8PDg5SUFNLS0oS4JBAIBAIT3Nzc8PHxMXEueXh4aGJP79696dWrF0ePHs2370CdBcKScwmgbl3l0TxyR3U9/PDDDwAWJ92g5Gtyd4c2bWw/J4HAWRxxLvXv35/nnnvOZJnBkAzEExvrgyRJrFq1innz5pkcJy0tjXfeeYdWrVqxePFiLQ+M+aT3wgWA3sAvjBgxgq5du1KtWjWr4pJKeno6Pj6WhCrH6d69u8lrNHLkSPz9/fHz89PEpbVr1wLwySefkJ2dna+gjLlzqXt3Zfm6dQUfOysrCw7A8KDhrD25lubzmhOfGm9z36MuRHEzveBiBXgCfQCD8volJyeTkpJCWFgY5cuX584dU9eTJXFJLsiCJSgRVHHJ29ub4bmJAAMDA0uyS6UaIS4JBGWYgsQllWbNmhVXdwQCgUBwn+Dv72+Sc8nLy0sTl9RJ1KFDh7Tt1d+SRYsWaW6L1NRU/v7773xhQbVqKY/mboTbuVamjIwMatWqRatWrfL1a/Nm+PprJbxHVIkWFCeOiEvHjh3TrgcVxcn0M1u2+JGSoggqL+myafv4+JCens6BAwcAeO655zRxSR9CBkqya/AA5rFkyRIqVKhAkyZNrIbFqaSnp1txQTmOv78/nTp10v7v1q0bAMHBwSZJkvU0atTI5H99Qm9QEnl36KBUvtNFLeVDdUkOrjqYfc/vIz41nje3v2lz36dsm0J6TnrhG3oBLRUnmZqEPTQ0tEBxSS+yC3Gp9BEYGEjjxo2ZMmWK9ptj7kIT5CHEJYGgDGOpfDPkiUtubm757hoJBAKBQKAXlzIyMvD09MznmlArjoIygRo0aBB+fn7ab8ywYcNo0qQJEydONNlPFZfMnUv6XE76UBk9r78OjRvDl186dFoCgcM4Ii6lpaXl+ywrosNcUlIM/Pe/ecunTZvGlClT8Pb2Ji0tzaTam+r8Mb8GlRDRW8AZLX9mkyZNOHHihCbQWOuXPpG2q1BzLAGaGB0SEmIxobckSTQwKwOnT+itsn49tGgBL7wA6Vb0H1XAcXd3p3Voaya0ncD8g/OJuhBVaJ9XxKzgj6t/FLodoIhLPSHpXhJxcXEAVK1a1WbnUkHviaDkiImJ4e2339bEpXr16pVwj0ovQlwSCMow7u7uAFqyQRV14F+lShWX37kSCAQCwf2Pn5+fSc4lT09PAgICTH4zDh8+rD1X3U2Q99uj8ssvv5i1DUFB1p1LYFlcio2Fv/9WcsYI15KguHFUXBo9ejQtWrTQEtQrzqX9RETksHRp3rbvvPMOM2bM0JxL+vw8cXFxBAYG5huzKdFvR0yWhYWFkZaWRnJyMrIsExMTk69fReFcAiWnkop6DVsTl2rVqpVP4DIPiwOoVAmmT4c7dxTnoiXME55P6zqNsIAwui3pxsg1I8kwZljc71ryNV5Y/4KNZ5eLGyw7u0wTw6tUqUKFChW4efMmCQkJyLJMamoqP//8MyDEpfsBSZKQJIlu3bpx4MABxo8fX9JdKrUIcUkgKONcv36dNWvWmCxTf3z1d8UEAoFAIFAxdy55eXkhSZLJ5PHIkSNamIe6DeRPfGypQlLt2vmdS7dv36ZixYqAZXEpd67Go486dk4CgTPYm9BblmXS0tLw9fWlWbNmmhtHdbR06gTHjuWvhKY6l/Ti0sWLF/OFxGVnK2Krksw7D321x/nz59O0aVOioqJMtikqcUmP6qRSxSVZlnF3d6d+/foAVp3zBoPBxLkESu6loCDITceWDzUsThW2y3uX5+iYo0x5eApLjy6l4ZyGTN46magLUVxPuc7ey3vptKgTYZ+FcS/rnn0n5gnLryznasJVQBHin3nmGbKzs/n888+pW7euSeTAhg0btOdCXCr9REZGivlRAYhXRiAo4wQHB+cbQKgDJPWHXyAQCAQCPeY5l1TXxkMPPaRtk5iYqJUc14tLeudSrVq1tPARPS1awJ49kLs7RqORxMREHs1VjqyJS/XqKX8CQXFj7sgrjKysLHJycvDx8cHd3R2j0UhcXByrVq3C3d2dRo0M3L0L5umIfHx88oXFXbhwIV/1xP/9D5RLNMpkuV5c2rlzJ6BUNdOTmppaJGFxkF+ECwoKIiEhgffffx+j0chLL71E5cqViYyMtLi/wWDIJ8K4u8PQobBhA1hKh2PuXAKo4FOBGT1msGn4JupWrMtn+z+j25JuVP20Kh0XdmTv5b00D26Ou2Tf+wqQnpnOK6tfARRxqWXLlvTv35/58+ebVB0zR+RcEtzvCHFJIBDkQ4hLAoFAICgIf39/LSxOLxypok/VqlWBvLxLal4mMJ2Et2/fnnv37mlClcrkyZCTA++8o/wfH69UdnrooYeoW7cuddWScrncuwc7dkC/fq48S4HAduwVl9LS0gA0cSkpKYnQ0FCOHTuGm5sbarqhU6dM9/P29s6XBD82NtbEuWQ0wrvvQtOmAGtNttWLS8nJyQCUK1eOEydOaNfZ3bt3reY1c5br16+bhMElJSUBMHXqVEBxMsXExDB58mSL+7u5ueVzLgEMHw4ZGTBjRn63l+pcsuQu61uvL1tHbCV+Ujxrhqxn1iOzWT1kNWcmnEFGxigb7T9JdyA3d5xvbozuhAkTtIqXKu3btzf5XziXBPc7QlwSCAT5cCRvgEAgEAjKDn5+fhadS+3atQNgzJgxQN7EUZ9zST/BUydX5jlXataEl1+GxYshNtZXy7dUpUoVjh49yptvmlZ6WrFCmVgKcUlQUujD1GxxoOjFJTc3NxOB1WAwWBWXvLy8yMjI0MRdgHv37pmIS/v3w+nTShU1MO2LXlxSq16dO3eOVq1a8f777wOKuBQQEFDoOThCxYoVTVxW6neFGioWFBREcHCwxXBZsOxcAmjXDkaMgI8+gtdeM12nT+htjWPRAYzpNoAfX59IC+8nqF2hNkfGHEF+R+baizJtN93G/X2ZhIky8jumf/8v/f/BNEz/vlHaVcWlnj17EhYWph0vJCSEcuXKmfRBlLgX3O8IcUkgEORDDZMT9lyBQCAQWEIfFqcPoalduzYZGRmMGjUKUBxLKSkppKena04I/QRPza9iKTTuzTehXDlYtKiWJi5VrlxZm4yr7N+vCFEdO0LXri4/VYHAJvSfSUvOGnPMnUt6DAYD1auDj09+ccnDwwOj0ai5jlT0gs2ZM8pj27b5j2vJufTGG2+Qnp6uicF37twpMnHJnAYNGtCsWTNNLKtSpUqB27u5uVkUlyQJliyBcePg/7d372Fy1vXdx9/f7G6yOWA2HIKckgjI+YwVNIogcvBBr+fBWrWlgqf6gFoVRFsPVITCYz0gtaLPQ4uHllqVFrTaXlVEDoInCKCIJBAIVQhIAuQAyW6yu7/nj3vuYTY7m92dmZ17Zuf9uq5cmdz37O5vNt/97cxnvr/f/bnPwa23Pncuf5z5Y690++1w8MHwspdlSxBvuw322QfOPju76uQFF8Aee8Cddy7g0kthwYLRY9reHjx5XUTEiFB8yZIl5VD+rLPO4jOf+QwXXnjhdh+71OoMlySNkr9ImMiTI0lS56lcFrftEpqZM2eWXzRt2bKF5cuXA89t0Fv5QnrnnXcG4Kmnnhr1NXbaCd76VvjZz3bisceyF735ht6VPv7x7L7f+U6294pUhEaHSzNmwH77QenHp6y7u5utW7eycePGEXtmVnYuPfggdHXBokWjv261cCmXL7ebys6laipDn4ULF273vtU29M5FwKc/DfPnZ0FTLl/ut9NOO424/4MPwoknZstqP/tZeOIJ+O1v4S1vgS9/Gd77Xig1c3HVVbfzwQ9WH9NEt5E455xzOPnkk4EsXMofxz777MMHPvCBcpeT1K4MlySNkj9Zce23JKma2bNns2nTJiDrCth2eUe+pKUyXDrggAOAkcvi8mUgecfEtk4+GbZuncFdd2VLZrbtPLj/frj+enjHO7KASSpKo8MlgP33H925lG/+vXHjxhFBTGXn0oMPZsFStQvYjRUuHXzwwQwMDJT/TNWeS9VU/lzngfNYxupcys2ZA6ecAtdeC9//frZ3Wx4uVYbTn/oUHH54FkjfeCOcdx7ssgvstVcWLK1fD2vXZp2Rt90GixZtHvNrTubqYfnS4b322ovVq1cD2YUNpOnAcEnSKHnnkuGSJKmanp4eUkoMDQ1V7XLIO5cGBgbKV4xbVGqjyF9IL1q0qPwCdv369VW/znHHQU/PMLfcsifw3IvQH/84O3fqqdkL6HPOafADlCZpsuFSHs6OFy6tWpXtJ5arXBZXuYRs286lffap/nXzvY0q91yCbG+g/v7+cuBUROfSjjvuWHXT7Urb61zKfeQj2abep54KZ54Ja9c+yfz588vf53vuyS4acNxx8POfQ7VsZ/bsLLA+5hjYZt/tUSZzAZz8e75w4cLy3Gi4pOnC5mFJo9i5JEnanvwF4NatW7cbLm3ZsoU1a9bwvOc9r/yitru7m2uvvZZjjz22HC6N1bk0dy687nWP8M1vHgC8j3nz5rFmDfzhH2YvHg85JNtvqeJ1tVSIyYZLW7ZsAbIuv+2FS8PDWVhUWlU6YllcZedSZbj00EPw+tdX/7p5V+G1115b7p7Kj/f39/Poo48C1ZegTpU8XBpvSRyM37kEWUfS6tXZkrZLLoEXvvC19PTM4dOfzr6fX/pSFhx97WtZt1K9JtO5tGbNGiDbWyrvqDJc0nRhuCRpFPdckiRtT/5iuL+/n02bNo0Kl7q7u4kItmzZwurVq9ltt91GnD/99NPLt+fNmzdmuATwznc+xN13P8uKFX/DT3/aw+c+B+vWwZ13ZuGS1AoqA4aJPH/Kr2DW09MzIpiC54Kq/Ipxv/nNc+FST08PGzZsYHh4mCVLlpQ/Ju9iWr8ennxy7M6lPFy65ZZbRhzv7e1l06ZN/OhHPwLgZS972biPoVEmEy5NpHMJoLcXLroInn4avvjFk4CT+NCHsnO7754tmWtEsAQjO5fOPfdcjjjiCBYuXFjew6rSYYcdxte//nUOO+yw8pXvnm86rmnCcEnSKC6LkyRtT9659PTTTwOjl9BEBDNnzmRgYIDHHntsVLhUaf78+dsNl2bMgOOP/xYrVpzLySfPoqsLLrvMYEmtpbL7aCLPnwYHB4HsZ2mszqVDDoEdd4Rvfeu5TqTu7u7yz11+tcX880DW5QSw997jj7NSb28vTz31FD/4wQ844IADystYmyGfP8a7UhxQDmQmYsYMuOIKuPXWVzN37j58//tfoKsLZs3KNjxvlMpg8fTTT+flL3/5mPc9//zzee1rX8tBBx3EXXfdxT333DOpzieplVnJkkbJl8XZuSRJqiZ/IZsv66i2P8vAwACf+tSnuP3229l9993H/Fx9fX1j7rn0nMdYsOANfPrT2R40731vzUOXpsRkl8XlnUvd3d2j9hnKw4beXnjb2+C667JlXvn9846YakvX8nBprM6lsfYHmjVrFuvXr+fmm28uX9GsWfLweSIhy0SWxW1r48YV7L33OnbYIdvwu5HBEoyc/7a96MC2urq6ylfOPOywwzjjjDMaOxipQIZLkkaxc0mStD35i+GnnnoKYNTV4ipt2bJlu51LfX192+1cgmzz4QULHuD887OrOUmtZrLhUmXnUr5ULVcZspx9NgwOwle+Qvn+uWo/d+OFS9u68MILueuuu+jt7eXBBx9k8+bNTQ+X8ivd5ftQbc9El8Vt3LiRmTNncu211/Lkk0+y0xReTvKcc87hhz/8Iddddx1HHHHElH0dqdW5LE7SKG7oLUnannxpzfY6lyqNtyzu8ccf3+7Hr1+/ftyOAKlI9XQu5RvgRwQppRHh0j77wKGHwk9+Qvn+uR122IGHHnpoxLGHHsr2EtpO3jvCu9/9bnbeeecRAdc+E02mGiT/2vn3ZHsm2rn061//mq1bt/LRj36UDRs2TGm41NPTw4knnjhln19qF4ZLkkZxQ29J0vZMZFlcpfE6l1asWDHm+eHhYX7+859z0kkn1TBSqTlqDZcqO5e6uroYHBwctTzs8MPhxhsp3z+3ww47jLrS2MqVo/db2rYzqlK+tC5/Y3G8+0+FE044gUMPPZSLL7543PuO17l033338Y1vfIN9990XoHxFvKkMlyRlDJckjZI/qXjf+95X8EgkSa1o22Vx44VL4+25tL1lcQ888ABr1qzh1a9+dQ0jlZpj74pEZ7Ibek8kXLr66uwqcNt2LlX67W/hxz+G97znuWOPPPJI+U3Daqrtc1QZNDXD/Pnz+dWvfjWh+47XuXT66aezYsUK3vKWtwCwYcMGwHBJagbDJUmjzJgxo+qTG0mS4LkXuBMNlyZytbiUUtXNhm+//XaApu8DI03GMcccw3nnncdll11GSmnc+1dbFtfd3c3AwEDVcAngl7/c/p5Ln/1s9vf73//csT322GNC41+5cmX5drM7lyZjvM6lfG66+eabgeeuaGm4JE09XzlKqqqrq2vMK4pIkjpbo5fFDQ0N8eyzz1Y9/4tf/IKjjz6ahQsX1jhaqTmOOuoogAmFS2N1LsHobqJ8j+hly8buXBoYgH/4B/iTP4HFiyc/9s985jPl283uXJqM8cKlnXfeGYBVq1aNOG64JE09wyVJkiRNyrbL4sbbbHt7V5Pr6+sDsk27q3n00Uc58sgjaxmm1FT5m3ITWRZX2bmUh0t5KLVtuLTLLrBkCfziFyPDpcqfu1//GjZtgtNOq23s+++/f/l2K3cuzZo1a7tXlcv3kNqW4ZI09VwWJ0mSpEnJw6UHH3yQuXPnjtjMOHfvvffyu9/9btxO2Pnz5wOwbt26qkt4hoaGysuGpFaW1/lklsX19PSU6zvvZqq2LcErXwnXXANLluxSvk9l0HTnndnfRx898fE+8MAD5duVy+2q/Ty3itmzZ5c36a6m8nHsv//+5YsFGC5JU8/OJUmSJE1K/qJ2xYoVYy5nO+iggzjllFN41atetd3PlXcuLV++vOr5wcHBES8YpVaVh0KTWRZXrXNp7ty5o+7/3vdCBPzt374dOIpjjz12xPlly6Cvb/SV4rYnv6Ia0DZbIfT29tLf3z/m+crv/eKK9YHVvqeSGstwSZIkSZPSyLAnD5de//rXV91LZWhoaESHhtSqalkWV7nn0n777cfHPvYxrrnmmlH3P/xwuPdeSCmAM3jlK1854vxdd8GRR2YB1HQ2XudSHtoBfPSjH+WCCy5g8+bNbROeSe3McEmSJEmT0shwKV8WB3DHHXeMOj80NGTnktrCZJbF5VflrVzellLi4osvHtFxU2nPPWGPPR4CTh6x31JKsGIFHHRQ/Y+h1Y0XLlV2Ne2yyy5cdNFFLb1BuTSdGC5JkiRpUhrZSVQZLlXb1NvOJbWLySyL27p1a7mu84+bSMfTbrv9GjiEwcHnrp64Zg2sXw8vfOHExrlq1Sruueeeid25xYy3LK7ynKGS1FyGS5IkSZqURnYSVW60u+1VoIaHhxkeHrZzSW1hssvi8rqeTLi0007LAFi1Ktsvad06eOMbs3PHHDOxcS5ZsoRDDjlkYnduMbNnz+bhhx/mTW96U9XzlV1Ns2fPbtawJGG4JEmSpElqZNgzc+ZM7r77bmB0uFS56bHU6ia7LK6WzqWenuXA46xYkS2de/Ob4bbb4OqrYZs9vqelfH+qb37zm1XP27kkFcdwSZIkSZNSGS4tXbq07s+XX4o93+Q4V7npsdTqJhMuVXYuLVmyhL6+Pj75yU+O+3Hnn38eXV03smzZXpxwAnzve/Ce98AZZ9Q3doDjjjuu5TuaKq9OWe37XBku2bkkNZdvA0mSJGlSKsOe66+/vu7Pl4dL23Yu5eGSnUtqB5PZc2lwcLD8czR79myefvrpCX2NpUuXsmrVUt71rixYioA3vKH2MVe6+eabG/OJptDatWvLtzdv3sycOXNGnF+zZk35dj6vSGoOf1NLkiRpUhYtWsRFF13Ecccd15DugLHCpfPPPx+wc0ntYbJ7LtUamu61F3z3uzA8nG3kvWBBTZ+mLVWGS+vWrRsRLq1du5Ynn3yy/O/8/0NSc7gsTpIkSZMSEVxwwQW84hWvaMjnGytcuuqqqwA7l9QeJrMsbsuWLXWHpjNmdFawBIxYtrft1SVXrFjR7OFIqmC4JEmSpEKNFS7l7FxSO5josrjBwUFuuOEGFi9e3IxhTSuXX345F198MZB1LuVWrlzJm9/85qKGJQnDJUmSJBWsWrhU+QLdziW1g4kui9u4cSO///3vec1rXtOMYU0rs2fP5lWvehUwMlx64xvfyKpVq4oaliQMlyRJklSwvDOpMlzasGHDqPNSK5vosriBgQHAq5nVqq+vDxgZLlXur7Tnnns2fUyS3NBbkiRJBcvDo/zqcACrV68u37ZzSe1gosvi8nBp1qxZUz6m6WjbcGnr1q0sW7YMyLogly9fPmIukdQc/qaWJElSoSKCnp6eEZ1LleFSV1dXEcOSJmWiy+IMl+qTh0v5ht5XXHFF+dwhhxzC3LlzCxmX1OkMlyRJklS4mTNnlsOlyy+/nM2bN5fPDQ0NFTUsacImuyzOcKk2vb29zJo1q9y59Pjjj5fPffe73y1qWFLHM1ySJElS4fJw6bHHHuPcc88dcc4lLmoHLotrnvnz55fDpfz7CbD77rsXNSSp47mhtyRJkgqXh0uVS+Nyg4ODBYxImhyXxTVPX19f1XBJUnEMlyRJklS4fM+lTZs2jTpnuKR24LK45jFcklqP4ZIkSZIKl3cuVe619LrXvQ4wXFJ7GC9ceuKJJ1i3bp3hUgP09fWVN/Q2XJJag+GSJEmSCtfb20t/f3+5c+m0007jxBNPBGDx4sVFDk2akLH2XLryyiuJCHbddVcWL15suNQAlZ1Lzz77bMGjkQRu6C1JkqQWMHv2bDZv3lzuXPrwhz/MS1/6UiKCU089teDRSeMba8+lL3zhC+XbGzZsMFxqgMpw6f777y94NJLAziVJkiS1gDlz5rBp06Zy59KcOXOICA488MCCRyZNzFjL4vLjOcOl+uVXi9u8eTPLly8vejiSMFySJElSC8g7lyrDJamdjLUsznCp8fr6+ujv72fZsmXjXp1PUnMYLkmSJKlweedSvixu9uzZBY9ImpyxlsUZLjXe/PnzAbj55psLHomknOGSJEmSCjdnzhw7l9TWXBbXPPPmzQPg1ltv5XnPe17Bo5EEhkuSJElqAbNnzx6x55KdS2o3Yy2Ly4/nDJfqVxkuHXHEEdx6663cd999BY9K6mxeLU6SJEmFyzuXXBandjXRZXH9/f0AzJw5szkDm4bmzp0LwDPPPMOiRYtYunRpwSOSZOeSJEmSClfZudTb2zuq20NqdZNZFjdz5sxRxzVxeecSGERLrcLf2pIkSSrcnDlzGBgY4Nlnn/XFotrSRMOlDRs2uCSuTpXhUm9vb4EjkZQzXJIkSVLh8g28165d62beakt5t914y+Kefvppw6U6GS5JrcdwSZIkSYXr6+sDYPXq1XYuqS2N1bm07RLPZ5991nCpToZLUusxXJIkSVLhdtxxRwAeffRRO5fUlia6LC7fV0y1yzf0BsMlqVUYLkmSJKlwCxYsALJwyc4ltaO8Q8nOpalX+f0zXJJag+GSJEmSCpd3Lg0MDNi5pLaUdyiNt+fS8uXLDZfq1NPTU77t91JqDYZLkiRJKlweLgGGS2pLE10Wt379egOROlV+T+1cklqD4ZIkSZIKly+LA1wWp7Y01rK4bcMlsNumkQyXpNZguCRJkqTCzZs3j+7ubsDOJbWniS6LA8OlRjJcklqD4ZIkSZIKFxHlpXF2LqkdTXRZHBguNZLhktQaDJckSZLUEvJwyc4ltaOJXi0ODJcayXBJag2GS5IkSWoJ+b5LhktqRy6LK4bhktQaDJckSZLUElwWp3Y23rK4yro2XGocwyWpNRguSZIkqSW4LE7tbLxwqa+vr3ysq6ureQOb5gyXpNZguCRJkqSWkC+Ls3NJ7WisPZeqhUvV9mFSbQyXpNbgrCZJkqSWYOeS2tl4ey7Nnz9/1DHVz3BJag2GS5IkSWoJhktqZ3lg9I53vGPE8XwJ3OLFi8vH7FxqHPevklqDs5okSZJaght6q52NFRhFBAsXLuRd73rXiGNqDDuXpNZguCRJkqSWYOeS2tlYgdHQ0BCLFi0aUdd2LjWO4ZLUGpzVJEmS1BJOOOEELrnkEpYuXVr0UKRJqwyXKjf1Hhoaoqura8QV4uxcapzu7u6ihyAJ8CdRkiRJLaG3t5ePfOQjRQ9DqkllN9Lg4CA9PT1A9XDJziVJ042zmiRJkiTVqbIbaWBgoHw7D5cqO2zsXJI03RguSZIkSVKdKgOjxx57jK1btwJ2LknqDM5qkiRJklSnysBov/3244wzzgBgeHiYGTNmjOhcMlyq30477VT0ECRVcM8lSZIkSarTtkvdrrnmGsANvafKAw88wKYAtJz/AAATlElEQVRNm4oehqQSwyVJkiRJqtNYgVG1PZfOPvvsZg1r2lqwYAELFiwoehiSSuzHlCRJkqQ6jRcu5Z1L8+bN4wUveEEzhyZJU85wSZIkSZLqNNY+Sv39/fT09JTDJZfESZqODJckSZIkqU7VQqPNmzdz//33c+CBB5aXxRkuSZqODJckSZIkqU7VQqPly5czODjIkUce6RXiJE1rTZ3hIuLLEfFERPy64tiOEXF9RDxQ+ntB6XhExOcjYmVE/CoijmrmWCVJkiRpoqqFR1u2bAGyfZZSSoCdS5Kmp2bH518FTt3m2F8CN6SUXgjcUPo3wKuBF5b+vBP4UpPGKEmSJEmTUi00Gh4eBrLgad68eQBccsklTR2XJDVD9/h3aZyU0i0RsWSbw/8TOL50+2vATcBflI7/Y8oi/p9FRF9E7JZSeqw5o5UkSZKkiRkvXOrp6Sl3L0nSdNMKC393rQiMHgd2Ld3eA/hdxf0eKR2TJEmSpJZSbVlcZbgkSdNZUzuXxpNSShEx6Tg/It5JtnSOXXfdlZtuuqnRQyvEM888M20ei5rP+lEjWEeqlzWkellDqlezamhwcHDUsWXLlgHwy1/+kq6urikfg6aG85Dq1Qk11Arh0u/z5W4RsRvwROn4o8BeFffbs3RslJTSlcCVAC960YvS8ccfP4XDbZ6bbrqJ6fJY1HzWjxrBOlK9rCHVyxpSvZpVQ9XCpcMPPxyAo446yjpuY85Dqlcn1FAr9Gf+O3BW6fZZwHcqjp9ZumrcscB691uSJEmS1Iqq7bmU77HksjhJ011TO5ci4l/INu/eOSIeAT4OfBL4VkS8Hfhv4A2lu/8n8D+AlcAm4K3NHKskSZIkTZR7LknqZM2+Wtwfj3HqxCr3TcC7p3ZEkiRJklS/8a4WJ0nTmbOcJEmSJE0BwyVJncJZTpIkSZKmgOGSpE7hLCdJkiRJUyAPl6otmZOk6cRwSZIkSZKmgJ1LkjqFs5wkSZIkNcDRRx894t9DQ0OA4ZKk6c9ZTpIkSZIa4I477hjx7/7+fsBwSdL05ywnSZIkSVPAcElSp3CWkyRJkqQpYLgkqVM4y0mSJEnSFNi8eTNguCRp+nOWkyRJkqQGiYjy7bxzqfKYJE1HhkuSJEmS1CDd3d3l2wMDA4CdS5KmP2c5SZIkSWqQnp6e8m2XxUnqFM5ykiRJktQglZ1LbugtqVM4y0mSJElSgxguSepEznKSJEmS1CCGS5I6kbOcJEmSJDVIZbiU77nk1eIkTXeGS5IkSZLUIHYuSepEznKSJEmS1CCV4dKWLVsAwyVJ05+znCRJkiQ1iOGSpE7kLCdJkiRJDdLT01O+bbgkqVM4y0mSJElSg9i5JKkTOctJkiRJUoMYLknqRM5ykiRJktQg1cKliChqOJLUFIZLkiRJktQgn/jEJ8q37VyS1Cmc5SRJkiSpQU455RSefPJJALZu3QoYLkma/pzlJEmSJKmB8qVxdi5J6hTOcpIkSZLUQIZLkjqNs5wkSZIkNZDhkqRO4ywnSZIkSQ1kuCSp0zjLSZIkSVIDzZgxg4gob+gdEQWPSJKmluGSJEmSJDVY3r0Edi5Jmv6c5SRJkiSpwSrDJTuXJE13hkuSJEmS1GB5uGTXkqRO4EwnSZIkSQ1muCSpkzjTSZIkSVKDGS5J6iTOdJIkSZLUYHm45H5LkjqB4ZIkSZIkNZidS5I6iTOdJEmSJDWY4ZKkTuJMJ0mSJEkNZrgkqZM400mSJElSgxkuSeokznSSJEmS1GCGS5I6iTOdJEmSJDWY4ZKkTuJMJ0mSJEkNtu+++wIwODhY8EgkaeoZLkmSJElSg5155pkArF+/vuCRSNLUM1ySJEmSpAY79dRTix6CJDVNd9EDkCRJkqTppru7mw9/+MMsX7686KFI0pQzXJIkSZKkKXDppZcWPQRJagqXxUmSJEmSJKlmhkuSJEmSJEmqmeGSJEmSJEmSama4JEmSJEmSpJoZLkmSJEmSJKlmhkuSJEmSJEmqmeGSJEmSJEmSama4JEmSJEmSpJoZLkmSJEmSJKlmhkuSJEmSJEmqmeGSJEmSJEmSama4JEmSJEmSpJoZLkmSJEmSJKlmhkuSJEmSJEmqmeGSJEmSJEmSama4JEmSJEmSpJoZLkmSJEmSJKlmhkuSJEmSJEmqmeGSJEmSJEmSama4JEmSJEmSpJpFSqnoMTRURKwB/rvocTTIzsDaogehtmX9qBGsI9XLGlK9rCHVyxpSvawh1Wu61NDilNIu1U5Mu3BpOomIO1JKLyp6HGpP1o8awTpSvawh1csaUr2sIdXLGlK9OqGGXBYnSZIkSZKkmhkuSZIkSZIkqWaGS63tyqIHoLZm/agRrCPVyxpSvawh1csaUr2sIdVr2teQey5JkiRJkiSpZnYuSZIkSZIkqWaGS5IkSSpURETRY5AkSbUzXCpQRMwv/e3/g2oSEc8v/e2TctUsIg6OiN6ix6H2FBFLI2Kfosehtje76AGofUVEV+lvnw+pZtaRGqGTX9t37AMvSkTMiIjnRcT3gM8DpJSGCx6W2kxEHBkRNwAXAyQ3T1MNIuKwiLgV+Gtgp6LHo/YSEUdFxA+AHwHzix6P2lNEHBsR/wZcEREn5y/upImIiJdExN8D50bEDj4fUi1Kb5J8DfhYROxoHWmyIuLFEfFe6OzX9oZLTVYqto1AD7BHRLwROjvh1MRF5nPAPwJfSyn9WdFjUlv7GPCvKaXTU0qPgu/WaXwR0RMR/4/sqiefB74PHF865+8yTVhEHA98EbgWWAH8KbCgyDGpfUTEK4AvkAXcuwMfiYhTih2V2k1E7E02D90ILAYujojTih2V2klEvB+4jiycfHXpWEe+UeKTwGIcAKwFLgfOKL3TMuyLOo2n9E7KPOCulNI/AkTEPr6g02SUOij3Bp5JKV1eOnZSRPQBtoRrPLOAm4GXp5S+B/wbcGBEdHfyu3WqyaHA7Smlfwb+ieyNt2eKHZLayFHAbSmlfyHr5N4VeFO+ZYA0QX8A3JdS+irwAeBu4DURsVeho1I7eQh4DXAO8GGAlNJQJz6X9gXpFCu1e+9Xup0X2EpgC7Cq9OesiFhkC6aqqayhkg8Ax0TEBRFxG/Bp4KsRcXQxI1Q7qKyjUgCwFnh5RJwWEd8GzifrQvlg6T7ORyrbZh56NqX09ZTS5tK/u4GhlNKgQbe2p8rvsx8DfxQRfwXcCewGfDEi/qiQAaqlVamf+4G+iNgtpfQ0WTA5E/hfhQxQbSEiXhsR74mIY0uHbgf2ioi9SnV0G7AOeF1hg1RLq1JD/wH8qvT3M/nyOEpv2HYSnwROkYjoi4j/AK4H3hARcyterL0I2JBSuhe4F/g48KXSUgP/TwRUryGAlNIG4Arg9WTp+B8DjwF/GBG7FDVetaZx6ugrZO/2fjmldArwD8CxFb8s1eHG+l1WWqKb/766GTg9IhbYuaRqqtTRPICU0t3AqcAS4F0ppePJXtidGhEHFjRctZix6ocsXNoAfK20b9dewF1kHd524GqEiNgtIr4LfIhs+e1XIuKUlNJDwE+BN5TuugL4DbBjeLETVdhODQ0BwymlfuCzwNsjYueU0mCR4y2CQcbUmUu2D8Wfl24fV3Hut8AOEfFNsuJcBtyfUtrqE3NVGLOGUkqfB45PKd2SUhoAvk0WWm4qYqBqadubi75H9qIu3+PkDuD3wEATx6fWVrV+Uma4FDA9XLrPK4oapFretnX08vxESukXwC5kdQTZ/jk7AM82d4hqYWPNQw8A5wH/B7gmpXQ62Zu2x5fO24GrSi8CfpxSenlK6WLgb4F879IfA4dGxItLQcGjwNJSWCDltq2hy4GzYcR8cxPwM7L5ioh4cQHjLIzhUgNFxJkR8YqIeF5pc9wrgW8B/WTLmHYv3XUB2ROpx4EjydZn7u+7dJpEDVFq3c0dDTwCDDV1wGpJE6ijPQBSSr8iWwb3nojYmWwz3UOAJwsaulrAROehiIjSGyKzSh/anx8vYtxqLZOoo1nAT4B3lz70RLKrV/qiroONUz8vzusnpbQlpXRjSukbpQ89GvivYkatVlOqo+NL88wNZHu75Z4EHijd/jlZ19vnSp1xBwO/jYg5TR2wWs44NfQUcF/pfjMg22uJ7CrMfxER64GjOul5URjq16dULM8Hvg4MAw+SvavyvpTS2tJ9lpK1Wt6RUvqn0rGdK87PA2amlJ4q4CGoYJOsodtTSleXjs0CXgJ8hmxZ3AdSSvc3/xGoFdQ6F5WOnwfsDbwQODel9JsmD18Fq2Me6iptWnk1sDKldGER41drqOM50cFkWwQ8H9gKvCeldF/zH4GKVOs8VDr+MrJOlLXA/04pPdzc0atVjFdHEdGTUtpa2hfnoJTS2RUfexmwJ9lV485MKa1o/iNQ0SZZQwemlM6p+Lh9yLad2AK8P6V0TyEPoiB2LtWh9KQ6kbVvP5pSOpGsC+kpsndYAEgp3UbW7r1/RMwv7VmxNiK6ImJGSukZg6XOVEMNHVCqodml5XBbgL9OKb3WYKlz1TEX7VA6fhlZqHSKwVLnqWMemlN6hw7gbQZLna3Geaiv9PvsXuAs4C0ppRMNljpPHfPQ3NKph4ALSr/HHm7q4NUyJlhH+RYkJwH/Wvq4haVjHwLenlI6xmCpM9VQQ/9W+rgdSx+3Afir0u+yjgqWwHCpJqVQ6FLg0oh4BbA/peVIpSfa7wNeWjqX+3uyDQavBx6KiN1TSkPusdSZ6qyhHwIPl2roJymlbzd5+GoRDZiLVlYsLdja1MGrcA2on1WVS1OaOni1jAbU0cMRsUdKaXPKNtZVB2nQc+o9U0qrU0r/2eThq0VMpo5K3bYzgTXA/RFxCXB9ZBelGEwpbSzqcag4DaihG0sB0xMppRuLehxFM1yapFKxLSPbN2kl2ZWWtgInRGnDrlJgdGHpT+404F3AL4FDU0qrmzdqtZIG1NDdWEMdz7lI9bB+1AgN/H32aPNGrVbRwHnokeaNWq1mknX0idKH9QJvIdtDZwfgVWnkXqbqIA2soY5fidRd9ADa0DDw2Yp9Ao4EXgD8FfAl4OjINvT6NvDKiFhSas/tJyu6W4oZtlqINaRGsI5UD+tHjWAdqR7WjxphsnW0J7A7cDVwWUrp7mKGrRZiDTWInUuTtwz4VkR0lf59G7AopfRVoCsi/ryUbO4JDOXrvlNK3/GXoEqsITWCdaR6WD9qBOtI9bB+1AiTqaPhlNIjKaVfpJTONBRQiTXUIIZLk5RS2pRSGkjPbWJ6Etl6S4C3AgdGxPeAfwHuBC/LrJGsITWCdaR6WD9qBOtI9bB+1AiTrKNlYB1pJGuocVwWV6NSspmAXYF/Lx3eCHwEOARYle8hkFJKhQxSLc0aUiNYR6qH9aNGsI5UD+tHjWAdqV7WUP3sXKrdMNADrAUOK6WZF5C1yt3q5pSaAGtIjWAdqR7WjxrBOlI9rB81gnWkellDdQpDt9pFxLHAT0p/vpJSuqrgIanNWENqBOtI9bB+1AjWkeph/agRrCPVyxqqj+FSHUo7xb+ZbJf4gaLHo/ZjDakRrCPVw/pRI1hHqof1o0awjlQva6g+hkuSJEmSJEmqmXsuSZIkSZIkqWaGS5IkSZIkSaqZ4ZIkSZIkSZJqZrgkSZIkSZKkmhkuSZIkSZIkqWaGS5IkSTWKiAsjIpX+DEfE0xFxe0RcEhHPr+HzfSgijp+CoUqSJE0ZwyVJkqT6rAdeArwUeBNwLfBm4J6IOHqSn+tDwPENHZ0kSdIU6y56AJIkSW1uMKX0s4p/fz8ivgTcAnwjIg5IKQ0VNDZJkqQpZ+eSJElSg6WU1pF1Ie0LnAQQEZ+MiHsi4pmIeCQi/rly6VxEPAzsBHy8Yqnd8aVzMyLiLyNiZUQMRMT9EXFW0x+YJElSFYZLkiRJU+MmYBA4tvTvhcClwGnA+4G9gR9FRP587HSyJXZXkS2zewlwZ+nc3wEfA64sffx1wJcj4jVT/igkSZLG4bI4SZKkKZBS6o+ItcCupX+/LT8XEV3AT4FHgJcBt6SU7oqIQeCRymV2EbEvcA7w1pTS10qHfxgRuwEfB77XlAckSZI0BjuXJEmSpk6Ub0S8OiJ+EhHryTqaHimd2m+cz3EiMAxcFxHd+R/gBuCIUlAlSZJUGDuXJEmSpkBE9JLtofT7iPgD4N/JlrN9EngCSMDPgN5xPtXOQBfZkrlqduO5oEqSJKnpDJckSZKmxglkz7V+Sraf0hrgjSmlBBARiyf4eZ4i63RaStbBtK0n6h+qJElS7QyXJEmSGiwi+oC/AVYCPwROBbbmwVLJGVU+dAujO5l+RNa5ND+ldP0UDFeSJKkuhkuSJEn16Y6I/IpwOwBHk23APQc4NaU0FBHXA++PiMuB7wIvBf60yudaDpwWEf8FPAOsSCmtiIj/C3wjIj4F3EEWQB0M7JdSesdUPjhJkqTxGC5JkiTVZz7Z0rcEbCDrVroa+LuU0uMAKaX/jIi/AP4c+LPS/V8D3L/N5/ogcAXwH2Th1AnATcC7S/f9M+Ci0tf5DXDVFD4uSZKkCYmR3dmSJEmSJEnSxM0oegCSJEmSJElqX4ZLkiRJkiRJqpnhkiRJkiRJkmpmuCRJkiRJkqSaGS5JkiRJkiSpZoZLkiRJkiRJqpnhkiRJkiRJkmpmuCRJkiRJkqSaGS5JkiRJkiSpZv8fHhIBKJLaD2gAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Calculating Averages\n",
        "\n",
        "rolling_mean = IBM_data['Adj Close'].rolling(window=50).mean()\n",
        "rolling_mean2 = IBM_data['Adj Close'].rolling(window=200).mean()"
      ],
      "metadata": {
        "id": "biLL9c9bkpSO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##Set up collumn for strategy two\n",
        "strategy2 = np.where(rolling_mean > rolling_mean2, 1, -1)\n",
        "IBM_data[\"Strategy2\"] = strategy2\n",
        "IBM_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "PDbFZZcokv1H",
        "outputId": "a58a7fc4-29df-413d-967c-cf18f7486e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2015-01-02 00:00:00-05:00  154.216064  156.128113  153.919693  154.933075   \n",
              "2015-01-05 00:00:00-05:00  154.177826  154.177826  152.189301  152.495224   \n",
              "2015-01-06 00:00:00-05:00  152.648178  152.925430  148.346085  149.206497   \n",
              "2015-01-07 00:00:00-05:00  150.286804  150.286804  147.256210  148.231354   \n",
              "2015-01-08 00:00:00-05:00  149.369019  152.045883  148.709366  151.453156   \n",
              "\n",
              "                            Adj Close   Volume  Strategy1      50_SMA  \\\n",
              "Date                                                                    \n",
              "2015-01-02 00:00:00-05:00  110.782318  5779673         -1  154.933075   \n",
              "2015-01-05 00:00:00-05:00  109.039169  5104898         -1  153.714149   \n",
              "2015-01-06 00:00:00-05:00  106.687645  6429448         -1  152.211599   \n",
              "2015-01-07 00:00:00-05:00  105.990341  4918083          1  151.216537   \n",
              "2015-01-08 00:00:00-05:00  108.294060  4431693          1  151.263861   \n",
              "\n",
              "                              200_SMA  Signal  Position  Strategy2  \n",
              "Date                                                                \n",
              "2015-01-02 00:00:00-05:00  154.933075     0.0       NaN         -1  \n",
              "2015-01-05 00:00:00-05:00  153.714149     0.0       0.0         -1  \n",
              "2015-01-06 00:00:00-05:00  152.211599     0.0       0.0         -1  \n",
              "2015-01-07 00:00:00-05:00  151.216537     0.0       0.0         -1  \n",
              "2015-01-08 00:00:00-05:00  151.263861     0.0       0.0         -1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f0ac2ce6-dedc-44f7-85f9-7726db1bfaca\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Strategy1</th>\n",
              "      <th>50_SMA</th>\n",
              "      <th>200_SMA</th>\n",
              "      <th>Signal</th>\n",
              "      <th>Position</th>\n",
              "      <th>Strategy2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02 00:00:00-05:00</th>\n",
              "      <td>154.216064</td>\n",
              "      <td>156.128113</td>\n",
              "      <td>153.919693</td>\n",
              "      <td>154.933075</td>\n",
              "      <td>110.782318</td>\n",
              "      <td>5779673</td>\n",
              "      <td>-1</td>\n",
              "      <td>154.933075</td>\n",
              "      <td>154.933075</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05 00:00:00-05:00</th>\n",
              "      <td>154.177826</td>\n",
              "      <td>154.177826</td>\n",
              "      <td>152.189301</td>\n",
              "      <td>152.495224</td>\n",
              "      <td>109.039169</td>\n",
              "      <td>5104898</td>\n",
              "      <td>-1</td>\n",
              "      <td>153.714149</td>\n",
              "      <td>153.714149</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06 00:00:00-05:00</th>\n",
              "      <td>152.648178</td>\n",
              "      <td>152.925430</td>\n",
              "      <td>148.346085</td>\n",
              "      <td>149.206497</td>\n",
              "      <td>106.687645</td>\n",
              "      <td>6429448</td>\n",
              "      <td>-1</td>\n",
              "      <td>152.211599</td>\n",
              "      <td>152.211599</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07 00:00:00-05:00</th>\n",
              "      <td>150.286804</td>\n",
              "      <td>150.286804</td>\n",
              "      <td>147.256210</td>\n",
              "      <td>148.231354</td>\n",
              "      <td>105.990341</td>\n",
              "      <td>4918083</td>\n",
              "      <td>1</td>\n",
              "      <td>151.216537</td>\n",
              "      <td>151.216537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-08 00:00:00-05:00</th>\n",
              "      <td>149.369019</td>\n",
              "      <td>152.045883</td>\n",
              "      <td>148.709366</td>\n",
              "      <td>151.453156</td>\n",
              "      <td>108.294060</td>\n",
              "      <td>4431693</td>\n",
              "      <td>1</td>\n",
              "      <td>151.263861</td>\n",
              "      <td>151.263861</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0ac2ce6-dedc-44f7-85f9-7726db1bfaca')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f0ac2ce6-dedc-44f7-85f9-7726db1bfaca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0ac2ce6-dedc-44f7-85f9-7726db1bfaca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Identifying Variables\n",
        "##important to use double brackets\n",
        "\n",
        "xfeature = IBM_data[['Adj Close']]\n",
        "yfeature = IBM_data['Strategy2']\n",
        "\n",
        "xfeature.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "riQifIKbkyNb",
        "outputId": "074c2ab4-8419-4f42-be27-e611290c3e86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Adj Close\n",
              "Date                                 \n",
              "2015-01-02 00:00:00-05:00  110.782318\n",
              "2015-01-05 00:00:00-05:00  109.039169\n",
              "2015-01-06 00:00:00-05:00  106.687645\n",
              "2015-01-07 00:00:00-05:00  105.990341\n",
              "2015-01-08 00:00:00-05:00  108.294060"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a413ed16-febb-424d-8869-e8de41ce2a88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02 00:00:00-05:00</th>\n",
              "      <td>110.782318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05 00:00:00-05:00</th>\n",
              "      <td>109.039169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06 00:00:00-05:00</th>\n",
              "      <td>106.687645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-07 00:00:00-05:00</th>\n",
              "      <td>105.990341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-08 00:00:00-05:00</th>\n",
              "      <td>108.294060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a413ed16-febb-424d-8869-e8de41ce2a88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a413ed16-febb-424d-8869-e8de41ce2a88 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a413ed16-febb-424d-8869-e8de41ce2a88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(xfeature, yfeature, train_size = .8, random_state = 50)"
      ],
      "metadata": {
        "id": "Y30LqNs2k1Sv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "5-Ly8DApk256",
        "outputId": "5a32d6f1-5e5e-48c2-ef0f-00c9391e2593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Adj Close\n",
              "count  1410.000000\n",
              "mean    112.345099\n",
              "std       9.533904\n",
              "min      79.847000\n",
              "25%     107.222914\n",
              "50%     112.234531\n",
              "75%     117.461323\n",
              "max     135.368500"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-80c82970-c638-43b1-943e-91bf82fb9703\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1410.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>112.345099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.533904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>79.847000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>107.222914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>112.234531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>117.461323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>135.368500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80c82970-c638-43b1-943e-91bf82fb9703')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80c82970-c638-43b1-943e-91bf82fb9703 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80c82970-c638-43b1-943e-91bf82fb9703');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN"
      ],
      "metadata": {
        "id": "qyUSy56wk4q7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDe4WPPOk6nO",
        "outputId": "8330795f-d2d7-4170-af63-085c8f1e454a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_prediction = knn.predict(x_test)"
      ],
      "metadata": {
        "id": "2-01ADTXk7X5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, knn_prediction))\n",
        "\n",
        "S2listofmodels.append(\"KNN\")\n",
        "S2listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWtY7o-3k84U",
        "outputId": "d72c17b3-f9a7-4f1a-9784-b1558370952f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7025495750708215\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "U0IuJAUvk-pU",
        "outputId": "74e0d6bc-dee0-4270-fce4-c96e12363bfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest"
      ],
      "metadata": {
        "id": "nmTowlYylAuY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_clf = RandomForestClassifier(n_estimators = 100)\n",
        "rf_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-7mg0mslDU1",
        "outputId": "778ff445-281a-49d2-9b70-2c2564352447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf_clf.predict(x_test)\n",
        "print(\"ACCURACY OF THE MODEL: \", metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "S2listofmodels.append(\"Random Forest\")\n",
        "S2listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vz7KfH7lFB7",
        "outputId": "82f14d81-8fe0-4ebd-e59b-61e1e3429686"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACCURACY OF THE MODEL:  0.660056657223796\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "_E8osByNlG5u",
        "outputId": "a854c1a2-7308-4407-abdc-a663eae35f77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gradient Boosting"
      ],
      "metadata": {
        "id": "2q2jpIrElJSZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]\n",
        "for learning_rate in lr_list:\n",
        "    gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=1, max_depth=2, random_state=1)\n",
        "    gb_clf.fit(x_train, y_train)\n",
        "\n",
        "    print(\"Learning rate: \", learning_rate)\n",
        "    print(\"Accuracy score: {0:.3f}\\n\".format(gb_clf.score(x_test, y_test)))\n",
        "    \n",
        "    ## In gradient boosting, the learning rate corresponds to the weight of newly added predictions, to the original prediction.\n",
        "    ## If the learning rate is high it means that adding new predictions has a greater affect on the original prediction.\n",
        "    ## So, we expect higher learning rates to cause overfitting, the model learns to fit the data too quickly.  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7RMlMdcOlMcS",
        "outputId": "46a7d38a-2890-42f4-adbf-f410283d29a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate:  0.05\n",
            "Accuracy score: 0.683\n",
            "\n",
            "Learning rate:  0.075\n",
            "Accuracy score: 0.683\n",
            "\n",
            "Learning rate:  0.1\n",
            "Accuracy score: 0.683\n",
            "\n",
            "Learning rate:  0.25\n",
            "Accuracy score: 0.703\n",
            "\n",
            "Learning rate:  0.5\n",
            "Accuracy score: 0.680\n",
            "\n",
            "Learning rate:  0.75\n",
            "Accuracy score: 0.691\n",
            "\n",
            "Learning rate:  1\n",
            "Accuracy score: 0.697\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate = .05, max_features=1, max_depth=2, random_state=0)\n",
        "gb_clf.fit(x_train, y_train)\n",
        "y_pred = gb_clf.predict(x_test)\n",
        "print(\"Accuracy score: {0:.3f}\\n\".format(gb_clf.score(x_test, y_test)))\n",
        "\n",
        "S2listofmodels.append(\"Gradient Boosting\")\n",
        "S2listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbRlkn26lPo5",
        "outputId": "ea7f0d1b-3bf2-4ed5-a6d0-83c18e1873ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score: 0.683\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "UY1O7bkTlSPF",
        "outputId": "836186ce-a791-4218-90f3-bf1e4f5247bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM"
      ],
      "metadata": {
        "id": "QATa8vmxlT7l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm_clf = svm.SVC()\n",
        "svm_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYqB23gnlTB2",
        "outputId": "14b05a12-6831-4d8d-fc7f-0c8f6c6be5ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svm_clf.predict(x_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "S2listofmodels.append(\"SVM\")\n",
        "S2listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWAt_fg1lXVK",
        "outputId": "67fa9a52-258b-4149-aef3-a22f06de8bee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7025495750708215\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "uNhok_1jlaUG",
        "outputId": "6657c9fe-8563-48c7-a08f-96b86cb1450a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XGBoost"
      ],
      "metadata": {
        "id": "dfulgAb-lcB_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_clf = XGBClassifier()\n",
        "xgb_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "biMt8Vd4liKB",
        "outputId": "b0256919-736d-4086-82b3-a101283e29d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = xgb_clf.predict(x_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "S2listofmodels.append(\"XGBoost\")\n",
        "S2listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r9kXalRilk-Y",
        "outputId": "b35d133d-17ad-4359-85cc-5038fac9e454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6997167138810199\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "3xK5IkfElmeB",
        "outputId": "a59a2b28-be1e-44f8-9cf9-96614645a87b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Strategy #1 Results"
      ],
      "metadata": {
        "id": "sIg9evBMlpub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "    'Algorithm': S1listofmodels,\n",
        "    'Accuracy': S1listofaccuracies\n",
        "}\n",
        "df = pd.DataFrame(data=d)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "X2UTHwiOloeX",
        "outputId": "5b8cead7-dd5b-4d31-abb4-0620012259c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Algorithm  Accuracy\n",
              "0                KNN  0.501416\n",
              "1      Random Forest  0.504249\n",
              "2  Gradient Boosting  0.515581\n",
              "3                SVM  0.512748\n",
              "4            XGBoost  0.535411"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9f3cd6be-dbf2-4599-be09-4a8046198753\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.501416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.504249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.515581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.512748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.535411</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f3cd6be-dbf2-4599-be09-4a8046198753')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9f3cd6be-dbf2-4599-be09-4a8046198753 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f3cd6be-dbf2-4599-be09-4a8046198753');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Strategy #2 Results"
      ],
      "metadata": {
        "id": "MCmCKMpOlyF8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "    'Algorithm': S2listofmodels,\n",
        "    'Accuracy': S2listofaccuracies\n",
        "}\n",
        "df = pd.DataFrame(data=d)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "BsgKf4Fhl2ZL",
        "outputId": "3255279c-d1c6-4ae3-edf7-02fccdea635d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Algorithm  Accuracy\n",
              "0                KNN  0.436261\n",
              "1      Random Forest  0.660057\n",
              "2  Gradient Boosting  0.682720\n",
              "3                SVM  0.702550\n",
              "4            XGBoost  0.699717"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e643e2b3-d7b6-4521-92b8-000e0613a487\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.436261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.660057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.682720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.702550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.699717</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Bonus** : Try to tune some classifiers (you decide which ones, even 1 classifier suffices), by changing their default parameters."
      ],
      "metadata": {
        "id": "mFYWug6cYV2j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The ALMA indicator is a moving average (MA) variant**\n",
        "\n",
        "As the name suggests, the ALMA indicator is a moving average (MA) variant and was created by Arnaud Legoux in 2009. The goal was to decrease the lag commonly encountered with moving averages.\n",
        "\n",
        "As with most moving averages, this indicator aims to detect trends and trend reversals. It works similarly to other moving averages, but one of the main differentiators is that it calculates 2 moving averages, one from left to right and one from right to left. This output is then further processed through a customizable formula, for either increased smoothness or increased responsiveness.\n",
        "\n",
        "Because of its similarity to a moving average, you’ll notice the applications are also similar.\n",
        "*   Applying 2 ALMAs with different lengths can provide signals when they cross\n",
        "*   Crosses between the price and an ALMA can provide signals\n",
        "*   Combining any of the above with conditions derived from other indicators.\n",
        "\n",
        "The ALMA was created with the goal of reducing the price lag usually associated with moving averages. As with most MAs, the Arnaud Legoux Moving Average is used to follow trends and detect trend reversals in price. In short, the ALMA helps you:\n",
        "\n",
        "\n",
        "\n",
        "*   Identify trends \n",
        "*   Identify reversals in trends\n",
        "*   Find confluence with other technical indicators\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Yj-lgdMn4ZdZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Random Forest**"
      ],
      "metadata": {
        "id": "rhI_VNccY71b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install AlmaIndicator\n",
        "from AlmaIndicator import ALMAIndicator"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxx8zGvYbQQA",
        "outputId": "e7e76caf-aa9b-4461-ea98-4d3360faa8cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting AlmaIndicator\n",
            "  Downloading AlmaIndicator-0.0.4-py3-none-any.whl (4.3 kB)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from AlmaIndicator) (2.8.2)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from AlmaIndicator) (2022.2.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from AlmaIndicator) (1.3.5)\n",
            "Collecting ta\n",
            "  Downloading ta-0.10.2.tar.gz (25 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from AlmaIndicator) (1.15.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from AlmaIndicator) (1.21.6)\n",
            "Building wheels for collected packages: ta\n",
            "  Building wheel for ta (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ta: filename=ta-0.10.2-py3-none-any.whl size=29104 sha256=22e976bf651a81450fe1e326fda122675278d48e18b58138507e64fe4f45da0b\n",
            "  Stored in directory: /root/.cache/pip/wheels/31/31/f1/f2ff471bbc5b84a4b973698ceecdd453ae043971791adc3431\n",
            "Successfully built ta\n",
            "Installing collected packages: ta, AlmaIndicator\n",
            "Successfully installed AlmaIndicator-0.0.4 ta-0.10.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alma = ALMAIndicator(close = IBM_data['Adj Close'])"
      ],
      "metadata": {
        "id": "sdTZrconbSst"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_alma(dataset):\n",
        "\t\talma = ALMAIndicator(close = dataset['Adj Close'])\n",
        "\t\treturn (alma.alma())"
      ],
      "metadata": {
        "id": "2Rw6koA5bldX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alma_nums = calc_alma(IBM_data)\n",
        "alma_nums"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3r69iOtBbX7W",
        "outputId": "6d1bef7c-e936-4f89-b7d0-a7252a41b2a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2015-01-02 00:00:00-05:00           NaN\n",
              "2015-01-05 00:00:00-05:00           NaN\n",
              "2015-01-06 00:00:00-05:00           NaN\n",
              "2015-01-07 00:00:00-05:00           NaN\n",
              "2015-01-08 00:00:00-05:00           NaN\n",
              "                                ...    \n",
              "2021-12-27 00:00:00-05:00    124.901958\n",
              "2021-12-28 00:00:00-05:00    125.842611\n",
              "2021-12-29 00:00:00-05:00    126.748651\n",
              "2021-12-30 00:00:00-05:00    127.588956\n",
              "2021-12-31 00:00:00-05:00    128.241225\n",
              "Name: alma, Length: 1763, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_ema(dataset):\n",
        "\t\treturn dataset['Adj Close'].ewm(com=0.4).mean()"
      ],
      "metadata": {
        "id": "Y0fC_5hbb5IL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ema = calc_ema(IBM_data)\n",
        "ema.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qe-bOo2bo4y",
        "outputId": "95d43552-f353-484f-c386-dee50f0429e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1763,)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(40,14))\n",
        "plt.plot(alma_nums, color = 'orange')\n",
        "plt.plot(IBM_data['Adj Close'], color = 'green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "Bo2dMA4cb9Um",
        "outputId": "dfd1cadc-d703-41e4-c5ea-7a6fe818f01f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc1c3512810>]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x1008 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def strategy_three(dataset, ema, alma):\n",
        "\t\treturn np.where(ema > alma, 1, -1)"
      ],
      "metadata": {
        "id": "w8YKA7FUcIJe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strategy3 = strategy_three(IBM_data, ema, alma_nums)\n",
        "IBM_data[\"Strategy3\"] = strategy3\n",
        "IBM_data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "hUDLYV5DbV17",
        "outputId": "6a098e8b-eb53-4330-c634-5998166b6a88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2015-01-02 00:00:00-05:00  154.216064  156.128113  153.919693  154.933075   \n",
              "2015-01-05 00:00:00-05:00  154.177826  154.177826  152.189301  152.495224   \n",
              "\n",
              "                            Adj Close   Volume  Strategy1      50_SMA  \\\n",
              "Date                                                                    \n",
              "2015-01-02 00:00:00-05:00  110.782318  5779673         -1  154.933075   \n",
              "2015-01-05 00:00:00-05:00  109.039169  5104898         -1  153.714149   \n",
              "\n",
              "                              200_SMA  Signal  Position  Strategy2  Strategy3  \n",
              "Date                                                                           \n",
              "2015-01-02 00:00:00-05:00  154.933075     0.0       NaN         -1         -1  \n",
              "2015-01-05 00:00:00-05:00  153.714149     0.0       0.0         -1         -1  "
            ],
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-daaa22cd-5c01-4889-aafd-53c881282c18 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-daaa22cd-5c01-4889-aafd-53c881282c18');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Identifying Variables\n",
        "##important to use double brackets\n",
        "\n",
        "xfeature = IBM_data[['Adj Close']]\n",
        "yfeature = IBM_data['Strategy3']\n",
        "\n",
        "xfeature.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "4z1Te92HcRom",
        "outputId": "bd2602ee-c948-4337-e195-8a355da175d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Adj Close\n",
              "Date                                 \n",
              "2015-01-02 00:00:00-05:00  110.782318\n",
              "2015-01-05 00:00:00-05:00  109.039169"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0b258d23-055c-4e2a-b7c6-38f50b30eee7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-02 00:00:00-05:00</th>\n",
              "      <td>110.782318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05 00:00:00-05:00</th>\n",
              "      <td>109.039169</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b258d23-055c-4e2a-b7c6-38f50b30eee7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0b258d23-055c-4e2a-b7c6-38f50b30eee7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0b258d23-055c-4e2a-b7c6-38f50b30eee7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(xfeature, yfeature, train_size = .8, random_state = 50)"
      ],
      "metadata": {
        "id": "tAjHWuoCcYC9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = scaler.transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "5QASYYHrccSE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_clf = RandomForestClassifier(n_estimators = 1000, max_depth = 8)\n",
        "rf_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xxanUT0cieg",
        "outputId": "c1165322-a4bd-4e44-fb31-2e01d18b8476"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=8, n_estimators=1000)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf_clf.predict(x_test)\n",
        "print(\"ACCURACY OF THE MODEL: \", metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "S2listofmodels.append(\"Random Forest - Strategy 3\")\n",
        "S2listofaccuracies.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REIUjAAMcjc4",
        "outputId": "4ac59cfa-6d0c-472f-a0ca-a05f6bd5e827"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACCURACY OF THE MODEL:  0.5580736543909348\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['False','True'])\n",
        "ax.yaxis.set_ticklabels(['False','True'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "W8caF5NhcncM",
        "outputId": "a97f510c-b0c3-4b2b-d410-8e3a88a53652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "    'Algorithm': S2listofmodels,\n",
        "    'Accuracy': S2listofaccuracies\n",
        "}\n",
        "df = pd.DataFrame(data=d)\n",
        "df"
      ],
      "metadata": {
        "id": "jeyeqj0Kcssv",
        "outputId": "6857faf8-cd0c-4553-8065-6a012d56ad64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Algorithm  Accuracy\n",
              "0                         KNN  0.436261\n",
              "1               Random Forest  0.660057\n",
              "2           Gradient Boosting  0.682720\n",
              "3                         SVM  0.702550\n",
              "4                     XGBoost  0.699717\n",
              "5  Random Forest - Strategy 3  0.558074"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22298305-1d9f-4c0b-a836-2157053de867\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.436261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.660057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.682720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.702550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.699717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest - Strategy 3</td>\n",
              "      <td>0.558074</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22298305-1d9f-4c0b-a836-2157053de867')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-22298305-1d9f-4c0b-a836-2157053de867 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-22298305-1d9f-4c0b-a836-2157053de867');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    }
  ]
}