Principal_Component_Analysis_(PCA)_Stock.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Principal Component Analysis (PCA) for Stocks"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbols = ['AAPL','MSFT','AMD','NVDA','GE']\n",
        "start = '2012-01-01'\n",
        "end = '2019-09-11'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbols,start,end)['Adj Close']\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  5 of 5 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "                 AAPL   AMD         GE       MSFT       NVDA\n",
              "Date                                                        \n",
              "2012-01-03  51.269413  5.48  13.776225  22.156071  12.939396\n",
              "2012-01-04  51.544937  5.46  13.926292  22.677486  13.086854\n",
              "2012-01-05  52.117188  5.46  13.918791  22.909233  13.556875\n",
              "2012-01-06  52.662014  5.43  13.993821  23.265116  13.400198\n",
              "2012-01-09  52.578468  5.59  14.151395  22.958887  13.400198"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AMD</th>\n",
              "      <th>GE</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NVDA</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-03</th>\n",
              "      <td>51.269413</td>\n",
              "      <td>5.48</td>\n",
              "      <td>13.776225</td>\n",
              "      <td>22.156071</td>\n",
              "      <td>12.939396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>51.544937</td>\n",
              "      <td>5.46</td>\n",
              "      <td>13.926292</td>\n",
              "      <td>22.677486</td>\n",
              "      <td>13.086854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>52.117188</td>\n",
              "      <td>5.46</td>\n",
              "      <td>13.918791</td>\n",
              "      <td>22.909233</td>\n",
              "      <td>13.556875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>52.662014</td>\n",
              "      <td>5.43</td>\n",
              "      <td>13.993821</td>\n",
              "      <td>23.265116</td>\n",
              "      <td>13.400198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>52.578468</td>\n",
              "      <td>5.59</td>\n",
              "      <td>14.151395</td>\n",
              "      <td>22.958887</td>\n",
              "      <td>13.400198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Arrange the data in ascending order\n",
        "dataset = dataset.iloc[::-1]\n",
        "print(dataset.round(2))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              AAPL    AMD     GE    MSFT    NVDA\n",
            "Date                                            \n",
            "2019-09-11  223.59  29.76   9.35  136.12  184.33\n",
            "2019-09-10  216.70  30.23   9.13  136.08  183.18\n",
            "2019-09-09  214.17  30.50   8.95  137.52  180.50\n",
            "2019-09-06  213.26  30.56   8.70  139.10  178.65\n",
            "2019-09-05  213.28  31.50   8.80  140.05  179.74\n",
            "2019-09-04  209.19  30.95   8.79  137.63  168.76\n",
            "2019-09-03  205.70  30.90   8.32  136.04  164.17\n",
            "2019-08-30  208.74  31.45   8.24  137.86  167.51\n",
            "2019-08-29  209.01  31.45   8.10  138.12  167.00\n",
            "2019-08-28  205.53  30.78   7.93  135.56  161.22\n",
            "2019-08-27  204.16  30.20   7.92  135.74  161.64\n",
            "2019-08-26  206.49  30.28   8.04  135.45  165.29\n",
            "2019-08-23  202.64  29.54   7.96  133.39  162.28\n",
            "2019-08-22  212.46  31.90   8.18  137.78  171.31\n",
            "2019-08-21  212.64  31.70   8.15  138.79  171.06\n",
            "2019-08-20  210.36  30.72   8.37  137.26  167.70\n",
            "2019-08-19  210.35  31.48   8.66  138.41  170.61\n",
            "2019-08-16  206.50  31.18   8.78  136.13  159.40\n",
            "2019-08-15  201.74  29.67   8.00  133.68  148.62\n",
            "2019-08-14  202.75  30.24   9.02  133.98  149.92\n",
            "2019-08-13  208.97  32.11   9.34  138.14  155.90\n",
            "2019-08-12  200.48  32.43   9.04  135.34  151.30\n",
            "2019-08-09  200.99  34.19   9.14  137.25  154.03\n",
            "2019-08-08  202.66  33.92   9.48  138.43  158.10\n",
            "2019-08-07  198.29  29.19   9.45  134.83  153.74\n",
            "2019-08-06  196.25  28.86   9.56  134.24  152.20\n",
            "2019-08-05  192.61  27.99   9.65  131.77  150.64\n",
            "2019-08-02  203.25  29.44   9.99  136.45  161.03\n",
            "2019-08-01  207.64  29.86  10.07  137.60  164.76\n",
            "2019-07-31  212.23  30.45  10.44  135.82  168.55\n",
            "...            ...    ...    ...     ...     ...\n",
            "2012-02-14   63.52   7.32  14.21   25.20   14.97\n",
            "2012-02-13   62.66   7.29  14.31   25.31   14.88\n",
            "2012-02-10   61.52   7.05  14.17   25.24   14.65\n",
            "2012-02-09   61.49   7.24  14.35   25.47   15.02\n",
            "2012-02-08   59.43   7.25  14.44   25.38   15.03\n",
            "2012-02-07   58.45   7.13  14.39   25.12   14.51\n",
            "2012-02-06   57.84   6.92  14.29   24.99   14.47\n",
            "2012-02-03   57.31   7.08  14.27   25.03   14.58\n",
            "2012-02-02   56.74   6.93  14.07   24.79   14.28\n",
            "2012-02-01   56.87   6.90  14.08   24.74   13.76\n",
            "2012-01-31   56.91   6.71  14.04   24.44   13.61\n",
            "2012-01-30   56.48   6.74  14.18   24.51   13.64\n",
            "2012-01-27   55.76   6.82  14.28   24.19   13.74\n",
            "2012-01-26   55.43   6.77  14.31   24.42   13.56\n",
            "2012-01-25   55.69   6.73  14.35   24.47   13.69\n",
            "2012-01-24   52.41   6.53  14.14   24.28   13.77\n",
            "2012-01-23   53.29   6.52  14.21   24.61   13.46\n",
            "2012-01-20   52.40   6.42  14.37   24.59   13.11\n",
            "2012-01-19   53.33   6.22  14.37   23.27   13.23\n",
            "2012-01-18   53.50   5.97  14.27   23.36   12.88\n",
            "2012-01-17   52.95   5.73  14.06   23.39   12.46\n",
            "2012-01-13   52.34   5.66  14.14   23.38   12.65\n",
            "2012-01-12   52.54   5.82  14.20   23.17   12.99\n",
            "2012-01-11   52.68   5.81  14.17   22.94   13.08\n",
            "2012-01-10   52.77   5.71  14.05   23.04   13.34\n",
            "2012-01-09   52.58   5.59  14.15   22.96   13.40\n",
            "2012-01-06   52.66   5.43  13.99   23.27   13.40\n",
            "2012-01-05   52.12   5.46  13.92   22.91   13.56\n",
            "2012-01-04   51.54   5.46  13.93   22.68   13.09\n",
            "2012-01-03   51.27   5.48  13.78   22.16   12.94\n",
            "\n",
            "[1935 rows x 5 columns]\n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Compute stock returns and print the returns in percentage format\n",
        "stock_ret = dataset.pct_change()[1:]\n",
        "print(stock_ret.round(4)*100)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            AAPL    AMD     GE  MSFT  NVDA\n",
            "Date                                      \n",
            "2019-09-10 -3.08   1.58  -2.35 -0.03 -0.62\n",
            "2019-09-09 -1.17   0.89  -1.97  1.06 -1.46\n",
            "2019-09-06 -0.42   0.20  -2.79  1.15 -1.02\n",
            "2019-09-05  0.01   3.08   1.15  0.68  0.61\n",
            "2019-09-04 -1.92  -1.75  -0.11 -1.73 -6.11\n",
            "2019-09-03 -1.67  -0.16  -5.34 -1.16 -2.72\n",
            "2019-08-30  1.48   1.78  -0.96  1.34  2.03\n",
            "2019-08-29  0.13   0.00  -1.70  0.19 -0.30\n",
            "2019-08-28 -1.66  -2.13  -2.10 -1.85 -3.46\n",
            "2019-08-27 -0.67  -1.88  -0.13  0.13  0.26\n",
            "2019-08-26  1.14   0.26   1.51 -0.21  2.26\n",
            "2019-08-23 -1.86  -2.44  -0.99 -1.52 -1.82\n",
            "2019-08-22  4.85   7.99   2.76  3.29  5.57\n",
            "2019-08-21  0.08  -0.63  -0.37  0.73 -0.15\n",
            "2019-08-20 -1.07  -3.09   2.70 -1.10 -1.96\n",
            "2019-08-19 -0.00   2.47   3.46  0.84  1.73\n",
            "2019-08-16 -1.83  -0.95   1.38 -1.65 -6.57\n",
            "2019-08-15 -2.31  -4.84  -8.87 -1.80 -6.76\n",
            "2019-08-14  0.50   1.92  12.73  0.22  0.87\n",
            "2019-08-13  3.07   6.18   3.54  3.10  3.98\n",
            "2019-08-12 -4.06   1.00  -3.21 -2.03 -2.95\n",
            "2019-08-09  0.25   5.43   1.10  1.41  1.80\n",
            "2019-08-08  0.83  -0.79   3.72  0.86  2.65\n",
            "2019-08-07 -2.16 -13.94  -0.32 -2.60 -2.76\n",
            "2019-08-06 -1.02  -1.13   1.16 -0.44 -1.00\n",
            "2019-08-05 -1.86  -3.01   0.94 -1.84 -1.02\n",
            "2019-08-02  5.52   5.18   3.52  3.55  6.90\n",
            "2019-08-01  2.16   1.43   0.80  0.85  2.31\n",
            "2019-07-31  2.21   1.98   3.67 -1.30  2.30\n",
            "2019-07-30 -2.00  11.23   0.67  2.99  3.99\n",
            "...          ...    ...    ...   ...   ...\n",
            "2012-02-14  2.37   0.27   0.96  0.67  0.43\n",
            "2012-02-13 -1.35  -0.41   0.69  0.43 -0.55\n",
            "2012-02-10 -1.83  -3.29  -1.00 -0.26 -1.55\n",
            "2012-02-09 -0.05   2.70   1.32  0.89  2.52\n",
            "2012-02-08 -3.34   0.14   0.58 -0.36  0.06\n",
            "2012-02-07 -1.65  -1.66  -0.31 -1.01 -3.49\n",
            "2012-02-06 -1.04  -2.95  -0.68 -0.49 -0.25\n",
            "2012-02-03 -0.92   2.31  -0.16  0.13  0.76\n",
            "2012-02-02 -0.99  -2.12  -1.42 -0.96 -2.09\n",
            "2012-02-01  0.24  -0.43   0.11 -0.20 -3.62\n",
            "2012-01-31  0.06  -2.75  -0.32 -1.20 -1.07\n",
            "2012-01-30 -0.76   0.45   1.02  0.27  0.20\n",
            "2012-01-27 -1.26   1.19   0.69 -1.28  0.74\n",
            "2012-01-26 -0.59  -0.73   0.21  0.92 -1.34\n",
            "2012-01-25  0.46  -0.59   0.31  0.20  0.95\n",
            "2012-01-24 -5.88  -2.97  -1.52 -0.74  0.61\n",
            "2012-01-23  1.67  -0.15   0.53  1.33 -2.21\n",
            "2012-01-20 -1.66  -1.53   1.11 -0.07 -2.67\n",
            "2012-01-19  1.77  -3.12   0.00 -5.35  0.91\n",
            "2012-01-18  0.32  -4.02  -0.68  0.39 -2.58\n",
            "2012-01-17 -1.03  -4.02  -1.47  0.11 -3.29\n",
            "2012-01-13 -1.15  -1.22   0.53 -0.04  1.55\n",
            "2012-01-12  0.38   2.83   0.48 -0.88  2.69\n",
            "2012-01-11  0.28  -0.17  -0.26 -1.00  0.64\n",
            "2012-01-10  0.16  -1.72  -0.85  0.43  2.04\n",
            "2012-01-09 -0.36  -2.10   0.75 -0.36  0.41\n",
            "2012-01-06  0.16  -2.86  -1.11  1.33  0.00\n",
            "2012-01-05 -1.03   0.55  -0.54 -1.53  1.17\n",
            "2012-01-04 -1.10   0.00   0.05 -1.01 -3.47\n",
            "2012-01-03 -0.53   0.37  -1.08 -2.30 -1.13\n",
            "\n",
            "[1934 rows x 5 columns]\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "num_pc = 4\n",
        "\n",
        "X = np.asarray(stock_ret)\n",
        "[n,m] = X.shape\n",
        "print('The number of timestamps is {}.'.format(n))\n",
        "print('The number of stocks is {}.'.format(m))\n",
        "\n",
        "pca = PCA(n_components=num_pc) # number of principal components\n",
        "pca.fit(X)\n",
        "\n",
        "percentage = pca.explained_variance_ratio_\n",
        "percentage_cum = np.cumsum(percentage)\n",
        "print('{0:.2f}% of the variance is explained by the first 4 PCA'.format(percentage_cum[-1]*100))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of timestamps is 1934.\n",
            "The number of stocks is 5.\n",
            "95.23% of the variance is explained by the first 4 PCA\n"
          ]
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca_components = pca.components_"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(1,len(percentage)+1,1)\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.bar(x, percentage*100, align = \"center\")\n",
        "plt.title('Contribution of Principal Component',fontsize = 16)\n",
        "plt.xlabel('Principal Component Analysis',fontsize = 16)\n",
        "plt.ylabel('Percentage',fontsize = 16)\n",
        "plt.xticks(x,fontsize = 16) \n",
        "plt.yticks(fontsize = 16)\n",
        "plt.xlim([0, num_pc+1])\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(x, percentage_cum*100,'ro-')\n",
        "plt.xlabel('Principal Component Analysis',fontsize = 16)\n",
        "plt.ylabel('Percentage',fontsize = 16)\n",
        "plt.title('Cumulative contribution of Principal Component',fontsize = 16)\n",
        "plt.xticks(x,fontsize = 16) \n",
        "plt.yticks(fontsize = 16)\n",
        "plt.xlim([1, num_pc])\n",
        "plt.ylim([50,100])"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": [
              "(50, 100)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Statistical risk factors\n",
        "factor_returns = X.dot(pca_components.T)\n",
        "factor_returns = pd.DataFrame(columns=[\"factor 1\", \"factor 2\", \"factor 3\",\"factor 4\"], \n",
        "                              index=stock_ret.index,\n",
        "                              data=factor_returns)\n",
        "factor_returns.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": [
              "            factor 1  factor 2  factor 3  factor 4\n",
              "Date                                              \n",
              "2019-09-10  0.003604 -0.029151  0.025482 -0.001417\n",
              "2019-09-09 -0.000403 -0.020386  0.006553  0.011305\n",
              "2019-09-06 -0.004615 -0.013395  0.010374  0.021540\n",
              "2019-09-05  0.032175 -0.004606 -0.005984 -0.007376\n",
              "2019-09-04 -0.044815 -0.047094 -0.020641 -0.006933"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\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>factor 1</th>\n",
              "      <th>factor 2</th>\n",
              "      <th>factor 3</th>\n",
              "      <th>factor 4</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-09-10</th>\n",
              "      <td>0.003604</td>\n",
              "      <td>-0.029151</td>\n",
              "      <td>0.025482</td>\n",
              "      <td>-0.001417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-09</th>\n",
              "      <td>-0.000403</td>\n",
              "      <td>-0.020386</td>\n",
              "      <td>0.006553</td>\n",
              "      <td>0.011305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-06</th>\n",
              "      <td>-0.004615</td>\n",
              "      <td>-0.013395</td>\n",
              "      <td>0.010374</td>\n",
              "      <td>0.021540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-05</th>\n",
              "      <td>0.032175</td>\n",
              "      <td>-0.004606</td>\n",
              "      <td>-0.005984</td>\n",
              "      <td>-0.007376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-04</th>\n",
              "      <td>-0.044815</td>\n",
              "      <td>-0.047094</td>\n",
              "      <td>-0.020641</td>\n",
              "      <td>-0.006933</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "factor_exposures = pd.DataFrame(index=[\"Portfolio 1\", \"Portfolio 2\", \"Portfolio 3\",\"Portfolio 4\"], \n",
        "                                columns=stock_ret.columns,\n",
        "                                data = pca.components_).T"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "factor_exposures"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": [
              "      Portfolio 1  Portfolio 2  Portfolio 3  Portfolio 4\n",
              "AAPL     0.160969     0.359808    -0.496848     0.634381\n",
              "AMD      0.886794    -0.460958    -0.031533     0.010837\n",
              "GE       0.127969     0.270651    -0.604646    -0.730939\n",
              "MSFT     0.158041     0.322146    -0.227724     0.211936\n",
              "NVDA     0.382534     0.693560     0.578526    -0.135108"
            ],
            "text/html": [
              "<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>Portfolio 1</th>\n",
              "      <th>Portfolio 2</th>\n",
              "      <th>Portfolio 3</th>\n",
              "      <th>Portfolio 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.160969</td>\n",
              "      <td>0.359808</td>\n",
              "      <td>-0.496848</td>\n",
              "      <td>0.634381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMD</th>\n",
              "      <td>0.886794</td>\n",
              "      <td>-0.460958</td>\n",
              "      <td>-0.031533</td>\n",
              "      <td>0.010837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>0.127969</td>\n",
              "      <td>0.270651</td>\n",
              "      <td>-0.604646</td>\n",
              "      <td>-0.730939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.158041</td>\n",
              "      <td>0.322146</td>\n",
              "      <td>-0.227724</td>\n",
              "      <td>0.211936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NVDA</th>\n",
              "      <td>0.382534</td>\n",
              "      <td>0.693560</td>\n",
              "      <td>0.578526</td>\n",
              "      <td>-0.135108</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels = factor_exposures.index\n",
        "data = factor_exposures.values"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.subplots_adjust(bottom = 0.1)\n",
        "plt.scatter(\n",
        "    data[:, 0], data[:, 1], marker='o', s=300, c='g',\n",
        "    cmap=plt.get_cmap('Spectral'))\n",
        "plt.title('Scatter Plot of Coefficients of PC1 and PC2')\n",
        "plt.xlabel('factor exposure of PC1')\n",
        "plt.ylabel('factor exposure of PC2')\n",
        "\n",
        "for label, x, y in zip(labels, data[:, 0], data[:, 1]):\n",
        "    plt.annotate(\n",
        "        label,\n",
        "        xy=(x, y), xytext=(-20, 20),\n",
        "        textcoords='offset points', ha='right', va='bottom',\n",
        "        bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n",
        "        arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0')\n",
        "    )"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
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1a9YD9O3bhbPPHs43v3kfF13Uh7PO2p8+fV5hwoQZnH32l+qpbTWVlRVcdNEvsnnxkiRtgfI30GpSmwvc0ifZ+ecfyNChNzBmzJD1tp988t7ccccLvPTSh5xyyj4NnJ3z/PMfcMQRu1JdXcNdd73ExImv8uMfP0FKsHDhSpYtW02XLh2yfBmSJLUpzuGW2pDu3cv48pf35qabnl9v+ymnDOLWW2fy6KNvcvzx/1LvuSklfv3rf/D++8s55pgB/PWvb7Dvvn14550LmDPnfN5663y++MW9uOeel1vipUiS1GYYuKU25qKLDuLDD1eut22vvXrSsWM7Dj98Fzp1Wn8O+CWXPMy++17PwIH/w5Qp7/G3v32N9u2Luf32FzjxxD3WO/aLX9yT227b4HpLSZLUCKeUSG3A8uX/se7r3r07s3Ll9zY45p///MYG28aP/3yDbda37/jj/6XBEXJJklQ/R7ilrciWuqhQTc0WWpgkSVsAA7e0lSgtLaWiIm2R4XblykpKS72zuyRJ9TFwS1uJdu3a0bv37syZs7i1S9nA7NmL2Xnn/Vq7DEmStkgGbmkrss8+n+bJJxdSVVXT2qWss3z5GqZNq2bvvYe2dimSJG2RDNzSVuSAAw6kQ4cjuPXWN3j55Q+prKxulTpSSqxcWcn06R9w881zGTLkNLzDqyRJ9Wtzt3YvLy9P3mlSbVl1dTXPPTeNWbOe5J13XiSimoiWraGmBoqKOjBgQDmDBx/Cnnvu2bIFSJK0BYiIaSml8qaOc1lAaStTXFzMsGEHMGzYAaSUqKqqavEaIoKlS5dSWVlJ7969W7x/SZK2Jk4pkbZiEUG7du1a/FFSUsKUKVMYPHgwd911V2u/DZIkbdEM3JI2ydFHH81f/vIXvvvd73L22WezcuXKpk+SJOkTyMAtaZMdcMABPPfcc6xatYry8nL++c9/tnZJkiRtcQzckjbLNttsw4QJExg7dixHHnkk11xzDW3tYmxJkjaHgVtSszj99NN5+umn+d3vfsfxxx/PggULWrskSZK2CK5SIqnZDBgwgKeeeoof/OAH7Lffftxyyy0cccQR9R47f8V8xk8fz4x5M1hSsYSupV0Z3HswY4aMoWenni1cuSRJ2XEdbkmZePjhhxk9ejRnnHEGV155Je3atQNgyrtTuPrJq3lg9gMAVFRVrDunrKSMROLYAccydvhYhvUb1iq1S5JUiELX4TZwS8rM/PnzGT16NAsXLuT2229n0keTOO9H51HZuxL6NXxeEJS1K2PcyHGcU35OyxUsSdJG8MY3klpdr169uO+++/j1r3/Nvvvvy5qRa6gsroQHgTOBBu6QmUisrFzJxQ9dDGDoliRt1bxoUlKmIoKDTzqYqq9WseaRNfA6sAqY3fS5a0P31Pf8q5Ukaetl4JaUuaufvJqKNypgOLCaXOB+EKhp+txVlau4+omrsy1QkqQMGbglZWr+ivnc/9r9UE1uVHsRucC9EJjW9PmJxP2z72fBCpcZlCRtnZzDLSlT46ePJyLg4Fobq4H3gQJX/wuC8dPHc8khl2RQoSRJ2TJwS8rUjHkz1lv6D4BiYIfC21hVtYqZ82c2a12SJLUUp5RIytSSiiXN0s6iikXN0o4kSS3NwC0pU11LuzZLO91KuzVLO5IktTQDt6RMDe49mNKS0s1qo6ykjEG9BjVTRZIktSwDt6RMjR4yerPbSKRmaUeSpNZg4JaUqV6denHsgGOJhm4r2YQgGDVgFD07FbikiSRJWxgDt6TMjR0+lrJ2ZZt0blm7MsaOGNvMFUmS1HIM3JIyN6zfMMaNHEfHdh036ryO7ToybuQ4yvuWZ1SZJEnZcx1uSS3inPJzALj4oYtZVbmKRGrw2CAoa1fGuJHj1p0nSdLWyhFuSS3mnPJzmDx6MifucSKlJaWUlaw/zaSspIzSklJO3ONEJo+ebNiWJLUJjnBLalHlfcu56yt3sWDFAsZPH8/M+TNZVLGIbqXdGNRrEKOHjPYCSUlSm2LgltQqenbqySWHXNLaZUiSlDmnlEiSJEkZMnBLkiRJGTJwS5IkSRkycEuSJEkZMnBLkiRJGTJwS5IkSRkycEuSJEkZMnBLkiRJGTJwS5IkSRkycEuSJEkZMnBLkiRJGTJwS5IkSRkycEuSJEkZMnBLkiRJGTJwS5IkSRkycEuSJEkZMnBLkiRJGWrVwB0Rx0TEKxExOyIubeCYL0fEixExKyJua+kaJUmSpM1R0lodR0QxcC1wFDAXmBIRE1NKL9Y6ZndgLHBISmlRRPRqnWolSZKkTdOaI9wHALNTSm+klNYAdwAn1DnmLODalNIigJTS/BauUZIkSdosrRm4+wHv1Ho+N7+ttoHAwIh4KiKeiYhjWqw6SZIkqRm02pQSIOrZluo8LwF2Bw4DdgCeiIh9UkqL12so4mzgbID+/fs3f6WSJEnSJmrNEe65wI61nu8AvFfPMX9OKVWmlN4EXiEXwNeTUroxpVSeUirv2bNnZgVLkiRJG6s1A/cUYPeI2CUi2gMnAxPrHHMP8BmAiNiO3BSTN1q0SkmSJGkztFrgTilVAd8CJgEvAX9IKc2KiCsj4vj8YZOAhRHxIvA34JKU0sLWqViSJEnaeJFS3WnTW7fy8vI0derU1i5DkiRJbVxETEsplTd1nHealCRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyVNLaBWypFi9ezIsvvsB7773GmjUrW6TP0tJt2GWXfdhjjz0oKytrkT4lSZKULQN3PR5//FGeeeZW9twzMXBgKR06FBMRmfaZUmLFikpee+1RJk3qwBe+cCEDBw7MtE9JkiRlz8Bdx3PPTWXGjJv593/fkc6d27d4/0OHwrvvLuW22/6TU0+9nH79+rV4DZIkSWo+zuGuY8qUBzjuuB6tErbX6tdvGw46qIbnn3+m1WqQJElS8zBw17J06VKWLHmD/v27tnYp7LXXdrzyylOtXYYkSZI2k4G7lqVLl7LttkUUFWU7X7sQ225byvLli0gptXYpkiRJ2gwG7lpqamooLm54/5/+9BIRV/Dyyx+ut/0Xv3ia0tKrWLKkYt22xx6bQ9euP2W//W5gzz2v5YorHlu3/bjjbmuylqKiICJRU1OzSa9FkiRJWwYD90a4/fYXGD68P3fc8cIG24cN68ef/vTyettHjOjP889/nalTz+LWW2cybdp7LVmuJEmStgAG7gItX76Gp556h5tuOn69wP366x+xfPkarrrqM9x++wv1ntupU3v23397Xn99UUuVK0mSpC2EgbtA99zzMsccsxsDB/age/cynnvufSA3un3KKfswYsROvPLKh8yfv2KDcxcuXMkzz8xl7717tnTZkiRJamUG7gLdfvsLnHzyPgCcfPI+3H77TADuuCO3vago+MIX9uTOO2etO+eJJ95mv/1uYOTIW7n00uHsvXevVqldkiRJrccb3xRg4cKVPProm7zwwnwioLo6EQGnnTaY1177iKOOmgDAmjXV7LprN775zQOA3Bzue+89tTVLlyRJUiszcBfgj398kTPOGMwNN3xu3bZPf3o8558/icsv/zRjx45Yt32XXX7FW28tbo0yJUmStAUycOctWbKEadOmMm3aLKqq3lhv/evx46czfHh/rr/+sXXbdt21hKefnkt1dc/1tg8Z0onvfW8iO+ywDUuXLlxvH8CcOYuZPn02u+/+k/yW4Gtf259jjtmHvfbqSceO7bJ7kZIkSWpx0dZurFJeXp6mTp26Uee8/vrr3HXXOPr1+5DS0hc48MBtKWqh2e3V1bB4cRUvv1zJnDllnHbaIfTp0xmAK6+cw/e+dxPFjS0OLkmSpFYREdNSSuVNHfeJH+H+6KOPuOuu/+Tkk8vo2nUHXn/9dfr169CiNeywQwf22QdefHEFt976FN/61hGUln7ivzWSJEltwid+lZKZM6czaNBq+vfvSlFREdXVrTfiv9denejbdxWvvrqQyspqIkooaqmhdkmSJGXiE5/mXnvtH+yxxzYAdOzYkYqKIiorq1utnj32KOa1197nnXeW0qfPrkREq9UiSZKkzfeJD9wrViyia9dSAIqLi+nefQc++GB5q9XTtWsJy5evYtq0j9hrrxFNnyBJkqQtWqsG7og4JiJeiYjZEXFpI8edFBEpIpqclL6xamqqKSr6eBR5550H8s47HZg7dykRkzn99JfW7auqSvTs+XeOOy5305t589Zw3HEz2Xffqey11xRGjcptnzOngrKyJxgyZOq6xw03vLfu6/btH2fQoNzXl176xnr1VFXV8OKLC/noo73Zf/9hzf1yJUmS1MJa7cq8iCgGrgWOAuYCUyJiYkrpxTrHdQHOBf7REnV16tSJffcdweuvz6KsrIgpU5bx7LOLKCsr5oknltCjRwnLllUxc+ZSrrzybfbeu4yrr94JgFdfXcXMmUt5993V9OvXngkTBq7X9sEH554fc8wsrr12V7p1y739M2cuJaVEZSW8/noVVVWDOOOM8ygtLW2JlyxJkqQMteZSGAcAs1NKbwBExB3ACcCLdY77EfBz4OKWKqxTp04MHnwARUV/ZdSovZk1azuOP34Akyc/zFe+MoSnn36Pvn0PYdmyRey55x707TsAgL59c+dXVS2lXbsP6Nv3kHrbLy6eTZ8+B9CjR9l620tKSujatZKVK3emrKys3nMlSZK0dWnNKSX9gHdqPZ+b37ZOROwH7JhSurexhiLi7IiYGhFTFyxY0KxFjh5dzn33vUWnTl155ZXFfOYzA2nfvj09evTgggsO4fzz/8ZJJ93L9dfPYvXq3PZu3boxZ85SjjjiTo444k4uu+wZevTose5RVFRE9+7d19vWo0cPunbtyvvvv8+4ceN46aWXmi5OkiRJW7zWDNz1Lb+xbk2+iCgCfgFc1FRDKaUbU0rlKaXynj17NmOJMHhwb+bMWcztt89k1Kjd19t39NEDeOON8zjrrKG8/PJC9tvvBhYsWAHAbrt1Y/r0bzB9+je49trPFtxfv379GDFiBIceeih33HFHs74WSZIktbzWDNxzgR1rPd8BeK/W8y7APsBjETEHOBCYmMWFk005/vh/4eKLH+aUU/bZYF/37mWceuogJkw4kWHD+vL4429tVl8Rwac+9Skefvhhvv/97/Ptb3+b1atXb1abkiRJaj2tGbinALtHxC4R0R44GZi4dmdKaUlKabuU0s4ppZ2BZ4DjU0obd9/2ZnDmmftx2WWHMmhQ7/W2P/rom6xcWQnAsmWref31RfTv37VZ+hwyZAhTp05l7ty5HHroobz11uYFeUmSJLWOVgvcKaUq4FvAJOAl4A8ppVkRcWVEHN9addVnhx224bzzDtxg+7Rp71FefiODB1/HQQfdxL/9234MG9avnhY2zbbbbsvdd9/Nl7/8ZT71qU/x4IMPNlvbkiRJahmRUuvdyjwL5eXlaerUwgfBf/GLixgzph3bbrtlLMH35puLmDx5R0aPXn9RlieeeIJTTjmFMWPGcPnll1NcXFzv+fNXzGf89PHMmDeDJRVL6FralcG9BzNmyBh6dmre+e2SJEmfZBExLaXU5HTn1lwWcAuxddw6fcSIEUybNo1TTz2Vo48+mttuu41evXqt2z/l3Slc/eTVPDD7AQAqqirW7bv7pbv54WM/5NgBxzJ2+FiG9fOGOpIkSS3lE39r9w4dOlJRUdXaZaxTUVFFhw6d6t3Xu3dvHnroIQ466CD2339/nnrqKQAu+8NlDL90OPe8fA8VVRXrhW2AVVWrqKiq4J4V0YijAAAgAElEQVSX7+GwWw7juqnXZf46JEmSlPOJGOFevXo1y5Yto7q6eoN9nTr15cUXn6Bz5+0bPL+oqIgOHTo0OI2jOc2Zs4p+/fZocH9xcTE/+tGPOOigg/jCF77AiFNHcG/1vay5Z03ufpztG247kVhZuZKLH8pNVzmn/Jxmrl6SJEl1tenAPXv2bJ555kHeeWc6nTsnSup5tfPnL2XatOcpLe1Eu3b1D/hXV0NlZRHdu+9A//4D6NKlSyb1Ll++hlmzYPToQU0eO2rUKH4z8Tec+MUTqelSA9sDz5FbPLEJa0P3sL7DKO/b4qssSpIkfaK02cA9a9YLPPDAf3P00R348pe3p337+kenU0pMnJh4+eVXOfLIDuy4YwciNpzXXVlZzbx57zBjxvsMGjScbbbZptlqra6u4c03FzNp0iKGDRvNdtttV9B5P7vnZ9QMr4GXgTfJrWJeTkHf1VWVq7j6iau56yt3bUblkiRJakqbXKXk6aef5r/+63xGjy6jT5/OTZ6TUuLpp9/mn/98k2XLFtO5c1DUwOz2jz6qYN68Mvbcc79mqbe6GpYtS2y33UD2338k++03tKDz5q+YT79T+1H1QhV8AFQDq4GDgZGF9V1aUsrb57/t6iWSJEmb4BO9Ssns2bPZfvuV9OlTWJCMCA4+eCcOPngnli1bzcqVldTU1P9BJKXEDTe8y/DhF9CjR4/NrrWoqIjOnTuTUqJz56Y/HKw1fvp4Sg4ooWpoFSRgKfAqUNjgOABBMH76eC455JKNLVuSJEkFapOB+91332aXXTbt3C5dOtClS4dGjxk8eDHV1dVsv33DF1pujJQSe+yxByeffDKXXXZZQRdnzpg34+PVSALoCmzkan+rqlYxc/7Mja5XkiRJhWuTywKuXr2C0tLsPkt06JBYvXp1s7UXEUyePJknn3ySo446ivfff7/Jc5ZULGmWvhdVLGqWdiRJklS/NjnCnZtjUb9585ZzwQWTeOaZuXTrVkb79sV85zsH061bGSeccAe77LLtumPHjRvJkUfuukEb9VxTudn69OnDQw89xFVXXcX+++/PhAkTOOKIIxo8vmtp12bpt1tpt2ZpR5IkSfVro4G7fiklPv/53/O1r+3Lbbd9EYC33lrMxImv0K1bGSNG9Ofee09ttfqKi4v54Q9/yPDhwzn99NM5++yz+cEPflDvFJPBvQdz10t3bXCTm41RVlLGoF5NL0EoSZKkTdcmp5Q05NFH36R9+2K+8Y2PLybdaadt+fa3P9WKVW3oiCOO4LnnnuOJJ57gqKOO4oMPPtjgmNFDRm92P4nULO1IkiSpYZ+owD1r1gKGDu3T4P4nnnibIUOuX/d4/fWPWrC69a2dYvLpT3+aoUOH8sgjj6y3v1enXhw74FiCTZvfEgSjBoxySUBJkqSMfaICd13f/OZ97Lvv9Qwb9hsARozoz/Tp31j32G237q1a39opJhMmTOD000/niiuuWO/29Cf8ywn13qSnEGXtyhg7YmxzlSpJkqQGfKIC99579+S55z6ennHttZ/lkUfOYMGCFa1YVdPWTjGZPHkyI0eO5IMPPuC6qddx9hVnU7OwZqPb69iuI+NGjvO27pIkSS3gExW4Dz98Fyoqqrjuuinrtq1cWdmKFRWuT58+PPzww4wYMYI9Bu/BBdddwJrla+DRjWtnbdg+p/ycbAqVJEnSej5Rq5REBPfc8xUuuGASP//53+nZsyOdOrXnZz87Evh4Dvda3//+oZx00l6tVe4GiouL+exZn+Wn7/yU1X9YDUOAN4H5QK8Czo9irvvsdZyx7xkZVypJkqS1PlGBG2D77btwxx0n1btvyZJLW7iajffjx37M6g9Ww0jgWaA98AhwStPn1qQa/vzynw3ckiRJLahNTikpKiqmpqbhm99srpqaoKio5d+6+Svm8+DsB2EJ8DzwEbAUeAV4t+nzE4n7Z9/PghULMq1TkiRJH2uTI9ydOnVjyZKqzNpfuhQGDOiUWfsNGT99PFEScFR+QwKWAbOB7QprIwjGTx/PJYdckk2RkiRJWk+bHOHeffeBvPJKkFLzj3KvXl3F228Xs8suuzR7202ZMW/G+neWDGAbYCjQobA2VlWtYub8mRlUJ0mSpPq0ycDdp08f2rffnaeeeq9Z200p8cAD77D77sMpLS1t1rYLsaRiSbO0s6hiUbO0I0mSpKa1ySklEcFXv/ptxo8fx1tvzWHvvUvp2bMjJSUb//kiJaisrOa995Yzc+YaSkr259RTC7hCMQNdS7s2SzvdSrs1SzuSJElqWpsM3ABdunTh61//Hq+88govvfQszz77HtXVmzavu6SkPdttV86hhw5jt912Y8WKFRQXF1NcXNzMVTducO/B3PXSXetPK9lIZSVlDOo1qBmrkiRJUmMii3nOram8vDxNnTo10z6++93v8tJLL/H73/+esrKyTPuqbf6K+ez0y502K3CXlpTy9vlv07NTz2asTJIk6ZMnIqallJq8dXebnMOdtauuuoouXbpw9NFHs2RJ88yrLkSvTr04dsCxBLFJ5wfBqAGjDNuSJEktyMC9Cdq1a8eECRMYPHgwhx12GPPmzWuxvscOH0tZu00bVS9rV8bYEWObuSJJkiQ1xsC9iYqKirjmmms44YQTGDFiBHPmzGmRfof1G8a4kePo2K7jRp3XsV1Hxo0cR3nfJv/qIUmSpGbUZi+abAkRweWXX0737t0ZMWIEkyZNYq+99sq833PKzwHg4ocuZlXlKhINz8MPgrJ2ZYwbOW7deZIkSWo5Bu5mcO6559K9e3cOP/xwJk6cyAEHHLDBMfNXzGf89PHMmDeDJRVL6FralcG9BzNmyJhNmlN9Tvk5DOs7jKufuJr7Z99PEKyqWrVuf1lJGYnEqAGjGDtirCPbkiRJrcRVSprRvffey5gxY7j99ts58sgjAZjy7hSufvJqHpj9AMB6K4ysDcXHDjiWscPHMqzfsE3qd8GKBYyfPp6Z82eyqGIR3Uq7MajXIEYPGe0FkpIkSRkpdJUSA3cze/zxxznppJO4/vrrmdd/HhfecSEVyytg+4bPcdqHJEnS1qfQwO2UkmZ26KGHMmnSJD4z8jOsGrGKNWVr4AngXxs+J5FYWbmSix+6GMDQLUmS1Ia4SkkGqnpVsfr01ax5dA3MAz4CPmz6vLWhe+p7rTdCL0mSpOZl4M7AVY9eRcXMCjgamA50zf9bgFWVq7j6iaszrE6SJEktycDdzOavmM+kNyZBJfA4sAJYAPwDqG76/ETi/tn3s2DFgkzrlCRJUsswcDez8dPHEyUBnwG+Afw7cCSwLbCmsDaCYPz08VmVKEmSpBbUYOCOiOKI+HpE/CgiDqmz7/vZl7Z1mjFvxnpL/7EN8Cngm0CBd2RfVbWKmfNnZlCdJEmSWlpjI9w3AJ8GFgK/joj/rrXvC5lWtRVbUrGkWdpZVLGoWdqRJElS62oscB+QUjo1pfRLcmO0nSPi7ojoAETLlLf16VratVna6VbarVnakSRJUutqLHC3X/tFSqkqpXQ2ubU2HgU6Z13Y1mpw78GUlpRuVhtlJWUM6jWomSqSJElSa2oscE+NiGNqb0gpXQncDOycZVFbs9FDRm92G4nULO1IkiSp9TUYuFNKp6WUHqxn+29TSu2yLWvr1atTL44dcCyxibNugmDUgFH07NSzmSuTJElSa2hslZLTIuL0erafFRGnZlvW1m3s8LGUtStwSZI6ytqVMXbE2GauSJIkSa2lsSklFwH31LP9jvw+NWBYv2GMGzmOju06btR5Hdt1ZNzIcZT3Lc+oMkmSJLW0kkb2FaeUltXdmFJaFhFOKWnCOeXnAHDxQxezqnIVidTgsUFQ1q6McSPHrTtPkiRJbUNjI9ztIqJT3Y0R0YVaK5ioYeeUn8Pk0ZM5cY8TKS0ppaxk/WkmZSVllJaUcuIeJzJ59GTDtiRJUhvU2Aj3TcAfI+KclNIcgIjYGbg2v08FKO9bzl1fuYsFKxYwfvp4Zs6fyaKKRXQr7cagXoMYPWS0F0hKkiS1YQ0G7pTSuIhYDkyOiM5AAlYAP00pXddSBbYVPTv15JJDLmntMiRJktTCGhvhJqV0PXB9PnBHfXO6JUmSJDWssWUBD4yIf+ZHuR8Gdmi5siRJkqS2obGLJv8HuBjoAfw38MsWqUiSJElqQxoL3EUppYdTSqtTSncCXtknSZIkbaTG5nBvGxFfaOh5Sunu7MqSJEmS2obGAvdk4HMNPE+AgVuSJElqQmPLAo5pyUIkSZKktqixOdySJEmSNpOBW5IkScpQY+twfyn/7y4tV44kSZLUtjQ2wj02/+9dWXUeEcdExCsRMTsiLq1n/4UR8WJEzIiIRyJip6xqkSRJkrLQ2ColCyPib8AuETGx7s6U0vGb03FEFAPXAkcBc4EpETExpfRircOeB8pTSisj4hzg58BXNqdfSZIkqSU1Frg/CwwFJgD/lUHfBwCzU0pvAETEHcAJwLrAnVL6W63jnwFOy6AOSZIkKTONLQu4BngmIg5OKS2IiC65zWl5M/XdD3in1vO5wKcaOf5fgQeaqW9JkiSpRTQ2wr1W74h4COgOREQsAL6WUnphM/uOeraleg+MOA0oBz7dwP6zgbMB+vfvv5llSZIkSc2nkGUBbwQuTCntlFLqD1yU37a55gI71nq+A/Be3YMi4kjge8DxKaXV9TWUUroxpVSeUirv2bNnM5QmSZIkNY9CAnen2nOpU0qPAZ2aoe8pwO4RsUtEtAdOBta7ODMi9gNuIBe25zdDn5IkSVKLKmRKyRsR8QNyF09C7sLFNze345RSVUR8C5gEFAP/l1KaFRFXAlNTShOB/wQ6A3dGBMDbm7s6iiRJktSSCgncZwJXAHfnnz8OjGmOzlNK9wP319l2Wa2vj2yOfiRJkqTW0mTgTiktAs5tgVokSZKkNqeQOdySJEmSNpGBW5IkScqQgVuSJEnKUJOBOyIGRsQjEfFC/vngiPh+9qVJkiRJW79CRrh/A4wFKgFSSjPIrZktSZIkqQmFBO6OKaVn62yryqIYSZIkqa0pJHB/GBG7AQkgIk4C3s+0KkmSJKmNKOTGN98EbgT2iIh3yd1l8quZViVJkiS1EY0G7ogoAspTSkdGRCegKKW0rGVKkyRJkrZ+jU4pSSnVAN/Kf73CsC1JkiRtnELmcD8cERdHxI4R0X3tI/PKJEmSpDagkDncZ+b//WatbQnYtfnLkSRJktqWJgN3SmmXlihEkiRJaouaDNwRcUZ921NKv2v+ciRJkqS2pZApJcNqfV0KHAE8Bxi4JUmSpCYUMqXk27WfR0RXYEJmFUmSJEltSCGrlNS1Eti9uQuRJEmS2qJC5nD/hfxt3ckF9L2AP2RZlCRJktRWFDKHe1ytr6uAt1JKczOqR5IkSWpTCgncU4FVKaWaiBgIDI2IeSmlyoxrkyRJkrZ6hczhfhwojYh+wCPAGGB8lkVJkiRJbUUhgTtSSiuBLwDXpJROJDePW5IkSVITCgrcEXEQ8FXgvvy2QqaiSJIkSZ94hQTu84GxwJ9SSrMiYlfgb9mWJUmSJLUNhdz4ZjIwOSK6RETnlNIbwLnZlyZJkiRt/Zoc4Y6IQRHxPPAC8GJETIuIvbMvTZIkSdr6FTKl5AbgwpTSTiml/sBFwG+yLUuSJElqGwoJ3J1SSuvmbKeUHgM6ZVaRJEmS1IYUstrIGxHxA2BC/vlpwJvZlSRJkiS1HYWMcJ8J9ATuBv6U/3pMlkVJkiRJbUUhq5QsAs6NiK5ATUppWfZlSZIkSW1DIauUDIuImcA/gZkR8c+I2D/70iRJkqStXyFzuG8C/j2l9ARARAwHbgYGZ1mYJEmS1BYUMod72dqwDZBSehJwWokkSZJUgEJGuJ+NiBuA24EEfAV4LCKGAqSUnsuwPkmSJGmrVkjgHpL/94d1th9MLoAf3qwVSZIkSW1IIauUfKYlCpEkSZLaokJWKZmQXxJw7fOdIuKRbMuSJEmS2oZCLpp8EvhHRIyKiLOAh4FfZluWJEmS1DYUMqXkhoiYBfwN+BDYL6X0QeaVSZIkSW1AIVNKTgf+DzgDGA/cHxH7ZlyXJEmS1CYUskrJF4HhKaX5wO0R8SfgFj5evUSSJElSAwqZUvL5Os+fjYgDsitJkiRJajsKmVIyMCIeiYgX8s8HA9/JvDJJkiSpDShklZLfAGOBSoCU0gzg5CyLkiRJktqKQgJ3x5TSs3W2VWVRjCRJktTWFBK4P4yI3cjdxp2IOAl4P9OqJEmSpDaikFVKvgncCOwREe8CbwJfzbQqSZIkqY0oZJWSN4AjI6ITUJRSWpZ9WZIkSVLbUMgINwAppRVZFiJJkiS1RYXM4ZYkSZK0iRoN3BFRFBEHt1QxkiRJUlvTaOBOKdUA/9VCtUiSJEltTiFTSh6KiC9GRGRejSRJktTGFHLR5IVAJ6A6IlYBAaSU0jaZViZJkiS1AYUsC9ilJQqRJEmS2qKClgWMiOOBQ/NPH0sp3ZtdSZIkSVLb0eQc7oj4KXAe8GL+cV5+myRJkqQmFDLCPQoYkl+xhIi4BXgeuDTLwiRJkqS2oNAb32xb6+uuzdV5RBwTEa9ExOyI2CDAR0SHiPh9fv8/ImLn5upbkiRJagmFjHBfDTwfEX8jt0LJocB/bG7HEVEMXAscBcwFpkTExJTSi7UO+1dgUUppQEScDPwM+Mrm9i1JkiS1lCZHuFNKtwMHAnfnHwflt22uA4DZKaU3UkprgDuAE+occwJwS/7rPwJHuB64JEmStiaFXDT5SErp/ZTSxJTSn1NKH0TEI83Qdz/gnVrP5+a31XtMSqkKWAL0qKfGsyNiakRMXbBgQTOUJkmSJDWPBgN3RJRGRHdgu4joFhHd84+dgb7N0Hd9I9VpE44hpXRjSqk8pVTes2fPZihNkiRJah6NzeH+OnA+uXA9jY/D71Jyc68311xgx1rPdwDea+CYuRFRQu6CzY+aoW9JkiSpRTQ4wp1S+lVKaRfg4pTSrimlXfKPfVNK/9MMfU8Bdo+IXSKiPXAyMLHOMROBr+W/Pgl4NKW0wQi3JEmStKUqZFnAmohYtyxgfnrJv29ux/k52d8CJgEvAX9IKc2KiCvzd7YEuAnoERGzgQtx7W9JkiRtZaKpAeOImJ5SGlJn2/Mppf0yrWwTlZeXp6lTp7Z2GZIkSWrjImJaSqm8qeMKGeEuqr0UX3797PabU5wkSZL0SVHIjW8mAX+IiOvJrRDyDeDBTKuSJEmS2ohCAvd3ya1Ycg65lUoeAn6bZVGSJElSW9Fk4E4p1QDX5R+SJEmSNkKTgTsidgeuBvYCStduTyntmmFdkiRJUptQyEWTN5Mb3a4CPgP8DpiQZVGSJElSW1FI4C5LKT1CbgnBt1JKlwOHZ1uWJEmS1DYUctFkRUQUAa9FxLeAd4Fe2ZYlSZIktQ2FjHCfD3QEzgX2B07j49utS5IkSWpEgyPcETEhpXQ6cHBKaQqwHBjTYpVJkiRJbUBjI9z7R8ROwJkR0S0iutd+tFSBkiRJ0tassTnc15O7o+SuwDRyN71ZK+W3S5IkSWpEgyPcKaVfp5T2BP4vpbRrSmmXWg/DtiRJklSAJi+aTCmd0xKFSJIkSW1RIauUSJIkSdpEBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUMGbkmSJClDBm5JkiQpQwZuSZIkKUOtErgjontEPBwRr+X/7VbPMUMi4umImBURMyLiK61RqyRJkrQ5WmuE+1LgkZTS7sAj+ed1rQTOSCntDRwD/DIitm3BGiVJkqTN1lqB+wTglvzXtwCfr3tASunVlNJr+a/fA+YDPVusQkmSJKkZtFbg7p1Seh8g/2+vxg6OiAOA9sDrDew/OyKmRsTUBQsWNHuxkiRJ0qYqyarhiPgr0KeeXd/byHa2ByYAX0sp1dR3TErpRuBGgPLy8rSRpUqSJEmZySxwp5SObGhfRMyLiO1TSu/nA/X8Bo7bBrgP+H5K6ZmMSpUkSZIy01pTSiYCX8t//TXgz3UPiIj2wJ+A36WU7mzB2iRJkqRm01qB+6fAURHxGnBU/jkRUR4Rv80f82XgUGB0REzPP4a0TrmSJEnSpomU2taU5/Ly8jR16tTWLkOSJEltXERMSymVN3Wcd5qUJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4JYkSZIyZOCWJEmSMmTgliRJkjJk4Jb0/9u79yi7yjLP498n9yKGhEtgACHIPQRibAoGJQ3hKqANImpgZCQ9oHLTpRJWw3TL0nGNqASYWa0gaLsCTZNESBvUBgXlDoEhkCsR5RKUiJBAJyAhFZLwzB9nE4tYSU4Vtc8+VfX9rHVWnbPP3vv91VOnqp7a9e59JElSiWy4JUmSpBLZcEuSJEklsuGWJEmSSmTDLUmSJJXIhluSJEkqkQ23JEmSVCIbbkmSJKlENtySJElSiWy4JUmSpBLZcEuSJEklsuGWJEmSSmTDLUmSJJXIhluSJEkqkQ23JEmSVCIbbkmSJKlENtySJElSiWy4JUmSpBLZcEuSJEklGlB1AEmSJJVn5cqVLF68iKVLn+TNN98g861Sxonox+DBQxk16gBGj96fYcOGlTJOT2TDLUmS1EvNnn0/998/ldGj32L//VsYPLg/EVHKWJnJ6tXreOaZB7jrrgGccML5vP/940oZq6ex4ZYkSeqFFi5cwKOP/oDPf35nhg8f0rBxx46F5ctXccMN/5dhw77KHnvs0bCxm5VzuCVJknqhOXPu5MMf3rqhzfbbRo4cyoQJA3nssXsbPnYzsuGWJEnqZdra2njxxcXsuee2lWUYPXokTz31CJlZWYZmYcMtSZLUy7z++usMHZoMGFBdq7fVVgOJWMOaNWsqy9AsbLglSZJ6mbfeeov+/Td9cuRPfvIbIr7Ok0++DMBzz60k4ut89at3bVjn5ZffYODAb3DBBbcB8LWv3cMuu1zJuHHfZ++9/5mPf3wGixcv32yO/v1h/fr13fAZ9Ww23JIkSX3MtGmLGD9+N6ZPX7Rh2R57bMPPf/7Uhsc33/wEY8aMfMd2X/7yocybdw5PPfUFJk4cw1FHXc/y5asalrunsuGWJEnqQ15//U0efPB5/uVfTnpHw93SMoDRo7dnzpwXAJgx4wk+9akxm9zPxIkHcNxxe3LTTQtLz9zT2XBLkiT1IbNmPcnxx+/JPvtsx7bbtvD443/a8Nxppx3A9OmLWLr0Nfr378fOO2/+zWv+5m922jAtRZtmwy1JktSHTJu2iNNOOwCoNdjTpv3lCPXxx+/FnXc+y7RpC5k4cdNHt9/mFUjq4xvfSJIk9RGvvPIGd921hEWLlhEB69cnEXDeeQcDMGhQfw46aCeuuGI2TzxxHj/72e82u7+5c1+ktXXnRkTv0SppuCNiW2AGsDvwHPCpzFyxiXW3Bn4D/CQzL2hURkmSpN7mllsW85nPjOXaa/9uw7IjjpjK0qWvbXh84YUf5IgjRrHddlttdl8zZy7mjjue4Yorjistb29R1RHui4FfZ+a3IuLi4vE/bGLdbwC+TZEkSdK7NG3aIi6+ePw7lp166mi++c0HNjweM2YHxozZocPtr7rqYW68cQGrVq3lgAN24K67zmTkyKGlZu4Nooq5NxHxW2BCZv4pInYC7snMfTtY7yDgIuAXQGs9R7hbW1tzzpw53Z5ZkiSpp1i2bBk333wJ55+/a6U5vvOd33P++d9l6NDe2ZRHxGOZ2bql9ao6aXLHzPwTQPHxr/6Mioh+wBXUGu7NiojPRcSciJizfPnmL8AuSZKkRtn0m+/0JaVNKYmIXwH/pYOn/rHOXZwH3JaZz0ds/ouVmdcB10HtCHdnckqSJPU2gwYN4s03q82QmaxZ8xaDBw+uNkgTKK3hzsxjNvVcRLwUETu1m1KyrIPVPgj8bUScB7wHGBQRr2fmxSVFliRJ6hW23npr1q4dysqVbYwYMaSSDC+88GdGjNiFAQO8KF5VU0p+CpxZ3D8TuHXjFTLz05m5W2buDkwGbrDZliRJ2rJ+/fqx337jeeyxFysZPzN57LGXGTNmQiXjN5uqGu5vAcdGxFPAscVjIqI1In5YUSZJkqRe4/DDj2Phwp24777nWb16bcPGff31N7nzzj+wdOneHHroYQ0bt5lVcpWSMnmVEkmSpJqVK1fyy1/O5Nln/x/bb7+OwYNhC6fGdVkmrF4NK1YMYp99PsSHP/zxXnt1krfVe5USJ9VIkiT1UiNGjGDixLN4883/zksvvcSaNWtKGysiGDx4MDvuuCPr1q2jpaWltLF6GhtuSZKkXm7QoEHsumvjrsn90Y9+lH333ZcrrriCgQMHNmzcZlXVHG5JkiT1UjNmzGDJkiUcddRRvPhiNSduNvkvdGIAAA2sSURBVBMbbkmSJHWrESNGcOutt3LsscfS2trK7Nmzq45UKRtuSZIkdbt+/fpx6aWXcu2113LyySdzzTXX0Nsu1lEvG25JkiSV5iMf+QgPPfQQV199NWeddRZtbW1VR2o4T5qUJElSqfbaay9mz57N2Wefzfjx45k5cyajRo3qcN1lq5Yxdd5UFry0gFfbXmX4kOGM3XEsfz/u7xk5dGSDk3cPr8MtSZKkhshMrrzySqZMmcKNN97I0UcfveG5R//4KJc9cBm3P307AG3r/nIkvGVAC0lywl4ncMn4Szh4l4Mbnr0j9V6H2yklkiRJaoiI4MILL+Smm27ijDPO4PLLLyczuez2yzjss4cx68lZtK1re0ezDbB63Wra1rUx68lZTLh+AtfMuaaiz6BrnFIiSZKkhjryyCN55JFHOPXUU5l+x3QWH7SYtfeshb2BbTe9XZK8sfYNJt8xGYBzW89tTOB3ySPckiRJarjddtuNq6ZfxfwV82n7URvsDzxU37ZvN91zXugZ04htuCVJklSJC6++kPUj18M+wBPAfODP9W27eu1qLrv/shLTdR8bbkmSJDXcslXLmLtiLiwDngHWFrf/qG/7JLnt6dtYvmp5eSG7iXO4JUmS1HBT502l/579WTtqbW3BWmAJnepOg2DqvKlcdNhFZUTsNjbckiRJargFLy1459VIBlKbWtIJq9etZuGyhd2aqwxOKZEkSVLDvdr2arfsZ0Xbim7ZT5lsuCVJktRww4cM75b9bDNkm27ZT5lsuCVJktRwY3ccy5ABQ97VPloGtHDgDgd2U6Ly2HBLkiSp4SaNm/Su95Fkt+ynbDbckiRJargdhu7ACXudQBBd2j4ITtzrREYOHdnNybqfDbckSZIqccn4S2gZ2NKlbVsGtnDJ317SzYnKYcMtSZKkShy8y8FMOW4KWw3cqlPbbTVwK6YcN4XWnVtLSta9vA63JEmSKnNu67kATL5jMqvXribJTa4bBC0DW5hy3JQN2/UEHuGWJElSpc5tPZd7J93LKfudwpABQ2gZ8M5pJi0DWhgyYAin7HcK9066t0c12+ARbkmSJDWB1p1bmTlxJstXLWfqvKksXLaQFW0r2GbINhy4w4FMGjepR5wg2REbbkmSJDWNkUNHctFhF1Udo1s5pUSSJEkqkQ23JEmSVCIbbkmSJKlENtySJElSiWy4JUmSpBLZcEuSJEklsuGWJEmSSmTDLUmSJJXIhluSJEkqkQ23JEmSVCIbbkmSJKlENtySJElSiWy4JUmSpBLZcEuSJEklsuGWJEmSSmTDLUmSJJUoMrPqDN0qIpYDv++m3W0PvNxN++pLrFvnWbOusW5dY906z5p1jXXrPGvWNVXVbVRmjtzSSr2u4e5OETEnM1urztHTWLfOs2ZdY926xrp1njXrGuvWedasa5q9bk4pkSRJkkpkwy1JkiSVyIZ7866rOkAPZd06z5p1jXXrGuvWedasa6xb51mzrmnqujmHW5IkSSqRR7glSZKkEtlwAxFxfET8NiKejoiLO3j+8Ih4PCLWRcQnqsjYbOqo2VciYnFELIiIX0fEqCpyNps66nZORCyMiHkR8UBE7F9Fzmazpbq1W+8TEZER0bRnqjdKHa+1SRGxvHitzYuIs6vI2Wzqea1FxKeKn29PRMRNjc7YbOp4rV3V7nX2u4hYWUXOZlNH3XaLiLsjYm7xu/TEKnI2mzrqNqroOxZExD0R8d4qcv6VzOzTN6A/8AywBzAImA/sv9E6uwNjgRuAT1SduepbnTU7EtiquH8uMKPq3FXf6qzb1u3unwT8ourcVd/qqVux3jDgPuBhoLXq3M1eM2AS8N2qszbTrc667Q3MBbYpHu9Qde5mr9lG638B+FHVuau+1flauw44t7i/P/Bc1bmrvtVZt5uBM4v7RwH/WnXuzPQIN3AI8HRmPpuZbwLTgZPbr5CZz2XmAuCtKgI2oXpqdndmvlE8fBhojr8wq1VP3V5r93Ao4EkWddSt8A3gO0BbI8M1qXprpneqp26fBb6XmSsAMnNZgzM2m86+1k4HpjUkWXOrp24JbF3cHw680MB8zaqeuu0P/Lq4f3cHz1fChht2AZ5v93hpsUyb1tmanQXcXmqinqGuukXE+RHxDLXm8YsNytbMtli3iPgAsGtm/ryRwZpYvd+jpxb/dr0lInZtTLSmVk/d9gH2iYgHI+LhiDi+YemaU92/D4qphe8D7mpArmZXT92+BpwREUuB26j9d6Cvq6du84FTi/unAMMiYrsGZNssG26IDpZ5VHHz6q5ZRJwBtAKXl5qoZ6irbpn5vczcE/gH4J9KT9X8Nlu3iOgHXAVc2LBEza+e19rPgN0zcyzwK+D60lM1v3rqNoDatJIJ1I7W/jAiRpScq5l15nfoacAtmbm+xDw9RT11Ox2YmpnvBU4E/rX4edeX1VO3ycARETEXOAL4I7Cu7GBb0te/cFD766j9kZ334r9ttqSumkXEMcA/Aidl5poGZWtmnX2tTQc+VmqinmFLdRsGHADcExHPAYcCP+3jJ05u8bWWma+0+778AXBQg7I1s3q+R5cCt2bm2sxcAvyWWgPeV3Xm59ppOJ3kbfXU7SzgxwCZORsYAmzfkHTNq56fbS9k5scz8wPUehAy89XGReyYDTc8CuwdEe+LiEHUfiD8tOJMzW6LNSv+xX8ttWa7r89xfFs9dWv/i/sjwFMNzNesNlu3zHw1M7fPzN0zc3dq5wyclJlzqonbFOp5re3U7uFJwG8amK9Z1fP7YBa1k8KJiO2pTTF5tqEpm0tdv0MjYl9gG2B2g/M1q3rq9gfgaICIGE2t4V7e0JTNp56fbdu3+0/AJcCPGpyxQ32+4c7MdcAFwC+p/cL5cWY+ERH/KyJOAoiIg4s5VJ8Ero2IJ6pLXL16akZtCsl7gJuLS0H1+T9i6qzbBcWlxuYBXwHOrChu06izbmqnzpp9sXitzad2rsCkatI2jzrr9kvglYhYTO2ErIsy85VqElevE9+fpwPTs7h0RF9XZ90uBD5bfI9OAyb19frVWbcJwG8j4nfAjsD/riTsRnynSUmSJKlEff4ItyRJklQmG25JkiSpRDbckiRJUolsuCVJkqQS2XBLkiRJJbLhlqQ6RMQXI+I3EfFvXdj2SxGxVRm5eoOIGBwRvyouITpxo+emRsSS4rnHI+KD7Z6bHBFPRsSiiJgfEZ8pll8QEU9HRBbXypakStlwS1J9zgNOzMxPd2HbLwGdargjon8XxqlEN2T9ADAwM8dl5owOnr8oM8cBF1N7Qy0i4hzgWOCQzDwAOJy/vO3zg8AxwO/fZS5J6hY23JK0BRHxfWAPam8Z/+WIOCQiHoqIucXHfYv1+kfElIhYGBELIuILEfFFYGfg7oi4u1jv9GKdRRHx7XbjvF68gcMjwAc3yrBnRPwiIh6LiPsjYr9i+a3tjux+/u0j8BFxT0T8nyLfoog4pFi+bUTMKvI9HBFji+VHFEeR5xWf17CImBARP2+X4bsRMam4/1xEXBoRDwCf3FS+jT6Hvxo7InYAbgTGFWPvuZkvxX3AXsX9/wmcl5mvwYZ3HL2+uD83M5/b0tdVkhplQNUBJKnZZeY5EXE8cGRmvhwRWwOHZ+a6iDgG+CZwKvA54H3AB4rnts3M/4yIr7Tbdmfg28BBwArgjoj4WGbOAoYCizLz0g5iXAeck5lPRcR/Ba4GjirGfDAillB7Z7pD220zNDM/FBGHU3t74wOArwNzM/NjEXEUcAMwDpgMnJ+ZD0bEe4C2OkrTlpnjASLi15vI195fjZ2Z4yLibGByZn50C+P9HbAwIoYBwzLzmToySlLlbLglqfOGA9dHxN5AAgOL5ccA3y/efpjM/M8Otj0YuCczlwMUR6QPB2YB64GZG29QNMAfAm6OeHvWBIOLMV6KiEupvc34KRuNOa1Y576I2DoiRgDjqf1xQGbeFRHbRcRwatMwrizy/HtmLm031qbM2FK+jWxq7C25PCL+CVgOnEVt6ohvkyypx7DhlqTO+wZwd2aeEhG7A/cUy+tpBDfXxbZl5voOlvcDVhbzmDtyIPAKtakr7W2cJTcxfmbmtyLiP4ATgYeLI/freOfUwyEbbbeqznxv63DsLWwDtTnct7xjRxGrImKPzHy2ju0lqVLO4ZakzhsO/LG4P6nd8juAcyJiANTmLBfL/wwMK+4/AhwREdsXJxueDty7ucGKecpLIuKTxX4jIt5f3D8EOIHaiYeTI+J97TadWKwzHng1M1+lNg/608XyCcDLmflaROyZmQsz89vAHGA/aicd7h+1q4gMB47ubL6NdDj25j73zbgM+F4xvYfiCP7nurgvSSqVDbckdd53gMsi4kGg/RU6fgj8AVgQEfOB/1Ysvw64PSLuzsw/AZdQmwIyH3g8M2+tY8xPA2cV+30CODkiBgM/AP5HZr5AbQ73j+Iv8zpWRMRDwPepTcUA+BrQGhELgG8BZxbLv1ScXDkfWA3cnpnPAz8GFgD/BsztTL4O1tnU2F1xDbUaPhoRi6j90fIGbLiE41LgvdS+Fj98F+NI0rsWmU6Dk6TeJiLuoXYi4pyqs0hSX+cRbkmSJKlEHuGWJEmSSuQRbkmSJKlENtySJElSiWy4JUmSpBLZcEuSJEklsuGWJEmSSmTDLUmSJJXo/wMU4YNU3kdahgAAAABJRU5ErkJggg==\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.subplots_adjust(bottom = 0.1)\n",
        "plt.scatter(\n",
        "    data[:, 0], data[:, 2], marker='o', s=300, c='g',\n",
        "    cmap=plt.get_cmap('Spectral'))\n",
        "plt.title('Scatter Plot of Coefficients of PC1 and PC2')\n",
        "plt.xlabel('factor exposure of PC1')\n",
        "plt.ylabel('factor exposure of PC3')\n",
        "\n",
        "for label, x, y in zip(labels, data[:, 0], data[:, 2]):\n",
        "    plt.annotate(\n",
        "        label,\n",
        "        xy=(x, y), xytext=(-20, 20),\n",
        "        textcoords='offset points', ha='right', va='bottom',\n",
        "        bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n",
        "        arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0')\n",
        "    )"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.subplots_adjust(bottom = 0.1)\n",
        "plt.scatter(\n",
        "    data[:, 0], data[:, 3], marker='o', s=300, c='g',\n",
        "    cmap=plt.get_cmap('Spectral'))\n",
        "plt.title('Scatter Plot of Coefficients of PC1 and PC2')\n",
        "plt.xlabel('factor exposure of PC1')\n",
        "plt.ylabel('factor exposure of PC4')\n",
        "\n",
        "for label, x, y in zip(labels, data[:, 0], data[:, 3]):\n",
        "    plt.annotate(\n",
        "        label,\n",
        "        xy=(x, y), xytext=(-20, 20),\n",
        "        textcoords='offset points', ha='right', va='bottom',\n",
        "        bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n",
        "        arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0')\n",
        "    )"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.subplots_adjust(bottom = 0.1)\n",
        "plt.scatter(\n",
        "    data[:, 1], data[:, 2], marker='o', s=300, c='g',\n",
        "    cmap=plt.get_cmap('Spectral'))\n",
        "plt.title('Scatter Plot of Coefficients of PC1 and PC2')\n",
        "plt.xlabel('factor exposure of PC2')\n",
        "plt.ylabel('factor exposure of PC3')\n",
        "\n",
        "for label, x, y in zip(labels, data[:, 1], data[:, 2]):\n",
        "    plt.annotate(\n",
        "        label,\n",
        "        xy=(x, y), xytext=(-20, 20),\n",
        "        textcoords='offset points', ha='right', va='bottom',\n",
        "        bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n",
        "        arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0')\n",
        "    )"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.subplots_adjust(bottom = 0.1)\n",
        "plt.scatter(\n",
        "    data[:, 1], data[:, 3], marker='o', s=300, c='g',\n",
        "    cmap=plt.get_cmap('Spectral'))\n",
        "plt.title('Scatter Plot of Coefficients of PC1 and PC2')\n",
        "plt.xlabel('factor exposure of PC2')\n",
        "plt.ylabel('factor exposure of PC4')\n",
        "\n",
        "for label, x, y in zip(labels, data[:, 1], data[:, 3]):\n",
        "    plt.annotate(\n",
        "        label,\n",
        "        xy=(x, y), xytext=(-20, 20),\n",
        "        textcoords='offset points', ha='right', va='bottom',\n",
        "        bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n",
        "        arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0')\n",
        "    )"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.subplots_adjust(bottom = 0.1)\n",
        "plt.scatter(\n",
        "    data[:, 2], data[:, 3], marker='o', s=300, c='g',\n",
        "    cmap=plt.get_cmap('Spectral'))\n",
        "plt.title('Scatter Plot of Coefficients of PC1 and PC2')\n",
        "plt.xlabel('factor exposure of PC3')\n",
        "plt.ylabel('factor exposure of PC4')\n",
        "\n",
        "for label, x, y in zip(labels, data[:, 2], data[:, 3]):\n",
        "    plt.annotate(\n",
        "        label,\n",
        "        xy=(x, y), xytext=(-20, 20),\n",
        "        textcoords='offset points', ha='right', va='bottom',\n",
        "        bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),\n",
        "        arrowprops=dict(arrowstyle = '->', connectionstyle='arc3,rad=0')\n",
        "    )"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": [
              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zGDNmowlIOOWUfVi48M33rB8zZm+mTZvX6vnuvPN5Bgz4wYbH3/72YofXLEmSpLbZw91J3HPPuPesO++8UZx33qj3rP/BDz624fvRowe9Z/vo0YOor/9mR5YnSZKkLWQPdwl0putU15X8fpeSJEldm4G7yLp3787q1Ym1a0ufdFNK1Nevo6qqqtSlSJIkdVkG7iIrLy+nf/99eO65ZaUuhVdfXUH37u9nu+22K3UpkiRJXZaBuwT23/8IHnhgGY2NpevlXrcucd99i9lvv9FERMnqkCRJ6uoM3CVQWzuSnj0/ynXXPc9TT73OmjVri9b22rXrmD9/KdOmLaC+/mCOOOLoorUtSZK0LXKWkhIoLy/n5JNP5+9/34cHH7yfP/zhccrL12be05xSYu3aMnbZZSj77TeW2tqR3mFSkiQpYwbuEikvL2fkyIMZOfJgUkqsWbOGlPH0JRFBZWUla9euZcGCBYZtSZKkIjBwdwIRQbdu3YrW3pIlSxg9ejTnnnsuF110EWVljiySJEnKiklrG7Tzzjsze/ZsbrnlFk488USWLl1a6pIkSZK6LAP3NmrAgAHMmjWLoUOHctBBB1FXV1fqkiRJkrokA/c2rLKykh/84AdMnjyZ4447jiuuuCLzceSSJEnbGgO3OOWUU3jggQe44oor+PznP8+KFStKXZIkSVKX4UWTAmDo0KE8+OCDnH322YwaNYobbriBffbZp9l9F69czJTHpjDntTksb1hO76reDN9pOGeOOJOanjVFrlySJKlzi642hKC2tjY5Hrl9rr76ai666CIuv/xyxo4du2H97Jdnc+n9l3Lb/NsAaGhs2LCtuqKaROK4PY5j0uGTGNl/ZNHrliRJKqaIeCSlVNvWfg4p0Xt86UtfYubMmVx88cWcc845rF69mu9O/y6HfvJQpj81nYbGho3CNkB9Yz0NjQ1Mf2o6o68ZzRV1V5SoekmSpM7FwK1mjRgxgrq6Ol5++WX2Pmhvvnfv91j77FrSi63/RSSRWLVmFRPumGDoliRJwsCtVvTp04dJl0/i5YEvs/qq1TAUuLewY9eH7rpXHN4jSZK2bQZutWr898az5o018AHgcWAh8HJhx9avqefS+y7NrjhJkqStgIFbLVq8cjHzquZBH2A5UAWsAW4p7PhEYsb8GSxZuSS7IiVJkjo5pwVUi6Y8NoWyHcvgg01WNgCNhZ8jCKY8NoWJh03s6PIkSZK2CgZutWjOa3PeMxsJVZt3jvrGeuYunttxRUmSJG1lHFKiFi1vWN4h51nWsKxDziNJkrQ1MnCrRb2renfIefpW9e2Q80iSJG2NDNxq0fCdhlNVsZljSDZRXVHNsH7DOqgiSZKkrY+BWy0aN2Jcu8+RSB1yHkmSpK2VgVst6tezH8ftcRxBbNHxQXD8HsdT07OmgyuTJEnaehi41apJh0+iurJ6i46trqxm0ocmdXBFkiRJW5eSBu6IODYino6I+RFxUQv7fCYinoiIxyPid8WucVs3sv9IJh8zmR6VPTbruB6VPZh8zGRqd6nNqDJJkqStQ8nm4Y6IcuBnwEeBl4DZEXFzSumJJvvsCUwCDkspLYuIfqWpdts2vnY8ABPumED9mnoSqcV9g6C6sprJx0zecJwkSdK2rJQ93AcD81NKz6WU3gGmASdtss9XgJ+llJYBpJQWF7lG5Y2vHc+scbMYs/cYqiqqqK7YeJhJdUU1VRVVjNl7DLPGzTJsS5Ik5ZXyTpP9gRebLL8EjNpkn6EAEfEAUA58O6X05+KUp03V7lLLjafeyJKVS5jy2BTmLp7LsoZl9K3qy7B+wxg3YpwXSEqSJG2ilIG7uakvNh2rUAHsCYwGBgD3RcT+KaU3NzpRxFnAWQADBw7s+Eq1kZqeNUw8bGKpy5AkSdoqlHJIyUvArk2WBwCvNLPP/6aU1qSUngeeJhfAN5JSuiqlVJtSqq2psYdVkiRJnUcpA/dsYM+I2D0iugFjgZs32Wc6cBRAROxIbojJc0WtUpIkSWqHkgXulFIjcA5wO/Ak8IeU0uMR8Z2IODG/2+3AGxHxBHA3MDGl9EZpKpYkSZI2X6TU8hRvW6Pa2tpUV1dX6jIkSZLUxUXEIymlNm864p0mJUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAyVNHBHxLER8XREzI+Ii1rZ71MRkSKitpj1SZIkSe1VssAdEeXAz4DjgH2B0yJi32b22w44D3iouBVKkiRJ7VfKHu6DgfkppedSSu8A04CTmtnvu8B/Aw3FLE6SJEnqCKUM3P2BF5ssv5Rft0FEfADYNaV0SzELkyRJkjpKKQN3NLMubdgYUQb8ELigzRNFnBURdRFRt2TJkg4sUZIkSWqfUgbul4BdmywPAF5psrwdsD9wT0QsBA4Bbm7uwsmU0lUppdqUUm1NTU2GJUuSJEmbp5SBezawZ0TsHhHdgLHAzes3ppSWp5R2TCkNSikNAh4ETkwp1ZWmXEmSJGnzlSxwp5QagXOA24EngT+klB6PiO9ExImlqkuSJEnqSBWlbDylNAOYscm6b7Ww7+hi1CRJkiR1JO80KUmSJGXIwC1JkiRlyMAtSZIkZcjALUmSJGXIwC1JkiRlyMAtSZIkZcjALUmSJGXIwC1JkiRlyMAtSZIkZcjALUmSJGXIwC1JkiRlyMAtSZIkZcjALUmSJGWootQFSGpeSonGxkZSSkVpr7y8nPLy8qK0JUnStsTALXUiKSXmzZvHnDn38fzzjwJrKCuLorS9di10774de+99GAceeBgDBgwoSruSJHV1Bm6pk0gpcccdt/Dcc9dz+OE9OeWUnaiqKt6vaEqJ5ctX8+STM5k69S+cdNJEhg4dWrT2JUnqqhzDLXUSzzzzDPPn38C4cbsybFhxwzZARNCnTxWHHjqAz352O/74xx/S0NBQ1BokSeqKDNxSJzFv3kMcfHA3qqsrS10K/fv3YuDAep555plSlyJJ0lbPwC11Ei+88A/22GP7UpexwR57VPLCC0+VugxJkrZ6Bm6pk2hoWEmPHqXv3V6vR49KGhreLnUZkiRt9bxoUuo0EhHNz0gScQmnnz6ca68dA0Bj4zp23vkyRo3qzy23fJbXXlvBl750My+++BZr1qxl0KA+zJjxORYufJN99vkZe+21w4ZzjR9fyxVX1AHwxBNL2GuvHSkvD449dg/+678+smG/srIgpXUZPl9JkrYNBm5pK9CzZyXz5i2mvn4N1dWVzJy5gP79t9uw/VvfupuPfnQw3/jGIQDMmfPahm1DhvTlsce+ttH5vvrVWgAGDfoRd9/9BXbcsUcRnoUkSdsmh5RIW4njjtuDW299FoCpU+dx2mn7b9i2aNEKBgzotWF5+PCdil6fJElqnoFb2kqMHbs/06bNo6GhkTlzXmPUqHdvTHP22SP50pdu5qijruF737uXV155d+z1ggXLGDHiF4wY8QvOPvvWUpQuSdI2zSEl0lZi+PCdWLjwTaZOncvxx++50baPfWwPnnvuG/z5z/O57bb5fOADVzJv3nig+SElkiSpeOzhlrYiJ564FxMmzNxoOMl6229fzWc/O4xrrx3DyJG7cO+9L5SgQkmStCkDt7QV+eIXP8C3vnUEw4ZtPEb7rrueZ9WqNQC8/fZqFixYxsCBvUtRoiRJ2oRDSqStyIABvTbMRNLUI4+8wjnnzKCioox16xJf/vIHGDmyPwsXvlmCKiVJUlMGbmkrsGLFv79n3ejRgxg9ehAAEycexsSJh71nn0GD+jBv3tdbPO/Ched3WI2SJKl5DimROomIXO90Z7FuXSLCfyIkSWov/zeVOomqqp6sXPlOqcvYYMWKd6iudhy4JEntZeCWOonBg2t55pmlpS5jg2eeWcvgwfuWugxJkrZ6Bm6pkxg27GAeeqiRt95aXepSWLBgKa++2os999yz7Z0lSVKrvGhS6iQGDx5Mbe04fv3rKYwaVcHQodvTs2c3ysqiKO2vXbuON96o58kn3+Sxx6oZO/ZCKisri9K2JEldmYFb6kQOP3w0u+wykLlzH+ahhx6hvv4NUlpXlLbLyiro1WtH9trrY3zxi7XssMMORWlXkqSuzsAtdTKDBw9m8ODBwNiit51S4p577mH77bcvetuSJHVVjuGWtMHatWu58MILOfnkk1m2bFmpy5EkqUswcEvaoKKigvvuu4+BAwdy4IEH8vDDD5e6JEmStnoGbkkb6d69Oz/+8Y+57LLL+MQnPsGPf/xjUuo8N+SRJGlrY+CW1KyTTz6ZBx98kGuvvdYhJpIktYMXTUpq0eDBg3nggQeYOHEiBx10EL///e8ZOXJki/svXrmYKY9NYc5rc1jesJzeVb0ZvtNwzhxxJjU9a4pYuSRJnUd0tT8V19bWprq6ulKXIXU5N954I+PHj+fiiy/m3HPPJeLd+cFnvzybS++/lNvm3wZAQ2PDhm3VFdUkEsftcRyTDp/EyP4tB3ZJkrYmEfFISqm2zf0M3JIKtWDBAk499VQGDhzIr3/9a/r06cMVdVcw4Y4J1K+pJ9HyvydBUF1ZzeRjJjO+dnwRq5YkKRuFBm7HcEsq2JAhQ3jggQfo378/Bx54IBdecyET7pjAqhtXkRa1/uE9kVi1ZhUT7pjAFXVXFKliSZJKzzHckjZL9+7dufzyy+m/f38mnT0JjgB2Au4GPtv28etD98hdRlK7S5udApIkbfXs4Za0RR7u8zB8CXgMeA54Jf8oQP2aei6979LsipMkqRMxcEvabItXLmbGYzPgLmAIsBZYA/y5sOMTiRnzZ7Bk5ZLsipQkqZMwcEvabFMemwI9gH3J/StSBpQD/wQKnK47iNx5JEnq4hzDLWmzzXltDqvXrc4F7n2bbFgDVBZ2jvrGeuYunptBdZIkdS72cEvabMsblje/ocCwvd6yBu9eKUnq+koauCPi2Ih4OiLmR8RFzWz/14h4IiLmRMSdEbFbKeqUtLHeVb075Dx9q/p2yHkkSerMSha4I6Ic+BlwHLk/Sp8WEftustujQG1KaThwA/Dfxa1SUnOG7zScqoqqdp2juqKaYf2GdVBFkiR1XqXs4T4YmJ9Sei6l9A4wDTip6Q4ppbtTSqvyiw8CA4pco6RmjBsxrt3nSKQOOY8kSZ1dKQN3f+DFJssv5de15EvAbc1tiIizIqIuIuqWLHGaMSlr/Xr247g9jiOILTo+CI7f43hqetZ0cGWSJHU+pQzczf1P3ey9oSPidKAW+H5z21NKV6WUalNKtTU1/gcuFcOkwydRXVm9RcdWV1Yz6UOTOrgiSZI6p1IG7peAXZssD6CZ+9RFxEeAbwInppRWF6k2SW0Y2X8kk4+ZTI/KHpt1XI/KHkw+ZrK3dZckbTNKGbhnA3tGxO4R0Q0YC9zcdIeI+ABwJbmwvbgENUpqxfja8RtCd1vDS4LYELbH144vUoWSJJVeyQJ3SqkROAe4HXgS+ENK6fGI+E5EnJjf7fvA+4DrI+KxiLi5hdNJKpHxteOZNW4WY/YeQ1VFFdUVGw8zqa6opqqiijF7j2HWuFmGbUnSNidSanbY9FartrY21dXVlboMaZu0ZOUSpjw2hbmL57KsYRl9q/oyrN8wxo0Y5wWSkqQuJyIeSSm1OUbSW7tL6jA1PWuYeNjEUpchSVKn4q3dJUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMGbglSZKkDBnfl75+AAAgAElEQVS4JUmSpAwZuCVJkqQMGbglSZKkDBm4JUmSpAwZuCVJkqQMbVbgjohnsipEkiRJ6ooqWtoQEW8Daf1i/muP9etTSr2yLk6SOpM33niDZ599lhUrlpPSuszaqajozg477MjQoUOpqqrKrB1JUnG0GLiBKUBvYGJK6TWAiHg+pbR7MQqTpM5i1apVTJv2C5YuncteeyV69w7Ky6PtA7dAStDYuI7HHw9uvbWCUaNO4aijPkZENu1JkrLXYuBOKZ0bEQcBUyNiOvBT3u3xlqRtQmNjI9dc80P23HMB48YNpKyseMF35cp3uO6631FWVsbo0ccUrV1JUsdqdQx3SukR4CP5xVmAf9uUtE159tlnqa5+lg9/eNeihm2Anj27cdpp/Xnoof+lsbGxqG1LkjpOmxdNppTWpZR+AnwG+HL2JUlS5/Hkk3Xsv3+3kg3p6NWrO/361fP888+XpH1JUvu1GLgj4vSI+Pz65ZTSopTSjIj4SkR8tjjlSVJpLV36Iv369SxpDf36wbJly0pagyRpy7XWw30BML2Z9dPy2ySpy1u7dg0VFaW9ZUFFRXJIiSRtxVr7X6Q8pfT2pivz6yqzK0mSth433fQkEZfw1FOvA7Bw4ZtEXMJ//MddG/Z5/fVVVFZ+l3POmQHAt799D/37/4ARI37Bnntezskn/54nnlhSkvolSdlrLXBXRsR7/o4aEdsB3bIrSZK2HlOnzuPwwwcybdq8DesGD+7LLbc8u2H5+usfZ7/9ajY67l/+5RAee+xrPPvsuZx66n4cffQ1LFmysmh1S5KKp7XAfTVwQ0QMWr8i//20/DZJ2qatWPEODzzwIldffeJGgbu6uoJ99tmRurpXAPj97x/nM5/Zr8XznHrq/hxzzBB+97u5mdcsSSq+1ubhnhwRK4BZEfE+cnNwrwT+K6V0RbEKlKTOavr0pzj22CEMHboD229fzd//vojtt68GYOzY/Zk2bR7vf//7KC8vY5ddtuOVV94zSm+DAw/cecOwFElS19LWPNy/SCntBuwG7J5S2s2wLUk5U6fOY+zY/YFcwJ469d0e6mOP3YOZM59j6tS5nHpqy73b66XkfcUkqatqsYc7Ig4BrgSGAHOBLwJPFqkuSerU3nhjFXfd9Tzz5i0mAtauTUTA178+EoBu3co56KCdueyyv/H441/nT396ptXzPfroq9TW7lKM0iVJRdZi4CZ3K/cJwL3AicCPgI8VoyhJ6uxuuOEJzjhjOFdeecKGdUceOYWXXnprw/IFFxzKkUfuxg479Gj1XDfe+AR33LGAyy7z9u2S1BW1FrjLUkoz899fHxGTilGQJG0Npk6dx0UXHb7RulNO2Yf//M/7Nyzvt18/9tuvX7PH//CHD3LddXNYuXIN++/fj7vu+gI1NaW9wY4kKRvR0rjBiHiOXA/3epObLqeU/phtaVumtrY21dXVlboMSV3ElVdewgknvMUuu2xXshpuv/0FttvuK3zwgx8sWQ2SpPeKiEdSSrVt7ddaD/cs4IQWlhPQKQO3JHWkioruNDauK2kNa9ZAZaX3G5OkrVVr0wKeWcxCJKkz2mGH3Vi0aAEDB/YuWQ2LFsE++2xfsvYlSe3T6rSAkrSt23ffg5g7d03Jpu1burSeZcvex6BBg0rSviSp/QzcktSKIUOGUFZ2ALfc8gJr1qwtattLl9bz298u4sgjx1JeXl7UtiVJHae1iyY/nVK6PiJ2Tyk9X+S6tpgXTUrqaKtXr+amm65l4cK/sfvu6+jdG7LKvykl1qwJFi2CN97oyZFHjuWQQw5v+0BJUtEVetFka4H77ymlA9d/7fAKM2LglpSVlStXsmDBAlasWJHpEJOKigp22GEHdt99d3u2JakT64hZSt6IiLuB3SPi5k03ppRObE+BkrS16dmzJ8OHDy9ae7/73e+4+eabueqqq+jVq1fR2pUkdazWAvfHgQOBa4HLilOOJGm9U045hVmzZjFq1Chuuukm9t5771KXJEnaAi1eNJlSeiel9CDwwZTSLODvwCMppVn5ZUlShrp3786VV17JBRdcwIc+9CGmT59e6pIkSVugkFlKdoqIR4F5wBMR8UhE7J9xXZKkvC9/+cvceuutnHfeeXzzm99k7drizpYiSWqfQgL3VcC/ppR2SykNBC7Ir5MkFcnBBx9MXV0df/3rX/n4xz/O0qVLS12SJKlArY3hXq9nSunu9QsppXsiomeGNUmSmtGvXz9mzpzJhRdeSG1tLX/84x8ZMWJEs/suXrmYKY9NYc5rc1jesJzeVb0ZvtNwzhxxJjU9a4pcuSRt21qcFnDDDhE3kRu/fW1+1elAbUrpkxnXtkWcFlDStmDatGmce+65/OhHP+Jzn/vchvWzX57Npfdfym3zbwOgobFhw7bqimoSieP2OI5Jh09iZP+RRa9bkrqSds/D3eREfYFLgPV3XrgXuCSltKzdVWbAwC1pWzFnzhxOPvlkPvGJT/D973+fX/3jV5x/2fm8M+QdqGz5uCCorqxm8jGTGV87vngFS1IX0xHzcAOQD9bndUhVkqQOM3z4cGbPns3pp5/OvqP25eWPvcw7f38HVgCt/POfSKxas4oJd0wAMHRLUsYKuWhSktRJ9e3bl2/94lss7LuQ+p/Xw57AA0ABE5msD911r/hXQUnKkoFbkrZy373juzTu1AhHAXflVz5R2LH1a+q59L5LsypNkoSBW5K2aotXLmbmvJlQB8wEAlgO3FbY8YnEjPkzWLJySXZFStI2rs3AHRFDI+LOiJiXXx4eERdnX5okqS1THptC2Y5l8DlgInAWcCIwpPBzBMGUx6ZkUp8kqbAe7l8Ck4A1ACmlOcDYLIuSJBVmzmtz3p36L4A+wAjglMLPUd9Yz9zFczOoTpIEhQXuHimlhzdZ19gRjUfEsRHxdETMj4iLmtnePSJ+n9/+UEQM6oh2JamrWN6wvEPOs6yhU870KkldQiGB+/WIGAIkgIj4FLCovQ1HRDnwM+A4YF/gtIjYd5PdvgQsSyntAfwQ+H/tbVeSupLeVb075Dx9q/p2yHkkSe9VSOA+G7gS2DsiXgbOB77WAW0fDMxPKT2XUnoHmAactMk+JwHX5L+/AfhwREQHtC1JXcLwnYZTVVHVrnNUV1QzrN+wDqpIkrSpVgN3RJSRu437R4AaYO+U0uEppRc6oO3+wItNll/Kr2t2n5RSI7lr73dops6zIqIuIuqWLPFKe0nbjnEjxrX7HInUIeeRJDWv1cCdUloHnJP/fmVK6e0ObLu5nupN7zNfyD6klK5KKdWmlGpramo6pDhJ2hr069mP4/Y4jmj2n8u2BcHxexxPTU//7ZSkrBQypGRmREyIiF0jYvv1jw5o+yVg1ybLA4BXWtonIiqA3sDSDmhbkrqMSYdPorqyeouOra6sZtKHJnVwRZKkpioK2OeL+a9nN1mXgMHtbHs2sGdE7A68TG6qwc9uss/NwBeAvwGfAu5KKb2nh1uStmUj+49k8jGTmXDHBFatWVXwcT0qezD5mMnU7lKbYXWSSimlxJo1a2hs7JAJ5jZLeXk53bp1w8vvCgjcKaXds2g4pdQYEecAtwPlwK9TSo9HxHeAupTSzcDVwLURMZ9cz7bzf0tSM8bXjgdgwh0TqF9TT3rv6LsNgqC6sprJx0zecJykruXNN9/koYfu54knZrFy5RIqK6PowbexMVFW1pO99z6MkSOPoH//TS/V23ZEWx3GEXFGc+tTSr/JpKJ2qq2tTXV1daUuQ5JKou6VOi6971JmzJ9BENQ31m/YVl1RTSJx/B7HM+lDk+zZlrqopUuXcs01/4/993+NAw6ooaamR8l6mZcvb+CJJ17nvvvKOOWUCxkyZDNug7sViIhHUkpt/mNaSOC+vMliFfBh4O8ppU+1r8RsGLglCZasXMKUx6Ywd/FcljUso29VX4b1G8a4EeO8QFLq4v7nf37Avvs+xahRu5S6lA1eeOFNpk1bxwUX/ICKikJGNG8dCg3chQwpOXeTE/cGrm1HbZKkjNX0rGHiYRNLXYakInvrrbdYvHgeZ5wxoNSlbGS33frQr98LLFiwgL322qvU5RRdIbOUbGoVsGdHFyJJkqT2+ec//8luu0F5+ZZEvGwNGQL//OdzpS6jJNrs4Y6IP/Hu3Ndl5G7D/ocsi5IkSdLmW716NT16lLqK5vXoUcny5R15S5etRyEffyYDl+UflwJHpJQuyrQqSZIkbZGWro+MuIQLLrh9w/LkyX/l29++h3vuWcihh1690b6NjevYaafJLFr0NuPGTWf33X/MAQf8gqFDL+eMM27i5Zff2mj/Rx9dRMQl3H77/M2ua1tQSOCuA+5LKc0ClgAHRkRltmVJkiSpI3XvXs4f//gUr7++8Xz9RxyxGy+99BYLF765Yd1f/vIc++/fj5133g6A73//o/zjH1/j6afP4QMfeD9HHXUN77yzdsP+U6fO4/DDBzJ16rziPJmtTCGB+16gKiL6A3cCZwJTsixKkiRJHauiooyzzjqQH/7wbxutLysLPv3pffn9798Ny9OmzeO00/Z/zzkign/5l0N5//vfx223PQvkbq5zww1PMGXKSdxxxwIaGop/k53OrpDAHSmlVcDJwOUppTHkxnFLkiRpK3L22Qfz29/OZfnyho3Wn3ba/kyb9jgAq1c3MmPGs5xyyj4tnufAA3fmqadeB+CBB15k9937MmTI9owePYgZM57N7glspQoK3BFxKPA54Nb8uq4zgaIkSdI2olev7pxxxgH85CcPbbR+5Mj+rFjxDk8//Tq33TafQw4ZQN++1S2ep+l9XKZOncvYsfsBMHbs/g4raUYhwfl8YBJwU/7W64OBu7MtS5IkSVk4//xDOPDAKznzzBEbrR87dj+mTZvHk0++3uxwkqYeffRVPvzhwaxdu44bb3ySm29+hu997z5SgjfeWMXbb69mu+26Z/k0tipt9nCnlGallE4Efh4R70spPZdSOq8ItUmSJKmDbb99NZ/5zH5cffWjG60/7bRhXHfdXO6663lOPLH5m9OklPjJTx5i0aIVHHvsHvzlL89xwAHv58UX/4WFC8/nhRfO55RT9mX69KeK8VS2Gm0G7ogYFhGPAvOAJyLikYjYL/vSJEmSlIULLjj0PbOV7LtvDT16VHL00bvTs2e3jbZNnDgzPy3gT5k9+xXuvvsLdOtWztSp8xgzZu+N9j3llH343e8cVtJUNB2D0+wOEX8FvplSuju/PBr4z5TSB7Mvb/PV1tamurq6UpchSZJUdI888givvPIzTjhhYKlLeY9HHnmFV175CCec8JlSl9JhIuKRlFJtW/sVctFkz/VhGyCldA/Qsx21SZIkKQMRwbp1pa6ieevWJWIbvftNIRdNPhcR/wFcm18+HXg+u5IkSZK0Jaqrq1mxotRVNG/Fikaqq3uXuoySKKSH+4tADfBH4Kb892dmWZQkSZI236BBg/jnP2Oju0B2Fk8/DUOGDC11GSXRZg93SmkZcF5E9AbWpZTezr4sSZIkba7q6mp23/0Q7rrrr3zsYwM7zRCOOXNeo77+/Qwc2PnGlhdDm4E7IkYCvwa2yy8vB76YUnok49okSZK0mU466XR+85s3mDbtCYYPfx/9+/eisrKs6OG7sXEdr7++iscfX84zz+zA5z//L5SVFTK4ouspZJaSOcDZKaX78suHAz9PKQ0vQn2bzVlKJEnStm716tU8/vjjPP74A7z++kLWrHmn6DWUl1fQu/dO7LPPYQwbdgC9evUqeg1ZK3SWkkIumnx7fdgGSCndHxEOK5EkSeqkunfvzoEHHsiBBx5Y0joeeugh9txzzy4ZtjdHIf36D0fElRExOiKOjIifA/dExIERUdp3UZIkSZ3W3XffzYgRI7j33ntLXUpJFdLDPSL/9f9ssv6DQAKO7tCKJEmS1CVcdNFFDBs2jFNPPZWvfvWrXHzxxVRUFBI/u5Y2x3BvbRzDLUmS1LksWrSIM844g/r6en7729+y2267lbqkDtFhd5qMiGvzUwKuX94tIu5sb4GSJEnaNuy8887cfvvtnHTSSYwcOZLrr7++1CUVVSF9+vcDD0XEvwL9gYnABZlWJUmSpC6lrKyMiRMnMnr0aE477TTuuOMOfvSjH9GzZ88Wj1m8cjFTHpvCnNfmsLxhOb2rejN8p+GcOeJManrWFLH69iloSEl+KsC7gdeBD6SUXs26sC3lkBJJkqTO7e233+bss89m9uzZTJ06lREjRmy0ffbLs7n0/ku5bf5tADQ0NmzYVl1RTSJx3B7HMenwSYzsP7KotTfVkUNKPk/uxjdnAFOAGRFxQLsrlCRJ0jZpu+224ze/+Q3f/OY3+ehHP8pPfvIT1ncCX1F3BaOvGc30p6bT0NiwUdgGqG+sp6GxgelPTWf0NaO5ou6KUjyFzVLIjW+mA2ellBbnlw8GrkopjWj1wBKxh1uSJGnrsWDBAk477TT69evHEd84gktmX8Kqa1fBiUCPto/vUdmDycdMZnzt+Mxr3VSH9XCnlD65Pmznlx8GDm5nfZIkSRJDhgzh/vvvZ4fdduCiUy5i1VOroCfwQGHHr1qzigl3TKDulc7b4VrIkJKhEXFnRMzLLw8H/i3zyiRJkrRN6NatG28f8TbpkwmmAwH8HSjw3ub1a+q59L5LM6ywfQq50+QvgUnAGoCU0hxgbJZFSZIkaduxeOVibn34Vnia3DiKRfkNfyns+ERixvwZLFm5JKMK26eQwN0jP4ykqcYsipEkSdK2Z8pjU4ieATsAy8lNXN0I/ANYW9g5gmDKY1OyKrFdCpmH+/WIGELuNu5ExKd493OHJEmS1C5zXpvD6vLVMKrJygS8A5QXdo76xnrmLp6bQXXtV0jgPhu4Ctg7Il4Gngc+l2lVkiRJ2mYsb1j+3pUBdN+88yxrWNYh9XS0NgN3Suk54CMR0RMoSykVOHxdkiRJalvvqt4dcp6+VX075DwdrZAx3ACklFYatiVJktTRhu80nKqKqnado7qimmH9hnVQRR2r4MAtSZIkZWHciHHtPkcidch5stBq4I6Isoj4YLGKkSRJ0ranX89+HLfHcQSxRccHwfF7HE9Nz5oOrqxjtBq4U0rrgMuKVIskSZK2UZMOn0R1ZfUWHVtdWc2kD03q4Io6TiFDSu6IiFMiYss+ckiSJEltGNl/JJOPmUyPyh6bdVyPyh5MPmYytbvUZlRZ+xUyLeC/kruj/dqIqCc3SUtKKfXKtDJJkiRtU8bXjgdgwh0TqF9TT8rdBqZZQVBdWc3kYyZvOK6zarOHO6W0XUqpLKVUmVLqlV82bEuSJKnDja8dz6xxsxiz9xiqKqqorth4mEl1RTVVFVWM2XsMs8bN6vRhGwrr4SYiTgSOyC/ek1K6JbuSJEmStC2r3aWWG0+9kSUrlzDlsSnMXTyXZQ3L6FvVl2H9hjFuxLhOe4Fkc9oM3BHxX8BI4Lf5Vd+IiMNTShdlWpkkSZK2aTU9a5h42MRSl9FuhfRwHw+MyM9YQkRcAzwKGLglSZKkNhR645s+Tb7vmHtvSpIkSduAQnq4LwUejYi7yc1QcgTw75lWJUmSJHURbQbulNLUiLiH3DjuAC5MKb2adWGSJElSV9DmkJKIuDOltCildHNK6X9TSq9GxJ3FKE6SJEna2rUYuCOiKiK2B3aMiL4RsX3+MQjYpT2N5s8zMyKezX/t28w+IyLibxHxeETMiYhT29OmJEmSVAqt9XB/FXgE2Dv/df3jf4GftbPdi4A7U0p7AnfS/Iwnq4AzUkr7AccCP4qIPs3sJ0mSJHVaLY7hTin9GPhxRJybUrq8g9s9CRid//4a4B7gwk3af6bJ969ExGKgBnizg2uRJEmSMlPItIDrmvYs54eXfL2d7e6UUloEkP/ar7WdI+JgoBuwoIXtZ0VEXUTULVmypJ2lSZIkSR2nkMD9lZTShl7llNIy4CttHRQRf4mIec08TtqcAiNiZ+Ba4Mz1N9/ZVErpqpRSbUqptqZm67nNpyRJkrq+QubhLouISCklgIgoJ9fb3KqU0kda2hYRr0XEzimlRflAvbiF/XoBtwIXp5QeLKBWSZIkqVMppIf7duAPEfHhiDgamAr8uZ3t3gx8If/9F8hdiLmRiOgG3AT8JqV0fTvbkyRJkkqikMB9IXAXMB44m9ysIv/Wznb/C/hoRDwLfDS/TETURsSv8vt8htxdLcdFxGP5x4h2titJkiQVVeRHinQZtbW1qa6urtRlSJIkqYuLiEdSSrVt7dfmGO6I2BO4FNgXqFq/PqU0uF0VSpIkSduAQoaU/A9wBdAIHAX8htysIZIkSZLaUEjgrk4p3Ulu+MkLKaVvA0dnW5YkSZLUNRQyLWBDRJQBz0bEOcDLtHGjGkmSJEk5hfRwnw/0AM4DDgJO590p/SRJkiS1osUe7oi4NqX0eeCDKaXZwArgzKJVJkmSJHUBrfVwHxQRuwFfjIi+EbF900exCpQkSZK2Zq2N4f4FuTtKDgYeAaLJtpRfL0mSJKkVLfZwp5R+klLaB/h1SmlwSmn3Jg/DtiRJklSANi+aTCmNL0YhkiRJUldUyCwlkiRJkraQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJypCBW5IkScqQgVuSJEnKkIFbkiRJylBJAndEbB8RMyPi2fzXvq3s2ysiXo6InxazRkmSJKkjlKqH+yLgzpTSnsCd+eWWfBeYVZSqJEmSpA5WqsB9EnBN/vtrgE82t1NEHATsBNxRpLokSZKkDlWqwL1TSmkRQP5rv013iIgy4DJgYlsni4izIqIuIuqWLFnS4cVKkiRJW6oiqxNHxF+A9zez6ZsFnuLrwIyU0osR0eqOKaWrgKsAamtr0+bUKUmSJGUps8CdUvpIS9si4rWI2DmltCgidgYWN7PbocCHIuLrwPuAbhGxIqXU2nhvSdL/b+/eg+wqy3yPfx/SCd0JIelgAoiDIBdDgBiGJuMlgMFAkRxHQg2CymjicBH0aAEnTMHRss4ZLLllLjXKwYk6kziMx6hwIIpIMBBAJFYCgYSLym0MKJMEDMGQi5A854+9iE3oTu+k99q73fv7qUrtvdd617ue7jfd/eu3372WJGlAKS1w92EBMAO4qni8ZccGmXn2688jYibQZdiWJEnSn5pGreG+Cjg5Ip4ATi5eExFdEfGNBtUkSZIk1VxkNteS566urly2bFmjy5AkSVKTi4gHMrOrr3beaVKSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqkYFbkiRJKpGBW5IkSSqRgVuSJEkqUUMCd0SMiog7IuKJ4rGzl3YHRsTCiHg8Ih6LiIPqW6kkSZLUP42a4b4MWJSZhwGLitc9+RZwbWYeAUwE1tSpPkmSJKkmGhW4TwPmFc/nAdN3bBAR44C2zLwDIDM3ZObG+pUoSZIk9V+jAve+mfk8QPE4poc2hwMvRcRNEbE8Iq6NiEF1rVKSJEnqp7ayOo6InwD79bDr81V20QYcDxwDrALmAzOBb/ZwrvOB8wEOPPDA3ahWkiRJKkdpgTszp/S2LyJWR8T+mfl8ROxPz2uznwOWZ+bTxTE3A++mh8CdmXOAOQBdXV1Zi/olSZKkWigtcPdhATADuKp4vKWHNkuBzogYnZlrgZOAZX11vHXrVpYsuZ9Vqx5ly5YNZO56/o7Yg6FDR3LIIe9i7NixtLe373IfkiRJEjQucF8FfDcizqGyXOTDABHRBVyQmedm5taImAUsiogAHgC+vrNO29vj2GOO2YfVq+dwxBHtdHQMJmLXi9u2Ldmw4Q/84heLWbhwOB/5yCyXqkiSJGm3xO7MAA9EEXHY1Kl8fsqUfWdccskFNev3qad+x003bePcc6+gs7PHy4VLkiSpBUXEA5nZ1Ve7prnT5MEH8/7zzmPzXnvV9kM65JBRHHnkK6xc+VBN+5UkSVJraIrAHRGDRo3iuMmTeaGM/o84Ym9+9aslZXQtSZKkJtcUgRtoHz6cQSNH8loZnXd2dvD7379YRteSJElqco1602St7dG2k49k9eoNXHzx7SxZ8hydnR0MGTKIv/3b99LZ2cFpp32Hgw8eub3t7NmnMGXKO95w/KBBwbZtW8uqXZIkSU2sWQJ3rzKT6dPnM2PGu/j2t/8KgF//+iUWLPglnZ0dHH/8gfzwhx9rcJWSJElqVs2ypKRXd975DEOGDOKCC/74BtK3v30kn/3sXzSwKkmSJLWKpp/hfvTRtfz5n/d0h/mKe+9dxYQJX9v++rgZ7u8AABCHSURBVMYbz+SQQ0bVozRJkiS1gKYP3Dv6zGdu5ac/fZYhQwZx7bUnu6REkiRJpWr6JSVHHjmaBx/8r+2vr7vuv7Fo0SdYu/aVBlYlSZKkVtH0gfukkw5m8+bXuP76pdu3bdz4agMrkiRJUitp+iUlEcHNN5/FxRffzjXX/IzRo4cybNgQrr56CvDmNdxf+MIJnHHGuEaVK0mSpCbT9IEbYP/9h/Od75zR47716y+rczWSJElqJU2zpCSTKLFvSZIkabc0S+B+dfNmYtu2cjrfsuU1hgxpL6dzSZIkNbVmCdxbXn6Z/3rkEfYqo/NVq9az336Hl9G1JEmSmlxTBO7MzFWruOfWWxld6763bt3G8uWbOfJI70wpSZKkXdcUgRtg/XrumTuXp1au3MKGDX+oSZ8vvriR733vPxk27ATe+c531qRPSZIktZbIJnpHYETsNW7cQb//+MdPpbPzD7S3Q+zGWym3bYMNG5LNm0cwfvwHOOmkU2lra4kLukiSJKlKEfFAZnb12a6ZAjdAV1dX3n///axZs4YtW7awOx9fRDB06FDGjBnDHnvswQsvvMCXvvQlrrzySjo6OkqoWpIkSX9qqg3cTTltO3jwYA444ICa9Tdy5EhWr17N1KlTWbBgAXvvvXfN+pYkSVJza5o13GVqa2vjhhtuYNy4cUyePJm1a9c2uiRJkiT9iTBwV2nQoEFcd911TJs2jUmTJrFq1apGlyRJkqQ/AU25pKQsEcEVV1zBPvvsw6RJk1i4cCFjx45tdFmSJEkawAzcu+Giiy6is7OTyZMn84Mf/ICurj7XykuSJKlFGbh304wZM+js7GTatGnMnz+fyZMnv2H/mlfWMPehuaxYvYL1m9czon0E4/cdzycnfJLRw2p+fx5JkiQNUE15WcBly5bV7XyLFy/mzDPPZM6cOUyfPp2lv1nKl+/9Mj9+6scAbH5t8/a2HW0dJMnUQ6dy+aTLOe6A4+pWpyRJkmqrpa/DXc/ADfDAAw/wwQ9+kJPPO5kb229k41c3wseA4T23D4KOwR3MPmU2F3ZdWNdaJUmSVBvVBm6vUlIDxx57LJ/6yqe44Ss3sPHujbAfsKL39kmy8dWNzFo4i+uXXV+3OiVJklR/Bu4aWPqbpVzz+DXkzIQHga3AQ0Affzx4PXQv+219Z+QlSZJUPwbuGrjyp1eyadEmmAOMAZ4F1gHP9X3splc3ceW9V5ZboCRJkhrGwN1Pa15Zw21P3gaTgfOAg4BRVGa57+z7+CT50ZM/Yu0r3r1SkiSpGRm4+2nuQ3P/+KITmAh8HLgMOLO6PoJ4Yz+SJElqGl6Hu59WrF7xhkv/bbdn9X1sem0TK9esrF1RkiRJGjCc4e6n9ZvX16SfdZvX1aQfSZIkDSwG7n4a0T6iJv10tnfWpB9JkiQNLAbufhq/73ja29r71UdHWwdHjzm6RhVJkiRpIDFw99PMCTP73UeSNelHkiRJA4+Bu5/GDBvD1EOnEsRuHR8E0w6dxuhho2tcmSRJkgYCA3cNXD7pcjoGd+zWsR2DO7j8+MtrXJEkSZIGCgN3DRx3wHHMPmU2QwcP3aXjhg4eyuxTZtP11q6SKpMkSVKjeR3uGrmw60IAZi2cxaZXN5Fkr22DoGNwB7NPmb39OEmSJDUnZ7hr6MKuC7l75t2cPvZ02tva6Wh74zKTjrYO2tvaOX3s6dw9827DtiRJUgtwhrvGut7axY1n3cjaV9Yy96G5rFyzknWb19HZ3snRY45m5oSZvkFSkiSphRi4SzJ62Ggufd+ljS5DkiRJDeaSEkmSJKlEBm5JkiSpRAZuSZIkqUQGbkmSJKlEBm5JkiSpRAZuSZIkqUQNCdwRMSoi7oiIJ4rHzl7aXRMRj0bE4xHxzxER9a5VkiRJ6o9GzXBfBizKzMOARcXrN4iI9wLvA8YDRwHHASfWs0hJkiSpvxoVuE8D5hXP5wHTe2iTQDswBNgTGAysrkt1kiRJUo00KnDvm5nPAxSPY3ZskJn3A3cBzxf/bs/Mx3vqLCLOj4hlEbFs7dq1JZYtSZIk7ZrSbu0eET8B9uth1+erPP5Q4AjgbcWmOyLihMy8Z8e2mTkHmAPQ1dWVu1exJEmSVHulBe7MnNLbvohYHRH7Z+bzEbE/sKaHZqcDSzJzQ3HMbcC7gTcFbkmSJGmgatSSkgXAjOL5DOCWHtqsAk6MiLaIGEzlDZM9LimRJEmSBqpGBe6rgJMj4gng5OI1EdEVEd8o2nwfeApYCTwMPJyZP2hEsZIkSdLuKm1Jyc5k5ovAB3rYvgw4t3i+FfhUnUuTJEmSaioym+s9hhGxFvh1o+vYibcALzS6CG3neAw8jsnA45gMPI7JwOJ4DDz1GpO3Z+bovho1XeAe6CJiWWZ2NboOVTgeA49jMvA4JgOPYzKwOB4Dz0Abk0at4ZYkSZJagoFbkiRJKpGBu/7mNLoAvYHjMfA4JgOPYzLwOCYDi+Mx8AyoMXENtyRJklQiZ7glSZKkEhm4SxYRoyLijoh4onjs7KXdgRGxMCIej4jHIuKg+lbaGqodj6Lt3hHxm4j4aj1rbDXVjElETIiI+yPi0YhYERFnNaLWZhcRp0bELyPiyYi4rIf9e0bE/GL/z/0+Va4qxuOS4ufFiohYFBFvb0SdraSvMenW7oyIyIgYMFfJaFbVjElEnFl8rTwaEd+ud41g4K6Hy4BFmXkYsKh43ZNvAddm5hHARGBNneprNdWOB8AVwN11qaq1VTMmG4FPZOaRwKnAP0XEyDrW2PQiYhBwHTAVGAd8NCLG7dDsHGBdZh4K/CNwdX2rbB1VjsdyoCszx1O5O/M19a2ytVQ5JkTEcOBzwM/rW2HrqWZMIuIw4HLgfcXPkIvqXigG7no4DZhXPJ8HTN+xQfGfoy0z7wDIzA2ZubF+JbaUPscDICKOBfYFFtaprlbW55hk5q8y84ni+W+p/ELa540GtEsmAk9m5tOZ+QfgO1TGprvuY/V94AMREXWssZX0OR6ZeVe3nxVLgLfVucZWU83XCFQma64BNtezuBZVzZicB1yXmesAMrMhE5oG7vLtm5nPAxSPY3poczjwUkTcFBHLI+La4rc21V6f4xERewB/D1xa59paVTVfI9tFxERgCPBUHWprJQcAz3Z7/Vyxrcc2mfkasB7Ypy7VtZ5qxqO7c4DbSq1IfY5JRBwD/Flm/rCehbWwar5ODgcOj4j7ImJJRJxat+q6aWvESZtNRPwE2K+HXZ+vsos24HjgGGAVMB+YCXyzFvW1mhqMx6eBH2Xms07e1UYNxuT1fvYH/h2YkZnbalGbtuvpP/uOl7Gqpo1qo+rPdUT8NdAFnFhqRdrpmBSTNf9I5ee36qOar5M24DDg/VT+CnRvRByVmS+VXNubilA/ZeaU3vZFxOqI2D8zny/CQk9/yngOWJ6ZTxfH3Ay8GwP3bqnBeLwHOD4iPg3sBQyJiA2ZubP13tqJGowJEbE3cCvwhcxcUlKprew54M+6vX4b8Nte2jwXEW3ACOB39Smv5VQzHkTEFCq/uJ6YmVvqVFur6mtMhgNHAYuLyZr9gAUR8aHMXFa3KltLtd+3lmTmq8AzEfFLKgF8aX1KrHBJSfkWADOK5zOAW3posxTojIjX16SeBDxWh9paUZ/jkZlnZ+aBmXkQMAv4lmG7VH2OSUQMAf4flbH4Xh1rayVLgcMi4uDi8/0RKmPTXfexOgO4M72ZQ1n6HI9i+cK/AB9q1LrUFrPTMcnM9Zn5lsw8qPj5sYTK2Bi2y1PN962bgckAEfEWKktMnq5rlRi46+Eq4OSIeAI4uXhNRHRFxDcAMnMrlWC3KCJWUvkTydcbVG+z63M8VHfVjMmZwAnAzIh4qPg3oTHlNqdiTfZ/B24HHge+m5mPRsTfRcSHimbfBPaJiCeBS9j5VX7UD1WOx7VU/gr3veJrYsegoRqqckxUR1WOye3AixHxGHAXcGlmvljvWr3TpCRJklQiZ7glSZKkEhm4JUmSpBIZuCVJkqQSGbglSZKkEhm4JUmSpBIZuCWpChHxuYh4PCL+YzeOvSgihpZRVzOIiD0j4ifFpe3O2mHf3Ih4ptj3YES8p9u+WRHxi4h4JCIejohPFNu/WbxeERHfj4i96v0xSVJ3Bm5Jqs6ngWmZefZuHHsRsEuBOyIG7cZ5GqIGtR4DDM7MCZk5v4f9l2bmBCrX/f6X4pwXULlu+8TMPIrKddpfv83zxZn5rswcD6yicp1eSWoYA7ck9SEivga8g8ptmi+OiIkR8bOIWF48vrNoNygiZkfEymJ29bMR8TngrcBdEXFX0e6jRZtHIuLqbufZUNyw4efAe3ao4ZCI+HFEPBAR90bE2GL7Ld1mdj/1+gx8RCyOiH8q6nskIiYW20dFxM1FfUsiYnyx/cRuNxVaHhHDI+L9EfHDbjV8NSJmFs//MyK+GBE/BT7cW307fAxvOndEjAFuACYU5z5kJ0NxD3Bo8fx/Ap/OzJdh+13+5hXPXy7OF0AH4A0nJDVUW6MLkKSBLjMviIhTgcmZ+UJE7A2ckJmvRcQU4MvAXwHnAwcDxxT7RmXm7yLikm7HvhW4GjgWWAcsjIjpmXkzMAx4JDO/2EMZc4ALMvOJiPgL4P8AJxXnvC8ingH+B/DubscMy8z3RsQJwL8CRwH/G1iemdMj4iTgW8AEKne7/Uxm3lcswdhcxadmc2ZOAoiIRb3U192bzp2ZEyLiXGBWZn6wj/P9JbAyIoYDwzPzqd4aRsS/AdOAx6h8XiSpYQzckrTrRgDzIuIwKrOng4vtU4CvFbcbJjN/18OxxwGLM3MtQDEjfQJwM7AVuHHHA4oA/F4qt/B+ffOexTlWR8QXqdyy+PQdzvl/izb3RMTeETESmETllwMy886I2CciRgD3Af9Q1HNTZj7X7Vy9md9XfTvo7dx9uTYivgCsBc6hsnRkp7PWmfnJYqnLV4CzgH+r4jySVAoDtyTtuiuAuzLz9Ig4CFhcbO8zCPLHdcY92ZyZW3vYvgfwUrGOuSdHAy9SWbrS3Y61ZC/nz8y8KiJupTIrvKSYuX+NNy49bN/huFeqrO91PZ67j2Ogsob7+2/oKOKViHhHZj7d20GZuTUi5gOXYuCW1ECu4ZakXTcC+E3xfGa37QuBCyKiDSprlovtvweGF89/DpwYEW8pZmA/Cty9s5MVa5KfiYgPF/1GRLyreD4RmErljYezIuLgboeeVbSZBKzPzPVU1kGfXWx/P/BCZr4cEYdk5srMvBpYBowFfg2Mi8pVREYAH9jV+nbQ47l39rHvxJXAdcXyHooZ/POLcx/6eh1UlqH8YjfPIUk1YeCWpF13DXBlRNwHdL9CxzeoXBVjRUQ8DHys2D4HuC0i7srM54HLqSwBeRh4MDNvqeKcZwPnFP0+CpwWEXsCXwf+JjN/S2Wt8r/GH9d1rIuInwFfo7IUA+B/AV0RsQK4CphRbL+oeHPlw8Am4LbMfBb4LrAC+A9g+a7U10Ob3s69O66n8jlcGhGPUPmlZSOVWfR5EbESWAnsD/xdP84jSf0Wmb55W5KaTUQspvJGxGWNrkWSWp0z3JIkSVKJnOGWJEmSSuQMtyRJklQiA7ckSZJUIgO3JEmSVCIDtyRJklQiA7ckSZJUIgO3JEmSVKL/D1elmJRp77wPAAAAAElFTkSuQmCC\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "pygments_lexer": "ipython3",
      "name": "python",
      "mimetype": "text/x-python",
      "version": "3.5.5",
      "nbconvert_exporter": "python",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.15.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}