Nelson_Seigel_Paramters_Forecasting.ipynb

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      "name": "Nelson Seigel Paramters Forecasting.ipynb",
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      "source": [
        "#importing necessary libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy import exp\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "!pip install quandl\n",
        "import quandl"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "text": [
            "Collecting quandl\n",
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            "Building wheels for collected packages: inflection\n",
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            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: more-itertools, cryptography, pyOpenSSL, ndg-httpsclient, inflection, quandl\n",
            "  Found existing installation: more-itertools 7.2.0\n",
            "    Uninstalling more-itertools-7.2.0:\n",
            "      Successfully uninstalled more-itertools-7.2.0\n",
            "Successfully installed cryptography-2.8 inflection-0.3.1 more-itertools-5.0.0 ndg-httpsclient-0.5.1 pyOpenSSL-19.0.0 quandl-3.4.8\n"
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      "source": [
        "df = quandl.get(\"USTREASURY/YIELD\")"
      ],
      "execution_count": 0,
      "outputs": []
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        "id": "zNI55P-rU_sw",
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      "source": [
        "df.head() #Checking data"
      ],
      "execution_count": 0,
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        {
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              "      <th></th>\n",
              "      <th>1 MO</th>\n",
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              "      <th>Date</th>\n",
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              "      <th>1990-01-02</th>\n",
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              "      <th>1990-01-03</th>\n",
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              "      <td>7.89</td>\n",
              "      <td>7.94</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.94</td>\n",
              "      <td>7.96</td>\n",
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              "      <td>7.84</td>\n",
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              "      <td>7.82</td>\n",
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              "      <td>7.93</td>\n",
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              "      <td>8.06</td>\n",
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              "      <td>7.79</td>\n",
              "      <td>7.88</td>\n",
              "      <td>7.81</td>\n",
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              "      <td>7.95</td>\n",
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              "      <td>8.05</td>\n",
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              "            1 MO  2 MO  3 MO  6 MO  1 YR  ...  5 YR  7 YR  10 YR  20 YR  30 YR\n",
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        "colab": {
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      "source": [
        "df.count() #Checking no. of data points "
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1 MO     4575\n",
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        "id": "hv5OjSPzU_s3",
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      "source": [
        "df = df.drop([\"2 MO\"],  axis =1) #Dropping columns with very few data points"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A50Fh9RSU_s6",
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      "source": [
        "df = df.drop([\"1 MO\"], axis = 1 )"
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      "execution_count": 0,
      "outputs": []
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      "source": [
        "df.count() #Checking data points again"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3 MO     7471\n",
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              "1 YR     7474\n",
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        "df = df.dropna() #Dropping NANs "
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      "outputs": []
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              "      <th>1993-10-01</th>\n",
              "      <td>2.98</td>\n",
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              "      <th>1993-10-04</th>\n",
              "      <td>3.02</td>\n",
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              "      <td>3.85</td>\n",
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              "      <th>1993-10-05</th>\n",
              "      <td>3.06</td>\n",
              "      <td>3.20</td>\n",
              "      <td>3.38</td>\n",
              "      <td>3.86</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.72</td>\n",
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              "      <th>1993-10-06</th>\n",
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              "      <td>3.85</td>\n",
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              "      <th>1993-10-07</th>\n",
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              "      <td>3.83</td>\n",
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              "      <td>6.11</td>\n",
              "      <td>6.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            3 MO  6 MO  1 YR  2 YR  3 YR  5 YR  7 YR  10 YR  20 YR  30 YR\n",
              "Date                                                                     \n",
              "1993-10-01  2.98  3.11  3.35  3.84  4.18  4.72  5.03   5.34   6.12   5.98\n",
              "1993-10-04  3.02  3.17  3.35  3.85  4.16  4.71  5.04   5.34   6.10   5.99\n",
              "1993-10-05  3.06  3.20  3.38  3.86  4.18  4.72  5.05   5.35   6.12   6.01\n",
              "1993-10-06  3.04  3.19  3.36  3.85  4.16  4.70  5.05   5.35   6.12   6.01\n",
              "1993-10-07  3.05  3.17  3.35  3.83  4.15  4.69  5.05   5.33   6.11   6.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CN3kBccKU_tK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Fuction to estimate betas' coefficient \n",
        "\n",
        "import math\n",
        "\n",
        "def constants(lamb, t):\n",
        "    c1 = 1\n",
        "    c2 = (1 - exp(-t * lamb))/(t * lamb)\n",
        "    c3 = (1 - exp(-t * lamb))/(t * lamb) - exp(-t * lamb)\n",
        "    \n",
        "    return c1,c2,c3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoGeVKwNU_tM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a2y,b2y,c2y = constants(0.0609,24)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gr0UmZixU_tO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a10y,b10y,c10y = constants(.0609,120)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPGp_HYlU_tR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a20y,b20y,c20y = constants(.0609,240)\n",
        "a1y,b1y,c1y = constants(.0609,12)\n",
        "a3y,b3y,c3y = constants(.0609,36)\n",
        "a5,b5,c5 = constants(.0609,60)\n",
        "a7,b7,c7 = constants(.0609,84)\n",
        "a3,b3,c3 = constants(.609,3)\n",
        "a6,b6,c6 = constants(.609,6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cHqhcPLU_tT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['beta_0'] = 0\n",
        "df['beta_1'] = 0\n",
        "df['beta_2'] = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odrtIRODU_tY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Fuction to estimate betas ussing OLS\n",
        "\n",
        "\n",
        "import statsmodels.api as sm\n",
        "def parameters(a2y,b2y,c2y,a10y,b10y,c10y,a20y,b20y,c20y,a3y,b3y,c3y,a5,b5,c5,a7,b7,c7,a1y,b1y,c1y,a3,b3,c3,a6,b6,c6):\n",
        "    for i in range(len(df)):\n",
        "        y1 = df['2 YR'].iloc[i]\n",
        "        y2 = df['10 YR'].iloc[i]\n",
        "        y3 = df['20 YR'].iloc[i]\n",
        "        y4 = df['3 YR'].iloc[i]\n",
        "        y5 = df['5 YR'].iloc[i]\n",
        "        y6 = df['7 YR'].iloc[i]\n",
        "        y7 = df['1 YR'].iloc[i]\n",
        "        y8 = df['3 MO'].iloc[i]\n",
        "        y9 = df['6 MO'].iloc[i]\n",
        "    \n",
        "        y = np.array([y1,y2,y3,y4,y5,y6,y7,y8,y9])\n",
        "        y = np.reshape(y,(-1,1))\n",
        "\n",
        "        X = np.array([[a2y,b2y,c2y],[a10y,b10y,c10y],[a20y,b20y,c20y],\n",
        "                      [a3y,b3y,c3y],[a5,b5,c5],[a7,b7,c7],[a1y,b1y,c1y],\n",
        "                      [a3,b3,c3],[a6,b6,c6]])\n",
        "\n",
        "        X = sm.add_constant(X)\n",
        "\n",
        "        est = sm.OLS(y,X)\n",
        "        est = est.fit()\n",
        "\n",
        "        df['beta_0'].iloc[i] = est.params[0]\n",
        "        df['beta_1'].iloc[i] = est.params[1]\n",
        "        df['beta_2'].iloc[i] = est.params[2]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuwP4YsvU_tc",
        "colab_type": "code",
        "outputId": "37dc54d7-819b-4498-c80a-22655884f9b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "parameters(a2y,b2y,c2y,a10y,b10y,c10y,a20y,b20y,c20y,a3y,b3y,c3y,a5,b5,c5,a7,b7,c7,a1y,b1y,c1y,a3,b3,c3,a6,b6,c6)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:205: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_with_indexer(indexer, value)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJMCcvf5U_th",
        "colab_type": "code",
        "outputId": "80dacc94-f2e5-4792-8a4d-19888be51477",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>3 MO</th>\n",
              "      <th>6 MO</th>\n",
              "      <th>1 YR</th>\n",
              "      <th>2 YR</th>\n",
              "      <th>3 YR</th>\n",
              "      <th>5 YR</th>\n",
              "      <th>7 YR</th>\n",
              "      <th>10 YR</th>\n",
              "      <th>20 YR</th>\n",
              "      <th>30 YR</th>\n",
              "      <th>beta_0</th>\n",
              "      <th>beta_1</th>\n",
              "      <th>beta_2</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",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1993-10-01</th>\n",
              "      <td>2.98</td>\n",
              "      <td>3.11</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3.84</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.72</td>\n",
              "      <td>5.03</td>\n",
              "      <td>5.34</td>\n",
              "      <td>6.12</td>\n",
              "      <td>5.98</td>\n",
              "      <td>6.666589</td>\n",
              "      <td>-1.943584</td>\n",
              "      <td>-7.765616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-04</th>\n",
              "      <td>3.02</td>\n",
              "      <td>3.17</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3.85</td>\n",
              "      <td>4.16</td>\n",
              "      <td>4.71</td>\n",
              "      <td>5.04</td>\n",
              "      <td>5.34</td>\n",
              "      <td>6.10</td>\n",
              "      <td>5.99</td>\n",
              "      <td>6.644550</td>\n",
              "      <td>-1.975793</td>\n",
              "      <td>-7.581481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-05</th>\n",
              "      <td>3.06</td>\n",
              "      <td>3.20</td>\n",
              "      <td>3.38</td>\n",
              "      <td>3.86</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.72</td>\n",
              "      <td>5.05</td>\n",
              "      <td>5.35</td>\n",
              "      <td>6.12</td>\n",
              "      <td>6.01</td>\n",
              "      <td>6.656880</td>\n",
              "      <td>-1.952744</td>\n",
              "      <td>-7.581914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-06</th>\n",
              "      <td>3.04</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.36</td>\n",
              "      <td>3.85</td>\n",
              "      <td>4.16</td>\n",
              "      <td>4.70</td>\n",
              "      <td>5.05</td>\n",
              "      <td>5.35</td>\n",
              "      <td>6.12</td>\n",
              "      <td>6.01</td>\n",
              "      <td>6.664764</td>\n",
              "      <td>-1.969146</td>\n",
              "      <td>-7.642901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-07</th>\n",
              "      <td>3.05</td>\n",
              "      <td>3.17</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3.83</td>\n",
              "      <td>4.15</td>\n",
              "      <td>4.69</td>\n",
              "      <td>5.05</td>\n",
              "      <td>5.33</td>\n",
              "      <td>6.11</td>\n",
              "      <td>6.01</td>\n",
              "      <td>6.649469</td>\n",
              "      <td>-1.970819</td>\n",
              "      <td>-7.616373</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            3 MO  6 MO  1 YR  2 YR  ...  30 YR    beta_0    beta_1    beta_2\n",
              "Date                                ...                                     \n",
              "1993-10-01  2.98  3.11  3.35  3.84  ...   5.98  6.666589 -1.943584 -7.765616\n",
              "1993-10-04  3.02  3.17  3.35  3.85  ...   5.99  6.644550 -1.975793 -7.581481\n",
              "1993-10-05  3.06  3.20  3.38  3.86  ...   6.01  6.656880 -1.952744 -7.581914\n",
              "1993-10-06  3.04  3.19  3.36  3.85  ...   6.01  6.664764 -1.969146 -7.642901\n",
              "1993-10-07  3.05  3.17  3.35  3.83  ...   6.01  6.649469 -1.970819 -7.616373\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F57UAT_rU_tn",
        "colab_type": "code",
        "outputId": "0cb002fb-0e7f-4d72-f4c3-7ce5b788b91c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "#Plotting beta values\n",
        "\n",
        "df['beta_0'].plot(figsize = (10,6))\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"beta_0\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lteZGSZ9GQgghRA0CmoPFOT8CYCAAMMa0AIoA/OzqPeW1TfjXD3tlr8VHBTdlTG7JTWmF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\nxEwBloiIiEjMFGCJiIiIxOz/ATUfA51XjYBPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQTcjntuU_ts",
        "colab_type": "code",
        "outputId": "95ce5dc2-167c-4ee5-a647-91ffe6042499",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "df['beta_1'].plot(figsize = (10,6))\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"beta 1\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S80LlzvfEzcD51yTnNAldBpieYUBWCh5bOA7/vn6K+BzmNiAJbfHa+gpkJBmwaKp3r9Yv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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAhDT1u3U_tx",
        "colab_type": "code",
        "outputId": "a7bffce3-a862-42b5-8a1d-93b13a112d34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "df['beta_2'].plot(figsize = (10,6))\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"beta_2\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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my2Dksk0BF/dKxUMTe2Jsj/ayKc+jJbWKixa9ITU+EjeNUA5i1OqDKSz03nqi\nEgYjx6DMJIfnj85KQWmNDnOX5jo8b/cp0843I7ctm1FW22TT3UBN48014IQMGnHduvxSrD1chtmT\nstEhiP/+KHNGiHd98vtJ/HdzgeyYsPwlLUA2HFFwpqJGq8zXr49eDMCUOVt58Jx4vFNiFB6f0hsa\nDZO1FvpieyEueXW9V8YqxTlHdUMLEqMjZA3OAeC56X1U628ojZvKanX4/Xil/ZNhWoM38mXbis7W\nvtt1GoC5VZPCldCTwZnwfr7caRuIdC0GvLAsDz1SY3HH2O6+Ho5HUWxGiHe9sCwPL6/Mlx07fb4B\njJmKygYCCs5UZL2rUJiyNBiNsmar0ppY1tf00hp1SjJwzvG/3WdcWsfW2GJAs8GIpJhwNOnlAeao\nrBTVSmlIA6f7v9jt8NzIMA3qFXZfOmLgtmULAOfTp20hvB3FZq3z4eYCFFY2YN5V/fymbZmnKP1M\nAsDOk+e9PBJCgp/RzhrvT7eeQoRWY/f30d8E96eil0mrxE/p20EMah79bj/G9LBkpBbvseyEVCoT\n0RafbT2FnSfPY8fJ83jih/2Yv/yw0+cIpSiSosORbrW+TM0yGtIKzQXl9Q7PHZLZDvVN8kDxbxN6\nOHxOY7MeMeFaLJs1DnNm9BWPezJzxhgDY7brG4h9xVWNeGf9cVzRPx3js1N9PRyPs/cbdP1/tnl1\nHISEAkezGIHUyYWCMxW1GEw/FJ/cPhyLbhsmC2x+OaTcO1PN6TDOOeYuzcX1/9mGGnMgVFLd6ORZ\nQKW5sXn7uEgsuPYiZKfFiY8xFSdlkmNdb5MRFxWGivomWRX+/5uag2Wzximev7vwAo6W1iE2MgwD\nMhIxtKul/U9spGeLDGoYg5s94EPSSytMBYafnR7MmwAsAuSLOiFBwRAkX5QpOFORUERWaHfkynRg\n+zj1FidKy3gIbaSkY6hv0uPuz3aJtcS+2VmEIyW12FZgWvuVEheBhKhwTMxJU21MUl2SY+w+ltEu\nWlYcMC4yDAXl9TYtNwZkJCo+X6i4/qN5J6g0MJbWkvMEDaM1Z67acqwCKw+W4MEJPZHRzv7PQzBR\n8wsOIcQxY+Akxxyi4MxNeWdrbOp0CbWuhAWHYS70dEyKiUD+/KnifWkz7daq0VmmDV80tz+S9sVc\nnVuCtYdL8Zq5hdTTiw/i8rc2YcEvpoWTQgPYv47pJj6nNdmu1mix+g36723DcKOklZV1+YxVj4y3\neY2HJ2XbHBNiUekF0dNrDBhlzlzSrDdi7tJD6JoSg3suzvL1cLyGMmeEeM/LK50v5QkEnluME+Sm\n/WszAGD2xJ7YcrwCix8Yi4yD404AACAASURBVPI602J+oWSEvczZE1N6ye5HSTI7bUnJFl+wncKU\n1nIRmq5faGhWXCMVbl6Y3SkpGidenobK+iaP9TmUZsQ2PXkpMlNMWZRxPdtjRPdkm5pxOemWOmIH\n501BrU6PjolR2F14AVuOV4iP9TIHt3pJ8Bfl4cxZs96IDzaewFNXBFfDbrV9uvUkTpTX4+Pbh3n8\n34QQEpqEzjKC2AitWEP07RsH+WJIbqHMmRukNcv+te449hRV4VBxNZ79yVTRPiXOlG2yDs7SE6Lw\n0wNjcP8ltgvbZ5uzQHa7CbigTmF3Y7iGgXOO7QWVYi/IPh0TxPVxUp0kmwG0Goa0eO/UnhICMwD4\n8u6RmD0p22Hh2fiocHRKigZjzOYif+VAU69SaewZ6YXdgPb6pxKT0hod3l57DJNy0jAxp4Ovh+NV\nlDkjxDcGZybhP7cOE+9fNbCTg7P9C2XO3LB0f7HNsRn/3iLeFqYSo8O16N4+FicrTDsTtRqGwZnt\nbJ4LAI9d1gunzzdgd+EFt8elVMA2XKvB6twS3P/lHnHKr1NilKych8Cfthg76wogsO5l+oB5R6f0\nuKcLxA7MSEQ7D03/BotXVh5Gi4FjzpV9nZ8cZOz11g1TqUQNIcQ+YVNZZ/MX+kBBmTM3uJrc0moY\n1j8xQbwfH+U44GAAis43uD0uYUOAdJNBRJgGz5gzesK4T5TX497Pd7n9Pt4gXcS/6zn7PdCOldXJ\n7gu/fNkd4vHWDYPw1d0j0a29496cbRUVrrUpQEwsdhRU4ud9Z3HfJVnomuLZfwt/ZO96EEDXCUIC\n0k0jMsXZKLXaEHoLBWducHfq8aPbhzt8fPFeU0ZO2E3ZWo99vx8A0D7OksVJigkX65gJPt16CltP\nOK7O7ymvXneR7L69DNm+M1XibUc7WjPaRdt97OrBnTFWpdZTjkSFa33Wdsvf6Q1GzF2ai85J0Xhg\nQk9fD8cn7O3WbDFwqo9HiIqkX5JfnNkP1w/rIiYlNAGWqabgzA3hDgqzDlboF7nu8UvwzT2j0DnJ\nfiAhJdQoyz1bbVOk9lRFPQrK65SeJpK+j70pFV+xHs8vD9vuwgSAdi6u4Rpl1W7KF6LDtTYlP4jJ\nl9sLkV9Si+dn9EG0Bzs1+DNHv4JCGRtCSNuV1OgAmJYM3GTe/S8kALoHWNaegjM3lFTbb7G0t6jK\n5lhWahxG93A9iGAMOFFeh+n/2oKFq+T9wSa8tgETX9+o+DxhZ2WCpHeY9a5HX5NeqG4d1dVu7bOr\nB3UGIN+koKRBsglioVVWzluiwjV+9/fsDyrqmvD6mqMYn90el/dL9/VwfMbR16NgqclEiD+Y9PoG\nAMArfxogrv0ekJGID24ZinlX9fPhyFqPgrNW4pyLhU7VJhR/bWoxorzWFADuO20K9o6X1Yn1yQCg\n/9zV6PbUCrHwLQDkpMdjSGaSrKr+ok0FLr13Trr79dVaQ5o5c7SIXigB4qzvorT0yMxBvtmJExWu\npeBMwcJV+dC1GDD3yn4BtRBXbUp/9JHdkwHQLl9C1CRMYVpn6af2Tw+4zD0FZ630z1VHbI5J1029\nONP96Pyucd0BmHZdCuU6GGM4XlaHyW9sxDvrj4vnCmUzTlVaelTW6PSIiwpHZJjtD+Hd5te2Z9Uj\nF7s97taQXqgclbjomBiF9IQoPD/D8e4+6XoerY8CgKhwrSxIJsDeogv4ftcZ3DmuO3pK2oGFJtuf\ny1tHdwXgnTIvhIQa6138gYg+GVqhsq4JH2w8Id6fay4L8H9Te4vHBnRWbi/kCuGD+rmfD2Hu0lwA\nQG5xNc5csL+DU7o5Yf/pKsRFahW/IcRHhWPprLFuj00t0gyKowtTVLgW25+ZhEl9XKuJdcOwLrKC\nu94UGaZBk54yZwKDkWPOklx0SIjEQxNtuziEso1PTsDGJyeIXyqoswQh6guGItd+HZwxxh5njHHG\nmOe33Dmx4UgZhv5jrezYHWO749SC6bh1dDfxWHyU+9MUwqJyaXmI+mYDfthlfxpV2Dyw2DzVuvJg\nCb7dWWRzXkpcBC7KkG9W8FRrJkekOQQ1sgbXDctAp8QozJrou52ApuDMSDvvzL774zQOFlfjmWl9\nXK5XF8ykm1u6psSia0qsmEHmoJ8ZQtQWExH4nzt+G5wxxroAmALANtLwgR/3yAvP2luoLl3v1Vr2\nWiWtOHjO7nNmf7sXgKWXJgBcOyTD5jxpeQ0A+OCWIeK24/f/MqTVY3WXRpY5a/u3m85J0dj69CSH\nTdU9LTJcC86hWNg31Fyob8bC1fkY0T05oKpxe5LSjmlhVz/F8ySY/WN5HjYdLff6+yY4qSkaCPw2\nOAPwJoC/A/7x1VJvdeG1lyFztoDdkd5uLMovrWnCvtNVqGowZdCSYsIxua/tVKDWqgl79/Zx0Jmn\n4ryZQZNep9ryd+VPhAygrpmCs9fXHEGtTo8XZ4b2JgAp5XI2wrSmX3y8EaI6zjk+3HISt328U7XX\nLCivw3sbjivOUsz+Zq94O6Od776sq8Uvr46MsZkAijnn+52cdy9jbBdjbFd5uWejc+ufBXvV/iPa\nuO6pQ4JrjcazJFXvr373d/H2V3ePVNy6n2mVWcpMjsFQcyspb7Ye0ri4ISCQpJuzqO4WDw4Wh4qr\n8dWOItw2uqusUX2oYwo/5owyZyTINXiga8rE1zdi4aojYjICAIxGjg83F2Dp/rMAgIcm9gyKL/4+\ny/0xxtYCUCp+9CyAZ2Ca0nSIc74IwCIAGDZsmFc/5uwtOGzrD4Wrz1/58HjkPL/K5ni/TonYfEwe\nqH5/32gxK7ds1jhoNQzREVr8++bBWHWoBNle3E0n2xDQhilgfyJ0MLjQ0OzkzOBlNHLMWXIIKbER\neGRyL18Px68oT2tSVpEEt6rGFucnuena97fi6sGdMbV/Oqa8uUn2WLB86fdZcMY5V2yYyBgbAKA7\ngP3mC3kGgD2MsRGc8xIvDlHGeuGuvZIZbc2chbvw/L+MzERUuBZxkWFiSQ0poXTBv28ajGkDOkIr\nSVcNyLDsJu2YGI07xjousaE26SUpQhv4O2oAy4dBKJfTWLy3GHuKqvDqdRchMZpqd0kpdY0RDtG0\nJglWVSp/WZUuLSqoqMcba44iTWGddjDs1AR8GJzZwzk/CCBNuM8YOwVgGOe8wmeDgu30Q1aqcrap\nreUc7AV3afGRKKttwtieKXjpmgEAYBOY9etkmkrqmBiNUwumt2kcnqIJwsyZkO1sCtHgrEbXggW/\nHMbgzCTFzSihTilLJhw6WVFvs4uakGBQLZl6vPS1DThZUY8wDcPxl6e59Xq5Z2tsjil9tYkMkuAs\nOK6OXuDsG+6Tl/du005Ngb3MWa1Oj1MLpuOru0fZfe6Dl/p/Y2lXi9AGkkgxOAvNWmdvrTmGyvpm\nzJ/ZP+CaC3uD0gymELA9/O0+L4+GEO+QTmuerDAVS9e3obBfgkJG/unFB22OBUtJI7+/OnLOu/k6\nawY471H54KU9kT//ija/j701Z0rvP6iL/Bt3twBo7CrNIgTDok3AUhLk4W/3ieVJQkV+SQ0+23YK\nN4/IRP82FGAOZorryyiGJUFOumhfyuBmgGZdMcGeMa3oY+3PguPq6AWe2HmiJEWyc9JZI+8XrBq5\n9u0UADvkZJmz4Eg/S4PMvHO2qfdgxTnH3CW5iI8KwxNTejt/QohSnNb0wTgI8aaqRuU1Z3qje8s/\njpbWOT3n7RsHoWead/pEexoFZy7yVkZkeLdk8fb1w7rgjesH2j13YJfAW6uicbF9UyCRrhO89v2t\nIZM9W3bgHHacPI8nL+/t1XIsgUZpppc2ApBgV6ez3awGuJ85e/DrPc7fU2GDXKAKjqujF0inFb+9\n1/66r7aKNbe7uWFYFwDOp/7e82J1fzXIdmsGS3Bm9eeobfLcFnJ/Ud+kx0sr8tC/cwJuHJ7p6+H4\nNaVivC0GCs5IcCurbVI8/sepC6q/l9DTWh9Ev1d+t1vTX+Wkx6OwsgH3X9IDo7I8N6cdrjV9kAut\ngCrs/IALpg3o6LGxeEIwZs6s/xxhmuD4czny73XHUVrThPdvGSor1UJcE0wXEUKU/G+3ck/o+jZm\nt4RexoLNf78U7WIj8MavR3G9OakRDCg4c5Gw2+SpK3I8+j5Co+gw8wVP50J5hu/vG4365sBI50qT\nCK7UdAsE1uVTgn3K6kR5HT7aUoA/D83AEHOXCdI67q67ISRQXdwrFZuOlovXuNY4c6EBAPDX0V2R\n3SEez/18CADwxJReYl/lOVf2VW+wfoCCMxe5shhRDZf17YAHJvTAPeOzAAAZ7aIBAK/8aYDd54zo\nnmz3MX8TjKU0rAVzcMY5x7yluYgK1+LvUz37RSWYubvuhpBAM2dGX6zJK8Ujk7Ox6Wg5DG58Po77\n53oApgKzt4zqiqn907EmrxQ3jQjeJRUUnPmZMK1GdtGbPqAj0u6LwvBuwZGh0Eqis2BtjF1SrUNa\nfJSvh+ERq3NLsflYBeZe2RepCtW5iWtoWpMEu+hwLaLCNbhzXHfcOa479p+uAmCq2ck5d+vzXygw\n2z4uMqgDM4CCM7/HGAuozJgzKXHBf0H/cfcZxESEIT0xyq0Uvr9qbDZg/vI85KTH49ZRXX09nIDW\nlmKchLRGeW0T4qPCvN7WKC0hUlaLU1ibOvubvajVteAvI51/hlQ3tuDrHUXifWMI/d4E57wS8Vuh\nkG35bFshJr+xEf3nrra7KDYQvb/hOIqrGvHCVf3a3KYs1AVETUIS8AxGjuEvrcXwl9Z69X1LqnUo\nrGyQlRmSbgb7fpfzz8WvdhTimnd/xz9X5YvHnBWDDyb0CUu8KtSaYj/xw35sL6j09TDarLCyHh9s\nKsDMQZ0w0oO7lUPFoC5JmNK3A+KjgiezSvzPweJqAKapRG8a9cpvAIDdhZayGdJd3VFO1htzzvHs\nT4dQYN6IJxiX3V7FUfo3Cs4I8bAbF2339RDabP7yPIRrGJ6Z1sfXQwkaGe1iEMR7R4iPcc6xLr/M\np2OolZTNkAZnzmpcnrnQqHi8S7sYdQYWACg4c4Ewzz17UraPR0KI963LL8Xaw2WYPSkbHRKCc6OD\nLxSdr0ddkx75JTU4W9UYNA2biX9YebAE//rtmMdev6Rah7IancNzWiT9MKXBmbMySmsPlyoe75kW\n14oRBjYKzlwgFLyLiQiOXpBEfYHWqcFVuhYDXliWhx6psbhjbHdfDydgTRuQjoXXynvlrj1sympM\nfWszxixYhw82FvhiaCRIFZ6XTwmer1fudemO8tomjHrlN4x4+TccMk+diu9baXlfoXI/IO8Oc7C4\nGkdKahVf+0J9M15YlmdzvHeH4OiZ6SqnwRljLIEx9gpj7AvG2M1Wj73nuaH5jwZzgddoL+92CVZv\nXD8QX9090tfDUFVwFgUBPtxcgMLKBsy7ql/QtNvyhff+MhTXD5dXL0+Kka+/3Hys3JtDIkHOYFWu\n5bOtp1R7bekGg/c3nJDV7RMKxALAWzcMEm9L6z+W1zbh8rc2yV7zg40n8PPeYny5vVDxPd+5eXCb\nxx1IXFmN+gmAYwB+BHAnY+xaADdzzpsAeK7JpB/ZZV7USMGZOv40JMPXQ1BdME5IFVc14p31x3FF\n/3SMz0719XCCjrB7LUKrQbPBSOvPiKqsy7VEe2jmZ8XBc7jQ0Iyv7xmFZfvPYvOxCgCm2QRnpZPy\nS2qQk54AvcGIBb+YdmVe3q+D7JznpvfB3eai7KHEla/CPTjnT3HOf+acXwVgD4B1jLGQ2bJ13xe7\nAQBRNK1J7JjaLx2zLu1p9/ET5XVodqEVlz95aYVpauG5GcHVFsVfCJmEFnMrJx6UIT7xFesuFJ5s\ngbv1hGlH+vNLLFmzzGT54n2ln+5bP9oJwBTgCYZ2tRRcf+8vQ0J2OYUrwVkkY0w8j3P+EoD/AtgE\nIGQCNACIocwZsUOjYbhrnP0PkUmvb0Sv537B8TLldRb+ZsuxCqw8WIJZl/ZE56RoXw8nKAmZMuv/\nE6KGOqsG478dLkO3p1Zg1aGSNr2uvY0rjc0GJMdEiPdz0p2vESuvbQIg7xbz8kpLXbPL+naQbSQI\nJa4EZ8sATJQe4Jx/CuBxAOqtMAwAnkoLk+CgceFDZO7SXC+MpG2a9UbMXXoIXVNiQnI6wVuse7BK\nd7YR0hZlNTp8arXGbMfJ8wCA+7/c3abXtlcIdtGmAlwzuDMAYMXscTaFqjOTYzC2p3I+p7KuyebY\nzEGdnO7qDGZO/+Sc879zzm3KC3POV3HOxdoSjLG/qj04fxOsjbqJOsJcCM40AdBP9NOtJ3GivB5z\nr+zr9ZYvocQ6AVHj5UKhJHidlBRvVbvKQHVji+LxN9ceRe7ZGgBATrptB4xwrQZf3T0KoyVFrAdm\nmHZzKhXJffvG0NoAYE3NaONhFV/LLwXAdZX4kNK3vKzUWNl9fw/OSmt0eHvtMUzKScPEnA7On0Dc\nNudK+Vq+42V1PhoJCTY6yfrW3BcuV+U1r//PNny+7ZTDkhyrck1Tpo6mIudf3V+8vf9MNd7fcAJN\n+tBpy+QqNXuH+PdVRwWURSCOSEtNbHxyAhgYDhRXYdbXe8Xj/t4I/ZWVh9Fi5DaBA1Hf9cO6YOOR\nctliaELUsHTfWfE2U+kL4c6T57Hz5Pk2zyD1TIvDlL4d8GueqdDsP1fl426r9bqnFkxv03sEAzUz\nZ0G7nPXKgZ0AAP06JTo5kxCTrimxyEyJsSm/0jHRfyvs7yioxM/7zuL+i7PQNSXW+RNIm1FgRjzh\nxz2mxuKv/Xmg4uOt7UYhPb9JhV3n71oV7dZR5syGmsFZ0GbOlu0/6/wkQgD87/7R+PC2YeL9iTlp\nmH91f2x9yrSnxl/bH+kNRsxdmovOSdH42wT7JUGIujr5cbBOAl+snfVmj363r1Wv05qAbPpFHZ2e\nY70E5MvtRa0aTyhQMzj7XcXX8hvU7460xrBuyZjc17JWizGGW0d1RTvzFnPrwpD+4svthcgvqcXz\nM/rQrmQvevSyXrL7SrvWCHGXvaU4P+9rXcKhsVk5s9UpMQrv3qxu67rrhwVfkXJ3tGoBDGNsOoB+\nAMSve5zzF83/n6Xu0PxDi8E/L6YksAgLZA1G/yuXUFHXhNfXHMX47Pa4vF+6r4cTUqwD4W//OI0H\nHRQzJsQVF/dKxaaj5ZjQ235nD865y+vRGhTKZyRGh+PjO4ajV5q8nllClPvrammtmYXLmTPG2AcA\nbgDwEExTmH8G0NVD4/Ib9r4xENIa4VrTh6A/dglYuCofuhYD5l7ZT7XFw8Q11rt3I0K4rhNRz/7T\nVUiNj3T4+7ykFdmz83W2OzR/f2oictITZFUM+nVKwMOTetmc64rstDi3nhesWvNJMIZzfhuAC5zz\nFwCMBuDev4IfK6nWyeq4vLfxuA9HQ4IFYwzR4Vo0+Fmwv7foAr7fdQZ3juuOnvTh6HXW09xhWgqO\nSdtVN7aI1fcBU5bL2p6iCy6/3vsK10Hhi4Q0AFwxezzS3VxHufiBMW49L1i1JjhrNP+/gTHWCUAL\nAOcr/9zEGHuIMZbPGMtljC301PtYG/XKb5jw6nrx/n82FnjrrUmQi4nQKk4P+IrByDFnSS46JETi\noYnZzp9AVGc9ze1KIWNCWmv705NsjlW0Yn3jb4fLbI6Ft/GLxLie7WX346NsA8hQ1prgbDljLAnA\nqzA1Pz8F4BtPDIoxdimAmQAGcs77AXjNE+9jz4UGU+ZsXX6pN9+WBLnoCC10fpQ5++6P0zhYXI1n\npvXx+/prwcq6Y5N1yxtC1CBd2zg4MwmAcqV/zjlWHTpns/xCabdmW5dAfHn3SPzXvLNdKbMX6lrz\nibyQc94E4EfG2HKYNgXoPDMs/A3AAvP7gXNuG7Z7WFVDM+78dJe335YEsahwrd/U87lQ34yFq/Mx\nsnsyrjLX8SPed1GGvHYiZc6IpzwxpRcYY8hMjsFDRXvx+/FKm3O6P70SAPDYZb0we5Lr2fQnL++N\nMT2U+2Y60r19DABTFp/IteZr2jbhBue8iXNeLT2msl4AxjPGdjDGNjLGhnvofeyy/lkZ2CXJ20Mg\nQYbBtp+ir7y+5ghqdXq8MJM2AfhSrw7xOPbSFeL9UG70TNQhBDqPWZVpmTUxGw9e2lMsqu7IhQbL\nBoBaXQtmDnL8nAcv7YnBme1aPdbIMFNGLzk2otXPDXZOM2eMsXQAnQFEM8YGw1JsNgFAjLtvzBhb\nC0Bp3/6z5nElAxgFYDiA7xljWdyq6Bhj7F4A9wJAZmamu0NRZLS6ii55cKyqr09Cj4YxvwjODhVX\n46sdRbh9TDfFBsXEu8K1GrSPi0RFXZPDnoSEuKLFPFfuaHNJ/84JuFCv3MAcABIk678GzPtV9ti6\nxy9BcVWj9VPc0jExCmN6pOD6YV1Ueb1g4sq05uUAbgeQAeANyfEaAM+4+8ac88n2HmOM/Q3AYnMw\ntpMxZgTQHkC51WssArAIAIYNG9bmy94FSUNXSrMStTFmG/R7m9HIMWfJIaTERuCRyUG32Tpg/fe2\nobjmva2+HgYJAs3m4MxRWZYeqXHY21hl9/G3fzuGRy/rBb31okgAWalxyEpVZ2d3mFaDr+8Zpcpr\nBRunwRnn/DMAnzHGruWc/+iFMQHAzwAuBbCeMdYLQASACk+/6dXvWZocnLmgzjcDQgSMMZvpcm9b\nvLcYe4qq8Op1F9EiXD8i7FTzdfBOAp/eXDjd0RR5dLgWOhd2jv+Wb1nunZMej2/vpUDKW1qzwOF3\nxthHjLFfAIAx1pcxdpeHxvUxgCzG2CEA3wL4q/WUpicUVjaIt+/+7A9Pvx0JMaYZK99dfGt0LVjw\ny2EMzkzCtUOoRYo/EaYzKTgjbeXKtGaUi8HZfV/sFm+fPt+ApBhaG+YtrQnOPgGwGoCwMvAogEdU\nHxEAznkz5/wWznl/zvkQzvk6T7yPI0I5DQDomuL20jpCRBofZ87eWnMMlfXNmD+zPzS0tsmvaJnQ\n3svHAyEBTyiD4ShzFhmugc6qPIaz/Ee9H5UBCgWtCc7ac86/B2AEAM65HkBI/Gv959ahvh4CCQK+\nXHOWX1KDz7adws0jMtG/c6LT84l3acyfxEZfz3uTgCd0nXC05iwqTItmvVG2tlpayywzmRISvtaa\n4KyeMZYC87wMY2wUgGqPjMoHlBY+AsBNI7rQjjaiCuaj3Zqcc8xdkov4qDA8MaW39wdAnBKmNQ00\nrUnayJVpzYZmPQDgxWW5qDKXzahv0ouPF51vgMHIMYhKSPlMa4KzxwAshWkt2O8APoepCXpQKDzf\noHh8aNdkL4+EBCsG32TOlh04hx0nz+PJy3ujHdUT8kuWaU0KzkjbCD01HU1rHi2tAwB8tq0Q939p\nWldm3ff3WFktGpr16Gjulfn2jYM8MVxiR2s6BOQB+AlAA4BamHZUHvXEoLylVtci7pLaf9r+tmJC\n1KBh3i9CW9+kx0sr8jCgcyJuHK5uLUCiHg1tCCAquedzU2cbR6tKa3WWNdXHy+oBANsL5B0Dpr61\nGQBw4/AueOqKHNoM4GWtyZx9DiAHwMsA/g1TFf8vPDEobzh8rgYD5v2KT38/Cb3BiJdXHlY8j5ZN\nE7VoGAP38m7Nf607htKaJrwwsx8VOPVjQtsmypyRthJK5DhaW7qnyJKMEBqYP/m/A4rn/rS3mAIz\nH2hN5qw/57yv5P56xlie2gPylm93FgEA5i3Lw5HSWlTUNSueR51tiFoYA4xe3I13vKwOH285iT8P\nzcAQN1qrEO/RUnBGVFDd0IIuyTFIS4hCp6Rou+cN7JIkzhY5WpsGKDc9J57XmszZHvMmAAAAY2wk\ngIDtDP7ZtkLx9jc7T9s9j4IzohbmxcwZ5xwvLMtFVLgWf5+a45X3JO4TgjM9BWekDQa++Ct2njyP\nrk52W07p20G8HW7eKjw+u71Hx0Zax2lwxhg7yBg7AGAogK2MsVOMsZMwNT0f5ukBeoKj4ntv3zhI\nVjmd0cQmUYmGwWt1zlbnlmLzsQo8dlkvpMZHeudNidsoc0baYt/pKizadEK8//txxw11xvRIEW8X\nVNTj9PkGHC2tVTz3y7tGqjNI0iquTGvO8PgovMze3Dpg6jlWJ9lSTIhaGBg49/wUQWOzAfOX5yEn\nPR63jurq8fcjbRdmzl5QcEbccfW7v8vuV9YrL9MRWJfIGL9wvXj7t8cvwdwludhiDvDGUUbNJ1zp\nrVno7JxAs+1Epd3HWgxG3DSiC77cblqT5mg7MiGtodEA3Atlm9/fcBzFVY347t5RCKOf34Ag7NVw\nNK1pNHKU1urQMdH+WiISWr7YXogBCgv/rxrYSeFsC+ZgvU6P1DgcOhs0JUwDVms2BAQR+x+Amckx\neHhSLzE469Mx3luDIkHO1L7Js5mRwsp6fLCpADMHdcLIrBTnTyB+gTGGMA2DwcGOkaxnVgIAls0a\nhwEZ1OUh1FU3tuD5nw8pPtbWmmRVkvaFxDdC8mu1vZTvjcO7ICUuUlz/ERGmQVZqnDeHRoKcp2et\n5i/PQ7iG4ZlpfTz7RkR1Wg1zaUPA6QvKBbNJ6+laDGgM0J6RNY3KAdTnd45wmBlzJKOdPCsbFxmi\n+Rs/EFTB2XM/H8SDX+1xep40eRERZvkrOFutA2CqEzOuZ3t8/Nfhqo+RhC5TnTPPWZdfirWHy/Dw\n5Gx0SIjy4DsRTwjTMBgMzn9CFu8544XRhIaLF65HnzmrAABlNTpsOFLm4xG5zl4WPj7K/YBqar90\nAJYgbfqAjm6/FmmboAnOOAe+3F6EFQfPtep53VIsW44jzYGaVsPw5d0jaSEkURVjphIXnqBrMeCF\nZXnokRqL28d098h7EM/SuJg5W3s4cAIIf1dmbnUEmBbV3/7JHz4cTevY+1nRtKH+U2S46RrY2Vwj\nbeZgx2vXiOcETc7ybFUjnBUMOFRcjZgIrexYdLjlvjSLRojaNB5sfP7h5gIUVjbgi7tG0M9xgDKt\nOaPdmr4izJwEimY77tusIwAAIABJREFUxWHbEpxFaLXy16AfR58Jmk/xKjvz71Iz/r0FE1/fKN7v\nkhwty47dPY4yDsRzTHXO1P+0K65qxDvrj+OK/ukYn52q+usT72CMyXoeEu/57o8i8bYxQAJke8HZ\n2epGt19T6BYwqU8aAKBzO9oZ7CtBE5w5u+i1GGx/kK/o3xGPTu4l3reu/UKIuphHNgS8tMLURe25\nGX2dnEn82fn6Zvy876zi1Ld1gVC9wucZcd+vuaXi7UBpPu+JtkpCj9e7xnXH7ucmo2tKrOrvQVwT\nNMGZ1K0f7cCAuavF+9e9vxXZz/5icx5jkNWBcneHCyGu0HhgzdmWYxVYebAEsy7tKa4TIYFNaWpz\nypubZPebDUacPt+AC3Z2nutaDOj5zEq8v+GE4uNE7rd8yzq+9UfKfTgS1zz700Fc/59tsmPf3zca\nY3umyFozObJ01lj89MAYdJWsuxYqFTDGkBJHnUV8KSiDs83HKlArqfK/q/CC4nlaCsaIF6m95qxZ\nb8TcpYfQNSUGd4/PUu+FiU+5simgsLIB4xeux4iX1yo+frS0Fnojxz9X5as9vKB3z+f+3zL6qx1F\nNsdGdE/GV3ePcjnJcFFGEgZntkNhpaU0S0mArbsLZkEZnLnq/gk9fD0EEkKYZM3ZNe/9jge+2t2m\n1/t060mcKK/H3Cv7Iipc6/wJJCBYZ86Usq23frQTANBip/QGNVAn7thx8ryvh0DMQjo4S4gKd34S\nISqR1jnbW1SFlQdL3H6t0hod3l57DJNy0jAxx7VpDBIYDFbB2J6iKptzKuqabI7JXoOCM7f5+/KA\n4ir3F/wreWhiT/H2vKto3aq/CJpSGkoMRm53jc+QTMvi/2/uGYVjZbWK5xGiGhV3a76y8jBajBxz\nrqQP02BjXYhW12JbwT4iTCPu1msxGG16AOtdKGZLlEX6eSma0hp1px6TYiLE20My26n62sR9/v1T\n2EYNzXrU22nN0Ts9Qbw9ukcKbhvdzUujIqFKw5gqdYN2FFTi531ncf/FWbSbKghZT0laB14AkCCp\nAp/97C8orKyXPU6ZM/cVVNQ7P8mHlIL1tpg5qBNy0uOx+e+X0qY4PxLUwVllXTPqJBsDpKJpjQ7x\nMjXqnOkNRsxdmovOSdH424Sezp9AAoaQsfk1Tz7drVQGaELvNNl96aJuANA7aKBO7OvVIQ6X9e2A\nrccrxL93g5Fj3tJcHC+r8/HoTOqb1A3O2sdFYtUjF6NLcozzk4nXBFVwJtRoETzw1R7U2wnOqNgj\n8TaGtjc+/3J7IfJLavH8jD6IjqAvGMHkMnMJhEWbCmTHmxWCM+um19b9FClz5tw5hWKt0RFhWJNX\nips/3IFv/zgNAMg9W41Pt57CUz8e8PYQFTXpA7NRO2mdoArOls4ah+y0OPF+3rkaHCs1fdv557UD\nZOcGQi0bElxMGwLcv2hW1DXh9TVHMT67PS43NygmwaPRvAQjNkIeaFlXgk+Nj0RVgzw401p9MaXd\nms5NeWOTzbH9py2bL+p0pi/2pTWmzRf2SjJ5W1OL5efh4l7UESRYBVVw1iMtFsesUs8Pfr0HgCl1\ne+ylK8TjidFBvReC+CHGGNoy27RwVT50LQbMu6ofrQ0JQkKAZT31LZ3WvO/iLIRpGKqtMmfWmTLK\nnDkn1MK8bmgG+nRMwF9GZsoeTzBfI6SZKrUX47tDJxnPHWO64Zt7RuHHv4324YiIJwRNhMIARIbZ\nn+YJ02pk057xVEaDeNmPe84AMG1Uaa09RRfw/a4zuO+SLPRIjXP+BBJwhOAsv0S+c1xnzpRsfHIC\nuqbEYuWhczZTW9bBmJA5oxjeOaOR45eHxwMADp+rEUuXCH+n0uB45Mu/4dSC6d4fpIROkjnrkhyN\nnmnxPhwN8RS/zJwxxgYxxrYzxvYxxnYxxkY4e05OxwSHj1+c3V6WbXh+Rp+2D5QQNzTa2UFsj8HI\nMXdJLjokROKhidkeGhXxtaFdLWUMpMFXVYOpRVNStKnkQbhGgxqdPMC3zZyZLuAUmzlXJclCXj24\ns3hbKEeyNq/M5jm+NH+5qZfu8ofGUWAWxPwyOAOwEMALnPNBAOaY7zskZMX6dTIFac9My5E9bj0N\nNLRrsioDJaS1hv5DueWOPd/9cRoHi6vxzLQ+iIsMmmQ3sXLXuO7i7YWrjoi3qxpaoGGWRf81Oj3O\nW/XUtC5cKwQWNP3t3DpJX03p2r2VB88h72wNVhw854thOSVc60hw8tfgjAMQfvISAZx19YkrZo/H\nqQXTkRxLTVuJf3nhqn6tfs6F+mYsXJ2Pkd2TcdXATh4YFfEX0kBKWrfsnfXHYeSAxhw4WO/UBGjN\nWVtIi85Kl77sKryAaf/abHO+vcLm3iCtcUaBd3Dz1+DsEQCvMsZOA3gNwNOtfYFwrfIP7uyJPfHF\nXU5nSQlRnfWOOgDYfMzxruHX1xxBrU6PF2bSJoBQ4uj6Hxlu+diedamp1p3dNWfqD80vLdp0Auvy\nS9167uxJlqUCWo39S6Lw66tU2sQbqhtakPP8Kp+8N/E+nwVnjLG1jLFDCv/NBPA3AI9yzrsAeBTA\nR3Ze417zmrRd5eXyi9z0AR1x38VZNs95bEpvjM+m7cfE+5Riq1s/2okT5crFLQ8VV+OrHUW4bXRX\n5KTTFEYoEUItxSyN5JBQZDvUM2cvr8zHnZ/ucuu5HROjxNvWtTKlHp3cCwAw+pV1br1PW20rqBRv\nXyNZG0eCk8+CM875ZM55f4X/lgD4K4DF5lN/AKCY6uKcL+KcD+OcD0tNlQdcYVoNnp5Gi/6J/9DY\nyXw99t0+m2NGI8ecJYeQEhuBR8wXBRI6hHVQTXrbLI10fVmEeUrOXnAWCsnW9fnyBftfbi/E3CWH\n0O2pFTZr86QGdE4EAEwb0FE8ppTdFgh/7+frm6FXyJ79uPsMuj21AgfPVLdq/M406Q2oamiWFVT3\n5dQq8Q5/ndY8C+AS8+2JAI65+0JT+nZAVir1HyS+Z/dz33wF3X+6Crd9vBO1uhYs3luMPUVV+L+p\nOUiMprIvoUpYX3ZRRqJ4rEGy21cIJqyDM6FWWigk0O749A/Z/ed+PoTPthUCgE3PUan4qDAM79YO\nUZJWfo7aq329o0i8rRQ0P/7DfgDAh1sKbB5ri3s/341BL66Rla+OpY1BQc9f/4XvAfA2YywMgA7A\nve6+0KLbhqk2KELagtlZASR8C160uQCbjpZj+YFzeP3XIxiSmYRrh2R4c4jEx6LDtWiULPquMbeZ\nu3u87RINwDINZ71bU7jb1l6ugU5aE8z2MYNNkHOi3H4wJ818N+mNkO45Oylplm7d0aGtNh41Ldlp\nlNRHdDT9SoKDX2bOOOdbOOdDOecDOecjOee7fT0mQtpK+Gwf1CUJnSTrXIQP8xUHTFv2X1l5GJX1\nzXhxZn9xhx4JDcmxplpmmeYm1NWNpgtyQpTy92ghYLDOnAltwkI8NsNN/91u97EmvVG2UxOA7PdS\n6oEJPdC/s2Xdp3UR4Etf2yDebmhlHUNXPb8kV7xNm4OCn18GZ4QEI+FCqmFAeV2TeNy6InyNTo+b\nR2Sif+dEkNAiXPSFenZC5izBztS2/WlNT40weJiCM3lXmRuGd8FPD4zB+Oz2suOPXdYL86/uL953\nlJGbmJOm7kAVdEpSDiJJ8KDgjBAvEb7sahjDy9cMcHjuE1N6e2FExN8I3SOEchmHz9UAAOLtrDES\npuXsrTkj9ulaDDaZM8YYBme2s/n7C9NqZAWgrTNn8nM9m9WaP7Mf7hqnPM1NggcFZ4R4iSVzxvDn\nYV3w+1MT7Z7bzjy9RUJLzzRT39SYCFNGJzbCFBCkxNkW1X7y8t6Y2j8dgMK0ZgjFZtIdlq3Zxdik\nNyIyXLkfs9LUZLjWcrlscpA583TW8s/DujjcVUqCAwVnhHiJuEzE/P8wNy8qJHh9coe8apDQdFvp\nYnzfxVkIt7shgCveDkbS36MWg+2f9VSF8iJ/pcyZQKn/bVS4FjeNyBSfa0+LihsC8s7WKI6DBD8K\nzgjxEmERrzA9khZvyYZ0f3qlT8ZE/EtybARGdE8We2MuM28SUdqdp2FM3DDiaM1ZsBeklf7d1DXp\nbR6fIFmsL9XUYpR1W5ASdsy+/ueB2PnsJPH4dUNNxV9vWLQdn287pfjcF82NydXwr9/criJFAhwF\nZ4R4icFo+kYtBGeMMfTtSJX/iVyYhsHIOTjnOGzOnChlSzQaZiml4WDN2arcEg+O1vekWUV7AVN+\nSQ1ynv8FZ6saAZh2SDcbjIiLUF7LJ0xrpiVEIi3esvheuoFgjmT3pKecqWqQ3X/35iEef0/iHyg4\nI8RLhGyIdMGw0nTVw5JefyT0aDUMeiPHu+uPo9lgBGP2K9fby5xJZzJnfb3XY2P1B9KyEvbqf325\nvRC6FiPWHjb132ww1wyzV8xV1yzfNSuQToNKS2t0TooGAIzKSkbvDvGt/SMoem/DcRwqlk9rTr+o\no52zSbDx1yK0hAQdIZuhlVxMrOuYTR/QEY9eRu2aQplWw2Awciw3T2k6WjJmL3MW7OvMpKR/1n52\nys98ud1U3V/YlFNvDr5iI+1sCDBPa8ZHWQdnlvN7psbhaGktOiZGITk2Ar3T46HVMFQ1tLj5J5Fb\nuOqIeHvDExOQ0S5aldclgYGCM0K8RG++gEqzINZf9GfQN+OQF2YOzgTWWbO1j12CPHOJDSHY0Idw\nnTPpH1Vod2WP8Hcp9Km0lzkT/v7jIuX15aTBWl2TAVPe3ISJOWloMRgRpmEI12rQrNB3s7UqJHUQ\nL+6Vim7tqQVhqKFpTUK8RLhgSrNle4uqZOcIpRRI6BIyZ0KmVSirIeiZFoerBnYSzwUAo50OAaFA\nmiRU2hAgJWStK+tMDdGToh2XrLHOrLWLjRC7NVTWmwKodfllaDYYER6mQWSYBgXl9Tb/Hq0l3S0a\n4eG6acQ/UXBGiJcIH9iO+uKFaelXMtQJa86ENYqOLs1CsGFdSiOUMmfSzQ+O6o8BwMZj5TAaudjW\nyVml/ViFDQMH5l2OcT3bo7zWkt0qKK/HigPnsHhvMQDgl0Nt24Shk/VXdRxwkuBEVwJCvESYetI4\n6ItHDY2JVqOB0chRYK7P5SjQ0mgYGAvtDgHSP2qVk2nNFQfOobK+Wbxvry2WwF5v25gILUqqdXaf\n9+baow5f1xlpEdydJ8+36bVIYKLgjBAvMSqsObMWTpmzkBdmzpwJ9EbH2SDrNWpAaHUIkE7hulIX\n7O//2y/etp4yFrw4s59Nf02p2Mgwm3V+b984CPOu7AsAOF5W53QcjjgqcktCA10JCPESYerJ8bQm\nZc5CnYaZgq1Uc5FiZ0VkhfOlQmm3Zqck+7sY58zoa3Ns/ZFy8XZUmHJwdtvobvjirpF2X1cpqOuR\nGodBme0cDdVl0sBvcGaSKq9JAgsFZ4R4iXABtTdVAgDhGvqVDHWmzJlRXBRunaFROt9Rh4BgN7QN\nAZGj30VHlHZ59u+ciEFdLIHUd38UuT0u6b95p0QqoRGK6EpAiJcIF1BpnbO7x3WXnUOZM6LVMpTW\nNIk7D50lwTRW06BAiK05c/N5B+ZNcfs9W1wol/F/Px50+/WFbiIzB3XCy9cMcPt1SOCi4IwQL1HK\nnD03oy9OvjJNnLqw14iZhA6tgw0jSjSM2UxjhlBs5vDPKn1oSt8OsscSohxvBnDks62n3H6uK4Sd\nuveMz0JijPvjJIGLrgSEeEnXlBgAQJZVQUnGGD64ZShWP3IxldIgDjeMKFGK5Y6W1qo0Gv/nak23\nDgmOy2a0xu1jTBnvcT3tbxpoC+GLHGXSQxddCQjxkmsGd8YP94/GzEGdbB7rkBCF3unq9OQjga21\n5VSqGlrw2bZC8X5lXRO2nqgEADw62dQKrK1FUdvq822n0O2pFWjWt716vjWlzNn8mf2w6pHxiDBn\noiflpGFAhqW1k6OdmK546oocrJg9DqN7pAAAJvdJEx8b2T25Ta8NWNacUWmd0EXtmwjxEsYYhndr\n+wc3CW7aNmZL6pssZRiEa7uRc2gclrP1rNd/NdX9qm/SIyLMcVX+1mhsNuAnc+FXqVtHdwMAdG8f\nizPnG/DQpGyslhSGzWnjF6GIMA36dUpEl+QYtIuJwA3Du4iPfXffaHR7agUA4PT5BnRJjmn164vr\nU2mDUMiif3lCCPEj1mvObh/TzaXnnSg31daSPl1Y3+jr3ZtGF3Yqu+PAmSqbY6/8ybKAPjJMi6en\n9UFcZJhsijBCpbWdCVHhuHlkpt2p6E9+P+XW664yB5KUOQtdFJwRQv6/vfuOk6o+9zj+ebawsIBL\nRwRpIlaKSkDQqLFcNYnR2KLGGDWWGDXlqtF477XFm2ZuYozGxBJTjTdXY7k2MNHoFaOiggUlimJB\nighIlWXLc/84Z2bP7s7szuxOOTP7fb9evJg5c87sb34Ms8/8yvNIjLT9hXz5Ee1zdaVy34JlQOsA\nKFGN4rF/flDU3Gcbwp2nr4UF23OlbdkqCEbLUolW5ihUV/x67pIuXffwwiA4y3b9oZQPBWciIjES\nncr60bGTsQx3b25pDKYzo+vLEr/bz/7989z34rLcNbKLfv5o5xn8s/HkGx+2O5auykY06H2jmxn8\nO7P/xKHJ290JijtLQCzlS8GZiEiMRGOLHYb2y/i6RFAWzXEWHXlZ9lH6WpCl6hd/fzN5e6fhwTqy\nXmmCs9dXtgRkfdOUbcqVn50wNXm7syTCHdHIWc+l4ExEJEaiI2fZ/HJOxGTRYCA68vKj2Yt46OXl\njL3kAdZt7rhAeL7kczqxIUzcWl2Vus/eWbMpefuiw3bOX0OAAbW9kpsOEjnLMrX4g5Y0KB2VppLy\npuBMRCRGotNv2SwIT8Rh0WnNuWFKDQgCo5v/7y0AFq3I7dqvTNX2yl+CgEQQlG5a84fHTGb8kL78\n7YL9GVmAoOfYvUYBLUFjpq66/7V8NEdKjIIzEZEYSbWgPxOJZKzRRfJtr37h3WB349YMyg+ddPPT\n/Ps9XS9BlMrQ/rlLo9FWY/ia0k1rVldW8OiFB2Q1VdwdiVHPeUvWZHR+U7PzxsoN1Dc0tbpeeiYF\nZyIiMdJq5CyLnGfJac2m1GvOojZvbUp5POqpN1fzh6e7Xrw7KtGMmqr8rfVqjFlW/Y1bgh2qX/nt\ncxmdv8OlD3LIT5/gmTCY02aAnq1owZmZHWdmC82s2cymtXnsO2a22Mz+aWaHFquNIiKF1tWRs3sX\nBMlYoxsC0mXk/8Yd85O3129pYHFk9+LcxR+y4L32+cO6Y2BtMGL2mxzWpLx7/tLk7R2H9WspeRST\nxK3VkVxq763ZnPX1+0wYnMvmSIkp5rv4FeBo4InoQTPbFTgB2A04DPiFmeV3a42ISExEw7Fs1pyt\nDRf5J4KUW788jdGDU2en39LQErRNvmIOB//kcT4OR9O+eMszHHXD3Cxb3bHVm7bm9PkAfv7o4uTt\nu742KzlyVh2TkbNDIoXW127eyqV3v8yWhs5HLAHq+lTz+9Nn5KtpUgKKFpy5+2vu/s8UDx0J3OHu\n9e6+BFgMTC9s60REiiM6WNaVdUeJkbPqygqOmNy+jms6899dy1urcp//a2OYgDbXEhsfantVsk3v\n6kjJo3gEZ7WRdB2X3buQ2595lxNvfjrt+dPGDEzenj5uUM6rKUhpicf4b2sjgfci95eGx9oxs7PM\n7Dkze27VqlUFaZyISD5FpzK7Emgk1pxVVhgzdxjMbad9IqPrHPjBQ4vaH+9m/ouPM1jf1hWJ5LyJ\nYLQx3BUZl+Asums0MU08P9yQce7tL/DQy8tbnb9oRUsKjXznYZP4y2twZmZ/NbNXUvw5MhfP7+43\nufs0d582dOjQzi8QEYm5aJ6yrtRWTOzWTAR5YwenLmfU1tamZua8urLd8cdf794X33yVjUo8b+Lp\n/3jGDI7baxR9quMR2Azum3pn6n/Pe5cHXlrOOX98IXnM3dm8tWWEcenaj/PePom3/CWdAdz94C5c\n9j6wfeT+qPCYiEjZW/ZRyy/mbKe2mpqdRFqtxK7FVLUmT99nXPL2xOH9eH3lRhrSbB4Y2r8mqza0\nlUnaju5IhH57jRnEXmMG5fVnZcPMmDl+MP94a3Wr4xff1T49SX1jc6vi9HtGpjilZ4rjtOZ9wAlm\nVmNm44AdgWeL3CYRkYKIBknZjpyd84fnk9N70enRHx83pdV5v567hPfDIHD1xmCx/uyF7UfNIPus\n/gf++O988475HHvjUxx27RPtdoyu35Lj6gQxzjgxY3xmwWJio8DlR+zKExd9iosO3SmfzZISUMxU\nGp83s6XATOABM5sN4O4LgT8DrwIPA+e6e34WLYiIxFi2I2dzXl2ZXIMVXXuV6lnueDbIYZbYSZku\n3UO2+bbe+nAT9yxYxnPvrGXRig00hGvgEln5E+uuuuvt1UF7PcbR2QmfGN3h44m+Taw361VVwejB\ntWmrHEjPUczdmne7+yh3r3H34e5+aOSx/3T3Hdx9J3d/qFhtFBEppi6tOQsHqiojI2ep0qX1brM2\nqynNEFk2hbuj66YSNtYHI2WJUaQv//rZjFNKpLPu45bRtzjnat22rneHjz+zJJjyvD5MC7JyfX3e\n2ySlQeG5iEiMjBvaskasKzsPU6WUSJXMtldlRasgKV0QdsyNT2X8s19d1r5m59pNQSAVTS1Rn2Z9\nW6aiO0DzteGgEFZtqGfFui307x0s/z5x+vadXCE9hYIzEZEYOWl6y1RYZRYVAhJSBWfRqb+T9w6e\nf97ba5h8xZzk8eYcDEGlKmx+xu+C8kW9q6LBWfdGzhojxcTjPHLWmbo+1Rxz41M89MoKAPr3ri5y\niyQuFJyJiMSIdTPP2durNwEt9SwBIrEMp8wcC8CaTVtb7aR8+f11rZ5nRGRK7oP1WzL62R2t/4pO\noybqTnZVc343gBZMQ5MnN2ZAfKobSPEpOBMRiZmDdwlK/1gXRs6umR0UXlny4abksQ3hDsnJo+qY\nOLw/44f07XQ9VGIXJ8AHGzJbCxUtut5W7+qWXzfn3j4/7XmZSLc+rlT87ISpADS2STNSHZO6oFJ8\neieIiMTMDV/cg2cvPahL104eVQfAhGH9ksceeS1Ik/HS0mB0bEtDE/e/tLz9xcAFh0wEupafrLGD\nIa3oyNlry9uvTctGtjtIi+m1qw5rd2y37YJ/o7Z9rJJNkqDgTEQkZmqqKhm2TccjW+kctvu2AGwX\npq4A+M7huwAtyWeXrUs/TXnijGBN2tTtBySPbc6wBFNDByNnNTnM3N9cQiNnfSIbIczgq/vvQK8w\nVUZjk7PD0MwqOEjPktcKASIiUlhNYYAUTcOx+8g6nrjoU4wc2CfdZUmVZvzvefsyenAtU64MNgys\nysG0ZtspvO7o6OfE2ZLvfwZoqQLR0NTMziO24c1Vmzq6THogjZyJiJSRhhS7NQFGD65td2zSyDqG\n9GtdA7Kiwpg0qo66PtUcP20UEBTqzsTazVvTPjZpZB3jU5SS6opSGjmDYI1ZYroYWoqiX/KXl7u0\nI1fKn4IzEZEy0tTcTFWFZbSZ4OX317XLgRaN387ef4eMf25zs3P+n1oW+pvBXefMSt4fUFvNoxce\nkPHzdSS65uzVqw7t4Mx4OHLqSM4/aMfk/eiuzDVhhQZNb0qUpjVFRMpIY5NnlYKjbQwXvXZw315k\nKpHCI+GRb+3Hx1tbpjIrw52Ih+w6nKVrP6Y7ors1U+VWi7uqSHmm+sYmqiuNe8/bt4gtkrjRyJmI\nSBlpbPasajMaxo1f3DN5PzqSNqC2F0P71wAw9pIHUu6S3LClgc1bG1ke2WTwo2MmM2FYf6oiI0SJ\n6btKM5q6maislHZrplJT1fLvM+/ttTQ0Of1qSi/IlPxRcCYiUkYWLlvHxvqOk7xe+bndkrfNoHdk\nR2HbUbdRkU0EH6eoiTnpijl88oePJRe5A0wfF9TRjG5KqAwDtcpK63ZwVerBWXVlBWftN77YzZAY\nU3AmIlJGnn5rTafnjI0szDdal1Zqu0A9WtYpVWFzgNWbtnLRnS+lfP6ExBRpVUX3g7NS3a0ZFa01\nKtKWgjMRkR4mOqJlZq2y97dNhBrNXbYhy7JL0QLniSS0lWZpi6xn6tYn3+rW9XGQbV9Kz6LgTESk\nh4nmLXPveI1aNOv/8o8yq7GZkGrDaGWFZVRk/dYnl/DjsBRVW4/9c1VW7Yij6PTx4xcdULyGSCwp\nOBMR6WH2HD0webuhuePdndEpxPVhjc5MDe5b0+5YVWVmI2ffvf9Vrn9scYfnHLTzsKzaEyfRHu+T\nw+oJUh4UnImI9DCjB9cmk6Ku2lDPpg42EGyJbAKob+y4jFOvygrmfGu/5P1UxdUrLLs1Z95BwtmD\nwgLxJSkSnVVlsbtWega9I0REykj/mipOnTW20/Oi8VFHZZ2idTi3NDSzpaGJsZc8wBm/ndfu3BED\nejNxeP9Wx64+aneuP2mP5P0nF3/I6k1bWbQidfHzTfWNvLdmc/J+qh2iidd30C6lO3L2mUkjkrej\nSWlFQMGZiEhZ2bS1kV5VnX+0R9eDjajrw87b9k9/cuie+e+z8388DMBfX/ug3ePRoCrh5L3H8NnJ\n2yXvv7M6OOes3z2f8mcceu0TfPJHjyXvu8Odzy/l9ZUbkse2GxCMyPUt4dxgk0cN4K5zZnHqrLHK\ncSbt6B0hIlJGmr11ktNM3XnOLNZsbF8bc8zg2mRA9cyS1mk62hYzz2YT5rtrNrPgvY8Yvk0NI+qC\nkbtX3l/XrnpAY7Nz4f+8CMDbPwgKhyd+bKnXpdxrzED2GjOw8xOlx9HImYhICfvpF6a0yvAPXQvO\n+tVUMXpwbbvj9527L3+7YP+U17yyrPXU5Jf2HtPpz9kusg7tqBvmcsA1f+fZJWtwdy6+66V250+5\nck67Y4nC5xX6DSZlSm9tEZES9vk9RnH4pBFc9tldk8cymdbMVF1tNTsM7UevFIvWj//lP1rdvyJS\neSCdiw/fudX7XaIKAAAQ80lEQVT9+sZmjv/VP3jw5RUZbxRIpOJoW7RdpFxoWlNEpAxE02FUZTCk\nlE39TYA+vSrZ+nHracyt4fziHWftzfSxg9olsE0lMYXZ1tK1m5MjYum88v46dh9Zlyx8XurTmiLp\naORMRKQMRIOzjvKWJUwZVZfV83eUi6u6siKjwAxa6m4evvu2rY7fu2BZp2vWvv/Qa0Bk5CzDnylS\nahSciYiUgcpWJZly//wd1YKs6kKQ9NArK1rdf3X5+k5Hwur6VAPQ5B0nzhUpdQrORETKQDSwsSyi\ns5ED0uc4i+polCpXgZLT8dDZjHGDgWC3pqY0pZwpOBMRKQPRACmbWGn0oPY7NFNZ/MHGtI9V5SCJ\n6rD+NWyqb51wtm3utUTZp2Z37dSUsqa3t4hIGWg1rUlhR5Uy2YDQmd2224ZdRmzT6tj3j54EwPgh\nfQFoCDcgNDW7Rs6krBUtODOz48xsoZk1m9m0yPFDzOx5M3s5/PvAYrVRRKRUdHXkLBcG9e3V7efY\n2tRMY3MzkyMbFRKZ8/eZMASAHzy0iDdXbQxGzhScSRkrZiqNV4CjgV+1Of4hcIS7LzOz3YHZwMhC\nN05EpJS0Ds4yCFys/XWZ2q6ud7Lm5ikzx+QkOKtvaMY9KJ5+2j5jmTFuEDsO78+dX53JlO0H8Pun\n3wHgtrlLcIfqHOZyE4mbogVn7v4atF+46u7zI3cXAn3MrMbd6wvYPBGRktIqyMog3poxbjCnzhrL\n2fuPz+j5Pz1pWx58eQVvfe/TmMG47zwIwHmfmtCV5rbz3DtrAZg5fjCXH9GSzHba2EGtzlu5vp6q\nCstJQCgSV3FPQnsM8EK6wMzMzgLOAhg9enQh2yUiEivRNViZjJxVVlhGGf0TbjhpT9zb79octk3v\nNFd0TWfVDZav+5h+NVUMrK3O6c8ViZO8jgub2V/N7JUUf47M4NrdgB8CZ6c7x91vcvdp7j5t6NCh\nuWy6iEhJqazM75ozM2sVmP3u9OkcP21Ul55rXLjAP5VMplnrG5vp3UFSXJFSl9eRM3c/uCvXmdko\n4G7gFHd/M7etEhEpP63znOX/5+03cSj7Tezal+KOArBHF32Q8virVx3KrpfNZoeh/Vj8wUZ61WrN\nmZSv2L27zWwA8ABwibvPLXZ7RERKQVW2GwKKKFFD8+z9MlvvBlDbq4oJw/px74JlLFy2nrWbt+ar\neSJFV8xUGp83s6XATOABM5sdPnQeMAG4zMwWhH+GFaudIiKloKJV+aZ4B2d7jw8y/X/lk+PaPda/\nJv2ETjQAfXHputw3TCQmirlb826Cqcu2x68Gri58i0RESldVqyS08XbFEbtx+j7jGNa/N4P69uKo\nqSOpqjRueuItvjRzTNrrFq3YkLxdnYOqBCJxFffdmiIikgHLcrdmMfWqqmDCsH4AvPAfhwDwy8eD\n5cWJKgCd2dKQ2XkipSh2a85ERKQrWoqGF7pCQC5UVwa/jhqaOi5+LtITKDgTESkzMR84S6kmzG9W\n00GesxF1LTnVzv3UDnlvk0ixKDgTESkDHhlwivu0ZirHTRvF2fuN57wD01ccuPHkvZK3PzdFVf2k\nfGnNmYhImUlMEZaSmqpKvvPpXTo8Z8qoOi45fGcqzdhp2/4FaplI4Sk4ExEpA9GVWlVlupPRzPjq\n/prOlPJXel+vRESkQ5mUQBKR+FJwJiJSBvpEak2W4rSmiLTQ/2ARkTKw+8i65O0qjZyJlDQFZyIi\nZWJY/xogSPIqIqVL/4NFRMrEmk1BMfBh/Xt3cqaIxJmCMxGRMtHYHOzZHFhbXeSWiEh3KDgTESkz\nVdoQIFLSlOdMRKRM3H7GDOa8urLYzRCRblJwJiJSJmZNGMKsCUOK3QwR6SaNfYuIiIjEiIIzERER\nkRhRcCYiIiISIwrORERERGJEwZmIiIhIjCg4ExEREYkRBWciIiIiMaLgTERERCRGFJyJiIiIxIiC\nMxEREZEYUXAmIiIiEiPm7sVuQ06Y2SrgnWK3oxuGAB8WuxFlSP2ae+rT/FC/5p76ND/Ur7kxxt2H\npnqgbIKzUmdmz7n7tGK3o9yoX3NPfZof6tfcU5/mh/o1/zStKSIiIhIjCs5EREREYkTBWXzcVOwG\nlCn1a+6pT/ND/Zp76tP8UL/mmdaciYiIiMSIRs5EREREYkTBmYiIiEiMKDiTsmFmVuw2lAszqw3/\nVp/mkJlVF7sNIhJ/Cs4KyMyqit2GcmJm+5rZjWb2NQDXAspuMbMKMxtkZnOAi0B9mitmtreZ3QFc\nY2a7F7s95URfIHLPzHYzs97FbkdPpuCsAMxsppndDHyi2G0pF2a2J3Aj8DzwaTP7qZlNLXKzSpq7\nNwONQB0w3swOBv3y6y4zO47gvXo/0Bv41/C4+rUbzGx6+Ll6sZmlzLIu2TGzyWb2JHA1MLjY7enJ\nFJzlmZmdSbDt+AVgvplVFrlJ5WI6MM/dbwHOADYTBGlDituskrcrsBL4P+AIM+uj0bNu2xH4X3f/\nA/BTCKY31a9dY2aVZvZ9gs/VucCewOVmNry4LSsL/w7c6e6fd/f3QV8iikXBWf6NBv7N3W909y3u\n3lTsBpUiMzvezP7VzGaFh14A+pnZtu6+AngUGArsW7RGlphIn+4dOfwO8ArwOtAMHGZm2xalgSUq\n0q8zw0P/BI42s28D/wC2A24wM5W/6ZoK4F3geHf/DfBNYG+gTzEbVcrCJQ3jgY3ufm147BAzGwBU\nhvcVpBWQgrMcM7NxZlYT3h4E7A48a2YHmtlsM7vUzI4OH9ebvRPht+TLgIvDQ78ysyOATcDbwP7h\n8ceBj4BR4XXq2zRS9OnNifckMBWodfcnCPrz58DVZlalPu1Ymn79HPAX4BvAfsAp7n4YsAo4VoFv\nZsI1exPDu83An9z9dTOrcfdlwFKCYtySoWifhksaPgQ+aWafMbN7gAuB69D606JQcJYjZjbWzB4C\nbgFuN7Nd3H0NsBr4I3AUcAOwHLjMzKbozd65cKRxJ+ACd/8JcCVwHlAFLAOmmtmu7t5IMELx+fA6\n9W0aKfr0cuDr4Qf1MmCTmd0GnEYwgvaSuzeqTzuWpl+/BUx0978BWwjeowD3ApMJvmRIGmY2wMwe\nAB4Bjjezfu7e5O4fAbh7vZn1B8YRvHelEyn6tC+Au68HbgO+C/za3Q8l+H22d5vRdSkABWfd0GYk\n4ULgGXc/iGCK7WozG0fwAT0JWO7u97n7bcCDwJEFb3CJMLNTzGz/cEgdgjVQA82syt3vBN4EDiHo\n5y0Ei1cBRgLztCu2vU769C/AQoIvEEOBQ4H1wBTgGmAPMxtb+FbHXyf9ehdBv54YjpC9CRwbnrcH\nwXtXOtYXmA2cH97+ZIpzZgAL3X2ZmfUzsx0L2cAS1LZP94s8dj8wFhgY3n+O4D1dX8D2CQrOuqs3\ntEqR8SqAu98A7AWcTTBUfAtwTOS6YcBThWtm/FlghJk9BnwZ+CLBupx+BH04CegXnn4dcDKw0t2v\nBD4KvwmeANwSjqL1eFn26fXAicCLwIHu/g13XwcsAL7t7m8X/AXEVBf69SigCZgDfMLMngaOAy51\n9w0FfwExFwl4twkXpd8E/JkgmJ1hZtuF5yU+dwcA75nZacA8gql5icigT0cCuPtLBNOY54Wbq04m\nWJqzukhN77EUnHVBuFDyEYKcRceHwcAaghGGKWY2hWBR9fbA9u5+KfCumf0g/GAeRPCNWgjW6oRT\nZv2B98PRx3MIRm+uA34BzAImm1mtuy8imG47KXyKs4FT3f0T7r648K8gfrrQp68BbwAnufv6cIFw\nhbsvd/dVxXodcdPF9+obwHHh1OYpwJnufnD4mJA24L3RzIaEG6k2A38lGNE5ECDyJexIgoBiP+AL\n7v4/hX8F8dOVPgVw91uBPwFXEAwqnOHu7xb8BfRwmv7JkplNIJhG+x7BjqFvh98wriHYNfSfBN/k\nvkmQz+gIgu3zpxLs0nrU3ecUvuXxY0Fake8ClWb2ILANwQgD7t5kZucRrNH7L+B2gpGxEcB/Aw2E\no4/u3kCwwLrHy0GfPh2e21z41sdXN/t1K0E+Ptx9I/BywV9AjIUBb1O4dux9dz857O9rCUZ4jgZw\n97lmNh3Y2cy2AZrD/nwAuDdc8iB0qU93MrM6gj7d4O4/sSDdS0PxXkXPppGzDCRGEcK7M4Dn3f1e\nd59P8M3je0Bvd/8u8HV339fdnyPIwZOYttjg7osUmAXMbH+CX1gDgcUEv/gagE+FHxaJBdZXAte4\n++8IpoVOMbP5BF8s9EsuQn2aH+rX/LBgd+v3gO+FfbwTkYCXYIfrrPCxhJsJpoz/BrxpZiPc/Q4F\nZoFu9ukjwOLEtLECs+JScNaJcB3DUoIPZAg+ZE+wYLE/BB+8bxImlwSWhNedBXyFIB+Xdg+21wz8\nl7uf4+43E0wDjwMuI8imThgQ3wVsNrPt3f0egj49xt2/EA7LSwv1aX6oX3Msw4C3mWBq7YrIpZ8B\nvkawFnKSuy8vXKvjLQd9+iJBn2rXawwoOOtAuMD3SOCHwOFmtnO4YPJ3BN9M5hLsHjqVYIfWcHd3\nM/smcCZwtru/UKTmx93zwJ+tpWLCXGC0B0klK83s/PCDZBTQ4O7vAbj7Cnd/qygtjj/1aX6oX3Mv\n04D3HmCVtewW3gIc7O5nuvsHBW91vKlPy4iCsw6E6xm+7u4/I5imuDJ86ALgXOBidz+ZIFnnqvBv\ngJvCxenzCt3mUuHum9293lsqJhxCy7qx04BdzOx+goWpCnAzoD7ND/VrXmQT8DZ5uFs4XE7yRDEa\nXALUp2VEGwI6Edmlci1wn5kd6u6zzWyduz8ZPvZVgtqOjeE1msLIUPhB4sBw4L7w8AbgUoIt3Es8\nrPEmmVGf5of6NXdSfEYeArwU3j4NODMMeHciWMCOmZmWh6SnPi0vCs4y5O4rzOxWgg/i2eFOmOnA\nvwHVwOmuupld0Qz0IsgPNdnMriXIqXN+JPiV7KhP80P9mmPZBLwKIjKjPi0Ppn+bzIQ5n5rN7E6C\nLfP1BDs133D3N4vbutJmQWmQp8I/t3mQZ0e6QX2aH+rX3DIzIwh4bwHuBk6nJeBdX8y2lSr1aXlQ\ncJYFM6sFHgZ2Ba5y9+uK3KSyYGajgC8BP3F3lQnJAfVpfqhfc08Bb+6pT0ufgrMsmNmFBIspL9YH\ns4hI9yngzT31aelTcJaFxNRmsdshIiIi5UvBmYiIiEiMKM+ZiIiISIwoOBMRERGJEQVnIiIiIjGi\n4ExEehwzazKzBWa20MxeNLMLwrqDHV0z1sxOKlQbRaTnUnAmIj3Rx+4+1d13IyhzczhweSfXjAUU\nnIlI3mm3poj0OGa20d37Re6PB+YBQ4AxwO+BvuHD57n7U2b2NLALsAT4LXAd8APgAKAGuMHdf1Ww\nFyEiZUvBmYj0OG2Ds/DYRwRFoTcAze6+xcx2BP7k7tPM7ADgQnf/bHj+WcAwd7/azGqAucBx7r6k\noC9GRMqOCp+LiLRWDVxvZlOBJmBimvP+haAA+rHh/TpgR4KRNRGRLlNwJiI9Xjit2QR8QLD2bCUw\nhWBd7pZ0lxEUk55dkEaKSI+hDQEi0qOZ2VDgl8D1HqzzqAOWh6XavgRUhqduAPpHLp0NnGNm1eHz\nTDSzvoiIdJNGzkSkJ+pjZgsIpjAbCTYA/CR87BfAXWZ2CvAwsCk8/hLQZGYvAr8Bfkawg/MFMzNg\nFXBUoV6AiJQvbQgQERERiRFNa4qIiIjEiIIzERERkRhRcCYiIiISIwrORERERGJEwZmIiIhIjCg4\nExEREYkRBWciIiIiMaLgTERERCRG/h//S59To2p0MwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0sVU1GMU_t1",
        "colab_type": "code",
        "outputId": "7d67ce05-7c18-4570-f58d-22d0d7ada2f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "source": [
        "#Normalising Data\n",
        "df.iloc[:,10:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>beta_0</th>\n",
              "      <th>beta_1</th>\n",
              "      <th>beta_2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1993-10-01</th>\n",
              "      <td>6.666589</td>\n",
              "      <td>-1.943584</td>\n",
              "      <td>-7.765616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-04</th>\n",
              "      <td>6.644550</td>\n",
              "      <td>-1.975793</td>\n",
              "      <td>-7.581481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-05</th>\n",
              "      <td>6.656880</td>\n",
              "      <td>-1.952744</td>\n",
              "      <td>-7.581914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-06</th>\n",
              "      <td>6.664764</td>\n",
              "      <td>-1.969146</td>\n",
              "      <td>-7.642901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-10-07</th>\n",
              "      <td>6.649469</td>\n",
              "      <td>-1.970819</td>\n",
              "      <td>-7.616373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-07</th>\n",
              "      <td>2.283254</td>\n",
              "      <td>-0.274315</td>\n",
              "      <td>-1.950714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-08</th>\n",
              "      <td>2.323858</td>\n",
              "      <td>-0.285491</td>\n",
              "      <td>-2.087325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-12</th>\n",
              "      <td>2.282163</td>\n",
              "      <td>-0.280528</td>\n",
              "      <td>-1.936297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-13</th>\n",
              "      <td>2.239041</td>\n",
              "      <td>-0.236426</td>\n",
              "      <td>-1.944157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-14</th>\n",
              "      <td>2.178368</td>\n",
              "      <td>-0.186441</td>\n",
              "      <td>-1.931833</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5538 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              beta_0    beta_1    beta_2\n",
              "Date                                    \n",
              "1993-10-01  6.666589 -1.943584 -7.765616\n",
              "1993-10-04  6.644550 -1.975793 -7.581481\n",
              "1993-10-05  6.656880 -1.952744 -7.581914\n",
              "1993-10-06  6.664764 -1.969146 -7.642901\n",
              "1993-10-07  6.649469 -1.970819 -7.616373\n",
              "...              ...       ...       ...\n",
              "2019-11-07  2.283254 -0.274315 -1.950714\n",
              "2019-11-08  2.323858 -0.285491 -2.087325\n",
              "2019-11-12  2.282163 -0.280528 -1.936297\n",
              "2019-11-13  2.239041 -0.236426 -1.944157\n",
              "2019-11-14  2.178368 -0.186441 -1.931833\n",
              "\n",
              "[5538 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lrcv2DrmU_t5",
        "colab_type": "code",
        "outputId": "c5e88403-db75-4c35-f444-0f7ec5161aa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "input_features = df.iloc[:,10:].values\n",
        "input_data = input_features\n",
        "print(input_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 6.66658893 -1.94358423 -7.76561639]\n",
            " [ 6.64454955 -1.97579251 -7.58148149]\n",
            " [ 6.65687992 -1.95274401 -7.5819138 ]\n",
            " ...\n",
            " [ 2.28216346 -0.28052841 -1.93629654]\n",
            " [ 2.23904105 -0.23642555 -1.94415665]\n",
            " [ 2.17836824 -0.18644112 -1.93183315]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEXccZbnU_t9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sc = MinMaxScaler(feature_range = (0,1))\n",
        "input_data[:,0:3] = sc.fit_transform(input_features[:,:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsQEvk6iU_uC",
        "colab_type": "code",
        "outputId": "aa2e7f86-6ee7-4a30-f44f-22b92ad573b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "input_data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.69120055, 0.39939833, 0.36212712],\n",
              "       [0.68817898, 0.39179372, 0.37477972],\n",
              "       [0.68986946, 0.39723564, 0.37475002],\n",
              "       ...,\n",
              "       [0.09010261, 0.79205793, 0.76268151],\n",
              "       [0.08419059, 0.80247094, 0.76214141],\n",
              "       [0.07587244, 0.81427263, 0.76298821]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyQ7j2pkU_uG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#The parameter in y will change depending on what you will be predicting \n",
        "\n",
        "def create_dataset(beta):\n",
        "  lookback = 50\n",
        "\n",
        "  test_size = int(.3*len(df))\n",
        "  X = []\n",
        "  y = []\n",
        "\n",
        "  for i  in range(len(df) - lookback - 1):\n",
        "      t = []\n",
        "      for j in range(0,lookback):\n",
        "          \n",
        "          t.append(input_data[[(i+j)], :])\n",
        "      X.append(t)\n",
        "      y.append(input_data[i+ lookback,beta])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzd6tJEcU_uU",
        "colab_type": "code",
        "outputId": "33f5301e-810a-40c9-f78e-92b40b36281c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "X, y= np.array(X), np.array(y)\n",
        "train_size = len(df) - test_size\n",
        "X_train = X[:train_size - lookback]\n",
        "X_test = X[len(df) - (test_size+lookback):]\n",
        "X_train = X_train.reshape(X_train.shape[0],lookback, 3)\n",
        "X_test = X_test.reshape(X_test.shape[0],lookback, 3)\n",
        "y_train = y[:train_size - lookback]\n",
        "y_test = y[len(df) -(test_size+lookback):]\n",
        "print(y_test.shape )\n",
        "print(X_test.shape + X_train.shape)\n",
        "print(X.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1660,)\n",
            "(1660, 50, 3, 3827, 50, 3)\n",
            "(5487, 50, 1, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sL8BMdbGU_uY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from keras import Sequential\n",
        "\n",
        "from keras.layers import Dense,LSTM,Dropout"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zx2QdqOWxJ-",
        "colab_type": "code",
        "outputId": "e58ba5c2-5ecb-4ced-e91e-15449468a2d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units = 200,return_sequences=True,input_shape = (X_train.shape[1],3)))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "model.add(LSTM(units = 200, return_sequences=True))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "model.add(LSTM(units = 200, return_sequences=True))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "model.add(LSTM(units = 200))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "model.add(Dense(units = 1,activation= 'linear'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_5 (LSTM)                (None, 50, 200)           163200    \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 50, 200)           0         \n",
            "_________________________________________________________________\n",
            "lstm_6 (LSTM)                (None, 50, 200)           320800    \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 50, 200)           0         \n",
            "_________________________________________________________________\n",
            "lstm_7 (LSTM)                (None, 50, 200)           320800    \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 50, 200)           0         \n",
            "_________________________________________________________________\n",
            "lstm_8 (LSTM)                (None, 200)               320800    \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 201       \n",
            "=================================================================\n",
            "Total params: 1,125,801\n",
            "Trainable params: 1,125,801\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMSgnnEaZ9Z4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss = 'mse')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cUpTgFnuk01",
        "colab_type": "code",
        "outputId": "fad981d2-cf9a-42b8-b56c-87685ca07171",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "model.fit(X_train,y_train,epochs = 1, batch_size = 32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Epoch 1/1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "3827/3827 [==============================] - 64s 17ms/step - loss: 0.0103\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efd0c78dbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8JPkWYwumz0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_value = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mZcVXlKwTLh",
        "colab_type": "code",
        "outputId": "4b22663f-fa78-4bb0-a9db-29d3fe967a54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(predicted_value)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aALbLPjmwWoX",
        "colab_type": "code",
        "outputId": "d9e08089-cba6-4e8e-f78c-acac881bbe23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(predicted_value.shape)\n",
        "print(input_data[lookback:test_size + lookback,0].shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1660, 1)\n",
            "(1661,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGNfUH-mwYty",
        "colab_type": "code",
        "outputId": "1f09138a-030e-4361-8f5a-44325d2f5176",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "#For beta_0\n",
        "\n",
        "plt.plot(predicted_value, color='red')\n",
        "plt.plot(input_data[lookback:test_size + (2*lookback),0],color = 'green')\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"beta 0\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YuWM8cvUjtKzUkpS0FP5v+v+ReD4x5I4XviAjBiFHjLhtBNdVuY6XW73Mm23e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\nD3uAndhhv/C+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHIZCd1rwjnn",
        "colab_type": "code",
        "outputId": "77410c11-72b1-454e-9756-544425df19c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "#For beta_1\n",
        "\n",
        "plt.plot(predicted_value, color='red')\n",
        "plt.plot(input_data[lookback:test_size + (2*lookback),1],color = 'green')\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"beta 1\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fTIuMb7+FL76A7dtVMlmPHjDXuQdX/rhrazbyZVj+f38Aykf11LVPOYSy+low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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5qNL6P8xQqX",
        "colab_type": "code",
        "outputId": "c96d0591-e5d0-4d5d-d30b-ccacea4ec899",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "#For beta_2\n",
        "\n",
        "plt.plot(predicted_value, color='red')\n",
        "plt.plot(input_data[lookback:test_size + lookback,2],color = 'green')\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"beta 2\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1nRyRrnoJWM1FVjNI7JFY9pzaA2RGwrjD+kC8ssyVHucFHQkJ8MMPJH07FaxN\nvxo25OiZw8SuyZxW740vmFqvHbRyv8r8qbdzMbiG5Ru6VATeUK9sPZfjDcubaqauanF5cuDbr6AB\n7ro6+zIYwYqsCPIg1uqHVr7Z9g1PL8m03/ed3Zdbv7uVd9eY1oTW5ieezEIAL7V/iadbPs0r7V9x\nGLdfxjeu0DjHpXbLFS7H1B5TKVKgCEWjigaNIth9cjdgcigGNjXx7hviNnDi4gmX89Mz0nlw7oN0\nnNqRibETHZyf1ixaaxSS1tpWzMwaSmv/xvhwzMPkO5KToWVLeOghU//fjt4/OZZnKBRVxNQWyiHV\ni1d3GnuhnfdluV1RuVhlAN5d+67TsZMj3Telsc/paFm5pW+b8OQyogjyINbyutkxdetUh313b0RW\nShQswXs3v0eNkjUclEZ2b6854UzSGcZvDHwsvNaaJxY9ARhFZU3gOnj2ILd8d4vLc77Zlvngevjn\nhynwaqbDPCktibiEOAqMLUDf2X35LPYzW3SL1RxnH02TXSmPPMmkSbBzJ0yezKPTMuP1M3SGrYmR\nlbBFi6Fq1Rzfwj66aH6f+eiXNa9e7zoZzFuuKeeduS4r7au35+yzZ9Eva9Y+uPayZAg0ogjyOJ7e\nQnbE77CZeCLCIuhV37mdnzsW9F1A26ptOfvsWb7s7vt4+sRU59yEDJ3hEBqYHclpyRw4c+CS7n/w\n7EHbtlLKFpoYlxBncwJWLFLR4Zz+c/o77NuvlJLTk5m7ay4AP+z4wdZwBoxp6OP1H9NrZub3n5dq\nBnnFxYvw2mvQvj088ACFoovx2vWvAWaVZL+KbFOlDVxzaQ/fKsUzO5PdWtc3WdbhYeHcVPMmp3F7\npeOOnObTBCuiCPIY1nBHK+93cixNkNX8Y0246Vi9Y47uE3NFDKseWEWxqGJ+SZvPmtgD8Pqq1yn/\nbnmPFSbt6T+nPzXG1bikTljWPrRWu25URBRlCpUh7lycLVzQXbapKy6mXuTfs/+6PJaakcrjix53\nsClnl62a5xg/Ho4dM8rA8vcSGWZ8TqkZqTbfyPlR520JX5dCTv+OvaVNlTZOY7/0+8Uv9wpGRBHk\nIVb9u4qxKzMzIfs37u/UY3Zh34UMbzXc6dzL7UXrK+xNMFmZs2sOkJnzYM+BMwdYe2gti/YuYsxv\npvWitbNUjxk9+Gj9RyzZtwQ1WhH9WrRzUlMW9p7aC+DwXVUqWom4c3G22vIAZd4ugxqt2Hw0swjc\nlsFbHEoggEnY23Zsm8t7/XvGWUHk1Zo0LklKgrffhi5dTF9hC9bVqr0p075fwaXgKr/AH+iXtVPI\naX4m73o3QogLKRd4+deXeXn5UDoAACAASURBVG/te7axrQ9vpUG5BrZYaitNKzal2RXNePumt2n7\nZVtbaOm4zp4rQeYWc3rPoeZHNfn2z2+5saYJT9Va8/zy59l0xETxuMpArTHO0ab+UvuXbNuL9i6y\n1UACY6/fd2ofDco7Fm6zZ/vx7SiUQ8XKSsUqse/UPnae2Enp6NKcTDxpWzk0nWRWCU+0eIJGFRo5\n2JVLR5cm9kgsC/cudLjHky2e5MP1H7L39F7b2OvXv55tmGOeY9EiOHkSnnjCYdgahWYNS/ZVXsTX\nt3/ttDK+XKxdyEIVWREEMT9s/4Ftx7YxbMkwByUApsCVUopmVzTjpete4qXrXuLr27+2vWkqpWz2\nyx5X9aB4weK5Lr8rKhUz1SOtbRrBvJ2/8fsbtv+MrSa3yvY6F1MveixZcCHVOZfCyvJ/ljNm5RgK\nhBewPays19x5wjg5H2zyoMtzR7QeARi7cuLziRwbfoziBYszb/c8h3nTe07ng84fEK7Cbc1LIGdZ\ny3mGmTOhdGm4/nqHYatpKDk9mTAVxs21bvbJ7e5teC+Pt3jcJ9eyYq1q+tmtn6FfDj2lICuCICVD\nZ3DXj8Z+XadUHYdjnWt3pnwRE55XILwAozuOdnkNa2mDS7Gh+wv70hQJyQlEhkWy+pBzGesLKRf4\n+8TfNLuimcu3v1OJpzw2R281uZXL/9B7Tu7hhmkmwD1rApC9aad2qdpO5zYq38imyMBklhaMKOjg\nMOzboC8fdfnIFn8eFRFlM1M92eJJnmr1lFuZ8ySJiTB3rmkxGRnpcMiqZI9fOE6Gzsg2fDmQPNj0\nQYoXLJ6jgIr8hKwIghT7Us1WR5uVzrU6e3UNq9lo7eHgCm3rc00fAPad2keh1wvxwJwHnOb0nNmT\nmM9jSEhOcAo9BNPQPTtW/buKr7d+bds/dv4Ydce7T4ZbcM8C2/aBMwecVgWuHNzg2NRkcrfJDklI\n1hj1zrU780HnD/JfL+eFC+HCBbjLOZnK6iOwKthqJao5zQkWwlQYd119l8sM8VDA7adWSlVRSk1X\nSq1SSj2nlIq0O+ac8if4FE9vu9463KylIL65PeeJO/7Emky177TzA35QU9MsfPG+xYBxtLqa9+Ti\nJx32Tz9zmsX3LmbXkMxG5Nd9dR39furHxriNfLrxU6ca/191/8phv0nFJrbtYa2H2cJIrdVAe1/t\nWG3SirVq6611bnUqPGaNQMquM1yeZcYMKFvWhI1mwWoasgYGZF3ZCsGDJ9PQFGAWsA54EPhNKXWb\n1vokELyqPZ+QNX3dnuwazFuxJjLZmzOCAWtM+ZFzR5yO3XblbbaMXDAmoL6znZuG29OmShtKFCxh\nK4r3QrsXGLsqM7qq+RemfaC9WWpsx7Hc39gxUc7+eKnoUvS5pg8TNk7gngb3cF/D+1yaiwA2DdrE\n9vjtdLuym9OxCbdMoHbJ2gxs5typK89z4QLMnw/9+kGE86PEahqy1r7Ky9U58zueFEFZrbW1aehQ\npdS9wEqlVDfwzsWulOoMjAPCgS+01k596JRSdwGvWK65VWt9Tw7kz7d4WhF0ru2dacha4ybYlrtW\nRWDfacpK1gSdkctG2pzCP975I71+yLThDrl2CJWLVeaBJo6mpUHNBjkoAiv23+lz7Z5zKdsL7V6w\nmTTaVG3jleOwVqla1CpVy+WxUtGlLjvzNWhZsMAkkt19t8vD1u/R6ri/nNLlgn/xpAgilVIFtdZJ\nAFrrb5RS/wGLgWxtE0qpcGACcBNwGNiolJqrtd5hN6cOMApoo7U+rZTKp+vnnOOqaxbAUy2f8rpL\nVa1Stfjz+J9BVx7Xqgiy5gscH37cyQ6/IW4DRQsUZWDTgfSs35OMlzIY9cso7m14r9vSANmtgOb3\nme82jj/fPrT9wcyZpqZQu3YuD1tNQ1ZnuSiC4MXTq+IXQAv7Aa31MuBO4C+XZzjSHNirtd6vtU4B\npgPds8wZCEzQWp+2XN/7+gL5HHemoawNPTzxVfev+O6O74KugqW1L4E1b8BK2cJlXX6+cynnbKGl\nSinevPFNj/VhwlQYI1uPdHvcV6UJQppz5+Dnn6FXLwh3/TdpMw3JiiDocasItNYfaK1/czG+WWvt\nXJjDmUqA/evdYcuYPXWBukqp1UqpdRZTkhNKqUFKqVilVGx8fGg0Pne3IrAmYXlD8YLF6dOgj69E\n8hllCpWhcrHKDr2LJ3adCLiPLDmfcj5H93DX5ctdfkDIkZoKW7dChvtcDLfMmgVdu5rQ0fvuczvN\nuiKYuX0mIIogmAm08TgCqAN0APoAnyulnOLrtNaTtNYxWuuYsmWDNxbZl7hbEVhb8eVllFL0rNfT\ntt+uajtb/9cSBUugX9bEj4hnao/M6qlZ2z1mR3hYOOdGnXPqqhaqceJO9O4NjRvD0KGe5x08CJs3\nm6qiGzbAtGlmFbByJTzyiGlD6YasBRFFEQQv/lQEcUAVu/3KljF7DgNztdapWut/gN0YxRDyZHUW\nv9z+Zeb3ubSGHMGINZcAYOUDK52cxGUKlXGoj2Qf2uktRQoUYVLXSdxR7w7bWE4VSo5ITIQDB/x3\nfV+xZw/MtrTW/OQTWLsWDh+G3bvB/vv56SeoWxeaNoX69c1D//77oVUr4yT+5BOPt7HP2obMFYIQ\nfPgzs3gjUEcpVQOjAHoDWSOCfsKsBL5USpXBmIr2+1GmPIN9Nu3mwZsdauLkB6oWz74Wvb0ieCTm\nkUu6T7US1Zh11yzUaOMc9rbNZo65cMG8Ye/dax6cY8eaImxgTDCLFsHDD0PxICj1MWmSCffcudPE\n/7dpY+z8aWkmJ6BFCyPn999Ds2Zm1ZCRYcaOHjVZxNHZV0/N+uDPV4X28hleKQKl1K3A1YAtXEVr\nPcbTOVrrNKXUEEyUUTgwRWu9XSk1BojVWs+1HOuklNoBpAMjLHkKIY3Wmjt/uBOAPwb9ke+UAGAr\nkdG/cX+3c6z1X+DyHyIXn7vI4YTD1CntpwXnrFlGCTz0EKxaZWzojz4KhQrBuHGme9fevfD55/65\nv7ckJcGXX0L37lC7Nrz3HvTpAw0awIMPwsaN5icuzuy//z4UcW7D6Q1ZVwRC8JKtIlBKfQYUAjpi\nIol6ARu8ubjWegGwIMvYS3bbGnja8iNYsC9M5m2oaF4jTIVx5pkzHrOki0UV4/Hmj/ukF2x0ZLT/\nlADAihVQpox5275wAfr3h88+M2/Z3bqZN+offjDmlMgAPiB//NFUCh082OxbfQU1akCUb8OMxRSU\nd/BmRdBaa91QKbVNaz1aKfUesDDbs4RL5mxyZvP06Mh81sDEjuwqoiqlGNclOMpnZ8u6daZfr1Lm\nDfrHHyE9HVJSjBll1iyThbtxI7RuHTg5P/4YrrwSbrghc+yqq/xyK/u6SkUL5LOObPkMb5zF1p6C\nF5VSVwCpQEUP84XL5GySnSLIb52s8iNnzsDffxtFYE94eKYt3VqL5zeniOzcY/16E/kzdCiE+T9g\n0L6JzOy7Z/v9fsKl481fw3xLSOc7wB/AAeB7fwoV6pxJOmPblvoseYANFkuph1BKypQxfXp//TVX\nRHLJxx9DsWKmNlAuYB8+GswlqAXvFMHbWuszWutZmGJzVwHOhVwEn2E1DQ25dkj+K1ucH1m3zpiE\nrr3W87z27WH1apPMldvs3m0qhQ4YAEVzz0xzddmrgeCrdyU44s2/jq2YvdY6WWt91n5M8D1W09Dg\nmMEBlkTwihUroGHD7ENDO3QwjuRNmzzP8wVaO+YEPPssFCxofuci1oKBogiCG0/9CCoopZoB0Uqp\nJkqpppafDpgoIsFPWE1D1jr3QhBz7hysWQM3eVF1pUMHKFAARo3yr69g9WqoVg2uuMKYg1asgP/9\nD555xhSJy0Web/c8ENxNaQRQ7jItlVL3A/2BGCDW7lACMFVrHRDvT0xMjI6Njc1+Yh5l14ldtJnS\nhpOJJ0l4NoGiURJtEbSkpsKQISZkdM0ak3GbHR9+aN7Kk5Nh2TLH6J3LZc8eeP5589CvWhUqVTI5\nDWC2d+2Cwt41NRLyH0qpTVrrGJfHsku5V0r1tPgHgoL8rgiqfVjN1tEp46UMycYMZh58EKZMgWHD\n4N13vT/v/HljSipTxkTy+OLfODnZOKOPHzdlIMaMMaaqhQtNOYkhQ0y+gBCyeFIE3hjuViulJiul\nFlouVl8pJSUc/URiqonWDVNhwa8EzpyBJk1MUtKlVLHMyyxaZJTAM8/kTAmAyTN49lmTU7B8uW/k\nmTjRZC7PmAEffQQlShgFc8st8MUXogQEj3ijCL7ElIKwFn7ZDTzpfrpwOVgrNFYpViWbmUHAM8/A\nli3m4fO//wVamkvnn3+MUvOWQ4dMCGa9evDKK5d2z/vvh4oV4bXXLu18e86cgbfeguuug87eda8T\nBHu8UQRltNYzgQwwNYQwdYEEP2DNG/DUszgo+OADYxsfNsw4Jb/5JtAS5Zz//jOlIGrVMrV2jh7N\n/pzkZOjZ09TsmTXLROJcClFRMGKEceT+9NOlXQNMBnPt2nDsGLzxxqVfRwhpvFEEF5RSpbH0KVZK\ntQTOej5FuFSsBeYUQWwW2rTJKICePc2baK9exhadmJj9ucHCkSOmxeL06XDHHca2fvfd2cf4v/mm\nMel89ZVZEVwOQ4ZAo0amrv/p0zk//9AhkxdQubKJFApk6QohT+ONIngamAvUUkqtBqYB2XSzEC6V\n4xdMt84FfRdkMzOAvP02lCoFkyebMgqdO5s35d9/D7Rk3jNokFkB/Pqreav+6isTYdOpk1EOaWnO\n5yQlmXDM7t2N8rhcIiONnyE+3ihWT4webSKMjhwx+xkZ5jNkZBiznKesZkHIDq11tj+Y4nRXA9cA\nkd6c46+fZs2a6fzK5qObNa+geYVAi+KexEStCxfWevDgzLHz57WOjNR6xIjAyZUTDhww6VZjxjiO\nf/SR1hUrmmOPPOJ83nffmWNLlvhWnlGjzHU3bHB9/K+/rOlhWnfqpHVGhtZDhpj9Tz7xrSxCvgVT\n/t/lczXbFYFSqiDwOPAqMBp4zDIm+JjDCYcDLUL2LFtmsmNvvz1zrHBhY5ZYtixz7MIFU4///fcd\nM1yDgYWW4rl33uk4PnSoMbc8/jh8+qmJDLJn4kSoWdO3sf9gfAVhYTB3ruvj339vjr/8MixZYnIE\nxo83q4iHH/atLEJI4o1paBpmNfAxMN6y/bU/hQpVTidegp04t1m40DRb6dDBcfymm0xv2+PGtMUb\nb5iH6bBh5uEVTCxcCNWrm3LMWQkPN36Pq682kUGjRpny0Tt3mmzggQN9X7mzZElj2lm82PXxH380\n3/dLL0HHjqat5OOPwzvv+CYHQRDcLRWsP8AOb8Zy6ycvmoZS01N1WnqaxzmHzx62mYWC2jRUq5bW\nXbs6j2/bprVSWt99t9bJyVqXK6d1585aV6midevWxpwRDCQlGdPWo496nrd1q9ZXXql1RIQxwRQo\noHWRIlofO+YfuUaPNt/ff/85ju/cae7/8cdm/9w5rbdvD57vU8gzcDmmIeAPS6QQAEqpFjiWnBCy\nIfLVSFpN9lx+4JVfX8kdYS6Hv/+Gfftcx6o3aGBWATNmwM03m5XBww/DyJGm/IK11EGg+eUXY7a6\n9VbP8xo2NJ/34kXTaaxTJ9NhrFw5/8jVrZsxoWU1D82ZY353725+FyliGsnLSkDwIZ6Kzv2plNoG\nNAPWKKUOKKX+wVQedZmmLLhn45GNTmPL9i/j94Mm0sa+dnvQYk0asz6UsjJypHmg/fqryS3o0sWU\nYShXzpg1AukrOHbMZN5+/bV5mHpr54+MNG0d583zb7JWo0amRMSgQaZg3I8/mu/rq6+M2ahKHkgw\nFPIsnp4+XXNNihDlpq9Nxcr9j++nWFSxAEvjBT//DDExJm7dFUqZkNIPPoDbbjOVNsFk3z76qEnA\n6tUr18Tl6FGjgNatg7/+yhx/8kmf9+e9bJQyivazz8z3fP/9EBtrViXTpgVaOiGfk23RuWAjLxad\nU6PNMl6/rF2O29OzXk9uqXMLA5oMyBXZvEZr092qf38TS58T0tOhaVNTCmH37tx7CHfpYkxB7dsb\n007ZsibfoX//4FME9hw5YlYIJ06YiqarVhkntiBcBp6KzuUBe0Te5u8Tf+do/o93/egnSS6TgwdN\n1cxrrsn5ueHhMHasMRv98osphOZvjh0z4Z+vvGLCLvMSV1xh/CorVsBdd4kSEPyOtA3yIwfPHqTe\nhMwyBKsPruZU4qkASnQZWE0rl6IIwLyRFy2a6fz0N9achq551MJZp47xF5SQVqWC/xFF4Edqf1Tb\nYb/tl20ZMMe9yadZxWb+FunSsSqCq6++tPOjoqBNG1ibS11OlyyB0qVNmWxBEDwiisBPpGeku6wg\nuuW/LQCM3zDe6djvA4K4Vs/27cZJfDlvqM2awY4dOS9Ol5hoktP++8+7+VobRXDTTb5P/hKEfIj8\nL/ET32xzXZY5PCyc/87/x9CFpm5fVLhxWi7su5CCEUFcueOvvy7dLGSlWTPjON66NWfnPfusiTp6\n0Mt+SH/8YZTGzTfnXEZBCEFEEfiJ7//63uX4/tP72XVil23/4ZiHiR8RT+faQdxQJD3dvMlfriKw\nmmlyoggSEuDzz832woXe9QyYPdusBPKqf0AQchlRBH5i8T5TN6Z4VHHAvPFXL1EdgN0nd9vmRYZF\nUqZQmVyXL0fs25fZE/dyqFbN9NHdssX7c3791ZiGPv7YmHzcOZsvXjQO6eLF4fXXTWRSmSD/XgUh\nSBBFcAn8sv8Xir5RlNOJp0lKSyJDZ/brXfHPCt5dk9nD9qoyVwFQpEARPu5i4u93nthpOx7U5iAr\nlxsxZEUp0zs3J4pg6VJT5G7gQNNJzJ0imDTJzL31VpOM9emnlyerIIQQoghyyM74ndz49Y2cTznP\nb//+RvRr0QxbnNlUpOfMnoxYOgKAQU0H2cYVikpFKwHwwboPbOOFCxTOJckvg61bjanlcjtygVEE\n27YZc5M3LFtmevFGRZnSFsuXw7lzjnOSk00D+fbt4bvvTFkGd9nPgiA4IYoghzy24DHb9u0zTE3+\nD9d/SOyRWHbE7yAxLTMiplhUMXpc1QOAKsWrULZwWafr5YkaQ5s2GSVQqNDlX6txY2PG2bs3+7nx\n8abEQseOZr97d0hJce7xO20axMXBc89dvnyCEIKIIvCS9Ix0RiwZ4bJ4HMC1n1/L1Z9czfU1rreN\nFS9YnGfaPMPx4cepWrwqlYvlwbdUrY0iaOajHIfGpiezVw7j9evN75aW4rdt2xqF9O67mQXs0tJM\n/4CYGBMuKghCjhFF4CU7T+zk3bXvcj7lvMd5aRmZvW7PJZ9DKeVyJWAlXAV5+YAdO0woZps2vrle\nvXoQEeGdn2DDBmOSsiqhsDDTKGbbNtMsBowpaN8+eOEFKc0sCJeIKAIvSc/wzqadmJppGoqKcC5s\ntuy+ZQ77tUvVdpoTVEydamrd+CoUMyrK1NP3RhGsX28c1IXt/Ci9e5t2kWPGwMaN8MQT0Ly5qXYq\nCMIlIYrAS5LSkryat+rgKuqUqsPiexfzXDtnm/UNNTPr4H9z+zd0rRvEse7nz5tonF69TCE0X+FN\n5FBGhlkRtGjhOB4ZaXwBsbFGAZQoYZrhSAaxIFwyecBTGRwkpyc7ja24fwU1S9ak2ofVHMb3nNpD\np1qd3F7r9qtu54qiV9C3YV+fy+lT3n4bzp6Fp5/27XUbNzYO3g8/NBE/w4YZc5E9e/eastVZFQGY\nXsJr1pguaJMmQcWKvpVPEEIMvyoCpVRnYBwQDnyhtX7TzbyewI/AtVrroGw24GpF0KF6BwCql6jO\ngTMHvL7W7Ltn+0gqH2N1wCoFBw4YRXDPPebN25dYr/fUU+Z3aqqx8dtjdRS7undkpGmAIwiCT/Db\nelopFQ5MALoA9YE+Sqn6LuYVBZ4A1vtLlpySmp5Kyy9aMm7dONtYVkXQvlp723b9so4fq1CkD8Is\nc5tffjEtHB96yOyPHWsUw5sudffl0bq1yfy97jpj2x8zxtzfng0bMvvzCoLgV/xpWG0O7NVa79da\npwDTAVfNbl8F3gK8M8LnAjtP7GR93HqeXPwkqemmguiJiydsx0+MOMHiexfb9sd0GONw/saBrkNM\ng5oXXjDx/VOmwPffGyfxwIH+6ZWrlGnH+Ntv8OWXUKEC3Hij6SdsZf16ExIqTVkEwe/4UxFUAg7Z\n7R+2jNlQSjUFqmitf/ajHNmSnJbM3T/ezQ/bf6DLt11o9Fkj2zGryeef0//YxkoXKu0QEdTsimbs\nGbrHtl+vjA8ycHOTuDjT1/eFF8xD+Z57zGpg+HD/37t0aVPiukULeOYZU1coMRE2b87MHxAEwa8E\nLNRCKRUGvA8M82LuIKVUrFIqNj4+3ue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U2qSUGmQZK6+1PmrZ/g8ob9kOJrmt9Aa+t9sP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\nWyMuy/IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dQlqdd-xkCQ",
        "colab_type": "code",
        "outputId": "06a68fcb-7571-4cd5-e471-8419524a5163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5485,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFcBXaIb3y0c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}