MAIN_Stock_Prediction.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as data "
      ]
    },
    {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas_datareader/tiingo.py:234: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  return pd.concat(dfs, self._concat_axis)\n"
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              "      <th rowspan=\"5\" valign=\"top\">TSLA</th>\n",
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            "text/plain": [
              "                                   close    high  ...  divCash  splitFactor\n",
              "symbol date                                       ...                      \n",
              "TSLA   2017-02-06 00:00:00+00:00  257.77  257.82  ...      0.0          1.0\n",
              "       2017-02-07 00:00:00+00:00  257.48  260.00  ...      0.0          1.0\n",
              "       2017-02-08 00:00:00+00:00  262.08  263.36  ...      0.0          1.0\n",
              "       2017-02-09 00:00:00+00:00  269.20  271.18  ...      0.0          1.0\n",
              "       2017-02-10 00:00:00+00:00  269.23  270.95  ...      0.0          1.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
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      "source": [
        "df = data.get_data_tiingo('TSLA',  api_key=\"3601f79afce502a82f275b485b1e01387c34352d\")\n",
        "df.head()"
      ]
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              "                                   close    high  ...  divCash  splitFactor\n",
              "symbol date                                       ...                      \n",
              "TSLA   2022-01-27 00:00:00+00:00  829.10  935.39  ...      0.0          1.0\n",
              "       2022-01-28 00:00:00+00:00  846.35  857.50  ...      0.0          1.0\n",
              "       2022-01-31 00:00:00+00:00  936.72  937.99  ...      0.0          1.0\n",
              "       2022-02-01 00:00:00+00:00  931.25  943.70  ...      0.0          1.0\n",
              "       2022-02-02 00:00:00+00:00  905.66  931.50  ...      0.0          1.0\n",
              "\n",
              "[5 rows x 12 columns]"
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            "text/plain": [
              "  symbol                      date   close  ...  adjVolume  divCash  splitFactor\n",
              "0   TSLA 2017-02-06 00:00:00+00:00  257.77  ...   17812585      0.0          1.0\n",
              "1   TSLA 2017-02-07 00:00:00+00:00  257.48  ...   21223875      0.0          1.0\n",
              "2   TSLA 2017-02-08 00:00:00+00:00  262.08  ...   19665070      0.0          1.0\n",
              "3   TSLA 2017-02-09 00:00:00+00:00  269.20  ...   39101110      0.0          1.0\n",
              "4   TSLA 2017-02-10 00:00:00+00:00  269.23  ...   18098695      0.0          1.0\n",
              "\n",
              "[5 rows x 14 columns]"
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          "metadata": {},
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        "df.head()"
      ]
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        "id": "2HydK1rrz4Se",
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        {
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              "      <td>262.08</td>\n",
              "      <td>263.36</td>\n",
              "      <td>256.20</td>\n",
              "      <td>257.35</td>\n",
              "      <td>3933014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>269.20</td>\n",
              "      <td>271.18</td>\n",
              "      <td>266.15</td>\n",
              "      <td>266.25</td>\n",
              "      <td>7820222</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>269.23</td>\n",
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              "      <td>266.11</td>\n",
              "      <td>269.79</td>\n",
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              "\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-f699787e-239e-4056-b2d2-5b0303c1f368 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f699787e-239e-4056-b2d2-5b0303c1f368');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    close    high     low    open   volume\n",
              "0  257.77  257.82  250.63  251.00  3562517\n",
              "1  257.48  260.00  256.42  258.19  4244775\n",
              "2  262.08  263.36  256.20  257.35  3933014\n",
              "3  269.20  271.18  266.15  266.25  7820222\n",
              "4  269.23  270.95  266.11  269.79  3619739"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df = df.drop(['date','symbol','adjClose','adjHigh','adjLow','adjOpen','adjVolume','divCash','splitFactor'],  axis=1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
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        },
        "id": "SAvr13R90YfY",
        "outputId": "959f07e9-1b35-4e39-a859-5fec89297165"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(df.close)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "NVTuoNUK1hdz",
        "outputId": "81bfd3fb-6a79-4324-85b8-7e3043bb6925"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bce6f035-2d56-42b2-ae60-7216aaf23b03\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>close</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>257.77</td>\n",
              "      <td>257.82</td>\n",
              "      <td>250.63</td>\n",
              "      <td>251.00</td>\n",
              "      <td>3562517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>257.48</td>\n",
              "      <td>260.00</td>\n",
              "      <td>256.42</td>\n",
              "      <td>258.19</td>\n",
              "      <td>4244775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>262.08</td>\n",
              "      <td>263.36</td>\n",
              "      <td>256.20</td>\n",
              "      <td>257.35</td>\n",
              "      <td>3933014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>269.20</td>\n",
              "      <td>271.18</td>\n",
              "      <td>266.15</td>\n",
              "      <td>266.25</td>\n",
              "      <td>7820222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>269.23</td>\n",
              "      <td>270.95</td>\n",
              "      <td>266.11</td>\n",
              "      <td>269.79</td>\n",
              "      <td>3619739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1253</th>\n",
              "      <td>829.10</td>\n",
              "      <td>935.39</td>\n",
              "      <td>829.00</td>\n",
              "      <td>933.36</td>\n",
              "      <td>49036523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>846.35</td>\n",
              "      <td>857.50</td>\n",
              "      <td>792.01</td>\n",
              "      <td>831.56</td>\n",
              "      <td>44929650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>936.72</td>\n",
              "      <td>937.99</td>\n",
              "      <td>862.05</td>\n",
              "      <td>872.71</td>\n",
              "      <td>34812032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>931.25</td>\n",
              "      <td>943.70</td>\n",
              "      <td>905.00</td>\n",
              "      <td>935.21</td>\n",
              "      <td>24379446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>905.66</td>\n",
              "      <td>931.50</td>\n",
              "      <td>889.41</td>\n",
              "      <td>928.18</td>\n",
              "      <td>22264345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1258 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bce6f035-2d56-42b2-ae60-7216aaf23b03')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bce6f035-2d56-42b2-ae60-7216aaf23b03 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bce6f035-2d56-42b2-ae60-7216aaf23b03');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       close    high     low    open    volume\n",
              "0     257.77  257.82  250.63  251.00   3562517\n",
              "1     257.48  260.00  256.42  258.19   4244775\n",
              "2     262.08  263.36  256.20  257.35   3933014\n",
              "3     269.20  271.18  266.15  266.25   7820222\n",
              "4     269.23  270.95  266.11  269.79   3619739\n",
              "...      ...     ...     ...     ...       ...\n",
              "1253  829.10  935.39  829.00  933.36  49036523\n",
              "1254  846.35  857.50  792.01  831.56  44929650\n",
              "1255  936.72  937.99  862.05  872.71  34812032\n",
              "1256  931.25  943.70  905.00  935.21  24379446\n",
              "1257  905.66  931.50  889.41  928.18  22264345\n",
              "\n",
              "[1258 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yc1AB8Bd1zyA",
        "outputId": "75ac28d8-42e1-47ee-8ea7-51824fbf6e43"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            NaN\n",
              "1            NaN\n",
              "2            NaN\n",
              "3            NaN\n",
              "4            NaN\n",
              "          ...   \n",
              "1253    964.4807\n",
              "1254    965.4150\n",
              "1255    967.2435\n",
              "1256    969.0074\n",
              "1257    970.7013\n",
              "Name: close, Length: 1258, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#moving average \n",
        "ma100=df.close.rolling(100).mean()\n",
        "ma100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "uYD5aARE18p_",
        "outputId": "db6f5540-6907-4fcf-f338-cb99d42d460e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff9f591ad10>]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(df.close)\n",
        "plt.plot(ma100, 'r')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXCNK6Hw2hj2",
        "outputId": "95e586a7-f395-4410-f0a5-0fe8d5004f2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            NaN\n",
              "1            NaN\n",
              "2            NaN\n",
              "3            NaN\n",
              "4            NaN\n",
              "          ...   \n",
              "1253    964.4807\n",
              "1254    965.4150\n",
              "1255    967.2435\n",
              "1256    969.0074\n",
              "1257    970.7013\n",
              "Name: close, Length: 1258, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "#moving average \n",
        "ma200=df.close.rolling(100).mean()\n",
        "ma200"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "SceMdNEa2kxK",
        "outputId": "cad31664-a153-4322-e422-eeef8a8e2fa7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff9f588ce10>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xehxz3fRXxy5/ACCzi5lIEQzMREREIcRsrV1h7pAahw6pcR7HnCPmvI2g4xxmImWxh5mIiChEyLLsaMloOPE6K8z1nssCM5EiWGEmIiIKEd2eXgWzTUKiY95yfRKjtdCoBDReWjJYYCZSFivMREREIUBvtMBskwAAbZJjGjxfpRJIjY/iHGaiJsDATEREFAIOn9O7HvsSmAEgNU7ndaycYBMzkaLYkkFERBQCThVXT8Fo4bihryGPXXlRvT3MnMNMpAwGZiIiohBgcbRjdEqLQ4/MRJ9ec2WPDK/HWWAmUhZbMoiIiEKA2WYvBy+5fwiitWpFrsk5zETKYGAmIiIKAVZHhTmqjl3+/MECM5GyGJiJiIhCgNVRYfa2EUmg2MNMpAwGZiIiohBgkewVZm9zlf3FHmYiZTEwExERhQCL1V4OrrktdjBYYCZSBgMzERFRCLBKElQCUCtQYWYXM5GyGJiJiIhCgMUmK9q/DAAym5iJFMHATEREFAIsNglaRarL7GEmUhoDMxERUQiw2iTlK8yKXo3owsXATEREFAIskqzYDX8sMBMpi4GZiIgoBFhtErRqhaMuS8xEimBgJiIiCgH2m/6U6mFmjZlISQzMREREIcB+05/SPcwsMRMpgYGZiIgoBFht7GEmClUMzERERCHAYpMUa8lw4hhmImUwMBMREYUAi6TcxiVsYSZSFgMzERFRCLAquHGJEyvMRMpgYCYiIgoBFpukYA8zS8wUPG6tXo2BmYiIKAQoOVbOiXGHAvXE13sw5tUNDM0ODMxEREQhwCopWGFmgZmC9Olvp5FTZMDuM2XNvZSQwMBMREQUAqw2GRrFe5hZHST/FRvMrseniiubcSWhg4GZiIgoBJgV7GEmClSJwYz+L6xxfa03WppxNaGD/2cSERGFAJNFgk6r9E5/RP7JKTJ4fK03WoO+ZrHBjCeX7oXRYgv6Ws2FgZmIiCgEGC02xGjVilyLPcwUqKgan3JUKBCY//PjESzeegpf/54b9LWaCwMzERFRCKhSMDA7sYWZ/GWyVleB1SqhSEuG2tGbX2kOPnw3FwZmIiKiZibLMowWG6KVqjBzDjMFyGiRXI9bJUYr0pLh/LlmSwYREREFzGyTIMlATJSyFWZ2MZO/3ENtQrQGepMCgVnjDMxSA2eGLgZmIiKiZmY024OEUhVmQIYEjgMj/1U5AvOUfm2QGK1VpCUj2nEzaxUrzERERBQoo6NvNFqhKRmfHVyA0zE3Y9aaMfgt9zdFrkkXBmeF+Y9jL0KsTg2DyTPkrjtUgINny/26prOHmS0ZREREFLAqsz1IKHXT3295GwEAlVY9rvjwCqw/sV6R61LkM1qdn3aoEKVWwWKrbqOQZRl3frANE97Y5Nc1rZK9NYgVZiIiIgqYM0goFZj3F+1CrG0oXh31HTokdcDkzybjUNEhRa5Nkc3k+FnUadWI0qhgdgvMhXqT6/GOk8V45NOdsEkN98lbHCHcxB5mIiIiCpTzo2olephLqkpwuvwEoqQuaBHdEiunrUSUOgpXfXwVckpygr4+Rbbqn8XaFebD+XrX4xvmbcGy3Xk4bzDVukZNzmtwrBwREREFrErBwLzz3E4AQJTUBbIMZCVn4ftp30Nv1uPyhZez0kz1MlokCGHfwESrVsFsrQ7MuSVVAKp7kgGgrLLhmwLNNnsVWokRdc2FgZmIiKiZOat6SoyV2563HQAQJXV2Heuf2R8b7tgAm2TDiIUjsCd/T9DvQ5HJueOkEAJajYDFVt1ycd5gBgBIbjvilFU1HJidFWYGZiIiIgpYpYI3/W3N3Yp2iVlQIwmy2xzmXi17YeOdG6HT6DDqg1GcnkFeGa3VG+ho1SpX/zEAnK+wB2b3HST9CczlCoyoay4MzERERM2sxFG5S4nTBn2trWe2ok/LAV6fuyj1Imy6cxNSYlIwdtFYhmaqxWCq3qK95k1/xV76lQOpMBeUG8Oun5mBmYiIqJkVOSp3KbFRQV0ntzwXufpcV2CWvQwwyErOwsY7NiItNg1XL74a2eezg3pPiiznDWakxdt/Dmve9HfeYIZO4xkdS33pYbbafxArTFbYJBmD/rkWt723VcFVNz4GZiIiomZWbDAjOVYLrTq4v5a35tpDSN9WA+s9r01iG6yevhoAMP7j8civyA/qfSlyFOlNSI3XAbC3ZEgyXKPjCvUmDOuShqSY6k9C6qsw7z1Thu0nij1Ct/P8XadLG2P5jYaBmYiIqJmdN5jQIi646jIA/HrmV0Spo3Bx2iUAgPom5HZN7YoVt61AviEfExdPhN6kr+dsulAUVZhcFWbnL3Bmq4TD5/Q4dE6PAVkpHr32e3PL6rzWlHm/4MZ3tmDPmepwfKo4PLdsZ2AmIiJqZkUVZqTF6YK+ztbcrejbqi906mifzh/UZhC+uOkL7D63Gzd+cSPMNnPQa6DwJUkyig1mpLkqzPbxcWab5ArGE3plIjFGAwAY1S0dP2cXeVSQ3TknbJw4Xx2SD/m5rXaoYGAmIiJqZqWV9paMYFglK7bnbcfgNoNdx2RvTcw1TOw6Ee9d+x5+OPYD7ll2j0+vochUVmWBVZJdLRlRjn5li01CUYX9hr/0BB3mTb8Utw/pgOFd0mC2Sag01d7y2mS1QVSPa3b1Pv9+qqSR/xSNg4GZiIiomRlMNsTrNEFdY3/BflRaKjGk7RCPoOKLO/vdiReueAEf7fkIT6x9Iqh1UPhyhmL3m/4A4O11R1GkNyFaq0JclBqd0+PxwvW9kBBt/5ldtOUE7l203eNaZ0uNkGXg6t6ZAACTVYJaJfD7KXt7hsrPn9HmFtz/nURERBS0SrMVcUEGZucNf4PbDAZqF/wa9NTlTyG3PBcv/fIS2iS0wcODHw5qPRR+nNNa0t1u+gOAhb+cQLeMBKTF6yDcfhuLjbL/zL665ggA+ycazucLHeF7fK9WWLH3LACgTXIMjhZUAEDQP+9NjRVmIiKiZmYw2xCrC27Tkl/P/Iq02DR0SumEQIp3Qgj8d+J/cX336/Hoqkfxxf4vgloPhR9XhTnBEZjdRsgdzte7epudan4q4tyABwAqHDOX26XEALBvp90yofr1UWpVWLX/MDATERE1I4tNgtkqIS4quIrbr2d+xaA2gzwqgP7mEbVKjcVTFmNou6GYvnQ61p9YH9SaKLycdwTm1DhnS4bnr141A3Nsja3cDabqzUgqHI/jdRps+L9R2PSXKzz69M8bzHh22X7lFt/IGJiJiIiakfOGqZrhwx8FhgIcLDqIy9tfDgAeodlfMdoYLJu6DJ1TOmPSkknYXxA+oYaCU1RhhlolXBvo1Pw5cvY2O9Vsq9B7CcxxOg06pMahdXIMpBq/wC3aclKppTc6BmYiIqJmZDBXV+ICtfHkRgDAqKxRHsfleicx161FTAusmr4KMZoY3PrVrTBajQGvjcJHXlkV0uN1UDnuyKsyezbD+1Nhdj6Oj67+ua5r/Fw4YGAmIiJqAofP6bFq39laxysdgTk2iMC87vg6xEfF49LMSwEgoB7mmtontcfCSQuxr2Afnv7paQWuSKEsp7ACX/+ei64Z8a5jFW4BGKhdYa75S577+XpHD7N7q5Hz8YzLOtT5HqGqwcAshFgghCgQQuxzO9ZCCLFGCJHt+HeK47gQQrwphDgqhNgjhOjv9pqZjvOzhRAzG+ePQ0REFJrG/2cjHvj491rHDY6WjLggWjLWn1yP4e2HQ6v2nOUc7D1VE7pOwKwBs/Daltew7vi64C5GIe3JpXsBeAbcnq0TPc5JjvUMzDV/yfvlaJHrscFkRWyUGmq3+XF/v74nHh7dBc9e2xOv3dwHgH277XDgS4X5AwBX1Tg2G8BaWZa7Aljr+BoAJgDo6vjnPgDzAHvABvAsgMEABgF41hmyiYiILiRSjUZO50fXsQHe9FdgKMCBwgMY1WGU61gQLcy1vDLuFXRN7YqZ38xEqbG04RdQWMpItO8OecewLNexfu1TsP/58bhrWEcAtX+uYrWev+S9ve6Y67HBy6jElgnR+PO4bo6JGfb3KygPj3afBgOzLMsbARTXODwJwIeOxx8CuN7t+CLZ7lcAyUKITADjAayRZblYluUSAGtQO4QTERFFvAqzFXvPlGHtwXzklVZ5TBMIxNqctQBq9y8DwVeYASBWG4uPJ3+MsxVn8fD3nM0cqbRqFVonRWNIp1SP43E6Df487iL86cqLMKFXpsdzKpVAn7ZJHsesjj5lvdFa7890umPEXEEEVZi9yZBl2dmIdQ5AhuNxGwCn3c474zhW13EiIqILSnmVBXd/uA13f7gdQ//1E9YcyAcAJMUEtjX2siPLkB6bjgGtB7iOCUW6mKsNbDMQs4fNxsd7PsaW01sUvTaFBrNVgk7rvS0oTqfBI2O6urbKdvflrKHY8H+jXF87e5cNpvoDs3Mm89KduUGsuukEfdOfbJ86rdjkaSHEfUKI7UKI7YWFhUpdloiIKCQ8+MnvHlW1A2fLAQQWmM02M1Zmr8S1F10Ltap22FFyW4jZw2cjMz4Tj61+DJIcvtMOyDuT1ebaCtsfWrUKHVLj8OpN9p7ksioLAPvNfHH1bMbjnMn806GCsJieEWhgzne0WsDx7wLH8VwA7dzOa+s4VtfxWmRZfleW5QGyLA9IT08PcHlEREShac+ZMgDASzf0BgDsz7MH5oRo/1syNpzYgHJTOSZ1n+RxXMkeZqe4qDi8NPYlbM3divd2vKf8G1CzsleYA6+jOn/hqw7MNsTr6v4lUAiBP195EQD7py6hLtD/MssAOCddzATwrdvxGY5pGUMAlDlaN1YDGCeESHHc7DfOcYyIiOiCdNOl7Ty+Vqn8T7nLDi9DjCYGYzuN9fq80lsPT79kOq7IugJ//fGvyK/IV/Ta1LxMVimgCrNTkqNi/MHmEyg3WhwtGfVPfmnbwr5tdrmxerRcqG6X7ctYuU8BbAHQTQhxRghxN4B/AbhSCJENYKzjawBYCSAHwFEA7wF4EABkWS4G8AKAbY5//u44RkREdMH54oHLAgrI7mRZxreHv8W4zuMQq41VaGX1E0Jg3tXzUGmpxOy1sxt+AYUNpSrMS3fm4qXvD6HCZPXYtKS+1zir0mtz1uKy/12G7XnbA15HY2nw8x9ZlqfW8dQYL+fKAB6q4zoLACzwa3VEREQRIkqtgtnRq5lSY55tIHad24XT5afx3Kjn6jynMWp13dK64bEhj+Hfm/+N+y+9H0PaDmmEd6GmZrJKAbUFObn/TBstkqOHuf7rJUZrIcOKBbvm4sdlH+Ng0UG0S2yH85XnA15HY+FOf0RERE3BraCcGGMPEjV3TvPHt4e/hYDANRddU/utGqGH2d3TI55GZnwmHv7+Yd4AGCHMVgk6TeCb5zjHxAH2fnyzVUJ8A7PFC405OKt7BG/9/hRaxLTA3Ilzkf1wNsZ3GR/wOhoLAzMREVEj25RdCLO1OlgmRts/iv75r6Px059H4rcna31o26Clh5ZiaLuhaBnXsu6TGqkdNEGXgH9f+W9sz9uOhTsXNs6bUJMyWW1ex8b548c/jQAAnC2rAoB6WzLWHFuDm76+AjZRjsf6vYtNd27CrIGzoNPo6nxNc2JgJiIiamSzamyJrXMEk2itGp3S49HSscuarw4WHsSe/D24pectXp8XjV1iBjCt9zQMazcMT6x9gjsARgB7hTm4WNilZQK6toxHbqk9MNfVkrEjbwcmfzYZnVI6IdP4H3ROuKJJfmaDwcBMRETUyHq1SfT4Othw8Nn+zyAgcGOPG+s9T26sEjPsf4a3JryFosoiPLvu2UZ7H2oaJqsUdIUZsM9X3pfrGJXoJTDnlORg4uKJSItNw+rpq5CV0g4vrz6M8a9vxO3/24rNR4uCXkNjYGAmIiJqZPE6Dbq3SsC/pvTG7UM6BHUtWZaxZN8SjMoahcyETK/nNFWtrl9mP9x/6f14e9vb2Fewr4nelRqDWaHAvO1EietxSpxnj36hoRBXfXwVrJIVq6avQmZCJoZ3SQMAHM7XY1N2EW57f2vQa2gMDMxERBRxQm2Wa4XJisQYLW4d1B4vXN8rqGvtzt+Nw+cP49ZetzZ4blP8Z5gzeg4SdYl45PtHQu6/O/nOZAvupj+nO9owuwwAACAASURBVIZmuR6nxVf3I1daKnHtp9fidPlpLJ+6HN3TugMAhjkCszubFHo/RwzMREQUUVbtO4eOT6zEmZLK5l6KS4XJivgGRmz5asm+JVALNaZcPKXOc5qyHTQ1NhVzRs/BuhPr8OWBL5vujUkxsiwrVmF+7rqersfOKTBWyYpbv7wV2/K24dMbPsXQdkNd51zWObXWNZw3DYYSBmYiIooo3+87CwD4JYR6IQ0mW4MzaX1htpnx4e4PMaHrBKTF1q7M1dRUdbr7L70fPdN74oWNL7DKHIZMjgkuwd70V1NitBayLOPBFQ9i+ZHl+O+E/+L67td7nJMWr8O0we09jp0qDp1fdp0YmImIKKK0SbZvt/vXr/Y2+0e7J4oMqDLboDcqU2FeenApzlWcw4MDHqz3PNFkXcx2apUajw99HHsL9uLHnB+b9L0peIfP6QEAHdPiFL2uSiUwZ+McvPf7e3hy+JOYNXCW1/O6t0rw+PpMMSvMREREjco9I+eVNt9fvLIsY9Qr6zFjwVYYTFbE64LvD527fS46pXTyeWOHpiz2Tu01FRlxGXjrt7ea7k1JEbtO28cC9mmXrMj1HhjZGWMvzsCCnQvwzPpnMKPPDMwZPafO8917p9+Z3h+T+7dRZB1KYmAmIqKwZbTY8My3+1BWaXEd0xurHxvM1uZYFgCgymIDYJ8aUGWxIV6nDep6e/P3YuPJjZg1YBZUov6/vptjpK1Oo8Nd/e7CiuwVyC3PVeSaRouNLR5N4GhBBRKjNWid5N888LrMntAdl/XejfuW34fxncfj/Wvfr3eUok5b/fN8UUYCtOrQi6ehtyIiIiIffbHjDBZtOYnXfzziOlZhqg7JBlPzBeaKGu+dEhdcYJ63fR6iNdG4s++dPr+mMecwe3NP/3sgyRIW7Fzg1+tOF1fixe8PQnL7eGDVvrPo/rdV+G7PWaWXSTXkllahTUqsIpuH2CQbHv/hcdyz/B6M6TQGX9z0BbTq+n/23Xuno7XBfxLTGBiYiYgobFlt9puV3KuQemN1UK0w2Zp8Ta73NnoG5k5p8QFfq9xUjo/2fIRbe92K1NjaUwVqaq490zqldMKVna7E+zvfh03y/b/9I0t2Yv6GHBzO17uOrTlQAADYknMem48W4asdZxRfLwGnzlfieJHB1fsfjCpLFaZ8PgWvbnkVfxj4B6y4bQUSdAkNvs69JSOGgZmIiEhZzoKke2VMb7SghWPDhFCqMHdpGXhg/mj3R6gwVzR4s19NzdHNcN+l9+FU2Sn8cOwHn19TZbaHa/f1FuiNAIAj5/S47f2t+PMXu1GgN+JcmVHR9V7IbJKMES+vcwTm4NoxDGYDrvn0Giw/bJ+G8dbEt6BR+Xajq/s4u5goBmYiIiJFOSvL7p8k641WZCTa//KvGVqbkvO9B2alAAAyEnX1nV4nWZYxb/s8DGg9AAPbDPTtRc1VYgZwXbfrkB6bjoW7Fvr8GsnxfTRZq6vShXoTACC7oMJ1bNA/1mLIi2sVWinll1f/8jGuZ6uAr1NhrsDVi6/G+hPrsWjyIjw06CG/Xu/ekqH0aDulhOaqiIiIfOCsSLqPUdMbrch03LxU2ZyB2dGS8ey1PXHiX1cH3B/686mfsb9wP2YN8D6Sqz7NcbtclDoKt/W+DcsOL0NJVUnDL0D193Hy3M14f1MOfjlahCOO9oyyKkut870dI/+ddsw7XnjnQK877vnCKllx4+c3YtOpTfh48seYfsl0v6/h3pKhRB91Y2BgJiKisOWsTKo8KswWV4XZYG7GHmZHWA92/vK87fOQpEvCLT1v8fk1TT2HuaYZfWbAZDPh8/2f+3S+5NaLMWfFQUx7fyskGeic7n0u8L7cMkXWeaE7XWIfu9gxNbD5y7Is49HvH8XqY6sx/5r5mNp7akDXcZ+SEapCf4VERER1cMYslSMxy7KMCpMVqXFR0KpFs7ZkOPung9nhr8BQgC8PfImZfWYiLiqAUNNMI9n6teqHnuk9sWjPIp/O97bK/u2T8ciYrq6v/z6pJ1Y8MhwAcN5gVmKZF7xTxZUQAmgd4A1/b/32FuZun4v/G/p/uKf/PQGvI1TbMNyF/gqJiIjq4KxMOuuplWYbJBlIiNYgTqepNamiKVSarbDYJJQ73jshOvDAvHDnQlgkC+4fcL9fr2vuT7WFEJjZZyY2n96Mo8VHGzzfW67/+sFhGJDVwvV1RmI0WibYPzkorWRgVsKZ4kpkJkZ73HTnq9VHV+Ox1Y/h+u7X419j/xXUOgJ5/6YW+iskIiKqg1xjSobeFVK1SI2LwnmDqYnXI6Pns6vxh8W/I7e0Csmx2oDnytokG+bvmI+RHUaiR3qPwNYT0KuUMe2SaVAJFRbtbrjKXHNzEmcPeqpj2gkApMRGITnWPs+3mBVmRZwuqUTbFrF+v+6s/ixuX3o7eqb3xMeTP25wI52GJETbv6+Pun2iEGoYmImIKGw5N7pw9jBXmOw3g8VHa9AyIRr55Y0bmE8XV2LAnB+x50wpZFnGztOlkGVg9f58LN56Kqjq8mf7P8Px0uN4eNDDfr82FG6bap3QGmM7jcVHez6CJEv1nmt127DktZv7YMl9QwB4bmKRHKuFVq1CQrQGq/adw54zpY2z8AuELMs4cb4S7VL8C8ySLGHGNzNQYa7AZzd+FlirUA3xOg0O/v0q/HEsAzMREZHiLM6NSxxfu7dBZCTqXLN8G8u2E8UoqjBh2vtbseZAPqbM3ezxfKAzg22SDXM2zkHP9J6YfPHkgNfX3LtKz7hkBk6UnsDPp36u9zz3edlT+rdFBy83oTmry3qjFYfO6THtva3KLvYCs+5wAQr1JlzWueGNcNy9/MvL+DHnR7w54U1cnH6xYuuJiVKH7IQMgIGZiIjCmMkRmJ07/r2+xr5FdmK0Bi0T7RXmmh/3B6vCZEVRhb1ybbTY31dvtOK+j3YAAEZ3b4n9z48HALQL4ONuAPhg1wc4WHQQz416LqCPu0MleEy+eDLio+Lx4a4P6z3PYLJBoxKuyrI3yTFRHl/rTVbFv7cXkn99fwhZqbG4rk9rn1+z+fRmPL3uadzU4ybc3e/uRlxd6GFgJiKisGVyBFaLzR6cNmUXAQBS43RomaCD2SqhtFK5mb3/+v4Qej27GgPm/AgAKK7RIx2tVWHutP6I02mw8M6BWHTXIL/fw2A24Jn1z2BI2yG44eIbglpvcwfKWG0sbupxE7448AUqLZVezzFZbTDbJDx25UUY0qnuaqfzxrD3ZgzAUEdV9Lll+5VfdISTJNkx57oCM4dm+XzD3amyU5j82WR0SOqAd699N2R+KWsqDMxERBS2zDZnYJYgyzI0KoGBWSnISotDxzT7x/o5RRX1XcIv72w45npcbrSgqMLs0af83cPDXX23V3RribZ+9ocCwOu/vo48fR5eufKVgENJKEWZGX1mQG/W49tD33p9vqKBaSLO76PTlT0y8K8plwAAlmw7DZvEKnN9ckurMPqV9ThWaP//YMEvxzHtfXs7y6COLep7qYvBbMCkJZNgtBqxfOpyJEcnN9p6QxUDMxERhS2z1dmSYZ+/bJVkjL04AwDQpWU8AOBogXKB2d3OU6X4YPMJVJptmDetPz64cyC6tEwI6poFhgK89MtLmNx9Moa1Hxb0GkMhSo7oMAIdkjrgw93e2zIa2uBl+cPDse2psR7H2qfGYt60/jBZJazce1bZBUeYb3flIqfIgIlvbMKPB/Lx5tpsAIBWLdAjM7HB10uyhJnfzMSe/D1YcsMSRfuWwwkDMxERhS2TtbrCvGKPPTilJ+gAAG1TYhGlViGnyNAo7/3RlhMAgK4t4zGhdyZGdWsZ9DWfX/88qixVeHHMi0FdJ5Q+LVcJFW6/5HasyVmDPH1ereedowDr2uAlXqdxfU/djevZCplJ0Vi1/5yyC44wxRX2EXwmq4R7Fm1HudGKG/q3xU9/HuXTJxjPr38eXx38Cq9c+QomdJ3Q2MsNWQzMREQUtsxW+9bXe3LLMPvrvQCqpymoVQLx0RqPCQxK2nikCCoBfHb/ZYpc73DRYczfMR/3X3o/uqV1U+SaoXJP3O19bockS1i8d3Gt55wV5gQ/d0RUqwRaJuiaZXOacHLonB6A/Re7Li3jsfqPI/DqzX18uiH1i/1f4O8b/467+t6FPw75Y2MvNaQxMBMRUdhytmS4t11c7PYxc7RG5boxUAmd3PppzTYJo7u3RFKMNujryrKMx9c8jhhtDJ4Z+UzQ1xMh1cUMXJR6ES5rexkW7lpY60ZEZ+CND2BmdZwu+F+IjhZU4Kmle12TViKBJMnYcbIENknGoXN63HhpW6z500j8+KeR6NbKt7ahUmMpZq2YhSFth2Du1XMvuJv8amJgJiKisGV2CzlRahWO/XMiMpNiXMd0WjWM1uCC0IkiA77cccb1fte6jeGa0r9tUNd2em79c/juyHd44YoXkBGfocg1gdDoYXa6t/+9OFB4ABtObvA4rndsNlNXS0Z94nQaV4U6UH/5cjc+2XoKB86WB3Wd5rbmQD6e+Hov9pwpxcQ3N+GGeZvx5tpsFFWY0N3HkOxuzsY5KK4qxryr50Gnqd0Sc6EJfAsiIiKiZuZePW6TEgO1yrMKptOoYLTY8P6mHGzMLsKHdw70u1J21wfbkFNkQPdWCag025AUU/1Xp/vWzYE4XHQYs9fOxjeHvsEdfe/Ao4MfDep6LiFYDLy11614fM3jeHvb2xiVNcp1PLekCgDQKjHa72vG6zQwmIMLzFq1vXZYXmWFJMkw26SAtzNvLuVGC+77aDtkGfjlaBFOFdtH+L3huMFvWJc0v66XfT4bb259E3f1uwt9W/VVfL3hiBVmIiIKW+4V5vE9W9V6XqdVY82BfMxZcRAbjxSixM+ZzEaLzXXT4PrDBag0WxEXVR2YA2kjcDpRegKXL7wca3PW4onhT+B/1/1P8Y+9m3sOs7sYbQzu6nsXlh5citzyXNfx7IIKtEmOCbDCrMbp4irc9cE2mBz97P5yjrN7+YfDeODjHej+t1U4VlgR8PWaQ3a+3tWvfqq4EsmxWsQ4Qv/tQzp4tCn54i8//gU6jQ5zRs9Reqlhi4GZiIjCltmt3eKvV9W+US66xqYMx/2cyXzYccMUAKzenw+jRUJMVHX1MTE6sP7lclM5rll8DSySBb/d+xv+OeafAe3oV5dQbTedNXAWJFnCuzveBWAP9AfyytHZMQLQX86Q/dOhAo/vlT+c4+x2ny7FDwfyAQBjXt3gGr8WyirNVtwyfwumvmufq5wWb//EY+60/qiy2AP/6O7+TW9Zd3wdvjn0DZ4c/iRaxdf+JfRCxZYMIiIKW86xci/d0NtrdbbmR+vHiypxaQffNmsAgP151X2te3PLAMCzwhxAVdQm2TD1q6k4VHQIq6avQve07n5fI1x1SumEiV0n4t3f38VTI57ClmNlyC6owB3DsgK6Xrzb9yLQGx3Nddzs93vuIby1dQ1ySnJwRn8G+RX5SI5OxmVtL8OYTmNwaealUKuat3Xjs22nsfV4MQDgsk6peHFKbxzJ12No5zS0TopGXpkRw7v63o5RbirHPcvvQVZyFh677LHGWnZYYmAmIqKwZbZKuKF/W9wysL3X53WOCnN6gg7FBrPfFeb9eWVIiNa4ZgUD8JiKEUhLxuM/PI6V2Ssx7+p5GNtpbMMvCECIFpgBAA8NfAgTF0/E1we/RknRQADAdW43UvrDvY1jw5ECZCTq0NLPXmi90YpL2iahTXIMVu7LQ6VqC/Sab7Dk9EEsOQ3EaePQNrEtMuIzcLT4KJYfWQ78BCTpkjAyayTGdByDMR3HoEd6j0abJHGssAK3v78Vb0zth4FZ1b/w7cstR3qCDr89OQYAIIRAlmOSy1cPDoXeaHX1aDdElmXc/939OFl6Ehvu2IBojf895ZGMgZmIiMKWySohSlN3IHBWmBOjNYjXaXCiqNKv6+/PK0ePzERXFQ8AumRUtw/4GkacPtj1Af6z9T94ZNAjeGDAA369NhAh1MLsMr7LeHRO6Yy3t72NYYlvIy0+CgkBtra4V/hf+eEIFm05id+e8u+XkAqTFUkxWlTIh3BW9xQsqpPQSK1wScIfsGT6o+iW1gkqVfX3ucBQgJ+O/4S1OWux9vhaLDu8DAAwssNIfDLlE7RJbBPQn6Wms2VVmL8hB5/+dgoy7L8c/u2bfVj1xxEAgJ2nSrApuxDdWyV4DeqZSTHITPL9/RbuWogl+5ZgzhVzFNllMtKwh5mIiMKW2WpzVZG9cT4Xp9MgKzUWx/3c9S87X1/rhqmuAfbbbs/bjge+ewBjOo7Bq+NfDegavgrlmbkqocKsAbPw86mfsevcbrT3YQONutTcfKNAb3I91hstGPf6Bmw7UVzzZR7Kqyw4ZFiAT4/PRFy0CZ9MXox7L1oGS+nVmPDaYXzy22mP81vGtcStvW7Fe9e9h5xHc5DzSA5eH/86dpzdgX7z+2HFkRUB/3lcf45yI4a/tA4fbD4Bk1Vy9eo7//36miOYPHczCvQm9G7jRyquw+f7P8dDKx/C6I6jMXv47KCvF4kYmImIKGyZrFK9gdlZYY7RqtEhNQ6ni32vMJutEgxmm+tGKgDolB4XUDX0rP4srl9yPTLiM7DkxiXQqJrmA145pCYxV7uz352I1kRja8Gn6O7nBAd3l7T1HhYlScaVr23EkfwK3PTOFqyuY/tsi82CvYaXsb3kXUztPRXHHzuI2y6Zio5p9hGCALDkt1P1rqFjSkf8ccgfse3ebWid0BrXfHoNHl75MKosVQH/uQ7n62GTZGSlVv9C0KVlPM6WGSHLMj7ccsJ13N+b+tyVGcsw85uZuOXLW9C3VV8suWFJs/dlhyoGZiIiCkuybJ+ZW39Lhv252Cg14nRqVFp8HxXm3BDD/WP/pQ/aP6pe+cjlWHzPYJ+uU2WpwqQlk1BqLMXyqcuRFuvfTNxAhGp9eeq7v+KKV9ajRUwLjOkwBWXiJwzuFPimGHE6DSb2rj3JIbe0CufKja6vv9juWSWuMFkx5/stGPjuCBTKKzG27QNYdP0iJEcnAwDGXFy9eYzJx41vuqd1x9Z7tuKxIY/hv9v+i0HvD8Le/L2u540WG4w+/vzlFNo/CXlvxgAM65KKdY+Pwq0D26HKYsOeM2UorbTgnuEd8fx1PXFphxSfrlnTppOb0OedPvh4z8d4ZsQz2HjHRqTHpQd0rQsBAzMREYUlqyRDlu07/NVFp7FXy2KjNIjWqGGTZFh83AJZb7TPbE6I1sK5H0qi4ya/Hq0TMdSHzSBkWcZdy+7C9rztWHzDYlyScYlP762UUOth3pJz3tUWM7jlbZCFCXtKvg3qmnOnXerxtU2Ske8WlgEgscb25e9sXo3nfr0Oe/J3Is38OB7o+5RHG0uftsmux76GXADQaXR4bfxrWDVtFQoNhRj43kC8tfUtyLKMm+dvwfCX1rnOPXnegGe+3ef15zG7QI94nQZdWsbjk3uGoGNaHFol2W/Cm/T2LwCAfu1TMHNolt/tNwazAX9Z8xeM/GAkNCoNfr7zZzx/xfPQqoPf4j2SMTATEVFYcvZz6rQNV5i1auFqz/A1ADknYyREa7Dikcvxj8m9/A4nczbOwZJ9S/DimBdxXbfr/HptMEK4hdklSuqMaKkn5v/+BoxWY8Mv8FFRhQlnyzyvF+uYnW22mfHMumfw142TAKjQyvRvxNlGoX2qZy+0+46RBeUmVPq5m+D4LuOxZ9YejO00Fo+segTd3hiCrXnrUVhhxC3zt+CbnbmYs+IgFm05ic3Hznu8VpJk/HSwAIM7tvD4eavZ692uRQz8Icsyvjn0DXrM7YGXN7+Mu/vdjV0P7MJl7S7z6zoXKgZmIiIKS86PyuurMHdItY/YOlZocIVno8XXCrOjJSNag4szEzFtcAe/1vflgS/xzPpnMKPPDPxl2F/8eq1SQqnA7L7roNUmIa+0Cp2jZyBXn4ulB5cGde2re2e6HueWVuHhT3cCgKtdY/fpMny1ZysGvz8YL2x8ARclTkCm6Q1EyZ0BABe1TKh1zduH2L/fZpuEK15Zj3nrj/m1ppZxLbF86nLMu3oeckqPokD3NxREPYUNJzdi/sYcaNX2MHy0wHPUYW5pFfLKjBh9sWdvcud0z5tNu7dquPdbkiUcLT6Kl395GT3m9sDkzyYjUZeIjXdsxHvXvYf4qMBuYL0QcawcERGFJWeFOUpT901KI7vZezLbt4iFzs8Ks7OHOZDd/Hbk7cCMpTMwtN1QvHvNu00+tSLQTTwak95UXaXdm1uG30+V4OKUYag0dcT7O9/H1N5TA772W1P7YWLvTDy0+HccL6yehPL2bf1xzVubsOXcl1jx9TtIiY3HK1d8jLdWJkMNoG+7ZHxw50Akxdb+Hr9wfS88d11P3LHwN2zKLsJLqw5h1qjOfq1LCIH7L70fL37ZCnr1KqiTlyK/cjbWF/eEpL0fMjpiz5lSAIDFJkGrVrmq4zUryu4zpz+8uyf2FPyOM+VnkFuei3xDPlRCBYPZgDJTGUqNpThdfhp78/fCYLH/9xjWbhgWTlqIab2nsf0iAAzMREQUlqoDc90V5sRoLdY8NgKtkqKx7nAhAMBk9bUlw97D7O9ufmf1ZzFpySSkx6Vj6S1LodMEflNbsEKph7nQbeTbXR9sg9Um47lre2JL4V3427q/IackB51SOgV0bZVKYGCW/ea3k45JKDMu6wC9WY99VXNwPmoNdLbeeHP0h5AsKQD2o01yDL55qP55w2qVwD+u740RL6+r97z65JebIKDFG9fMxg0DXsNj372G/+3+D34ofAQaXRsszRkB9bIx+GyLDfOnXoU31x6FWeTgcGkVTu0xobiqGOerzuNYyTFEtz6Ac5UnMWrxea/vFaOJQVJ0EpJ0SchMyMTd/e7GJRmXYHj74eiWVnvrePIdAzMREYUlZ/Ctb6wcAHTNSPA4z9+WjAQ/dvNzn4ix+e7NaBkX+MivYIRKD/Ohc+U4kl+B6/q09gjMJZUWPDCyM8ZcnIFube7As+ufxcKdC/HC6BcCfq/UeB3UKoFT5+0V1ejYE+g3fxJOG08i2XI7Eq03wmJKRlmV/Reh7x4e7tN126fG4tExXfHmT9mw2iRo/Nys5kyJPcC3axGLWG0snhzxGFZt7YUK9TqYtJtw2vw5Xt/5KRANXPu1ChASEA3cu7L6GgIC7ZLaoUuLLhjZcQC6tOiCzi06o0NSB7ROaI1W8fbWk1Cevx3uGJiJiCgs7TlTBgBom+LbzU/Om/5ue+9X7HlufIPnO1sy4nysMMuyjHuW34Ptedux9JalTT4Rw+uamrmL+ar/bAIAXNM7E7klnnOJ+7azz1Bum9gW4zuPx8JdC/HcqOcCngOsVgm0TNDhWFEBSjSL8MyWr9EmsTW+mLIaiepeePCTHdiXVw6tWoXUuCikxEU1fFGHFnFRkGWgrMqC1Hj/PjFwjrdzTrlo3yIWAlok2Mbh0Ox/44b5PyL7/CFYVLmwijwAGmjkVlgx6yakxKQgJToFydHJbKNoZgzMREQUljYcKURGog592yU3fDKAaEeFudxoRU5hBTql13/DU5XZBpVouILt9PLml7F472L8Y/Q/MKn7JJ9eE8nKKi2ux3/5ag++3HEGgD3Y2iQZIy6qnvk7s89M3PrVrdhyZguGt/et8uuNKWodvj//FmzaclzRdgq+mvo+UmLsrRoWm4wvd5yBVi3Q1ctNfvVxhuuSSrPfgTm/3F5Zb5VoD8xCCMy5vhdaJuiQnqDDGzcPxQebW+Ph0V3x3LL9qDBZ8X/ju2Fw28af102+Y2AmIqKwVFJpRmZSjM8fQzsrzADw2/HiBgNzpdmG2CiNT9dfmb0Ss3+cjVt63oInhj/h03qaQnP2MBcZqlswnGEZAH7562hIsozYqOoIMqHrBGhVWiw7vCygwFxhrsBDKx/CzopF0Nl6IN1yH/42dKorLLuz2GRkOqq9vmoRaw/MxQZLreeeXLoX/dun4MZL23p9bUG5EVEaFZLcZkFPH1I9caVf+xT0a29f58c+boZDTY9j5YiIKCxVmKx+9Re7B+YKU8NzdassVsRENdwecKjoEKZ+NRV9W/XFgkkLQqKPNASWgBKD2evxVknRaJ3s2UaTqEvEyKyRWH5kud/vc+T8EQx6bxA+2v0RRrZ6EBnmF6GTu9T6ZOC1m/u4Htecu9yQNo62n8P5eo/jBXojFm89hce/2I21B/NrvW757jzM35iDjERdSPxcUOAYmImIKCzpjf4G5uq/8gymhidl2CvM9QfmUmMpJi2ZBJ1ah29u/QaxWv+CWCQr9hKY/3BFlzrPv+6i63Co6BCyz2f7/B4HCw9i2IJhKKwsxI8zfsSt3f8EAfv3rOb0lCn927o2+8hyzOf2VVZqLNqmxGDjkUKP41vcNh356vfqKvqCn4/j9v9txSNL7POgb+zfzq/3o9DDwExERGGpwmj1a+Sb+3QDX3ZuqzTbEKOtOzDbJBumfjUVx0uO46ubv0L7pPY+r6WxhcIc5pqB+epLMvH4+LpHm13b7VoA8LnKfLzkOMZ+NBZqocbmuzZjdMfRyHKrHOu8zOce0z0DAJCR6F9LhhACfdolI9utwmyTZMzfkIMEnQZjL87AobN6VJqtWLY7D3//7gA2ZRdBloEFdwzAo2O7+vV+FHoYmImIKCzZWzJ8nxyQGhflCsCV5oYrzFUNVJifWPsEVh1dhf9O/C8u73C5z+toSnIzNjEXV3oG5qcmXlzv+VnJWejVspdPgTlPn4exH41FlaUKa25fg66p9kDawa1y7G3L9Ccmdse/b7gEYy/2f9xf2+QY5JUaIUn2/6bLd+fhwNly9GqThB6tE5FTZED/F9bgEccug06Xd033djkKMwzMREQUdmySjAqTfxXmaK0aB1+4Cm2SY2DwqcJs9bgxzd0nez7By5tfL6DLPAAAIABJREFUxqwBs3Dfpff5vIamEgrtsrtOlSIxWoN3pvfH3ufG1epb9ubai67FppObUGosrfOc4qpijPtoHPIr8vH9tO/RO6O36zn3Vgtv0010GjVuHtjO71nKgH18odkmobDCfjNjdoG92vzilN6u0YbOGd839LffADhtcHtoA3gvCj38LhIRUdhxBl5/epid4nRqVPrYw+ztpr/d53bj3uX3YkSHEXjjqjf8fv+m1BwF5iP5euzPK8MPB/Jx2+AOuKpXps+fBEzoMgE22YZ1x73vrGe2mTHlsynILs7GsqnLMLit51QJ9++Xt5aMYLRNsbd7DP7nWhwtqMDb646hTXIMstLiPFo81jw2Av+Y3Au/PjEGc67vpegaqPlwrBwREYWdQHbhc4qJ0vhUYa6y1G7JKKkqwZTPp6BFTAt8fuPnIbuZRHMVmCtMVox7faPra19nZDsNaTsECVEJWH1sNSZfPNnjOVmWce/ye7Hh5AZ8MuUTjO44ut5r1bdleiA6u40hHPvaBgBAbql9M5ZWboHZubNkqyRlAzs1LwZmIiIKG7Is45ej52G02CvErZJ82+XPXVyUGlU+9DDXnJIhyRKmL52O02WnseGODciIz/D7vZtaUxeY3adGAEAHP8e3adVajO44GquPrYYsyx6j2ObvmI9Fuxfh+VHP47betzV4LV83nPGVtx0l/z6pJwDPwEyRiYGZiIjCxv9+Po45Kw4CAFQC6N/evwomAMRGaVBSWVXvOZIko6zSgkS3VoJn1z2LldkrMXfiXFzW7jK/37cpOYNmU7dkbD9Z7PF1+xb+j9kb13kcvj38LY4WH3XdzHfk/BH8+Yc/Y1zncXh6xNM+XUfpwKxSCSRGa1BurP50YsZlWQCAxBgN1CqBxzgNI2IxMBMRUdj4Nac6kHVIjfNrSoZTnE7d4Fi5s+VGmG2Sa4OLhTsXYs6mObir7114YMADfr/nheLQWfuNcIM6tsB1fVojzo+bMp3GdR4HAPjh2A/omtoVFpsF07+eDp1ahwXXLYBK1B+E27eIxaniyoBu7GvIlifGQAigxzOrEeV2fSEEjv1zouLvR6GDgZmIiMJGTlEFOqXHIafQgHsu7xjQNTKTYrBy71lU1XFTHwCcPG8AYJ+6sObYGtz33X24stOVeOead8JixzbnCuUmbso4fE6PKf3a4LVb+gZ8jc4pndE+qT3Wn1yPhwY9hOfWP4dtedvw+Y2fo01imwZf//WDQ3G0oCLg96+P8xeAH/80ssFNbSiycEoGERGFBatNwqnzlRjfsxV2PXMlbhsU2EYhgzu2gMUmY+epkjrP+elgAQCgQsrGDZ/fgB7pPfDlzV+G7E1+ocBik3Cu3Oj3ttM1CSEwosMIbDy5EZ/u/RT//PmfuLvf3bip500+vT4tXochnVKDWkNDurSM92lMHkUOBmYiIgp5P2cX4dA5PaySjDbJMUiOjQq40ts1wz7t4EyJvY/ZYLLi36sOwWCqbtP4fPtp9OlYgTuX34Dk6GSsvG0lEnWJwf9BmojzP01T9jAX6u3zif3dRc+bkR1GosBQgBnfzMDl7S/H3KvnBn1NomCwJYOIiEJaicGM6f/b6rqJKz1BF9T1oh27/Zms9kkZy3fnYe76YzBbJTx9TQ/klRfhhPUd7M//DsnRiVhz+xqfWgEudPnlRgDKTIwY0WEEAKBdYjt8fcvXiFJHBX1NomAwMBMRUUj7Ncc+qsxkte+iFmxgdgZv565sNkcZdlN2Ph5dvhKfHHwJes15jG03FZ/c/Dpaxvm/jXJzc03JaML3dAbmlonBfX8AoGuLrnjzqjcxoesEpMWmBX09omAFFZiFECcA6AHYAFhlWR4ghGgB4DMAWQBOALhZluUSYf+/9w0AEwFUArhDluXfg3l/IiKKfIfO6T2+To9XtsJcUG6CUbUP60vfww+/H0PHxEuhNT2N5y6fhpZxDGu+yi9XriVDCIGHBz8c9HWIlKJED/MVsiz3lWV5gOPr2QDWyrLcFcBax9cAMAFAV8c/9wGYp8B7ExFRhCursnh8nRZkYNaoBFQCMJjN+On4T3h3/8PI182GDeVIM/8F17Z6Fzq5CzIUqJQ2uyZsYs4vN0KjEmgRy/YJijyN0ZIxCcAox+MPAawH8FfH8UWyLMsAfhVCJAshMmVZPtsIayAioghRWmlG+xaxGNAhBYfz9XWOgvNHZdQKzNnxOf6/vfsOb+o6Hzj+PZIt2bK897ZZBmM2YSVhhhGyaEIKGWQ0q+nKatJmN03S9tc0AxqymrRJsxMyyGjCDoRlttlggzHGe2/t+/tDsrADGDBgg3k/z6PH0tXV1ZGurvzq3Pectz6zAh/lR5xuFvqmq9DhR06Ze0q5KKnedlJKaq1EBRrR6c7+afeEOFmnGjBrwEKllAa8rmnaG0B0iyC4GGiuHRoP5Ld47CHPMgmYhRBCHFNVo51Qk+8pze3bTNM0fvvdbynRvUqK33DevvR1/vtDCAG+Aaz2lHXeVVSLv6+ewHYU3TibKNVxOcybD1bx2aZD9Igyd9AzCtGxTjUl4yJN0wbjTrf4tVJqdMs7Pb3JJ3W8KqXuVEptUEptKCsrO8XmCSGEOJc9NC+L5XvLCD4Np/k1TeO+Bfcxd/1cYnTXMj3pNa5JvwarzZcAow9ZT7grzLk0iAn2OycKlLQl0OhDdaP9+CueBn/9bjfAGSsYIkRnO6WAWdO0As/fUuALYBhQopSKBfD8LfWsXgAktnh4gmfZT7f5hqZpQzVNGxoZGXkqzRNCCHEOs9idfLLhEAChplMvGPLMimeYnTmbe4ffSy/jL7E53f05jTYnZqMPwSZfwgPcgXnUKc7EcTZICneXiO4IVQ02AB6aktYhzydER2t3wKyUClBKBTZfByYB24GvgJs9q90MzPdc/wq4SbmNAGokf1kIIcSx5FUcDvZOta8381AmTy1/ihv63cALk1/Az9cHi909S0ajzeEtcxxudgfMp2Omh86WFGYiv4MC5uJaC7eMSuFXY3t0yPMJ0dFOJUErGvjCc8rKB/hA07TvlVLrgU+UUrcBecDPPev/D/eUcjm4p5W79RSeWwghRBeXW+4efDcuLZJbL0xt93YabA3M+mIW8UHxzJ06F6UUfr4677zODVYnAZ585eggP/aW1JMQeu6XPU4MM7F4Zykul3ZGB+I5nC7qLA5CTsNZACHOVu0OmDVN2w8MOMryCmDCUZZrwK/b+3xCCCHOLzml7vmX51w3iEC/9gdjDy56kOzKbJbetJRgv2AAjD56LHYnTpdGk93p7WH28QSWSWGmU2x950sKM2FzuiipsxAbfHI/ADL3V9Ajykz4CUzhV+2Z9i9UppMTXdjpmIdZCCGEOO12FNaSHG46pWD5m73f8OqGV3lg5AOMSx3nXW709DA32hwABBjc/UcuzzD1sIBzP/hLDHUH/QcrTi4to97qYMYba7nxrXUntH51ozt/WXqYRVcmAbMQQoizjqZpbD1UQ9+4oHZvo9pSzZ1f30m/qH48O/7ZVve5e5hdNNrcecwmo7uH+bphSQBkxAe3+3nPFs295M0D/yx2J/d9vIUt+dU88sU2KurdlfkabQ7ez8xD8xQ52XywCnBPr6edQOGTqkbpYRZd37k9yaQQQoguaV9ZPQXVTdw9tnu7t/HAggcobSjl6+u+xujTOrXA36Cnweqgwdq6h3lKRgwH/nZZ+xt+FokLcadhFFQ3Ae4e+y82F/DFZvcEVX1iApk1MoWnv9nJh+vySY0IYFT3CN5ZnefdxvaCWvolHPnjoWVedPMMGRIwi65MepiFEEKcNTRNw+XSWLTTPSPphD5R7dpOVnEW/97yb+4feT9D4oYccX9atJmDlY0U11gAvDnMXYnBR0ewvy+VnoC2rM7S6v7mnuGdRe5c8Uark+0FNSzeVcJdY7ph8NExb2M+P7W3pI5uj/yPH7PdtRIqPNsPM0vALLou6WEWQghxVnC5NH7++hpyyuoxG33IiA866cFqzR5f9jghfiE8cvEjR71/cFIoANe/mQlAQui5P8jvaMIDDFTUuwPawurWAXNzqkZzD/EPe0spqGrCz1fHr8b0oKCqiflZhVw9OIG0mEBcmobJ4MOKve5A+fmFe7m4ZyQlte7tRp7AAEEhzlUSMAshhDgrfLbpEBvy3Pmz1Y12pg9JaNd2Mg9l8vXer3lm3DOE+IUcdZ2WAXJadCDpp5ArfTYLNxvYVVSLw+miqKap1X0HKxv5IPOgN3B+b+1BAG4YnkSwyZfpQxL4ZmsRV81dBUBKuIkP7xzBM9/uAmB7QQ1ldVZK66yEBxgw+MhJa9F1yadbCCHEWeGtlbmkxx4OXCelx5z0NjRN45GljxBhiuCeEfccc73mQX4AT0/LOOnnOVfYnBr7yxv459IcSmqtJIT68/RVfRmXFklFvZVHvth2xGOeurIvABf3jOTaIQn46t25ygcqGlm4owSA6CAjDpfGBc8u5rttRUR1gUIvQrRFAmYhhBCdLr+ykd3FdUztF8PvJvTk4Ut7t6vX95Mdn7A0dylPjnkSs8F8zPXMxsMnWAOMXS9/udmuolrAnXdc3WQn3Gxk1sgU4kL82VfmLgzz8KW9eeyyPpiNPjw9LQMfvTs00OsUz107gOxnp/KfWy4A4MmvdgCw+P4x3ueoarR3iVLiQrRFUjKEEEJ0use+3A7AuN5R9I1r35RuNZYa7l1wL0Nih3D30LvbXNfYIn2gZfDc1cyeMZC7399ESkQAq3LKvTNZtJxnemxaFGkxgdx+cbdjbqd/i5kyQky+BPr58v29F5Nb1sD8LYVcMSDuzL0IIc4CXfdbQgghxDmh0eZg9b5ybhyR1O5gGdwD/UrqS/j6uq/R69ruNVbqcKnogC4cMF/aL5ZAow8Wu5PqRjupEQEAhLSYAq55WVvCzUb+fk1/Hvpsq/cHRu+YIHrHBHFpv9gz03ghziKSktGFWOxO8itPrqLTqdqSX012SV2HPqcQomvJzK3E7tTalbPcbGPhRuaun8uvLvgVQ+OGntRju3IPM4CfQe8JmG0tepjdVfkMet0JD9aLDXHnKfc/yrzMQnR1EjCf415ctJeXl2Zjd7q48uWVjH5uGRs9o8zPtG2Hapg2dxVXvLyyQ55PnLr5WwpYuruks5txQmoa7d5KZKLrqqi3cut/1gMwLDWsXdtwupz88ttfEmmK5Jnxz5z0441dfHYHf1899VYntRYHwf6+3mUAg5OPPovI0YzoFs7vxvfgmWn9zkg7hTibde1viS6sqsHGop0lzF6SzT8W7uWKf65kb0k9mgZ3vbuxQ3qa7/tkCwAWu4s3Vuw74893vttfVk9uecMpbeOej7bwi7c34HIdv9xtZ5vwwg8MeWZxZzdDnGFb8qsBd6+ln2/7Bt+9tuE1NhRu4MXJLx5zGrm2tEzP6Ir8ffWs2VcBQKjJHTCP7B7BNYMTmHPdoBPejq9ex/2T0lrlPwtxvpCA+Rw1+rll3PHfDQAkhZnYXVzHjSOSGNEtjPJ6K9NfW31S2yuvt/LS4r3kljewYEcx+8rqj7mupmn8Z1UuOaX1/GFKbwYnhTB7cTZ1FvspvSbRtvHPL2fcP37A6nCe8rZ2ekbOn412FdXywCdZlHuKLXR0mpHoWM0/At+5dVi7Hl9cX8wjSx9hQuoEZmbMPJ1N6zJ89Iryeitmow9Dkt29+MH+vjz/8wFEBcp0cEKcCAmYz0GltRbqLA4AlIIlD4zhh9+P5emrMrzVq0pqrfxzSfYJbc/udHHjm5m8tDibcf/4gbve3ciUl1ZQVtf6dHhOaT01TXbW5Vby1Nc7AbhmcDz3T0yjweZslQpSb3Xww55Sckrr2VlYS05pPfd8tLnNQPxsUVDdxOKdZ1fawqIW7Znx+tp2bcNiPxxov7sm76xLd3C6NB75YhuXzv6RzzYd8k5TtS63spNbJk7FX/+3i++2FaFpR57VqGm088y3u9ApCG1nr+X9C+7H4rDwymWvdPme4vZqLo396GV96Cf5x0K0S9ce6dBF/XNpjvf6vRN64avXkeIZ5XzPJT0ZmBjCne9u5L3MPH49rgc6Xdv/RD5cd5DdxYcH7gX7+1LTZOf77UWsyqnAZNTTMyqQ//t+NzFBfvj5un9nLbpvNFFBfpiMPigFmw9WMzYtiupGG5e8sJzS+kqcqtp9oQaXqmFNmZNRPf0I8w8j1hxLQlACg2IHEWNu/2Cf48mraODlpTlEBRm5aWQKd7+3kQizkddnDTniH6zLpfHr9zeRdaiaRfeNoUfUsedx7UjPL9zjvb4lvxqXS2NvaR1RgX4ndHpU0zQW7Cj23v54Qz7l9Vbe8syt2hE0TePzTQXUWtwV3K6auwqz0YcP7hiB2ejD4l0lfJDprjQ2KT2aV28cQsaTC9heWMM17az4JjqH1eFkzb4KPl6fz3fb3Z+7R6b25s7R3Vut98oP7u+yaYPi2/U8n+74lA+3f8gTo5+gV3ivk378wvtGezsfurJ6z2uMC2lfmXEhhATM5xSXSyO7tJ4P1h1k1ojko1anMvromdQ3hqenZfD4l9spqbMQG9z6S7Ki3kpmbiXRQX4MSQ7l261FAKz643hKai0MSgzhipdX8vj8Ha0eFx/iT0H14dKqEUEOMg9lsqdiD8awJTz2Yw5zd9ZS2lBKnaMS/I/8R1RZCbs2BVJvbz2zRmJQIsPihzEsfhiDYwfTLbQbiUGJ+Op9j9iG06VhdThZvqeMS9Kj8dUf+0TJnCXZvLBor/f23GWHc61fX7Gfsjorf5jS2ztKfH5WgTen8o0V+/j79AEAfL7pELnlDdw/sVeH92LtKKxhd3Edt12Uyn9W5eLS4IvNBTzwaRbJ4SaWPzjuuNtYf6CKez5y55yP6h7Olvxqlu0ppaTWQvQZrtBVa7Hz4KdZbMyr8qZZfLu1iP2eogm3v7OetfvdvcgBBj1bnpzk3afpcUHe/fFTmqbx4LytfLm5gNSIACLMRj68c8QZfS3ixDz9zU5vmeVmS3eXtgqYs0vqeH3Ffn4+NMF7nJ2M1fmruenLmxiVOIpHLn6kXe3sFR3Yrseda+qs7u/iCLPkHgvRXhIwn0P+9PUO/rsmD1+94jfje7S5blKYCYD8yqYjAubffriZ1Z4BIOsfvYSdhbXMGpFMfIg/8Z4eiIcv7cMNb2ai4eQXowPYWrKTAamNLNizmY0FO1C+hYT9/fCpcr3So9fFUFgRi04bwKCIeKICotiQ6+S9WyYSGxgDriCufnkbiaZA5t0zjEZHBQeqD7CxcCPrCtexrmAdn+36zLtNndIR7h+OXqfHoDcQ4BuAyddEQZWTqgYboBEd7Et8SACh/qHEBMQQFxhHiDGKyrpADpT4smx3DUrpefKKfvz56z2g6YgODECv0/OX77agw4/JfWO4ICWU7QW1vPljLvEh/oxJi+SDzIPcfnE3ekUH8tC8rThcGoOTQhnXO+p07M4T9qevduDnq+OmkcncMiqFi/++jAc+zQIgr6KRrYeq6Z/Q9kCn4lqL9/rjl6dj9NEx/vnl/HtVLg9f2ueMtb3R5mD24mwWeMrpKgWaBhvyqrjtolR2FdV6P4sAU/vFtvoBdGGPCF5emk15vZUIsxFN09A0cGoab686wLyNhwDILq0nu7QeTdPktHwn21VUy0fr8gGYPXMgl2bEct/HW/h2WxEV9VbK622U1Fr4Zmshep3ioSm9T/o5Vh5cydT3p5IYlMiXM77E6CNV5k5EpFTjE6LdJGA+R9Q02vlkQz7DUsK4cWTycXsFmwPmg5WNhAUYKKppokeUmaz8mlYBymVzfqTO6vCmHjhcDjYVbWJ50RJc4fMpsWTxp/XuYOvzgxBhimBoShpp4cNIi0ijd0Rv0sLT6BbajSY7vLPqAGFmA1cPSsDoo8PucmH0OTzy/epBtXy68RDfbS3DpUGAsRv3jRztvb+8sZytJVvJq87jQPUBShtKcWpObE4bDfYG6qz1bD9UAPigUJTW6HA5nTQFFbGleAvF9cW4NNfhN8LzNt216PD1guaOb3/QaSH84ttehPslsuWADh3+DE6MxmaMpl5fxEWzV6Mwui/Kj292Qe/4wUSYIo7a+326ldVZ2ZBXxb0TepEcHoCmaRh8dNgcLv4+vT9/+moH8zYeOm7AXNPkHpD58Z0j6BPrLjc8KT2aN1bs567R3c/IqPcGq4Nx//iB0jorY3pFct2wJHpEmbnkheUAPDCpF19sLmD1vgrigv14YcbAI+Z3ndovhpeXZvP8wj389er+XPPqavKrmrhpRDLPL9pLSriJAxWHBwW2nDZLdIyc0jrCA4yEBhjQNHdKk49eseSBMSSHu1PF4kPdP8R/OuvJbRelEmE+uSBuae5SrvjwChKDElly0xIiAyJPzws5D4QHSMAsRHtJwHwWyMqvRq9TZMQHszK7nL99v4vUCDP/uLa/N9j8dGM+FruLP13Zl/S4oONuMz7EH52C7QU1/N7TG9nSG7OG8NbKXNbmVuBQBWRVbeOqj9bww4EfqLW6Z1DIiMpgWsYdDI4dTFp4GmkRaYT5H3ueVF89/HZCz1bLjD+ptvX36f1Zd6CyVbrH1YPivXnWEaYIxqeOP+ZzrM4pZ/f2TN66eSgJoSYmv7QCKuHWIb347fge1Fls3Pzfhaw9sI+HL48mIlCBcuF0OXG4HN5Lo93Gwl15rD6wgwZrJbvLVuD0qQDlYmkxLC0GjhJDvrDVfQEI8w8jKiCK6IBo4gLjCDIGYdQbMfoYCfANIMIUQWRApPuvKZLIgEjC/cNPKtBeu78CTYOxae6gQCnFO7cOY0t+NVcPiuf9tXnsL2ugtM7CjoJaxvWO4pMN+VQ22LhrdDdvb2tNozsVYmDS4cB6+pAEFu4s4dttRRj0CpPBh8v7x562HtpVOeWUegaOPjg5jYx4dzD8yzHdSYsxYzL4MLqn+3WFmAyM6BZ+xDZ6xwQxuW8MK/aWc+d/N7DpoDs946P1+fjqFQvuG82e4jqy8qt5fP4OqhttrQLmqgYbOp06Ioh2uBxYHVYabU3YXFYsDgsN9gaqLdVUNVVhdVrRKz1RAVFkRGUQ6h96Wt6TrqaguolLXlhBiMmXzY9PZNPBavaXN/D3a/p7g2WAW0al8OXmAkrrrCSFmeifEExNk50/nGTv8oKcBUz7eBrdQ7uz5KYlRJujT/dL6pIendqHj9YfRH+c8SxCiGOTgLkTWexO7E4XV81dBcCKB8fx0LwsCmssbC+opaCqkU/uGolOKd5bm8fQ5NATCpYBDD7ugYBvrz5wxH0vXp9ImXMJrpAFFBi/x6kr48UN0C20GzP6zmBC6gTGpow9I/+MlFL86cq+3kIF4J7iLCM+mDqLnY15VfRPCOHDdQdZva+cHpFmgk0G7p/YC4fTxYrscvQ6xbDUMAL9fNn15yn86v2NvLBoLz9ml7H+QBWg4/rB4/jdhW3nRd4/UqP3499jLXORAPxmXHd+d0k3Gu2N3ktlYy3FdbVkl5Zj9HUwe+l2HFo96QkaiZEOKpvKKK4vJrMgk3pbPVaHFavTHYAdS4hfiDeIjjBFEB0QTUJQAknBSSQGJxIXGEewMZj315Qxd9kh/Hz0rfb7yO7hjOzuDi6TwwPYmFfFjNfXHjFHs9now40jkgGobrRjMuhb9fY3DwB6/MvtaNhxqir2V/Zgcj93EOvv44+fjx8GvQGn5sTutGNz2rC77DhdTvx9/Qk0BBJoDMRHd+RXSdYh9w/BHU9NbjW/7h8vPRwkJYaZePZnGVzYPQKAGksNeTXuswsHqg+QW5XLqupt7G7ax7p9DnRGf/RaMBUNkfSOTuH5NVuot9Wzq7iUct887l/4KfEhQfjqfKm11fLFlj002GvoFaujxlpFlaWKBlsDTu3kpuaLD4ynX3Q/+kf1Z3jCcEYmjCQ2UMoB53ry0Ksb7fzlf7t4Z00efr46pvZv/d7EhfiT+cgEmuxOTIb2/dv5Zu83XPPJNaRHprNo1iIiTBGn3P7zxR2ju3HH6G6d3QwhzmnqaFP9nC2GDh2qbdiwobObcVq8n5nHx+vzeevmC4gMNOJ0aVz72mpvjxlAQqg/h6qaeOrKvjzz7U7sTve+iQv2o7DGwtzrB3NZ/xP/J/2zVxey9uB2QoMqmTnSyNJ9WeTVb+ZA9X7AHbgNjr6YCxPGc8uQK+kW2nFfqEU1TXy0Lp/ZS7J5/toBXDkwjifm7+DDdQePuv4z0zLYlFfF55sLGJ4axsd3jfTet72ghsv/ebjaoFLw6g1DmJJx/Jk3xj63jAMVjdw4Iomnr8o4bu/qJ+vzeegzdxfzk1ekc+uFqUddz+60U9lUSVljGeWN5ZQ1eP56bhfWlVDZVE5lUwUlDSUU1xcfdTtoOvx9zESZQzEbzFgcFiwOC0op9EpPncVJbZMLhQFfnRGn0wcd/gT4RGDUhXFZ3z5ckJjKou2N7Cyw8eJ1aRTWFZJXncfeilzmbdmMQ5Xg1JWg4Tp6G05AgG8AZoOZQGMgZoMZs8HM/lI7FruNnjEGbE4bBr0Bf19/DHp3173D5aDOWkedrY46ax011hrqba2nHTT5mogNSKKwIhClGXGpJpyqCqcqx6XcZ0L0So/J10yjVU+wvw8ubFgdNhwOI7jM6DQzOsyYfUMYlZpESng44SYza/fXsianll5RYfxqbB9MviZC/UIJ8QvBz8cPh8tBYV0h20q3sb10O9tKt7GzbCc2p7u3Pjk4mZGJIxmZMJKLki5iQPQA9Lr2Fd44V7U8HprdP7EXv/vJmaZT9d7W97h1/q0MjBnIghsXtHmmSwgh2ksptVHTtKFHvU8C5jNr7f4KvtlayPzNhd6RyklhJg62KMZwYY9wdErxY3Y5AIvvH0OE2cDAPy/yruPvq2fHU5OPO0Wcw+Xg0SWPsjh3MZuLNqNxeP/GBcYxLH4Yo5NGMyZlTKf/g7c6nKTYiG3WAAAXgUlEQVQ99v0RywckBJN1qIbhqWHcc0lPrv9XZqv7Vzw4jqRwU6tl72fmsXBHCb+b0IP4EBMxwSc288OW/Gr+9eN+np2WQYjp+Hm8DqeL5xbs4fUV7h8dL80YyLRB8XyztZCHP99GakQA1wxO4JL0aMIDDDRYHYR7cjRrLXa2HKymqtHGvR9vITzAwAd3jKBXdCBWh5WCugIO1hykuL6YGksNf1+4mVCzg5E9/Kmx1tBgb8DPxw8/vR8aGi7NRVWjhVX7SnFpNoammimuraWkrgo7VVRZykAdOwiODoimqi4YH1c0vx09Gps1jP+uKeCC5AiuHZqAUg4sDos3PcFH50t1o4vnF7pfu4aNaYPCSY5U1FjcwW69vd7911bPhrwiTAY/+sdHYtQbvT3vVoc7TUOv03t7qIMMQQQaA4kPjCclJIXkkGRSQlKINEWilGLWW5n8mF3Oc9P78+A8d4D26o19Gd87FoPeQG55A+OfX87/XdOPGRckMenF5ewtqee6YYmsyqlodbyZDHrev32456xEORf2COf9209sdg2rw8rm4s2syV/DmkPuy6Fa98DDYGMwl/e6nJkZM5nUfZL3h0FX9uKivcxZmk3Wk5PYU1xHoJ8PvWNO7CzYibA77Ty46EFmZ85mTPIY5s+cT7CfzCMshDgzJGDuJJqmkfrw/455/9PTMrhxeBKa5p654tttRVw3LJG/Xt0fgDX7KkiJMDF7cTbThyQwNOXEelXS56YTGRDJhNQJDIgeQI+wHqSGpmLyNR3/wR0s5Y/fAtAtIgCXpvGrcT34+dDEVrMdHKpqZGV2OUt2l/LYZX1a5UZ2lqW7S/jF2xvoHRPI9/eOZsbra8j8SYGNHlFm9pfVk/3sVAqqmrjkxeXYHK0D2It6RPDe7cOP2P7CHcXc+e5G7rg4lUcvSz9ue346O0RNk53+T32PizqcqgqXqqN/ooHHpg4l1hxLYnAifj5+fLetiJSIAPrEBqFpGn/9bjdvrNjP7yf14jfj3b2Emfvd8+l+vrngiOcd1T2cD+44MtgsrrEw4q9LuP2iVB67/PjtP5673t3Agh0lzJ45kM0Hq3l79QHWPTrBW6WsyeZk1N+W4KPX0Sc2iBV7y5jcN5rXbhxCaZ2VVTnlpEQE8Oevd7KjsMZ79gYgMcyfHx86dt788eTX5LMibwVLcpfw5e4vqbJUEeIXws96/4zrMq5jfOr4Ltvz/OCnWSzfW8a6Ry857dveWrKVu7+9m9X5q7ln+D08N/G5DhloK4Q4f0nA3EkKq5sY9belAPx2fA8GJISQmVvBVQPj6RMb1GoAxvaCGn753kY+u3vUKc+L63Q5z5l/0DsLawkw6s+KIPhk3fPRZtbnVrL64QlcNXcVWceYL/jd24bxt+92s6PwcDnqD+8YwYYDlTy/aC9v3jSUS9Ld+eI1TXaG/2UxFrs7sL5nQk/um3jyBRnAXR1wb0kdzy1wFz1Z+YdxJIQe/0fTZXN+xGz04cM7RvDq8n3exwP8flIv4kL8uf+TwwNJf/j9WG/hnGYj/7qEohoLf5jSm7vHti5W0R6bDlZx/b/WsvzBcUSYjRRUNR1xluHdNQe8g0lHdQ9nznWDjjoDwx8/28pH6/PpExtEr2gzX2UVsuaPE/h2WxGvLMthaEooj12WTmLY4e2X1lm4fM5KqhptbHx8IkF+Rw/cbE4bi/cv5qPtH/Hl7i+ps9URa45lRt8ZTE+fzsjEkehU1ymwetvb6ymssfDdPReftm1WNlXy5LIneWXDK4T4hTBnyhxu6H/Dadu+EEIcS1sBswz6a0NNo535WQUMSAhhQGLb03Ydze5id4D00Z0jvDMANAdGP5URH8zKP7S/l6ulcyVYBk54EOPZqHukmflbCmm0OcguqaNPbBAZcUE8MrUPj325nS351VQ32pj11joA7hrTjdeXu9MZhqeGkRoRwPOL9nL7f90/Cu+f2Aubw+UNlq8fnuQdtNceE9OjmZgeTXSQH002xwkFywDje0fxz6U59H7i+yN6xH8zvucRgwtX76s4ImBurix2cc/TMzBrcFIou5++1Hv7p8EywOS+Mby4OJtbRqW0mUP7wKQ0+sYFcd2wJA5VNTF/SyH/XXOAV35wF7VZsKOEAYkh/Grs4bnOn1+w1zvjR3ZJHUOSj362x6A3MLXnVKb2nEqTvYlv9n7Du1vf5ZUNr/BS5kvEBcYxvc90bh98O/2i+7XnrTirVDXaCDWdnl5fi8PCm5ve5E8//IkqSxV3D72bP4/7s+QrCyHOChIwt+GNH/d5K8MlhZm47aJUbh6VctzHOV0at/xnnTcn+XypJnW+aQ4SV2aX02hzcvPIZGYOSwJg7g2Dcbo0Fu0s5m/f7cbf4MN9l/RiTK9IfHQ6dDpFTLAfs2cO9Fbga65I2D0ygIem9GZy39NTLnz6SZaVnnFBIv9cmuMNll+cMYDukWbvGZGwFrne4QEGtuRXcf3wJGwOF3e/t5FfXJRKkL8vE/tGe6eS6whRQX6sf/SS406dFRloZNbIFMC9D4ckh3qD5UFJIWTlV5O5v5L4EHcFwcz9lXy8Id87//Uv39vEoMQQbr0wFaOvjtJaK2PTIlvNBALg7+vPtX2v5dq+11JjqeHb7G+Zt3Mer298nTnr5jAqcRS/HPJLpqdPx9/33CxZXN1op0/sqbW9xlLDvzb9i+fXPE9xfTFjkscwe8psBsScfPU/IYQ4UyRgPoas/GrmLtuHXqdwujQOVjby5Fc7mDkssdXUXEezaGcJP2aXM/OCRMamRZ2RohCi86V60kiaUxZ6x7buLdfrFFMyYpncNwanS8NHr2NU99Y9rlcNjCcjPpgHPsnCYndy5cA4bhqZgtnYeYdmQqiJP17am799t5veMYFMGxjfKj86yP9w23pFB7K3xD2zxc6iWpbsLmXJ7lKAM15y+2jaM8/sg5PTeGf1AfrGBfGb8T35y/928caK/SzfW4bBR4fC3ev+ws8HMPDPiyirs7JwZwkrsstOOHUm2C+Y6/tdz/X9rqeisYJ3st7htQ2vcdOXN3Hvgnu5ZcAt3DnkTtIi0k66/fVWBwEGfadUOKxqtBEacHI9zA6Xg/UF61m0fxEL9y1k7aG1ODUnE1In8MHVHzA2ZaxUaxRCnHUkYD6K/MpGnvjKnQv55s1DWb6njLI6K99uKyK7pP6IXjOL3cm/V+WyKa+KqwbG8+6aPBJC/Xn2Z/1kovguLCXCnRaQXeoOGHtFm4+6nlIKH/2xPwfdI818+esLT38DT0H3SPdrGZwcekTw0nw7LMBAr2gz76zJ4/Z3NpAY1rqnMfocKcM7olt4q6Ipvx7bgy0Hq1l3oNLby/7I1D6EmAxM7htNz6hAzH4+zF2W4w2YZy/JJjncxNWDj9+bH24K5/6R93PfiPtYdmAZr214jTnr5vDC2hdIDUllZOJILky8kPGp40kLTztm8Ohwuli8q4RfvreJV24YzNR+HTMvdE5pPa8v38fMYYnUNNkJPYHZZWqttXy+63O+2P2FtziSQjEkbgh/uPAPTOs9jQviL+iA1gshRPvIoL+jGPXXJRTWWHhmWoY3hzS3vIFx//gBgN1PTyG3vIHYYD9CTAau/9faVuWmAR67rA+3XywTxXd1zbN8ABz422Wd2JLTy+nSeH3FPm4YlkzwUXJU9xTXERZg4MfsslYDAKODjPRPCGF1Tjnv3zGCge3I/T9b2Bwu7np3A7Eh/vzlZ0fmG9udLix2JzsLa5nxxloizEbWPTKBWoud33+6lWGpodw5+sQGPBbXF/PR9o9YeXAlaw6tobCuEIBYcyzjUscxJnkMFyddTO+I3hTVWPjPqlw+XJdPvWeqyrvGdOPhS/ucvhd/DJ9uyPdO69fs8cvTue2iI+cjtzvtLNy3kHe3vsv8PfOxOCwkByczuftkLul2CeNTxxNuOrK6oxBCdBaZJeMkfbetCL1OMTE92tu7o2kaV7y8ku0Fh2c6MBt9mDUymVc9+Y+/m9CTOUuySY0IYP5vLjzmSHrRdewsrGXqnB+5ckAcc64b1NnN6XAV9VaGPLMYAD9fHYvuG0NCqD8urX3pEeeqLzYf4r6Ps5j/6wvZWlDD419uB2D1H8d7KyqeKE3T2F+1n6W5S1l6YCnLcpdR0lACQKhfKH4kU1cfha+WQJBvIjZLNLcOG8azPxvSrrZvOljFxgNVjOsdyfaCWmKD/Rju6XEvrbVg8NERYjJQVNPEzDfW4nRpPPuzfjz82VaC/H154ecDvYN37U47y/OW8/muz/l056eUN5YT7h/OjL4zmDVgFsPjh0u6hRDirCUB82lS3WjjipdXMjQ5jJHdw/k6q5Afs8tRCr7+zUX0jDazYm85E3pHHbfAiOg6mmxOfPUKH33XmS7sZKzdX0HPKDMGHx2B5+mPxJY/HHQKXJq7QIqfr55XbxjsDUDbQ9M0cipzWJG3gk+3LeOHfVvQGQppcla1WEvhp4skwZzKuB79iTal0C20O0Pj0+ke1r3NOdinv7qaDXlVrZbNGpHM4l0lFNVYCPb35YWfD+C3H26mye7ktRuHtBqQanPaWJCzgHm75vH1nq+pslRh8jVxWc/LmNV/FpN7TD4virgIIc59EjCfRi0LRGiaxpb8avx89fSJPXenRxNCnLqW6Tm3X5TKzGFJ3PKfdRyqamLOdYO4vF9su39I/+mrHVQ02Pg6y52qsebh8RgNTeRU5nDPvO/YVZaNQxXiUEXoDMU0OVvPCR4XGEePsB70jezLwJiBZERlkBiUiMUSxOSXVjJ9SAI9osyU1Fp5a2Wu93GBRh9vhdLoICOv3DCY9Dh/NhVtIrMgk8yCTBbtW0SVpYpQv1CuSLuCq3tfzcTuE8/KQklCCNEWCZiFEOIMW51Tztr9FWwvrOWJy9NJiQjgb9/t5rXl7pStByen8etxPdrcxv6yer7fUUxKeAADEkMIDzDwyBfb+HzT4SqLH985olWP9e8+3MxXWYVc1COCrEPV1FkcuKjHropx6AoZ2cvOgZp96A0l7K/eRa31cFoZmg5fFU7/2G4E+wWgV3ryKpvIq6jFzxfS4wLYU1JNg9VGcoQfKDv7q/bjcLmD6OTgZMakjGFG3xlM7DZRKvEJIc5pUrhECCHOsFE9IhjVo/W0gTMvSGR3cS07CmtZsqvkmAFzk81JndXOfZ9kHbNi5Cs3DCbIz/eI9I7bL04lwKjnV2N7EGLy5Y0V+5k+JIG8ikZu+vc6tu4CmADA85f1ocZWwP8tWYJTleFQ5VzUG6yuCqxOKw6XA6VsgIPIwEACDAFckBSMpukwGQz46Hy4ps81DI8fzvCE4cSYT89c4UIIcbaTHmYhhDjDnvp6Bx+uO8iOp6YcMRjS5nBx4f8tpcxTSXBiejTl9VZKa62YDHp+O6En49Ii25Ufftvb61myu5QrBsR50znAXaBlQEII1w9POqKwksulsTa3ghGp4TIWQwhxXpEeZiGE6ERDkkP5z6oDPPnVdp6+KsM7DsLhdHHV3FXeYLlffDAvzhh42grXvDRzIKtyypmUHsPE9Gge/DSLy/vH8eDkNGKCj15YRqdTRxTYEUKI850EzEIIcYZN6RtDWnQg7609yGX94hjZ3Z1W8WNOObuKajEZ9Lx3+3Ay4oIx+Jy+2VYC/XyZkuEuaHLlgDiu6B8r07oJIUQ7nJ/zYAkhRAfy0et497ZhAFz3r7W8n5nHoapGnvpqB2EBBjY/MZHBSaGnNVg+GgmWhRCifaSHWQghOkBUkB+/uDCVf6/K5dEvtnuXPze9P0YffSe2TAghxPFID7MQQnSQJ65IJ+uJSbwx63BVvjFpkZ3YIiGEECdCepiFEKIDBZt8mdQ3hg9uH86K7HKiAo8++E4IIcTZQwJmIYToBEebt1kIIcTZSVIyhBBCCCGEaIMEzEIIIYQQQrRBAmYhhBBCCCHaIAGzEEIIIYQQbZCAWQghhBBCiDZIwCyEEEIIIUQbJGAWQgghhBCiDRIwCyGEEEII0QYJmIUQQgghhGiDBMxCCCGEEEK0QQJmIYQQQggh2iABsxBCCCGEEG2QgFkIIYQQQog2KE3TOrsNx6SUKgPyOunpI4DyTnpu4Sb7oPPJPuhc8v53PtkHnU/2Qec7X/ZBsqZpkUe746wOmDuTUmqDpmlDO7sd5zPZB51P9kHnkve/88k+6HyyDzqf7ANJyRBCCCGEEKJNEjALIYQQQgjRBgmYj+2Nzm6AkH1wFpB90Lnk/e98sg86n+yDznfe7wPJYRZCCCGEEKIN0sMshBBCCCFEGyRg/gml1BSl1B6lVI5S6o+d3Z6uSimVqJRappTaqZTaoZS6x7M8TCm1SCmV7fkb6lmulFJzPPtlq1JqcOe+gq5DKaVXSm1WSn3juZ2qlMr0vNcfK6UMnuVGz+0cz/0pndnurkIpFaKUmqeU2q2U2qWUGinHQcdSSt3n+R7arpT6UCnlJ8fBmaWU+rdSqlQptb3FspP+3Culbvasn62UurkzXsu56hj74DnPd9FWpdQXSqmQFvc97NkHe5RSk1ssPy/iJgmYW1BK6YG5wKVAOnCdUiq9c1vVZTmABzRNSwdGAL/2vNd/BJZomtYTWOK5De590tNzuRN4teOb3GXdA+xqcfv/gBc1TesBVAG3eZbfBlR5lr/oWU+cutnA95qm9QYG4N4Xchx0EKVUPPA7YKimaRmAHpiJHAdn2tvAlJ8sO6nPvVIqDHgSGA4MA55sDrLFCXmbI/fBIiBD07T+wF7gYQDP/+eZQF/PY17xdLacN3GTBMytDQNyNE3br2maDfgIuKqT29QlaZpWpGnaJs/1OtxBQjzu9/sdz2rvANM8168C/qu5rQVClFKxHdzsLkcplQBcBrzpua2A8cA8zyo/3QfN+2YeMMGzvmgnpVQwMBp4C0DTNJumadXIcdDRfAB/pZQPYAKKkOPgjNI0bQVQ+ZPFJ/u5nwws0jStUtO0KtzB3k8DQHEMR9sHmqYt1DTN4bm5FkjwXL8K+EjTNKumablADu6Y6byJmyRgbi0eyG9x+5BnmTiDPKc0BwGZQLSmaUWeu4qBaM912TdnxkvAQ4DLczscqG7xhdnyffbuA8/9NZ71RfulAmXAfzxpMW8qpQKQ46DDaJpWAPwDOIg7UK4BNiLHQWc42c+9HA9n1i+A7zzXz/t9IAGz6FRKKTPwGXCvpmm1Le/T3FO4yDQuZ4hS6nKgVNO0jZ3dlvOYDzAYeFXTtEFAA4dPQwNyHJxpnlP4V+H+8RIHBCC9lJ1OPvedSyn1KO7Uyfc7uy1nCwmYWysAElvcTvAsE2eAUsoXd7D8vqZpn3sWlzSfYvb8LfUsl31z+l0IXKmUOoD7NNp43Pm0IZ5T09D6ffbuA8/9wUBFRza4CzoEHNI0LdNzex7uAFqOg45zCZCraVqZpml24HPcx4YcBx3vZD/3cjycAUqpW4DLgRu0w3MPn/f7QALm1tYDPT2jow24E9y/6uQ2dUmenL+3gF2apr3Q4q6vgOaRzjcD81ssv8kzWnoEUNPi1J1oB03THtY0LUHTtBTcn/WlmqbdACwDpntW++k+aN430z3rSw/QKdA0rRjIV0qleRZNAHYix0FHOgiMUEqZPN9LzftAjoOOd7Kf+wXAJKVUqOdMwSTPMtFOSqkpuNP0rtQ0rbHFXV8BMz2zxKTiHoC5jvMpbtI0TS4tLsBU3CND9wGPdnZ7uuoFuAj36batwBbPZSruXMAlQDawGAjzrK9wj8TdB2zDPaK9019HV7kAY4FvPNe74f4izAE+BYye5X6e2zme+7t1dru7wgUYCGzwHAtfAqFyHHT4PngK2A1sB94FjHIcnPH3/EPcOeN23GdabmvP5x53nm2O53JrZ7+uc+lyjH2Qgzsnufn/8mst1n/Usw/2AJe2WH5exE1S6U8IIYQQQog2SEqGEEIIIYQQbZCAWQghhBBCiDZIwCyEEEIIIUQbJGAWQgghhBCiDRIwCyGEEEII0QYJmIUQQgghhGiDBMxCCCGEEEK0QQJmIYQQQggh2vD/s+Zm5e0GuUYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(df.close)\n",
        "plt.plot(ma200,'g')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPsyhNVd3lFD",
        "outputId": "801792fd-b11d-40a4-c809-ebd17c88a19c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1258, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.shape #to find data column and row"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lbu-xV2311U",
        "outputId": "131c702b-16d5-414e-af89-3b133adb7677"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(880, 1)\n",
            "(378, 1)\n"
          ]
        }
      ],
      "source": [
        " #splitting data into training and testing\n",
        " training=pd.DataFrame(df['close'][0:int(len(df)*0.7)])\n",
        " testing=pd.DataFrame(df['close'][int(len(df)*0.7): int(len(df))])\n",
        " print(training.shape)\n",
        "print(testing.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QzGJT9nV4QLk",
        "outputId": "a8ad74fc-6cbe-499b-e79d-1ac2c9eb834d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c9d35dad-3711-495e-9617-04cc2f101d34\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>257.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>257.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>262.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>269.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>269.23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9d35dad-3711-495e-9617-04cc2f101d34')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c9d35dad-3711-495e-9617-04cc2f101d34 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c9d35dad-3711-495e-9617-04cc2f101d34');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    close\n",
              "0  257.77\n",
              "1  257.48\n",
              "2  262.08\n",
              "3  269.20\n",
              "4  269.23"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "training.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cawiewpI4cmp",
        "outputId": "1dd71e42-a9c8-4026-8eb3-1f3f0f4bd379"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2350ef72-ec25-4c8e-95ed-25dc9ce58401\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>880</th>\n",
              "      <td>1485.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>881</th>\n",
              "      <td>1489.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>882</th>\n",
              "      <td>1452.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>883</th>\n",
              "      <td>1418.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>884</th>\n",
              "      <td>1374.39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2350ef72-ec25-4c8e-95ed-25dc9ce58401')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2350ef72-ec25-4c8e-95ed-25dc9ce58401');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       close\n",
              "880  1485.02\n",
              "881  1489.58\n",
              "882  1452.71\n",
              "883  1418.57\n",
              "884  1374.39"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "testing.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "7IwrzcIb4gV3"
      },
      "outputs": [],
      "source": [
        "#scaling down the data between 0 and 1\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "koWJ3r9B4w9Z",
        "outputId": "3d414550-87c0-476b-8262-47ecf29a087d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " [0.3223841 ]\n",
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            " [0.4236252 ]\n",
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            " [0.43482032]\n",
            " [0.4430442 ]\n",
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            " [0.42816063]\n",
            " [0.44809874]\n",
            " [0.49119895]\n",
            " [0.47990137]\n",
            " [0.48085763]\n",
            " [0.46816664]\n",
            " [0.48270186]\n",
            " [0.5265944 ]\n",
            " [0.52027622]\n",
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            " [0.54224982]\n",
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            " [0.54859532]\n",
            " [0.55519354]\n",
            " [0.56350621]\n",
            " [0.56141609]\n",
            " [0.55692165]\n",
            " [0.56201717]\n",
            " [0.53406009]\n",
            " [0.55122504]\n",
            " [0.53330191]\n",
            " [0.56718783]\n",
            " [0.61531526]\n",
            " [0.64251416]\n",
            " [0.70332575]\n",
            " [0.81460762]\n",
            " [0.82709371]\n",
            " [0.81071426]\n",
            " [0.83011277]\n",
            " [0.93282241]\n",
            " [0.90031625]\n",
            " [0.91379958]\n",
            " [0.93375136]\n",
            " [0.90276156]\n",
            " [0.90289816]\n",
            " [1.        ]\n",
            " [0.94901744]\n",
            " [0.96539005]\n",
            " [0.91125182]\n",
            " [0.84563158]\n",
            " [0.92937303]\n",
            " [0.88626599]\n",
            " [0.90171649]\n",
            " [0.8937795 ]\n",
            " [0.85503029]\n",
            " [0.89207871]\n",
            " [0.89344481]]\n"
          ]
        }
      ],
      "source": [
        "#to convert it into an array\n",
        "training_array=scaler.fit_transform(training)\n",
        "print(training_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rUfHMmLjHzqe",
        "outputId": "5c398c0e-30c6-4aea-ec05-d9b173a7d310"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(880, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "training_array.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "-oYWT_m-HyOv"
      },
      "outputs": [],
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(100,training_array.shape[0]):\n",
        "  x_train.append(training_array[i-100: i])\n",
        "  y_train.append(training_array[i,0])\n",
        "\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r00-c8tQ86xn",
        "outputId": "e1549463-4776-4214-c703-00dd14fe3997"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(780, 100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htUsP1zIH4kk"
      },
      "source": [
        "#ML MODEL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "XA1KW0xK8H6V"
      },
      "outputs": [],
      "source": [
        "from keras.layers import Dense, Dropout, LSTM\n",
        "from keras.models import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "gl9EeXjj8kkC"
      },
      "outputs": [],
      "source": [
        "#model is sequential, then we set up our LSTM LAYERS, Sequential model is a linear stack of layers.\n",
        "#the units -- poaitive integer that is dimention of the output space, what's going out into the next layer\n",
        "#so basically even if we have 50 inputs, we will be having 60 outputs\n",
        "#relu is a common activation unit for convuational layers, \n",
        "#Applies the rectified linear unit activation function.\n",
        "#With default values, this returns the standard ReLU activation: max(x, 0), the element-wise maximum of 0 and the input tensor.\n",
        "#return data in sequence == truw\n",
        "#1,1 to tell Sequential what model to use, in what format \n",
        "#dropout layer == we can overtrain our data, so basically it will become rattu tota and ONLY print what it KNOWS, 0 prediction\n",
        "#20%==0.2 (random) of our neurons are just going to be turned off at all times to train other ones\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=50,activation=\"relu\", return_sequences=True, \n",
        "               input_shape=(x_train.shape[1],1)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=60,activation=\"relu\",return_sequences=True))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(LSTM(units=80,activation=\"relu\", return_sequences=True))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(LSTM(units=120,activation=\"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "#this is output layer \n",
        "model.add(Dense(units=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOphKGq99mgD",
        "outputId": "a424eeb0-24af-4a43-ad65-f95a057997eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 100, 50)           10400     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100, 50)           0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 120)               96480     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 120)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 121       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,761\n",
            "Trainable params: 178,761\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smEJKCBc9ool",
        "outputId": "1a5b87a5-9d03-443e-905d-4442a106aad8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "25/25 [==============================] - 12s 276ms/step - loss: 0.0274\n",
            "Epoch 2/50\n",
            "25/25 [==============================] - 7s 277ms/step - loss: 0.0073\n",
            "Epoch 3/50\n",
            "25/25 [==============================] - 7s 268ms/step - loss: 0.0108\n",
            "Epoch 4/50\n",
            "25/25 [==============================] - 9s 381ms/step - loss: 0.0076\n",
            "Epoch 5/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0058\n",
            "Epoch 6/50\n",
            "25/25 [==============================] - 7s 283ms/step - loss: 0.0047\n",
            "Epoch 7/50\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.0046\n",
            "Epoch 8/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0041\n",
            "Epoch 9/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0039\n",
            "Epoch 10/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0058\n",
            "Epoch 11/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0046\n",
            "Epoch 12/50\n",
            "25/25 [==============================] - 7s 274ms/step - loss: 0.0044\n",
            "Epoch 13/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0042\n",
            "Epoch 14/50\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.0044\n",
            "Epoch 15/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0038\n",
            "Epoch 16/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0038\n",
            "Epoch 17/50\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.0040\n",
            "Epoch 18/50\n",
            "25/25 [==============================] - 7s 282ms/step - loss: 0.0033\n",
            "Epoch 19/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0034\n",
            "Epoch 20/50\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.0035\n",
            "Epoch 21/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0030\n",
            "Epoch 22/50\n",
            "25/25 [==============================] - 7s 283ms/step - loss: 0.0041\n",
            "Epoch 23/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0034\n",
            "Epoch 24/50\n",
            "25/25 [==============================] - 7s 275ms/step - loss: 0.0041\n",
            "Epoch 25/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0034\n",
            "Epoch 26/50\n",
            "25/25 [==============================] - 7s 283ms/step - loss: 0.0030\n",
            "Epoch 27/50\n",
            "25/25 [==============================] - 7s 283ms/step - loss: 0.0029\n",
            "Epoch 28/50\n",
            "25/25 [==============================] - 7s 285ms/step - loss: 0.0035\n",
            "Epoch 29/50\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.0029\n",
            "Epoch 30/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0027\n",
            "Epoch 31/50\n",
            "25/25 [==============================] - 7s 285ms/step - loss: 0.0032\n",
            "Epoch 32/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0026\n",
            "Epoch 33/50\n",
            "25/25 [==============================] - 7s 282ms/step - loss: 0.0024\n",
            "Epoch 34/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0030\n",
            "Epoch 35/50\n",
            "25/25 [==============================] - 7s 281ms/step - loss: 0.0032\n",
            "Epoch 36/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0026\n",
            "Epoch 37/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0025\n",
            "Epoch 38/50\n",
            "25/25 [==============================] - 7s 276ms/step - loss: 0.0034\n",
            "Epoch 39/50\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.0023\n",
            "Epoch 40/50\n",
            "25/25 [==============================] - 7s 276ms/step - loss: 0.0024\n",
            "Epoch 41/50\n",
            "25/25 [==============================] - 7s 271ms/step - loss: 0.0033\n",
            "Epoch 42/50\n",
            "25/25 [==============================] - 7s 273ms/step - loss: 0.0025\n",
            "Epoch 43/50\n",
            "25/25 [==============================] - 7s 271ms/step - loss: 0.0028\n",
            "Epoch 44/50\n",
            "25/25 [==============================] - 7s 277ms/step - loss: 0.0028\n",
            "Epoch 45/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0029\n",
            "Epoch 46/50\n",
            "25/25 [==============================] - 7s 275ms/step - loss: 0.0029\n",
            "Epoch 47/50\n",
            "25/25 [==============================] - 7s 276ms/step - loss: 0.0030\n",
            "Epoch 48/50\n",
            "25/25 [==============================] - 7s 280ms/step - loss: 0.0029\n",
            "Epoch 49/50\n",
            "25/25 [==============================] - 7s 278ms/step - loss: 0.0026\n",
            "Epoch 50/50\n",
            "25/25 [==============================] - 7s 279ms/step - loss: 0.0025\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff97c797850>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "#compiling our data, adam is used as its most common optimizer in big data\n",
        "#mean squared error = os the error in our model \n",
        "#epochs are how many times we are going over the data \n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(x_train, y_train, epochs=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "HmpHT2do93n7"
      },
      "outputs": [],
      "source": [
        "model.save('keras_model.h5')"
      ]
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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              "       close\n",
              "880  1485.02\n",
              "881  1489.58\n",
              "882  1452.71\n",
              "883  1418.57\n",
              "884  1374.39"
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          "execution_count": 28
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        "testing.head()"
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        "id": "2DubDg0mH4Al",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "870  1592.33\n",
              "871  1513.07\n",
              "872  1417.00\n",
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              "874  1476.49\n",
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              "878  1485.00\n",
              "879  1487.00"
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      "source": [
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    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "-Ma5sZFz_j0k"
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      "source": [
        "past_100_days= training.tail(100)\n"
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    {
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      "execution_count": 32,
      "metadata": {
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              "      close\n",
              "473  829.10\n",
              "474  846.35\n",
              "475  936.72\n",
              "476  931.25\n",
              "477  905.66"
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          "metadata": {},
          "execution_count": 33
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      "execution_count": 34,
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        "outputId": "836978a9-8607-494c-e9f3-94d937a2de23"
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              "       [0.40144299],\n",
              "       [0.42275771],\n",
              "       [0.43313737],\n",
              "       [0.40807109],\n",
              "       [0.4117231 ],\n",
              "       [0.39386652],\n",
              "       [0.42272103],\n",
              "       [0.42679221],\n",
              "       [0.40071992],\n",
              "       [0.39527073],\n",
              "       [0.35878735],\n",
              "       [0.35566454],\n",
              "       [0.37805862],\n",
              "       [0.38707598],\n",
              "       [0.3529347 ],\n",
              "       [0.3598667 ],\n",
              "       [0.33334381],\n",
              "       [0.32920452],\n",
              "       [0.33836336],\n",
              "       [0.3126526 ],\n",
              "       [0.31561298],\n",
              "       [0.29851614],\n",
              "       [0.31873579],\n",
              "       [0.35559118],\n",
              "       [0.38604902],\n",
              "       [0.40016452],\n",
              "       [0.39729846],\n",
              "       [0.39610383],\n",
              "       [0.38779905],\n",
              "       [0.38069414],\n",
              "       [0.45562053],\n",
              "       [0.42932294],\n",
              "       [0.39711507],\n",
              "       [0.38484391],\n",
              "       [0.36506963],\n",
              "       [0.38139625],\n",
              "       [0.38468672],\n",
              "       [0.40659876],\n",
              "       [0.36747985],\n",
              "       [0.37693734],\n",
              "       [0.36692969],\n",
              "       [0.34866442],\n",
              "       [0.34898928],\n",
              "       [0.32154946],\n",
              "       [0.3142664 ],\n",
              "       [0.30818846],\n",
              "       [0.31814895],\n",
              "       [0.26139877],\n",
              "       [0.27043709],\n",
              "       [0.31778742],\n",
              "       [0.31492135],\n",
              "       [0.3015132 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "#again scale it down\n",
        "input_data = scaler.fit_transform(final_df)\n",
        "input_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VT9fLeXtGjb1",
        "outputId": "90dfef9c-a545-4301-a25d-5049d70224f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(478, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "input_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "4KtHa25dGnFr"
      },
      "outputs": [],
      "source": [
        "x_test = []\n",
        "y_test = []\n",
        "\n",
        "for i in range(100,input_data.shape[0]):\n",
        "  x_test.append(input_data[i-100:i])\n",
        "  y_test.append(input_data[i,0])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SA_zlh9_K_17",
        "outputId": "8d25539e-c448-4d94-dbde-b2b964bcf744"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(378, 100, 1)\n",
            "(378,)\n"
          ]
        }
      ],
      "source": [
        "x_test, y_test = np.array(x_test), np.array(y_test)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "rtypc2hGJV7j"
      },
      "outputs": [],
      "source": [
        "#Making Predicitions\n",
        "y_predicted = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWUL3O3ZLQEw",
        "outputId": "096c778f-31ab-4f6f-c5de-ac662e0d0fd9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(378, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "y_predicted.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFm64bZmJcYv",
        "outputId": "751fafa8-bcaf-4bfc-f6b7-50e9e31d4f62"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.00052396])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "#scale the values back \n",
        "scaler.scale_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "ofKmqrn9JkS6"
      },
      "outputs": [],
      "source": [
        "scale_factor=1/0.00052396\n",
        "y_predicted= y_predicted* scale_factor\n",
        "y_test = y_test * scale_factor\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "XmU6BJR5Jzbc",
        "outputId": "9f8ceed5-f077-43f9-fbb6-a39de301e2f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(y_test)\n",
        "plt.plot(y_predicted)\n",
        "plt.show()\n",
        "#There are a lot of ways to make this basic graph much better!\n",
        "#Play with other functions of the plt module and make your graph more appealing. "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "MAIN: Stock Prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}