NN LSTM ARIMA prediction timeseries.ipynb

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        "полезные ссылки:\n",
        "https://habr.com/ru/articles/495884/"
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      "source": [
        "#df = pd.read_csv('/kaggle/input/usd-rub-timeseries/UsdRubTime.csv', delimiter=';', decimal='.')"
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        "df = pd.read_csv('https://raw.githubusercontent.com/TAUforPython/machinelearning/refs/heads/main/test_datasets/UsdRubTime.csv',delimiter=';', decimal='.')\n"
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              "   Close        Data  Position\n",
              "0  0.988  22.08.1993         1\n",
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              "0   1993-08-22\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1993-08-22</th>\n",
              "      <td>0.988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-08-29</th>\n",
              "      <td>0.993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-09-05</th>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-09-12</th>\n",
              "      <td>1.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-09-19</th>\n",
              "      <td>1.300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d8b0b9c-406a-4d2e-a0b4-87f99b1c9333')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3d8b0b9c-406a-4d2e-a0b4-87f99b1c9333 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3d8b0b9c-406a-4d2e-a0b4-87f99b1c9333');\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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              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d988320a-de38-4cba-be2c-9277019c0cfe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_close",
              "summary": "{\n  \"name\": \"df_close\",\n  \"rows\": 1542,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1993-08-22 00:00:00\",\n        \"max\": \"2023-03-05 00:00:00\",\n        \"num_unique_values\": 1542,\n        \"samples\": [\n          \"2022-03-27 00:00:00\",\n          \"2015-10-18 00:00:00\",\n          \"2017-08-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.89067728520827,\n        \"min\": 0.988,\n        \"max\": 133.417,\n        \"num_unique_values\": 1476,\n        \"samples\": [\n          66.8,\n          5.772,\n          62.452\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "C9fB4ubaVTQb",
        "outputId": "a762bb6e-a73a-490e-cc44-f55b6cac8858"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Close       0\n",
              "Data        0\n",
              "Position    0\n",
              "dtype: int64"
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              "  </tbody>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_close.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "q_SH5sZcCmH0",
        "outputId": "1b5af802-6724-47b5-fab4-3d4c9b58905c",
        "execution": {
          "iopub.status.busy": "2024-09-19T16:42:36.691238Z",
          "iopub.execute_input": "2024-09-19T16:42:36.691639Z",
          "iopub.status.idle": "2024-09-19T16:42:36.712244Z",
          "shell.execute_reply.started": "2024-09-19T16:42:36.691601Z",
          "shell.execute_reply": "2024-09-19T16:42:36.711023Z"
        },
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Close\n",
              "count  1542.000000\n",
              "mean     35.306973\n",
              "std      21.890677\n",
              "min       0.988000\n",
              "25%      25.754750\n",
              "50%      29.871000\n",
              "75%      57.287250\n",
              "max     133.417000"
            ],
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              "        document.querySelector('#df-8988f23c-d7e8-477c-be13-0d9f00fbe355 button.colab-df-convert');\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8988f23c-d7e8-477c-be13-0d9f00fbe355');\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",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7891bb11-c663-4766-a071-8b616dae3dd3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_close\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 531.2956888486674,\n        \"min\": 0.988,\n        \"max\": 1542.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.306973411154345,\n          29.871000000000002,\n          1542.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.plotting import register_matplotlib_converters\n",
        "register_matplotlib_converters()\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(df_close, color='g')\n",
        "plt.title('Closing Price', weight='bold', fontsize=16)\n",
        "plt.xlabel('Time', weight='bold', fontsize=14)\n",
        "plt.ylabel('Rub', weight='bold', fontsize=14)\n",
        "plt.xticks(weight='bold', fontsize=12, rotation=45)\n",
        "plt.yticks(weight='bold', fontsize=12)\n",
        "plt.grid(color = 'grey', linewidth = 0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "9kBeq-AGCxZP",
        "outputId": "44b4b449-ebfe-4962-a410-b1214fd97bd4",
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.hist(df['Close'], bins=25)\n",
        "plt.title('Частота распределения значений курса')\n",
        "plt.xlabel('стоимость')\n",
        "plt.ylabel('частота')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "k3vCtHP7FaNh",
        "outputId": "4e27d770-4262-47e6-ea3a-42b244aefbf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/"
      ],
      "metadata": {
        "id": "kp48PNoit7nP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction model (one step) - NN LSTM"
      ],
      "metadata": {
        "id": "fvMMhNy4GWzz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense"
      ],
      "metadata": {
        "id": "DcSPu8eTGWi5",
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_close[[\"Close\"]] = df_close[[\"Close\"]].values.astype('float32')\n",
        "\n",
        "train_size = int(len(df_close) * 0.6)\n",
        "train_data, test_data = df_close[:train_size], df_close[train_size:]"
      ],
      "metadata": {
        "id": "y1m3fXvDGdnt",
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot correctly made splits\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_data['Close'], label=\"Train data\")\n",
        "plt.plot(test_data['Close'], 'r-', label=\"Test data\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"USD RUB Price\")\n",
        "plt.legend(fontsize=14)\n",
        "plt.grid()\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "7tgpnaMnH2su",
        "outputId": "ef436806-90b5-485e-cd96-53e8c307d143",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_close"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "NIDgLyTJnOsS",
        "outputId": "2cc5bcb7-cf25-4848-9825-f560fbb3290f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Close\n",
              "Data                 \n",
              "1993-08-22   0.988000\n",
              "1993-08-29   0.993000\n",
              "1993-09-05   1.000000\n",
              "1993-09-12   1.020000\n",
              "1993-09-19   1.300000\n",
              "...               ...\n",
              "2023-02-05  73.348000\n",
              "2023-02-12  74.230003\n",
              "2023-02-19  76.135002\n",
              "2023-02-26  75.711998\n",
              "2023-03-05  76.071999\n",
              "\n",
              "[1542 rows x 1 columns]"
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              "\n",
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              "      <th>1993-08-22</th>\n",
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              "      <td>1.300000</td>\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_close",
              "summary": "{\n  \"name\": \"df_close\",\n  \"rows\": 1542,\n  \"fields\": [\n    {\n      \"column\": \"Data\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1993-08-22 00:00:00\",\n        \"max\": \"2023-03-05 00:00:00\",\n        \"num_unique_values\": 1542,\n        \"samples\": [\n          \"2022-03-27 00:00:00\",\n          \"2015-10-18 00:00:00\",\n          \"2017-08-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 1476,\n        \"samples\": [\n          66.80000305175781,\n          5.771999835968018,\n          62.45199966430664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "scaled_train_data = scaler.fit_transform(train_data[['Close']])\n",
        "#scaled_test_data = scaler.transform(test_data[['Close']])\n",
        "#scaled_test_data = scaler.fit_transform(test_data[['Close']])"
      ],
      "metadata": {
        "id": "0Bgh_RMlGm9I",
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_data['Close'].index, scaled_train_data, label=\"Train data\")\n",
        "#plt.plot(test_data['Close'].index, scaled_test_data, 'r-', label=\"Test data\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"USD Price\")\n",
        "plt.legend(fontsize=14)\n",
        "plt.grid()\n",
        "plt.show();"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "89G6AZJ9Tped",
        "outputId": "ec1cf244-499a-4b3c-cc67-4c7e67ca3f50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**epochs**: Обучение разбито на *эпохи*. Эпоха это одна итерация по всем входным данным (это делается небольшими партиями).\n",
        "\n",
        "**batch_size**: При передаче данных NumPy, модель разбивает данные на меньшие блоки (batches) и итерирует по этим блокам во время обучения. Это число указывает размер каждого блока данных. Помните, что последний блок может быть меньшего размера если общее число записей не делится на размер партии."
      ],
      "metadata": {
        "id": "bDuqZiGAXIQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM model\n",
        "def create_lstm_model(n_steps):\n",
        "    model = Sequential()\n",
        "    model.add(LSTM(64, activation='relu', input_shape=(n_steps, 1)))\n",
        "    model.add(Dense(1))\n",
        "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "    return model"
      ],
      "metadata": {
        "trusted": true,
        "id": "IhRqElK0Tped"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def split_sequence(sequence, n_steps):\n",
        "  X, y = list(), list()\n",
        "  for i in range(len(sequence)):\n",
        "    # find the end of this pattern\n",
        "    end_ix = i + n_steps\n",
        "    # check if we are beyond the sequence\n",
        "    if end_ix > len(sequence)-1:\n",
        "      break\n",
        "      # gather input and output parts of the pattern\n",
        "    seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
        "    X.append(seq_x)\n",
        "    y.append(seq_y)\n",
        "  return np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "ihAOrDTof2Yn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_steps = 20\n",
        "# split into samples\n",
        "X, y = split_sequence(scaled_train_data, n_steps)"
      ],
      "metadata": {
        "id": "7XVMwCfiez8P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model = Sequential()\n",
        "#model.add(LSTM(7, input_shape=(1, 1)))\n",
        "lstm_model = create_lstm_model(n_steps=n_steps)\n",
        "\n",
        "n_features = 1\n",
        "X = X.reshape((X.shape[0], X.shape[1], n_features))\n",
        "lstm_history = lstm_model.fit(X,y,epochs=10, verbose=1)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9M-x5Y1Tped",
        "outputId": "d0a4d224-9f86-400c-c461-0dac16cee71d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.3240\n",
            "Epoch 2/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.0134\n",
            "Epoch 3/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0028\n",
            "Epoch 4/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0014\n",
            "Epoch 5/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0013\n",
            "Epoch 6/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.0012\n",
            "Epoch 7/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 9.7099e-04\n",
            "Epoch 8/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 8.7497e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0010\n",
            "Epoch 10/10\n",
            "\u001b[1m29/29\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 8.1691e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.inverse_transform(scaled_train_data[-n_steps:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQnYpPxLlJcN",
        "outputId": "0977f50f-358b-4816-ef8c-1914a50b96dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30.432999],\n",
              "       [30.534998],\n",
              "       [30.003   ],\n",
              "       [29.813997],\n",
              "       [29.693   ],\n",
              "       [29.306   ],\n",
              "       [29.222   ],\n",
              "       [29.066998],\n",
              "       [28.791998],\n",
              "       [28.075998],\n",
              "       [28.516998],\n",
              "       [28.344997],\n",
              "       [28.229998],\n",
              "       [28.199999],\n",
              "       [27.936   ],\n",
              "       [28.030998],\n",
              "       [27.91    ],\n",
              "       [27.284996],\n",
              "       [27.603998],\n",
              "       [28.042997]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_input = scaled_train_data[-n_steps:]\n",
        "x_input = x_input.reshape((1, n_steps, n_features))\n",
        "yhat = lstm_model.predict(x_input, verbose=1)\n",
        "print(scaler.inverse_transform(yhat))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_Yw0yPqg4sI",
        "outputId": "10a5b896-75bc-4cc0-93e7-20c0eaeeafed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step\n",
            "[[28.41176]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_data['Close'].index[-10:],train_data[-10:],'-b.', label = 'History')\n",
        "plt.plot(test_data['Close'].index[0],test_data['Close'][0],'gx', label = 'True Future')\n",
        "plt.plot(test_data['Close'].index[0],scaler.inverse_transform(yhat),'ro', label = 'Model Prediction')\n",
        "plt.legend(loc='best')\n",
        "plt.grid()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "SKBYQ7ZMhvS_",
        "outputId": "c5429edb-1154-4964-eab3-b13cad0e538d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-8536b9a855e3>:2: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
            "  plt.plot(test_data['Close'].index[0],test_data['Close'][0],'gx', label = 'True Future')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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fvmLV9pdeEsnOrFnAc88B16/LHRkREdkTJj1kFjVqANu3A199JSY3PHVKrN/15Zcc2k5ERObBpIfMRqEAXntN1Pp07y46No8fDwwYAPzyC3DsGHD7ttxREhGRrWLSQ2ZXuzZw+DCwbBng5ATs2yc6OffoIRYzXb9e7giJiMgWMekhWTg4iGUr9u0ruF+rBV5/XdQEffAB8O23YjFTtVqeOImIyHao5A6A7JtSWfT+qCjx0HFyApo2BVq0EH2BdM+enuaIkoiIbAGTHpJVgwai1if/6uwODsCCBWLh0vPnRR+gjAwx78+5cwW/39f3cRKkS4QaNQIcHc37PoiIyPIx6SFZ+fmJEVzjxwMajaj5+eILYNy4x8dotcCNG8CFC+Jx/rx4TkwE7t0Tj4MHHx/v5AQ0aVK4VsjLy/zvj4iILAeTHpLduHFAnz5i3p6AAJEI5efgANSvLx6DBz/e//AhcOnS4yRI93j4UCx+Gh9fsBwfn8KJUKNGIkkiIiLbZ1DSs3jxYuzatQu//vorXFxc0KlTJ3zyySdo1KiR/pjk5GRMnz4dkZGRePjwIRo1aoSZM2fixRdfrFC5ZNv8/AonO6VxdxczPHfs+HifVgv8/vvjRCh/rVBysngcOvT4eEfHomuFvL2N876IiMhyGJT0REdHIzQ0FO3atUNeXh4+/PBDBAcH48qVK3BzcwMAjBw5EqmpqdizZw88PT0RHh6OoUOH4uzZs2jdunW5yyUqCwcHoF498QgJebw/I0PUCuVPhC5cANLTxevz58XEiTpPPVU4EWrcuGCt0O3bwLVrol+SoQkbERGZn0FJz8H8HScAbNq0Cd7e3oiLi0PXrl0BACdOnMDq1avRvn17AMBHH32E5cuXIy4urtikpyzlElVE5crAs8+Kh44kFV0rdP06cP8+EBkpHjqOjkBgoEiCsrOBXbtEzZKDg+iXlL8fEhERWZ4K9elJS0sDAFSvXl2/r1OnTti2bRv69++PqlWrYvv27cjOzkZQUFCFys0vJycHOTk5+tfp6ekAALVaDTUndCkX3XWzt+tXq5Z49Ov3eN+jR8DlywpcvAhcuKDAxYvikZam0NcQ5afVAuPHS+jRI8+sNT72es/kwGttuXhvzMNU19nc900hSeVb+Uir1WLgwIFITU1FbGysfn9qaiqGDRuGQ4cOQaVSwdXVFTt27EBwcHCFys1v7ty5mDdvXqH94eHhcHV1Lc/bISqRJAF//OGCpCQP/PyzL44cqVPomAULYtG8+Z8yREdEZJ0yMzPx6quvIi0tDR4eHiY/X7mTnokTJ+LAgQOIjY2FX75/bydNmoTTp09j0aJF8PT0xO7du7F8+XIcP34czZs3L3e5+RVV0+Pv748HDx6Y5aLZIrVajcjISPTu3RuOnOSmRLdvAwEBKmi1Cv0+hUJCYqL5a3p4z8yD19py8d6Yh6muc3p6Ojw9Pc2W9JSreSssLAx79+5FTExMgcQkMTERK1euxKVLl9C0aVMAQMuWLXH8+HGsWrUKa9asKVe5T3J2doazs3Oh/Y6OjvyhryBew9LVq1dwbiEAcHBQICfHUZZJEXnPzIfX2nLx3piHsa+zue+ZQWtvSZKEsLAwRERE4OjRo6hXr16Br2dmZopCHQoWq1Qqoc0/5a6B5RJZmnHjgKQk4OhRsU6YRgNMmCCawYiIyDIZlPSEhobi66+/Rnh4ONzd3ZGcnIzk5GRkZWUBABo3boyAgACMHz8ep0+fRmJiIpYtW4bIyEiE5Bs/3LNnT6xcubLM5RJZIj8/kfBs2AC4ugLR0cCmTXJHRURExTEo6Vm9ejXS0tIQFBQEX19f/WPbtm0ARDXV/v374eXlhQEDBqBFixbYsmULNm/ejH75hsckJibiwYMHZS6XyJLVrQvo+tW/+y7wxx+yhkNERMUwqE9PWfo8N2jQADt37izxmKSkJIPLJbJkb78NfP21mOvn3XeBzZvljoiIiJ5kUE0PERXN0VF0blYogC1bgCNH5I6IiIiexKSHyEjatwdCQ8X2hAli1mYiIrIcTHqIjGjhQqBmTbGUxaJFckdDRET5MekhMqIqVYD//ldsL1kCXLkibzxERPQYkx4iIxsyBBgwAFCrxQSGJUxRRUREZsSkh8jIFApg5UrAzQ2IjQU2bpQ7IiIiApj0EJlE7drA/Plie/p0ICVF3niIiIhJD5HJTJ4MtG4N/P03MG2a3NEQERGTHiITUanE3D0ODsA33wCRkXJHRERk35j0EJnQM88AYWFie+JEgMvJERHJh0kPkYktXAjUqgUkJoptIiKSB5MeIhNzdxejuQBg6VLg8mV54yEisldMeojMICQEGDQIyMvj3D1ERHJh0kNkJv/7H1C5MvDTT8C6dXJHQ0Rkf5j0EJmJv//jPj3vvw8kJ8sbDxGRvWHSQ2RGYWFA27ZAairn7iEiMjcmPURmpFQ+nrvn22+BgwfljoiIyH4w6SEyszZtgLffFttvvQVkZsobDxGRvWDSQySD+fNFH58bN4AFC+SOhojIPjDpIZJB5crAqlVi+9NPgYsX5Y2HiMgeMOkhksmAAcCQIWLunjff5Nw9RESmxqSHSEb//a+YsfnUKdHBmYiITIdJD5GMatUCFi0S2x98ANy7J288RES2jEkPkcwmTgTatQPS0oApU+SOhojIdjHpIZKZbu4epRLYvh3Yv1/uiIiIbBOTHiIL0KoVMHWq2H7rLeDRI1nDISLC3Ki5WBBd9JwaC6IXYG7UXPMGZARMeogsxNy5QO3awO+/A/PmyR0NEdk7pUKJ2VGzCyU+C6IXYHbUbCgVSpkiKz+V3AEQkeDmBnz+OfDCC8B//gMMHw60bCl3VERkr2Z1mwUAmB01GxqtBq3RGh/Hfox5MfMwP2i+/uvWhEkPkQXp3x94+WVgxw4xd8+JE6KvDxGRHPInPiqFCnlSntUmPACbt4gszooVgIcHcPo0sGaN3NEQkb2b1W0WnJROyJPy4KR0stqEB2DSQ2RxatYEFi8W2zNmAHfvyhsPEdm3BdELkKvJhUqhQq4mt9jOzdaASQ+RBZowAejQAXj48PGK7ERE5qbrtDyn6xx81/I7zOk6p8jOzdaCSQ+RBXJweDx3z3ffAXv3yh0REdkbXcIzP2g+ZnaZCQCY2WUm5gfNt9rEh0kPkYVq0QJ45x2xHRoKZGTIGw8RGcft28CxY+LZkmkkTZGdlmd1m4X5QfOhkTQyRVZ+HL1FZMHmzBGzNCcliXl8Pv1U7oiIqCLWrwfeeAOQpMc1uuPGyR1V0eYGzS32a9bamZk1PUQWzNVVzN0DiFFd587JGg4RVcDt22IqCkkSr7VaYPx4y6/xsSVMeogs3PPPA8OGARqN+IWpsb4a5QqxlqYAotJcuyYSnfw0GuD6dXnisUdMeoiswIoVQJUqwNmzj2t+7MH69UCdOkCPHuJ5/Xq5IyIqvzp1Cu9TKoGAAPPHYq+Y9BBZAR8f4JNPxPaHH9pHrYeuKUD3nzGbAsjanT9f8LVSCXzxBeDnJ0889ohJD5GVeOMNoFMnMYpr8mS5ozGt7GzRcbuopoDYWFlCIqqwTZvE84QJosk2KclyOzHbKiY9RFbCwUH8V6hSARERwJ49CrlDMjpJAnbuBAIDi2/KGj8e2LXLvHERVdT9+8C+fWJ70iQgKIg1PHJg0kNkRZo1A6ZPF9tTpiiRlWU7s07ExwPduwMvvST+A65VC3j99ccLrjo4APXqAenpwIsvAhMnAllZckZMVHbffCNqKtu3B5o0kTsa+8Wkh8jKzJoFPP00cPu2AuHhjeUOp8JSUkTfnTZtgOhooFIl8R4TEoC1a0UCdOwY8PvvYt9774nvW7NG/AG5ckXW8IlKJUnAxo1ie8wYeWOxdwYlPYsXL0a7du3g7u4Ob29vhISEICEhocAxycnJGDFiBHx8fODm5oY2bdpg586dpZa9atUq1K1bF5UqVUKHDh1w+vRpw94JkZ1wcQFWrxbb+/Y9jV9+kTee8srNFZMtNmggkhtJEkPzf/0VmD8fcHMTx/n5PW4KcHQUHbp//BHw9gYuXQKeeQZYt+7x3CdEluaXX8TPqrOz+Bkn+RiU9ERHRyM0NBSnTp1CZGQk1Go1goOD8ejRI/0xI0eOREJCAvbs2YOLFy9iyJAhGDp0KM6VMKvatm3bMG3aNMyZMwe//PILWrZsiT59+iAlJaX874zIhgUHA6+8ooVWq8DEiSrk5ckdUdlJEvDDD0DTpqKpLj0daNsWOH4c2Lq16GG9TwoOBi5cEM9ZWaKT9yuvAGlppo+fyFC6DswhIUC1anJGQpAqICUlRQIgRUdH6/e5ublJW7ZsKXBc9erVpbVr1xZbTvv27aXQ0FD9a41GI9WsWVNavHhxmeJIS0uTAEhpaWkGvgPSyc3NlXbv3i3l5ubKHQqV0a1buZKbW44ESNLy5XJHUzaXLklS796SJFIfSXrqKUnasEGSNJrylafRSNInn0iSSiXKq1tXkk6eNG7MksTPhyWz9HuTnS1J1auLn8+DB+WOpvxMdZ3N/fe7Qr0g0/75t6p69er6fZ06dcK2bdvQv39/VK1aFdu3b0d2djaCgoKKLCM3NxdxcXGYMWOGfp+DgwN69eqFkydPFvk9OTk5yMnJ0b9OT08HAKjVaqjV6oq8Jbulu268ftajenU1Ro9OwKpVrfHRRxIGDsyDv7/cURXtzz+B+fMd8OWXDtBoFHBykvD221p88IEW7u6ig2d5Z5qeOhXo3FmBESOUuHFDgeeekzBvnhbvvKOFg5F6LfLzYbks/d5ERCjw118q1KoloVu3PFhomKUy1XU2931TSFL5WsK1Wi0GDhyI1NRUxOabOCM1NRXDhg3DoUOHoFKp4Orqih07diA4OLjIcu7evYtatWrhxIkT6Nixo37/e++9h+joaPz888+Fvmfu3LmYN29eof3h4eFwdXUtz9shskpaLTBzZhdcvVoD7dvfw4wZp6GwoJHseXkKHDxYD1u3NkJGhhMA4Nln72L06Mvw8ck06rkePVJh9eqWiI0V44BbtkzBlCm/oFq1nFK+k8h0FizogLg4H7z44m8YMeKq3OFYnMzMTLz66qtIS0uDh4eHyc9X7pqe0NBQXLp0qUDCAwCzZs1CamoqDh8+DE9PT+zevRtDhw7F8ePH0bx58woHDAAzZszAtGnT9K/T09Ph7++P4OBgs1w0W6RWqxEZGYnevXvD0dFR7nCoDHT37KuvXNGxo4TTp32hVvdHSIhl9Oj98UcFPvhAiV9/FVlY8+YSli3TICjIC0CQSc750kvApk15mDJFifPnvfH++32wcaMGvXtX7Jrw82G5LPne3LsHnDsn/szOnVsPjRrVkzmi8jPVdda11JhLuZKesLAw7N27FzExMfDLN7tSYmIiVq5ciUuXLqFp06YAgJYtW+L48eNYtWoV1qxZU6gsT09PKJVK3L9/v8D++/fvw8fHp8jzOzs7w9nZudB+R0dHi/uhtza8htanRQsV3n9fgYULgalTVejTB5Az909IAKZNA/bvF689PYGFC4HXX1dAqTT9vEJvvgl06SI6Nl+8qED//ipMny5icHKqWNn8fFguS7w327aJ2tiOHYFmzSwrtvIy9nU29z0zqMVbkiSEhYUhIiICR48eRb16BbPWzExRXe3wREO6UqmE9sn55P/h5OSEtm3b4siRI/p9Wq0WR44cKdDcRUTF+/BDsWjhnTvARx/JE0Nqqkh2mjUTCY9KJV5fuyZmUdZNMmgOTZoAP/8MvPWWeP3vfwPPPQf83/+ZLwayb5ybxzIZlPSEhobi66+/Rnh4ONzd3ZGcnIzk5GRk/TMtauPGjREQEIDx48fj9OnTSExMxLJlyxAZGYmQkBB9OT179sTKlSv1r6dNm4a1a9di8+bNuHr1KiZOnIhHjx5hDH9SiMrExUVM1gcAK1cCZ86Y79x5eeLcDRoAy5eL1y+8AFy+DCxbBlStar5Y8nNxAVatEktWVK0KnD4NtG4thsUTmdqZM8DVq+LncOhQuaMhHYOSntWrVyMtLQ1BQUHw9fXVP7Zt2wZAVFPt378fXl5eGDBgAFq0aIEtW7Zg8+bN6Nevn76cxMREPHjwQP962LBh+PTTTzF79my0atUK8fHxOHjwIJ566ikjvU0i29ezJzBihPgP8803YZa5e44eFTMpT5wIPHggalh+/FHMw9OwoenPXxaDB4vVrTt3FnMC/etfYpHHfNOLERmdrpZnyBCgShV5Y6HHDGpgL8tArwYNGpQ6A3NSUlKhfWFhYQgLCzMkHCJ6wrJlYlHD+Hjgv/8VzUumkJgIvPsusHu3eF2tmphFefx4MWuypaldG4iKEjEuXAhs2ACcOCH6XLRoIXd0ZGuys4FvvxXbo0fLGgo9gWtvEdkQLy/RfwUQ61f9/rtxy09PB95/X9To7N4t+umEhYl+O2Fhlpnw6KhUIuk5cgSoWVMsd9G+PfD551zCgoxr924xO7i/P9Cjh9zRUH5MeohszJgxQNeuQGYmEBpqnD/oGg2wfr1oslq6VKybFRwsmo3+9z+gRo2Kn8NcuncXNWH9+wM5OeIavfgi8NdfckdGtkK37MSoUTDaBJlkHLwdRDZGoQC++ELUuuzbJzryVsTx40C7dsDrrwP374sOyz/8ABw8KNbPskZeXuI9LF8urlNEBNCqFfDEtGNEBrtzB4iMFNujRskbCxXGpIfIBjVuDOhWdpk0qXwLcf7+u1gRumtX4Nw50Rlz2TKxWvQLL8CiZn4uD4UCmDIFOHVKJHK3bgHdugELFpR/SQyiLVvE3DzPPSemkSDLwqSHyEbNmCGao+7dA2bOLPv3ZWSI/kCNGwPbt4vq+fHjRb+dadMqPsGfpWnTBoiLEyPftFpg9mygVy/xHzuRISTpcdMWOzBbJiY9RDaqUqXHc/d8/rmYrK8kWi3w1VdAo0ZihFN2tuj/cu6cKMfLy/Qxy8XdXfyHvmUL4OYmRnq1bAns3St3ZGRNTp4EfvsNcHUFXn5Z7mioKEx6iGxY9+6iX4Fu7p7iFjQ+dQro1AkYORK4exd4+mnRF+jIEfsa0j1ihEjy2rQRK8MPGCCawHK4ZimVga6W56WXRCJNlodJD5GN+/RTMbrqwgVgxYqCX7t9G3jtNbE20M8/A5UrA4sXi9mUBw+2/n475dGggZjDZ8oU8fqzz4DnnlPhzh03WeMiy5aZKeZ9ArjshCVj0kNk4zw9RQdkQPRX+fZb0T9n/nzRlPXNNyK5GTNGVM1/8IFoGrNnzs5iZNcPP4iEMT5egXfeCcJXX9lhFkhlEhEh5rGqW1d0/ifLxKSHyA6MHCkSnOxs4NVXRQfnOXPEf6edO4t1gjZsAHx95Y7UsrzwgpiLqFs3LbKzVRg3ToURI4CHD+WOjCwN5+axDrw1RHbgzh1Ru/OkVavEPDxt25o/JmtRqxZw8KAGr756FQ4OEr7+WvT5+eUXuSMjS3Hzpuj/BnBuHkvHpIfIDly7JkZnPalJE/vst2MopRIYOvQ3HDmigb8/cP068Oyzoo8Ul7CgLVvEz0FQEFCvntzRUEmY9BDZgQYNCle5K5WcPM1QnTtLiI8XnbzVamDqVDHC648/5I6M5JJ/bh52YLZ8THqI7ICfH/DllyLRAcTzF1+I/WSY6tWBnTvF3EfOzmKpj5YtgWPH5I6M5BAbCyQmipGPL74odzRUGiY9RHZi3DggKUn8cU5KEq+pfBQKYOJE4PRpIDBQzHrds6eYyTovT+7oyJx0tTwvvywmtiTLxqSHyI74+Yl+B6zhMY4WLcTIt3HjRDPHwoXi+t68KeZAOnZMPJNtevRILNUCsGnLWjDpISKqADc3YN06YOtWwMMD+OknUftTuzbQowdQpw6wfr3cUZIp7Nwp1qqrXx/o0kXuaKgsmPQQERnBsGFiCYtWrcT8R7pRXVqtWLCVNT62J//iohwFaR2Y9BARGcnTTwNLlxber9GIYe5kO27cEM2XCoWY/JOsA5MeIiIjCgzk9AD2YMsW8dyzp2jKJOvApIeIyIh00wPoODhwegBbo9UWbNoi68Gkh4jIyMaNA4YPF9tjxnB6AFsTEyOmffDwEBNVkvVg0kNEZAKDBonnn3+WNw4yPl0tz7BhgKurrKGQgZj0EBGZQPfuopPrpUtAcrLc0ZCxPHwI7Nghttm0ZX2Y9BARmYCnJ9C6tdg+elTeWMh4vvtOTEnQsCHQsaPc0ZChmPQQEZlIr17i+fBheeMg49m4UTxzbh7rxKSHiMhEevYUz4cPP56skKzX9evA8eNiRB7n5rFOTHqIiEykSxfAyQm4dYuTE9qCzZvFc+/eQK1a8sZC5cOkh4jIRFxdgc6dxTabuKybVvs46WEHZuvFpIeIyITyN3GR9Tp2TNTYVakChITIHQ2VF5MeIiIT0nVmPnpUrMFF1knXgflf/wIqVZI3Fio/Jj1ERCbUtq2oHUhNFauwk/VJSwN27RLbY8bIGwtVDJMeIiITUqnERIUAm7is1fbtQFaWWEy2XTu5o6GKYNJDRGRi7Ndj3fIvLsq5eawbkx4iIhPT9euJjRU1BmQ9fvsNOHFCzM0zYoTc0VBFMekhIjKxRo3EvC45OeIPKFkPXS1P376Ar6+soZARMOkhIjIxhYJLUlgjjQbYskVsswOzbWDSQ0RkBuzXY30OHwbu3AGqVwcGDJA7GjIGJj1ERGagS3ri4oC//5Y3FiobXdPWq68Czs6yhkJGwqSHiMgMatYEmjQRC48eOyZ3NFSa1FQgIkJsc9kJ28Gkh4jITNjEZT22bhUdz5s1A9q0kTsaMhYmPUREZsLOzNZD17Q1Zgzn5rElTHqIiMykWzdAqQSuXQNu3pQ7GirO1avAzz+LezV8uNzRkDEx6SEiMpMqVYD27cX2kSPyxkLF09Xy9O8PPPWUrKGQkRmU9CxevBjt2rWDu7s7vL29ERISgoSEBP3Xk5KSoFAoinzs2LGj2HIzMjIQFhYGPz8/uLi4oEmTJlizZk353xURkYVivx7LlpcHfPWV2GYHZttjUNITHR2N0NBQnDp1CpGRkVCr1QgODsajR48AAP7+/rh3716Bx7x581C5cmU8//zzxZY7bdo0HDx4EF9//TWuXr2KKVOmICwsDHv27KnYuyMisjC6fj1HjoiRXGRZDh0C7t0DPD1FTQ/ZFpUhBx88eLDA602bNsHb2xtxcXHo2rUrlEolfHx8ChwTERGBoUOHonLlysWWe+LECYwaNQpBQUEAgDfffBNffPEFTp8+jYEDBxoSIhGRRXv2WcDVFbh/H7h8WYwOIsuha9oaPhxwcpI1FDIBg5KeJ6WlpQEAqlevXuTX4+LiEB8fj1WrVpVYTqdOnbBnzx6MHTsWNWvWRFRUFH777TcsX768yONzcnKQk5Ojf52eng4AUKvVUKvV5Xkrdk933Xj9rAfvmfkY81o7OABduihx6JADfvxRg0aNtBUu054Z89789Rfw/fcqAAoMH64GP1qPmer3jbl/fykkqXwVrFqtFgMHDkRqaipiY2OLPOatt95CVFQUrly5UmJZOTk5ePPNN7FlyxaoVCo4ODhg7dq1GDlyZJHHz507F/PmzSu0Pzw8HK6uroa/GSIiM9q9uz42bWqGZ55Jxkcf/Sx3OPSP/fvr4csvW6BevVQsXx4tdzh2ITMzE6+++irS0tLg4eFh8vOVO+mZOHEiDhw4gNjYWPj5+RX6elZWFnx9fTFr1iy88847JZb16aefYu3atfj0009Rp04dxMTEYMaMGYiIiEAvXQN4PkXV9Pj7++PBgwdmuWi2SK1WIzIyEr1794ajo6Pc4VAZ8J6Zj7GvdXw80L69IypXlnD/fh54+8rPmPemY0cl4uIcsGyZBpMmsQYuP1P9vklPT4enp6fZkp5yNW+FhYVh7969iImJKTLhAYDvvvsOmZmZxdbW6GRlZeHDDz9EREQE+v/Ta6xFixaIj4/Hp59+WmTS4+zsDOciFkJxdHTkL/8K4jW0Prxn5mOsa922rego++CBAufOOaJzZyMEZ+cqem8uXRLrojk6AiNHKuHoqDRidLbD2L9vzP27y6DRW5IkISwsDBERETh69Cjq1atX7LHr16/HwIED4eXlVWKZun44Dg4FQ1EqldBqmWkTke1xcAB69BDbHLpuGXQdmF94QSSkZJsMSnpCQ0Px9ddfIzw8HO7u7khOTkZycjKysrIKHHf9+nXExMTg9ddfL7Kcxo0bI+Kfldw8PDzQrVs3TJ8+HVFRUbhx4wY2bdqELVu2YPDgweV8W0RElo1LUlgOtZpz89gLg5q3Vq9eDQD6oeU6GzduxOh8PykbNmyAn58fgoODiywnISFBP/ILALZu3YoZM2Zg+PDh+Ouvv1CnTh18/PHHmDBhgiHhERFZDV3Sc+oUkJEBlDCrB5nYwYNASgrg7Q2UMKUc2QCDkp6y9nletGgRFi1aVOZyfHx8sHHjRkNCISKyavXqiceNG0BMDNCvn9wR2S9d09Zrr4Gdym0c194iIpIJm7jk9+AB8MMPYptNW7aPSQ8RkUzyL0lB8ggPF3162rYFmjeXOxoyNSY9REQy0Y3gunBBLEtB5qfrWTFmjLxxkHkw6SEikomnJ9Cqldg+elTWUOxSfLx4ODkBr7widzRkDkx6iIhkxH498tF1YB44EKhRQ9ZQyEyY9BARySh/0lO+RYGoPHJzgW++Edts2rIfTHqIiGTUpYsYJn3zJpCYKHc09mP/fjFyy9cXKGZKObJBTHqIiGTk5gZ06iS22cRlProOzCNGAKpyrUJJ1ohJDxGRzDh03bzu3wf27RPbnJvHvjDpISKSmS7pOXoU0GjkjcUefPONuM4dOgCBgXJHQ+bEpIeISGbPPAN4eAB//SWGUJPpSNLjpi3W8tgfJj1ERDJTqQDdOs7s12Na584Bly4Bzs7AsGFyR0PmxqSHiMgCsF+PeehqeQYPBqpVkzcWMj8mPUREFqBnT/F8/DiQnS1vLLYqJ0estQWwacteMekhIrIAgYFizpjsbODECbmjsU0//CD6TdWq9bhmjewLkx4iIgugUHBJClPTLTsxciSgVMoaCsmESQ8RkYVgvx7TuXcPOHhQbLNpy34x6SEishC6fj1nzwJ//y1vLLbm66/F3DydOgENG8odDcmFSQ8RkYWoVQto3BjQaoGoKLmjsR2S9Lhpi7U89o1JDxGRBWETl/GdOQNcuQK4uABDh8odDcmJSQ8RkQVhZ2bj09XyDBkCVKkiaygkMyY9REQWpFs3wMEBSEgAbt2SOxrrl50NfPut2B4zRt5YSH5MeoiILEjVqkC7dmKbTVwV9/33QGoq4O8PdO8udzQkNyY9REQWhv16jEfXtDVqlKhBI/vGHwEiIgujG7p++LAYeUTlc+cOcOiQ2OaoLQKY9BARWZyOHcVIo+RkMeqIyuerr8Tw/+eeA+rXlzsasgRMeoiILEylSuIPNcAmrvLKPzcPOzCTDpMeIiILxKHrFXPqlBgB5+oKvPSS3NGQpWDSQ0RkgXT9eqKiALVa1lCskq6W5+WXAXd3WUMhC8Kkh4jIArVqBVSvDjx8KGYUprLLzAS2bhXb7MBM+THpISKyQA4Oj2t72K/HMLt3A+npQN26QNeuckdDloRJDxGRhco/dJ3KbuNG8Tx6NOfmoYL440BEZKF0nZlPngQyMuSNxVrcvPm4ZmzkSHljIcvDpIeIyEI9/bRoolGrgePH5Y7GOnz1lRiu3r07UK+e3NGQpWHSQ0RkoRQKLklhiPxz87ADMxWFSQ8RkQVjv56y++kn4Pp1oHJl4MUX5Y6GLBGTHiIiC9ajh3g+fx5ISZE3Fkun68A8dCjg5iZvLGSZmPQQEVkwb2+gZUuxfeyYvLFYskePgO3bxTabtqg4THqIiCwcm7hKFxGhQEaGWFi0Sxe5oyFLxaSHiMjC6TozR0aKzrpU2JYt4s/Z6NGiAzhRUZj0EBFZuOeeAxwdgd9/B/7v/+SOxvLcv++CqCgHKBScm4dKxqSHiMjCVa4MdOwotjl0vbBjx2oDEM2AtWvLHAxZNCY9RERWgP16iqbVAseO+QNgB2YqnUFJz+LFi9GuXTu4u7vD29sbISEhSEhI0H89KSkJCoWiyMeOHTtKLPvq1asYOHAgqlSpAjc3N7Rr1w43b94s37siIrIx+Scp1GrljcWSREQocP++GypXljB4sNzRkKUzKOmJjo5GaGgoTp06hcjISKjVagQHB+PRo0cAAH9/f9y7d6/AY968eahcuTKef/75YstNTExEly5d0LhxY0RFReHChQuYNWsWKlWqVLF3R0RkI9q1A9zdgb/+AuLj5Y7GMqxfD/zrX0oAYsj6t9/KHBBZPJUhBx88eLDA602bNsHb2xtxcXHo2rUrlEolfHx8ChwTERGBoUOHonLlysWWO3PmTPTr1w9Lly7V76tfv74hoRER2TRHR6BbN2DvXlHb06aN3BHJ6/Zt4M03AUAM1ZIkBcaPB/r0Afz8ZA2NLFiF+vSkpaUBAKpXr17k1+Pi4hAfH49x48YVW4ZWq8W+ffvQsGFD9OnTB97e3ujQoQN2795dkdCIiGyOromL/XqAa9cKN/NpNGIZCqLiGFTTk59Wq8WUKVPQuXNnNGvWrMhj1q9fj8DAQHTq1KnYclJSUpCRkYElS5Zg4cKF+OSTT3Dw4EEMGTIEx44dQ7du3Qp9T05ODnJycvSv09PTAQBqtRpqtbq8b8mu6a4br5/14D0zH0u51uLXoSOOH5eQkZEHZ2dZw5GVlxcg/oQ9npRHqZRQp04e+JEwPlN9Bsz9mSp30hMaGopLly4hNja2yK9nZWUhPDwcs2bNKrEc7T+p+qBBgzB16lQAQKtWrXDixAmsWbOmyKRn8eLFmDdvXqH9hw4dgqurq6FvhfKJjIyUOwQyEO+Z+ch9rSUJqFatD/7+uxJWrPgZzZv/KWs8ctqxowGAJgAkAAo4OGgxYcJ5XLhwExcuyBycDTP2ZyAzM9Oo5ZWmXElPWFgY9u7di5iYGPgV03j63XffITMzEyNLmSnK09MTKpUKTZo0KbA/MDCw2IRqxowZmDZtmv51eno6/P39ERwcDA8PDwPfDQEi246MjETv3r3h6OgodzhUBrxn5mNJ17pvXyW+/RZ49Kgj+vWzz2FcqanA6NHiz9eyZbnIzDyDYcPaom7dZgCKbnmgijHVZ0DXUmMuBiU9kiRh0qRJiIiIQFRUFOrVq1fssevXr8fAgQPhJeogi+Xk5IR27doVGPoOAL/99hvq1KlT5Pc4OzvDuYh6XUdHR9l/IVk7XkPrw3tmPpZwrYODxSilY8eUcHRUyhqLXD77TCQ+zZoBoaEOOHjwT9Stq5L93tgDY38GzH3PDEp6QkNDER4eju+//x7u7u5ITk4GAFSpUgUuLi76465fv46YmBjs37+/yHIaN26MxYsXY/A/kypMnz4dw4YNQ9euXdG9e3ccPHgQP/zwA6Kiosr5toiIbJNuksIzZ8Qf/qpV5YzG/P74A1ixQmwvWAA4cIpdMoBBPy6rV69GWloagoKC4Ovrq39s27atwHEbNmyAn58fgoODiywnISFBP/ILAAYPHow1a9Zg6dKlaN68OdatW4edO3eiC5fKJSIqwN8faNRIjFyKjpY7GvNbskTMyfPMM8CgQXJHQ9bG4Oatsli0aBEWLVpkUDljx47F2LFjDQmHiMgu9ewJJCSIoev29If/zh1g1SqxvXAhV1Mnw7FikIjIytjrfD0ffwzk5IhV54tpSCAqEZMeIiIrExQk+rL8+quo/bAHN24Aa9eKbdbyUHkx6SEisjLVqgFt24rtI0fkjcVc5s0D8vJEDU/XrnJHQ9aKSQ8RkRWypyauX38FvvpKbC9cKG8sZN2Y9BARWaH8SU8Zx5hYrTlzxGi1kBCx2jxReTHpISKyQp06AZUqAffuiZoQWxUfD2zfLvrwzJ8vdzRk7Zj0EBFZoUqVAN1UZrbcxKVbvvGVV4DmzeWNhawfkx4iIitl6/16Tp0C9u4FlEpg7ly5oyFbwKSHiMhK6ZKeqCgxssnWfPSReB41CmjYUN5YyDYw6SEislKtWonh6+npwNmzckdjXMeOieH4jo7A7NlyR0O2gkkPEZGVUiqBHj3Eti01cUnS41qe8eOBOnXkjYdsB5MeIiIrZov9eg4cAE6cAFxcgA8/lDsasiVMeoiIrJgu6Tl5Uqw+bu202se1PGFhgK+vvPGQbWHSQ0RkxerXB2rXBnJzgdhYuaOpuIgI4Nw5wN0deP99uaMhW8Okh4jIiikUttPEpdE8npdn6lSgRg154yHbw6SHiMjK2UrSEx4OXL0qRqRNmyZ3NGSLmPQQEVk53Qiu+HjgwQNZQyk3tfrxBITvvw9UqSJrOGSjmPQQEVm5p556vETD0aPyxlJeGzcC//d/4r2EhckdDdkqJj1ERDbAmpu4srOBBQvE9ocfAm5u8sZDtotJDxGRDbDmpOeLL4DbtwF/fzEZIZGpMOkhIrIBXbsCKhVw44ZoJrIWjx4BixaJ7dmzAWdneeMh26aSOwBz0mg0UKvVcodhkdRqNVQqFbKzs6HRaOQOx6Y5OjpCqVTKHQbZmMqVgWefFXP1HDkCPP203BGVzX//C6SkiPmGRo2SOxqydXaR9EiShOTkZKSmpsodisWSJAk+Pj64desWFAqF3OHYvKpVq8LHx4fXmoyqVy+R9Bw+DLzxhtzRlC41FVi6VGzPmycWFyUyJbtIenQJj7e3N1xdXfmHpgharRYZGRmoXLkyHBzY6mkqkiQhMzMTKSkpAABfzrFPRtSzpxj2ffSoWM7B0j/K//mPSHyaNgVeeUXuaMge2HzSo9Fo9AlPDU7vWSytVovc3FxUqlSJSY+Jubi4AABSUlLg7e3Npi4ymg4dRDPXgwfAhQtAq1ZyR1S8P/4Ali8X2/PnixXjiUzN5v+66frwuLq6yhwJ0WO6n0f2MSNjcnQEunUT25Y+imvpUiAjA2jbFhg8WO5oyF7YfNKjwyYtsiT8eSRTsYah63fvAitXiu2FC8X6YUTmYDdJDxGRPejZUzwfPw7k5MgbS3E+/lhMSNi5M9Cnj9zRkD1h0mPlNm3ahKpVq8odBhFZiGbNAG9vIDMTOHVK7mgKS0oC1q4V2x9/zFoeMi8mPRZs9OjRCAkJKbQ/KioKCoUCqampGDZsGH777bcylccEicj2KRSW3cQ1f75YXLR378f9j4jMhUmPlXNxcYG3t7dZz6nRaKDVas16TiIqO10T15Ej8sbxpIQEYPNmsb1wobyxkH1i0mOg27eBY8fEsyV4svbm/Pnz6N69O9zd3eHh4YG2bdvi7NmziIqKwpgxY5CWlgaFQgGFQoG5c+cCAP7++2+MGjUKdevWReXKlfH888/j2rVrhc6xZ88eNGnSBM7OzoiNjYWjoyOSk5MLxDNlyhQ899xz5njrRFQMXU3P6dNAWpq8seQ3Z46YP2jgQKB9e7mjIXtkl0mPJIn1Xgx9fP45UKcO0KOHeP78c8PLkCTTvrfhw4fDz88PZ86cQVxcHD744AM4OjqiU6dOWLFiBTw8PHDv3j3cu3cP7777LgDRjBYXF4fw8HD89NNPkCQJ/fr1KzCcOjMzE5988gnWrVuHy5cv45lnnsHTTz+Nr776Sn+MWq3GN998g7Fjx5r2TRJRiWrXBho0ADQaIDpa7miECxeAbdvEtm5FdSJzs/nJCYuSmSkm8KoIrRYIDRUPQ2RkAG5uZT9+7969qPxEsCWtjXXz5k1Mnz4djRs3BgA0aNBA/7UqVapAoVDAx8dHv+/atWvYs2cPjh8/jmbNmsHDwwPffPMN/P39sXv3brz88ssARELz+eefo2XLlvrvHTduHDZu3Ijp06cDAH744QdkZ2dj6NChZX+DRGQSvXoB166Jfj0DB8odDTBrlngeNgxo0ULeWMh+2WVNjzXp3r074uPjCzzWrVtX7PHTpk3D66+/jl69emHJkiVITEwssfyrV69CpVKhQ4cO+n01atRAo0aNcPXqVf0+JycntHjiN9Xo0aNx/fp1nPpniMimTZswdOhQuBmS1RGRSVhSv56ffwb27BHLYsybJ3c0ZM/ssqbH1VXUuBjizh0gMFDU8OgolcCVK0CtWoad2xBubm4ICAgosO92CR2K5s6di1dffRX79u3DgQMHMGfOHGzduhWDKzjlqYuLS6EJ9by9vTFgwABs3LgR9erVw4EDBxAVFVWh8xCRcXTvLkZyXbkiJgOsWVO+WHS1PKNGAY0ayRcHkV3W9CgUoonJkEfDhsCXXz5eH0apBL74Quw3pBxzzEnRsGFDTJ06FYcOHcKQIUOwceNGAKK25smmscDAQOTl5eHnn3/W7/vzzz+RkJCAJk2alHqu119/Hdu2bcOXX36J+vXro3PnzsZ9M0RULtWriyUeAHlre6KjgchIsUTG7NnyxUEE2GnSU17jxomJtY4dE8/jxskdUUFZWVkICwtDVFQUfv/9d/z00084c+YMAgMDAQB169ZFRkYGjhw5ggcPHiAzMxMNGjTAoEGDMH78eJw8eRLnz5/Ha6+9hlq1amHQoEGlnrNPnz7w8PDAwoULMWbMGFO/RSIygNzz9UgSMHOm2H7jDaBuXXniINJh0mMgPz8gKEg8WxqlUok///wTI0eORMOGDTF06FA8//zzmPdPI3qnTp0wYcIEDBs2DF5eXli6dCkAYOPGjWjTpg1eeeUVdO7cGZIkYf/+/XB0dCz1nA4ODhg9ejQ0Gg1Gjhxp0vdHRIbJ36/H1CNHi/Ljj8BPPwGVKj1OfojkZJd9eqzFpk2bitwfFBQE6Z/fYKNHj8bo0aMBiOarb7/9tsQyV69ejdWrVxfYV61aNWzevBnp6enw8PCAg0PBXDj/OYpy584d9OvXD76+viW/ISIyq86dAWdn0ScxIQH4Z1CnWUgS8NFHYjs0VN4+RUQ6rOmhcktLS0NsbCzCw8MxadIkucMhoie4uABduohtczdxRUQAcXFiepAPPjDvuYmKw6SHym3QoEEIDg7GhAkT0Lt3b7nDIaIiyDF0XaN53Gl56lTA09N85yYqCZu3qNw4PJ3I8vXqBXz4oRiAkZcHqMzwW3/rVuDyZaBqVWDaNNOfj6isWNNDRGTD2rQRyUdammhuMjW1WqyxBQDvvSfOTWQpDEp6Fi9ejHbt2sHd3R3e3t4ICQlBQkKC/utJSUn6xSyffOzYsaNM55gwYQIUCgVWrFhh0BshIqLClEqxXiBgnn49mzcDiYmAtzcwebLpz0dkCIOSnujoaISGhuLUqVOIjIyEWq1GcHAwHj16BADw9/fXL2ape8ybN0+/cndpIiIicOrUKdRkN38iIqMxV7+enBxg/nyxPWOGYesMEpmDQa27Bw8eLPB606ZN8Pb2RlxcHLp27QqlUllgMUtAJDJDhw4ttGjmk+7cuYNJkybhxx9/RP/+/Q0Ji4iISqCbpPCnn8SCy4Yuh1NWX3wB3Lol5jGbMME05yCqiAp1aUtLSwMAVK9evcivx8XFIT4+HqtWrSqxHK1WixEjRmD69Olo2rRpqefNyclBTk6O/nV6ejoAsRK4Wq0ucKxarYYkSdBqtdDmXziLCtDN+6O7VmRaWq0WkiRBrVZDqVvbxEC6n/Unf+bJ+Kz9WtetC/j7q3DrlgJRUXno3dv4MxU+egR8/LEKgAIffpgHpVKCOS6Xtd8ba2Gq62zu+1bupEer1WLKlCno3LkzmjVrVuQx69evR2BgIDp16lRiWZ988glUKhUml7EBePHixfpZhvM7dOgQXJ/4F0alUsHHxwcZGRnIzc0tU/n27OHDh3KHYBdyc3ORlZWFmJgY5OXlVaisyMhII0VFpbHma92gQWvculUb69bdgFp9xejl79oVgJSUpnjqqUfw9j6C/fvNOwW0Nd8ba2Ls65yZmWnU8kpT7qQnNDQUly5dQmxsbJFfz8rKQnh4OGbpltctRlxcHD777DP88ssvhVbxLs6MGTMwLd84yPT0dPj7+yM4OBgeHh4Fjs3OzsatW7dQuXJlVKpUqUzl2yNJkvDw4UO4u7uX+T5Q+WVnZ8PFxQVdu3Yt98+lWq1GZGQkevfuXaYlQ6j8bOFap6YqcPQokJQUgH796hq17LQ0YMwY8edk8WJnDBxYeh9OY7GFe2MNTHWddS015lKupCcsLAx79+5FTEwM/IpZhOq7775DZmZmqesxHT9+HCkpKahdu7Z+n0ajwTvvvIMVK1YgKSmp0Pc4OzvD2dm50H5HR8dCN0Oj0UChUMDBwaHQ8gqWrLTEY86cOZg7d67Rzqdr0tJdq/yCgoIQHR1d6HvUajVUZZj0o27dupgyZQqmTJlilFhtgYODAxQKRZE/s4YyRhlUNtZ8rfv0Ec/x8QqkpTkadcLA//0P+PtvoEkTYORIFcrZYlsh1nxvrImxr7O575lBSY8kSZg0aRIiIiIQFRWFevXqFXvs+vXrMXDgQHh5eZVY5ogRI9BL18vuH3369MGIESMsZtXuuVFzoVQoMatb4VqrBdELoJE0mBs016jnvHfvnn5727ZtmD17doHpAfJ3DJckCRqNpkwJSHm98cYbmK8blvEPU56vKLm5uXBycjLrOYlshY8P0KwZcOmSmKjw5ZeNU+6DB8Dy5WJ7/nzIkvAQlZVBVR+hoaH4+uuvER4eDnd3dyQnJyM5ORlZWVkFjrt+/TpiYmLw+uuvF1lO48aNERERAQCoUaMGmjVrVuDh6OgIHx8fNGrUqJxvy7iUCiVmR83GgugFBfYviF6A2VGzoVQY/1Pu4+Ojf1SpUgUKhUL/+tdff4W7uzsOHDiAtm3bwtnZGbGxsRg9ejRCQkIKlDNlyhQEBQXpX2u1WixevBj16tWDi4sLWrZsie+++67UeFxdXQvEpBulFxQUVKgGJyQkRL9AaVBQEH7//XdMnTpVP2cTAMydOxetWrUq8H0rVqxA3bp19a917+fjjz9GzZo19T8Pt27dwtChQ1G1alVUr14dgwYNKrJGkIgKMsXQ9aVLgYcPgdatgcGDjVcukSkYlPSsXr0aaWlpCAoKgq+vr/6xbdu2Asdt2LABfn5+CA4OLrKchIQE/cgvazCr2yzMD5pfIPHRJTzzg+YXWQNkDh988AGWLFmCq1evokWLFmX6nsWLF2PLli1Ys2YNLl++jKlTp+K1114rsvnKGHbt2gU/Pz/Mnz9fP3eTIY4cOYKEhARERkZi7969UKvV6NOnD9zd3XH8+HH89NNPqFy5Mvr27cuO6kSl0FWqG2uSwnv3gJUrxfbChYAV9SAgO2Vw81ZZLFq0CIsWLSp3OZb4X7susZkdNRsLjy9EriZX1oQHAObPn2/QQp85OTlYtGgRDh8+jI4dOwIAnn76acTGxuLLL7/E6tWri/3ezz//HOvWrdO/Hj9+PJYtW1bqOatXrw6lUgl3d/dCcziVhZubG9atW6dv1vr666+h1Wqxbt06fa3Rxo0bUbVqVURFRRWbaBMR0K2baH5KTARu3ABK6KFQJosWAVlZQKdOQBnmnyWSHRccNcCsbrP0CY+T0knWhAcAnnnmGYOOv379OjIzMwslSrm5uWjdunWJ3zt8+HDMnDlT/7qqmRbUad68eYF+POfPn8f169fh7u5e4Ljs7GwkJiaaJSYia+XuDnToAJw4IZq4iumBUCa//y4mIwRELQ8HfZI1YNJjgAXRC/QJT64mFwuiF8ia+Lg9Mce7g4NDoVq0/BM/ZWRkAAD27duHWrVqFTiutB70VapUQUBAQKH9pZ2zOGX9viffY0ZGBtq2bYtvvvmm0LGldZonItHEZYykZ/58sbhoz55A9+7Gi4/IlNgCW0b5+/DkfJRTqI+PJfDy8irUZyY+Pl6/3aRJEzg7O+PmzZsICAgo8PD39zfKOTUaDS5dulTgGCcnJ2g0mkLfl5ycXCDxyR9rcdq0aYNr167B29u70HuoUqVKud4DkT3R9es5cgQo7+Trv/0mFhYFRC0PkbVg0lMGRXVaLqpzs9x69OiBs2fPYsuWLbh27RrmzJlTIAFxd3fHu+++i6lTp2Lz5s1ITEzEL7/8gv/973/YrPsNVo5z7tu3D/v27cOvv/6KiRMnIjU1tcAxdevWRUxMDO7cuYMHDx4AEKO6/vjjDyxduhSJiYlYtWoVDhw4UOr5hg8fDk9PTwwaNAjHjx/HjRs3EBUVhcmTJ+P27dvleg9E9qRDB7EQ6B9/ABcvlq+MuXMBjQZ44QXg2WeNGh6RSTHpKQONpCmy07Iu8dFImmK+07z69OmDWbNm4b333kO7du3w8OHDQpNDLliwALNmzcLixYsRGBiIvn37Yt++fSXOuVSSsWPHYtSoURg5ciS6deuGp59+Gt2fqOueP38+kpKSUL9+fX0TVGBgID7//HOsWrUKLVu2xOnTp/Huu++Wej5XV1fExMSgdu3aGDJkCAIDAzFu3DhkZ2cXmo2biApzcgK6dhXb5Rm6fvEisHWr2F5gGf/vEZWZQirrkCwLlp6ejipVqiAtLa3IZShu3LiBevXqcRmKEmi1WqSnp8PDw8OqZq62Vsb4uVSr1di/fz/69evHmWhNzNau9X/+A7zzjhhxtX+/Yd87eDCwezcwdCjwxGwlsrC1e2OpTHWdS/r7bQr860ZEZGd0/XqiowFDprc6c0YkPA4OQBFrPhNZPCY9RER2plkzwMsLyMwETp0q+/d99JF4HjECaNzYNLERmRKTHiIiO+PgYPiSFDExwKFDgEoFzJljutiITIlJDxGRHTJkSQpJelzL8/rrFZ/JmUguTHqIiOyQLun5+WcgPb3kYw8dAo4fBypVepz8EFkjJj1ERHaoTh2gfn0x305J6w3nr+V56y3gicnciawKkx4iIjuVf3bm4nz/PXD2rJjQ8P33zRMXkakw6SEislOl9evRaIBZ/8zJOmUK4O1tlrCITIZJDxGRnereXayOfvky8MSyfQDE5IOXLgFVqwJlmDCdyOIx6bFzUVFRUCgUhdbLKkndunWxYsUKk8VkiLlz56JVq1b616NHj0ZISEiFyjRGGUTWoEYNoHVrsX30aMGv5eU9Hpr+7rsi8SGydkx6DKHRAFFRwLffimeNadfcGj16NBQKBSZMmFDoa6GhoVAoFBg9erRJYyiPuXPnQqFQQKFQQKVSoW7dupg6dSoyMjJMfu7PPvsMmzZtKtOxSUlJUCgUhVZ3N6QMImtXXBPX5s3A9etiEsO33zZ/XESmwKSnrHbtAurWFfXBr74qnuvWFftNyN/fH1u3bkVWVpZ+X3Z2NsLDw1G7dm2TnrsimjZtinv37iEpKQmffPIJvvzyS7zzzjtFHptryDz4pahSpQqqVvBfUmOUQWQt8ic9upUYc3KA+fPF9owZQOXK8sRGZGxMespi1y7gpZeA27cL7r9zR+w3YeLTpk0b+Pv7Y1e+c+zatQu1a9dGa1299D9ycnIwefJkeHt7o1KlSujSpQvOnDlT4Jj9+/ejYcOGcHFxQffu3ZGUlFTonLGxsXjuuefg4uICf39/TJ48GY8ePTIobpVKBR8fH/j5+WHYsGEYPnw49uzZA+Bxk9S6desKLLiZmpqK119/HV5eXvDw8ECPHj1w/vz5AuUuWbIETz31FNzd3fWrq+f3ZNOUVqvF0qVLERAQAGdnZ9SuXRsff/wxAOhXlm/dujUUCgWCgoKKLKO066prIjxy5AieeeYZuLq6olOnTkhISDDomhHJoXNnsfL67dvAb7+JfWvXAjdvAjVrAkVUNBNZLSY9pdFoRN1uUYvR6/ZNmWLSpq6xY8di48aN+tcbNmzAmDFjCh333nvvYefOndi8eTN++eUXBAQEoE+fPvjrr78AALdu3cKQIUMwYMAAxMfH4/XXX8cHH3xQoIzExET07dsXL774Ii5cuIBt27YhNjYWYWFhFXoPLi4uBWp0rl+/jp07d2LXrl365qWXX34ZKSkpOHDgAOLi4tCmTRv07NlTH//27dsxd+5cLFq0CGfPnoWvry8+//zzEs87Y8YMLFmyBLNmzcKVK1cQHh6Op556CgBw+vRpAMDhw4dx7969AollfqVdV52ZM2di2bJlOHv2LFQqFcaOHVuua0VkTq6uIvEBxND1zEzgn/8LMGsW4OIiX2xERifZgLS0NAmAlJaWVuhrWVlZ0pUrV6SsrKzyFX7smCSJ9Kbkx7FjFXoPRRk1apQ0aNAgKSUlRXJ2dpaSkpKkpKQkqVKlStIff/whDRo0SBo1apQkSZKUkZEhOTo6St98843++3Nzc6WaNWtKS5culSRJkmbMmCE1adKkwDnef/99CYD0559/Sn///bc0duxY6c033yxwzPHjxyUHBwf9NaxTp460fPnyYuOeM2eO1LJlS/3rs2fPSp6entJLL72k/7qjo6OUkpJS4BweHh5SdnZ2gbLq168vffHFF5IkSVLHjh2lt956q8DXO3ToUOBcumsmSZKUnp4uOTs7S2vXri0yzhs3bkgApHPnzhXYn7+MslzXY8eOSQCkw4cP64/Zt2+fBKDYn7sK/1z+E8fu3bul3NzccpdBZWPr1/rjj8WvscGDJemTT8R2vXqSlJMjd2Sls/V7YylMdZ1L+vttCio5Ey6rUNQ4zoocVw5eXl7o378/Nm3aBEmS0L9/f3h6ehY4JjExEWq1Gp11/7IBcHR0RPv27XH16lUAwNWrV9GhQ4cC39exY8cCry9cuIALFy7gm2++0e+TJAlarRY3btxAYGBgmWK+ePEiKleuDI1Gg9zcXPTv3x8rV67Uf71OnTrw8vLSvz5//jwyMjJQo0aNAuVkZWUhMTFRH/+Tnbo7duyIY8eOFRnD1atXkZOTg566lRXLoSzXVadFixb6bV9fXwBASkqKRfe9IgJEv56ZM0W/Hl2H5jlzRLMXkS1h0lOaf/54Ge24cho7dqy+iWnVqlUmO09GRgbGjx+PyZMnF/qaIX+8GzVqhD179kClUqFmzZpweuK3p5ubW6Hz+vr6IioqqlBZ5e1U7GLmenlHR0f9tkKhACD6FBFZurZtRTPWw4eP9xlxfAGRxWCfntI89xzg5ydm8CqKQgH4+4vjTKhv377Izc2FWq1Gnz59Cn29fv36cHJywk8//aTfp1arcebMGTRp0gQAEBgYqO/HonPq1KkCr1u3bo0rV64gICCg0OPJxKUkTk5OCAgIQN26dcv0fW3atEFycjJUKlWh8+pqtQIDA/Hzzz+XGH9+DRo0gIuLC44UM8e+Li5NCf2xynJdiazdvXtAvgGiAICJEwuP3SCydkx6SqNUAp99JrafTHx0r1esEMeZNAwlrl69iitXrkBZxLnc3NwwceJETJ8+HQcPHsSVK1fwxhtvIDMzE+PGjQMATJgwAdeuXcP06dORkJCA8PDwQvPRvPfeezhx4gTCwsIQHx+Pa9eu4fvvv69wR+bS9OrVCx07dkRISAgOHTqEpKQknDhxAjNnzsTZs2cBAG+//TY2bNiAjRs34rfffsOcOXNw+fLlYsusVKkS3n//fbz33nvYsmULEhMTcerUKaxfvx4A4O3tDRcXFxw8eBD3799HWlpaoTLKcl2JrN21a4X3aTRinh4iW8KkpyyGDAG++67w8sJ+fmL/kCFmCcPDwwMeHh7Ffn3JkiV48cUXMWLECLRp0wbXr1/Hjz/+iGrVqgEQzVM7d+7E7t270bJlS6xZswaLFi0qUEaLFi0QHR2N3377Dc899xxat26N2bNno2bNmiZ9bwqFAvv370fXrl0xZswYNGzYEK+88gp+//13/WirYcOGYdasWXjvvffQtm1b/P7775g4cWKJ5c6aNQvvvPMOZs+ejcDAQAwbNgwpKSkAxLD6//73v/jiiy9Qs2ZNDBo0qMgySruuRNauQQPA4Ym/BkolEBAgTzxEpqKQpKLGYluX9PR0VKlSBWlpaYWSguzsbNy4caPAfDDlptEAx4+LumBfX9GkZeIaHnPRarVIT0+Hh4cHHJ787UdGZ4yfS7Vajf3796Nfv34F+hOR8dnDtV6/Hhg/XvyaUyqBL74ArKEy0x7ujSUw1XUu6e+3KbAjsyGUSuCfCeyIiGzJuHFAnz6iSSsgQFRkE9kaJj1ERARAJDpMdsiWsR2DiIiI7AKTHiIiIrILTHqIiIjILthN0sOZccmS8OeRiMj8bL4js5OTExwcHHD37l14eXnByclJv0QAPabVapGbm4vs7GwOWTchSZKQm5uLP/74Aw4ODgbNck1ERBVj80mPg4MD6tWrh3v37uHu3btyh2OxJElCVlYWXFxcmBSagaurK2rXrs0Ek4jIjGw+6QFEbU/t2rWRl5dX4jpL9kytViMmJgZdu3blBF8mplQqoVKpmFwSEZmZXSQ9gFjmwNHRkX/Qi6FUKpGXl4dKlSrxGhERkU1i3ToRERHZBSY9REREZBeY9BAREZFdsIk+PbqF4tPT02WOxHqp1WpkZmYiPT2dfXqsBO+Z+fBaWy7eG/Mw1XXW/d3W/R03NZtIeh4+fAgA8Pf3lzkSIiIiMtTDhw9RpUoVk59HIZkrvTIhrVaLu3fvwt3dncOAyyk9PR3+/v64desWPDw85A6HyoD3zHx4rS0X7415mOo6S5KEhw8fombNmmaZt8wmanocHBzg5+cndxg2wcPDg784rAzvmfnwWlsu3hvzMMV1NkcNjw47MhMREZFdYNJDREREdoFJDwEAnJ2dMWfOHDg7O8sdCpUR75n58FpbLt4b87CV62wTHZmJiIiISsOaHiIiIrILTHqIiIjILjDpISIiIrvApIeIiIjsApMeM1u8eDHatWsHd3d3eHt7IyQkBAkJCQWOyc7ORmhoKGrUqIHKlSvjxRdfxP379wscM3nyZLRt2xbOzs5o1apVofNkZ2dj9OjRaN68OVQqFUJCQsoc444dO9C4cWNUqlQJzZs3x/79+wt8fe7cuWjcuDHc3NxQrVo19OrVCz///HOp5ZYWc1JSEhQKRaHHqVOnyhy7KdjCPctvwoQJUCgUWLFiRanl3rx5E/3794erqyu8vb0xffp05OXl6b9+7949vPrqq2jYsCEcHBwwZcqUMsdcFHu+1pb++bCFezN69OhC169v376llmvOz4E9X2dzfAaY9JhZdHQ0QkNDcerUKURGRkKtViM4OBiPHj3SHzN16lT88MMP2LFjB6Kjo3H37l0MGTKkUFljx47FsGHDijyPRqOBi4sLJk+ejF69epU5vhMnTuBf//oXxo0bh3PnziEkJAQhISG4dOmS/piGDRti5cqVuHjxImJjY1G3bl0EBwfjjz/+KLX8kmLWOXz4MO7du6d/tG3btszxm4It3DOdiIgInDp1CjVr1iy1XI1Gg/79+yM3NxcnTpzA5s2bsWnTJsyePVt/TE5ODry8vPDRRx+hZcuWZY65OPZ6rcsSs45cnw9buTd9+/YtcP2+/fbbEss19+fAXq9zWWLWqdBnQCJZpaSkSACk6OhoSZIkKTU1VXJ0dJR27NihP+bq1asSAOnkyZOFvn/OnDlSy5YtSzzHqFGjpEGDBpUpnqFDh0r9+/cvsK9Dhw7S+PHji/2etLQ0CYB0+PDhMp2juJhv3LghAZDOnTtXpnLkYq337Pbt21KtWrWkS5cuSXXq1JGWL19eYrn79++XHBwcpOTkZP2+1atXSx4eHlJOTk6h47t16ya9/fbbZYq5rOzlWpclZkv7fFjjvTGkPB25Pwf2cp3zM+VngDU9MktLSwMAVK9eHQAQFxcHtVpdIPNu3LgxateujZMnT5o8npMnTxbK+vv06VPsuXNzc/Hll1+iSpUqRvlPHwAGDhwIb29vdOnSBXv27DFKmcZkjfdMq9VixIgRmD59Opo2bVrmcps3b46nnnqqQLnp6em4fPmycYIvhb1ca0NYyufDGu8NAERFRcHb2xuNGjXCxIkT8eeff5ZarpyfA3u5zoaoyGfAJhYctVZarRZTpkxB586d0axZMwBAcnIynJycULVq1QLHPvXUU0hOTjZ5TMnJyQU+3MWde+/evXjllVeQmZkJX19fREZGwtPTs0Lnrly5MpYtW4bOnTvDwcEBO3fuREhICHbv3o2BAwdWqGxjsdZ79sknn0ClUmHy5MkVLlf3NVOzp2tdFpb0+bDWe9O3b18MGTIE9erVQ2JiIj788EM8//zzOHnyJJRKpUHl6r5mSvZ0ncvCGJ8B1vTIKDQ0FJcuXcLWrVvNfu6bN2+icuXK+seiRYsM+v7u3bsjPj4eJ06cQN++fTF06FCkpKQAAJ5//nl9uYb8p+vp6Ylp06ahQ4cOaNeuHZYsWYLXXnsN//73vw2KzZSs8Z7FxcXhs88+w6ZNm6BQKIo8prz3zJR4rQuypM+HNd4bAHjllVcwcOBANG/eHCEhIdi7dy/OnDmDqKgoAJb3OeB1LsgYnwHW9MgkLCwMe/fuRUxMDPz8/PT7fXx8kJubi9TU1AKZ/P379+Hj42O089esWRPx8fH617qqUx8fn0KjAIo6t5ubGwICAhAQEIBnn30WDRo0wPr16zFjxgysW7cOWVlZAABHR8cKxdmhQwdERkZWqAxjsdZ7dvz4caSkpKB27dr6r2s0GrzzzjtYsWIFkpKSirxnPj4+OH36dKFydV8zJXu71uUlx+fDWu9NUZ5++ml4enri+vXr6Nmzp0V9DuztOpeXoZ8B1vSYmSRJCAsLQ0REBI4ePYp69eoV+Hrbtm3h6OiII0eO6PclJCTg5s2b6Nixo9HiUKlU+qQlICBA/wPdsWPHAucGgMjIyFLPrdVqkZOTAwCoVauWvtw6depUKM74+Hj4+vpWqIyKsvZ7NmLECFy4cAHx8fH6R82aNTF9+nT8+OOPAIq+Zx07dsTFixf1NXi6cj08PNCkSROjva/87PVal5c5Px/Wfm+Kcvv2bfz555/6a2gJnwN7vc7lZehngDU9ZhYaGorw8HB8//33cHd317eDVqlSBS4uLqhSpQrGjRuHadOmoXr16vDw8MCkSZPQsWNHPPvss/pyrl+/joyMDCQnJyMrK0ufkTdp0gROTk4AgCtXriA3Nxd//fUXHj58qD+mqPkPdN5++21069YNy5YtQ//+/bF161acPXsWX375JQDg0aNH+PjjjzFw4ED4+vriwYMHWLVqFe7cuYOXX365xPdeWsybN2+Gk5MTWrduDQDYtWsXNmzYgHXr1hl6mY3K2u9ZjRo1UKNGjQLf4+joCB8fHzRq1KjYcoODg9GkSROMGDECS5cuRXJyMj766COEhoYWWGlZF2NGRgb++OMPxMfHw8nJqVx/EOz1WpclZrk/H9Z+bzIyMjBv3jy8+OKL8PHxQWJiIt577z0EBASgT58+xZZr7s+BvV7nssRslM9Aucd9UbkAKPKxceNG/TFZWVnSW2+9JVWrVk1ydXWVBg8eLN27d69AOd26dSuynBs3buiPqVOnTpHHlGb79u1Sw4YNJScnJ6lp06bSvn37CsQ2ePBgqWbNmpKTk5Pk6+srDRw4UDp9+nSp5ZYW86ZNm6TAwEDJ1dVV8vDwkNq3b19gWKZcrP2eFaWsw6iTkpKk559/XnJxcZE8PT2ld955R1Kr1aVenzp16pRadlHs+Vpb+ufD2u9NZmamFBwcLHl5eUmOjo5SnTp1pDfeeKPAUPTimPNzYM/X2RyfAYUkSVKxGRERERGRjWCfHiIiIrILTHqIiIjILjDpISIiIrvApIeIiIjsApMeIiIisgtMeoiIiMguMOkhIiIiu8Ckh4iIiOwCkx4iIiKyC0x6iIiIyC4w6SEiIiK7wKSHiIiI7ML/A7bqIljzWlN3AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction model - LSTM self time series"
      ],
      "metadata": {
        "id": "UWqI-_lj0kCM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class LSTMstepsPretiction(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.lstm = nn.LSTM(input_size=1, hidden_size=50, num_layers=1, batch_first=True)\n",
        "        self.linear = nn.Linear(50, 1)\n",
        "    def forward(self, x):\n",
        "        x, _ = self.lstm(x)\n",
        "        x = self.linear(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "AfzgOzgW0r62"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "def create_dataset(dataset, lookback):\n",
        "    \"\"\"Transform a time series into a prediction dataset\n",
        "\n",
        "    Args:\n",
        "        dataset: A numpy array of time series, first dimension is the time steps\n",
        "        lookback: Size of window for prediction\n",
        "    \"\"\"\n",
        "    X, y = [], []\n",
        "    for i in range(len(dataset)-lookback):\n",
        "        feature = dataset[i:i+lookback]\n",
        "        target = dataset[i+1:i+lookback+1]\n",
        "        X.append(feature)\n",
        "        y.append(target)\n",
        "    return torch.tensor(X), torch.tensor(y)"
      ],
      "metadata": {
        "id": "mkbo6g4R1TXh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch.optim as optim\n",
        "import torch.utils.data as data"
      ],
      "metadata": {
        "id": "P5CRbTGD00an"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d = {'col1': train_data['Close'].index[:], 'col2': train_data['Close'].values}\n",
        "df = pd.DataFrame(data=d)"
      ],
      "metadata": {
        "id": "BFtdUJ_R5EpX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lookback = 1\n",
        "X_train, y_train = create_dataset(train_data.values, lookback=lookback)\n",
        "X_test, y_test = create_dataset(test_data.values, lookback=lookback)\n",
        "print(X_train.shape, y_train.shape)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "metadata": {
        "id": "gyt7irQR1v1I",
        "outputId": "97c403d4-7fcf-4d7a-f540-030fd0bf9d9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([924, 1, 1]) torch.Size([924, 1, 1])\n",
            "torch.Size([616, 1, 1]) torch.Size([616, 1, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LSTMstepsPretiction()\n",
        "optimizer = optim.Adam(model.parameters())\n",
        "loss_fn = nn.MSELoss()\n",
        "loader = data.DataLoader(data.TensorDataset(X_train, y_train), shuffle=True, batch_size=8)\n",
        "\n",
        "n_epochs = 200\n",
        "for epoch in range(n_epochs):\n",
        "    model.train()\n",
        "    for X_batch, y_batch in loader:\n",
        "        y_pred = model(X_batch)\n",
        "        loss = loss_fn(y_pred, y_batch)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    # Validation\n",
        "    if epoch % 100 != 0:\n",
        "        continue\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        y_pred = model(X_train)\n",
        "        train_rmse = np.sqrt(loss_fn(y_pred, y_train))\n",
        "        y_pred = model(X_test)\n",
        "        test_rmse = np.sqrt(loss_fn(y_pred, y_test))\n",
        "    print(\"Epoch %d: train RMSE %.4f, test RMSE %.4f\" % (epoch, train_rmse, test_rmse))\n"
      ],
      "metadata": {
        "id": "o4pyl5i20zKo",
        "outputId": "3f2f8c3c-1d34-4a37-ff86-264b2599f75b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: train RMSE 19.2687, test RMSE 52.9324\n",
            "Epoch 100: train RMSE 0.4365, test RMSE 23.6404\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = (17,5)\n",
        "with torch.no_grad():\n",
        "    # shift train predictions for plotting\n",
        "    train_plot = np.ones_like(df_close.values) * np.nan\n",
        "    y_pred = model(X_train)\n",
        "    y_pred = y_pred[:, -1, :]\n",
        "    train_plot[lookback:train_size] = model(X_train)[:, -1, :]\n",
        "    # shift test predictions for plotting\n",
        "    test_plot = np.ones_like(df_close.values) * np.nan\n",
        "    test_plot[train_size+lookback:len(df_close.values)] = model(X_test)[:, -1, :]\n",
        "# plot\n",
        "plt.plot(df_close.values, c='b')\n",
        "plt.plot(train_plot, c='r')\n",
        "plt.plot(test_plot, c='g')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fPqTw7eG1FXF",
        "outputId": "78c160ea-433d-4c7c-91ea-a5ca36b5e99f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "InvalidArgumentError",
          "evalue": "Exception encountered when calling LSTMCell.call().\n\n\u001b[1m{{function_node __wrapped__MatMul_device_/job:localhost/replica:0/task:0/device:CPU:0}} Matrix size-incompatible: In[0]: [915,1], In[1]: [25,160] [Op:MatMul] name: \u001b[0m\n\nArguments received by LSTMCell.call():\n  • inputs=tf.Tensor(shape=(915, 1), dtype=float32)\n  • states=('tf.Tensor(shape=(915, 40), dtype=float32)', 'tf.Tensor(shape=(915, 40), dtype=float32)')\n  • training=False",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-90-d49556447f32>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m# shift train predictions for plotting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtrain_plot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_close\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mtrain_plot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlookback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtrain_size\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   5981\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5982\u001b[0m   \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5983\u001b[0;31m   \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5984\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: Exception encountered when calling LSTMCell.call().\n\n\u001b[1m{{function_node __wrapped__MatMul_device_/job:localhost/replica:0/task:0/device:CPU:0}} Matrix size-incompatible: In[0]: [915,1], In[1]: [25,160] [Op:MatMul] name: \u001b[0m\n\nArguments received by LSTMCell.call():\n  • inputs=tf.Tensor(shape=(915, 1), dtype=float32)\n  • states=('tf.Tensor(shape=(915, 40), dtype=float32)', 'tf.Tensor(shape=(915, 40), dtype=float32)')\n  • training=False"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction model - NN LSTM loopback"
      ],
      "metadata": {
        "id": "CuWfHOWmGTbx",
        "_kg_hide-input": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "df_close_scaled = scaler.fit_transform(df_close['Close'].values.reshape(-1, 1))\n",
        "train_size = int(len(df_close_scaled) * 0.6)\n",
        "train_data, test_data = df_close_scaled[:train_size], df_close_scaled[train_size:]"
      ],
      "metadata": {
        "trusted": true,
        "id": "ZroaqjusTpef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_data)\n",
        "plt.plot(test_data)"
      ],
      "metadata": {
        "id": "LHXwlaDRyRFc",
        "outputId": "58295f8c-449f-45bb-9be5-591df4ea7a2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7dd8e11923e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert an array of values into a dataset matrix\n",
        "def create_dataset(dataset, look_back=25):\n",
        "    dataX, dataY = [], []\n",
        "    for i in range(len(dataset)-look_back-1):\n",
        "        a = dataset[i:(i+look_back), 0]\n",
        "        dataX.append(a)\n",
        "        dataY.append(dataset[i + look_back, 0])\n",
        "    return np.array(dataX), np.array(dataY)"
      ],
      "metadata": {
        "trusted": true,
        "id": "Exezkn1-Tpef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train = create_dataset(train_data, look_back=25)\n",
        "x_test, y_test = create_dataset(test_data, look_back=25)\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], 1, x_test.shape[1]))"
      ],
      "metadata": {
        "trusted": true,
        "id": "gDz1-3nCTpef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x_train[:,0,:])\n",
        "#plt.plot(x_test[:,0,:])"
      ],
      "metadata": {
        "id": "LXfXXtetxYUn",
        "outputId": "ca04d56e-67b8-446b-eeb6-147e24cfde7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7dd8e1df8b20>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df8b80>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df8bb0>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df8ca0>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df8d90>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df8e80>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df8f70>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9060>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9150>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9240>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9330>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df8b50>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9420>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df95d0>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df96c0>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df97b0>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df98a0>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9990>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9a80>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9b70>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9c60>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9d50>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9e40>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1df9f30>,\n",
              " <matplotlib.lines.Line2D at 0x7dd8e1dfa020>]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "look_back = 25\n",
        "model = Sequential()\n",
        "model.add(LSTM(40, input_shape=(1, look_back)))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "model.fit(x_train, y_train, epochs=17, batch_size=1, verbose=2)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rlq6XZCHTpef",
        "outputId": "f730021a-85d1-4f3e-ea0b-321521330754"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/17\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "899/899 - 4s - 4ms/step - loss: 6.9488e-04\n",
            "Epoch 2/17\n",
            "899/899 - 1s - 2ms/step - loss: 8.4592e-05\n",
            "Epoch 3/17\n",
            "899/899 - 4s - 4ms/step - loss: 5.0047e-05\n",
            "Epoch 4/17\n",
            "899/899 - 4s - 4ms/step - loss: 4.9225e-05\n",
            "Epoch 5/17\n",
            "899/899 - 3s - 3ms/step - loss: 5.1834e-05\n",
            "Epoch 6/17\n",
            "899/899 - 1s - 2ms/step - loss: 4.2294e-05\n",
            "Epoch 7/17\n",
            "899/899 - 1s - 2ms/step - loss: 4.2956e-05\n",
            "Epoch 8/17\n",
            "899/899 - 2s - 2ms/step - loss: 4.1417e-05\n",
            "Epoch 9/17\n",
            "899/899 - 2s - 3ms/step - loss: 3.8168e-05\n",
            "Epoch 10/17\n",
            "899/899 - 2s - 2ms/step - loss: 3.3162e-05\n",
            "Epoch 11/17\n",
            "899/899 - 2s - 2ms/step - loss: 3.6574e-05\n",
            "Epoch 12/17\n",
            "899/899 - 2s - 2ms/step - loss: 3.2551e-05\n",
            "Epoch 13/17\n",
            "899/899 - 1s - 2ms/step - loss: 3.1050e-05\n",
            "Epoch 14/17\n",
            "899/899 - 2s - 2ms/step - loss: 3.3014e-05\n",
            "Epoch 15/17\n",
            "899/899 - 1s - 2ms/step - loss: 2.9949e-05\n",
            "Epoch 16/17\n",
            "899/899 - 2s - 2ms/step - loss: 2.6682e-05\n",
            "Epoch 17/17\n",
            "899/899 - 3s - 3ms/step - loss: 2.8883e-05\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7dd8e1aa3fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_predictions = model.predict(np.expand_dims(x_test, axis=-1))\n",
        "lstm_predictions = scaler.inverse_transform(lstm_predictions)\n",
        "\n",
        "testScore = np.sqrt(mean_squared_error(scaler.inverse_transform(x_test[:,:,0]), lstm_predictions[:,0]))\n",
        "print('Test Score: %.2f RMSE' % (testScore))"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1NX4XSATpef",
        "outputId": "e573b22c-4727-4d28-e1e9-021a586ff322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "Test Score: 9.09 RMSE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(scaler.inverse_transform(x_test[:,:,0]))\n",
        "plt.plot(lstm_predictions)\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "1fQjltXNTpeg",
        "outputId": "528dfe67-b7af-42e9-f925-20e541a4133b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction model - ARIMA"
      ],
      "metadata": {
        "id": "6amiqrsmTpeg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-19T17:00:41.113481Z",
          "iopub.execute_input": "2024-09-19T17:00:41.113923Z",
          "iopub.status.idle": "2024-09-19T17:00:41.119536Z",
          "shell.execute_reply.started": "2024-09-19T17:00:41.113882Z",
          "shell.execute_reply": "2024-09-19T17:00:41.118207Z"
        },
        "trusted": true,
        "id": "SSFrJAgyTpeg"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_size = int(len(df_close) * 0.6)\n",
        "train_data, test_data = df_close[:train_size], df_close[train_size:]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-19T17:00:46.145778Z",
          "iopub.execute_input": "2024-09-19T17:00:46.146214Z",
          "iopub.status.idle": "2024-09-19T17:00:46.151927Z",
          "shell.execute_reply.started": "2024-09-19T17:00:46.146174Z",
          "shell.execute_reply": "2024-09-19T17:00:46.150674Z"
        },
        "trusted": true,
        "id": "EGh7PS5aTpeg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_data)"
      ],
      "metadata": {
        "id": "FNd7kr7L1nS6",
        "outputId": "fee34f26-f692-42c3-d9ac-61c046383991",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7a3050a76a10>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The P value is defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance.\n",
        "\n",
        "**Dickey–Fuller test** tests the null hypothesis that a unit root is present in an autoregressive (AR) time series model. The alternative hypothesis is different depending on which version of the test is used, but is usually stationarity or trend-stationarity"
      ],
      "metadata": {
        "id": "137Js44jFZ2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "def test_stationarity(series, title=''):\n",
        "    print(f\"Results of ADF Test on {title}:\")\n",
        "    result = adfuller(series, autolag='AIC')\n",
        "    print(f\"ADF Statistic: {result[0]}\")\n",
        "    print(f\"p-value: {result[1]}\")\n",
        "    if result[1] > 0.05:\n",
        "        print(\"Timeseries is non-stationary\")\n",
        "    else:\n",
        "        print(\"Timeseries is stationary\")\n",
        "    for key, value in result[4].items():\n",
        "        print(f\"Critical Value ({key}): {value}\")\n",
        "    print(\"\\n\")\n",
        "\n",
        "test_stationarity(train_data, 'Counts')"
      ],
      "metadata": {
        "id": "LLYRaRRKFyM6",
        "outputId": "36c8720a-cf0f-4bf5-eb44-28a2eaa826ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results of ADF Test on Counts:\n",
            "ADF Statistic: -1.8500120248286613\n",
            "p-value: 0.3559297136490945\n",
            "Timeseries is non-stationary\n",
            "Critical Value (1%): -3.4375643702748078\n",
            "Critical Value (5%): -2.8647248254388096\n",
            "Critical Value (10%): -2.568465808810804\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Отрисовка ACF и PACF\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "\n",
        "\n",
        "diff1 = train_data.diff()\n",
        "diff1.dropna(inplace=True) #delete all rows with NaN value\n",
        "\n",
        "fig = plt.figure(figsize=(12,8))\n",
        "ax1 = fig.add_subplot(211)\n",
        "half_size = len(diff1) // 2\n",
        "fig = plot_acf(diff1, lags=half_size, ax=ax1)\n",
        "ax2 = fig.add_subplot(212)\n",
        "fig = sm.graphics.tsa.plot_pacf(diff1, lags=half_size, ax=ax2)"
      ],
      "metadata": {
        "id": "OwwCnDx9HP7e",
        "outputId": "8a08f6ef-2d78-442d-8e53-97ea4038e546",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Модель ARIMA(p,d,q) - интегрированная модель авторегрессии — скользящего среднего\n",
        "\n",
        "\\begin{equation}\n",
        "a(L)(1-L)^{d} y_{t}=\\alpha+b(L) \\varepsilon_{t}\n",
        "\\end{equation}\n",
        "\n",
        "где ε(t) — белый шум, L — оператор сдвига.\n"
      ],
      "metadata": {
        "id": "eyepEBt6RWsh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install optuna --quiet"
      ],
      "metadata": {
        "id": "B8ZDEpbpJP64"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import optuna\n",
        "import itertools"
      ],
      "metadata": {
        "id": "8BTJoYYXJIiz"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p = q = range(1, 40)\n",
        "d = range(1, 5)\n",
        "pdq = list(itertools.product(p, d, q))\n",
        "\n",
        "\n",
        "def objective_arima(trial):\n",
        "    order=trial.suggest_categorical('order',pdq)\n",
        "    model=ARIMA(train_data['Close'].values,order=order)\n",
        "    mdl = model.fit() #disp=0\n",
        "    predictions = mdl.forecast(len(test_data))\n",
        "    predictions = pd.Series(predictions.values, index=test_data.index)\n",
        "    # predictions\n",
        "    residuals = test_data['Close'].values - predictions\n",
        "    mse=np.sqrt(np.mean(residuals**2))\n",
        "    accuracy=mse\n",
        "    return accuracy\n"
      ],
      "metadata": {
        "id": "JsjEkMddJZOl"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "study=optuna.create_study(direction=\"minimize\")\n",
        "study.optimize(objective_arima,n_trials=1)"
      ],
      "metadata": {
        "id": "WTFrBT49JpDZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 15 lags for autoregression (AR)\n",
        "# 1st order differencing (I)\n",
        "# No moving average term (MA)\n",
        "arima_model = ARIMA(train_data['Close'].values, order=(31, 3, 22))\n",
        "arima_model_fit = arima_model.fit()\n",
        "print(arima_model_fit.summary())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-19T17:17:28.514000Z",
          "iopub.execute_input": "2024-09-19T17:17:28.514441Z",
          "iopub.status.idle": "2024-09-19T17:17:29.382994Z",
          "shell.execute_reply.started": "2024-09-19T17:17:28.514400Z",
          "shell.execute_reply": "2024-09-19T17:17:29.381843Z"
        },
        "trusted": true,
        "id": "7dV-z3o3Tpeg",
        "outputId": "6c41869a-2307-4725-ace5-3d01e1635971",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                  925\n",
            "Model:               ARIMA(31, 3, 22)   Log Likelihood                -449.579\n",
            "Date:                Fri, 01 Nov 2024   AIC                           1007.157\n",
            "Time:                        10:20:19   BIC                           1267.791\n",
            "Sample:                             0   HQIC                          1106.606\n",
            "                                - 925                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "ar.L1         -1.8419     17.609     -0.105      0.917     -36.355      32.671\n",
            "ar.L2         -2.3123     29.900     -0.077      0.938     -60.916      56.291\n",
            "ar.L3         -2.4224     35.545     -0.068      0.946     -72.089      67.244\n",
            "ar.L4         -2.4390     36.862     -0.066      0.947     -74.686      69.808\n",
            "ar.L5         -2.6168     37.019     -0.071      0.944     -75.173      69.940\n",
            "ar.L6         -2.6515     39.940     -0.066      0.947     -80.932      75.629\n",
            "ar.L7         -2.5809     40.116     -0.064      0.949     -81.207      76.045\n",
            "ar.L8         -2.4781     38.941     -0.064      0.949     -78.800      73.844\n",
            "ar.L9         -2.3693     37.559     -0.063      0.950     -75.983      71.245\n",
            "ar.L10        -2.3977     35.811     -0.067      0.947     -72.586      67.791\n",
            "ar.L11        -2.4864     36.588     -0.068      0.946     -74.197      69.225\n",
            "ar.L12        -2.5628     37.997     -0.067      0.946     -77.035      71.909\n",
            "ar.L13        -2.7568     38.956     -0.071      0.944     -79.109      73.596\n",
            "ar.L14        -2.9670     42.245     -0.070      0.944     -85.767      79.832\n",
            "ar.L15        -3.1555     45.321     -0.070      0.944     -91.984      85.673\n",
            "ar.L16        -2.9655     48.002     -0.062      0.951     -97.048      91.117\n",
            "ar.L17        -2.6657     44.295     -0.060      0.952     -89.482      84.150\n",
            "ar.L18        -2.4472     39.474     -0.062      0.951     -79.814      74.920\n",
            "ar.L19        -2.2078     36.592     -0.060      0.952     -73.926      69.510\n",
            "ar.L20        -2.0712     32.861     -0.063      0.950     -66.477      62.334\n",
            "ar.L21        -1.9119     31.034     -0.062      0.951     -62.738      58.914\n",
            "ar.L22        -1.7498     28.835     -0.061      0.952     -58.266      54.767\n",
            "ar.L23        -1.4828     26.313     -0.056      0.955     -53.055      50.089\n",
            "ar.L24        -1.2797     21.974     -0.058      0.954     -44.348      41.789\n",
            "ar.L25        -1.0058     19.073     -0.053      0.958     -38.389      36.378\n",
            "ar.L26        -0.8107     14.804     -0.055      0.956     -29.826      28.205\n",
            "ar.L27        -0.6810     11.900     -0.057      0.954     -24.005      22.643\n",
            "ar.L28        -0.6010     10.194     -0.059      0.953     -20.581      19.379\n",
            "ar.L29        -0.3766      9.035     -0.042      0.967     -18.084      17.331\n",
            "ar.L30        -0.1580      5.211     -0.030      0.976     -10.371      10.055\n",
            "ar.L31        -0.0135      1.929     -0.007      0.994      -3.795       3.768\n",
            "ma.L1         -0.0993     17.605     -0.006      0.996     -34.605      34.407\n",
            "ma.L2         -0.4279      4.563     -0.094      0.925      -9.371       8.516\n",
            "ma.L3         -0.1322      7.880     -0.017      0.987     -15.576      15.312\n",
            "ma.L4         -0.0847      1.035     -0.082      0.935      -2.114       1.944\n",
            "ma.L5          0.2856      1.829      0.156      0.876      -3.300       3.871\n",
            "ma.L6         -0.3904      4.791     -0.081      0.935      -9.780       8.999\n",
            "ma.L7         -0.1950      7.196     -0.027      0.978     -14.299      13.909\n",
            "ma.L8          0.1834      2.579      0.071      0.943      -4.871       5.238\n",
            "ma.L9         -0.1223      4.067     -0.030      0.976      -8.093       7.848\n",
            "ma.L10         0.1820      3.223      0.056      0.955      -6.135       6.499\n",
            "ma.L11         0.1114      3.589      0.031      0.975      -6.923       7.146\n",
            "ma.L12        -0.1004      1.898     -0.053      0.958      -3.821       3.620\n",
            "ma.L13         0.2289      2.509      0.091      0.927      -4.689       5.146\n",
            "ma.L14         0.1045      4.542      0.023      0.982      -8.798       9.007\n",
            "ma.L15         0.0522      1.301      0.040      0.968      -2.498       2.603\n",
            "ma.L16        -0.4418      0.784     -0.564      0.573      -1.978       1.095\n",
            "ma.L17        -0.3507      7.668     -0.046      0.964     -15.379      14.678\n",
            "ma.L18         0.2449      5.228      0.047      0.963     -10.002      10.492\n",
            "ma.L19        -0.0041      5.555     -0.001      0.999     -10.893      10.884\n",
            "ma.L20         0.0751      1.288      0.058      0.953      -2.449       2.599\n",
            "ma.L21        -0.1242      1.439     -0.086      0.931      -2.945       2.697\n",
            "ma.L22         0.0097      1.797      0.005      0.996      -3.513       3.533\n",
            "sigma2         0.1529      0.004     37.646      0.000       0.145       0.161\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):            183653.83\n",
            "Prob(Q):                              0.89   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               0.57   Skew:                             2.75\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                        71.92\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ARIMA\n",
        "arima_predictions = arima_model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-19T17:17:52.280068Z",
          "iopub.execute_input": "2024-09-19T17:17:52.280517Z",
          "iopub.status.idle": "2024-09-19T17:17:52.304072Z",
          "shell.execute_reply.started": "2024-09-19T17:17:52.280476Z",
          "shell.execute_reply": "2024-09-19T17:17:52.302851Z"
        },
        "trusted": true,
        "id": "ZZXWDTxsTpeh"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Forecast\n",
        "fc = arima_model_fit.forecast(len(test_data), alpha=0.05)  # 95% conf\n",
        "\n",
        "# Make as pandas series\n",
        "fc_series = pd.Series(fc, index=test_data.index)\n",
        "\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(12,5), dpi=100)\n",
        "plt.plot(train_data, label='training')\n",
        "plt.plot(test_data, label='actual')\n",
        "plt.plot(fc_series, label='forecast')\n",
        "\n",
        "plt.title('Forecast vs Actuals')\n",
        "plt.legend(loc='upper left', fontsize=8)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-19T17:17:54.964013Z",
          "iopub.execute_input": "2024-09-19T17:17:54.964440Z",
          "iopub.status.idle": "2024-09-19T17:17:55.359557Z",
          "shell.execute_reply.started": "2024-09-19T17:17:54.964398Z",
          "shell.execute_reply": "2024-09-19T17:17:55.358365Z"
        },
        "trusted": true,
        "id": "Ls0g1D2cTpeh",
        "outputId": "b5f31167-10cd-4750-f79f-b0442fc526e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/representation.py:374: FutureWarning: Unknown keyword arguments: dict_keys(['alpha']).Passing unknown keyword arguments will raise a TypeError beginning in version 0.15.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(fc[:10],'r')\n",
        "plt.plot(test_data.values[:10],'b')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "zoXXWM3WQDD7",
        "outputId": "091eb5ee-ec64-4d98-b607-4753f2d63370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Dg1KAUjqdUq+9ptSFC0Vyd5ZlyxalateWJw5QqlYtpSIitI6KCkFamlJDhxpf2sGDlXrwQOuoTNPNm0p5esrzNH261tFQfty9q9Ty5Uq99JJSJUoY/+51OqVatVJq/nylbtx49P8lJChVrpwc+/rrxR+3qcvt9zcTljz6+2+levUy/qE6OSn10UdKJSYW6d2av9RUpb75RqlSpYxPXufOSp06pXVklE9XryrVtq3x5Zw+XSmDQeuoTNuiRfJcuboq9c8/WkdDuZGaqtQvvyjVp49SLi7Gv3dAqXr1lPrsM6XOn3/8ebZvV8rGRv7fwoVFHbV5YcJSxPbuVap5c+MfrpeXZNepqcVy9+br+nWl3ntPKVtbeeLs7ZUaNYoZn5n580+lfHzkJXR0VCosTOuIzENamlJNmsjz1quX1tFQdgwGpXbvVio4WKmyZTMnKRUrKjV2rFLHjuX9vJMmGX/oRkcXftzmiglLMTAYlPrxR6WqVDH+MdeuLdk4PcaxY0oFBGTO+BYskE90MlkGg1JffKGUnZ28bE89pdTRo1pHZV4OHpQuBECpHTu0joYeduKEtJhXqpQ5SSlTRqmgIEnUC9KKmJZm/NirUUOp27cLL3ZzxoSlGKWkKDVzZubejoAApY4cKfZQzIvBoNSGDZkzvqefVur337WOjLJw65ZSXbsaX6oePZTS4O1mEd55x/gDh62y2vrnH6VmzFCqfv3MSYqLi1KvvqrUzz8rdf9+4d3flStKeXvLffTty25UpZiwaOLGDaU++MA4GMvGRqkBA5S6eFGzkMxDSop0BLu7Gz8tevXKXccwFYtDh4y/OkuUUGr2bH7QFsT160qVLi3P58yZWkdjfW7eVOq775R67jljaxcgLYcdOkgX5507RXf/O3cae8W//77o7sdcMGHR0OnTSnXvbnwTODsrNWFC0b4BLEJCglIDBxo/QZyc5Im7e1fryKyWwaDUvHnG2XF+fkrt26d1VJZh3jx5Tt3d5U+filZyslKrVyvVpYsMnXu4NaV5c6W+/VYGkheXkBDjGDBrb41nwmIC/vhDqWeeMb4pvL1lmAanfT7GoUNKPfus8Ynz9ZW5hPxJX6zu3JGZEekvQ8eO0jJAhePBA6UaNuRU16L04IFUUHj99cwNuOnVFaZMkZmfWkhLU6p9e+NYMGued8CExUQYDEqtXCkjy9PfKP/3fyxD8ljpT1yFCsYnrlkzpQ4c0Doyq3DsmFI1a8rTbmur1LRpHA9dFPbsMf55//mn1tFYBoNBqf37pT5Qeu2Th3/7jBplOi0aV68aZ9v16mW9v8mKJGGZMmWKatiwoXJ1dVVly5ZVnTt3VidPnsx0THx8vOrTp4/y8vJSzs7Oqn79+mrNmjWPPffs2bOVn5+fcnBwUI0aNVJ79+7NS2gmm7Cku3dPBnaVLGl887Rrx6ltj5WUpNTEidI99HDFvk2bZBQoFbqlS6UbE1CqfHmlIiO1jsiy9e8vz3X9+mx9LYjYWOlBfuqpzElKqVJKvfWWjBsxxaT7jz+M41nmztU6Gm0UScLSpk0btXDhQhUdHa2ioqJU+/btVYUKFdSdhwZnvPjii8rf31/t3btXnT59Wk2aNEnZ2NioQ4cOZXveFStWKHt7e7VgwQJ17Ngx9eabb6qSJUuqy5cv5zo2U09Y0l27ptSQIcZpoTY28mZiH/ZjnD+vVO/emT+JbGyUatBAqfffV+qnn9hfUUDJyfK3mP70tm6tVB7egpRPV64Yf8jMmaN1NOYlPl4GLfv7Z/5ocHJS6pVXlFq/Xsb0m7rp0yVuBwfpEbc2xdIldOXKFQVART70E8zFxUUtWbIk03GlSpVS3333XbbnadSokQoKCsq4nJaWpry9vVVISEiuYzGXhCVdbKwM/kp/g7m6KvXppxxf+lh//ik/SStXzvwJlb7VqSPVnlatYhaYB3FxxmmdOp1SH3/MX/vFadYsee6feKJ4B36aI71eKsW++KKxcmx612WbNkotWWJ+40HS0mSMGCBVHszka6zQFEvCcurUKQVA/fXXXxnXvfjii6pDhw7q+vXrKi0tTS1fvlw5OzurU9mUYE9JSVG2trYqPDw80/X9+vVTnTp1yva+7927p/R6fcZ24cIFs0pY0u3cmfnXgY+PvOFMsenS5Pzzj8w/fOstqcKUVQJTvbpSgwYptWwZa6FnY+1apTw8jAWyfv1V64isT2qqUnXrymswaJDW0Ziee/eUCg+X2ZeOjpnf4o0bK/X11+b/++T6deOQve7drWs8S5EnLGlpaapDhw6qWbNmma6/efOmCggIUACUnZ2dcnd3V7/m8Al48eJFBUD9+Z8RZyNGjFCNGjXK9v+NHz9eAXhkM7eERSlJTpYtyzy+tEEDWXuC8uDyZZm3GBxs/PT/71apklJvvCGLuvz9t3V9KvzH/ftKDRtmfGqaNuVinlratcvYwrV/v9bRaC8tTT4DBw7MPPYPUKpaNRnaFhendZSFa/du43CB2bO1jqb4FHnC8vbbbys/Pz914T+fcMHBwapRo0Zqy5YtKioqSk2YMEF5eHioo9nU785vwmIpLSwPS05WaurUzNPvXnpJykVTPly/LmNbhg2TCroPtx8/PG2gTx+pIhUTYzUJzPnzxjVtACl4WJjVPCl/0qeRN25sva2sqalS/uHJJzO/Vb295e/04EHLfpt+8YU8Xnt765kUWaQJS1BQkPLx8VFnzpzJdH1cXJwCoKL/M/WldevW6q233sryXPntEvovcxvDkpMrV2TdivSR47a2cvnKFa0jM3O3bkmd7VGjpEBO+k+Zh7dy5WS03jffyBQuC/zW2LzZWGXVw0Oa2sk0XLqklJubvDahoVpHU7wMBumefLh318NDqoVv3Wo9Y6oMBqUCA+XxV6woVXktXZEkLAaDQQUFBSlvb28VGxv7yO1Hjx5VANTx48czXR8QEKDefPPNbM/bqFEjFRwcnHE5LS1N/e9//7PoQbe5ceKEtLCkv3nd3aUFJjlZ68gsxJ07UhDno4+UatHi0fKX6YM6unSRqQiHD5v1p+aDB0qNG2csJFy/vlRlJtPy+efy+pQtK8t9WINt25Rq1Mj4tnviCVmtIylJ68i0ceOGsYWpSxfLblFSqogSlnfeeUd5eHioHTt2qPj4+Iwt6d+/qvv376sqVaqoZ599Vu3du1fFxcWpGTNmKJ1OpzZt2pRxnueff17NmjUr4/KKFSuUg4ODWrRokTp+/LgaNGiQKlmypErIwygqS0xY0m3bJmNa0t/MFSrImBcL/PGvreRkWT534kSZ05te++XhrWRJGc7/2WdK7d1rNivXJSQo9fzzxofx9ttMfE3V/fvGVoaHfsdZpAMHMi/a7uys1Nix1tGq8Dj79hnXpbP09aaKJGHJapArALVw4cKMY2JjY1XXrl2Vp6encnZ2VnXr1n1kmrOfn58aP358putmzZqlKlSooOzt7VWjRo3Unj178hKaRScsSklysnixUv/7n/HN7e8vs4yoiKSkyDTqkBCl2rY1ttU/vLm6ylzKyZNllWkTLPoQGSkF4ABZgXbZMq0josfZutVYaigqSutoCl9MjKz2nf42KlFCkrP4eK0jMy1ff218fvJYS9Ws5Pb7W6eUUrAAiYmJ8PDwgF6vh7u7u9bhFJmkJODLL4GpU4E7d+S6Ll2AadOAqlW1jc3iPXgAREUBkZHAzp3Arl3AzZuZj3F0BJo0AVq2lK1xY8DJSZNwDQbgs8+AsWOBtDSgZk1gzRqgRg1NwqE8euUVYNUqoHlz+XPT6bSOqOD++QeYOBFYsED+JnU64NVXgU8+ASpV0jo606MU0KOHvG/9/IBDh4BSpbSOqvDl9vubCYuZSkgAJkwAvvtOvpjs7ICgIGDcOKB0aa2jsxIGA/DXX/Jtkp7EXL2a+Rh7e6BRI0leWrQAmjYFXF2LPLQbN4DXXgM2bpTLffsCc+YALi5FftdUSC5cAKpXlx8pP/wA9OmjdUT5d+OG/MiaNQu4d0+u69gRmDwZqFtX29hMnV4PPP00cPo00KkT8NNPlpG8PowJi5U4dgwYMQL45Re5XLIk8NFHQHAw4OCgaWjWRyng5ElJXtK3+PjMx9jZyadPixbA888DL74I2NoWahj79smvsnPn5G9g1ixg4EDL+5CzBiEhwIcfAuXKATExgLl9tN29C3z1FTB9unzxAtJiNHUq0KyZtrGZk0OHpOH2/n3g88+BYcO0jqhw5fr7u8g7p4qJpY9heZzffstcK61iRVns2NJHl5s0g0GpU6eU+v57pfr1U8rP79ExMM8+W2jr2xsM0uedPlCvcmWZ2ETm6949papWNdbKMRcpKVL4zMvL+Kdet66sWcrPpPz59lt5Hu3spMCcJSmW0vymxNoTFqVk2mpoqHGAJSDlRrhsvQk5e1bWXhgwQAbsAjKYd+HCAn2S6/VSzjv9de/alYtZW4pffjF+UR07pnU0OUtLk9W+K1bMXFyasxoLzmCQElHp9S6vXdM6osLDhMWK3bkjy6w7Oxs/NLp3Z80Nk3P6tFLNmmXOMvKx8t2RI8Zf4XZ2MgWSv2ItS+fO8vo+/7xpvrYGg1IbN2Zu5fXykvqLJjhxzmzp9cb3eocOlpMEMmEhdfGi/JBPLxTm4CBNsmRCHjyQadPp/TheXnl6kUJDjYvB+fpaXlMxiTNnjK/zqlVaR5PZrl1KNW+euTrt5Mnyw4kKX1SU8W9h2jStoykcTFgow5EjSrVqZUxatmzROiJ6xKFDStWsmbmyWw6f+HfvKvX668bD27XLV+MMmZEJE+S19vFR6vZtraORz5UOHYx/g46OSo0cKUt4UdGaP9+4bMuuXVpHU3C5/f62KerRv6S9unWB334DOncGUlJkatyuXVpHRZnUrw8cOAAMHSqX586V6/bufeTQmBjgmWeARYsAGxuZGrpxI1CmTLFGTMVs5EigYkWpZTJlinZxnDkjU6zr1QM2bZJJboMGAXFxUg/KEuuEmJqBA4HevaWWTc+ej1ZTsFRMWKxEiRLAypVA27ZS16F9+yy/C0lLTk5SFXDLFuB//wNOnZK5nxMmAKmpAOQ1bNhQyr94ecmhH34oiQtZNicnYOZM2Z8xA4iNLd77T0iQcgnVqgHLlkm7yiuvAMePA/PmyZ8sFQ+dTp7zatWAixelzpLBoHVURY8fc1bEwQFYuxZ47jmpktu2LXD4sNZR0SNat5aMpFcv+Qn1ySdIafocgvvcQs+e8tq1bCmv3XPPaR0sFaeXXgLatZP8dcgQSRqKml4vtZ0qVwa++UYKPrdpAxw8CKxYATz1VNHHQI9ydQVWr5ZE9tdfpbaNpWPCYmWcnIANG+SH+61bUrcsOlrrqOgRTzwBhIUBYWE461YHzQ98iW+WlQQAfDhGYcsWoHx5bUOk4qfTSSE2e3tg82Zg/fqiu6/kZFnaoVIl6XZMSpKVJrZtk/tu0KDo7ptyp04dYPZs2R83TmpVWjImLFbIxQX4+WfA3x+4fl1+0MfEaB0VZWWDay/U10XhAPxRCtexCe0xOaoD7K7GP/4/k0WqWhUYPlz2hw6VxKIwPXggS35UrSrjZm7ckPWnwsOB3bvZqmdq3ngD6NdPuoR69QIuX9Y6oqLDhMVKubtLM2K9esCVK5K0nDmjdVSU7sEDYNQoGSB9K9EGjRsrHB6/Du0dtsk6DHXqSP8eWaUPPwR8fICzZ2Wga2EwGKSLoVYtGUR78SJQoQKwcKH0UAYGcnkHU6TTAd9+K4ubxsfLgOi0NK2jKhpMWKzYE0/I7KGaNeXD6fnngfPntY6K0l+L6dPl8pAhwM6dOlSY0F8WFalXT5rGunUDXn8dSEzUMlzSgIsL8MUXsj91asF+bCglnwP+/rIGVWyszDibOVP2X3+90Je7okLm4iLJprOzDMSfPFnriIoGExYrV7YssHWrNP+eOydflJcuaR2V9dqyRWYz79oFuLnJh9DMmTJmAYBkl3v3AmPGyNSgxYtl3vrOnVqGTRp4+WVpGU1Jyf9ieHv3yjnatJFc2NVVJqWdOSOJMhdQNR81a8qK7IC8htu2aRpOkWDCQihXTv64K1aUJcxbt5ZuIio+aWnAxIlAQIDUVPi//5MvkJdfzuJge3spxBEZKS/auXNAq1bSh5SSUtyhk0Z0OuDrr2UB8HXrjCu258bx40DXrlLPZ/t2+ZMaOlQSlfHjJVkm89OvH9C/v7Sa9e4tU9EtCRMWAiD94du2yb8nTwIvvCC9DlT0rl6Vujjjx8sHzcCBMrixSpXH/MfmzYEjR4yfUNOnyzQOTvuyGjVrSksIALz33uPz1fPn5c+lTh0ZRGtjI4M2Y2OlBFDZskUfMxWtWbOA2rVl8G16cTlLwYSFMjz5pCQt5crJILs2bWTqMxWdP/6QLqDffpMp54sXywwNJ6dcnsDNDQgNlW+fMmUkgXn6aRngYA2VpAgffyxT3OPijONa/uvqVek2qlpVBtEaDECXLvI+X7AA8PMr3pip6Dg7S1eyi4u0nn3yidYRFR4mLJRJ1aoypqVMGSkM1b49cPu21lFZHqWAzz+XAnAXLwLVqwP79kmTbr4EBkrLSocOwP37wAcfSDMZR1FbPHd3qZcCAJ9+mvklv31bvrAqV5YWlPv3pfdwzx6ZZFazpiYhUxGrXh2YP1/2P/1UfhBZAiYs9IiaNWXw5xNPSNdEx45SNIoKx82b8ut2+HBpru3VC9i/X5pxC8TLS6oCzpsnP7O2b5cBuel11Mli9e4NPPusvE+HD5euoa++kqJvEyZI4tKggZQy2LZNeg7JsvXuLdPTlZKpzpYwmUKnlGV8kiUmJsLDwwN6vR7u7u5ah2MR9u+XH+mJiVIRd/16wNFR66jM24EDQPfuUj/D3l5mAL39dhHUt4iLkwVG9uyRyz16yBQCrkxnsY4ele5Fg0G6iOL/rS1Ytar8yn75Za45ZW2Sk4EmTaSnuEULaT23s9M6qkfl9vubf76ULX9/mXng4gJERMgX7f37WkdlnpSSEtrNmkmyUqkS8OefwDvvFFExripVZG70xIlSRGPVKhlpGRFRBHdGpqBuXSAoSPbj4wFvb+kWOHZM8lUmK9bHyUnGs7i5SeWD8eO1jqhg2MJCj7Vjhyy4du+e1CpbscI0s3RTlZgoM39Wr5bLXbrIQMeSJYspgP37pbUlff2FwYOl2pizczEFQMUlMVFK9FSqBLz7bh4Gb5NFW7VKVtYG5Edo27baxvNfuf3+ZsJCufLrr1Im/v596RtdsoTVL3MjKkpapuLiJMn77DOZhlrsJc6TkqROS/pKadWrA0uXyowiIrJ4QUFSwr90aflc8vHROiIjdglRoWrTBlizRr50w8JkMBdnzWZPKZme/MwzkqxUqCA9NEOHarQei7OzFGjYvFkGOJw8KcFNniwLFxGRRfv8cxnjdP060LMnkJqqdUR5x4SFcu2llyRZsbGRLo3gYE4+ycqdO9IDM2iQzNbo2BE4fFjyA821aSPFN15+WRKVjz6S0XinT2sdGREVIUdH6ZZ2d5f6Tx99pHVEeceEhfKke3cpbqbTyaST4cOZtDzs2DEZrLxsmXSZTZsmZdNNanJO6dLSqb1kiXx67d4tawF89x1fTCILVrmy/NgEpDD2xo3axpNXTFgoz/r0ke82QCprjhunbTymYvFiSVZOnpQZGjt2ACNHmujsDJ1OmoGOHpXqdXfvSpNQ585S05uILFK3bjLuHgBee828akua4kcpmYEBA4zjNydPljoP1iopSZ6P11+XugcBATKorXlzrSPLBT8/qSQ2Y4YUhtmwQaY/r1+vdWREVEQ++0x+XN24IbOHzKVcBRMWyregIPmeA6SVJX3fmsTESNXQBQukJWXSJJk2aFaLyNnYSCn/AwckWbl6VVpa3nyT6zIQWSAHB2DlSimtsGePTIU3B0xYqEA++EC+pAFgxAhjq4s1WL4caNhQlvDx8pKabB99ZKJdQLlRp47UbBkxQrqMvv8eqFdPKtwRkUWpWFEWwgSka3/dOm3jyQ1z/WglE/LRR8DYsbI/eLB8z1mye/ekQm3v3jIjqFUr6QJ6/nmtIysEDg4yGm/7dpmLfeaMLFIzdqz5tBsTUa4EBgLvvy/7r78O/P23ltE8HhMWKhSTJsny9YCM3Vy6VNt4isrp00DTpsDcudII8dFHslBkuXJaR1bIWraUAbn9+knBnSlTZFGSEye0joyICtHUqdKtfeuW6Y9nyVPCEhISAn9/f7i5ucHT0xOBgYGISS/3DeDs2bPQ6XRZbqvT65Jn4c6dOwgODoaPjw+cnJxQs2ZNzJ07N/+PioqdTidjWN59V2bGvvaasRS9pVi7Vla8PXwYKFNGxqpMmmTBFX89PGTq0+rVMi/70CF5Ar7+mlUDiSyEvb2MZ3niCWOPsMlSedCmTRu1cOFCFR0draKiolT79u1VhQoV1J07d5RSSj148EDFx8dn2j755BPl6uqqbt++ne1533zzTVW5cmW1fft29ffff6t58+YpW1tbtW7dulzHptfrFQCl1+vz8pCokKWlKdW/v1KAUnZ2SuXhJTRZKSlKDRkijwlQqlkzpS5c0DqqYnbpklJt2xqfhBdfVOqff7SOiogKyYYNxrf3mjXFe9+5/f7OU8LyX1euXFEAVGRkZLbH1KtXT/Xv3z/H89SqVUtNnDgx03UNGjRQY8eOzXUsTFhMx4MHSvXuLX/49vZKbd6sdUT5d/asUo0aGd/II0cqdf++1lFpxGBQ6ptvlHJykifjiSeUWrFC66iIqJCMGCFvbXd3peLiiu9+c/v9XaAxLHq9HgBQKpsyngcPHkRUVBQGDBiQ43maNm2K9evX4+LFi1BKYfv27YiNjUVAQEC2/yclJQWJiYmZNjINtrbSk9Ctm/SHBgbKGE5zs2GDrL2xb580l65fL5VrS5TQOjKN6HTS53f4sBRxuHlTFiV59VXg388CIjJfkyfLGL3ERKBHD5lgYFLymxGlpaWpDh06qGbNmmV7zDvvvKNq1Kjx2HPdu3dP9evXTwFQdnZ2yt7eXi1evDjH/zN+/HgF4JGNLSymIyVFqY4dJWN3cVHq99+1jih37t83/tIApIXl7FmtozIx9+8r9fHHStnaypPUvLlSyclaR0VEBXThglKlS8vb+t13i+c+i7xL6O2331Z+fn7qQjad+UlJScrDw0PNmDHjsef67LPP1FNPPaXWr1+vjhw5ombNmqVcXV1VREREtv/n3r17Sq/XZ2wXLlxgwmKCkpNluEN6M+O+fVpHlLMLF2SMSnqyMmSIJF6UjT//VMrDQ56sHj1kEBMRmbWffzZ+Bq5cWfT3V6QJS1BQkPLx8VFnzpzJ9pglS5aoEiVKqCtXruR4rqSkJFWiRAm1cePGTNcPGDBAtWnTJtcxcQyL6bp7V6mWLY3DHqKitI4oa5s3K1WmjDG5Ku6BZ2Zr2zalSpSQJ274cK2jIaJCMGaMvKXd3JSKjS3a+yqSMSxKKQQHByM8PBzbtm1DxYoVsz02NDQUnTp1QtnH1ChPTU1FamoqbP5THtTW1hYGTp20CM7OsipokyYy7OGFF4Djx7WOyigtTZYWaNcOuHZNxq0cOiRjcCgXnnvOWDJzxgzrKndMZKEmTpSakbdvm9B4lrxkQe+8847y8PBQO3bsyDR1OSkpKdNxp06dUjqdTv3yyy9ZnqdatWpq7dq1GZdbtmypatWqpbZv367OnDmjFi5cqBwdHdW3336b69jYwmL6bt1S6umnJWsvV67os/bcuHRJqVatjM2f77zDoRj5NmWKPIk2Nkr99JPW0RBRAf3zj1Jly8rb+q23iu5+iqRLCFkMcgWgFi5cmOm4MWPGKF9fX5WWTX/2f/9PfHy8ev3115W3t7dydHRU1apVU59//rkyGAy5jo0Ji3m4dk2punXlDeDjo1QOvYpFbutWpby8JBZXV6XCwrSLxSIYDEoNGiRPqJOTUnv2aB0RERXQr78qpdPJ27qoPiNz+/2tU0opjRp3ClViYiI8PDyg1+vh7u6udTiUgytXZP2dEydkAa7ISMDXt/ju32CQ6XsTJsh+nTpSzLVateKLwWI9eCArPf/8syxZvXs3ULmy1lERUQGMGwd8+ing6iqLuhf2Z2Vuv7+5lhAVO09PWX+ncmVZbKt1ayA+vnju++pVGavy8ceSrPTvL8urM1kpJHZ2Uue7QQPjk33tmtZREVEBTJggPzJr1gQcHbWLgwkLacLbG9i2DfDzA06dkoG4V68W7X3u2gXUqwf89hvg5AQsWgSEhsqgYCpErq7Apk3GF7dTJyA5WeuoiCifbG2BH3+Uz1A/P+3iYMJCmqlQQZKW//1PZg0FBAA3bhT+/RgMwPTpMpnl0iWgenWpXvvaa4V/X/SvcuVkdciSJaVbqE8fmY5FRGapVClZKFFLTFhIU5UqAVu3Al5eQFQU0LZt4VZ5v35dfuCPGiXfl6++KiuS1q5dePdB2ahRA1i3Tj7l1q4Fhg/XOiIiMmNMWEhz1arJmJbSpSWZ6NABuHOn4Ofdu1eGUmzaBDg4APPnAz/8ID0WVExatJCFpQBg5kzZiIjygQkLmYTatYGICOlB+OOPgg17UEq+F599Fjh/HqhSRQbWvvmmrN9HxaxnT1k1EgCGDZPOcCKiPGLCQiajfn1g82bAzU1Wd+7SBUhJyds5bt2SCrXvvw+kpgLduwMHD8pgW9LQiBGy0rNSMp7lzz+1joiIzAwTFjIpjRtLF46zM/Drr1ISOjU1d//30CHg6aeB8HCgRAlg1iyZYcuyPCZApwO++gp46SWp8d2pExAbq3VURGRGmLCQyXn2WWD9ehl3sn69DJR98CD745UC5syRtYrOnAGefFK6lYKD2QVkUuzsgOXLAX9/GQ3drp1UESQiygUmLGSSWrc2tpSsXi0F3rJaC/P2baBXL+ltuH9fiqweOiTfiWSCXFyADRukxPGZM9LikpSkdVREZAaYsJDJatcOWLVKihb98APw1luZk5ajR4GGDaXbx84O+PxzSXKeeEK7mCkXvLykRkupUlIQp3dv1mghosdiwkImLTAQWLYMsLEBvv8eGDJEuoBCQ2W8S2ws4OMD7NwpE1DYBWQmqlWTGi0ODvLv0KHywhIRZYMJC5m8V14BFi6UZGT2bKmtMnCgjN1s1w44fFjGr5CZad5cms7SX9gvvtA6IiIyYUxYyCz06wfMnSv7UVHS4jJlCrBxI1CmjKahUUF07w7MmCH7w4fLgCUioizYaR0AUW4NGiRjVZYuBcaPB1q21DoiKhTvvw+cPSvz0Pv2BcqXl9YXIqKH6JSyjI7jxMREeHh4QK/Xw52FN4jMS1oa8PLLwE8/yajp3btlnAsRWbzcfn+zS4iItGdrK6OrGzcGbt6UwUmXL2sdFRGZECYsRGQanJ2lRkvlysDffwMdOwJ372odFRGZCCYsRGQ6ypaVGi2lSwMHDsjCiTmVOSYiq8GEhYhMS9Wq0tLi6CjTwN57jzVaiIgJCxGZoCZNZEyLTicLRU2frnVERKQxJixEZJq6dgW+/FL2R4+WhROJyGoxYSEi0zVkiNRpAYDXXwciIzUNh4i0w4SFiEzbjBlAt26yHHdgIHD8uNYREZEGmLAQkWmzsZE1h5o2BW7dAtq3B+LjtY6KiIoZExYiMn1OTrKqc9WqwLlzUqPlzh2toyKiYsSEhYjMQ5kyUqOlbFng0CGgRw/WaCGyIkxYiMh8VK4sNVqcnCR5efdd1mghshJMWIjIvDRuLFOcbWyA774DQkK0joiIigETFiIyP507A19/LftjxwJLl2obDxEVOSYsRGSegoKA4cNlv39/YNs2beMhoiLFhIWIzNe0aTL4NjUV6NIFiI7WOiIiKiJMWIjIfNnYAIsXA82bA4mJQLt2wMWLWkdFREUgTwlLSEgI/P394ebmBk9PTwQGBiImJibj9rNnz0Kn02W5rV69OsdznzhxAp06dYKHhwdcXFzg7++P8+fP5+9REZH1cHSUGi3VqgH//AN06CDJCxFZlDwlLJGRkQgKCsKePXsQERGB1NRUBAQE4O7duwAAX19fxMfHZ9o++eQTuLq6ol27dtme9/Tp02jevDmqV6+OHTt24OjRoxg3bhwcHR0L9uiIyDqUKiXTnD09gSNHgO7dpZuIiCyGTqn8FzG4evUqPD09ERkZiRYtWmR5TP369dGgQQOEhoZme56ePXuiRIkS+OGHH/IbChITE+Hh4QG9Xg93d/d8n4eIzNiBA0DLlkBSEvDGG0BoKKDTaR0VEeUgt9/fBRrDotfrAQClSpXK8vaDBw8iKioKAwYMyPYcBoMBmzZtwlNPPYU2bdrA09MTjRs3xk8//VSQ0IjIGjVsCKxcKWNbFi4EJk3SOiIiKiT5TlgMBgOGDh2KZs2aoXbt2lkeExoaiho1aqBp06bZnufKlSu4c+cOpk6dirZt2+K3335Dly5d0LVrV0TmsJR8SkoKEhMTM21EROjYEfjmG9kfP14G5RKR2ct3whIUFITo6GisWLEiy9uTk5MRFhaWY+sKIIkPAHTu3Bnvv/8+6tWrh9GjR6Njx46YO3dutv8vJCQEHh4eGZuvr29+HwoRWZq33wZGjZL9gQOBiAht4yGiAstXwhIcHIyNGzdi+/bt8PHxyfKYNWvWICkpCf369cvxXGXKlIGdnR1q1qyZ6foaNWrkOEtozJgx0Ov1GduFCxfy/kCIyHJNmQL06iULJHbrBhw9qnVERFQAdnk5WCmFwYMHIzw8HDt27EDFihWzPTY0NBSdOnVC2bJlczynvb09/P39M02PBoDY2Fj4+fll+/8cHBzg4OCQl/CJyJqkj2O5dAmIjATatwf27AGy+ZFFRKYtTy0sQUFBWLp0KcLCwuDm5oaEhAQkJCQgOTk503FxcXHYuXMnBg4cmOV5qlevjvDw8IzLI0aMwMqVK/Hdd98hLi4Os2fPxoYNG/Duu+/m4yEREf3LwQEIDwdq1JCCcu3bA/9OFiAi85KnhGXOnDnQ6/Vo1aoVypcvn7GtXLky03ELFiyAj48PAgICsjxPTExMxgwjAOjSpQvmzp2L6dOno06dOvj+++/x448/onnz5vl4SERED3niCanRUq4c8Ndf0j10/77WURFRHhWoDospYR0WIsrRoUNAixbA3btAv37AokWs0UJkAoqlDgsRkdlo0ABYvRqwtQWWLJEpz0RkNpiwEJH1aNcOmDNH9idNkkq4RGQWmLAQkXV5801g7FjZf+stYPNmbeMholxhwkJE1mfSJKBPHyAtTRZKPHxY64iI6DGYsBCR9dHppDvoueeAO3eADh2AHApVEpH2mLAQkXWytwfWrgVq1QLi42V8y40bWkdFRNlgwkJE1qtkSanR4u0NHD8O+PnJ2kN//glYRsUHIovBhIWIrJuvL/Dzz8BTT0n3UGgo0KyZVMedNk1aX4hIc0xYiIj+7/+AkydlzaHXXgOcnYGYGGD0aEloOnaU7iNWyCXSDCvdEhH91+3bwKpVwIIF0j2UrkwZmV3Uvz9Qp4528RFZkNx+fzNhISLKSUyMrPq8eDGQkGC8vmFD4I03gF69ZL0iIsoXJixERIXpwQPg11+l1WXDBiA1Va53cAC6dJFWl9atARv2tBPlBRMWIqKicvUqsGyZJC9//WW8vkIFGQPz+utApUqahUdkTpiwEBEVNaVkFegFC4CwMODWLeNtrVpJq0u3bjKIl4iyxNWaiYiKmk4HPP008M03Mv15+XIgIECu37ED6NcPKFcOGDQI2LOHtV2ICoAtLEREhe38eRmku2gRcOaM8foaNWSgbt++ksgQEbuEiIg0ZzAAO3fKLKPVq4HkZLne1hZo3166jDp0AEqU0DZOIg0xYSEiMiWJicDKlZK87N5tvN7T01jbpVYt7eIj0ggTFiIiU3XihHQXLV4MXL5svL5RI+ky6tlT1jkisgJMWIiITF1qKrB5s7S6bNggtV4AwNER6NpVWl2ee461XciiMWEhIjInV65IbZfQUODYMeP1fn5S1+X114Enn9QoOKKiw4SFiMgcKQUcOCCtLmFhgF5vvO3556XVpWtXwMlJuxiJChETFiIic5ecDISHS/KydauxjouHh4xz6d8f8PeXui9EZooJCxGRJTl3TgbpLlwInD1rvL5mTUlc+vQBvLw0C48ov5iwEBFZIoNBquguXAisWQPcuyfX29lJTZf+/aXGi52dpmES5RZL8xMRWSIbGxnL8sMPQEICMG8e0LixzDBatw7o3Bl4+WUuA0AWhwkLEZG58vAwrlN07BgwfDjg4CCJy/LlWkdHVKiYsBARWYKaNYHPPgM++kguDx0K3LihaUhEhYkJCxGRJRk5UhZZvHoVGDVK62iICg0TFiIiS2JvL+NaAOD774Fdu7SNh6iQMGEhIrI0zz4LDBwo+2+9BaSkaBsPUSFgwkJEZImmTZOVoE+ckLEtRGaOCQsRkSUqVQr48kvZ//RTIDZW23iICogJCxGRperVCwgIkC6hd95hbRYya3lKWEJCQuDv7w83Nzd4enoiMDAQMTExGbefPXsWOp0uy2316tW5uo+3334bOp0OM2fOzNMDISKi/9DpgG+/BRwdgW3bpNgckZnKU8ISGRmJoKAg7NmzBxEREUhNTUVAQADu3r0LAPD19UV8fHym7ZNPPoGrqyvatWv32POHh4djz5498Pb2zt+jISKizCpXBsaPl/1hw4Br17SNhyif8rTYxObNmzNdXrRoETw9PXHw4EG0aNECtra2KFeuXKZjwsPD0aNHD7i6uuZ47osXL2Lw4MH49ddf0aFDh7yERUREOfngA2DZMiA6GhgxQtYhIjIzBRrDotfrAQClSpXK8vaDBw8iKioKAwYMyPE8BoMBffv2xYgRI1CrVq1c3XdKSgoSExMzbURElIUSJYD582V/0SJZPJHIzOQ7YTEYDBg6dCiaNWuG2rVrZ3lMaGgoatSogaZNm+Z4rmnTpsHOzg7vvfderu8/JCQEHh4eGZuvr2+e4icisipNmgBvvy37b71lXOWZyEzkO2EJCgpCdHQ0VqxYkeXtycnJCAsLe2zrysGDB/HVV19h0aJF0Ol0ub7/MWPGQK/XZ2wXLlzIU/xERFYnJAQoV06mOE+dqnU0RHmSr4QlODgYGzduxPbt2+Hj45PlMWvWrEFSUhL69euX47l27dqFK1euoEKFCrCzs4OdnR3OnTuHDz74AE8++WS2/8/BwQHu7u6ZNiIiykHJksBXX8l+SAhw8qSm4RDlhU6p3E/MV0ph8ODBCA8Px44dO1C1atVsj23VqhXKlCmDNWvW5HjO69evIz4+PtN1bdq0Qd++ffHGG2+gWrVquYotMTERHh4e0Ov1TF6IiLKjFNCxI/Dzz0CLFjKeJQ+t20SFLbff33lqYQkKCsLSpUsRFhYGNzc3JCQkICEhAcnJyZmOi4uLw86dOzEwfS2L/6hevTrCw8MBAKVLl0bt2rUzbSVKlEC5cuVynawQEVEu6XTAN98Azs7Azp2cMURmI08Jy5w5c6DX69GqVSuUL18+Y1u5cmWm4xYsWAAfHx8EBARkeZ6YmJiMGUZERFTMnnwS+OQT2R8+HLhyRdNwiHIjT11CpoxdQkREeZCaCvj7A0eOAH36sAouaaZIuoSIiMhCpNdm0emApUuBLVu0jogoR0xYiIisVaNGQFCQ7L/9NvCf8YhEpoQJCxGRNZs8GfD2Bk6fln0iE8WEhYjImrm7A7Nmyf60acCxY9rGQ5QNJixERNauSxegUyfgwQMp228waB0R0SOYsBARWTudTlpZXFyAP/4Avv9e64iIHsGEhYiIgAoVgE8/lf1Ro4CEBG3jIfoPJixERCSCg4EGDYBbt4D339c6GqJMmLAQEZGws5PaLDY2wIoVwObNWkdElIEJCxERGT39NPDee7L/7rtAUpK28RD9iwkLERFlNmkS4OsL/P03MHGi1tEQAWDCQkRE/+XqCsyeLfszZgBHj2obDxGYsBARUVY6dQK6dgXS0oBBg1ibhTTHhIWIiLL29deAmxuwdy8wb57W0ZCVY8JCRERZ+9//gClTZH/0aODSJW3jIavGhIWIiLL3zjuyqnNiIjB0qNbRkBVjwkJERNmztZXuIFtbYPVqYNMmrSMiK8WEhYiIclavnrHy7bvvAnfvahoOWScmLERE9HgTJgB+fsD588D48VpHQ1aICQsRET2eiwvw7beyP3MmcPiwpuGQ9WHCQkREudO+PdCjh7E2S1qa1hGRFWHCQkREuTdzJuDhARw4YGxxISoGTFiIiCj3ypcHpk6V/Q8/BP75R9t4yGowYSEiorwZNAho0gS4c8e4sjNREWPCQkREeWNjI7VZ7OyA8HBg3TqtIyIrwISFiIjyrk4dYPhw2Q8OBm7f1jYesnhMWIiIKH/GjQMqVZJxLOPGaR0NWTgmLERElD/OzsCcObI/a5bMHCIqIkxYiIgo/wICgN69AYNBBuM+eKB1RGShmLAQEVHBfPEFULKkVL+dNUvraMhCMWEhIqKC8fICPvtM9seNk/WGiAoZExYiIiq4/v2B5s1lJeegIEAprSMiC8OEhYiICi69NkuJEsDGjcDatVpHRBYmTwlLSEgI/P394ebmBk9PTwQGBiImJibj9rNnz0Kn02W5rV69OstzpqamYtSoUahTpw5cXFzg7e2Nfv364dKlSwV7ZEREVLxq1gRGjZL9wYMBvV7beMii5ClhiYyMRFBQEPbs2YOIiAikpqYiICAAd+/eBQD4+voiPj4+0/bJJ5/A1dUV7dq1y/KcSUlJOHToEMaNG4dDhw5h7dq1iImJQadOnQr+6IiIqHiNHQtUqQLEx8s+USHRKZX/jsarV6/C09MTkZGRaNGiRZbH1K9fHw0aNEBoaGiuz7t//340atQI586dQ4UKFXL1fxITE+Hh4QG9Xg93d/dc3xcRERWyrVuBF14AdDpg926gcWOtIyITltvv7wKNYdH/29xXqlSpLG8/ePAgoqKiMGDAgDyfV6fToWTJktkek5KSgsTExEwbERGZgNatgX79ZODtoEFAaqrWEZEFyHfCYjAYMHToUDRr1gy1a9fO8pjQ0FDUqFEDTZs2zfV57927h1GjRqFXr145ZlohISHw8PDI2Hx9ffP8GIiIqIjMmAGULg0cPQrMnKl1NGQB8p2wBAUFITo6GitWrMjy9uTkZISFheWpdSU1NRU9evSAUgpz0ss9Z2PMmDHQ6/UZ24ULF/IUPxERFaGyZSVpAYDx44G//9Y2HjJ7+UpYgoODsXHjRmzfvh0+Pj5ZHrNmzRokJSWhX79+uTpnerJy7tw5REREPHYcioODA9zd3TNtRERkQl57DWjVCkhOBt59l7VZqEDylLAopRAcHIzw8HBs27YNFStWzPbY0NBQdOrUCWXLln3sedOTlVOnTmHLli0oXbp0XsIiIiJTpNMBc+cC9vbA5s1ANuUtiHIjTwlLUFAQli5dirCwMLi5uSEhIQEJCQlITk7OdFxcXBx27tyJgQMHZnme6tWrIzw8HIAkKy+//DIOHDiAZcuWIS0tLeO89+/fz+fDIiIik1CtGvDhh7I/ZAhw65am4ZD5ylPCMmfOHOj1erRq1Qrly5fP2FauXJnpuAULFsDHxwcBAQFZnicmJiZjhtHFixexfv16/PPPP6hXr16m8/7555/5fFhERGQyRo+WxCUhARgzRutoyEwVqA6LKWEdFiIiExYZKeNZAOCPP4A8zB4ly1YsdViIiIhypWVLWSARAN56i7VZKM+YsBARUfGYPh0oUwaIjjZOeSbKJSYsRERUPEqXBr78UvYnTgROn9Y2HjIrTFiIiKj4vPqqlO6/dw945x3WZqFcY8JCRETFJ702i4MDEBEBLF+udURkJpiwEBFR8apSBRg3TvaHDgVu3NA0HDIPTFiIiKj4jRgB1KwJXL0KjBqldTRkBpiwEBFR8bO3B+bNk/3vvwd27dI2HjJ5TFiIiEgbzZsDgwbJ/qBBQEqKtvGQSWPCQkRE2pk6FfDyAk6elDotRNlgwkJERNp54glg5kzZnzwZiI3VNBwyXUxYiIhIW6+8ArRpI11Cb7/N2iyUJSYsRESkLZ0OmDMHcHICtm8HfvhB64jIBDFhISIi7VWsCIwfL/vDhgHXrmkbD5kcJixERGQahg0D6tQBrl+XOi1ED2HCQkREpqFECWD+fOkiWrRIuoeI/sWEhYiITMczz8iiiAAwYAC7higDExYiIjItU6bImJa//wZefhm4f1/riMgEMGEhIiLT4uEBbNgAuLkBkZHA4MGc6kxMWIiIyATVqgUsXy7jWebPB2bN0joi0hgTFiIiMk0dOgCffSb7778P/PqrtvGQppiwEBGR6Ro2DHjjDcBgkIq4J09qHRFphAkLERGZrvQquM2bA3o98NJLwI0bWkdFGmDCQkREps3BAVi7FnjySSAuDujeHUhN1ToqKmZMWIiIyPSVLSszh1xdgW3bgPfe48whK8OEhYiIzEPt2saZQ3PnAt98o3VEVIyYsBARkfno2BGYNk32hw4FIiI0DYeKDxMWIiIyL8OHA6+9BqSlyXiWmBitI6JiwISFiIjMi04HzJsHNGvGmUNWhAkLERGZn/SZQxUqAKdOAT16cOaQhWPCQkRE5snTU2YOubgAW7fKmBayWExYiIjIfNWtCyxbJt1E334rG1kkJixERGTeOncGpkyR/ffek9YWsjh5SlhCQkLg7+8PNzc3eHp6IjAwEDEPjc4+e/YsdDpdltvq1auzPa9SCh9//DHKly8PJycnvPDCCzh16lT+HxUREVmXUaOAvn2NM4f4HWJx8pSwREZGIigoCHv27EFERARSU1MREBCAu3fvAgB8fX0RHx+fafvkk0/g6uqKdu3aZXve6dOn4+uvv8bcuXOxd+9euLi4oE2bNrh3717BHh0REVkHnQ6YPx945hng5k2ZOXTrltZRUSHSKZX/2sZXr16Fp6cnIiMj0aJFiyyPqV+/Pho0aIDQ0NAsb1dKwdvbGx988AGGDx8OANDr9fDy8sKiRYvQs2fPXMWSmJgIDw8P6PV6uLu75+8BERGRebt8GfD3By5cAAICgE2bADs7raOiHOT2+7tAY1j0ej0AoFSpUlnefvDgQURFRWHAgAHZnuPvv/9GQkICXnjhhYzrPDw80LhxY+zevTvb/5eSkoLExMRMGxERWTkvL2D9esDZGfjtN2DYMK0jokKS74TFYDBg6NChaNasGWrXrp3lMaGhoahRowaaNm2a7XkSEhIAAF5eXpmu9/LyyrgtKyEhIfDw8MjYfH198/EoiIjI4tSrByxdKvuzZkmROTJ7+U5YgoKCEB0djRUrVmR5e3JyMsLCwnJsXSmIMWPGQK/XZ2wXLlwokvshIiIz1KULMHmy7AcHA9u3axsPFVi+Epbg4GBs3LgR27dvh4+PT5bHrFmzBklJSejXr1+O5ypXrhwA4PLly5muv3z5csZtWXFwcIC7u3umjYiIKMOYMcCrrwIPHgDdugFxcVpHRAWQp4RFKYXg4GCEh4dj27ZtqFixYrbHhoaGolOnTihbtmyO56xYsSLKlSuHrQ/Nm09MTMTevXvRpEmTvIRHRERkpNMB338PNG7MmUMWIE8JS1BQEJYuXYqwsDC4ubkhISEBCQkJSE5OznRcXFwcdu7ciYEDB2Z5nurVqyM8PBwAoNPpMHToUHz66adYv349/vrrL/Tr1w/e3t4IDAzM36MiIiICAEdH4KefAB8f4ORJoGdPaXEhs5OnhGXOnDnQ6/Vo1aoVypcvn7GtXLky03ELFiyAj48PAgICsjxPTExMxgwjABg5ciQGDx6MQYMGwd/fH3fu3MHmzZvh6OiYj4dERET0kHLlgHXrACcn4NdfgX9LaJB5KVAdFlPCOixERJSjH38EXn5Z9ufPB958U9t4CEAx1WEhIiIyG926ARMnyv677wI7dmgaDuUNExYiIrIeH31kHMfSrRtw5ozWEVEuMWEhIiLrodMBCxZI+f4bN2TmECulmwUmLEREZF2cnGTm0P/+Bxw/DvTqJas8k0ljwkJERNbH29s4c+jnn4GRI7WOiB6DCQsREVmnp58GFi+W/S++AEJDtY2HcsSEhYiIrFf37sCECbL/zjvAzp2ahkPZY8JCRETW7eOPgR49gNRUmTn0999aR0RZYMJCRETWTacDFi4EGjYErl3jzCETxYSFiIjI2VlmDpUvDxw7BvTuzZlDJoYJCxERESDTnNetkwUTN20CRo/WOiJ6CBMWIiKidP7+0j0EADNmAIsWaRoOGTFhISIieljPnsC4cbI/aBDw++/axkMAmLAQERE9asIEmTGUmgp06QKcPat1RFaPCQsREdF/2dhIUbn69Y0zh27f1joqq8aEhYiIKCsuLsD69UC5ckB0NPDqq5w5pCEmLERERNnx8ZHpzg4OwIYNwNixWkdktZiwEBER5aRxY2DBAtmfNg1YskTbeKwUExYiIqLH6d3b2Lry5pvAn39qG48VYsJCRESUGxMnyoyh+/fl33PntI7IqjBhISIiyg0bG+CHH4B69YArV4BOnYA7d7SOymowYSEiIsqt9JlDXl7A0aNA376AwaB1VFaBCQsREVFe+PoaZw799BPw0UdaR2QVmLAQERHl1TPPAN9/L/shIcDSpdrGYwWYsBAREeVHnz7GFZ0HDgT27NE2HgvHhIWIiCi/Jk8GOncGUlKAwEDg/HmtI7JYTFiIiIjyy8ZGuoPq1gUuX5bk5e5draOySExYiIiICsLVVWYOeXoCUVGcOVREmLAQEREVlJ8fEB4O2NvLvx9/rHVEFocJCxERUWFo2hT47jvZnzwZCAvTNh4Lw4SFiIiosPTrB4wcKfv9+wP79mkbjwVhwkJERFSYpkwBXnpJZg517gz884/WEVkEJixERESFydYWWLYMqFMHSEgAmjcHpk+Xfco3JixERESFzc1NZg55e8uqzqNGAT4+Uqtl40bgwQOtIzQ7eUpYQkJC4O/vDzc3N3h6eiIwMBAxMTGPHLd79248//zzcHFxgbu7O1q0aIHk5ORsz5uWloZx48ahYsWKcHJyQuXKlTFp0iQopfL+iIiIiEzBk08CJ09KCf8mTYC0NGDdOukuqlAB+PBDIC5O6yjNRp4SlsjISAQFBWHPnj2IiIhAamoqAgICcPehIjm7d+9G27ZtERAQgH379mH//v0IDg6GjU32dzVt2jTMmTMHs2fPxokTJzBt2jRMnz4ds2bNyv8jIyIi0pqbGzBgAPDnn8CxY8CwYUCZMkB8vKxBVLUq0KqVFJ/L4Yc9ATpVgGaMq1evwtPTE5GRkWjRogUA4JlnnsGLL76ISZMm5fo8HTt2hJeXF0JDQzOu69atG5ycnLA0lwtKJSYmwsPDA3q9Hu7u7nl7IERERMXl/n1gwwYgNBT49VdjkTkPD6B3b1mXqEEDbWMsRrn9/i7QGBa9Xg8AKFWqFADgypUr2Lt3Lzw9PdG0aVN4eXmhZcuW+P3333M8T9OmTbF161bExsYCAI4cOYLff/8d7dq1y/b/pKSkIDExMdNGRERk8uztgW7dgJ9/Bs6eBSZOlO4jvR6YMwd4+mmgfn1g9mzg5k2tozUdKp/S0tJUhw4dVLNmzTKu2717twKgSpUqpRYsWKAOHTqkhg4dquzt7VVsbGyO5xo1apTS6XTKzs5O6XQ6NWXKlBzvf/z48QrAI5ter8/vQyIiItJGWppSW7Yo1auXUg4OSgGyOTjIdVu3yjEWSK/X5+r7O98tLEFBQYiOjsaKFSsyrjP826z11ltv4Y033kD9+vXx5Zdfolq1aliwYEG251q1ahWWLVuGsLAwHDp0CIsXL8aMGTOwePHibP/PmDFjoNfrM7YLFy7k96EQERFpy8YGaN1aquNeugR8/TXwf/8ntVyWL5fbqlQBPv3Uauu65CthCQ4OxsaNG7F9+3b4+PhkXF++fHkAQM2aNTMdX6NGDZzPYcntESNGYPTo0ejZsyfq1KmDvn374v3330dISEi2/8fBwQHu7u6ZNiIiIrNXqhQweDBw+DBw4ADw9tuAuzvw99/AuHGyblH79sDatTIexkrkKWFRSiE4OBjh4eHYtm0bKlasmOn2J598Et7e3o9MdY6NjYWfn1+2501KSnpkFpGtrW1Giw0REZHV0elkPMucOTKraMkSoGVLGaT7yy8yDsbHBxg+HDhxQutoi1yeEpagoCAsXboUYWFhcHNzQ0JCAhISEjJqrOh0OowYMQJff/011qxZg7i4OIwbNw4nT57EgAEDMs7TunVrzJ49O+PySy+9hMmTJ2PTpk04e/YswsPD8cUXX6BLly6F9DCJiIjMmLMz0LcvsGMHEBsLjB4NlC8PXL0KfP45ULOmLL64YAFw547W0RaNvAyMQRaDXAGohQsXZjouJCRE+fj4KGdnZ9WkSRO1a9euTLf7+fmp8ePHZ1xOTExUQ4YMURUqVFCOjo6qUqVKauzYsSolJSXXseV20A4REZFFSE1Vav16pTp3VsrW1jhQ19VVqQEDlNq9WymDQesoHyu3398FqsNiSliHhYiIrFZ6l1FoKHDqlPH6mjWlcF3fvkDZstrFl4Pcfn8zYSEiIrIUSgG7dknisnq1sXpuiRJAp05SlO7FF2WBRhPBhIWIiMia6fXAihWSvOzfb7ze1xd4/XXgjTeA/0ye0QITFiIiIhJHj0risnQpcOOG8foXXpAuo8BAwNFRk9CKpTQ/ERERmYG6dYGvvgIuXpRWlxdflOu3bAF69QK8vYH33gOOHNE2zhywhYWIiMganT0LLFwo28PV4hs2lFaXXr1kQcYixi4hIiIiery0NGlp+f57YN06IDVVrndyArp3l+Tl2WelkF0RYMJCREREeXP1qoxzCQ0Fjh0zXl+1KtC/v8wyKlOmUO+SCQsRERHlj1LAvn3S6rJihbF67vHjQI0ahXpXuf3+tivUeyUiIiLzp9MBjRvL9uWXUtNl//5CT1byFBJbWIiIiEgrnNZMREREFoMJCxEREZk8JixERERk8piwEBERkcljwkJEREQmjwkLERERmTwmLERERGTymLAQERGRyWPCQkRERCaPCQsRERGZPCYsREREZPKYsBAREZHJY8JCREREJs9O6wAKS/qi04mJiRpHQkRERLmV/r2d/j2eHYtJWG7fvg0A8PX11TgSIiIiyqvbt2/Dw8Mj29t16nEpjZkwGAy4dOkS3NzcoNPpCu28iYmJ8PX1xYULF+Du7l5o56X84etheviamBa+HqaFr8fjKaVw+/ZteHt7w8Ym+5EqFtPCYmNjAx8fnyI7v7u7O//YTAhfD9PD18S08PUwLXw9cpZTy0o6DrolIiIik8eEhYiIiEweE5bHcHBwwPjx4+Hg4KB1KAS+HqaIr4lp4ethWvh6FB6LGXRLRERElostLERERGTymLAQERGRyWPCQkRERCaPCQsRERGZPCYsj/HNN9/gySefhKOjIxo3box9+/ZpHZJVCgkJgb+/P9zc3ODp6YnAwEDExMRoHRb9a+rUqdDpdBg6dKjWoVitixcvok+fPihdujScnJxQp04dHDhwQOuwrFZaWhrGjRuHihUrwsnJCZUrV8akSZMeu14OZY8JSw5WrlyJYcOGYfz48Th06BD+7//+D23atMGVK1e0Ds3qREZGIigoCHv27EFERARSU1MREBCAu3fvah2a1du/fz/mzZuHunXrah2K1bp58yaaNWuGEiVK4JdffsHx48fx+eef44knntA6NKs1bdo0zJkzB7Nnz8aJEycwbdo0TJ8+HbNmzdI6NLPFac05aNy4Mfz9/TF79mwAsl6Rr68vBg8ejNGjR2scnXW7evUqPD09ERkZiRYtWmgdjtW6c+cOGjRogG+//Raffvop6tWrh5kzZ2odltUZPXo0/vjjD+zatUvrUOhfHTt2hJeXF0JDQzOu69atG5ycnLB06VINIzNfbGHJxv3793Hw4EG88MILGdfZ2NjghRdewO7duzWMjABAr9cDAEqVKqVxJNYtKCgIHTp0yPQ+oeK3fv16NGzYEN27d4enpyfq16+P7777TuuwrFrTpk2xdetWxMbGAgCOHDmC33//He3atdM4MvNlMYsfFrZr164hLS0NXl5ema738vLCyZMnNYqKAGnpGjp0KJo1a4batWtrHY7VWrFiBQ4dOoT9+/drHYrVO3PmDObMmYNhw4bhww8/xP79+/Hee+/B3t4er732mtbhWaXRo0cjMTER1atXh62tLdLS0jB58mS8+uqrWodmtpiwkNkJCgpCdHQ0fv/9d61DsVoXLlzAkCFDEBERAUdHR63DsXoGgwENGzbElClTAAD169dHdHQ05s6dy4RFI6tWrcKyZcsQFhaGWrVqISoqCkOHDoW3tzdfk3xiwpKNMmXKwNbWFpcvX850/eXLl1GuXDmNoqLg4GBs3LgRO3fuhI+Pj9bhWK2DBw/iypUraNCgQcZ1aWlp2LlzJ2bPno2UlBTY2tpqGKF1KV++PGrWrJnpuho1auDHH3/UKCIaMWIERo8ejZ49ewIA6tSpg3PnziEkJIQJSz5xDEs27O3t8fTTT2Pr1q0Z1xkMBmzduhVNmjTRMDLrpJRCcHAwwsPDsW3bNlSsWFHrkKxa69at8ddffyEqKipja9iwIV599VVERUUxWSlmzZo1e2Saf2xsLPz8/DSKiJKSkmBjk/kr1tbWFgaDQaOIzB9bWHIwbNgwvPbaa2jYsCEaNWqEmTNn4u7du3jjjTe0Ds3qBAUFISwsDOvWrYObmxsSEhIAAB4eHnByctI4Ouvj5ub2yPghFxcXlC5dmuOKNPD++++jadOmmDJlCnr06IF9+/Zh/vz5mD9/vtahWa2XXnoJkydPRoUKFVCrVi0cPnwYX3zxBfr37691aOZLUY5mzZqlKlSooOzt7VWjRo3Unj17tA7JKgHIclu4cKHWodG/WrZsqYYMGaJ1GFZrw4YNqnbt2srBwUFVr15dzZ8/X+uQrFpiYqIaMmSIqlChgnJ0dFSVKlVSY8eOVSkpKVqHZrZYh4WIiIhMHsewEBERkcljwkJEREQmjwkLERERmTwmLERERGTymLAQERGRyWPCQkRERCaPCQsRERGZPCYsREREZPKYsBAREZHJY8JCREREJo8JCxEREZk8JixERERk8v4fs3boz1mCC4sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}