stock_market_prediction_and_forecasting.ipynb

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        "!pip install pandas==1.5.3\n",
        "!pip install pandas_datareader==0.10.0"
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          "text": [
            "Collecting pandas==1.5.3\n",
            "  Downloading pandas-1.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
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            "Downloading pandas-1.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.1/12.1 MB\u001b[0m \u001b[31m55.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pandas\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.0.3\n",
            "    Uninstalling pandas-2.0.3:\n",
            "      Successfully uninstalled pandas-2.0.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pandas<2.2.2dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.\n",
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            "\u001b[0mSuccessfully installed pandas-1.5.3\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas_datareader==0.10.0) (2024.1)\n",
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      "source": [
        "import pandas_datareader as pdr\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "key=\"API_KEY\"\n",
        "df=pdr.get_data_tiingo(\"AAPL\",api_key=key)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctzoPkCmku85",
        "outputId": "90ef2cd3-fd62-4b1e-bd72-6b5d5b9184bc"
      },
      "execution_count": 3,
      "outputs": [
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          "text": [
            "<ipython-input-3-b7ec59fe52e0>:2: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
            "  df=pdr.get_data_tiingo(\"AAPL\",api_key=key)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"AAPL.csv\")"
      ],
      "metadata": {
        "id": "MCEJpTREk1c9"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"AAPL.csv\")"
      ],
      "metadata": {
        "id": "0VuU8Ew-qJe2"
      },
      "execution_count": 3,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df2=df.reset_index()[\"close\"]"
      ],
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        "id": "4JohnaGJqTcb"
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      "execution_count": 4,
      "outputs": []
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      "source": [
        "df.head()"
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      },
      "execution_count": 5,
      "outputs": [
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              "  symbol                       date   close    high     low    open    volume  \\\n",
              "0   AAPL  2019-07-29 00:00:00+00:00  209.68  210.64  208.44  208.46  21673389   \n",
              "1   AAPL  2019-07-30 00:00:00+00:00  208.78  210.16  207.31  208.76  33935718   \n",
              "2   AAPL  2019-07-31 00:00:00+00:00  213.04  221.37  211.30  216.42  69281361   \n",
              "3   AAPL  2019-08-01 00:00:00+00:00  208.43  218.03  206.74  213.90  54017922   \n",
              "4   AAPL  2019-08-02 00:00:00+00:00  204.02  206.43  201.63  205.53  40862122   \n",
              "\n",
              "    adjClose    adjHigh     adjLow    adjOpen  adjVolume  divCash  splitFactor  \n",
              "0  50.591067  50.822693  50.291883  50.296708   86693556      0.0          1.0  \n",
              "1  50.373917  50.706880  50.019239  50.369092  135742872      0.0          1.0  \n",
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              "3  50.289470  52.605734  49.881711  51.609258  216071688      0.0          1.0  \n",
              "4  49.225436  49.806915  48.648783  49.589765  163448488      0.0          1.0  "
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              "      <td>AAPL</td>\n",
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              "      <td>AAPL</td>\n",
              "      <td>2019-07-31 00:00:00+00:00</td>\n",
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              "    }\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-52bc3a67-fa07-4cd8-8469-32c5dd25465e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1258,\n  \"fields\": [\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"AAPL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1258,\n        \"samples\": [\n          \"2021-10-18 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.31858937842382,\n        \"min\": 106.84,\n        \"max\": 506.09,\n        \"num_unique_values\": 1196,\n        \"samples\": [\n          162.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.97516311084995,\n        \"min\": 110.19,\n        \"max\": 515.14,\n        \"num_unique_values\": 1211,\n        \"samples\": [\n          284.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68.31182788958431,\n        \"min\": 103.1,\n        \"max\": 500.33,\n        \"num_unique_values\": 1222,\n        \"samples\": [\n          353.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.04026148427118,\n        \"min\": 104.54,\n        \"max\": 514.79,\n        \"num_unique_values\": 1201,\n        \"samples\": [\n          385.67\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37226035,\n        \"min\": 11654363,\n        \"max\": 332607163,\n        \"num_unique_values\": 1258,\n        \"samples\": [\n          85589175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adjClose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.50713575048164,\n        \"min\": 46.6485924822,\n        \"max\": 234.82,\n        \"num_unique_values\": 1245,\n        \"samples\": [\n          144.2307217024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adjHigh\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.81134944828633,\n        \"min\": 47.7898350727,\n        \"max\": 237.23,\n        \"num_unique_values\": 1246,\n        \"samples\": [\n          145.0020089843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adjLow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.208758564134115,\n        \"min\": 46.4652215797,\n        \"max\": 233.09,\n        \"num_unique_values\": 1251,\n        \"samples\": [\n          130.011348481\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adjOpen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.51521061512452,\n        \"min\": 47.148036914,\n        \"max\": 236.48,\n        \"num_unique_values\": 1244,\n        \"samples\": [\n          153.1352496075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adjVolume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52452819,\n        \"min\": 24048344,\n        \"max\": 426884920,\n        \"num_unique_values\": 1258,\n        \"samples\": [\n          85589175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"divCash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.055432578282774675,\n        \"min\": 0.0,\n        \"max\": 0.82,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"splitFactor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08458258116519014,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yx3AeFfxqh2l",
        "outputId": "a433b1d1-cc22-4a42-e457-8f1d3fc21af5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    209.68\n",
              "1    208.78\n",
              "2    213.04\n",
              "3    208.43\n",
              "4    204.02\n",
              "Name: close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "6pgo6OPzqnuv",
        "outputId": "50b7fc08-1963-4319-ed54-bbcf26768c27"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bcbbc28de40>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#feature scaling since lstm is sensitive to the scale of the data\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc=MinMaxScaler(feature_range=(0,1))\n",
        "df2=sc.fit_transform(np.array(df2).reshape(-1,1))"
      ],
      "metadata": {
        "id": "R3yVDKX9q3gc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAI8BPNCrmZT",
        "outputId": "f9f2d349-9b2b-4af1-ade3-e2ecb15021a9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1258, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting the dataset\n",
        "training_size=int(len(df2)*0.65)\n",
        "test_size=len(df2)-training_size\n",
        "train_data,test_data=df2[0:training_size,:],df2[training_size:len(df2),:1]"
      ],
      "metadata": {
        "id": "J1MqG8uOrrsX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_size,test_size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXXmsGHCvfAO",
        "outputId": "62f755e0-76c5-4b0d-b4dc-0182fc447fdb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(817, 441)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_dataset(dataset,time_step=1):\n",
        "  dataX,dataY=[],[]\n",
        "  for i in range(len(dataset)-time_step-1):\n",
        "    a=dataset[i:(i+time_step),0]\n",
        "    dataX.append(a)\n",
        "    dataY.append(dataset[i+time_step,0])\n",
        "  return np.array(dataX),np.array(dataY)\n"
      ],
      "metadata": {
        "id": "WXZ42A_hvirb"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reshaping X=t,t+1,t+2 and Y=t+4\n",
        "time_step=100\n",
        "X_train,y_train=create_dataset(train_data,time_step)\n",
        "X_test,y_test=create_dataset(test_data,time_step)"
      ],
      "metadata": {
        "id": "pTK0uhPk0qDD"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SjVT0Rw1ClM",
        "outputId": "44b69cc3-8b28-4f51-aa4b-b40b31db26a3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.25758297 0.25532874 0.26599875 ... 0.42156544 0.43336255 0.43474014]\n",
            " [0.25532874 0.26599875 0.2544521  ... 0.43336255 0.43474014 0.43306199]\n",
            " [0.26599875 0.2544521  0.24340639 ... 0.43474014 0.43306199 0.43376331]\n",
            " ...\n",
            " [0.08435817 0.09252348 0.107201   ... 0.09054477 0.07899812 0.08909205]\n",
            " [0.09252348 0.107201   0.10519724 ... 0.07899812 0.08909205 0.09244834]\n",
            " [0.107201   0.10519724 0.10487163 ... 0.08909205 0.09244834 0.09272386]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape,y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roRGWBNd1Ze-",
        "outputId": "5edbba6d-e1ed-45a0-845a-977449150db6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((340, 100), (340,))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELrRtAdA3IYp",
        "outputId": "6a141e01-fe01-4fac-fe10-4b495cb6032c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.25758297 0.25532874 0.26599875 ... 0.42156544 0.43336255 0.43474014]\n",
            " [0.25532874 0.26599875 0.2544521  ... 0.43336255 0.43474014 0.43306199]\n",
            " [0.26599875 0.2544521  0.24340639 ... 0.43474014 0.43306199 0.43376331]\n",
            " ...\n",
            " [0.08435817 0.09252348 0.107201   ... 0.09054477 0.07899812 0.08909205]\n",
            " [0.09252348 0.107201   0.10519724 ... 0.07899812 0.08909205 0.09244834]\n",
            " [0.107201   0.10519724 0.10487163 ... 0.08909205 0.09244834 0.09272386]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#reshaping the input to be [samples,time steps, features] which is required for LSTM\n",
        "X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],1)\n",
        "X_test=X_test.reshape(X_test.shape[0],X_test.shape[1],1)"
      ],
      "metadata": {
        "id": "tnpAxVlO1iqo"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing necessry libraries\n",
        "from tensorflow.keras.layers import Dense,LSTM\n",
        "from tensorflow.keras.models import Sequential"
      ],
      "metadata": {
        "id": "FwfWdVOVYWhB"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creation of stacked lstm\n",
        "model=Sequential()\n",
        "model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))\n",
        "model.add(LSTM(50,return_sequences=True))\n",
        "model.add(LSTM(50))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss=\"mean_squared_error\",optimizer=\"adam\")"
      ],
      "metadata": {
        "id": "41j5nlZ-ZQFs"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5J6gJqmPZ6QY",
        "outputId": "f0991000-96a5-43b2-aceb-9209a4ff1761"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 100, 50)           10400     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100, 50)           20200     \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 50)                20200     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 50851 (198.64 KB)\n",
            "Trainable params: 50851 (198.64 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100,batch_size=64,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqA0bkb_Z7rz",
        "outputId": "924481fe-db46-4ae9-d1d4-a75ef809fdda"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 12s 336ms/step - loss: 0.0390 - val_loss: 9.5542e-04\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 3s 282ms/step - loss: 0.0127 - val_loss: 0.0011\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0124 - val_loss: 6.8252e-04\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0102 - val_loss: 4.8014e-04\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0086 - val_loss: 4.7203e-04\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0078 - val_loss: 2.8527e-04\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 2s 186ms/step - loss: 0.0071 - val_loss: 3.9872e-04\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 3s 255ms/step - loss: 0.0062 - val_loss: 0.0015\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0064 - val_loss: 7.8469e-04\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 2s 169ms/step - loss: 0.0057 - val_loss: 3.9453e-04\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 2s 171ms/step - loss: 0.0046 - val_loss: 8.5794e-04\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0051 - val_loss: 9.3718e-04\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 3s 227ms/step - loss: 0.0052 - val_loss: 4.4238e-04\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 3s 239ms/step - loss: 0.0041 - val_loss: 3.3902e-04\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0038 - val_loss: 2.2961e-04\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0040 - val_loss: 3.3054e-04\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 2s 171ms/step - loss: 0.0034 - val_loss: 2.2585e-04\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0032 - val_loss: 2.2288e-04\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 3s 246ms/step - loss: 0.0034 - val_loss: 8.6457e-04\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 3s 219ms/step - loss: 0.0030 - val_loss: 5.7599e-04\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0034 - val_loss: 2.7947e-04\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0028 - val_loss: 4.7499e-04\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0033 - val_loss: 1.9962e-04\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 2s 177ms/step - loss: 0.0035 - val_loss: 2.1282e-04\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 3s 288ms/step - loss: 0.0029 - val_loss: 2.2137e-04\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0022 - val_loss: 2.9738e-04\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0033 - val_loss: 2.1636e-04\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 2s 177ms/step - loss: 0.0030 - val_loss: 5.4225e-04\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 2s 178ms/step - loss: 0.0023 - val_loss: 2.1664e-04\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 2s 189ms/step - loss: 0.0020 - val_loss: 2.1834e-04\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 3s 280ms/step - loss: 0.0019 - val_loss: 2.0450e-04\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0023 - val_loss: 2.3216e-04\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0022 - val_loss: 2.1201e-04\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 2s 177ms/step - loss: 0.0020 - val_loss: 2.5703e-04\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0026 - val_loss: 3.1255e-04\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 3s 220ms/step - loss: 0.0036 - val_loss: 4.3788e-04\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 3s 249ms/step - loss: 0.0025 - val_loss: 2.7206e-04\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0022 - val_loss: 1.9830e-04\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0019 - val_loss: 3.0397e-04\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0019 - val_loss: 4.7412e-04\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0018 - val_loss: 2.4061e-04\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 3s 248ms/step - loss: 0.0018 - val_loss: 3.2186e-04\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 3s 223ms/step - loss: 0.0020 - val_loss: 2.0582e-04\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0017 - val_loss: 2.3345e-04\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0020 - val_loss: 2.9083e-04\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0025 - val_loss: 2.2454e-04\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0031 - val_loss: 4.0748e-04\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 3s 256ms/step - loss: 0.0020 - val_loss: 4.0641e-04\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 3s 209ms/step - loss: 0.0021 - val_loss: 3.7508e-04\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0033 - val_loss: 4.0671e-04\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 2s 176ms/step - loss: 0.0024 - val_loss: 5.4869e-04\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0020 - val_loss: 1.9988e-04\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0035 - val_loss: 0.0032\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 3s 295ms/step - loss: 0.0098 - val_loss: 6.6457e-04\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0053 - val_loss: 0.0010\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0046 - val_loss: 2.5986e-04\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 3s 225ms/step - loss: 0.0040 - val_loss: 2.0880e-04\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 3s 294ms/step - loss: 0.0023 - val_loss: 3.7106e-04\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 3s 265ms/step - loss: 0.0024 - val_loss: 1.9567e-04\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0018 - val_loss: 2.6378e-04\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0019 - val_loss: 2.2644e-04\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0024 - val_loss: 2.6797e-04\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0022 - val_loss: 2.2554e-04\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 2s 208ms/step - loss: 0.0024 - val_loss: 2.0118e-04\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 3s 253ms/step - loss: 0.0019 - val_loss: 2.7205e-04\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0015 - val_loss: 1.9829e-04\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0016 - val_loss: 2.4073e-04\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 2s 171ms/step - loss: 0.0019 - val_loss: 2.6099e-04\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 2s 171ms/step - loss: 0.0016 - val_loss: 3.4400e-04\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 3s 222ms/step - loss: 0.0014 - val_loss: 2.0715e-04\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 3s 235ms/step - loss: 0.0086 - val_loss: 8.5417e-04\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 2s 171ms/step - loss: 0.0034 - val_loss: 2.2033e-04\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0035 - val_loss: 2.4333e-04\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0028 - val_loss: 2.0160e-04\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 2s 176ms/step - loss: 0.0024 - val_loss: 2.1013e-04\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 3s 268ms/step - loss: 0.0022 - val_loss: 2.0768e-04\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 2s 181ms/step - loss: 0.0020 - val_loss: 2.0684e-04\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 2s 176ms/step - loss: 0.0018 - val_loss: 2.0447e-04\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0017 - val_loss: 2.1971e-04\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 2s 176ms/step - loss: 0.0060 - val_loss: 2.8807e-04\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 2s 210ms/step - loss: 0.0045 - val_loss: 2.0816e-04\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.0042 - val_loss: 2.0236e-04\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0034 - val_loss: 5.4915e-04\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0026 - val_loss: 1.7134e-04\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0020 - val_loss: 2.0175e-04\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0021 - val_loss: 1.7758e-04\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 3s 218ms/step - loss: 0.0018 - val_loss: 1.9456e-04\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 3s 242ms/step - loss: 0.0018 - val_loss: 3.9949e-04\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 2s 174ms/step - loss: 0.0016 - val_loss: 1.7520e-04\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 2s 176ms/step - loss: 0.0016 - val_loss: 2.6540e-04\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 2s 172ms/step - loss: 0.0014 - val_loss: 2.5945e-04\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0013 - val_loss: 2.8425e-04\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 3s 235ms/step - loss: 0.0013 - val_loss: 2.4016e-04\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 3s 233ms/step - loss: 0.0015 - val_loss: 4.0904e-04\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0013 - val_loss: 2.1967e-04\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0012 - val_loss: 2.1713e-04\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 2s 173ms/step - loss: 0.0012 - val_loss: 3.1233e-04\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 2s 175ms/step - loss: 0.0011 - val_loss: 2.4322e-04\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 3s 251ms/step - loss: 0.0011 - val_loss: 3.0275e-04\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 3s 222ms/step - loss: 0.0012 - val_loss: 1.9327e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7bcb50932e60>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#evaluation of the model\n",
        "train_predict=model.predict(X_train)\n",
        "test_predict=model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJiJ-TTlaRx4",
        "outputId": "3da8d3f9-7d2f-423a-9562-7d2465a97e1c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23/23 [==============================] - 1s 35ms/step\n",
            "11/11 [==============================] - 0s 35ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_predict=sc.inverse_transform(train_predict)\n",
        "test_predict=sc.inverse_transform(test_predict)"
      ],
      "metadata": {
        "id": "kOu5Deg6cFSs"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "from sklearn.metrics import mean_squared_error\n",
        "math.sqrt(mean_squared_error(y_train,train_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiQI0_vtbgtB",
        "outputId": "a684ffd1-06e4-4211-90a0-d5a44a894980"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "209.07519444552412"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "math.sqrt(mean_squared_error(y_test,test_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8DvY73_b1jr",
        "outputId": "041ab1af-cdb9-4eb8-eb01-280f62dfaa2a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "182.12178924618206"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "look_back=100\n",
        "trainPredictPlot = np.empty_like(df2)\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = np.empty_like(df2)\n",
        "testPredictPlot[:, :] = np.nan\n",
        "testPredictPlot[len(train_predict)+(look_back*2)+1:len(df2)-1, :] = test_predict\n",
        "# plot baseline and predictions\n",
        "plt.plot(sc.inverse_transform(df2))\n",
        "plt.plot(trainPredictPlot)\n",
        "plt.plot(testPredictPlot)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "4coHMEB4b8TK",
        "outputId": "d554b053-cfbb-4eb7-e56a-29644c2ea242"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_input=test_data[341:].reshape(1,-1)\n",
        "x_input.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utZpZoPOc18W",
        "outputId": "8743b68f-f11d-47a9-d5f7-38d0d7de2ece"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_input=list(x_input)\n",
        "temp_input=temp_input[0].tolist()"
      ],
      "metadata": {
        "id": "MANGbC9XdLPv"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lst_output=[]\n",
        "n_steps=100\n",
        "i=0\n",
        "while(i<30):\n",
        "\n",
        "    if(len(temp_input)>100):\n",
        "        #print(temp_input)\n",
        "        x_input=np.array(temp_input[1:])\n",
        "        print(\"{} day input {}\".format(i,x_input))\n",
        "        x_input=x_input.reshape(1,-1)\n",
        "        x_input = x_input.reshape((1, n_steps, 1))\n",
        "        #print(x_input)\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(\"{} day output {}\".format(i,yhat))\n",
        "        temp_input.extend(yhat[0].tolist())\n",
        "        temp_input=temp_input[1:]\n",
        "        #print(temp_input)\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "    else:\n",
        "        x_input = x_input.reshape((1, n_steps,1))\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(yhat[0])\n",
        "        temp_input.extend(yhat[0].tolist())\n",
        "        print(len(temp_input))\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "\n",
        "\n",
        "print(lst_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zl3D3Ho5dNZx",
        "outputId": "0f0f0e43-4679-4dc1-b41e-d49ee55f1483"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.29194218]\n",
            "101\n",
            "1 day input [0.15599249 0.15569192 0.16002505 0.16508453 0.16628679 0.16102693\n",
            " 0.16571071 0.16475892 0.16751409 0.17342517 0.17991234 0.16162805\n",
            " 0.16390733 0.16032561 0.15747026 0.16648716 0.16190357 0.15827176\n",
            " 0.15529117 0.15731997 0.15524108 0.15714465 0.15431434 0.15737007\n",
            " 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723\n",
            " 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211\n",
            " 0.15644333 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946\n",
            " 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307\n",
            " 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658\n",
            " 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216\n",
            " 0.21392611 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479\n",
            " 0.2161052  0.25124609 0.26607389 0.26900438 0.26462116 0.2750908\n",
            " 0.26912962 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473\n",
            " 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121\n",
            " 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906\n",
            " 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996\n",
            " 0.27977458 0.27714465 0.27832185 0.29194218]\n",
            "1 day output [[0.2887532]]\n",
            "2 day input [0.15569192 0.16002505 0.16508453 0.16628679 0.16102693 0.16571071\n",
            " 0.16475892 0.16751409 0.17342517 0.17991234 0.16162805 0.16390733\n",
            " 0.16032561 0.15747026 0.16648716 0.16190357 0.15827176 0.15529117\n",
            " 0.15731997 0.15524108 0.15714465 0.15431434 0.15737007 0.15263619\n",
            " 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272\n",
            " 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333\n",
            " 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661\n",
            " 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348\n",
            " 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477\n",
            " 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611\n",
            " 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052\n",
            " 0.25124609 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962\n",
            " 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373\n",
            " 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816\n",
            " 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906 0.32055103\n",
            " 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458\n",
            " 0.27714465 0.27832185 0.29194218 0.28875321]\n",
            "2 day output [[0.28653032]]\n",
            "3 day input [0.16002505 0.16508453 0.16628679 0.16102693 0.16571071 0.16475892\n",
            " 0.16751409 0.17342517 0.17991234 0.16162805 0.16390733 0.16032561\n",
            " 0.15747026 0.16648716 0.16190357 0.15827176 0.15529117 0.15731997\n",
            " 0.15524108 0.15714465 0.15431434 0.15737007 0.15263619 0.17082029\n",
            " 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314\n",
            " 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306\n",
            " 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485\n",
            " 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923\n",
            " 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589\n",
            " 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447\n",
            " 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609\n",
            " 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297\n",
            " 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738\n",
            " 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722\n",
            " 0.31594239 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314\n",
            " 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465\n",
            " 0.27832185 0.29194218 0.28875321 0.28653032]\n",
            "3 day output [[0.28493625]]\n",
            "4 day input [0.16508453 0.16628679 0.16102693 0.16571071 0.16475892 0.16751409\n",
            " 0.17342517 0.17991234 0.16162805 0.16390733 0.16032561 0.15747026\n",
            " 0.16648716 0.16190357 0.15827176 0.15529117 0.15731997 0.15524108\n",
            " 0.15714465 0.15431434 0.15737007 0.15263619 0.17082029 0.17460238\n",
            " 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708\n",
            " 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317\n",
            " 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645\n",
            " 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979\n",
            " 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045\n",
            " 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447 0.21918597\n",
            " 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389\n",
            " 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768\n",
            " 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117\n",
            " 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722  0.31594239\n",
            " 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106\n",
            " 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185\n",
            " 0.29194218 0.28875321 0.28653032 0.28493625]\n",
            "4 day output [[0.28368902]]\n",
            "5 day input [0.16628679 0.16102693 0.16571071 0.16475892 0.16751409 0.17342517\n",
            " 0.17991234 0.16162805 0.16390733 0.16032561 0.15747026 0.16648716\n",
            " 0.16190357 0.15827176 0.15529117 0.15731997 0.15524108 0.15714465\n",
            " 0.15431434 0.15737007 0.15263619 0.17082029 0.17460238 0.16493425\n",
            " 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206\n",
            " 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333\n",
            " 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004\n",
            " 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493\n",
            " 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655\n",
            " 0.20901691 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311\n",
            " 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438\n",
            " 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574\n",
            " 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117 0.2841077\n",
            " 0.28731371 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198\n",
            " 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668\n",
            " 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218\n",
            " 0.28875321 0.28653032 0.28493625 0.28368902]\n",
            "5 day output [[0.28261262]]\n",
            "6 day input [0.16102693 0.16571071 0.16475892 0.16751409 0.17342517 0.17991234\n",
            " 0.16162805 0.16390733 0.16032561 0.15747026 0.16648716 0.16190357\n",
            " 0.15827176 0.15529117 0.15731997 0.15524108 0.15714465 0.15431434\n",
            " 0.15737007 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371\n",
            " 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202\n",
            " 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333 0.16578585\n",
            " 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291\n",
            " 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543\n",
            " 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691\n",
            " 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311 0.21951158\n",
            " 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438 0.26462116\n",
            " 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574 0.2560551\n",
            " 0.26652473 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371\n",
            " 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093\n",
            " 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003\n",
            " 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321\n",
            " 0.28653032 0.28493625 0.28368902 0.28261262]\n",
            "6 day output [[0.28161716]]\n",
            "7 day input [0.16571071 0.16475892 0.16751409 0.17342517 0.17991234 0.16162805\n",
            " 0.16390733 0.16032561 0.15747026 0.16648716 0.16190357 0.15827176\n",
            " 0.15529117 0.15731997 0.15524108 0.15714465 0.15431434 0.15737007\n",
            " 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723\n",
            " 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211\n",
            " 0.15644333 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946\n",
            " 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307\n",
            " 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658\n",
            " 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216\n",
            " 0.21392611 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479\n",
            " 0.2161052  0.25124609 0.26607389 0.26900438 0.26462116 0.2750908\n",
            " 0.26912962 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473\n",
            " 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121\n",
            " 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906\n",
            " 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996\n",
            " 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032\n",
            " 0.28493625 0.28368902 0.28261262 0.28161716]\n",
            "7 day output [[0.28066504]]\n",
            "8 day input [0.16475892 0.16751409 0.17342517 0.17991234 0.16162805 0.16390733\n",
            " 0.16032561 0.15747026 0.16648716 0.16190357 0.15827176 0.15529117\n",
            " 0.15731997 0.15524108 0.15714465 0.15431434 0.15737007 0.15263619\n",
            " 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272\n",
            " 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333\n",
            " 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661\n",
            " 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348\n",
            " 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477\n",
            " 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611\n",
            " 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052\n",
            " 0.25124609 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962\n",
            " 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373\n",
            " 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816\n",
            " 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906 0.32055103\n",
            " 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458\n",
            " 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625\n",
            " 0.28368902 0.28261262 0.28161716 0.28066504]\n",
            "8 day output [[0.27974552]]\n",
            "9 day input [0.16751409 0.17342517 0.17991234 0.16162805 0.16390733 0.16032561\n",
            " 0.15747026 0.16648716 0.16190357 0.15827176 0.15529117 0.15731997\n",
            " 0.15524108 0.15714465 0.15431434 0.15737007 0.15263619 0.17082029\n",
            " 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314\n",
            " 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306\n",
            " 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485\n",
            " 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923\n",
            " 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589\n",
            " 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447\n",
            " 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609\n",
            " 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297\n",
            " 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738\n",
            " 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722\n",
            " 0.31594239 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314\n",
            " 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465\n",
            " 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902\n",
            " 0.28261262 0.28161716 0.28066504 0.27974552]\n",
            "9 day output [[0.27885866]]\n",
            "10 day input [0.17342517 0.17991234 0.16162805 0.16390733 0.16032561 0.15747026\n",
            " 0.16648716 0.16190357 0.15827176 0.15529117 0.15731997 0.15524108\n",
            " 0.15714465 0.15431434 0.15737007 0.15263619 0.17082029 0.17460238\n",
            " 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708\n",
            " 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317\n",
            " 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645\n",
            " 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979\n",
            " 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045\n",
            " 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447 0.21918597\n",
            " 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389\n",
            " 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768\n",
            " 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117\n",
            " 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722  0.31594239\n",
            " 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106\n",
            " 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185\n",
            " 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262\n",
            " 0.28161716 0.28066504 0.27974552 0.27885866]\n",
            "10 day output [[0.27800733]]\n",
            "11 day input [0.17991234 0.16162805 0.16390733 0.16032561 0.15747026 0.16648716\n",
            " 0.16190357 0.15827176 0.15529117 0.15731997 0.15524108 0.15714465\n",
            " 0.15431434 0.15737007 0.15263619 0.17082029 0.17460238 0.16493425\n",
            " 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206\n",
            " 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333\n",
            " 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004\n",
            " 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493\n",
            " 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655\n",
            " 0.20901691 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311\n",
            " 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438\n",
            " 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574\n",
            " 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117 0.2841077\n",
            " 0.28731371 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198\n",
            " 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668\n",
            " 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218\n",
            " 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262 0.28161716\n",
            " 0.28066504 0.27974552 0.27885866 0.27800733]\n",
            "11 day output [[0.2771936]]\n",
            "12 day input [0.16162805 0.16390733 0.16032561 0.15747026 0.16648716 0.16190357\n",
            " 0.15827176 0.15529117 0.15731997 0.15524108 0.15714465 0.15431434\n",
            " 0.15737007 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371\n",
            " 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202\n",
            " 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333 0.16578585\n",
            " 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291\n",
            " 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543\n",
            " 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691\n",
            " 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311 0.21951158\n",
            " 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438 0.26462116\n",
            " 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574 0.2560551\n",
            " 0.26652473 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371\n",
            " 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093\n",
            " 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003\n",
            " 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321\n",
            " 0.28653032 0.28493625 0.28368902 0.28261262 0.28161716 0.28066504\n",
            " 0.27974552 0.27885866 0.27800733 0.27719361]\n",
            "12 day output [[0.27641794]]\n",
            "13 day input [0.16390733 0.16032561 0.15747026 0.16648716 0.16190357 0.15827176\n",
            " 0.15529117 0.15731997 0.15524108 0.15714465 0.15431434 0.15737007\n",
            " 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723\n",
            " 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211\n",
            " 0.15644333 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946\n",
            " 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307\n",
            " 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658\n",
            " 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216\n",
            " 0.21392611 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479\n",
            " 0.2161052  0.25124609 0.26607389 0.26900438 0.26462116 0.2750908\n",
            " 0.26912962 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473\n",
            " 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121\n",
            " 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906\n",
            " 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996\n",
            " 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032\n",
            " 0.28493625 0.28368902 0.28261262 0.28161716 0.28066504 0.27974552\n",
            " 0.27885866 0.27800733 0.27719361 0.27641794]\n",
            "13 day output [[0.27567887]]\n",
            "14 day input [0.16032561 0.15747026 0.16648716 0.16190357 0.15827176 0.15529117\n",
            " 0.15731997 0.15524108 0.15714465 0.15431434 0.15737007 0.15263619\n",
            " 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272\n",
            " 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333\n",
            " 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661\n",
            " 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348\n",
            " 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477\n",
            " 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611\n",
            " 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052\n",
            " 0.25124609 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962\n",
            " 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373\n",
            " 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816\n",
            " 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906 0.32055103\n",
            " 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458\n",
            " 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625\n",
            " 0.28368902 0.28261262 0.28161716 0.28066504 0.27974552 0.27885866\n",
            " 0.27800733 0.27719361 0.27641794 0.27567887]\n",
            "14 day output [[0.2749735]]\n",
            "15 day input [0.15747026 0.16648716 0.16190357 0.15827176 0.15529117 0.15731997\n",
            " 0.15524108 0.15714465 0.15431434 0.15737007 0.15263619 0.17082029\n",
            " 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314\n",
            " 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306\n",
            " 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485\n",
            " 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923\n",
            " 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589\n",
            " 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447\n",
            " 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609\n",
            " 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297\n",
            " 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738\n",
            " 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722\n",
            " 0.31594239 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314\n",
            " 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465\n",
            " 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902\n",
            " 0.28261262 0.28161716 0.28066504 0.27974552 0.27885866 0.27800733\n",
            " 0.27719361 0.27641794 0.27567887 0.27497351]\n",
            "15 day output [[0.274298]]\n",
            "16 day input [0.16648716 0.16190357 0.15827176 0.15529117 0.15731997 0.15524108\n",
            " 0.15714465 0.15431434 0.15737007 0.15263619 0.17082029 0.17460238\n",
            " 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708\n",
            " 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317\n",
            " 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645\n",
            " 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979\n",
            " 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045\n",
            " 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447 0.21918597\n",
            " 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389\n",
            " 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768\n",
            " 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117\n",
            " 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722  0.31594239\n",
            " 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106\n",
            " 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185\n",
            " 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262\n",
            " 0.28161716 0.28066504 0.27974552 0.27885866 0.27800733 0.27719361\n",
            " 0.27641794 0.27567887 0.27497351 0.27429801]\n",
            "16 day output [[0.27364802]]\n",
            "17 day input [0.16190357 0.15827176 0.15529117 0.15731997 0.15524108 0.15714465\n",
            " 0.15431434 0.15737007 0.15263619 0.17082029 0.17460238 0.16493425\n",
            " 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206\n",
            " 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333\n",
            " 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004\n",
            " 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493\n",
            " 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655\n",
            " 0.20901691 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311\n",
            " 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438\n",
            " 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574\n",
            " 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117 0.2841077\n",
            " 0.28731371 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198\n",
            " 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668\n",
            " 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218\n",
            " 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262 0.28161716\n",
            " 0.28066504 0.27974552 0.27885866 0.27800733 0.27719361 0.27641794\n",
            " 0.27567887 0.27497351 0.27429801 0.27364802]\n",
            "17 day output [[0.27301887]]\n",
            "18 day input [0.15827176 0.15529117 0.15731997 0.15524108 0.15714465 0.15431434\n",
            " 0.15737007 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371\n",
            " 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202\n",
            " 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333 0.16578585\n",
            " 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291\n",
            " 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543\n",
            " 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691\n",
            " 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311 0.21951158\n",
            " 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438 0.26462116\n",
            " 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574 0.2560551\n",
            " 0.26652473 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371\n",
            " 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093\n",
            " 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003\n",
            " 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321\n",
            " 0.28653032 0.28493625 0.28368902 0.28261262 0.28161716 0.28066504\n",
            " 0.27974552 0.27885866 0.27800733 0.27719361 0.27641794 0.27567887\n",
            " 0.27497351 0.27429801 0.27364802 0.27301887]\n",
            "18 day output [[0.27240574]]\n",
            "19 day input [0.15529117 0.15731997 0.15524108 0.15714465 0.15431434 0.15737007\n",
            " 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723\n",
            " 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211\n",
            " 0.15644333 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946\n",
            " 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307\n",
            " 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658\n",
            " 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216\n",
            " 0.21392611 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479\n",
            " 0.2161052  0.25124609 0.26607389 0.26900438 0.26462116 0.2750908\n",
            " 0.26912962 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473\n",
            " 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121\n",
            " 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906\n",
            " 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996\n",
            " 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032\n",
            " 0.28493625 0.28368902 0.28261262 0.28161716 0.28066504 0.27974552\n",
            " 0.27885866 0.27800733 0.27719361 0.27641794 0.27567887 0.27497351\n",
            " 0.27429801 0.27364802 0.27301887 0.27240574]\n",
            "19 day output [[0.27180436]]\n",
            "20 day input [0.15731997 0.15524108 0.15714465 0.15431434 0.15737007 0.15263619\n",
            " 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272\n",
            " 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333\n",
            " 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661\n",
            " 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348\n",
            " 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477\n",
            " 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611\n",
            " 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052\n",
            " 0.25124609 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962\n",
            " 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373\n",
            " 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816\n",
            " 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906 0.32055103\n",
            " 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458\n",
            " 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625\n",
            " 0.28368902 0.28261262 0.28161716 0.28066504 0.27974552 0.27885866\n",
            " 0.27800733 0.27719361 0.27641794 0.27567887 0.27497351 0.27429801\n",
            " 0.27364802 0.27301887 0.27240574 0.27180436]\n",
            "20 day output [[0.27121025]]\n",
            "21 day input [0.15524108 0.15714465 0.15431434 0.15737007 0.15263619 0.17082029\n",
            " 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314\n",
            " 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306\n",
            " 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485\n",
            " 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923\n",
            " 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589\n",
            " 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447\n",
            " 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609\n",
            " 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297\n",
            " 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738\n",
            " 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722\n",
            " 0.31594239 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314\n",
            " 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465\n",
            " 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902\n",
            " 0.28261262 0.28161716 0.28066504 0.27974552 0.27885866 0.27800733\n",
            " 0.27719361 0.27641794 0.27567887 0.27497351 0.27429801 0.27364802\n",
            " 0.27301887 0.27240574 0.27180436 0.27121025]\n",
            "21 day output [[0.27061957]]\n",
            "22 day input [0.15714465 0.15431434 0.15737007 0.15263619 0.17082029 0.17460238\n",
            " 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708\n",
            " 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317\n",
            " 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645\n",
            " 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979\n",
            " 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045\n",
            " 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447 0.21918597\n",
            " 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389\n",
            " 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768\n",
            " 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117\n",
            " 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722  0.31594239\n",
            " 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106\n",
            " 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185\n",
            " 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262\n",
            " 0.28161716 0.28066504 0.27974552 0.27885866 0.27800733 0.27719361\n",
            " 0.27641794 0.27567887 0.27497351 0.27429801 0.27364802 0.27301887\n",
            " 0.27240574 0.27180436 0.27121025 0.27061957]\n",
            "22 day output [[0.2700291]]\n",
            "23 day input [0.15431434 0.15737007 0.15263619 0.17082029 0.17460238 0.16493425\n",
            " 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206\n",
            " 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333\n",
            " 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004\n",
            " 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493\n",
            " 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655\n",
            " 0.20901691 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311\n",
            " 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438\n",
            " 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574\n",
            " 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117 0.2841077\n",
            " 0.28731371 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198\n",
            " 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668\n",
            " 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218\n",
            " 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262 0.28161716\n",
            " 0.28066504 0.27974552 0.27885866 0.27800733 0.27719361 0.27641794\n",
            " 0.27567887 0.27497351 0.27429801 0.27364802 0.27301887 0.27240574\n",
            " 0.27180436 0.27121025 0.27061957 0.2700291 ]\n",
            "23 day output [[0.26943576]]\n",
            "24 day input [0.15737007 0.15263619 0.17082029 0.17460238 0.16493425 0.15664371\n",
            " 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202\n",
            " 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333 0.16578585\n",
            " 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291\n",
            " 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543\n",
            " 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691\n",
            " 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311 0.21951158\n",
            " 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438 0.26462116\n",
            " 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574 0.2560551\n",
            " 0.26652473 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371\n",
            " 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093\n",
            " 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003\n",
            " 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321\n",
            " 0.28653032 0.28493625 0.28368902 0.28261262 0.28161716 0.28066504\n",
            " 0.27974552 0.27885866 0.27800733 0.27719361 0.27641794 0.27567887\n",
            " 0.27497351 0.27429801 0.27364802 0.27301887 0.27240574 0.27180436\n",
            " 0.27121025 0.27061957 0.2700291  0.26943576]\n",
            "24 day output [[0.2688372]]\n",
            "25 day input [0.15263619 0.17082029 0.17460238 0.16493425 0.15664371 0.15318723\n",
            " 0.15078272 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211\n",
            " 0.15644333 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946\n",
            " 0.18752661 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307\n",
            " 0.20185348 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658\n",
            " 0.21054477 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216\n",
            " 0.21392611 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479\n",
            " 0.2161052  0.25124609 0.26607389 0.26900438 0.26462116 0.2750908\n",
            " 0.26912962 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473\n",
            " 0.26865373 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121\n",
            " 0.30301816 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906\n",
            " 0.32055103 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996\n",
            " 0.27977458 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032\n",
            " 0.28493625 0.28368902 0.28261262 0.28161716 0.28066504 0.27974552\n",
            " 0.27885866 0.27800733 0.27719361 0.27641794 0.27567887 0.27497351\n",
            " 0.27429801 0.27364802 0.27301887 0.27240574 0.27180436 0.27121025\n",
            " 0.27061957 0.2700291  0.26943576 0.26883721]\n",
            "25 day output [[0.26823163]]\n",
            "26 day input [0.17082029 0.17460238 0.16493425 0.15664371 0.15318723 0.15078272\n",
            " 0.14567314 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333\n",
            " 0.16696306 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661\n",
            " 0.18925485 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348\n",
            " 0.20758923 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477\n",
            " 0.20047589 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611\n",
            " 0.21838447 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052\n",
            " 0.25124609 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962\n",
            " 0.25758297 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373\n",
            " 0.25993738 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816\n",
            " 0.3051722  0.31594239 0.30239198 0.30983093 0.31949906 0.32055103\n",
            " 0.30567314 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458\n",
            " 0.27714465 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625\n",
            " 0.28368902 0.28261262 0.28161716 0.28066504 0.27974552 0.27885866\n",
            " 0.27800733 0.27719361 0.27641794 0.27567887 0.27497351 0.27429801\n",
            " 0.27364802 0.27301887 0.27240574 0.27180436 0.27121025 0.27061957\n",
            " 0.2700291  0.26943576 0.26883721 0.26823163]\n",
            "26 day output [[0.2676177]]\n",
            "27 day input [0.17460238 0.16493425 0.15664371 0.15318723 0.15078272 0.14567314\n",
            " 0.14777708 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306\n",
            " 0.15902317 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485\n",
            " 0.19010645 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923\n",
            " 0.20788979 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589\n",
            " 0.20824045 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447\n",
            " 0.21918597 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609\n",
            " 0.26607389 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297\n",
            " 0.25209768 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738\n",
            " 0.27529117 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722\n",
            " 0.31594239 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314\n",
            " 0.29390106 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465\n",
            " 0.27832185 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902\n",
            " 0.28261262 0.28161716 0.28066504 0.27974552 0.27885866 0.27800733\n",
            " 0.27719361 0.27641794 0.27567887 0.27497351 0.27429801 0.27364802\n",
            " 0.27301887 0.27240574 0.27180436 0.27121025 0.27061957 0.2700291\n",
            " 0.26943576 0.26883721 0.26823163 0.2676177 ]\n",
            "27 day output [[0.26699442]]\n",
            "28 day input [0.16493425 0.15664371 0.15318723 0.15078272 0.14567314 0.14777708\n",
            " 0.15043206 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317\n",
            " 0.15644333 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645\n",
            " 0.19469004 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979\n",
            " 0.20796493 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045\n",
            " 0.2082655  0.20901691 0.2115216  0.21392611 0.21838447 0.21918597\n",
            " 0.22299311 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389\n",
            " 0.26900438 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768\n",
            " 0.25372574 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117\n",
            " 0.2841077  0.28731371 0.29931121 0.30301816 0.3051722  0.31594239\n",
            " 0.30239198 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106\n",
            " 0.29422668 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185\n",
            " 0.29194218 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262\n",
            " 0.28161716 0.28066504 0.27974552 0.27885866 0.27800733 0.27719361\n",
            " 0.27641794 0.27567887 0.27497351 0.27429801 0.27364802 0.27301887\n",
            " 0.27240574 0.27180436 0.27121025 0.27061957 0.2700291  0.26943576\n",
            " 0.26883721 0.26823163 0.2676177  0.26699442]\n",
            "28 day output [[0.26636147]]\n",
            "29 day input [0.15664371 0.15318723 0.15078272 0.14567314 0.14777708 0.15043206\n",
            " 0.15574202 0.1579211  0.15644333 0.16696306 0.15902317 0.15644333\n",
            " 0.16578585 0.19170946 0.18752661 0.18925485 0.19010645 0.19469004\n",
            " 0.19088291 0.19897307 0.20185348 0.20758923 0.20788979 0.20796493\n",
            " 0.21089543 0.21417658 0.21054477 0.20047589 0.20824045 0.2082655\n",
            " 0.20901691 0.2115216  0.21392611 0.21838447 0.21918597 0.22299311\n",
            " 0.21951158 0.2255479  0.2161052  0.25124609 0.26607389 0.26900438\n",
            " 0.26462116 0.2750908  0.26912962 0.25758297 0.25209768 0.25372574\n",
            " 0.2560551  0.26652473 0.26865373 0.25993738 0.27529117 0.2841077\n",
            " 0.28731371 0.29931121 0.30301816 0.3051722  0.31594239 0.30239198\n",
            " 0.30983093 0.31949906 0.32055103 0.30567314 0.29390106 0.29422668\n",
            " 0.29335003 0.29597996 0.27977458 0.27714465 0.27832185 0.29194218\n",
            " 0.28875321 0.28653032 0.28493625 0.28368902 0.28261262 0.28161716\n",
            " 0.28066504 0.27974552 0.27885866 0.27800733 0.27719361 0.27641794\n",
            " 0.27567887 0.27497351 0.27429801 0.27364802 0.27301887 0.27240574\n",
            " 0.27180436 0.27121025 0.27061957 0.2700291  0.26943576 0.26883721\n",
            " 0.26823163 0.2676177  0.26699442 0.26636147]\n",
            "29 day output [[0.2657188]]\n",
            "[[0.29194217920303345], [0.2887532114982605], [0.2865303158760071], [0.28493624925613403], [0.283689022064209], [0.2826126217842102], [0.2816171646118164], [0.2806650400161743], [0.27974551916122437], [0.27885866165161133], [0.2780073285102844], [0.2771936058998108], [0.27641794085502625], [0.2756788730621338], [0.2749735116958618], [0.2742980122566223], [0.2736480236053467], [0.27301886677742004], [0.272405743598938], [0.2718043625354767], [0.271210253238678], [0.27061957120895386], [0.27002909779548645], [0.2694357633590698], [0.26883721351623535], [0.2682316303253174], [0.26761770248413086], [0.2669944167137146], [0.2663614749908447], [0.2657187879085541]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "day_new=np.arange(1,101)\n",
        "day_pred=np.arange(101,131)\n"
      ],
      "metadata": {
        "id": "X9JUN90ZdSxV"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(day_new,sc.inverse_transform(df1[1158:]))\n",
        "plt.plot(day_pred,sc.inverse_transform(lst_output))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "NsDKJ-LxdYkf",
        "outputId": "0010af24-f3f4-4aa0-d7ee-99c687dd5f55"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'df1' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-3345ee299d0e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mday_new\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1158\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;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mday_pred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst_output\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;31mNameError\u001b[0m: name 'df1' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3=df2.tolist()\n",
        "df3.extend(lst_output)\n",
        "plt.plot(df3[1200:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "2Vj3jLuUdaw8",
        "outputId": "7dfc78d9-33bb-47e1-bf92-3ca134378201"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bcb4ae69300>]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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WEd/23x/vo7bRzpikSL4/ure3h3NGzjoWzbCIeJZHtjWHhYWxdetWNm3axGOPPcbcuXNZt25dh+/30EMPUVFR4frKz8/vusGKiEcdKKnmjU15ADx81TC3e654mg5BFPEOt7Y1x8bGYrFYKCoqavV4UVERCQkJp32d2Wxm0KDmiv+0tDR2797NokWLmDZtmut1RUVF9O594m9WRUVFpKWltXk/q9WK1Wp1Z+gi4qM+3VuCw4CLB8UwISXa28M5q8HxoZhMUFrdSGl1g6sIV0S6l1szLAEBAYwbN47MzEzXYw6Hg8zMTCZNmtTu+zgcDhoaGgBISUkhISGh1T0rKyvZsGGDW/cUkZ4pp6gagLSkSO8OpJ2CA/zoF918+vNezbKIeIzbjePmzp3L7NmzSU9PZ8KECSxZsoSamhrmzJkDwKxZs+jTpw+LFi0CmutN0tPTGThwIA0NDXz44Ye8/PLLLFu2DGg+n+O+++7jD3/4A4MHDyYlJYX58+eTmJjIdddd13XvVER80r6WWhDnacg9wdD4MHKP1bKnsIqLBnVtczoRaZvbgWXmzJmUlJSwYMECCgsLSUtLY9WqVa6i2by8PMzmExM3NTU13HHHHRw+fJigoCBSU1N55ZVXmDlzpuuaBx54gJqaGm6//XbKy8uZPHkyq1atIjAwsAveooj4KsMwyGkJLM7akJ5gaEIYH+0qUh2LiAeZDMMwvD2IzqqsrCQiIoKKigrCw8O9PRwRaaejFXVMWvQJfmYTu/7rSgL8esbxZu9vO8Jdr31NWlIkb995sbeHI9JjufP7u2f8dBCRc5JzhiI5NqTHhBVovbXZ4ejxf+cT6RF6zk8IETnn7GspuB3ag+pXoCVgWczUNto5fLzO28MROS8osIiI1+T0wIJbAH+LmYFxocCJ9+AuwzBYteMoX+cd78qhiZyzFFhExGv2ugpuQ708EvcNjW8JLIUdOxrktY15/OKVLdz+cnZXDkvknKXAIiJe4XAYrsAyuIfNsAAMTWguEHT2kXHH1vxyfv/uLgBKqhqoqLN16dhEzkUKLCLiFfnHa6m3OQjwM9O/pRFbT+KcFXJ3huVYdQN3vJJNo93heuzw8douHZvIuUiBRUS8Ym/LzMSgXqH4WXrejyLnDMuBkhoam06ED8MwaGiyt/kau8Pg3te3cqSingGxIQxuqYPJL1PhrsjZuN04TkSkK+ztgQ3jTpYYEUiY1Y+qhib+9FEO5bU29pdU821JNeW1NiamRHPLRclcMTzeFcie/jiHz78tJcjfwvKbx/HnzH3sK67WDItIOyiwiIhXOHuw9LQdQk4mk4khCWFk5x7nL58eOOX5DQfL2HCwjMSIQH42qT/xYYEsXbsfgMdvGM2Q+DD6RgUBaGu0SDsosIiIV+x1bWnueTuEnH5xyUCWrfuW3hFBDIwLZWCvEAbFhRJq9eOfm/P5x8Z8jlTU88SqHNdr5lyczDVjEgFIimqu3dEMi8jZKbCIiMfZ7A72lzTXsPTUGRaAK4bHc8Xw+Dafu39GKndfNpj3tx3lxS8PsqOgkgkp0fz2qmGua5wzLKphETk7BRYR8bjcYzXY7AYhARb6RAZ5ezjdJtDfwg3j+vKjC/qQe6yWxMgg/E8qME6KPjHDYhgGJpPJW0MV8Xk9rzRfRHq8nMLm2ZXB8WGYzef+L2mTydTmeUnOsFbTaKe8tvO9WAzDYNvh8la7lkTOFQosIuJxOedA/UpXCPS30CvMCjT3pems1TuLuOa5L7jj1WwMQ4cyyrlFgUVEPG5vD98h1JWSunCnUHZuGQBrdhfz7jdHOn0/EV+iwCIiHtfTe7B0pb4tO4Xyyzo/w3KgpMb1z79/bxdlNY2dvqeIr1BgERGPqrfZOXSs+RfrUM2wkBTddTMsB0ub/72GWv0oq2nk0fd3dfqeIr5CgUVEPGp/STUOAyKD/V31G+cz1wxLJ2tYbHYHeS2zNE/9eDRmE6z8uoB1OcWdHqOIL1BgERGPcjWMiwvTNl5Obh7XuRmW/LJamhwGQf4WZoxIYM7FKQA8vHIHNQ1NnR6niLcpsIiIRzm3NA9JOL93CDmdaM9f26mdPc76lZTYEEwmE7+ePoS+UUEUlNfx5Oqcs7xaxPcpsIiIR+1zFtyqfgWA3pGBmExQb3NQWt3xItkDpc1BcECvEACCA/z44/WjAPh71iGyc493frAiXqTAIiIedaIHiwILgNXPQkJ4INC5M4WcBbcDYkNcj00d0osfXdAXw4BfvbGV4qr6zg1WxIsUWETEY6obmly1GgosJ7jOFOpEHcv+liWhAb1aL7X97uph9IsOJq+sltkvbKKirvMddUW8QYFFRDzGuRzUK8xKVEiAl0fjO7ri1GbnDEvKSTMsAFEhAbzyHxPpFWZl99FK/vPvm6hrtHd8sCJeosAiIh6zV/Urbersqc1V9TZKqhoASOkVcsrz/WKCeenWCYQF+rHp0HHufG0LNrvOG5KeRYFFRDzC4TB4c/NhAIb1VmA5Wd/ozs2wOGdXYkOthAf6t3nNsN7hvHDLeAL9zXyyp5gH/m8bDofOG5KeQ4FFRDzi1Y15bM49TkiAhVtaeoRIM+cMS0EHa1gOuOpXTp1dOdn45GiW/XQcfmYTK78uYOG7O7ErtEgPocAiIt3uaEUdj/97DwAPXJlKn8ggL4/It5zcPK4jsx4HSlq2NMeeObAAXJoax1M/HgPAy1/lcsvfNurMIekRFFhEpFsZhsH8t3dQ3dDE2H6R/OzC/t4eks/pHRGIxWyi0e6gpLrB7dcfKG3fDIvTdWP78MxNYwnyt/DZvlK+/8xnbM0vd/v7iniSAouIdKsPtxeyZncx/hYTj/9oNBaz2vF/l5/F7OrF0pFTm11LQrHt7x58zZhE3r7zYgbEhnCkop4bl2fxyle5neq2K9KdFFhEpNtU1NpY+O5OAH45bZB6r5xBR09tNgzjxJbmds6wOA1NCOOduy7myhEJNNod/O7tHfz6n99QUateLeJ7FFhEpNv88cPdlFY3MLBXCHdeOtDbw/FprlOb3ZxhKaysp85mx2I20a9lt5E7wgL9WfazC/jtValYzCbe+rqAy/60jn9lH9Zsi/gUBRYR6RZf7i/ljc35ADz+o9FY/SxeHpFv6+ipzc7loH7RwfhbOvYj3WQycfvUgbx++4UMjgvlWE0jv37zG2b+5StyCqs6dE+RrqbAIiLdYtm6/QD87MJ+pCdHe3k0vs91anO5ezMsB9o4Q6ijxidH88E9U3jwe6kE+VvYeKiMq575jMc+2KWW/uJ1Ciwi0uWa7A7X6cA3X5js3cH0EEnRziUhd2dYmrc0f7clf0cF+Jn5xSUDWfPrS7hyRAJ2h8GKzw4y9Ym1LFu3X239xWsUWESky+0+WkVto53wQD8Gx7V/58r5zDnDcqS8zq1mbq5Tmnt17b/nPpFBLL95HH+bM54h8aFU1Nl4fNUepj65lpeyDtHYpNb+4lkKLCLS5TYdKgNgXP8ozNrG3C7x4YH4W0w0OQwKK+vb/TpnDUtXzbB816VD4/j3vVN5+sYxJEUHUVLVwIJ3dnLZn9bxxqY8BRfxGAUWEelyzuUg1a60n8VsIrGlA/Dhdu4Uamiyu84fGujmlmZ3x/bDC/qSOXcaj143krgwK4eP1zHvX9uZ1jLjUm/TUpF0LwUWEelShmG4ZljS+0d5eTQ9i6vwtp07hfKO1eIwINTqR68wa3cODWiub7n5wv6sv/9Sfnf1MOLCrBypqGfBOzuZ8sRanv/sALWNTd0+Djk/KbCISJc6fLyO4qoG/C0mxiRFens4PYpza3N+O09t3n/ScpDJ5Lmlt6AAC/85ZQCfPnApj147gsSIQEqqGvjDB7u5aPEnPP3xXo514IgBkTPpUGBZunQpycnJBAYGMnHiRDZu3Hjaa1esWMGUKVOIiooiKiqKjIyMU66vrq7mrrvuom/fvgQFBTF8+HCWL1/ekaGJiJc5Z1dG9okg0F+9V9zh7gzLQTfPEOpqgf4Wbp6UzLr7L+XxH42if0ww5bU2nsncx8WPf8KCd3Z06KgBkba4HVjeeOMN5s6dy8KFC9myZQtjxoxhxowZFBcXt3n9unXruOmmm1i7di1ZWVkkJSUxffp0CgoKXNfMnTuXVatW8corr7B7927uu+8+7rrrLt59992OvzMR8YrNLfUr41W/4rYTW5vb90u+q7c0d1SAn5mZ4/vxya+nsfT/XcCoPhHU2xy8lJXLtKfWcfc/vmbb4XKvjlF6PrcDy9NPP81tt93GnDlzXDMhwcHBvPDCC21e/+qrr3LHHXeQlpZGamoqzz//PA6Hg8zMTNc1X375JbNnz2batGkkJydz++23M2bMmDPO3IiIb9p80g4hcU/HZ1h8Y+u4xWzi6tG9efeui3ntPycydUgv7A6D9745wjXPfcGNy7P4aGchDje2bYs4uRVYGhsbyc7OJiMj48QNzGYyMjLIyspq1z1qa2ux2WxER5/429dFF13Eu+++S0FBAYZhsHbtWvbu3cv06dPbvEdDQwOVlZWtvkTE+ypqbewtav5bvwpu3ec8T+hoRR02+9m3C3dll9uuZDKZuGhQLC/dOoEP7pnMD8f2wc9sYuOhMm5/OZvLn17Py1mHqGlQga60n1uBpbS0FLvdTnx8fKvH4+PjKSwsbNc95s2bR2JiYqvQ8+yzzzJ8+HD69u1LQEAAV155JUuXLmXq1Klt3mPRokVERES4vpKSktx5GyLSTbLzmmdXBsSGEBPa/btWzjW9Qq2EWv1wGPDahrwzXlte20hZTSPg/SWhMxmRGMHTM9P4fN5l/HLaQMID/ThYWsP8d3Zy4aJMHvtgl+pcpF08ukto8eLFvP7666xcuZLAwEDX488++yxfffUV7777LtnZ2fzpT3/izjvvZM2aNW3e56GHHqKiosL1lZ+f76m3ICJnsOmQs/+KZlc6wmw2cV/GYAD+8MEuVz+btjhnV+LDrYRY/Twyvs5IiAhk3pWpZD10OY/8YDjJMcFU1Tex4rODXPLkWm5/aTNZ+4/phGg5Lbf+K4+NjcVisVBUVNTq8aKiIhISEs742qeeeorFixezZs0aRo8e7Xq8rq6O3/72t6xcuZKrr74agNGjR7N161aeeuqpVjMxTlarFatVf3sT8TXZzsDSXwW3HfUfk1PYknecD7cXcser2bx/95Q2e6w4O9wOiPWN+pX2CrH6ccvFKcyalMy6vcX87YtDfLavlI92FfHRriIGx4Vy86T+XD+2D2GB/t4ervgQt2ZYAgICGDduXKuCWWcB7aRJk077uieeeIJHH32UVatWkZ6e3uo5m82GzWbDbG49FIvFgsOhls8iPUVDk52tLTtBNMPScSaTiSduGMPAXiEUVTZw9z+20PSdepbqhiY+2tm8DO+tLc2dZTabuCw1npf/YyIf/2oqP53Yj+AAC/uKq1nwzk4u/GMmv3t7O3sKVaMozdxeEpo7dy4rVqzg73//O7t37+aXv/wlNTU1zJkzB4BZs2bx0EMPua5//PHHmT9/Pi+88ALJyckUFhZSWFhIdXVzYV54eDiXXHIJ999/P+vWrePgwYO8+OKLvPTSS1x//fVd9DZFpLvtKKiksclBTEiAT9dU9AShVj/+cvM4QgIsfHWgjCdX5wDgcBj8c3M+lz61jo92Nc90Tx3Sy5tD7RKD48N47PpRfPXby/n9NSMYFBdKTaOdV77K48oln/GjZV/y5uZ8nRR9njMZHVgwfO6553jyyScpLCwkLS2NZ555hokTJwK4tia/+OKLACQnJ5Obm3vKPRYuXMgjjzwCQGFhIQ899BAfffQRZWVl9O/fn9tvv51f/epX7ereWFlZSUREBBUVFYSHh7v7dkSkC/xl/X4W/XsP04fH89dZ6Wd/gZzVh9uPcserWwD4zfQhrN5ZxPaCCgD6xwTz8FXDmD7izMvxPZFhGGQdOMYrX+WyemeR6/TqsEA/rkvrw08mJDEiMcLLo5Su4M7v7w4FFl+jwCLifbe9tJmPdxXx8FXDuG3qAG8P55zx2Ae7WPHZQdefQ61+3H3ZIG65OBmr37nfSbi4sp43sw/z+qY88stO9KcZ2SecH49L4tq0RCKDA7w4QukMBRYR8SjDMBj3hzWU1TTy1h0XcUE/1bB0lSa7gzkvbuLzb0v5yfgk5l4x1CMHHfoah8Pgy/3H+MfGPD7aVYjN3vyrK8Bi5orh8dwwri9TBsfiZ9EReT2JAouIeNT+kmou/9N6rH5mtj8ygwA//dLoSnaHQXV9ExHB2jUDcKy6gXe2HuHN7MPsPnqiKLdXmJVrxiRy/dg+jEgM9+iBkNIx7vz+9v3N+yLi85zt+MckRSqsdAOL2aSwcpKYUCu3Tk7h1skp7Cio4P+yD/PO1gJKqhr4388P8r+fH2RwXCjXje3DtWmJrg7C0rMpsIhIp20+5DzwUEtB4lkj+0Qwsk8Ev71qGOv3lvD21wV8vLuIfcXVPLk6hydX5zCufxQ/GN2bq0b3Ji4s8Ow3FZ+kwCLiI3KP1bBkzT7uuXxwj9sWnJ2nhnHiXQF+zbUsVwyPp7Lexqrthbz19WE2HCwjO/c42bnH+a/3d3HhgBi+PzqRGSPidXxED6PAIuIjlq8/wMqvC6i32Vn2s3HeHk67Ndkd5B5rPgtmWG/VkIn3hQf6c+P4JG4cn0RRZT0fbDvKe9uO8HVeOV/uP8aX+48x/50dXDggmu+N7M2MEQnnZSFzT6PAIuIjdrT01/hkTzFV9bYe05b8SHk9dodBgJ+ZOP3QFx8THx7oqnfJL6vl/W1H+WD7EXYUVPLFt8f44ttjLHhnB+OTo7lyZALTRyTQJzLI28OWNiiwiPiAxiYHOYVVADQ0OVizu4jrx/b18qjaJ6/lpN2kqCDMZu3KEN+VFB3ML6cN5JfTBpJ3rJZ/7zjKhzsK+Sa/nA0Hy9hwsIzfv7eLUX0imDEinhkjEhgUF6rdRj5CgUXEB+wrrqLxpPNi3vvmaI8LLP1jelbdjZzf+sUE8/NLBvLzSwZy+Hgtq3YU8tHOIjbllrG9oILtBRU89dFeUmJDyBgWxxXDE7igX6T6vHiRAouID9hZ0NxLok9kEAXldXy6t4Ty2sYe0cHTGVj6RWvrqPRMfaOC+c8pA/jPKQMorW5gza4iVu8s5Itvj3GwtIYVnx1kxWcHiQr259LUOK4YFs+UIb0ItepXqCfp37aID9hxpLl+5apRCXz+7TF2H61k1Y5CfjKhn5dHdnb5ziUhBRY5B8SGWvnJhH78ZEI/qhua+HRvCR/vKuKTPcUcr7Xx1pYC3tpSgL/FxIUDYrg8NY7Lh8Xrv38PUGAR8QHOA+1G9okgOsTK7qOVvLftSI8ILLllNYBmWOTcE2r146pRvblqVG+a7A42HTrOmt1FZO4u4tCxWj7bV8pn+0p55L1dDI4L5bLUOC5NjWNc/yj8tXTU5RRYRLysye5wtRcf2SeCC/pF8fiqPWTtP0ZxVb3PN7rKO6YlITn3+VnMTBoYw6SBMfzu6mEcKK0hc3cRmbuL2Zx7nH3F1ewrruYvnx4gLNCPqUN6cdnQOKYN7aV+L11EgUXEyw6U1lBvcxASYCElJgSz2cTYfpF8nVfOh9uOcsvFKd4e4mlV1NqorG8CFFjk/GEymRjYK5SBvUK5fepAKmptrN9Xwto9xazLaV46+mDbUT7YdhSTCUb3jeTSob24dGgco/pEaDddBymwiHiZs//K8MRw1w+yH4xO5Ou8ct7z8cDiLLjtFWYlKMDi5dGIeEdEsD/XjEnkmjGJ2B0GW/PLWbunmLU5xew8Usk3+eV8k1/OkjX7iA0NYOqQ5vAydXAvnRHlBgUWES/b0bJDaERihOuxq0f35tEPdpGde5zDx2t99vA21a+ItGYxmxjXP4px/aP4zYyhFFXWsz6nhLU5xXy2r5TS6kZX4a7ZBBf0i+LS1DguGdJLJ0yfhQKLiJftOKng1ik+PJCJKdF8daCMD7Yd5eeXDPTW8M5IW5pFziw+PNB1TEBjk4Ps3OOsy2mefdlbVM3m3ONszj3Ok6tziAuzcsmQXlyaGsfkwbGE95Bu156iwCLiRQ6Hwc4jzsDS+hyeH4xJ5KsDZby37YjPBpZ8BRaRdgvwO1G4+9BVwygor2sOL3tK+HJ/KcVVDbyZfZg3sw+7ZmouHRrHZalxDIlXx10FFhEvOnSshppGO1Y/M4N6hbZ67nsje7PwnZ3sKKjkQEk1A77zvC/QDItIx/WJDOKnE/vz04n9aWiys+ngcdbmNBfu7i+pYePBMjYeLOPxVXtIjAhkWmoclw6N4+JBMQQHnH+/vs+/dyziYYeP13KsupExSZGnPLfjSHP9yrDe4ae0/I4OCWDy4FjW5ZTw/raj3HP5YE8M1y2uwBKjwCLSGVY/C5MHxzJ5cCzzvz+cvGO1rNtbzNo9xXy5/xhHKup5bUMer23II8BiZuKAaC5LbZ59OV+OxVBgEelGxZX1XPPcF5TXNvL2nRczum9kq+d3FrS9HOR01ajerMspYf3eEo8HFofD4L1tRxiRGM6guLBTnrfZHRwprwc0wyLS1frFBDNrUjKzJiVTb7OTdeCYa+dRflmdq2nd79/bxYBeIVw2NI7LhsUxPjn6nG1ap8Ai0k0Mw+CBf22jrKYRgGXr9rPsZ+NaXeNsyT/ypB1CJxvTEnD2FlZhGIbH1rANw+Dht7fzj435pCaEseq+qadcc6S8DrvDwOpnppcaY4l0m0B/C5cObV4OMgyD/SU1rN1TzCd7itl0qIwDJTUcKDnI858fJMzqx5QhsVyWGs+l51jTOgUWkW7yyle5rMspIcBiptHuYNXOQvaXVDOwpRbFMAzXluaTdwidbECvEPwtJqoamigor/PI9mbDMPj9e7v4x8Z8APYUVlFW00h0SOuDGE+uX1EjLBHPMJlMDIoLZVBcKLdNHUBlvY3P95WSubu59uVYTSMfbi/kw+2FmEwwNimSy4fFkzEsvscX7iqwiHSDb4ureezD3QA8dFUqX3xbyprdxfx1/QEev2E0AIeP11FRZ8PfYmJwfNsFtf4WMwN7hbKnsIo9R6u6PbAYhsHjq3J48ctDAIRZ/ahqaGLzoTKmj0hoda0KbkW8LzzQ33XekcNh8M3hcj7ZU0zm7mJ2Ha1kS145W/LKeXJ1Dn2jgshoCS8TUqIJ8OtZS0c9a7QiPUBjk4NfvbGVepuDKYNjmT0pmV9Oa96W/NbXhymsaK77cPZfGZoQhtXv9F1ih/Vurm/JKarq5pHDnzP3sXz9fgAeu34k3x+TCMDm3OOnXJunU5pFfErzsR5R/Hr6UD68dwpZD13GH64byWWpcVj9zBw+XseLXx7iZ/+7gXGPfsydr23h7a8LqKi1eXvo7aIZFpEu9kzmPrYXVBAR5M+TN4zBbDYxrn80E5Kj2XiojBe+OMhvrxp21voVp6EJzQWvzgMSu8vy9ftZsmYfAAu+P5yfTuzPW1sO84+NeWw6VHbK9Tr0UMS39Y4I4mcX9udnF/anrtHO59+Wkrm7iDW7iymtbnCdd2Qxm5iQHM0Vw+O5Yni8z/4lRIFFpAtl55bxP+u+BeCP148iIeLEScu/nDaQjS+W8epXudw5bdCJlvynqV9xSm0JLDmF3TfDsi6nmMX/3gPAA1cO5dbJzecXjU+OBmD74QrqGu2tzgvSkpBIzxEUYHEFEufS0ZrdRazZVUxOURVZB46RdeAY//X+LlITwpg+IoHpw+N96rgABRaRLtJkdzD3n9/gMOCHY/tw9ejerZ6fNrQXqQlh7Cms4uWvDp1oyZ/Y9pZmp9SE5ucPlNbQ0GQ/4/JRR63dUwzAjy7oyx3TBrke7xsVREJ4IIWV9WzNL2fSwBigudbFOcPSXz1YRHoU59LR2H5R3D8jldxjNXy8q4iPdxWx6VBZc81cYRXPZO6jT2QQ00fEM314AuOTo07pF+XRcXvtO4ucYw4dqyH3WC1B/hYeuXbEKc+bTCZXLcvy9Qc4VtOIxWxy1aicTny4lchgf+wOg2+Lq7tl7M4GdlOHxJ4y5vTkKAA2n7QsVFFno6qhCcBnD2YUkfbpHxPCf04ZwBs/n0T2767gqR+PYcaIeAL9zRSU1/G3Lw5x04qvSH9sDcVV9V4bpwKLSBfJbZlxSIkNOe2hZVeP6k3fqCCqW37ZD44LJdD/zDMmJpOJofHNy0J7jnb9spDdYbDryKknRjs5l4U2nVR463yvcWHWVstEItKzRYUEcMO4vvzl5nS+nj+dFbPSuWFcX6KC/YkKDvBqzyUtCYl0kUPtWCLxs5j5+dQBzH9nJ9B2QGjLsN7hbDhY1i07hQ6WVlNnsxMcYCEl9tQW387AsiX3OHaHgcVsUv2KyHng5LqXJruDoxX1Xq1n0QyLSBfJO1YDcNZzPX6cnkRMSxO2EWepX3Hqzp1C21tqaYb3DsfSRgO4oQlhhFn9qG5ocn1/BRaR84ufxez13UMKLCJdJLesfUWogf4WnrhhNN8bmcCPLujbrnt3506hs3XbtZhNXNC/dR1Lvg49FBEPU2AR6SK5buyauXxYPMt+No6I4LZrXb5rSEsNS3FVg+tsoq7i3K10ptme8S2Ft5sONdexaIZFRDxNgUWkCzTZHRw+7gwsXX/Ue4jVzxWE9hR23bKQ46SC29PNsMBJhbeHyjAMwxXOFFhExFMUWES6wNGKemx2gwCLmYTwwLO/oAO6Y6dQXlktVQ1NWP3MDI5r+zwjgDFJkfhbTBRXNbC/pIajFXWAAouIeI4Ci0gXcM44JEUHtVm42hVSnWcKdWEdi/N4gNTe4WdsCBXob2FUywzMu1sLcBgQ6G+mV9i5c3S9iPg2BRaRLnConTuEOsNZeNuVS0Lb29ltF2B8SvOy0L+2FADNsyu+0rJbRM59CiwiXSCvnTuEOsO5tTmnqAq7w+iSe+48yw6hk43v3xxYCsq1HCQintehwLJ06VKSk5MJDAxk4sSJbNy48bTXrlixgilTphAVFUVUVBQZGRltXr97926uueYaIiIiCAkJYfz48eTl5XVkeCIel+ucYenGX+LJMSFY/czU2xyugNQZhmG0+8RogHEtW5udvN2TQUTOL24HljfeeIO5c+eycOFCtmzZwpgxY5gxYwbFxcVtXr9u3Tpuuukm1q5dS1ZWFklJSUyfPp2CggLXNfv372fy5Mmkpqaybt06tm3bxvz58wkM7J7iRZGu5trS3Ean2K5iMZtc25v3dEEDuYLyOsprbfiZTQxJOH3BrVNUSECrwlzNsIiIJ7kdWJ5++mluu+025syZw/Dhw1m+fDnBwcG88MILbV7/6quvcscdd5CWlkZqairPP/88DoeDzMxM1zUPP/wwV111FU888QRjx45l4MCBXHPNNcTFxXX8nYl4yMnbfLtzhgVOrmPpfOGts2HckPiwdp8A7axjAQUWEfEstwJLY2Mj2dnZZGRknLiB2UxGRgZZWVntukdtbS02m43o6OYffA6Hgw8++IAhQ4YwY8YM4uLimDhxIm+//fZp79HQ0EBlZWWrLxFvKalqoM5mx2zq/pOLh3Zh4e3OluWgUe2oX3FyNpCD7q3XERH5LrcCS2lpKXa7nfj4+FaPx8fHU1hY2K57zJs3j8TERFfoKS4uprq6msWLF3PllVfy0Ucfcf311/PDH/6Q9evXt3mPRYsWERER4fpKSkpy522IdClnS/7EyCAC/Lq3jn1YF25tdu0Q6tO+84zgRAM5f4up28OZiMjJPHpa8+LFi3n99ddZt26dqz7F4XAAcO211/KrX/0KgLS0NL788kuWL1/OJZdccsp9HnroIebOnev6c2VlpUKLeI07Lfk7yznDkltWS21jE8EBHftf2DCMEy353Zhh6RsVzJKZaQT6mwn0b98ykohIV3Drp11sbCwWi4WioqJWjxcVFZGQkHDG1z711FMsXryYNWvWMHr06Fb39PPzY/jw4a2uHzZsGJ9//nmb97JarVitalglviHXAz1YnGJDrcSGWimtbmBvUTVpSZEduk9xVQOl1Y2YTTAsof0zLADXje3Toe8pItIZbs1fBwQEMG7cuFYFs84C2kmTJp32dU888QSPPvooq1atIj09/ZR7jh8/npycnFaP7927l/79+7szPBGv8FTBrZOr8LYTO4WcsyuD4kIJCtBMiYj4Prfnk+fOncvs2bNJT09nwoQJLFmyhJqaGubMmQPArFmz6NOnD4sWLQLg8ccfZ8GCBbz22mskJye7al1CQ0MJDW3eInn//fczc+ZMpk6dyqWXXsqqVat47733WLduXRe9TZHu48kZFmgOLJ9/W9qpnULOHULt6b8iIuIL3A4sM2fOpKSkhAULFlBYWEhaWhqrVq1yFeLm5eVhNp+YuFm2bBmNjY3ccMMNre6zcOFCHnnkEQCuv/56li9fzqJFi7jnnnsYOnQo//rXv5g8eXIn3pqIZ+R6oMvtybpip9D2DtSviIh4U4cq9u666y7uuuuuNp/77qzIoUOH2nXPW2+9lVtvvbUjwxHxmopaG+W1NsBzgcW5U2hHQSX1NnuHil87sqVZRMSbdJaQSCfkljUvB/UKs3Z4x467hvcOp09kENUNTXy0q+jsL/iO0uoGjlbUN9+rHYceioj4AgUWkU7wdMEtgNls4kfj+gLwf9mH3X79ziPNS0kDYkMItXq0s4GISIcpsIh0gqcLbp1+dEHz1uLP95VQ2DJb0l4d6b8iIuJtCiwineDJpnEn6x8TwoTkaBwGvPV1+2dZ9pdUs+KzAwCM7WAPFxERb1BgEekEbwUWgBtaloX+lX0YwzDOen1JVQO3/G0j5bU2xiRFctOEft09RBGRLqPAItIJzqJbTy8JAVw1ujdB/hb2l9SwNb/8jNfWNDRx64ubyC+ro39MMP87O10N40SkR1FgEemgukY7RZUNACR7YYYl1OrHlSObj8Q4U/Ftk93BXa9tYXtBBdEhAbw4ZwKxoTraQkR6FgUWkQ7Ka2kYFx7oR2RwgFfG4FwWeu+bI9Tb7Kc8bxgG89/ZydqcEqx+Zp6fnU5KrOdng0REOkuBRaSDvLVD6GSTBsSQGBFIZX0Ta3a37snicBg89VEO/9iYh9kEz940lgv6RXlppCIinaPAItJB3iy4dTpdT5aqehu/eCWbpWv3A/DINSOYPuLMJ6qLiPgyBRaRDjpRcOu9wALwwwuaA8une0soqqzn2+Jqrlv6BR/tKiLAYubxH41i1qRkr45RRKSz1OZSpINOzLB4tyYkJTaE9P5RbM49zu/e3kHW/mNUNzTROyKQZT8bR5r6rYjIOUAzLCId5I22/KfjLL79eFcR1Q1NTEyJ5r27JyusiMg5Q4FFpANsdgcF5XUAJPvArpurR/cmpKWvyq0Xp/DKf07U1mUROadoSUikAwqO12F3GAT6m4kL834wCAv0542fT6KqvolJA2O8PRwRkS6nwCLSAYePN8+uJEUFYzKZvDyaZiN1mKGInMO0JCTSARV1NgCivNQwTkTkfKPAItIBVfXNgSUsUJOUIiKeoMAi0gFV9U2AAouIiKcosIh0wIkZFn8vj0RE5PygwCLSAZWaYRER8SgFFpEOOLEkpBkWERFPUGAR6QAV3YqIeJYCi0gHqOhWRMSzFFhEOqCqoXmGJVxLQiIiHqHAItIBmmEREfEsBRaRDqhuCSyhCiwiIh6hwCLSAdolJCLiWQosIm6qt9lptDsALQmJiHiKAouIm5yzKyYThAYosIiIeIICi4ibnD1YQgP8MJtNXh6NiMj5QYFFxE3aISQi4nkKLCJuUsGtiIjnKbCIuElt+UVEPE+BRcRNWhISEfE8BRYRN1W6Zli0JCQi4ikKLCJu0gyLiIjnKbCIuElFtyIinqfAIuImFd2KiHieAouIm5wzLOEKLCIiHtOhwLJ06VKSk5MJDAxk4sSJbNy48bTXrlixgilTphAVFUVUVBQZGRlnvP4Xv/gFJpOJJUuWdGRoIt2uqkFFtyIinuZ2YHnjjTeYO3cuCxcuZMuWLYwZM4YZM2ZQXFzc5vXr1q3jpptuYu3atWRlZZGUlMT06dMpKCg45dqVK1fy1VdfkZiY6P47EfEQFd2KiHie24Hl6aef5rbbbmPOnDkMHz6c5cuXExwczAsvvNDm9a+++ip33HEHaWlppKam8vzzz+NwOMjMzGx1XUFBAXfffTevvvoq/v76m6v4LhXdioh4nluBpbGxkezsbDIyMk7cwGwmIyODrKysdt2jtrYWm81GdHS06zGHw8HNN9/M/fffz4gRI856j4aGBiorK1t9iXiKim5FRDzPrcBSWlqK3W4nPj6+1ePx8fEUFha26x7z5s0jMTGxVeh5/PHH8fPz45577mnXPRYtWkRERITrKykpqf1vQqSTKltmWEKtCiwiIp7i0V1Cixcv5vXXX2flypUEBgYCkJ2dzZ///GdefPFFTCZTu+7z0EMPUVFR4frKz8/vzmGLuDQ02WlscgAQriUhERGPcSuwxMbGYrFYKCoqavV4UVERCQkJZ3ztU089xeLFi/noo48YPXq06/HPPvuM4uJi+vXrh5+fH35+fuTm5vLrX/+a5OTkNu9ltVoJDw9v9SXiCdUtsysAoVoSEhHxGLcCS0BAAOPGjWtVMOssoJ00adJpX/fEE0/w6KOPsmrVKtLT01s9d/PNN7Nt2za2bt3q+kpMTOT+++9n9erVbr4dke7lLLgNCbBgMbdvRlBERDrP7b8izp07l9mzZ5Oens6ECRNYsmQJNTU1zJkzB4BZs2bRp08fFi1aBDTXpyxYsIDXXnuN5ORkV61LaGgooaGhxMTEEBMT0+p7+Pv7k5CQwNChQzv7/kS6lHYIiYh4h9uBZebMmZSUlLBgwQIKCwtJS0tj1apVrkLcvLw8zOYTEzfLli2jsbGRG264odV9Fi5cyCOPPNK50Yt4mHYIiYh4h8kwDMPbg+isyspKIiIiqKioUD2LdKtVOwr5xSvZXNAvkrfuuNjbwxER6dHc+f2ts4RE3HBihkVLQiIinqTAIuIGteUXEfEOBRYRN6joVkTEOxRYRNzgXBIK1wyLiIhHKbCIuEFLQiIi3qHAIuKGqgYV3YqIeIMCi4gbNMMiIuIdCiwibqhU0a2IiFcosIi4QZ1uRUS8Q4FFxA1aEhIR8Q4FFhE3nNjWrCUhERFPUmARaSeb3UG9zQFohkVExNMUWETaybkcBBBiVWAREfEkBRaRdnIuBwX5W/C36H8dERFP0k9dkXZSwa2IiPcosIi0kwKLiIj3KLCItNOJHizaISQi4mkKLCLtpBkWERHvUWARaSf1YBER8R4FFpF20gyLiIj3KLCItFNVgwKLiIi3KLCItJOKbkVEvEeBRaSdKrUkJCLiNQosIu10ooZFMywiIp6mwCLSTieWhDTDIiLiaQosIu2kXUIiIt6jwCLSTurDIiLiPQosIu2kGRYREe9RYBE5yeHjtSxZs5ealp4rTk12B7WNdkBFtyIi3qC/Koqc5Hdv72BdTgn+FjN3XjrI9Xj1SQEm1Kr/bUREPE0zLCItjtc08vm+UgCyc4+3es65HGT1MxPgp/9tREQ8TT95RVqs2llIk8MA4Ou84xiG4XquUl1uRUS8SoFFpMX72464/vl4rY28slrXn50zLOEquBUR8QoFFhGgpKqBrP3HAOgTGQTA13nlruertUNIRMSrFFjkvFBvs1PXssunLf/ecRSHAWOSIpk+Ih6ArfnlruerGrQkJCLiTfrropzz6hrtXP3MZ9Q0NvHhPVOICbWecs373xwF4Aeje9MrrPn5r/NOFN6qB4uIiHdphkXOeX/78iAHSmsoqmzgydU5pzxfWFHPptwyAK4a1ZsL+kUBsOtoJfW25lkZBRYREe9SYJFz2vGaRpat2+/68xub89l2uLzVNR9sP4phQHr/KBIjg+gbFURMSAA2u8HOI5WAdgmJiHibAouc0/5n3bdU1TcxrHc416YlYhiw4J2dOBwntiw7dwd9f3RvAEwmE2P7RQInloU0wyIi4l0dCixLly4lOTmZwMBAJk6cyMaNG0977YoVK5gyZQpRUVFERUWRkZHR6nqbzca8efMYNWoUISEhJCYmMmvWLI4cOXLae4q0x+Hjtfz9y1wA5l05lIevGkZIgIWt+eX8a8th1zVf55VjMjUvBzmNbVkWchbenggsmmEREfEGtwPLG2+8wdy5c1m4cCFbtmxhzJgxzJgxg+Li4javX7duHTfddBNr164lKyuLpKQkpk+fTkFBAQC1tbVs2bKF+fPns2XLFt566y1ycnK45pprOvfO5Lz33x/vo9HuYNKAGC4Z0ou48EDuuXwwAI+v2kNlvY0PtjUX205MiSYuPND12rSkSODE1uYq15KQZlhERLzB7cDy9NNPc9tttzFnzhyGDx/O8uXLCQ4O5oUXXmjz+ldffZU77riDtLQ0UlNTef7553E4HGRmZgIQERHBxx9/zI033sjQoUO58MILee6558jOziYvL69z707OW3sKK3nr6+ZZlAe/l4rJZAJgzsUpDIgNobS6kT+v2cf7LYHl+6MTW71+dN8ITCYoKK+juKpejeNERLzMrcDS2NhIdnY2GRkZJ25gNpORkUFWVla77lFbW4vNZiM6Ovq011RUVGAymYiMjGzz+YaGBiorK1t9iZzs8X/vwTDg6tG9GdMyWwIQ4GdmwQ+GA/Dil4fYXlCBxWzieyMTWr0+LNCfIXFhAGzNKz9phkVLQiIi3uBWYCktLcVutxMfH9/q8fj4eAoLC9t1j3nz5pGYmNgq9Jysvr6eefPmcdNNNxEeHt7mNYsWLSIiIsL1lZSU5M7bkHNc1v5jrM0pwc9s4jfTh57y/LShcWQMi8feUnh70cCYNnuzuJaF8stVdCsi4mUe3SW0ePFiXn/9dVauXElgYOApz9tsNm688UYMw2DZsmWnvc9DDz1ERUWF6ys/P787hy09SHFlPYv+vRuAmyb0IyU2pM3rFnx/uOvUZefuoO9y7hRqnmFR0a2IiDe59dfF2NhYLBYLRUVFrR4vKioiISHhNK9q9tRTT7F48WLWrFnD6NGjT3neGVZyc3P55JNPTju7AmC1WrFaT/0bsZyfmuwO1u8t4fVN+Xyypxi7wyA4wMLdlw867Wv6xQTzpx+PYcPBY1yb1qfNa9JaAss3h8upbWnrrxkWERHvcOunb0BAAOPGjSMzM5PrrrsOwFVAe9ddd532dU888QSPPfYYq1evJj09/ZTnnWFl3759rF27lpiYGPfehZyXmuwOlq7dz2sbcymqbHA9nt4/irlXDCEu7NRZvJP9YEwiPxiTeNrnB8eFERJgoeakM4gUWEREvMPtn75z585l9uzZpKenM2HCBJYsWUJNTQ1z5swBYNasWfTp04dFixYB8Pjjj7NgwQJee+01kpOTXbUuoaGhhIaGYrPZuOGGG9iyZQvvv/8+drvddU10dDQBAQFd9V7lHPPCFwf57zV7AYgOCeBHF/Rh5vgkBrUUy3aWxWxiTFIkX7ac4hzgZ8bqZ+mSe4uIiHvcDiwzZ86kpKSEBQsWUFhYSFpaGqtWrXIV4ubl5WE2nyiNWbZsGY2Njdxwww2t7rNw4UIeeeQRCgoKePfddwFIS0trdc3atWuZNm2au0OU80Bjk4P//fwgAL++Ygg/v2SgqyalK6WdFFi0pVlExHs69BP4rrvuOu0S0Lp161r9+dChQ2e8V3JyMoZhnPEake96Z2sBRZUNxIdbuy2swImOtwChVgUWERFv0VlC0uM4HAZ//fQAAP8xOaXbwgqc2NoM2iEkIuJNCizS46zNKWZfcTVhVj9umtCvW79XrzArfaOCABXcioh4kwKL9Dh/Wd88u/L/LuznkVkP57KQAouIiPcosEiPkp17nI2HyvC3mLj14hSPfM/Jg5q32SfHtN2ETkREup/+yig9yl8/3Q/A9WP7EB9+5j4rXeWGcUkkx4S0OpNIREQ8S4FFeoz9JdV8tKu5y/LtUwd47PtazCYmDlAzQxERb9KSkPQYz392AMOAjGHxXdYcTkREegYFFmnTc5/s46a/fsWx6oazX+wBxVX1/GtLAQA/v8RzsysiIuIbFFjkFBsPlvHUR3vJOnCMP32819vDwTAMFn24h8YmBxf0iyS9f9TZXyQiIucUBRZppbHJwW9Xbnf9+fWNeewprPTiiGDp2m9Z+XUBFrOJ+2ekYjKZvDoeERHxPAUWaeWvn+7n2+JqYkMDmDa0Fw4DHvtgt9eOT3jvmyM89VHzLM8j14xg0kAVv4qInI8UWMTlUGkNz3zyLQDzvz+c/7pmJAEWM5/tK2VtTrHHx5Ode5xfv/kN0NyC/+YL+3t8DCIi4hsUWARorhP53ds7aGxyMGVwLNeMSaRfTDBzJicDzbMsNrvDY+PJL6vl9pc209jkIGNYPL+9apjHvreIiPgeBRYB4J2tR/j821Ksfmb+cN1IV53InZcOIiYkgP0lNby2Ic8jY6moszHnxU0cq2lkRGI4f/5JGhaz6lZERM5nCixCeW0jj76/C4B7Lh9M/5Na0IcH+vOrK4YA8N9r9lJRa+vWsTTZHdz12ha+La4mITyQ/509nhCr+huKiJzvFFiERR/u4VhNI4PjQrltyqk9Tn4yPokh8aGU19p49pN93TuWf+/hs32lBPlbeH52OgkRnmm/LyIivk2B5Ty3NqeYNzbnA/DHH44iwO/U/yT8LGYevno4AH/POsSh0ppuGcubm/P5388PAvD0jWMY2SeiW76PiIj0PAos57GymkYe+L9tANxyUTLjk6NPe+0lQ3oxeVAsNrvBu98c6fKxZOce5+GVOwC49/LBfG9U7y7/HiIi0nMpsJynDMPgwX9to6SqgcFxoTz4vdSzvuay1DgAth2u6NKxHK2o4+cvZ9Nod3DliATuvXxwl95fRER6PgWW89Sbmw/z0a4i/C0m/ntmGoH+lrO+ZnTf5iWa7QXlXTaOukY7t7+UTWl1A6kJYfzpxjGYtSNIRES+Q9svzkN5x2r5/Xs7AZh7xdB214oMTwzHbIKiygaKKuuJD+94QWxDk521e4p54fNDbC+oIDokgBWz0rUjSERE2qTfDueZJruDX/1zKzWNdiYkR3P71PaffBwc4MfguDByiqrYdriCK4a7F1gMw2BLXjkrvz7Me98cpaKueYu0v8XE//z0ApKig926n4iInD8UWM5hhmHQ0OSgweagzman3mbnzex8snOPE2b14083jnG7IdvovhHkFFWx/XA5VwyPb/OasppGbn1xE0cr6rA7DNdXk8OgttHuui4hPJBrxyZyY3oSA3uFduq9iojIuU2B5Rxgdxhk5x5nX3EVh0prOFhaw4HSGvLLarHZ2z608PfXjujQjMbovhG8mX2YbQWnL7z9YPtRtuaXt/lckL+F741M4IcX9GXSwBh1sBURkXZRYDkH/M/ab/nTx3vPeI2f2USgv4VAfwszx/fl+rF9OvS9RvWNBGD74QoMw3C18D/ZF/tKAZhzcTI3pidhMZswm0xYzCYSwgMJCjh7ga+IiMjJFFh6OIfD4PVNzY3fJqREMzIxgpReIaTEhNA/JpjIYH8C/S34W7pmQ1hqQhh+ZhPHaho5UlFPn8igVs/bHQZZB44B8IMxiQzrHd4l31dERM5vCiw9XHbecQrK6wi1+vHSrRPatT25MwL9LQxNCGPnkUq2Hy4/JbDsPFJBRZ2NMKsfo9WpVkREuoj6sPRwK78uAODKkQndHlacRrcsC33TRgO5z79tXg6aOCAGvy6a1REREdFvlB6sscnBB9uOAnS4JqUjXA3k2ggsX37bvBw0eVCMx8YjIiLnPgWWHmxdTjEVdTbiwqxcOMBzAWFUy1LPtsPlGMaJXUj1NjsbD5UBMHlwrMfGIyIi5z4Flh7sna3NhxBem5bo0e3BQ+LDCPAzU1nfRF5Zrevx7NzjNDY5iAuzqq+KiIh0KQWWHqqy3sbHu4sAuM6Dy0EAAX5m1+6fkw9C/KKlfmXyoNg2tzuLiIh0lAJLD7VqRyGNTQ4Gx4Uy3Atbh8e4DkI8NbBcPEjLQSIi0rUUWHqot1t2B103to9XZjOcdSzftHS0rai1ubrfKrCIiEhXU2DpgQor6l3N2a5NS/TKGJxbm3cUVOBwGGQdKMUwYGCvEBIiOn6Ks4iISFsUWHqgd78pwDBgQnI0faO8c8LxwF4hBPlbqGm0c6C0hi9atjNrdkVERLqDAksPtPLr5t1Bni62PZmfxcyIxObame0F5apfERGRbqXA0sPkFFax+2gl/hYTV41K8OpYnMtCq3cUcaC0BrMJj/aDERGR84cCSw/z1teHAbh0aByRwQFeHYuz4+3qXYVA80nOEUH+3hySiIicoxRYepC9RVW8+MUhAH54QV/vDgYY1RJYnM1u1Y5fRES6S4cCy9KlS0lOTiYwMJCJEyeycePG0167YsUKpkyZQlRUFFFRUWRkZJxyvWEYLFiwgN69exMUFERGRgb79u3ryNDOWfU2O/f842samhxcOrQXM0bEe3tIpMSEEGo9ceC36ldERKS7uB1Y3njjDebOncvChQvZsmULY8aMYcaMGRQXF7d5/bp167jppptYu3YtWVlZJCUlMX36dAoKClzXPPHEEzzzzDMsX76cDRs2EBISwowZM6ivr+/4OzvHPLEqhz2FVcSEBPDEDWN8opOs2WxiZJ/mwlurn5kL+kV5eUQiInKuMhknn17XDhMnTmT8+PE899xzADgcDpKSkrj77rt58MEHz/p6u91OVFQUzz33HLNmzcIwDBITE/n1r3/Nb37zGwAqKiqIj4/nxRdf5Cc/+clZ71lZWUlERAQVFRWEh3u+62t3W7+3hNkvNM9KvXBLOpelen92xWnRh7v5y6cHmDI4lpf/Y6K3hyMiIj2IO7+/3ZphaWxsJDs7m4yMjBM3MJvJyMggKyurXfeora3FZrMRHR0NwMGDByksLGx1z4iICCZOnHjaezY0NFBZWdnq61x1rLqB37z5DQCzJvX3qbACcPOk/lyWGsd9GUO8PRQRETmHuRVYSktLsdvtxMe3/qUZHx9PYWFhu+4xb948EhMTXQHF+Tp37rlo0SIiIiJcX0lJSe68jR7DMAzm/WsbJVUNDI4L5bdXDfP2kE7RNyqYF24Zz7j+Wg4SEZHu49FdQosXL+b1119n5cqVBAZ2vH37Qw89REVFhesrPz+/C0fpO17ZkMea3cUEWMz8+SdjCfS3eHtIIiIiXuF39ktOiI2NxWKxUFRU1OrxoqIiEhLO3MTsqaeeYvHixaxZs4bRo0e7Hne+rqioiN69e7e6Z1paWpv3slqtWK1Wd4be49gdBn/6KAeAB64cyvDEc682R0REpL3cmmEJCAhg3LhxZGZmuh5zOBxkZmYyadKk077uiSee4NFHH2XVqlWkp6e3ei4lJYWEhIRW96ysrGTDhg1nvOe5bteRSsprbYRZ/bjlomRvD0dERMSr3JphAZg7dy6zZ88mPT2dCRMmsGTJEmpqapgzZw4As2bNok+fPixatAiAxx9/nAULFvDaa6+RnJzsqksJDQ0lNDQUk8nEfffdxx/+8AcGDx5MSkoK8+fPJzExkeuuu67r3mkPk3Wg+WyeCSnR+FnU309ERM5vbgeWmTNnUlJSwoIFCygsLCQtLY1Vq1a5imbz8vIwm0/8gl22bBmNjY3ccMMNre6zcOFCHnnkEQAeeOABampquP322ykvL2fy5MmsWrWqU3UuPV3W/ubTjycNVPdYERERt/uw+KJzrQ9Lk91B2n99THVDE+/fPZmRfSK8PSQREZEu1219WMQzthdUUN3QRESQP8N79/wAJiIi0lkKLD4o60DzctCElGjMZu+34BcREfE2BRYf5KpfGaD6FREREVBg8TmNTQ42HzoOqOBWRETESYHFx2w7XE6dzU5UsD9D48O8PRwRERGfoMDiYfU2O4+v2sPn+0rbfN65HHThgBjVr4iIiLRQYPGwl7NyWbZuP3e8mk1Fre2U550Ft1oOEhEROUGBxYNsdgd/++IgAJX1TSxbv7/V8w1NdrJzW+pXVHArIiLiosDiQf/eUciRinoC/Jr/tf/ti4McrahzPf91XjkNTQ5iQ60Migv11jBFRER8jgKLhxiGwfOfHQDgjmkDmZASTUOTgyUf73Ndc6J+JRqTSfUrIiIiTgosHrLp0HG2Ha7A6mfm5gv78+D3UgF4MzuffUVVgOpXRERETkeBxUNWtMyu/PCCvsSEWrmgXxQzRsTjMOCJ1TnU2+xszSsHVL8iIiLyXQosZ1FRZ2PTobJO3eNgaQ1rdhcB8B+TU1yP3z8jFbMJPt5VxF/WH6DR7iA+3EpKbEinvp+IiMi5RoHlDHIKq5j8+Cfc9tJmqhuaOnyfFz4/iGHAZalxrYppB8WFMnN8EgBLMvcCzbMrql8RERFpTYHlDAbFhdIr1Ep5rY1Xvsrt0D3Kaxt5MzsfgP+cknLK8/dePoRAfzOG0fxn1a+IiIicSoHlDCxmE3deOgiAFZ8eoLbR/VmWVzfkUW9zMLx3eJu1KQkRgdx68YkgM2lAbMcHLCIico5SYDmLa9MS6RcdzLGaRl7bkOfWaxua7Lz45SEAbpuactqlnp9fMpAh8aFcMqQXSdFBnR2yiIjIOUeB5Sz8LGbuvHQgAH/59AD1NvtZX9Nkd7B6ZyGzX9hISVUDCeGBXD0q8bTXRwT5s/q+qfz91gmqXxEREWmDn7cH0BNcP7Yvz2R+S0F5Ha9vzOOWi0+tRQEoqWrgjU15vLYhjyMV9QCYTPDAlUNd3W1PR0FFRETk9BRY2iHAz8wdlw7k4ZU7WL7+ADdN7IfVz+J63uEw+HPmPv5n3bfY7M3Vs9EhAcwcn8T/m9CPpOhgbw1dRETknKDA0k43jOvLc598y9GKet7cfJifXdgfgLpGO7958xs+2H4UgAv6RXLzpP58b2RvAv0tZ7qliIiItJNqWNrJ6mfhF5c017IsW7efxiYHxZX1/OSvWXyw/Sj+FhNP3jCat+64mOvH9lVYERER6UKaYXHDzPFJPLe2uZblydV7eH/bUY5W1BMV7M/yn41jolrqi4iIdAvNsLgh0N/Cz6cOAGDFZwc5WlHPwF4hvH3nxQorIiIi3UiBxU0/ndif2NAAAKYMjuWtOy6mf4zO/hEREelOWhJyU1CAhdduu5AdBRX8YEwi/hZlPhERke6mwNIBQ+LDGBIf5u1hiIiInDc0PSAiIiI+T4FFREREfJ4Ci4iIiPg8BRYRERHxeQosIiIi4vMUWERERMTnKbCIiIiIz1NgEREREZ+nwCIiIiI+T4FFREREfJ4Ci4iIiPg8BRYRERHxeQosIiIi4vPOidOaDcMAoLKy0ssjERERkfZy/t52/h4/k3MisFRVVQGQlJTk5ZGIiIiIu6qqqoiIiDjjNSajPbHGxzkcDo4cOUJYWBgmk6lL711ZWUlSUhL5+fmEh4d36b2l8/T5+C59Nr5Nn49vO18+H8MwqKqqIjExEbP5zFUq58QMi9lspm/fvt36PcLDw8/p/2h6On0+vkufjW/T5+PbzofP52wzK04quhURERGfp8AiIiIiPk+B5SysVisLFy7EarV6eyjSBn0+vkufjW/T5+Pb9Pmc6pwouhUREZFzm2ZYRERExOcpsIiIiIjPU2ARERERn6fAIiIiIj5PgeUsli5dSnJyMoGBgUycOJGNGzd6e0jnnUWLFjF+/HjCwsKIi4vjuuuuIycnp9U19fX13HnnncTExBAaGsqPfvQjioqKvDTi89fixYsxmUzcd999rsf02XhXQUEBP/vZz4iJiSEoKIhRo0axefNm1/OGYbBgwQJ69+5NUFAQGRkZ7Nu3z4sjPn/Y7Xbmz59PSkoKQUFBDBw4kEcffbTVuTr6fE5iyGm9/vrrRkBAgPHCCy8YO3fuNG677TYjMjLSKCoq8vbQziszZsww/va3vxk7duwwtm7dalx11VVGv379jOrqatc1v/jFL4ykpCQjMzPT2Lx5s3HhhRcaF110kRdHff7ZuHGjkZycbIwePdq49957XY/rs/GesrIyo3///sYtt9xibNiwwThw4ICxevVq49tvv3Vds3jxYiMiIsJ4++23jW+++ca45pprjJSUFKOurs6LIz8/PPbYY0ZMTIzx/vvvGwcPHjTefPNNIzQ01Pjzn//sukafzwkKLGcwYcIE484773T92W63G4mJicaiRYu8OCopLi42AGP9+vWGYRhGeXm54e/vb7z55puua3bv3m0ARlZWlreGeV6pqqoyBg8ebHz88cfGJZdc4gos+my8a968ecbkyZNP+7zD4TASEhKMJ5980vVYeXm5YbVajX/84x+eGOJ57eqrrzZuvfXWVo/98Ic/NH76058ahqHP57u0JHQajY2NZGdnk5GR4XrMbDaTkZFBVlaWF0cmFRUVAERHRwOQnZ2NzWZr9VmlpqbSr18/fVYecuedd3L11Ve3+gxAn423vfvuu6Snp/PjH/+YuLg4xo4dy4oVK1zPHzx4kMLCwlafT0REBBMnTtTn4wEXXXQRmZmZ7N27F4BvvvmGzz//nO9973uAPp/vOicOP+wOpaWl2O124uPjWz0eHx/Pnj17vDQqcTgc3HfffVx88cWMHDkSgMLCQgICAoiMjGx1bXx8PIWFhV4Y5fnl9ddfZ8uWLWzatOmU5/TZeNeBAwdYtmwZc+fO5be//S2bNm3innvuISAggNmzZ7s+g7Z+zunz6X4PPvgglZWVpKamYrFYsNvtPPbYY/z0pz8F0OfzHQos0qPceeed7Nixg88//9zbQxEgPz+fe++9l48//pjAwEBvD0e+w+FwkJ6ezh//+EcAxo4dy44dO1i+fDmzZ8/28ujkn//8J6+++iqvvfYaI0aMYOvWrdx3330kJibq82mDloROIzY2FovFcspuhqKiIhISErw0qvPbXXfdxfvvv8/atWvp27ev6/GEhAQaGxspLy9vdb0+q+6XnZ1NcXExF1xwAX5+fvj5+bF+/XqeeeYZ/Pz8iI+P12fjRb1792b48OGtHhs2bBh5eXkArs9AP+e84/777+fBBx/kJz/5CaNGjeLmm2/mV7/6FYsWLQL0+XyXAstpBAQEMG7cODIzM12PORwOMjMzmTRpkhdHdv4xDIO77rqLlStX8sknn5CSktLq+XHjxuHv79/qs8rJySEvL0+fVTe7/PLL2b59O1u3bnV9paen89Of/tT1z/psvOfiiy8+pQXA3r176d+/PwApKSkkJCS0+nwqKyvZsGGDPh8PqK2txWxu/WvYYrHgcDgAfT6n8HbVry97/fXXDavVarz44ovGrl27jNtvv92IjIw0CgsLvT2088ovf/lLIyIiwli3bp1x9OhR11dtba3rml/84hdGv379jE8++cTYvHmzMWnSJGPSpEleHPX56+RdQoahz8abNm7caPj5+RmPPfaYsW/fPuPVV181goODjVdeecV1zeLFi43IyEjjnXfeMbZt22Zce+215+22WU+bPXu20adPH9e25rfeesuIjY01HnjgAdc1+nxOUGA5i2effdbo16+fERAQYEyYMMH46quvvD2k8w7Q5tff/vY31zV1dXXGHXfcYURFRRnBwcHG9ddfbxw9etR7gz6PfTew6LPxrvfee88YOXKkYbVajdTUVOOvf/1rq+cdDocxf/58Iz4+3rBarcbll19u5OTkeGm055fKykrj3nvvNfr162cEBgYaAwYMMB5++GGjoaHBdY0+nxNMhnFSSz0RERERH6QaFhEREfF5CiwiIiLi8xRYRERExOcpsIiIiIjPU2ARERERn6fAIiIiIj5PgUVERER8ngKLiIiI+DwFFhEREfF5CiwiIiLi8xRYRERExOcpsIiIiIjP+/+trBULQYjaiwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NFSvi7jBdh63"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}