dec18_att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_0deg.ipynb

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        "<a href=\"https://colab.research.google.com/github/bharat-mullapudi/LSTM_SOC_RUL/blob/main/dec18_att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_0deg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        },
        "id": "V5wlvJX-tvun",
        "outputId": "d3ac32d8-81cc-4942-9361-743bad8e1296"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tI6EDld8uL52"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import files\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ju4NGqAquQk9"
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      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/calce_incremental_INR_18650-20R/Incremental_temp_0.xlsx')"
      ],
      "execution_count": 62,
      "outputs": []
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          "height": 423
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        "outputId": "850d5ed0-20dc-4983-9a48-904695a17985"
      },
      "source": [
        "df"
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      "execution_count": 63,
      "outputs": [
        {
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            "text/plain": [
              "         Test_Time(s)  Cycle_Index  ...  TimeDelta       SOC\n",
              "0           10.000019          1.0  ...          1      0.97\n",
              "1           20.015468          1.0  ...    10.0154  0.978693\n",
              "2           30.030954          1.0  ...    10.0155  0.978693\n",
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              "...               ...          ...  ...        ...       ...\n",
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      "source": [
        "df=df.dropna()"
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      "execution_count": 64,
      "outputs": []
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      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "HAjkmkUaqh0E"
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      "source": [
        "combined_data.to_csv('Incremental_Init_0.csv')\n",
        "combined_data=pd.read_csv('Incremental_Init_0.csv')"
      ],
      "execution_count": 66,
      "outputs": []
    },
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      "source": [
        "combined_data.describe()"
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          "data": {
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              "      <th>count</th>\n",
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              "      <td>-0.000120</td>\n",
              "      <td>3.756622</td>\n",
              "      <td>0.519926</td>\n",
              "      <td>1.392800</td>\n",
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              "      <td>0.280592</td>\n",
              "      <td>0.209131</td>\n",
              "      <td>0.616112</td>\n",
              "      <td>0.556578</td>\n",
              "      <td>0.628872</td>\n",
              "      <td>0.121619</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>-1.000412</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>46807.431234</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.600780</td>\n",
              "      <td>0.021223</td>\n",
              "      <td>0.902758</td>\n",
              "      <td>1.015400</td>\n",
              "      <td>0.646755</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>78784.500000</td>\n",
              "      <td>86664.620077</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.694451</td>\n",
              "      <td>0.136850</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015584</td>\n",
              "      <td>0.701856</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>126522.511848</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.950466</td>\n",
              "      <td>1.104041</td>\n",
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              "      <td>0.999904</td>\n",
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              "      <td>1.819286</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5609b4a3-f808-4f9b-8531-2025eaace744 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5609b4a3-f808-4f9b-8531-2025eaace744');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          Unnamed: 0   Test_Time(s)  ...      TimeDelta            SOC\n",
              "count  157570.000000  157570.000000  ...  157570.000000  157570.000000\n",
              "mean    78784.500000   86648.264899  ...       1.055854       0.736358\n",
              "std     45486.685295   46052.526517  ...       0.628872       0.121619\n",
              "min         0.000000      10.000019  ...       0.000000       0.045320\n",
              "25%     39392.250000   46807.431234  ...       1.015400       0.646755\n",
              "50%     78784.500000   86664.620077  ...       1.015584       0.701856\n",
              "75%    118176.750000  126522.511848  ...       1.015615       0.851428\n",
              "max    157569.000000  166379.962373  ...      10.017561       1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nMfX0Z056Fe"
      },
      "source": [
        "###\n",
        "#combined_data=combined_data.drop_duplicates()\n",
        "#combined_data.set_index('Data_Point',inplace=True)\n",
        "#cd2=combined_data.drop(63990)\n",
        "#cdcombined_data.drop(127979)\n",
        "\n",
        "#cd2=combined_data;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "6NHZKiuogMXL",
        "outputId": "0c5b859a-948c-49c0-fa6a-80e48fea310e"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Test_Time(s)</th>\n",
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              "      <th>Current(A)</th>\n",
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              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>TimeDelta</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20.015468</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015450</td>\n",
              "      <td>0.978693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>30.030954</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015485</td>\n",
              "      <td>0.978693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>40.046428</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015474</td>\n",
              "      <td>0.978693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>50.061910</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163940</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015482</td>\n",
              "      <td>0.978788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157565</th>\n",
              "      <td>157565</td>\n",
              "      <td>166376.509256</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163454</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015608</td>\n",
              "      <td>0.978502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157566</th>\n",
              "      <td>157566</td>\n",
              "      <td>166377.524866</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015610</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157567</th>\n",
              "      <td>157567</td>\n",
              "      <td>166378.540471</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015605</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157568</th>\n",
              "      <td>157568</td>\n",
              "      <td>166379.556097</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015626</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157569</th>\n",
              "      <td>157569</td>\n",
              "      <td>166379.962373</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>0.406276</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>157570 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f201c49-f396-48e7-a83c-6a6c9501c820')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f201c49-f396-48e7-a83c-6a6c9501c820 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f201c49-f396-48e7-a83c-6a6c9501c820');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Unnamed: 0   Test_Time(s)  ...  TimeDelta       SOC\n",
              "0                0      10.000019  ...   1.000000  0.970000\n",
              "1                1      20.015468  ...  10.015450  0.978693\n",
              "2                2      30.030954  ...  10.015485  0.978693\n",
              "3                3      40.046428  ...  10.015474  0.978693\n",
              "4                4      50.061910  ...  10.015482  0.978788\n",
              "...            ...            ...  ...        ...       ...\n",
              "157565      157565  166376.509256  ...   1.015608  0.978502\n",
              "157566      157566  166377.524866  ...   1.015610  0.978598\n",
              "157567      157567  166378.540471  ...   1.015605  0.978598\n",
              "157568      157568  166379.556097  ...   1.015626  0.978598\n",
              "157569      157569  166379.962373  ...   0.406276  0.978598\n",
              "\n",
              "[157570 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lYfOvKWBml25",
        "outputId": "1b69aaff-9836-4a72-8f4d-b12c81e12a68"
      },
      "source": [
        "combined_data=combined_data.iloc[:,1:] #drop first column\n",
        "combined_data.shape\n",
        "combined_data"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-738e4145-d856-4d5c-ad29-240d0ea3d17a\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>TimeDelta</th>\n",
              "      <th>SOC</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.015468</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30.030954</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40.046428</td>\n",
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              "      <th>4</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157565</th>\n",
              "      <td>166376.509256</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163454</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015608</td>\n",
              "      <td>0.978502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157566</th>\n",
              "      <td>166377.524866</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015610</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157567</th>\n",
              "      <td>166378.540471</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015605</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157568</th>\n",
              "      <td>166379.556097</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>1.015626</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157569</th>\n",
              "      <td>166379.962373</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>1.819286</td>\n",
              "      <td>1.805571</td>\n",
              "      <td>0.406276</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>157570 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-738e4145-d856-4d5c-ad29-240d0ea3d17a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-738e4145-d856-4d5c-ad29-240d0ea3d17a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-738e4145-d856-4d5c-ad29-240d0ea3d17a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Test_Time(s)  Cycle_Index  ...  TimeDelta       SOC\n",
              "0           10.000019          1.0  ...   1.000000  0.970000\n",
              "1           20.015468          1.0  ...  10.015450  0.978693\n",
              "2           30.030954          1.0  ...  10.015485  0.978693\n",
              "3           40.046428          1.0  ...  10.015474  0.978693\n",
              "4           50.061910          1.0  ...  10.015482  0.978788\n",
              "...               ...          ...  ...        ...       ...\n",
              "157565  166376.509256         19.0  ...   1.015608  0.978502\n",
              "157566  166377.524866         19.0  ...   1.015610  0.978598\n",
              "157567  166378.540471         19.0  ...   1.015605  0.978598\n",
              "157568  166379.556097         19.0  ...   1.015626  0.978598\n",
              "157569  166379.962373         19.0  ...   0.406276  0.978598\n",
              "\n",
              "[157570 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "65WCm6OYoFsX",
        "outputId": "62ec2cc8-7b84-47a0-d723-8a22989705bf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_67561a17-8732-4ee9-8805-347b2745beb9\", \"cd.csv\", 27699379)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data=combined_data.drop(columns=['Charge_Capacity(Ah)','Discharge_Capacity(Ah)','TimeDelta'])"
      ],
      "metadata": {
        "id": "AaFig_2fj2kU"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1gdmPPRs-kJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "50c8c37d-6de0-4430-e259-d3a55aae7730"
      },
      "source": [
        "combined_data.hist()\n",
        "plt.figure(figsize=(20,20))\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data=combined_data.drop(columns=['Test_Time(s)'])"
      ],
      "metadata": {
        "id": "V47_5QPvkYg1"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0OIEihXABYQ",
        "outputId": "7cad89b8-8ec8-4415-c785-71cdc51918f7"
      },
      "source": [
        "  fig = plt.figure(figsize = (15,15))\n",
        "  ax = fig.gca()\n",
        "  combined_data.hist(ax = ax)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcf283ecad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf2b577390>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcf2b562510>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcf2a5c7b50>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "AG_E_pvAvEvy",
        "outputId": "ac5f60d2-106f-4f9e-e3cb-6d0f24112a6a"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "IbtPgY8nDvbp",
        "outputId": "429156f6-987b-4210-ee1f-b524dda0b6eb"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-81f4901d-6eba-4e20-b9ac-17922e91672a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.490157</td>\n",
              "      <td>-0.000120</td>\n",
              "      <td>3.756622</td>\n",
              "      <td>0.736358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.748668</td>\n",
              "      <td>0.280592</td>\n",
              "      <td>0.209131</td>\n",
              "      <td>0.121619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000412</td>\n",
              "      <td>2.577044</td>\n",
              "      <td>0.045320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.600780</td>\n",
              "      <td>0.646755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.694451</td>\n",
              "      <td>0.701856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.950466</td>\n",
              "      <td>0.851428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.262149</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81f4901d-6eba-4e20-b9ac-17922e91672a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81f4901d-6eba-4e20-b9ac-17922e91672a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81f4901d-6eba-4e20-b9ac-17922e91672a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)            SOC\n",
              "count  157570.000000  157570.000000  157570.000000  157570.000000\n",
              "mean       10.490157      -0.000120       3.756622       0.736358\n",
              "std         5.748668       0.280592       0.209131       0.121619\n",
              "min         1.000000      -1.000412       2.577044       0.045320\n",
              "25%         5.000000       0.000000       3.600780       0.646755\n",
              "50%        11.000000       0.000000       3.694451       0.701856\n",
              "75%        16.000000       0.000000       3.950466       0.851428\n",
              "max        19.000000       0.999904       4.262149       1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jej3N8-4JDj4"
      },
      "source": [
        "Deleting 0 valued columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lA5X5VM0CvOI",
        "outputId": "dd752b54-9887-4915-b249-106d63d495be"
      },
      "source": [
        "#cd2=pd.read_csv('/content/drive/MyDrive/LSTM_SOC_Implementation/r1.csv')\n",
        "cd2=combined_data\n",
        "cd2.describe()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9cb164a2-eb23-4800-b7ba-4eb056798d68\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.490157</td>\n",
              "      <td>-0.000120</td>\n",
              "      <td>3.756622</td>\n",
              "      <td>0.736358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.748668</td>\n",
              "      <td>0.280592</td>\n",
              "      <td>0.209131</td>\n",
              "      <td>0.121619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000412</td>\n",
              "      <td>2.577044</td>\n",
              "      <td>0.045320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.600780</td>\n",
              "      <td>0.646755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.694451</td>\n",
              "      <td>0.701856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.950466</td>\n",
              "      <td>0.851428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.262149</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9cb164a2-eb23-4800-b7ba-4eb056798d68')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9cb164a2-eb23-4800-b7ba-4eb056798d68');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)            SOC\n",
              "count  157570.000000  157570.000000  157570.000000  157570.000000\n",
              "mean       10.490157      -0.000120       3.756622       0.736358\n",
              "std         5.748668       0.280592       0.209131       0.121619\n",
              "min         1.000000      -1.000412       2.577044       0.045320\n",
              "25%         5.000000       0.000000       3.600780       0.646755\n",
              "50%        11.000000       0.000000       3.694451       0.701856\n",
              "75%        16.000000       0.000000       3.950466       0.851428\n",
              "max        19.000000       0.999904       4.262149       1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "FSzDs6JUPobf",
        "outputId": "3885e472-85fd-4546-8263-41262ad576ff"
      },
      "source": [
        "cd2"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-14a87e33-e049-4d2c-bcf3-f48bb7480d13\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>4.163778</td>\n",
              "      <td>0.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163778</td>\n",
              "      <td>0.978693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163778</td>\n",
              "      <td>0.978693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163778</td>\n",
              "      <td>0.978693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163940</td>\n",
              "      <td>0.978788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>157565</th>\n",
              "      <td>19.0</td>\n",
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              "      <td>0.978502</td>\n",
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              "    <tr>\n",
              "      <th>157566</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.978598</td>\n",
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              "    <tr>\n",
              "      <th>157567</th>\n",
              "      <td>19.0</td>\n",
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              "      <td>4.163616</td>\n",
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              "    <tr>\n",
              "      <th>157568</th>\n",
              "      <td>19.0</td>\n",
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              "      <td>0.978598</td>\n",
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              "    <tr>\n",
              "      <th>157569</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.163616</td>\n",
              "      <td>0.978598</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>157570 rows × 4 columns</p>\n",
              "</div>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-14a87e33-e049-4d2c-bcf3-f48bb7480d13 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14a87e33-e049-4d2c-bcf3-f48bb7480d13');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Cycle_Index  Current(A)  Voltage(V)       SOC\n",
              "0               1.0         0.0    4.163778  0.970000\n",
              "1               1.0         0.0    4.163778  0.978693\n",
              "2               1.0         0.0    4.163778  0.978693\n",
              "3               1.0         0.0    4.163778  0.978693\n",
              "4               1.0         0.0    4.163940  0.978788\n",
              "...             ...         ...         ...       ...\n",
              "157565         19.0         0.0    4.163454  0.978502\n",
              "157566         19.0         0.0    4.163616  0.978598\n",
              "157567         19.0         0.0    4.163616  0.978598\n",
              "157568         19.0         0.0    4.163616  0.978598\n",
              "157569         19.0         0.0    4.163616  0.978598\n",
              "\n",
              "[157570 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVAxdxD_FSA2"
      },
      "source": [
        "y = cd2.SOC\n",
        "X = cd2.drop(['SOC'], axis=1)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lWEv98nHWPCz",
        "outputId": "caab6e4f-dab2-48a9-d898-9369e53f2516"
      },
      "source": [
        "X.describe()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-52f09656-76b1-4220-9f1d-b555cbd2a341\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "      <td>157570.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.490157</td>\n",
              "      <td>-0.000120</td>\n",
              "      <td>3.756622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.748668</td>\n",
              "      <td>0.280592</td>\n",
              "      <td>0.209131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000412</td>\n",
              "      <td>2.577044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.600780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.694451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.950466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.262149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-52f09656-76b1-4220-9f1d-b555cbd2a341 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-52f09656-76b1-4220-9f1d-b555cbd2a341');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)\n",
              "count  157570.000000  157570.000000  157570.000000\n",
              "mean       10.490157      -0.000120       3.756622\n",
              "std         5.748668       0.280592       0.209131\n",
              "min         1.000000      -1.000412       2.577044\n",
              "25%         5.000000       0.000000       3.600780\n",
              "50%        11.000000       0.000000       3.694451\n",
              "75%        16.000000       0.000000       3.950466\n",
              "max        19.000000       0.999904       4.262149"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7or5lAg9zv05"
      },
      "source": [
        "split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3KejRNIz8L6",
        "outputId": "37942519-5b65-47c0-e788-e3b804fe252e"
      },
      "source": [
        "cd2.values"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.        ,  0.        ,  4.16377831,  0.97      ],\n",
              "       [ 1.        ,  0.        ,  4.16377831,  0.97869312],\n",
              "       [ 1.        ,  0.        ,  4.16377831,  0.97869312],\n",
              "       ...,\n",
              "       [19.        ,  0.        ,  4.16361618,  0.97859775],\n",
              "       [19.        ,  0.        ,  4.16361618,  0.97859775],\n",
              "       [19.        ,  0.        ,  4.16361618,  0.97859775]])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZ7aC4j11Rpw"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihBUmg5bzu8E"
      },
      "source": [
        "dataset = cd2.SOC.values #numpy.ndarray\n",
        "dataset = dataset.astype('float64')\n",
        "dataset = np.reshape(dataset, (-1, 1))\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "dataset = scaler.fit_transform(dataset)\n",
        "train_size = int(len(dataset) * 0.80)\n",
        "test_size = len(dataset) - train_size\n",
        "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYjXEEeV1Xmy"
      },
      "source": [
        "# convert an array of values into a dataset matrix\n",
        "def create_dataset(dataset, look_back=1):\n",
        "    X, Y = [], []\n",
        "    for i in range(len(dataset)-look_back-1):\n",
        "        a = dataset[i:(i+look_back), 0]\n",
        "        X.append(a)\n",
        "        Y.append(dataset[i + look_back, 0])\n",
        "    return np.array(X), np.array(Y)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dw523Skl1al8"
      },
      "source": [
        "# reshape into X=t and Y=t+1\n",
        "look_back = 3\n",
        "\n",
        "X_train, Y_train = create_dataset(train, look_back)\n",
        "X_test, Y_test = create_dataset(test, look_back)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uk0IHjKKlUnK",
        "outputId": "58b2f40c-16a0-429e-97eb-f9c61072610a"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.96857586, 0.97768166, 0.97768166],\n",
              "       [0.97768166, 0.97768166, 0.97768166],\n",
              "       [0.97768166, 0.97768166, 0.97778155],\n",
              "       ...,\n",
              "       [0.8012376 , 0.8012376 , 0.8012376 ],\n",
              "       [0.8012376 , 0.8012376 , 0.8012376 ],\n",
              "       [0.8012376 , 0.8012376 , 0.80113771]])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu8EmIUW13Ts",
        "outputId": "bd460c50-52e6-4da5-f8fb-c15084fa5c28"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(126052, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaHqs7FL2FKn",
        "outputId": "c5b1a7d8-1212-4168-e19a-4a7d05884e01"
      },
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(126052,)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntkwGpFK2Kvv"
      },
      "source": [
        "# reshape input to be [samples, time steps, features]\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06NjbtWi2Z6m"
      },
      "source": [
        "import math\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import *\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.models import load_model\n"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFJgXQVc2QAj",
        "outputId": "726c361a-f945-4956-b8eb-517e3b9c560d"
      },
      "source": [
        "model = Sequential()\n",
        "#model.add(LSTM(,return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(LSTM(50, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(2))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "history = model.fit(X_train, Y_train, epochs=100, batch_size=64, validation_data=(X_test, Y_test), \n",
        "                    callbacks=[EarlyStopping(monitor='val_loss', patience=50), ModelCheckpoint(filepath='best_model0.h5', monitor='val_loss', save_best_only=True)], verbose=1, shuffle=False)\n",
        "#scores = model.evaluate(X_test, Y_test, verbose=1)\n",
        "#print(\"Accuracy: %.2f%%\" % (scores[1]*100))\n",
        "# Training Phase\n",
        "model.summary()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1970/1970 [==============================] - 9s 3ms/step - loss: 0.0177 - val_loss: 4.5434e-04\n",
            "Epoch 2/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 0.0012 - val_loss: 7.5565e-04\n",
            "Epoch 3/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 8.9336e-04 - val_loss: 0.0015\n",
            "Epoch 4/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 6.9093e-04 - val_loss: 0.0016\n",
            "Epoch 5/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 5.8306e-04 - val_loss: 0.0017\n",
            "Epoch 6/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 5.0820e-04 - val_loss: 0.0017\n",
            "Epoch 7/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 4.2524e-04 - val_loss: 0.0014\n",
            "Epoch 8/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 3.3395e-04 - val_loss: 0.0015\n",
            "Epoch 9/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 2.4233e-04 - val_loss: 0.0012\n",
            "Epoch 10/100\n",
            "1970/1970 [==============================] - 7s 3ms/step - loss: 1.7499e-04 - val_loss: 0.0013\n",
            "Epoch 11/100\n",
            "1970/1970 [==============================] - 7s 3ms/step - loss: 1.5190e-04 - val_loss: 0.0019\n",
            "Epoch 12/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.5655e-04 - val_loss: 0.0019\n",
            "Epoch 13/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.6523e-04 - val_loss: 0.0021\n",
            "Epoch 14/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.7080e-04 - val_loss: 0.0021\n",
            "Epoch 15/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.8661e-04 - val_loss: 0.0021\n",
            "Epoch 16/100\n",
            "1970/1970 [==============================] - 7s 3ms/step - loss: 1.9255e-04 - val_loss: 0.0021\n",
            "Epoch 17/100\n",
            "1970/1970 [==============================] - 7s 3ms/step - loss: 1.8729e-04 - val_loss: 0.0022\n",
            "Epoch 18/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 2.0225e-04 - val_loss: 0.0021\n",
            "Epoch 19/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.9848e-04 - val_loss: 0.0021\n",
            "Epoch 20/100\n",
            "1970/1970 [==============================] - 7s 3ms/step - loss: 1.9664e-04 - val_loss: 0.0021\n",
            "Epoch 21/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.9418e-04 - val_loss: 0.0021\n",
            "Epoch 22/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.9472e-04 - val_loss: 0.0020\n",
            "Epoch 23/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.8765e-04 - val_loss: 0.0020\n",
            "Epoch 24/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.8638e-04 - val_loss: 0.0019\n",
            "Epoch 25/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 1.7790e-04 - val_loss: 0.0019\n",
            "Epoch 26/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 1.7193e-04 - val_loss: 0.0018\n",
            "Epoch 27/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 1.6234e-04 - val_loss: 0.0017\n",
            "Epoch 28/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 1.5330e-04 - val_loss: 0.0017\n",
            "Epoch 29/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.4343e-04 - val_loss: 0.0017\n",
            "Epoch 30/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 1.4915e-04 - val_loss: 0.0016\n",
            "Epoch 31/100\n",
            "1970/1970 [==============================] - 7s 3ms/step - loss: 1.3324e-04 - val_loss: 0.0016\n",
            "Epoch 32/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 1.3499e-04 - val_loss: 0.0015\n",
            "Epoch 33/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.2741e-04 - val_loss: 0.0015\n",
            "Epoch 34/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.1713e-04 - val_loss: 0.0015\n",
            "Epoch 35/100\n",
            "1970/1970 [==============================] - 7s 4ms/step - loss: 1.1814e-04 - val_loss: 0.0015\n",
            "Epoch 36/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.1384e-04 - val_loss: 0.0014\n",
            "Epoch 37/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.0844e-04 - val_loss: 0.0014\n",
            "Epoch 38/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.1154e-04 - val_loss: 0.0014\n",
            "Epoch 39/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.0369e-04 - val_loss: 0.0015\n",
            "Epoch 40/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 1.0252e-04 - val_loss: 0.0013\n",
            "Epoch 41/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 9.7859e-05 - val_loss: 0.0014\n",
            "Epoch 42/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 9.6855e-05 - val_loss: 0.0014\n",
            "Epoch 43/100\n",
            "1970/1970 [==============================] - 7s 3ms/step - loss: 1.0089e-04 - val_loss: 0.0014\n",
            "Epoch 44/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 9.6716e-05 - val_loss: 0.0013\n",
            "Epoch 45/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 9.0776e-05 - val_loss: 0.0014\n",
            "Epoch 46/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 9.1521e-05 - val_loss: 0.0014\n",
            "Epoch 47/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 8.7204e-05 - val_loss: 0.0014\n",
            "Epoch 48/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 8.7274e-05 - val_loss: 0.0014\n",
            "Epoch 49/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 8.5885e-05 - val_loss: 0.0014\n",
            "Epoch 50/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 8.4013e-05 - val_loss: 0.0014\n",
            "Epoch 51/100\n",
            "1970/1970 [==============================] - 6s 3ms/step - loss: 8.6610e-05 - val_loss: 0.0014\n",
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_6 (LSTM)               (None, 50)                10800     \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 2)                 102       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,902\n",
            "Trainable params: 10,902\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''from keras.models import load_model\n",
        "saved_model = load_model('best_model45.h5')\n",
        "train_acc = saved_model.evaluate(X_train, Y_train, verbose=0)\n",
        "test_acc = saved_model.evaluate(X_test, Y_test, verbose=0)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))'''"
      ],
      "metadata": {
        "id": "Kv2fBEf4LBCk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xToSSRZGDqhL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9igKq7u2jvl",
        "outputId": "42514255-1b9d-4f47-b45b-e00057479c20"
      },
      "source": [
        "# make predictions\n",
        "from keras.models import load_model\n",
        "saved_model = load_model('best_model45.h5')\n",
        "train_predict = saved_model.predict(X_train)\n",
        "test_predict = saved_model.predict(X_test)\n",
        "# invert predictions\n",
        "train_predict = scaler.inverse_transform(train_predict)\n",
        "Y_train = scaler.inverse_transform([Y_train])\n",
        "test_predict = scaler.inverse_transform(test_predict)\n",
        "Y_test = scaler.inverse_transform([Y_test])\n",
        "\n",
        "print('Train Mean Absolute Error:', mean_absolute_error(Y_train[0], train_predict[:,0]))\n",
        "print('Train Root Mean Squared Error:',np.sqrt(mean_squared_error(Y_train[0], train_predict[:,0])))\n",
        "print('Test Mean Absolute Error:', mean_absolute_error(Y_test[0], test_predict[:,0]))\n",
        "print('Test Root Mean Squared Error:',np.sqrt(mean_squared_error(Y_test[0], test_predict[:,0])))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Mean Absolute Error: 0.009052819953582075\n",
            "Train Root Mean Squared Error: 0.012541559527130512\n",
            "Test Mean Absolute Error: 0.026210086671261786\n",
            "Test Root Mean Squared Error: 0.027320845484824937\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hADXDvCd2qzF",
        "outputId": "941d766f-ced1-4ff9-83fb-3312016fcbb0"
      },
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Test Loss')\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show();"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "stz_dngk2wpa",
        "outputId": "ac22ff50-a113-4c68-b673-2b546908bc69"
      },
      "source": [
        "aa=[x for x in range(20000)]\n",
        "plt.figure(figsize=(20,4))\n",
        "plt.plot(aa, Y_test[0][:20000], marker='.', label=\"actual\")\n",
        "plt.plot(aa, test_predict[:,0][:20000], 'r', label=\"prediction\")\n",
        "plt.tick_params(left=False, labelleft=True) #remove ticks\n",
        "#plt.tight_layout()\n",
        "#sns.despine(top=True)\n",
        "plt.subplots_adjust(left=0.07)\n",
        "plt.ylabel('SOC', size=15)\n",
        "plt.ylim((0,1))\n",
        "plt.xlabel('Time step', size=15)\n",
        "plt.legend(fontsize=15)\n",
        "plt.show();"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ukYrqC/vU7efbFW/n40lNXKNwL8i1yb0Wt2HtsQbqiq9Z3E6SGpQSH3/pGU5+7CZqFs6GoUPhv/4LvvY16Nmz3NFJkiRJasfa4wIORwGTU0qXR8RewG8jYueUUm25A8vSod2XwfS7YM2aXMn42X4qUkjKRdQvLakrvkbx8cZe2+LYhpzf2NchJaiszL0Wzivtm6Xvu5W3319Vy3sfrKayInjngzUsX7UGAiqigkSiR7dKKiqCt5evojaRTwTXXaheqKloP0Vj9bFe7anXvqjN+l4HqEgpl9yu9w7W/SyI0m9Bg21SSZsGrNOmZL+Bj6F12zRw7w24DhvwPhu+d+k1WhJf85+3vVetYMRbr/Je9RC44go47jjo0aPZ8yRJkiQp62TcQmB40f6wfF2x44BxACmlxyOiJzAIWJxJhO3FEUfkSkFKUFu7NjlXKE3VAaxeXT+hV1u79rV0uyXH1qxZm4ApLoV7wNo2hbizKoX7NXTfctQVf+8aiq+heFv72IaeX5qUK7SLqN8HoH7b0vfdBtu9gd757QEbep2MYt3o7Yjcv738qNM6DSVNS+vK2aYrxBcBY8fS59hjTcJJkiRJWi9ZJ+OeAraLiK3JJeGOBL5Y0mY+cCAwOSJ2BHoCr2caZXsUkRuRVFlZ7kgkSZIkSZK0gSqab9J6UkqrgQnAfcBscqumPhcRF0bE+Hyz04GvR8T/ATcBx6TUgjlDkiRJkiRJUjuX+TPjUkr3APeU1J1XtD0L2DvruCRJkiRJkqS2lunIOEmSJEmSJKkrMxknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGXEZJwkSZIkSZKUEZNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGXEZJwkSZIkSZKUEZNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGUk82RcRIyLiOcjYk5ETGykzRciYlZEPBcRN2YdoyRJkiRJktQWumV5s4ioBK4AxgILgKciYkpKaVZRm+2As4C9U0pLI6I6yxglSZIkSZKktpL1yLg9gTkppbkppZXAzcBhJW2+DlyRUloKkFJanHGMkiRJkiRJUpvIOhk3FHi5aH9Bvq7Y9sD2EfFYRDwREeMyi06SJEmSJElqQ5lOU22hbsB2wP7AMODRiNglpfRWWaOSJEmSJEmSNlLWI+MWAsOL9ofl64otAKaklFallP4FvEAuOSdJkiRJkiR1aFkn454CtouIrSOiO3AkMKWkzR3kRsUREYPITVudm2WQkiRJkiRJUlvINBmXUloNTADuA2YDt6SUnouICyNifL7ZfcCSiJgFPAx8O6W0JMs4JUmSJEmSpLYQKaVyx7DRampq0tSpU8sdhiRJkiRJkkRETEsp1TR0LOtpqpIkSZIkSVKXZTJOkiRJkiRJyojJOEmSJEmSJCkjzSbjImLTiOjZxPGeEbFp64YlSZIkSZIkdT5NJuMi4kDgTeCjTTT7KLnVT/dtzcAkSZIkSZKkzqa5kXETgN+llP7SWIP8sZuAk1szMEmSJEmSJKmzaS4ZtzfwhxZc53Zgn40PR5IkSZIkSeq8mkvG9QNeb8F13gD6b3w4kiRJkiRJUufVXDLuNWDbFlxnm3xbSZIkSZIkSY1oLhn3Z+DEiOjWWIP8sROB+1szMEmSJEmSJKmzaS4Z9z1ge+DuiBhVejAidgTuAj4MfL/1w5MkSZIkSZI6j0ZHvAGklOZGxKeAm4EZEfEKMB9IwFbAUOAV4NCU0ty2DlaSJEmSJEnqyJpMxgGklB6LiO2ALwD7kkvAQW5a6iPA71NKK9osQkmSJEmSJKmTaDYZB5BPtl2fL5IkSZIkSZI2QIuScQARsRWwD2tHxi0A/ppSerktApMkSZIkSZI6m2aTcRGxBfBLYDzrLvhQGxF3ABNSSq+2QXySJEmSJElSp9HkaqoRMRD4K7lnxV0IjAY2y5fRwCRgP+DRiBjQtqFKkiRJkiRJHVtzI+POBaqAj6SUXik59izwbET8CngcOAc4rfVDlCRJkiRJkjqHJkfGAYcBFzeQiKuTUloEfB/499YMTJIkSZIkSepsmkvGDQH+0YLrzM63lSRJkiRJktSI5pJxbwAjW3Cdkfm2kiRJkiRJkhrRXDLuPuDMiOjTWIP8sTOBe1szMEmSJEmSJKmzaS4ZdwGwOTAtIr4UEZsUDkTEJhHxRWAqUE1uZVVJkiRJkiRJjWhyNdWU0ssRcQDwP8BvgRQRb+UP9wcCmA58IqW0oE0jlSRJkiRJkjq4JpNxACml54BdI2I/4OPA0PyhhcCjKaVH2zA+SZIkSZIkqdNoNhlXkFL6C/CXNoxFkiRJkiRJ6tRanIwrFhG9geOAHYDXgOtSSvNaMzBJkiRJkiSps2kyGRcRlwOfSSltX1S3KfAUsB2wFOgHnBYRe6aUXmjLYCVJkiRJkqSOrLnVVAuLNxQ7A9ge+HpKaRCwJfAScG6rRydJkiRJkiR1Is0l40YC00rqPg/MSin9GiCl9DpwObB3q0cnSZIkSZIkdSLNJeO6ASsKOxExANgReKik3UvAFq0amSRJkiRJktTJNJeMewHYv2j/0/nX+0raVQNvtlJMkiRJkiRJUqfU3GqqvwCuiYh+5FZNPQn4F3B/SbuDgJmtH54kSZIkSZLUeTSZjEspTY6IIcCJQH/gaeDElNKqQpuIGAwcBkxqy0AlSZIkSZKkjq65kXGklC4BLmni+Ov4vDhJkiRJkiSpWc09M67VRcS4iHg+IuZExMQm2n0+IlJE1GQZnyRJkiRJktRWMk3GRUQlcAVwCDAKOCoiRjXQblPgZODJLOOTJEmSJEmS2lLWI+P2BOaklOamlFYCN5N73lypi4AfACuyDE6SJEmSJElqS1kn44YCLxftL8jX1YmI3YDhKaW7swxMkiRJkiRJamvNLuCQpYioAH4MHFPmUCRJkiRJkqRWl/XIuIXA8KL9Yfm6gk2BnYFHIuIlYAwwxUUcJEmSJEmS1BlknYx7CtguIraOiO7AkcCUwsGU0tsppUEppZEppZHAE8D4lNLUjOOUJEmSJEmSWl2mybiU0mpgAnAfMBu4JaX0XERcGBHjs4xFkiRJkiRJylrmz4xLKd0D3FNSd14jbffPIiZJkiRJkiQpC1lPU5UkSZIkSZK6LJNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGXEZJwkSZIkSZKUEZNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGWkW7kDyMqyZctYvHgxq1atKncoylBVVRXV1dX07du33KFIkiRJkiR1jWTcsmXLeO211xg6dCi9evUiIsodkjKQUmL58uUsXLgQwIScJEmSJEkquy4xTXXx4sUMHTqU3r17m4jrQiKC3r17M3ToUBYvXlzucCRJkiRJkrpGMm7VqlX06tWr3GGoTHr16uX0ZEmSJEmS1C50iWQc4Ii4LszvvSRJkiRJai+6TDJOkiRJkiRJKjeTcZIkSZIkSVJGTMZ1EYsXL+aCCy7gpZdearN7XHDBBQwaNKjNri9JkiRJktTRmYzrIhYvXsykSZPaNBknSZIkSZKkppmMkyRJkiRJkjKSeTIuIsZFxPMRMSciJjZw/LSImBURz0bEgxExIusY26PHH3+c8ePHM2TIEPr06cPo0aO54YYb6rWZN28eRx11FIMGDaJ379585CMf4cYbb+Sll15il112AeCAAw4gIupWGJ08eTIRwbvvvlvvWiNHjuSMM86o27/77rsZO3Ys1dXV9O3blzFjxnD//fe38buWJEmSJEnqXLplebOIqASuAMYCC4CnImJKSmlWUbNngJqU0vsR8U3gMuCILONszrR5S3li7hLGbDOQ3Udslsk9582bx957780JJ5xAz549eeyxxzj22GOpqKjgqKOOYvHixey111707t2bH/3oRwwfPpyZM2fy8ssvM2TIEG644Qa+9KUvccUVV7Dbbrut9/3/9a9/8ZnPfIYzzjiDiooK7r33Xg455BAeffRR9t577zZ4x5IkSZIkSZ1Ppsk4YE9gTkppLkBE3AwcBtQl41JKDxe1fwL4clsEMumPzzHrlWXrfd47K1bxj1ffoTZBRcAOW2zKpj2r1usao7bsy/mf2Wm9zjnyyCPrtlNK7LvvvixYsIBrrrmGo446ip/85Ce8/fbbTJs2jSFDhgBw4IEH1p3zkY98JHfvUaMYM2bMet0bYMKECXXbtbW1HHDAATz33HNce+21JuMkSZIkSZJaKOtpqkOBl4v2F+TrGnMccG+bRrSelq1YTW3Kbdem3H4Wli5dykknncSIESOoqqqiqqqKq6++mhdeeAGAhx56iHHjxtUl4lrbggUL+MpXvsLQoUPp1q0bVVVV3H///XX3lyRJkiRJUvOyHhnXYhHxZaAG2K8trr++I9MKps1bypd+9QSrVtdS1a2Cnx25ayZTVY855hieeOIJzj33XEaNGkXfvn258sorufPOOwFYsmQJe+yxR5vcu7a2lvHjx/POO+9w4YUX8qEPfYg+ffpw3nnnsXjx4ja5pyRJkiRJUmeUdTJuITC8aH9Yvq6eiPgkcDawX0rpg4xia5HdR2zGDV8bk+kz41asWMFdd93FFVdcwQknnFBXX1tbW7c9cOBAFi1atN7X7tmzJwArV66sV7906dK67Tlz5vDMM89w7733Mm7cuLr65cuXr/f9JEmSJEmSurKsp6k+BWwXEVtHRHfgSGBKcYOI2BX4b2B8SqldDrvafcRmnHjAhzJbvOGDDz6gtraWHj161NW98847TJmy9kt34IEHct999/Haa681eI3u3bsDucResWHDhgEwe/bsuronn3ySZcvWPk+vkHQrvv+8efN47LHHNvQtSZIkSZIkdUmZjoxLKa2OiAnAfUAl8OuU0nMRcSEwNaU0BfghsAlwa0QAzE8pjc8yzvamX79+7LHHHlx44YX07duXiooKLr30Uvr161eXNDv11FO5/vrr+fjHP87ZZ5/N8OHDmT17Nu+99x5nnnkmW221Fb169eK6666jX79+VFVVUVNTw5577snQoUM56aSTuOiii3jzzTe57LLL6Nu3b939d9hhB4YNG8bpp5/ORRddxDvvvMP555/P0KFNPe5PkiRJkiRJpbIeGUdK6Z6U0vYppW1TSt/L152XT8SRUvpkSmnzlNLofOnSibiCG2+8kW222Yajjz6ak08+mc9//vMcffTRdccHDx7MY489xq677sopp5zCpz/9aa6++mq22morIDcd9ZprrmHatGnst99+dc+X6969O7fffjsVFRUcfvjhXH755Vx55ZVsttnaUX89evTgtttuo1u3bhx++OGce+65nHXWWey3X5s8zk+SJEmSJKnTipRSuWPYaDU1NWnq1KmNHp89ezY77rhjhhGpvbEPSJIkSZKkrETEtJRSTUPHMh8ZJ0mSJEmSJHVVJuMkSZIkSZKkjJiMkyRJkiRJkjJiMk6SJEmSJEnKiMk4SZIkSZIkKSMm4yRJkiRJkqSMmIyTJEmSJEmSMmIyTpIkSZIkScqIyThJkiRJkiQpIybj1Khf/OIXRETd/iOPPEJEMHPmzBZf4+qrr+aOO15/OE8AABMNSURBVO5Yp37kyJGcccYZrRKnJEmSJElSR9Gt3AGo49htt914/PHH2XbbbVt8ztVXX83OO+/MZz/72Xr1t99+OwMHDmztECVJkiRJkto1k3Gd2PLly+nVq1erXa9v376MGTOmVa616667tsp1JEmSJEmSOhKnqXYQxxxzDDU1Ndxxxx3ssMMO9OzZk3322YdZs2bVtYkIfvzjH3PKKacwePBgdtllFwBWrFjBmWeeyfDhw+nRowf/9m//xj333FPv+h988AETJkygf//+DBgwgFNPPZVVq1bVa9PQNNU1a9ZwySWXsP3229OjRw+GDRvGMcccA8D+++/PtGnTuO6664gIIoLJkycDDU9TveWWW9hll13o0aMHw4cP5+yzz2b16tV1xydPnkxEMGPGDMaOHUufPn3YYYcduO222zb66ytJkiRJkpQFk3EdyLx58zjttNM499xzufHGG3n77bc5+OCDWbFiRV2bH/7whyxatIjf/va3/PznPwfg8MMPZ/LkyXz3u9/lj3/8I3vssQfjx49n+vTpdedNnDiRX/3qV5x77rnccMMNzJs3j8svv7zZmL7xjW9w/vnn84UvfIG77rqLyy+/nPfffx+AX/7yl+ywww4ceuihPP744zz++ON86lOfavA6999/P0cccQS77bYbd955J9/61rf40Y9+xIQJE9Zp+8UvfpHx48dz++23s91223HkkUeyYMGC9fpaSpIkSZIklUPXnaZ6yilQlIzK1OjR8NOfrvdpb7zxBnfeeScf+9jHANh9993ZdtttmTx5MieccAIAQ4YM4Xe/+13dOQ8++CB33303jzzyCPvttx8ABx10EC+88ALf+973uPXWW1myZAlXXXUVkyZN4vTTTwfg4IMPZtSoUU3G849//INrr72Wn/3sZ5x00kl19UcccQQAo0aNok+fPgwePLjZ6a3nnXce+++/P9dddx0A48aNA+Css87inHPOYdiwYXVtTz31VL761a/WfQ0233xz7rrrrrqvgSRJkiRJUnvlyLgOpLq6ui4RBzBixAh23313/v73v9fVHXroofXOeeCBB9hiiy3Ye++9Wb16dV058MADmTp1KgAzZsxgxYoVHHbYYXXnVVRU1NtvyMMPPwxQNy11Q61Zs4ann36a//iP/6hXf8QRR1BbW8vjjz9er/6ggw6q2x44cCDV1dWOjJMkSZIkSR1C1x0ZtwEj08qturq6wbpFixbV7W+++eb1jr/xxhu8+uqrVFVVrXNuZWUlAK+++mqD12/ofsWWLFlCnz596Nu3b8veQCPeeOMNVq1atU7shf0333yzXn3//v3r7Xfv3r3eVF1JkiRJkqT2qusm4zqgxYsXN1i300471e1HRL3jAwYMYOjQodxxxx2NXneLLbaou9aAAQOavF+xgQMH8t5777Fs2bKNSsgNGjSIqqqqde732muvAdSLSZIkSZIkqSNzmmoHsnjxYv72t7/V7c+fP5+nn36aPffcs9FzDjzwQF599VU22WQTampq1ikAu+yyCz179uTOO++sO6+2trbefkM+8YlPAHD99dc32qYlo9YqKyvZfffdufXWW+vV33LLLVRUVLDXXns1eb4kSZIkSVJH4ci4DmTQoEF8+ctf5uKLL6ZXr16cf/75VFdXN/nMtrFjx3LwwQczduxYvvOd77DTTjuxbNkypk+fzooVK7jkkksYOHAgxx9/POeffz7dunVjp5124pprruHdd99tMp4Pf/jDHH/88Zx++uksXryYfffdl7feeovf//733HzzzQDssMMO3Hfffdx3330MHDiQrbfemoEDB65zrUmTJnHwwQdz7LHHcuSRRzJjxgzOPfdcvv71r9dbvEGSJEmSJKkjMxnXgYwYMYLvfve7TJw4kXnz5lFTU8ONN95Iz549Gz0nIrjtttv4/ve/z09/+lPmz5/PgAEDGD16NN/61rfq2l122WWsWrWKCy+8kIqKCr785S9z2mmn1a2u2phf/vKXjBgxgl/96ldceumlVFdX11tg4ZxzzmH+/Pl84QtfYNmyZfzmN79pMHl40EEHcfPNN3PxxRdzww03UF1dzemnn86kSZPW/wslSZIkSZLUTkVKqdwxbLSamppUWBm0IbNnz2bHHXfMMKLWd8wxxzBz5kyaep9qXGfoA5IkSZIkqWOIiGkppZqGjvnMOEmSJEmSJCkjJuMkSZIkSZKkjPjMuA5i8uTJ5Q5BkiRJkiRJG8mRcZIkSZIkSVJGukwyrjMsVKEN4/dekiRJkiS1F10iGVdVVcXy5cvLHYbKZPny5VRVVZU7DEmSJEmSpK6RjKuurmbhwoW8//77jpLqQlJKvP/++yxcuJDq6upyhyNJkiRJktQ1FnDo27cvAK+88gqrVq0qczTKUlVVFZtvvnldH5AkSZIkSSqnLpGMg1xCzoSMJEmSJEmSyqlLTFOVJEmSJEmS2gOTcZIkSZIkSVJGTMZJkiRJkiRJGck8GRcR4yLi+YiYExETGzjeIyJ+lz/+ZESMzDpGSZIkSZIkqS1kmoyLiErgCuAQYBRwVESMKml2HLA0pfQh4CfAD7KMUZIkSZIkSWorWY+M2xOYk1Kam1JaCdwMHFbS5jDguvz274EDIyIyjFGSJEmSJElqE90yvt9Q4OWi/QXARxtrk1JaHRFvAwOBNxq76LRp096IiHmtHGt7MIgm3re6PPuHmmL/UFPsH2qK/UNNsX+oMfYNNcX+oaZ01v4xorEDWSfj2kRKaXC5Y2gLETE1pVRT7jjUPtk/1BT7h5pi/1BT7B9qiv1DjbFvqCn2DzWlK/aPrKepLgSGF+0Py9c12CYiugH9gCWZRCdJkiRJkiS1oayTcU8B20XE1hHRHTgSmFLSZgrwlfz24cBDKaWUYYySJEmSJElSm8h0mmr+GXATgPuASuDXKaXnIuJCYGpKaQpwLfDbiJgDvEkuYddVXV3uANSu2T/UFPuHmmL/UFPsH2qK/UONsW+oKfYPNaXL9Y9w0JkkSZIkSZKUjaynqUqSJEmSJEldlsk4SZIkSZIkKSMm49qpiBgXEc9HxJyImFjueNT2ImJ4RDwcEbMi4rmIODlff0FELIyI6flyaNE5Z+X7yPMRcXBRvf2nE4qIlyJiRr4fTM3XDYiIP0fEi/nXzfL1ERE/z/eBZyNit6LrfCXf/sWI+Epj91PHEREfLvqMmB4RyyLiFD8/uq6I+HVELI6ImUV1rfZ5ERG75z+P5uTPjWzfoTZGI/3jhxHxj3wfuD0i+ufrR0bE8qLPkauKzmmwHzTW19QxNNI/Wu3nSeQW83syX/+7yC3spw6gkb7xu6J+8VJETM/X+9nRxUTjf8/6+0dDUkqWdlbILW7xT2AboDvwf8CocsdlafPv+xBgt/z2psALwCjgAuCMBtqPyveNHsDW+T5Taf/pvAV4CRhUUncZMDG/PRH4QX77UOBeIIAxwJP5+gHA3PzrZvntzcr93iyt2k8qgVeBEX5+dN0C7AvsBswsqmu1zwvg7/m2kT/3kHK/Z8tG94+DgG757R8U9Y+Rxe1KrtNgP2isr1k6Rmmkf7TazxPgFuDI/PZVwDfL/Z4tG943So5fDpyX3/azo4sVGv971t8/GiiOjGuf9gTmpJTmppRWAjcDh5U5JrWxlNKilNLT+e13gNnA0CZOOQy4OaX0QUrpX8Accn3H/tO1HAZcl9++DvhsUf31KecJoH9EDAEOBv6cUnozpbQU+DMwLuug1aYOBP6ZUprXRBs/Pzq5lNKj5FalL9Yqnxf5Y31TSk+k3G/G1xddSx1AQ/0jpXR/Sml1fvcJYFhT12imHzTW19QBNPL50Zj1+nmSH8XyCeD3+fPtHx1IU30j/739AnBTU9fws6PzauLvWX//aIDJuPZpKPBy0f4Cmk7KqJOJiJHArsCT+aoJ+aG7vy4art1YP7H/dF4JuD8ipkXE8fm6zVNKi/LbrwKb57ftH13XkdT/RdjPDxW01ufF0Px2ab06j6+SG3FQsHVEPBMRf4mIj+frmuoHjfU1dWyt8fNkIPBWUeLXz4/O4+PAaymlF4vq/Ozookr+nvX3jwaYjJPamYjYBPgDcEpKaRlwJbAtMBpYRG74t7qmfVJKuwGHACdGxL7FB/P/Q5TKEpnahfxzd8YDt+ar/PxQg/y8UGMi4mxgNXBDvmoRsFVKaVfgNODGiOjb0uvZ1zoNf56oOUdR/z8D/ezoohr4e7aO39e1TMa1TwuB4UX7w/J16uQioorcB9cNKaXbAFJKr6WU1qSUaoFryA37h8b7if2nk0opLcy/LgZuJ9cXXssP2S4M+1+cb27/6JoOAZ5OKb0Gfn5oHa31ebGQ+lMY7SedREQcA3wa+FL+Dyby0w+X5LenkXsO2PY03Q8a62vqoFrx58kSclPRupXUqwPLfz8/B/yuUOdnR9fU0N+z+PtHg0zGtU9PAdvlVxrqTm7K0ZQyx6Q2ln/OwrXA7JTSj4vqhxQ1+3egsHrRFODIiOgREVsD25F7oKX9pxOKiD4RsWlhm9yDtmeS+94WVhj6CnBnfnsKcHR+laIxwNv54eH3AQdFxGb5KSYH5evUOdT7X2k/P1SiVT4v8seWRcSY/M+uo4uupQ4qIsYBZwLjU0rvF9UPjojK/PY25D4v5jbTDxrra+qgWuvnST7J+zBweP58+0fn8EngHymluimEfnZ0PY39PYu/fzRsfVZ7sGRXyK0s8gK5/0E4u9zxWDL5nu9Dbsjus8D0fDkU+C0wI18/BRhSdM7Z+T7yPEUrydh/Ol8htxrZ/+XLc4XvK7lnrzwIvAg8AAzI1wdwRb4PzABqiq71VXIPWJ4DHFvu92ZptT7Sh9yIg35FdX5+dNFCLim7CFhF7pkqx7Xm5wVQQ+6P8X8CvwCi3O/ZstH9Yw65Z/QUfge5Kt/28/mfO9OBp4HPNNcPGutrlo5RGukfrfbzJP87zd/zfe5WoEe537Nlw/tGvn4ycEJJWz87ulih8b9n/f2jgVLo9JIkSZIkSZLamNNUJUmSJEmSpIyYjJMkSZIkSZIyYjJOkiRJkiRJyojJOEmSJEmSJCkjJuMkSZIkSZKkjJiMkyRJKpOISC0o+0fESxHxo3LH25iIqI6ICyJiZLljkSRJau8ipVTuGCRJkrqkiBhTtNsLeAi4GLi7qH4WsC2wJKU0P8PwWiwidgZmAAeklB4pcziSJEntWrdyByBJktRVpZSeKGxHxCb5zX8W1+c9k11UkiRJaktOU5UkSWrnSqepRsTkiJgaEZ+KiFkR8X5E3B0RAyLiQxHxcES8l2/zkZJrVUTExIiYExEfRMQLEfGVFsRwXP5eyyPijYj4S0TslJ+aOiPf7OHC9Nqi8wZExNUR8VpErIiIv0XER0uunSLitIj4WUS8GRFvRcR/RUT3jfm6SZIktUcm4yRJkjqmrYALgXOA44GPAVcDN+fL4eRmQdwcEVF03n/lz7ka+BRwO/DriPh0YzeKiH2Bq4DfAocAXwX+BvQDFgFfyjc9EdgrX4iIHsADwCeBbwOfBV4HHoiILUpuczowLH+ti/Pv6Xvr8fWQJEnqEJymKkmS1DENAPZKKf0TID8C7tvAV1JK1+frgtzz53YAZkfEh4BvAsemlK7LX+eBiBgCnA/c1ci99gSeTSldUlQ3pbAREc/mN2eVTLH9MrAzsFNK6cV82weA58kl375d1PYd4D9SSrXAvflE3tkRcUlK6c0Wf1UkSZLaOUfGSZIkdUwvFRJxeXPyrw81UDc0/3ogUAvcHhHdCgV4EBgdEZWN3Gs6sGtE/CQi9l2P6aOfBKYB/yq6F8BfgJqStnfmE3EFt5Fb1GLnFt5LkiSpQzAZJ0mS1DG9VbK/soH6Ql3P/OsgoBJ4G1hVVCaTmzExpKEbpZQeAI4F9gUeAd6IiCsiok8zMQ4CxpTca1X+WsNL2i5uZL/BmCRJkjoqp6lKkiR1HW8Cq4G9yY2QK1WaEKuTn9Z6XUQMBj4H/ITc1NKJzdxvKrmpsaU+KNmvbmR/URPXlyRJ6nBMxkmSJHUdD5EbGdcvpfTnDblASul14L8j4nPAqHx16Qi8ggeBg4D5KaVGE315h0XEWUVTVT8HLAdmbkickiRJ7ZXJOEmSpC4ipfR8RFxFboXVy8iNWusJ7ARsn1L6WkPnRcQkcgtGPAK8AewK7MfaUXHzySXOvhIRbwOrUkpTgeuBE4BHIuJHwFxgILkFIV5NKf2k6DabArdGxDX5eM4FrnDxBkmS1NmYjJMkSepaTgReAL4OXAgsA2YB1zZxzlPAqcCR5JJm84ALgJ8BpJRWRMTXya3I+hegCoh8/QH5+0wCNic3FfbvFK3Gmnc5sA1wE7nnGl8LfHfj3qokSVL7EymlcscgSZKkLiwiEvCtlNIvyh2LJElSW3M1VUmSJEmSJCkjJuMkSZIkSZKkjDhNVZIkSZIkScqII+MkSZIkSZKkjJiMkyRJkiRJkjJiMk6SJEmSJEnKiMk4SZIkSZIkKSMm4yRJkiRJkqSM/D/nsXJWzgW8igAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aa=[x for x in range(110000)]\n",
        "plt.figure(figsize=(20,4))\n",
        "plt.plot(aa, Y_train[0][:110000], marker='.', label=\"actual\")\n",
        "plt.plot(aa, train_predict[:,0][:110000], 'r', label=\"prediction\")\n",
        "plt.tick_params(left=False, labelleft=True) #remove ticks\n",
        "#plt.tight_layout()\n",
        "#sns.despine(top=True)\n",
        "plt.subplots_adjust(left=0.07)\n",
        "plt.ylabel('SOC', size=15)\n",
        "plt.ylim((0,1))\n",
        "plt.xlabel('Time step', size=15)\n",
        "plt.legend(fontsize=15)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "NQzm3UICyFYt",
        "outputId": "c371edc0-95a9-44c7-868b-221ec2bdcdf5"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}