Dec18_att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_45deg.ipynb

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      "name": "att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_45deg.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_45deg.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_45.xlsx')"
      ],
      "execution_count": 4,
      "outputs": []
    },
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        "outputId": "9a41331d-bfad-4123-8389-64c974706dbc"
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      "source": [
        "df"
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      "execution_count": 5,
      "outputs": [
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              "         Test_Time(s)  Cycle_Index  ...  TimeDelta       SOC\n",
              "0           10.000502          1.0  ...          1      0.97\n",
              "1           20.016109          1.0  ...    10.0156  0.984508\n",
              "2           30.031595          1.0  ...    10.0155  0.984604\n",
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              "...               ...          ...  ...        ...       ...\n",
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      "outputs": []
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      "outputs": []
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      "cell_type": "code",
      "metadata": {
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      "source": [
        "combined_data.to_csv('Incremental_Init_45.csv')\n",
        "combined_data=pd.read_csv('Incremental_Init_45.csv')"
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      "execution_count": 8,
      "outputs": []
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              "      <td>0.568921</td>\n",
              "      <td>1.542466</td>\n",
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              "      <td>0.231053</td>\n",
              "      <td>0.687331</td>\n",
              "      <td>0.616400</td>\n",
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              "      <td>0.000435</td>\n",
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              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.578578</td>\n",
              "      <td>0.014028</td>\n",
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              "      <td>1.000212</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>87297.368555</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>3.678893</td>\n",
              "      <td>0.138467</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.015569</td>\n",
              "      <td>0.693371</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>3.946941</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-f5f340ab-2539-428f-bc41-c7a7234bd56c 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-f5f340ab-2539-428f-bc41-c7a7234bd56c');\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  159107.000000  159107.000000  ...  159107.000000  159107.000000\n",
              "mean    79553.000000   87279.243341  ...       1.054046       0.722902\n",
              "std     45930.378977   46456.798733  ...       0.621824       0.134872\n",
              "min         0.000000      10.000502  ...       0.000435       0.314054\n",
              "25%     39776.500000   47094.959959  ...       1.000212       0.633599\n",
              "50%     79553.000000   87297.368555  ...       1.015569       0.693371\n",
              "75%    119329.500000  127494.672648  ...       1.015621       0.850189\n",
              "max    159106.000000  167715.033539  ...      10.017457       1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "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": "497b8e56-07b5-4c3e-d961-2768517665cf"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>Discharge_Capacity(Ah)</th>\n",
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              "      <th>SOC</th>\n",
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              "      <td>10.015606</td>\n",
              "      <td>0.984508</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>10.015487</td>\n",
              "      <td>0.984604</td>\n",
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              "    <tr>\n",
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              "      <td>40.047191</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015596</td>\n",
              "      <td>0.984604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>50.062737</td>\n",
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              "      <td>4.173826</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015546</td>\n",
              "      <td>0.984604</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159102</th>\n",
              "      <td>159102</td>\n",
              "      <td>167711.283616</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176743</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.015685</td>\n",
              "      <td>0.986319</td>\n",
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              "    <tr>\n",
              "      <th>159103</th>\n",
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              "      <td>2.028843</td>\n",
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              "      <td>1.000003</td>\n",
              "      <td>0.986224</td>\n",
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              "    <tr>\n",
              "      <th>159104</th>\n",
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              "      <td>19.0</td>\n",
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              "      <td>1.015567</td>\n",
              "      <td>0.986224</td>\n",
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              "    <tr>\n",
              "      <th>159105</th>\n",
              "      <td>159105</td>\n",
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              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.015600</td>\n",
              "      <td>0.986224</td>\n",
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              "    <tr>\n",
              "      <th>159106</th>\n",
              "      <td>159106</td>\n",
              "      <td>167715.033539</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>0.718753</td>\n",
              "      <td>0.986224</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>159107 rows × 9 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1caa71b1-2098-4b6e-8fa8-e190a45f7247')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1caa71b1-2098-4b6e-8fa8-e190a45f7247 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-1caa71b1-2098-4b6e-8fa8-e190a45f7247');\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      10.000502  ...   1.000000  0.970000\n",
              "1                1      20.016109  ...  10.015606  0.984508\n",
              "2                2      30.031595  ...  10.015487  0.984604\n",
              "3                3      40.047191  ...  10.015596  0.984604\n",
              "4                4      50.062737  ...  10.015546  0.984604\n",
              "...            ...            ...  ...        ...       ...\n",
              "159102      159102  167711.283616  ...   1.015685  0.986319\n",
              "159103      159103  167712.283619  ...   1.000003  0.986224\n",
              "159104      159104  167713.299187  ...   1.015567  0.986224\n",
              "159105      159105  167714.314787  ...   1.015600  0.986224\n",
              "159106      159106  167715.033539  ...   0.718753  0.986224\n",
              "\n",
              "[159107 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lYfOvKWBml25",
        "outputId": "e90af729-d8e5-4c9b-9f7c-38cb8b555030"
      },
      "source": [
        "combined_data=combined_data.iloc[:,1:] #drop first column\n",
        "combined_data.shape\n",
        "combined_data"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>Test_Time(s)</th>\n",
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              "    <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>159102</th>\n",
              "      <td>167711.283616</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176743</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.015685</td>\n",
              "      <td>0.986319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159103</th>\n",
              "      <td>167712.283619</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.000003</td>\n",
              "      <td>0.986224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159104</th>\n",
              "      <td>167713.299187</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.015567</td>\n",
              "      <td>0.986224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159105</th>\n",
              "      <td>167714.314787</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.015600</td>\n",
              "      <td>0.986224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159106</th>\n",
              "      <td>167715.033539</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>0.718753</td>\n",
              "      <td>0.986224</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>159107 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5137b70d-49fd-45c9-bb87-3c3915c5a411')\"\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-5137b70d-49fd-45c9-bb87-3c3915c5a411 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-5137b70d-49fd-45c9-bb87-3c3915c5a411');\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           10.000502          1.0  ...   1.000000  0.970000\n",
              "1           20.016109          1.0  ...  10.015606  0.984508\n",
              "2           30.031595          1.0  ...  10.015487  0.984604\n",
              "3           40.047191          1.0  ...  10.015596  0.984604\n",
              "4           50.062737          1.0  ...  10.015546  0.984604\n",
              "...               ...          ...  ...        ...       ...\n",
              "159102  167711.283616         19.0  ...   1.015685  0.986319\n",
              "159103  167712.283619         19.0  ...   1.000003  0.986224\n",
              "159104  167713.299187         19.0  ...   1.015567  0.986224\n",
              "159105  167714.314787         19.0  ...   1.015600  0.986224\n",
              "159106  167715.033539         19.0  ...   0.718753  0.986224\n",
              "\n",
              "[159107 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "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": 12,
      "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": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0OIEihXABYQ",
        "outputId": "db8b8e2f-5f1e-4e05-8bbe-6a6d6e8ab8c6"
      },
      "source": [
        "  fig = plt.figure(figsize = (15,15))\n",
        "  ax = fig.gca()\n",
        "  combined_data.hist(ax = ax)"
      ],
      "execution_count": 15,
      "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 0x7fcfbc00dd50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcfbca4a750>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcfbcf83d50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcfbd0be390>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": "d17992a1-1a78-4b8c-d396-3abedb08e097"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "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": "2362142a-93c6-4227-91c5-503670d71b26"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-06774486-14bf-4c42-ae64-2ac545ea8790\">\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>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.493090</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>3.735026</td>\n",
              "      <td>0.722902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.747789</td>\n",
              "      <td>0.295084</td>\n",
              "      <td>0.231053</td>\n",
              "      <td>0.134872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>3.033892</td>\n",
              "      <td>0.314054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.578578</td>\n",
              "      <td>0.633599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.678893</td>\n",
              "      <td>0.693371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.946941</td>\n",
              "      <td>0.850189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.001522</td>\n",
              "      <td>4.328594</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06774486-14bf-4c42-ae64-2ac545ea8790')\"\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",
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              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-06774486-14bf-4c42-ae64-2ac545ea8790 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-06774486-14bf-4c42-ae64-2ac545ea8790');\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  159107.000000  159107.000000  159107.000000  159107.000000\n",
              "mean       10.493090       0.000390       3.735026       0.722902\n",
              "std         5.747789       0.295084       0.231053       0.134872\n",
              "min         1.000000      -1.000233       3.033892       0.314054\n",
              "25%         5.000000       0.000000       3.578578       0.633599\n",
              "50%        11.000000       0.000000       3.678893       0.693371\n",
              "75%        16.000000       0.000000       3.946941       0.850189\n",
              "max        19.000000       1.001522       4.328594       1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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": "d61602d2-c27b-43e1-8414-a61068ee1f80"
      },
      "source": [
        "#cd2=pd.read_csv('/content/drive/MyDrive/LSTM_SOC_Implementation/r1.csv')\n",
        "cd2=combined_data\n",
        "cd2.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1966313e-f57c-4c8f-97bb-cfaae4737191\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.493090</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>3.735026</td>\n",
              "      <td>0.722902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.747789</td>\n",
              "      <td>0.295084</td>\n",
              "      <td>0.231053</td>\n",
              "      <td>0.134872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>3.033892</td>\n",
              "      <td>0.314054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.578578</td>\n",
              "      <td>0.633599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.678893</td>\n",
              "      <td>0.693371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.946941</td>\n",
              "      <td>0.850189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.001522</td>\n",
              "      <td>4.328594</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1966313e-f57c-4c8f-97bb-cfaae4737191')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1966313e-f57c-4c8f-97bb-cfaae4737191');\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  159107.000000  159107.000000  159107.000000  159107.000000\n",
              "mean       10.493090       0.000390       3.735026       0.722902\n",
              "std         5.747789       0.295084       0.231053       0.134872\n",
              "min         1.000000      -1.000233       3.033892       0.314054\n",
              "25%         5.000000       0.000000       3.578578       0.633599\n",
              "50%        11.000000       0.000000       3.678893       0.693371\n",
              "75%        16.000000       0.000000       3.946941       0.850189\n",
              "max        19.000000       1.001522       4.328594       1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "FSzDs6JUPobf",
        "outputId": "bff38299-5206-463c-dbfa-32714a3b33a2"
      },
      "source": [
        "cd2"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3453a91d-b638-4c5a-9965-ffaf8883b543\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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",
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              "      <th>SOC</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>4.173664</td>\n",
              "      <td>0.970000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173664</td>\n",
              "      <td>0.984508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173826</td>\n",
              "      <td>0.984604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173826</td>\n",
              "      <td>0.984604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173826</td>\n",
              "      <td>0.984604</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>159102</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176743</td>\n",
              "      <td>0.986319</td>\n",
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              "    <tr>\n",
              "      <th>159103</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>0.986224</td>\n",
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              "    <tr>\n",
              "      <th>159104</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>0.986224</td>\n",
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              "    <tr>\n",
              "      <th>159105</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>0.986224</td>\n",
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              "    <tr>\n",
              "      <th>159106</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.176581</td>\n",
              "      <td>0.986224</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>159107 rows × 4 columns</p>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3453a91d-b638-4c5a-9965-ffaf8883b543 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-3453a91d-b638-4c5a-9965-ffaf8883b543');\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    4.173664  0.970000\n",
              "1               1.0         0.0    4.173664  0.984508\n",
              "2               1.0         0.0    4.173826  0.984604\n",
              "3               1.0         0.0    4.173826  0.984604\n",
              "4               1.0         0.0    4.173826  0.984604\n",
              "...             ...         ...         ...       ...\n",
              "159102         19.0         0.0    4.176743  0.986319\n",
              "159103         19.0         0.0    4.176581  0.986224\n",
              "159104         19.0         0.0    4.176581  0.986224\n",
              "159105         19.0         0.0    4.176581  0.986224\n",
              "159106         19.0         0.0    4.176581  0.986224\n",
              "\n",
              "[159107 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVAxdxD_FSA2"
      },
      "source": [
        "y = cd2.SOC\n",
        "X = cd2.drop(['SOC'], axis=1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lWEv98nHWPCz",
        "outputId": "f9e427b0-25b5-4b59-e80a-0209451c9965"
      },
      "source": [
        "X.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1d9135e0-4c2e-4a09-b91c-234d00b9c4ca\">\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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              "<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>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.493090</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>3.735026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.747789</td>\n",
              "      <td>0.295084</td>\n",
              "      <td>0.231053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>3.033892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.578578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.678893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.946941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.001522</td>\n",
              "      <td>4.328594</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d9135e0-4c2e-4a09-b91c-234d00b9c4ca')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\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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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-1d9135e0-4c2e-4a09-b91c-234d00b9c4ca 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-1d9135e0-4c2e-4a09-b91c-234d00b9c4ca');\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  159107.000000  159107.000000  159107.000000\n",
              "mean       10.493090       0.000390       3.735026\n",
              "std         5.747789       0.295084       0.231053\n",
              "min         1.000000      -1.000233       3.033892\n",
              "25%         5.000000       0.000000       3.578578\n",
              "50%        11.000000       0.000000       3.678893\n",
              "75%        16.000000       0.000000       3.946941\n",
              "max        19.000000       1.001522       4.328594"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7or5lAg9zv05"
      },
      "source": [
        "split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3KejRNIz8L6",
        "outputId": "32bd634d-ff61-4444-e105-19240624080e"
      },
      "source": [
        "cd2.values"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.        ,  0.        ,  4.17366409,  0.97      ],\n",
              "       [ 1.        ,  0.        ,  4.17366409,  0.98450829],\n",
              "       [ 1.        ,  0.        ,  4.17382622,  0.98460366],\n",
              "       ...,\n",
              "       [19.        ,  0.        ,  4.17658091,  0.98622406],\n",
              "       [19.        ,  0.        ,  4.17658091,  0.98622406],\n",
              "       [19.        ,  0.        ,  4.17658091,  0.98622406]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZ7aC4j11Rpw"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n"
      ],
      "execution_count": 23,
      "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": 48,
      "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": 49,
      "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": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uk0IHjKKlUnK",
        "outputId": "a974d6d7-ef8c-49d8-9a2f-597469b8638f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.95626476, 0.97741554, 0.97755457],\n",
              "       [0.97741554, 0.97755457, 0.97755457],\n",
              "       [0.97755457, 0.97755457, 0.97755457],\n",
              "       ...,\n",
              "       [0.70224374, 0.70210471, 0.70224374],\n",
              "       [0.70210471, 0.70224374, 0.70210471],\n",
              "       [0.70224374, 0.70210471, 0.70210471]])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu8EmIUW13Ts",
        "outputId": "a19175e4-fa24-4565-a7ec-171dfa9d9bc6"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127281, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaHqs7FL2FKn",
        "outputId": "94dab6fa-74b2-49ac-8e50-f5a4575f4185"
      },
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127281,)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "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": 54,
      "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"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFJgXQVc2QAj",
        "outputId": "ed45efd6-5e9a-4ee0-e617-e7acfe68166a"
      },
      "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_model45.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": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1989/1989 [==============================] - 9s 4ms/step - loss: 0.0169 - val_loss: 9.1553e-04\n",
            "Epoch 2/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 0.0010 - val_loss: 7.2694e-04\n",
            "Epoch 3/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 6.8490e-04 - val_loss: 0.0012\n",
            "Epoch 4/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 5.4454e-04 - val_loss: 0.0017\n",
            "Epoch 5/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 4.4742e-04 - val_loss: 0.0015\n",
            "Epoch 6/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 3.5081e-04 - val_loss: 0.0013\n",
            "Epoch 7/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 2.6450e-04 - val_loss: 8.2222e-04\n",
            "Epoch 8/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 2.0885e-04 - val_loss: 6.3312e-04\n",
            "Epoch 9/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.7619e-04 - val_loss: 6.8278e-04\n",
            "Epoch 10/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.6367e-04 - val_loss: 9.5613e-04\n",
            "Epoch 11/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.4775e-04 - val_loss: 0.0011\n",
            "Epoch 12/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.3388e-04 - val_loss: 0.0012\n",
            "Epoch 13/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.3295e-04 - val_loss: 0.0013\n",
            "Epoch 14/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.2435e-04 - val_loss: 0.0013\n",
            "Epoch 15/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.2177e-04 - val_loss: 0.0016\n",
            "Epoch 16/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.2134e-04 - val_loss: 0.0014\n",
            "Epoch 17/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.1074e-04 - val_loss: 0.0018\n",
            "Epoch 18/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.1544e-04 - val_loss: 0.0015\n",
            "Epoch 19/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.0874e-04 - val_loss: 0.0019\n",
            "Epoch 20/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.1430e-04 - val_loss: 0.0015\n",
            "Epoch 21/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.0804e-04 - val_loss: 0.0020\n",
            "Epoch 22/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.1267e-04 - val_loss: 0.0018\n",
            "Epoch 23/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.0639e-04 - val_loss: 0.0019\n",
            "Epoch 24/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.0935e-04 - val_loss: 0.0020\n",
            "Epoch 25/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.0764e-04 - val_loss: 0.0019\n",
            "Epoch 26/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.0535e-04 - val_loss: 0.0023\n",
            "Epoch 27/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.1441e-04 - val_loss: 0.0020\n",
            "Epoch 28/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.0394e-04 - val_loss: 0.0020\n",
            "Epoch 29/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.0594e-04 - val_loss: 0.0021\n",
            "Epoch 30/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.0783e-04 - val_loss: 0.0020\n",
            "Epoch 31/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.0186e-04 - val_loss: 0.0025\n",
            "Epoch 32/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.1146e-04 - val_loss: 0.0022\n",
            "Epoch 33/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.0944e-04 - val_loss: 0.0024\n",
            "Epoch 34/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.1119e-04 - val_loss: 0.0024\n",
            "Epoch 35/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.0951e-04 - val_loss: 0.0026\n",
            "Epoch 36/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.1415e-04 - val_loss: 0.0024\n",
            "Epoch 37/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.1172e-04 - val_loss: 0.0025\n",
            "Epoch 38/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.1360e-04 - val_loss: 0.0027\n",
            "Epoch 39/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.1742e-04 - val_loss: 0.0026\n",
            "Epoch 40/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.1217e-04 - val_loss: 0.0028\n",
            "Epoch 41/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.1942e-04 - val_loss: 0.0026\n",
            "Epoch 42/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.1538e-04 - val_loss: 0.0029\n",
            "Epoch 43/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.2172e-04 - val_loss: 0.0027\n",
            "Epoch 44/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.2408e-04 - val_loss: 0.0029\n",
            "Epoch 45/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.2484e-04 - val_loss: 0.0029\n",
            "Epoch 46/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.2467e-04 - val_loss: 0.0029\n",
            "Epoch 47/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.2708e-04 - val_loss: 0.0029\n",
            "Epoch 48/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.3136e-04 - val_loss: 0.0029\n",
            "Epoch 49/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.3023e-04 - val_loss: 0.0031\n",
            "Epoch 50/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.3450e-04 - val_loss: 0.0030\n",
            "Epoch 51/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.2923e-04 - val_loss: 0.0031\n",
            "Epoch 52/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.3181e-04 - val_loss: 0.0032\n",
            "Epoch 53/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.3798e-04 - val_loss: 0.0031\n",
            "Epoch 54/100\n",
            "1989/1989 [==============================] - 7s 4ms/step - loss: 1.3535e-04 - val_loss: 0.0031\n",
            "Epoch 55/100\n",
            "1989/1989 [==============================] - 6s 3ms/step - loss: 1.3607e-04 - val_loss: 0.0031\n",
            "Epoch 56/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.3572e-04 - val_loss: 0.0031\n",
            "Epoch 57/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.3695e-04 - val_loss: 0.0032\n",
            "Epoch 58/100\n",
            "1989/1989 [==============================] - 7s 3ms/step - loss: 1.3536e-04 - val_loss: 0.0033\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 50)                10800     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_2 (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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kv2fBEf4LBCk",
        "outputId": "698ec11a-d257-485a-e33e-ede3ff85b3b0"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 0.001, Test: 0.001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xToSSRZGDqhL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9igKq7u2jvl",
        "outputId": "f09646c8-82e7-4077-9cd3-a755939e2f16"
      },
      "source": [
        "# make predictions\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": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Mean Absolute Error: 0.015778747107616366\n",
            "Train Root Mean Squared Error: 0.019512803265306404\n",
            "Test Mean Absolute Error: 0.014952282050284692\n",
            "Test Root Mean Squared Error: 0.017199858247106726\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hADXDvCd2qzF",
        "outputId": "acce1ef3-0c64-4596-8443-9f7328d7abff"
      },
      "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": 59,
      "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": "7d054e45-6df4-47d8-ff69-c820dd2c5aa4"
      },
      "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": 60,
      "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"
          }
        }
      ]
    },
    {
      "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": "81ff5469-1f67-4362-a1db-dd8e9aaa3d47"
      },
      "execution_count": 61,
      "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"
          }
        }
      ]
    }
  ]
}