may5run_att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_25deg.ipynb

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        "<a href=\"https://colab.research.google.com/github/bharat-mullapudi/LSTM_SOC_RUL/blob/main/may5run_att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_25deg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "outputId": "5b3dfb64-151d-4ecb-e865-ecb18823b84a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 1,
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ju4NGqAquQk9"
      },
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/calce_incremental_INR_18650-20R/Incremental_temp_25.xlsx')"
      ],
      "execution_count": 3,
      "outputs": []
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        "outputId": "4b52a9ba-067a-4db4-b189-555b3453d101"
      },
      "source": [
        "df"
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      "execution_count": 4,
      "outputs": [
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              "         Test_Time(s)  Cycle_Index  Current(A)  Voltage(V)  \\\n",
              "0           10.000167          1.0         0.0    4.166209   \n",
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              "         Charge_Capacity(Ah)  Discharge_Capacity(Ah)  TimeDelta       SOC  \n",
              "0                        0.0                     0.0          1      0.97  \n",
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    {
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      "outputs": []
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        "combined_data=pd.read_csv('Incremental_Init_0.csv')"
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      "outputs": []
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              "        Test_Time(s)    Cycle_Index     Current(A)     Voltage(V)  \\\n",
              "count  142119.000000  142119.000000  142119.000000  142119.000000   \n",
              "mean    78705.582922       9.029032      -0.007765       3.684208   \n",
              "std     41479.490967       4.802561       0.303031       0.207378   \n",
              "min        10.000167       1.000000      -1.000233       2.499903   \n",
              "25%     42831.169001       5.000000       0.000000       3.577119   \n",
              "50%     78728.254274       9.000000       0.000000       3.661715   \n",
              "75%    114616.556029      13.000000       0.000000       3.839495   \n",
              "max    150507.800260      18.000000       0.999904       4.375753   \n",
              "\n",
              "       Charge_Capacity(Ah)  Discharge_Capacity(Ah)  \n",
              "count        142119.000000           142119.000000  \n",
              "mean              0.412327                1.481353  \n",
              "std               0.539736                0.625663  \n",
              "min               0.000000                0.000000  \n",
              "25%               0.016146                0.999809  \n",
              "50%               0.016146                1.799699  \n",
              "75%               0.815788                1.987336  \n",
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              "      <td>1.692307</td>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-07afa284-de67-43d9-8bdf-d3c028d1295e 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-07afa284-de67-43d9-8bdf-d3c028d1295e');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "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": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "6NHZKiuogMXL",
        "outputId": "aafe4775-b6ff-42bf-c954-7213f59c0cde"
      },
      "execution_count": 9,
      "outputs": [
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              "          const element = document.querySelector('#df-46782f5f-4695-421a-b495-b5129accc98b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "lYfOvKWBml25",
        "outputId": "35eb55e7-d504-4b86-ef6b-adabb57ff811"
      },
      "source": [
        "combined_data=combined_data.iloc[:,1:] #drop first column\n",
        "combined_data.shape\n",
        "combined_data"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>10.015526</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015553</td>\n",
              "      <td>0.980123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015548</td>\n",
              "      <td>0.980028</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142114</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249022</td>\n",
              "      <td>1.691192</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015622</td>\n",
              "      <td>0.88911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142115</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999364</td>\n",
              "      <td>4.249184</td>\n",
              "      <td>1.691470</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.000084</td>\n",
              "      <td>0.889249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142116</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249508</td>\n",
              "      <td>1.691752</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015528</td>\n",
              "      <td>0.88939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142117</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249671</td>\n",
              "      <td>1.692029</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.000153</td>\n",
              "      <td>0.889528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142118</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015849</td>\n",
              "      <td>0.889669</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142119 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f278687-d835-4c8e-b294-a13a52346682')\"\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-8f278687-d835-4c8e-b294-a13a52346682 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-8f278687-d835-4c8e-b294-a13a52346682');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65WCm6OYoFsX"
      },
      "source": [
        "#combined_data.to_csv('cd.csv')\n",
        "#files.download('cd.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data=combined_data.drop(columns=['Charge_Capacity(Ah)','Discharge_Capacity(Ah)','TimeDelta'])"
      ],
      "metadata": {
        "id": "AaFig_2fj2kU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1gdmPPRs-kJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "50e42179-8950-492c-8a0e-45ee0f0492d2"
      },
      "source": [
        "combined_data.hist()\n",
        "plt.figure(figsize=(20,20))\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "76b51b81-041e-4a8b-ac10-3c20a6cbb783"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-56c7de8f355b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcombined_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcombined_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Test_Time(s)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4911\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4912\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4913\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4914\u001b[0m         )\n\u001b[1;32m   4915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4150\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   4183\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4184\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4185\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4186\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6015\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6017\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6018\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6019\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Test_Time(s)'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0OIEihXABYQ",
        "outputId": "8e10cf9c-f8fd-4696-d73c-019ed98ea679"
      },
      "source": [
        "  fig = plt.figure(figsize = (15,15))\n",
        "  ax = fig.gca()\n",
        "  combined_data.hist(ax = ax)"
      ],
      "execution_count": null,
      "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 0x7ff4ce1f1310>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff4411265d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ff43f7ce990>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff4d1454690>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "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": "48d0a495-9cac-4f92-e274-5affe23a77a6"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "IbtPgY8nDvbp",
        "outputId": "f9ac87ff-0770-4ef1-88d6-3ced8d677824"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)\n",
              "count  142119.000000  142119.000000  142119.000000\n",
              "mean        9.029032      -0.007765       3.684208\n",
              "std         4.802561       0.303031       0.207378\n",
              "min         1.000000      -1.000233       2.499903\n",
              "25%         5.000000       0.000000       3.577119\n",
              "50%         9.000000       0.000000       3.661715\n",
              "75%        13.000000       0.000000       3.839495\n",
              "max        18.000000       0.999904       4.375753"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "  <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>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.029032</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.802561</td>\n",
              "      <td>0.303031</td>\n",
              "      <td>0.207378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>2.499903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.577119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.661715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.839495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.375753</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          document.querySelector('#df-9f2c9fb7-5c39-43df-8ca1-c29f7533c168 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f2c9fb7-5c39-43df-8ca1-c29f7533c168');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "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": "54211b60-89bf-46bc-9840-0df204ca9a9c"
      },
      "source": [
        "#cd2=pd.read_csv('/content/drive/MyDrive/LSTM_SOC_Implementation/r1.csv')\n",
        "cd2=combined_data\n",
        "cd2.describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)\n",
              "count  142119.000000  142119.000000  142119.000000\n",
              "mean        9.029032      -0.007765       3.684208\n",
              "std         4.802561       0.303031       0.207378\n",
              "min         1.000000      -1.000233       2.499903\n",
              "25%         5.000000       0.000000       3.577119\n",
              "50%         9.000000       0.000000       3.661715\n",
              "75%        13.000000       0.000000       3.839495\n",
              "max        18.000000       0.999904       4.375753"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
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              "  <tbody>\n",
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              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.029032</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.802561</td>\n",
              "      <td>0.303031</td>\n",
              "      <td>0.207378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>2.499903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.577119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.661715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.839495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.375753</td>\n",
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              "                                                     [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",
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          "height": 424
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        "id": "FSzDs6JUPobf",
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      "source": [
        "cd2"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Cycle_Index  Current(A)  Voltage(V)       SOC\n",
              "0               1.0    0.000000    4.166209      0.97\n",
              "1               1.0    0.000000    4.166047  0.980028\n",
              "2               1.0    0.000000    4.166047  0.980028\n",
              "3               1.0    0.000000    4.166209  0.980123\n",
              "4               1.0    0.000000    4.166047  0.980028\n",
              "...             ...         ...         ...       ...\n",
              "142114         18.0    0.999544    4.249022   0.88911\n",
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              "142116         18.0    0.999544    4.249508   0.88939\n",
              "142117         18.0    0.999544    4.249671  0.889528\n",
              "142118         18.0    0.999544    4.249832  0.889669\n",
              "\n",
              "[142119 rows x 4 columns]"
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              "<p>142119 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVAxdxD_FSA2"
      },
      "source": [
        "y = cd2.SOC\n",
        "X = cd2.drop(['SOC'], axis=1)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "lWEv98nHWPCz",
        "outputId": "eef3efb1-e4b9-4478-cfb7-555e3a6e461d"
      },
      "source": [
        "X.describe()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)\n",
              "count  142119.000000  142119.000000  142119.000000\n",
              "mean        9.029032      -0.007765       3.684208\n",
              "std         4.802561       0.303031       0.207378\n",
              "min         1.000000      -1.000233       2.499903\n",
              "25%         5.000000       0.000000       3.577119\n",
              "50%         9.000000       0.000000       3.661715\n",
              "75%        13.000000       0.000000       3.839495\n",
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              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.029032</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.802561</td>\n",
              "      <td>0.303031</td>\n",
              "      <td>0.207378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>2.499903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.577119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.661715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.839495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.375753</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ddb2710-7a87-4411-ac46-7d5ca8fe0142')\"\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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              "    <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-1ddb2710-7a87-4411-ac46-7d5ca8fe0142 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-1ddb2710-7a87-4411-ac46-7d5ca8fe0142');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7or5lAg9zv05"
      },
      "source": [
        "split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3KejRNIz8L6",
        "outputId": "93f5b506-dbc5-4bea-cdf6-c4275f753cd2"
      },
      "source": [
        "cd2.values"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0, 0.0, 4.1662092208862305, 0.97],\n",
              "       [1.0, 0.0, 4.166047096252441, 0.9800277036779067],\n",
              "       [1.0, 0.0, 4.166047096252441, 0.9800277036779067],\n",
              "       ...,\n",
              "       [18.0, 0.9995440244674683, 4.249508380889893, 0.8893895688644523],\n",
              "       [18.0, 0.9995440244674683, 4.249670505523682, 0.8895284156939548],\n",
              "       [18.0, 0.9995440244674683, 4.2498321533203125, 0.889669441477086]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZ7aC4j11Rpw"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n"
      ],
      "execution_count": 22,
      "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": 23,
      "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": 24,
      "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": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uk0IHjKKlUnK",
        "outputId": "8c073f06-3fbf-4f45-ae7f-0582c823ebe6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.98127282, 0.99141706, 0.99141706],\n",
              "       [0.99141706, 0.99141706, 0.99151354],\n",
              "       [0.99141706, 0.99151354, 0.99141706],\n",
              "       ...,\n",
              "       [0.69930767, 0.69950048, 0.69930767],\n",
              "       [0.69950048, 0.69930767, 0.6992112 ],\n",
              "       [0.69930767, 0.6992112 , 0.69930767]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu8EmIUW13Ts",
        "outputId": "cd3277a5-e29a-4d30-addf-827b97df820c"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(113691, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaHqs7FL2FKn",
        "outputId": "c344cab5-4b97-436b-b8f9-23ecc218e309"
      },
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(113691,)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "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": 29,
      "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"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint"
      ],
      "metadata": {
        "id": "SmWEX-YMCGIR"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFJgXQVc2QAj",
        "outputId": "1ba46fb4-0687-4f70-a88b-be9700febb2f"
      },
      "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='/content/drive/MyDrive/LSTM_SOC_Implementation/calce_incremental_INR_18650-20R/best_model25_soc.h5', monitor='val_loss', save_best_only=True)], verbose=1, shuffle=False)\n",
        "\n",
        "# Training Phase\n",
        "model.summary()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1777/1777 [==============================] - 9s 4ms/step - loss: 0.0186 - val_loss: 7.2891e-04\n",
            "Epoch 2/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 0.0014 - val_loss: 8.5841e-04\n",
            "Epoch 3/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 0.0010 - val_loss: 0.0012\n",
            "Epoch 4/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 8.2259e-04 - val_loss: 0.0016\n",
            "Epoch 5/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 7.3722e-04 - val_loss: 0.0018\n",
            "Epoch 6/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 6.6088e-04 - val_loss: 0.0017\n",
            "Epoch 7/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 5.6409e-04 - val_loss: 0.0013\n",
            "Epoch 8/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 4.5846e-04 - val_loss: 0.0011\n",
            "Epoch 9/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 3.5488e-04 - val_loss: 6.7242e-04\n",
            "Epoch 10/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 2.6857e-04 - val_loss: 3.7253e-04\n",
            "Epoch 11/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 2.0994e-04 - val_loss: 3.7630e-04\n",
            "Epoch 12/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.7672e-04 - val_loss: 3.0885e-04\n",
            "Epoch 13/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6025e-04 - val_loss: 4.4024e-04\n",
            "Epoch 14/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6424e-04 - val_loss: 5.4965e-04\n",
            "Epoch 15/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.5818e-04 - val_loss: 5.7083e-04\n",
            "Epoch 16/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.5672e-04 - val_loss: 6.1626e-04\n",
            "Epoch 17/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6307e-04 - val_loss: 6.6739e-04\n",
            "Epoch 18/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.5448e-04 - val_loss: 6.9889e-04\n",
            "Epoch 19/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5768e-04 - val_loss: 8.3247e-04\n",
            "Epoch 20/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6737e-04 - val_loss: 8.5896e-04\n",
            "Epoch 21/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.6323e-04 - val_loss: 8.1949e-04\n",
            "Epoch 22/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5999e-04 - val_loss: 8.3148e-04\n",
            "Epoch 23/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.5926e-04 - val_loss: 8.5962e-04\n",
            "Epoch 24/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.5950e-04 - val_loss: 8.4322e-04\n",
            "Epoch 25/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.5553e-04 - val_loss: 8.1518e-04\n",
            "Epoch 26/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4907e-04 - val_loss: 8.4071e-04\n",
            "Epoch 27/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5143e-04 - val_loss: 8.1537e-04\n",
            "Epoch 28/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5123e-04 - val_loss: 8.4116e-04\n",
            "Epoch 29/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4828e-04 - val_loss: 7.2748e-04\n",
            "Epoch 30/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.3981e-04 - val_loss: 7.8209e-04\n",
            "Epoch 31/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4213e-04 - val_loss: 7.5436e-04\n",
            "Epoch 32/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.3600e-04 - val_loss: 8.1163e-04\n",
            "Epoch 33/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4401e-04 - val_loss: 7.0905e-04\n",
            "Epoch 34/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.3160e-04 - val_loss: 7.3973e-04\n",
            "Epoch 35/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.3232e-04 - val_loss: 7.0945e-04\n",
            "Epoch 36/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.2687e-04 - val_loss: 7.0403e-04\n",
            "Epoch 37/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.3102e-04 - val_loss: 7.2174e-04\n",
            "Epoch 38/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.2731e-04 - val_loss: 6.5998e-04\n",
            "Epoch 39/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.2336e-04 - val_loss: 6.1321e-04\n",
            "Epoch 40/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1870e-04 - val_loss: 7.0483e-04\n",
            "Epoch 41/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.2332e-04 - val_loss: 6.0128e-04\n",
            "Epoch 42/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1806e-04 - val_loss: 6.3252e-04\n",
            "Epoch 43/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1707e-04 - val_loss: 6.5483e-04\n",
            "Epoch 44/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1962e-04 - val_loss: 6.8746e-04\n",
            "Epoch 45/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1977e-04 - val_loss: 5.7259e-04\n",
            "Epoch 46/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1357e-04 - val_loss: 6.1526e-04\n",
            "Epoch 47/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1575e-04 - val_loss: 6.1656e-04\n",
            "Epoch 48/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1493e-04 - val_loss: 6.3126e-04\n",
            "Epoch 49/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1470e-04 - val_loss: 6.4506e-04\n",
            "Epoch 50/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1577e-04 - val_loss: 5.6418e-04\n",
            "Epoch 51/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0926e-04 - val_loss: 6.5819e-04\n",
            "Epoch 52/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1501e-04 - val_loss: 5.5102e-04\n",
            "Epoch 53/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.0467e-04 - val_loss: 5.9539e-04\n",
            "Epoch 54/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0849e-04 - val_loss: 5.6742e-04\n",
            "Epoch 55/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0794e-04 - val_loss: 5.7822e-04\n",
            "Epoch 56/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1012e-04 - val_loss: 6.0606e-04\n",
            "Epoch 57/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0909e-04 - val_loss: 5.4100e-04\n",
            "Epoch 58/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0526e-04 - val_loss: 5.8140e-04\n",
            "Epoch 59/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0665e-04 - val_loss: 5.1349e-04\n",
            "Epoch 60/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0163e-04 - val_loss: 5.7693e-04\n",
            "Epoch 61/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.0998e-04 - val_loss: 5.5156e-04\n",
            "Epoch 62/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.0555e-04 - val_loss: 5.4134e-04\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_4 (LSTM)               (None, 50)                10800     \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_4 (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"
      ],
      "metadata": {
        "id": "2mSfVQqLFv-8"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9igKq7u2jvl",
        "outputId": "94177398-7063-4722-cdaf-e729e314c365"
      },
      "source": [
        "# make predictions\n",
        "saved_model = load_model('/content/drive/MyDrive/LSTM_SOC_Implementation/calce_incremental_INR_18650-20R/best_model25_soc.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": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Mean Absolute Error: 0.015158817222240498\n",
            "Train Root Mean Squared Error: 0.019292306657016835\n",
            "Test Mean Absolute Error: 0.014237852264039353\n",
            "Test Root Mean Squared Error: 0.01705844831334543\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hADXDvCd2qzF",
        "outputId": "77c08d67-7946-40c1-dcb6-42f0beb5a7c9"
      },
      "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": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "stz_dngk2wpa",
        "outputId": "1f0dd065-4dfb-49d9-ee91-16f5da7bc7ea"
      },
      "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": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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7b8l2N8v/h7GyspJevXrVe75CvXv35uOPP2bJkiUblJDr06cPFRUVa51v0aLcfycLY5LU9OpO8UnAFuOnmJCTJK230257hnufrX+iS7flnzDun1P53oxJ9P9wMWy/PfziJhg3Dor8Q7na9J9+FX4HGzXCyDhJUvN7+6MVzR1CZrJOxi0EBhVsD8yXFToOGAOQUnoiIjoDfYD6M0PtQGVlJf/4xz9qpqq+9tprPP300xx77LElj9l///258sor2WijjRg2bFjROjvssAOdO3dm0qRJNXWqqqqYNGlSvfF88YtfBOCmm24q+qAFWLdRa+Xl5YwYMYI77riDE088sab89ttvp6ysjD322KPe4yU1jvqmDqWMY5EktX4N3b+t70fvcfTTf+E/np5Cj+Ufw777wtlnw5gxUOcfzEV17QpDhnDKjLvp9ckHvLjplrzUZzALemzKR526srqsvHFeiCQpE3036tjcIWQm62TcU8DQiNiCXBJuHPCtOnVeA/YHJkbEtkBn4O1Mo2yh+vTpw3e+8x0uvfRSunTpwkUXXUS/fv3qvWfb6NGjOfDAAxk9ejQ//vGP2W677ViyZAnPPvssy5Yt47LLLqN3794cf/zxXHTRRXTo0IHtttuO66+/no8++qjeeLbZZhuOP/54zjzzTCorK9l77715//33ufPOO7ntttsAGDZsGFOnTmXq1Kn07t2bLbbYgt69e6/V1iWXXMKBBx7Isccey7hx43j++ee54IIL+P73v1/r4Q2SGt+ul/61wf9CrcOfRJIkAfDZc6dQ323htnh3Id9/8h4Om/UQHatWEV//OvzoR7B73bvXNKCsDB5+mC6nnsr3HnwAVtT5Xda1a256a5cua5aKiprl+UUf8/ayKlaVd2BlWTkryzuwOsqoinJWl5VRFUFVlFEVZawuKyMR+fLckoJcWZSRIoCoKUsRrCorpyrKANaU16pTVqt+gvzXgDrbtdYDIGrFUHj8Grn12sfWrpc7V652VeReY3W8Nftr1a/Tdp3yWu3Ws591PM9a7VUHux7HBYlIqeY11pXqJH7X+gdkwf5U5xNRqr1ZZH/hseted63963Geuueqt90Gjq2bFF+f178h71WkRFlKdfbXPjZF1Hxfc/vrfm9L/Su5IMY6/ahYLPW1VErh8cX63IaoG1vROuvwyX1dX1Ojna+BKlVRTo9NurWbKaqQcTIupbQqIk4GpgLlwB9TSi9ExARgRkppMnAmcH1EnE6ujxyTUnJQBrkHNpx77rmMHz+e+fPnM3LkSG699VY6d+5c8piI4O677+ZnP/sZv/nNb3jttdfo1asXO+20E6ecckpNvSuuuIKVK1cyYcIEysrK+M53vsMZZ5xR83TVUq6++moGDx7MH/7wBy6//HL69etX6wEL559/Pq+99hrf/OY3WbJkCf/93/9dNHl4wAEHcNttt3HppZdyyy230K9fP84880wuueSS9X+jJK2To26YzmOvLC65f+D7bzHq9Vnctf0XefXnh2QYmSSpNdr+wv/loxWrS+7f6Y2X+MH0uzjw5SdY1aGCjt/7Lpx5Jgwd+ulPusUW8Je/wKpVMGcOPP88LFwIS5asWZYtyy1Ll8Ly5bm6K1eyQ/cy6LIaVi3NJfKWr4DVq3NLVVX9X1fnX2f1tn+uSNKn95Wv5K7l7Ui0hTzXyJEj04wZM0runz17Nttuu22GETW+Y445hlmzZlHf61RpbaEPSI2pvqlD2y36N8dPv5sv/+v/o0Oq4oW7p7Ld1w4oWV+S1L6NvvJRXnn746L7IlWx79yZnDD9LnZ/fRZLOm9E9zN/mHsyap2Hd7UZKeWSdKtW5b6mVHppaH9DS93jC2Oo/lq9Xli37r7qtuoe2xa+Qm4UZd33qG6dhrY9Nrtjy8pyo/KqR2XVrV/dl8vK1uyv7r+FI7nqjuoq9TNSWBZRu61S+ZLqusVUx1ff8cXaa0l1sj7fVlvBYYc1XK+ViYiZKaWRxfa1xAc4SJKayFbnTGF1sd+HKfH5+f/khOl3sfe8Z/iwYxf+vft+bDPtIXj/g8zjlCS1fIVP3q6rYvVKxr74GMc/eRfbLH6NN7r3hV//mu7HHbfWk1HbnAgoL88tkiQVYTJOktqBUlOHyqpWc9BL/+AHT97Fjm/N4e1um3DTIT/gqJsuZ+59T7DNtId4eOarLPvie4wY3LMZIpcktUSlRlhXPxn1uBmT+MyHi5nddwjcfDOfOeKIep+MKklSe2IyrpWYOHFic4cgqRUqNXWo08rlfGPWQ3z/ybsZ/P5bzO35GSYc8kMuvP1yjurcmZnz3+OXjy/gIODf8yu58pp/cMLeWzL+YKd7S1J7tvV597OiyBDrvh+9xzEzJ/Mfz9xP9+Uf88TmOzD5/13ECZedtG5PRpUkqR0xGSdJbVCpqUPdl33Efzw9hWNnTqbPJx/wTP9tuPyL3+Wa2ydwYcF0mmlz3+GjstyjxbuuXAbAtY/N5drH5q5zDB3Kgj//YA9H1ElSG1Dqydu5J6PezWGzHqJi9Woe2Obz3Lv/kVx/1Uns0QxxSpLUGrSbZFxKifC/cu1SW3hIibQ+ik0d6vfhOxw3YxLffvYBNlqxlIe3HMm1ow7n9j+dzTVFro2jtuzNf1XkntTcZcWyTxXHqqrEYdf841MdK0lq2XZ64yVOmH4nB7w8jZXlHbhjh9HcvMdhTL36OL7c3MFJktTCtYtkXEVFBUuXLqVr167NHYqawdKlS6nwHiVqB4pNHdri3YUcP/0uvv7Cw3SoquIv2+7FtbsfzrAD9uT2cTuXbGvE4J58UtGZlWXlHDr77zzXfyhzeg/i/S4bk6KsqV+KJKkFqvtk1Pc7b8Tv9/gmN434CjP+8zt8p7kDlCSplYi2MGpo5MiRacaMGSX3L1myhEWLFjFgwAC6dOniCLl2IqXE0qVLWbhwIZtuuindu3dv7pCkJlFs6tD2b83hxGl3cNBL/2BFhwpu32E01+/2NdKQIfzf+P3Xue3zD/h/nPPof9MtP1V1ZVk573Ttwbtde/BJRWeWdujE8g4VrCivYFV5B1aVlVMVZayOMlaXlbG6rJzVUZYrK6suz5cVbufXqyK3noCqKCNF5L7W2a6KyK8HiSjYl9sGIGBVWTlR/eT6fHGuvahpAyCRW0+QLwtSUHPuQqngHIXnqimraZNa7a85Nq/WuSlZr3bZmvMVxl19vrXiq7NvneSPrxtTrfegoKy+erWOKfq7N+rUKX2eYm0U+97UF2vpOOo7R/3nXKdjGnhdde+nVfQ9b+T3puj3a30+H6VEWapaq/2GXluptj51HC1FwWuo/umNWmUUKauuV3u7sL3CsmL1oqbeGsXOv3b9BtqoUy8S7P3q0zVPRl3QvS837PpV/rzjAZx/xG58a/fN1zqXJEntXUTMTCmNLLavXYyMq07CvPHGG6xcubKZo1GWKioqTMSpzTrqhuk89sriNQUpscdrz3HitDvZe94zLOnUjav3+AYTRxzCsp59mDVhzHqf49IHr2bH0/dlj/n/pP+SxfT9+D36fvwevT75gM6rVtB15TJ6LV1Ch6pVdFy9kvKqKspSFeVVVZSnKsqrVlOeqiir3q4uq6pi7TSXJJVWVSfRV30NqSpIpgOU1UlCVSfqIyXKUqrZLqY6EV2W0lpJqsLkVnu9fs3uO4QffuVMpgzbi+0H9+bFk7/Q3CFJktQqtYuRcZLU1hTeFy5SFQe8Mo0Tp93JTm++TGW3ntyw66HcutNBfNipG/Muz/buPaWe4LqWlPLJuSrK0mo65BN5uSXV/OEc1dvU3i5LuT/Ny1JVvu6aeomoSQIWjgiqVa9g5EeQ2458ndw2kD9n9W/K6v3Vx1FTf037+Re3VtmaNuseWz10j4J6aycB6o52KRzzVlhWbERL9XtS/bWUNede+9jCOoXvZ81562k7iiQu6o7aWevItfbX2W7o+KJ16rbRQJwNHL8ubazvOYuNZlr7nBv23hQdMdVAnLk+UXtEZPXI0WLJqrptRlozum9N27mjU8HI0mIjwYqNR4x8Ui139lSTgAtSTXKuWvX1onCka3l+VF/dEYPV152q/CjcuhHUrl89ApWCeqVHpRYbPVh0NOdaI2qLtN/Q6NSi5yoeY6nXt2Ykbu7rqz0/w+ODP0ffjTvx1Pmj14pbkiTV1u5HxklSW7HVOVOovi1cxeqVfPWFRzlh+l1s9e4C5m3Sn3MOPJm7t/8iyzt05Gdf26FZpg799cx9Mz+nJEmSJLUWJuMkqRXY/sL/5aMVqwHoumIpR/5zKsc9dS+f+XAxL/TbkpPHns0D2+zJ6rJy9h7ah5uO272ZI5YkSZIkFWMyTpJasMIpnz0/+YCjn76Po2feR89lHzJt0PaMH3MKj22xC0TQd6OOTh2SJEmSpBbOZJwktUCn3fYM9z77BgCfWVLJ9568l3HPTaXryuU8OHQU1+5+GE8P2BaAjuXByz89uDnDlSRJkiStI5NxktTCVD+cYavFr3PC9Lv46ouPADB5+D5cs/vhzOmz5j5wWT+cQZIkSZK0YUzGSVILsfV597NidWKnN17ixGl3cOAr01jaoRN/2vlg/rDr11jYo19N3RP23pLxB2/bjNFKkiRJkj4Nk3Et1K3TX+Pce57foDbKA8rKgoryMrb/THe+uvNAHnmpksolyzhi182b5SmLkta20yVTef+Tlew17xlOnHYnn3/tOd7vvBG//fw4bhxxCO927VFTd2jfbj6tVJIkSZJasUgpNXcMG2zkyJFpxowZzR1Go7l1+mv86dpJjH3xUaKgPFIiRZAAItY6LhXUjlRVUycRVBWrX7C/VC9IRY6DYmWQihSnInWLt0nRGIrVLdZmfe2ua93isZY4vljdDXxfin1PGyWuoq+1xPHr8X4XjTfV7k11jy1LVTUlUXDtKTwmCoKr1TPXs35DdaLEta90XIXl61K/sNW126nVBol9585k+0X/5q2NenH9rl/jts8dyMedutbU2ahjObMmjCkasyRJkiSpZYmImSmlkcX2OTKuBXpg1pts8e5Cjnn6vpokWqQ1CZiyIkmE2omFRIqygrJUJzFQO2lQrL2128zXLZnGkVSfqoL0W2HSs/onal7Pz3D2mFO5d7v9WNGhomZ/GTDX+8JJkiRJUpthMq4FOmj7/pz7yl5M2Xav5g5lwxRLGpZI5hVL/BUdk1cqcVik3boJyHpjWK9Y17FN1uN1rcfxpRQ/V0OJ2wbiWs/3OwVURVnRY4NEVZTVGqWZSiSoCqVS9delTkGTtUbpFalfqm6xxFl95yw1wvHTuOvEzzNicM9Ga0+SJEmS1PxMxrVA39p9c15752OufWxuc4eyYdZrimlTByO1HnsP7cNNx+3e3GFIkiRJkpqAybgWavzB2zJ6u8249u//5slX3+GjZatY7QxRqU3ru1FHnjp/dHOHIUmSJElqQibjWrARg3ty/VFF7/UnSZIkSZKkVqisuQOQJEmSJEmS2guTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGXEZJwkSZIkSZKUEZNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlJPNkXESMiYiXImJORIwvUeebEfFiRLwQEbdmHaMkSZIkSZLUFDpkebKIKAeuAkYDC4CnImJySunFgjpDgXOAPVNK70VEvyxjlCRJkiRJkppK1iPjdgPmpJTmppRWALcBh9ap833gqpTSewAppcqMY5QkSZIkSZKaRNbJuAHA6wXbC/JlhbYGto6IxyNiWkSMySw6SZIkSZIkqQllOk11HXUAhgL7AgOBxyJih5TS+80alSRJkiRJkrSBsh4ZtxAYVLA9MF9WaAEwOaW0MqX0KvAyueScJEmSJEmS1KplnYx7ChgaEVtEREdgHDC5Tp17yY2KIyL6kJu2OjfLICVJkiRJkqSmkGkyLqW0CjgZmArMBm5PKb0QERMiYmy+2lTgnYh4EXgE+FFK6Z0s45QkSZIkSZKaQqSUmjuGDTZy5Mg0Y8aM5g5DkiRJkiRJIiJmppRGFtuX9TRVSZIkSZIkqd0yGSdJkiRJkiRlxJEcX6oAABTaSURBVGScJEmSJEmSlJEGk3ERsXFEdK5nf+eI2Lhxw5IkSZIkSZLannqTcRGxP/AusHs91XYn9/TTvRszMEmSJEmSJKmtaWhk3MnAn1NKfy9VIb/vf4AfNmZgkiRJkiRJUlvTUDJuT+CudWjnHuALGx6OJEmSJEmS1HY1lIzrAby9Du0sBjbZ8HAkSZIkSZKktquhZNwiYKt1aGfLfF1JkiRJkiRJJTSUjPsrcFJEdChVIb/vJODBxgxMkiRJkiRJamsaSsb9FNgamBIRw+vujIhtgfuAbYCfNX54kiRJkiRJUttRcsQbQEppbkR8GbgNeD4i3gBeAxKwOTAAeAM4OKU0t6mDlSRJkiRJklqzepNxACmlxyNiKPBNYG9yCTjITUt9FLgzpbSsySKUJEmSJEmS2ogGk3EA+WTbTflFkiRJkiRJ0qewTsk4gIjYHPgCa0bGLQD+L6X0elMEJkmSJEmSJLU1DSbjImIz4GpgLGs/8KEqIu4FTk4pvdUE8UmSJEmSJEltRr1PU42I3sD/kbtX3ARgJ6BnftkJuATYB3gsIno1baiSJEmSJElS69bQyLgLgApgx5TSG3X2PQc8FxF/AJ4AzgfOaPwQJUmSJEmSpLah3pFxwKHApUUScTVSSm8CPwO+1piBSZIkSZIkSW1NQ8m4/sC/1qGd2fm6kiRJkiRJkkpoKBm3GBiyDu0MydeVJEmSJEmSVEJDybipwNkR0a1Uhfy+s4EHGjMwSZIkSZIkqa1pKBl3MbApMDMivh0RG1XviIiNIuJbwAygH7knq0qSJEmSJEkqod6nqaaUXo+I/YA/ATcDKSLez+/eBAjgWeCLKaUFTRqpJEmSJEmS1MrVm4wDSCm9AOwcEfsAewED8rsWAo+llB5rwvgkSZIkSZKkNqPBZFy1lNLfgb83YSySJEmSJElSm7bOybhCEdEVOA4YBiwCbkwpzW/MwCRJkiRJkqS2pt5kXERcCRySUtq6oGxj4ClgKPAe0AM4IyJ2Sym93JTBSpIkSZIkSa1ZQ09TrX54Q6GzgK2B76eU+gCfAeYBFzR6dJIkSZIkSVIb0lAybggws07ZYcCLKaU/AqSU3gauBPZs9OgkSZIkSZKkNqShZFwHYFn1RkT0ArYFHq5Tbx6wWaNGJkmSJEmSJLUxDSXjXgb2Ldj+Sv7r1Dr1+gHvNlJMkiRJkiRJUpvU0NNUfw9cHxE9yD019VTgVeDBOvUOAGY1fniSJEmSJElS21FvMi6lNDEi+gMnAZsATwMnpZRWVteJiL7AocAlTRmoJEmSJEmS1No1NDKOlNJlwGX17H8b7xcnSZIkSZIkNaihe8Y1uogYExEvRcSciBhfT73DIiJFxMgs45MkSZIkSZKaSqbJuIgoB64CDgKGA0dGxPAi9TYGfghMzzI+SZIkSZIkqSllPTJuN2BOSmluSmkFcBu5+83V9RPg58CyLIOTJEmSJEmSmlLWybgBwOsF2wvyZTUiYhdgUEppSpaBSZIkSZIkSU2twQc4ZCkiyoBfAcc0cyiSJEmSJElSo8t6ZNxCYFDB9sB8WbWNge2BRyNiHjAKmOxDHCRJkiRJktQWZJ2MewoYGhFbRERHYBwwuXpnSumDlFKflNKQlNIQYBowNqU0I+M4JUmSJEmSpEaXaTIupbQKOBmYCswGbk8pvRAREyJibJaxSJIkSZIkSVnL/J5xKaX7gfvrlF1You6+WcQkSZIkSZIkZSHraaqSJEmSJElSu2UyTpIkSZIkScqIyThJkiRJkiQpIybjJEmSJEmSpIyYjJMkSZIkSZIyYjJOkiRJkiRJyojJOEmSJEmSJCkjJuMkSZIkSZKkjJiMkyRJkiRJkjJiMk6SJEmSJEnKiMk4SZIkSZIkKSMm4yRJkiRJkqSMmIyTJEmSJEmSMmIyTpIkSZIkScqIyThJkiRJkiQpIybjJEmSJEmSpIyYjJMkSZIkSZIyYjJOkiRJkiRJyojJOEmSJEmSJCkjJuMkSZIkSZKkjJiMkyRJkiRJkjJiMk6SJEmSJEnKiMk4SZIkSZIkKSMm4yRJkiRJkqSMmIyTJEmSJEmSMmIyTpIkSZIkScqIyThJkiRJkiQpIybjJEmSJEmSpIyYjJMkSZIkSZIyYjJOkiRJkiRJyojJOEmSJEmSJCkjJuMkSZIkSZKkjJiMkyRJkiRJkjJiMk6SJEmSJEnKSObJuIgYExEvRcSciBhfZP8ZEfFiRDwXEQ9FxOCsY5QkSZIkSZKaQqbJuIgoB64CDgKGA0dGxPA61Z4BRqaUdgTuBK7IMkZJkiRJkiSpqWQ9Mm43YE5KaW5KaQVwG3BoYYWU0iMppU/ym9OAgRnHKEmSJEmSJDWJrJNxA4DXC7YX5MtKOQ54oEkjkiRJkiRJkjLSobkDKCUivgOMBPZp7lgkSZIkSZKkxpB1Mm4hMKhge2C+rJaI+BJwHrBPSml5RrFJkiRJkiRJTSrraapPAUMjYouI6AiMAyYXVoiInYH/AsamlCozjk+SJEmSJElqMpkm41JKq4CTganAbOD2lNILETEhIsbmq/0C2Ai4IyKejYjJJZqTJEmSJEmSWpXM7xmXUrofuL9O2YUF61/KOiZJkiRJkiQpC1lPU5UkSZIkSZLaLZNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGXEZJwkSZIkSZKUEZNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGTEZJ0mSJEmSJGXEZJwkSZIkSZKUEZNxkiRJkiRJUkZMxkmSJEmSJEkZMRknSZIkSZIkZcRknCRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGck8GRcRYyLipYiYExHji+zvFBF/zu+fHhFDso5RkiRJkiRJagqZJuMiohy4CjgIGA4cGRHD61Q7DngvpfRZ4NfAz7OMUZIkSZIkSWoqWY+M2w2Yk1Kam1JaAdwGHFqnzqHAjfn1O4H9IyIyjFGSJEmSJElqEh0yPt8A4PWC7QXA7qXqpJRWRcQHQG9gcalGZ86cuTgi5jdyrC1BH+p53Wr37B+qj/1D9bF/qD72D9XH/qFS7Buqj/1D9Wmr/WNwqR1ZJ+OaREqpb3PH0BQiYkZKaWRzx6GWyf6h+tg/VB/7h+pj/1B97B8qxb6h+tg/VJ/22D+ynqa6EBhUsD0wX1a0TkR0AHoA72QSnSRJkiRJktSEsk7GPQUMjYgtIqIjMA6YXKfOZODo/PrhwMMppZRhjJIkSZIkSVKTyHSaav4ecCcDU4Fy4I8ppRciYgIwI6U0GbgBuDki5gDvkkvYtVfXNXcAatHsH6qP/UP1sX+oPvYP1cf+oVLsG6qP/UP1aXf9Ixx0JkmSJEmSJGUj62mqkiRJkiRJUrtlMk6SJEmSJEnKiMm4FioixkTESxExJyLGN3c8anoRMSgiHomIFyPihYj4Yb784ohYGBHP5peDC445J99HXoqIAwvK7T9tUETMi4jn8/1gRr6sV0T8NSJeyX/tmS+PiPhdvg88FxG7FLRzdL7+KxFxdKnzqfWIiG0KrhHPRsSSiDjN60f7FRF/jIjKiJhVUNZo14uIGJG/Hs3JHxvZvkJtiBL94xcR8a98H7gnIjbJlw+JiKUF15FrC44p2g9K9TW1DiX6R6P9Poncw/ym58v/HLkH+6kVKNE3/lzQL+ZFxLP5cq8d7UyU/nvWzx/FpJRcWthC7uEW/wa2BDoC/wSGN3dcLk3+fe8P7JJf3xh4GRgOXAycVaT+8Hzf6ARske8z5faftrsA84A+dcquAMbn18cDP8+vHww8AAQwCpieL+8FzM1/7Zlf79ncr82lUftJOfAWMNjrR/tdgL2BXYBZBWWNdr0AnszXjfyxBzX3a3bZ4P5xANAhv/7zgv4xpLBenXaK9oNSfc2ldSwl+kej/T4BbgfG5devBU5s7tfs8un7Rp39VwIX5te9drSzhdJ/z/r5o8jiyLiWaTdgTkppbkppBXAbcGgzx6QmllJ6M6X0dH79Q2A2MKCeQw4FbkspLU8pvQrMIdd37D/ty6HAjfn1G4GvFpTflHKmAZtERH/gQOCvKaV3U0rvAX8FxmQdtJrU/sC/U0rz66nj9aONSyk9Ru6p9IUa5XqR39c9pTQt5T4Z31TQllqBYv0jpfRgSmlVfnMaMLC+NhroB6X6mlqBEtePUtbr90l+FMsXgTvzx9s/WpH6+kb+e/tN4H/qa8NrR9tVz9+zfv4owmRcyzQAeL1gewH1J2XUxkTEEGBnYHq+6OT80N0/FgzXLtVP7D9tVwIejIiZEXF8vmzTlNKb+fW3gE3z6/aP9msctT8Ie/1Qtca6XgzIr9ctV9vxXXIjDqptERHPRMTfI2KvfFl9/aBUX1Pr1hi/T3oD7xckfr1+tB17AYtSSq8UlHntaKfq/D3r548iTMZJLUxEbATcBZyWUloCXANsBewEvElu+Lfapy+klHYBDgJOioi9C3fm/0OUmiUytQj5++6MBe7IF3n9UFFeL1RKRJwHrAJuyRe9CWyeUtoZOAO4NSK6r2t79rU2w98nasiR1P5noNeOdqrI37M1/L6uYTKuZVoIDCrYHpgvUxsXERXkLly3pJTuBkgpLUoprU4pVQHXkxv2D6X7if2njUopLcx/rQTuIdcXFuWHbFcP+6/MV7d/tE8HAU+nlBaB1w+tpbGuFwupPYXRftJGRMQxwFeAb+f/YCI//fCd/PpMcvcB25r6+0GpvqZWqhF/n7xDbipahzrlasXy38+vA3+uLvPa0T4V+3sWP38UZTKuZXoKGJp/0lBHclOOJjdzTGpi+fss3ADMTin9qqC8f0G1rwHVTy+aDIyLiE4RsQUwlNwNLe0/bVBEdIuIjavXyd1oexa57231E4aOBibl1ycDR+WfUjQK+CA/PHwqcEBE9MxPMTkgX6a2odZ/pb1+qI5GuV7k9y2JiFH5311HFbSlVioixgBnA2NTSp8UlPeNiPL8+pbkrhdzG+gHpfqaWqnG+n2ST/I+AhyeP97+0TZ8CfhXSqlmCqHXjvan1N+z+PmjuPV52oNLdgu5J4u8TO4/COc1dzwumXzPv0BuyO5zwLP55WDgZuD5fPlkoH/BMefl+8hLFDxJxv7T9hZyTyP7Z355ofr7Su7eKw8BrwB/A3rlywO4Kt8HngdGFrT1XXI3WJ4DHNvcr82l0fpIN3IjDnoUlHn9aKcLuaTsm8BKcvdUOa4xrxfASHJ/jP8b+D0Qzf2aXTa4f8whd4+e6s8g1+brHpb/vfMs8DRwSEP9oFRfc2kdS4n+0Wi/T/KfaZ7M97k7gE7N/ZpdPn3fyJdPBE6oU9drRztbKP33rJ8/iizVnV6SJEmSJElSE3OaqiRJkiRJkpQRk3GSJEmSJElSRkzGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSc0kItI6LPtGxLyI+GVzx1tKRPSLiIsjYkhzxyJJktTSRUqpuWOQJElqlyJiVMFmF+Bh4FJgSkH5i8BWwDsppdcyDG+dRcT2wPPAfimlR5s5HEmSpBatQ3MHIEmS1F6llKZVr0fERvnVfxeW5z2TXVSSJElqSk5TlSRJauHqTlONiIkRMSMivhwRL0bEJxExJSJ6RcRnI+KRiPg4X2fHOm2VRcT4iJgTEcsj4uWIOHodYjguf66lEbE4Iv4eEdvlp6Y+n6/2SPX02oLjekXEdRGxKCKWRcQ/ImL3Om2niDgjIn4bEe9GxPsR8Z8R0XFD3jdJkqSWyGScJElS67Q5MAE4Hzge+DxwHXBbfjmc3CyI2yIiCo77z/wx1wFfBu4B/hgRXyl1oojYG7gWuBk4CPgu8A+gB/Am8O181ZOAPfILEdEJ+BvwJeBHwFeBt4G/RcRmdU5zJjAw39al+df00/V4PyRJkloFp6lKkiS1Tr2APVJK/wbIj4D7EXB0SummfFmQu//cMGB2RHwWOBE4NqV0Y76dv0VEf+Ai4L4S59oNeC6ldFlB2eTqlYh4Lr/6Yp0ptt8Btge2Sym9kq/7N+Alcsm3HxXU/RD4RkqpCnggn8g7LyIuSym9u87viiRJUgvnyDhJkqTWaV51Ii5vTv7rw0XKBuS/7g9UAfdERIfqBXgI2Ckiykuc61lg54j4dUTsvR7TR78EzAReLTgXwN+BkXXqTson4qrdTe6hFtuv47kkSZJaBZNxkiRJrdP7dbZXFCmvLuuc/9oHKAc+AFYWLBPJzZjoX+xEKaW/AccCewOPAosj4qqI6NZAjH2AUXXOtTLf1qA6dStLbBeNSZIkqbVymqokSVL78S6wCtiT3Ai5uuomxGrkp7XeGBF9ga8DvyY3tXR8A+ebQW5qbF3L62z3K7H9Zj3tS5IktTom4yRJktqPh8mNjOuRUvrrp2kgpfQ28F8R8XVgeL647gi8ag8BBwCvpZRKJvryDo2Icwqmqn4dWArM+jRxSpIktVQm4yRJktqJlNJLEXEtuSesXkFu1FpnYDtg65TS94odFxGXkHtgxKPAYmBnYB/WjIp7jVzi7OiI+ABYmVKaAdwEnAA8GhG/BOYCvck9EOKtlNKvC06zMXBHRFyfj+cC4Cof3iBJktoak3GSJEnty0nAy8D3gQnAEuBF4IZ6jnkKOB0YRy5pNh+4GPgtQEppWUR8n9wTWf8OVACRL98vf55LgE3JTYV9koKnseZdCWwJ/A+5+xrfAJy7YS9VkiSp5YmUUnPHIEmSpHYsIhJwSkrp980diyRJUlPzaaqSJEmSJElSRkzGSZIkSZIkSRlxmqokSZIkSZKUEUfGSZIkSZIkSRkxGSdJkiRJkiRlxGScJEmSJEmSlBGTcZIkSZIkSVJGTMZJkiRJkiRJGfn/AZ5sUKzFZ801AAAAAElFTkSuQmCC\n"
          },
          "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": "65fa4c4b-6e87-4d8c-89e4-a657e8f79fbc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}