Copy_of_Copy_of_implementation_1_SOC_LSTM_CALCE_IC2_45deg.ipynb

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    "colab": {
      "name": "Copy of Copy of implementation_1_SOC_LSTM_CALCE_IC2_45deg.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1i7AAoUvs8slVYi7ARI5TC-oPVQIVDEbH",
      "authorship_tag": "ABX9TyO7mkzmt3GxvWJgoJiIBvkq",
      "include_colab_link": true
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bharat-mullapudi/LSTM_SOC_RUL/blob/main/Copy_of_Copy_of_implementation_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>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5wlvJX-tvun",
        "outputId": "b92bb480-4a04-40a9-f6b4-de9280b53a76"
      },
      "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": 3,
      "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_45.xlsx')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vKmhVn9DO0Mo",
        "outputId": "259f2631-07a0-4dc3-e08d-b440b74831e2"
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>TimeDelta</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>10.0155</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>10.0156</td>\n",
              "      <td>0.984604</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.062737</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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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",
              "3           40.047191          1.0  ...    10.0156  0.984604\n",
              "4           50.062737          1.0  ...    10.0155  0.984604\n",
              "...               ...          ...  ...        ...       ...\n",
              "1048568           NaN          NaN  ...                     \n",
              "1048569           NaN          NaN  ...                     \n",
              "1048570           NaN          NaN  ...                     \n",
              "1048571           NaN          NaN  ...                     \n",
              "1048572           NaN          NaN  ...                     \n",
              "\n",
              "[1048573 rows x 8 columns]"
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      "cell_type": "code",
      "metadata": {
        "id": "zXneLTAPvFFs"
      },
      "source": [
        "df=df.dropna()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vaZMCdn-xvYC"
      },
      "source": [
        "#not for all\n",
        "\n",
        "ws1 = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/12_2_2015_Incremental_OCV_test_SP20-1.xlsx', sheet_name = 1)\n",
        "ws2 = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/12_2_2015_Incremental_OCV_test_SP20-1.xlsx', sheet_name = 2)\n",
        "ws3 = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/12_2_2015_Incremental_OCV_test_SP20-1.xlsx', sheet_name = 3)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Z210Ew40tRK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcf44841-1fa0-4c66-c59f-93a693d2ba18"
      },
      "source": [
        "combined_data=ws1.append(ws2,ignore_index=True).append(ws3,ignore_index=True)\n",
        "combined_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(142121, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrVQ2ETgCrim"
      },
      "source": [
        "combined_data=df"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAjkmkUaqh0E"
      },
      "source": [
        "combined_data.to_csv('Incremental_Init_45.csv')\n",
        "combined_data=pd.read_csv('Incremental_Init_45.csv')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "oLqTe4j6GuNp",
        "outputId": "142dc3db-d992-4cea-aa5d-e00b932e0223"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
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              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>TimeDelta</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\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>79553.000000</td>\n",
              "      <td>87279.243341</td>\n",
              "      <td>10.493090</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>3.735026</td>\n",
              "      <td>0.568921</td>\n",
              "      <td>1.542466</td>\n",
              "      <td>1.054046</td>\n",
              "      <td>0.722902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>45930.378977</td>\n",
              "      <td>46456.798733</td>\n",
              "      <td>5.747789</td>\n",
              "      <td>0.295084</td>\n",
              "      <td>0.231053</td>\n",
              "      <td>0.687331</td>\n",
              "      <td>0.616400</td>\n",
              "      <td>0.621824</td>\n",
              "      <td>0.134872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000502</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>3.033892</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000435</td>\n",
              "      <td>0.314054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39776.500000</td>\n",
              "      <td>47094.959959</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.578578</td>\n",
              "      <td>0.014028</td>\n",
              "      <td>0.999794</td>\n",
              "      <td>1.000212</td>\n",
              "      <td>0.633599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>79553.000000</td>\n",
              "      <td>87297.368555</td>\n",
              "      <td>11.000000</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>119329.500000</td>\n",
              "      <td>127494.672648</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.946941</td>\n",
              "      <td>1.213525</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.015621</td>\n",
              "      <td>0.850189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>159106.000000</td>\n",
              "      <td>167715.033539</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.001522</td>\n",
              "      <td>4.328594</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>10.017457</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
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            "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]"
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          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
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        "id": "hpknBqegD2f3",
        "outputId": "1ea1dcb7-823c-406b-97b5-7da18970ffe1"
      },
      "source": [
        "combined_data.to_csv('merged0.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      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",
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      "cell_type": "code",
      "metadata": {
        "id": "TXN7irT9lVh_"
      },
      "source": [
        "files.download('merged0.csv')"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "2nMfX0Z056Fe"
      },
      "source": [
        "###\n",
        "combined_data=combined_data.drop_duplicates(subset=['Data_Point'])\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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "lYfOvKWBml25",
        "outputId": "fff59f10-d3ba-4ddb-eae6-1d3f66caf060"
      },
      "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": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>TimeDelta</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.000502</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173664</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.016109</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173664</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015606</td>\n",
              "      <td>0.984508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30.031595</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173826</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015487</td>\n",
              "      <td>0.984604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40.047191</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173826</td>\n",
              "      <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>50.062737</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>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>"
            ],
            "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": [
        "combined_data.to_csv('cd.csv')\n",
        "files.download('cd.csv')"
      ],
      "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",
      "metadata": {
        "id": "j8lLUYPgoehA"
      },
      "source": [
        "display(combined_data.loc[63990])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1gdmPPRs-kJ"
      },
      "source": [
        "combined_data.hist()\n",
        "plt.figure(figsize=(20,20))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0OIEihXABYQ",
        "outputId": "a54d54e9-28b9-4276-9aad-d2c3f9812858"
      },
      "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 0x7fa02c3fd750>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa03e4b0810>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa03e760e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fa03ecbb690>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa03f022c90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa03f4a32d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fa03f7bc950>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa03f999e90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fa03f999ed0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "AG_E_pvAvEvy",
        "outputId": "e645e7c0-9008-4539-93f9-34e715347fff"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "4d14a512-693a-48f0-eff7-2fb7878574c5"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>TimeDelta</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>27602.000000</td>\n",
              "      <td>27602.0</td>\n",
              "      <td>27602.000000</td>\n",
              "      <td>27602.000000</td>\n",
              "      <td>27602.000000</td>\n",
              "      <td>27602.000000</td>\n",
              "      <td>27602.000000</td>\n",
              "      <td>27602.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15040.741654</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.055701</td>\n",
              "      <td>3.897969</td>\n",
              "      <td>2.106916</td>\n",
              "      <td>0.804022</td>\n",
              "      <td>1.214452</td>\n",
              "      <td>0.842407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9710.525082</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.730714</td>\n",
              "      <td>0.324151</td>\n",
              "      <td>1.031030</td>\n",
              "      <td>0.891472</td>\n",
              "      <td>1.339952</td>\n",
              "      <td>0.200001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.001205</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.000525</td>\n",
              "      <td>2.499921</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.031246</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6972.425579</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.999981</td>\n",
              "      <td>3.640014</td>\n",
              "      <td>1.857298</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000245</td>\n",
              "      <td>0.642212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13942.747873</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.042780</td>\n",
              "      <td>3.980649</td>\n",
              "      <td>1.996313</td>\n",
              "      <td>0.230640</td>\n",
              "      <td>1.015536</td>\n",
              "      <td>0.997103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>20916.228757</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.913817</td>\n",
              "      <td>4.199773</td>\n",
              "      <td>1.996313</td>\n",
              "      <td>2.009444</td>\n",
              "      <td>1.015616</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>33521.293923</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000313</td>\n",
              "      <td>4.200097</td>\n",
              "      <td>4.005512</td>\n",
              "      <td>2.009444</td>\n",
              "      <td>10.016820</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Test_Time(s)  Cycle_Index  ...     TimeDelta           SOC\n",
              "count  27602.000000      27602.0  ...  27602.000000  27602.000000\n",
              "mean   15040.741654          1.0  ...      1.214452      0.842407\n",
              "std     9710.525082          0.0  ...      1.339952      0.200001\n",
              "min        1.001205          1.0  ...      0.031246      0.000000\n",
              "25%     6972.425579          1.0  ...      1.000245      0.642212\n",
              "50%    13942.747873          1.0  ...      1.015536      0.997103\n",
              "75%    20916.228757          1.0  ...      1.015616      1.000000\n",
              "max    33521.293923          1.0  ...     10.016820      1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jej3N8-4JDj4"
      },
      "source": [
        "Deleting 0 valued columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGcwcEaaItya"
      },
      "source": [
        "combined_data=combined_data.drop(columns=['Internal_Resistance(Ohm)','Is_FC_Data','AC_Impedance(Ohm)',\t'ACI_Phase_Angle(Deg)'])\n",
        "combined_data.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8j2W3tFJXVw"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UV6QQJwJomw"
      },
      "source": [
        "  fig = plt.figure(figsize = (15,15))\n",
        "  ax = fig.gca()\n",
        "  combined_data.hist(ax = ax)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwXxdNoxVNVG"
      },
      "source": [
        "# ***Can run from here***\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lA5X5VM0CvOI",
        "outputId": "62725ebe-cbe2-4a40-f964-b4529d35e199"
      },
      "source": [
        "#cd2=pd.read_csv('/content/drive/MyDrive/LSTM_SOC_Implementation/r1.csv')\n",
        "cd2=combined_data.drop(columns=['TimeDelta'])\n",
        "cd2.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>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",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>87279.243341</td>\n",
              "      <td>10.493090</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>3.735026</td>\n",
              "      <td>0.568921</td>\n",
              "      <td>1.542466</td>\n",
              "      <td>0.722902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>46456.798733</td>\n",
              "      <td>5.747789</td>\n",
              "      <td>0.295084</td>\n",
              "      <td>0.231053</td>\n",
              "      <td>0.687331</td>\n",
              "      <td>0.616400</td>\n",
              "      <td>0.134872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000502</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>3.033892</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.314054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47094.959959</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.578578</td>\n",
              "      <td>0.014028</td>\n",
              "      <td>0.999794</td>\n",
              "      <td>0.633599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>87297.368555</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.678893</td>\n",
              "      <td>0.138467</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>0.693371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127494.672648</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.946941</td>\n",
              "      <td>1.213525</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>0.850189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>167715.033539</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.001522</td>\n",
              "      <td>4.328594</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Test_Time(s)    Cycle_Index  ...  Discharge_Capacity(Ah)            SOC\n",
              "count  159107.000000  159107.000000  ...           159107.000000  159107.000000\n",
              "mean    87279.243341      10.493090  ...                1.542466       0.722902\n",
              "std     46456.798733       5.747789  ...                0.616400       0.134872\n",
              "min        10.000502       1.000000  ...                0.000000       0.314054\n",
              "25%     47094.959959       5.000000  ...                0.999794       0.633599\n",
              "50%     87297.368555      11.000000  ...                1.999575       0.693371\n",
              "75%    127494.672648      16.000000  ...                1.999575       0.850189\n",
              "max    167715.033539      19.000000  ...                1.999575       1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "FSzDs6JUPobf",
        "outputId": "c647f57c-f615-4f82-863b-5745ea1ed368"
      },
      "source": [
        "cd2"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.000502</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.016109</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.984508</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30.031595</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173826</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40.047191</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.173826</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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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              "      <td>4.176581</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "      <td>0.986224</td>\n",
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              "      <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>0.986224</td>\n",
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              "    <tr>\n",
              "      <th>159105</th>\n",
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              "      <td>4.176581</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
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              "      <td>4.176581</td>\n",
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              "<p>159107 rows × 7 columns</p>\n",
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            ],
            "text/plain": [
              "         Test_Time(s)  Cycle_Index  ...  Discharge_Capacity(Ah)       SOC\n",
              "0           10.000502          1.0  ...                0.000000  0.970000\n",
              "1           20.016109          1.0  ...                0.000000  0.984508\n",
              "2           30.031595          1.0  ...                0.000000  0.984604\n",
              "3           40.047191          1.0  ...                0.000000  0.984604\n",
              "4           50.062737          1.0  ...                0.000000  0.984604\n",
              "...               ...          ...  ...                     ...       ...\n",
              "159102  167711.283616         19.0  ...                1.999575  0.986319\n",
              "159103  167712.283619         19.0  ...                1.999575  0.986224\n",
              "159104  167713.299187         19.0  ...                1.999575  0.986224\n",
              "159105  167714.314787         19.0  ...                1.999575  0.986224\n",
              "159106  167715.033539         19.0  ...                1.999575  0.986224\n",
              "\n",
              "[159107 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        },
        "id": "B9hs5ar2DOBt",
        "outputId": "f67e6aca-8816-4a7c-beb5-ac6115ff51cd"
      },
      "source": [
        "  fig = plt.figure(figsize = (12,12))\n",
        "  ax = fig.gca()\n",
        "  cd2.hist(ax = ax)"
      ],
      "execution_count": 14,
      "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 0x7fad005d7a50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fad152c9190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fad15819790>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fad159f0b10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fad15f08610>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fad161e4c10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fad166632d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fad16a09810>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fad16a09850>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "qC51kWbhEywO",
        "outputId": "8958ca84-5868-49b3-8168-85ff9ee9a24a"
      },
      "source": [
        "correlations = cd2.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": {
        "id": "VVAxdxD_FSA2"
      },
      "source": [
        "y = cd2.SOC\n",
        "X = cd2.drop(['SOC'], axis=1)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lWEv98nHWPCz",
        "outputId": "31c31c40-eb52-4e4d-baeb-3e1952202552"
      },
      "source": [
        "X.describe()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "    </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",
              "      <td>159107.000000</td>\n",
              "      <td>159107.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>87279.243341</td>\n",
              "      <td>10.493090</td>\n",
              "      <td>0.000390</td>\n",
              "      <td>3.735026</td>\n",
              "      <td>0.568921</td>\n",
              "      <td>1.542466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>46456.798733</td>\n",
              "      <td>5.747789</td>\n",
              "      <td>0.295084</td>\n",
              "      <td>0.231053</td>\n",
              "      <td>0.687331</td>\n",
              "      <td>0.616400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000502</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>3.033892</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47094.959959</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.578578</td>\n",
              "      <td>0.014028</td>\n",
              "      <td>0.999794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>87297.368555</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.678893</td>\n",
              "      <td>0.138467</td>\n",
              "      <td>1.999575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127494.672648</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.946941</td>\n",
              "      <td>1.213525</td>\n",
              "      <td>1.999575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>167715.033539</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.001522</td>\n",
              "      <td>4.328594</td>\n",
              "      <td>2.028843</td>\n",
              "      <td>1.999575</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Test_Time(s)  ...  Discharge_Capacity(Ah)\n",
              "count  159107.000000  ...           159107.000000\n",
              "mean    87279.243341  ...                1.542466\n",
              "std     46456.798733  ...                0.616400\n",
              "min        10.000502  ...                0.000000\n",
              "25%     47094.959959  ...                0.999794\n",
              "50%     87297.368555  ...                1.999575\n",
              "75%    127494.672648  ...                1.999575\n",
              "max    167715.033539  ...                1.999575\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7or5lAg9zv05"
      },
      "source": [
        "split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3KejRNIz8L6",
        "outputId": "e7f899ca-63ac-4e93-befa-d3818d2e30f7"
      },
      "source": [
        "cd2.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.00120490e+00, 0.00000000e+00, 3.43425655e+00, 0.00000000e+00,\n",
              "        0.00000000e+00, 5.00000000e-01],\n",
              "       [2.01676330e+00, 0.00000000e+00, 3.43441844e+00, 0.00000000e+00,\n",
              "        0.00000000e+00, 5.49657906e-01],\n",
              "       [3.03225394e+00, 0.00000000e+00, 3.43458056e+00, 0.00000000e+00,\n",
              "        0.00000000e+00, 5.49753273e-01],\n",
              "       ...,\n",
              "       [3.35194814e+04, 0.00000000e+00, 4.19588470e+00, 4.00551180e+00,\n",
              "        2.00944414e+00, 9.97579238e-01],\n",
              "       [3.35204970e+04, 0.00000000e+00, 4.19588470e+00, 4.00551180e+00,\n",
              "        2.00944414e+00, 9.97579238e-01],\n",
              "       [3.35212939e+04, 0.00000000e+00, 4.19588470e+00, 4.00551180e+00,\n",
              "        2.00944414e+00, 9.97579238e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZ7aC4j11Rpw"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihBUmg5bzu8E"
      },
      "source": [
        "dataset = cd2.SOC.values #numpy.ndarray\n",
        "dataset = dataset.astype('float32')\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": 35,
      "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": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dw523Skl1al8"
      },
      "source": [
        "# reshape into X=t and Y=t+1\n",
        "look_back = 6\n",
        "X_train, Y_train = create_dataset(train, look_back)\n",
        "X_test, Y_test = create_dataset(test, look_back)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu8EmIUW13Ts",
        "outputId": "f21e9979-f812-4e62-a70a-b9f881ad440e"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127278, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaHqs7FL2FKn",
        "outputId": "7c21ad88-274c-4ea9-c338-822478e943b3"
      },
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127278,)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "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": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIfIWrpP2NcJ",
        "outputId": "8116592e-4a72-4a8b-eebb-d27d17bd29f8"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127278, 1, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "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": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFJgXQVc2QAj",
        "outputId": "82a6a335-8c5e-4ac1-9f9d-c806f592523f"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(50, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(Dropout(0.6))\n",
        "model.add(Dense(2))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "history = model.fit(X_train, Y_train, epochs=50, batch_size=32, validation_data=(X_test, Y_test), \n",
        "                    callbacks=[EarlyStopping(monitor='val_loss', patience=20)], verbose=1, shuffle=False)\n",
        "\n",
        "# Training Phase\n",
        "model.summary()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "3978/3978 [==============================] - 23s 5ms/step - loss: 0.0114 - val_loss: 0.0172\n",
            "Epoch 2/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0016 - val_loss: 0.0265\n",
            "Epoch 3/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0014 - val_loss: 0.0294\n",
            "Epoch 4/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0014 - val_loss: 0.0267\n",
            "Epoch 5/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0013 - val_loss: 0.0268\n",
            "Epoch 6/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0013 - val_loss: 0.0260\n",
            "Epoch 7/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0013 - val_loss: 0.0258\n",
            "Epoch 8/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0013 - val_loss: 0.0254\n",
            "Epoch 9/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0012 - val_loss: 0.0252\n",
            "Epoch 10/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0012 - val_loss: 0.0245\n",
            "Epoch 11/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0012 - val_loss: 0.0245\n",
            "Epoch 12/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0011 - val_loss: 0.0245\n",
            "Epoch 13/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0011 - val_loss: 0.0241\n",
            "Epoch 14/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0011 - val_loss: 0.0242\n",
            "Epoch 15/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0011 - val_loss: 0.0243\n",
            "Epoch 16/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0011 - val_loss: 0.0236\n",
            "Epoch 17/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0010 - val_loss: 0.0230\n",
            "Epoch 18/50\n",
            "3978/3978 [==============================] - 21s 5ms/step - loss: 0.0010 - val_loss: 0.0235\n",
            "Epoch 19/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 0.0010 - val_loss: 0.0234\n",
            "Epoch 20/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 9.9931e-04 - val_loss: 0.0228\n",
            "Epoch 21/50\n",
            "3978/3978 [==============================] - 22s 5ms/step - loss: 9.8260e-04 - val_loss: 0.0223\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 50)                11400     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 102       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11,502\n",
            "Trainable params: 11,502\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9igKq7u2jvl",
        "outputId": "ddb23513-7684-4454-98e7-fece42d99b6d"
      },
      "source": [
        "# make predictions\n",
        "train_predict = model.predict(X_train)\n",
        "test_predict = 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": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Mean Absolute Error: 0.093277231899141\n",
            "Train Root Mean Squared Error: 0.10117577252215415\n",
            "Test Mean Absolute Error: 0.08126673827008718\n",
            "Test Root Mean Squared Error: 0.10251687241601182\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hADXDvCd2qzF",
        "outputId": "02910ced-d0ec-4b9a-e653-70eeeef5f96b"
      },
      "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": 44,
      "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": 284
        },
        "id": "stz_dngk2wpa",
        "outputId": "52ccbd10-0f18-4e76-bcb8-c5b224377559"
      },
      "source": [
        "aa=[x for x in range(10000)]\n",
        "plt.figure(figsize=(20,4))\n",
        "plt.plot(aa, Y_test[0][:10000], marker='.', label=\"actual\")\n",
        "plt.plot(aa, test_predict[:,0][:10000], 'r', label=\"prediction\")\n",
        "#plt.tight_layout()\n",
        "#sns.despine(top=True)\n",
        "plt.subplots_adjust(left=0.07)\n",
        "plt.ylabel('SOC', size=15)\n",
        "plt.xlabel('Time step', size=15)\n",
        "plt.legend(fontsize=15)\n",
        "plt.show();"
      ],
      "execution_count": 56,
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9upe3CIKFjui",
        "outputId": "ffc98728-5edd-4171-b302-6596a02b14ac"
      },
      "source": [
        "y.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    142119.000000\n",
              "mean          0.691114\n",
              "std           0.118811\n",
              "min           0.000000\n",
              "25%           0.633504\n",
              "50%           0.672399\n",
              "75%           0.787366\n",
              "max           0.988512\n",
              "Name: SOC, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbXLDjCuW5EB"
      },
      "source": [
        "#split the dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size = 0.3, random_state = 1) #1 is seed for random state , useful for regenerating same"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1cFeJ6ZZLLL0",
        "outputId": "fe83febf-3913-4044-8ae3-7ae3e2541485"
      },
      "source": [
        "X_train[1:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Test_Time(s)</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22706</th>\n",
              "      <td>28576.552241</td>\n",
              "      <td>0.824601</td>\n",
              "      <td>4.199935</td>\n",
              "      <td>3.778262</td>\n",
              "      <td>2.009444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18466</th>\n",
              "      <td>18658.375942</td>\n",
              "      <td>-1.000162</td>\n",
              "      <td>3.419675</td>\n",
              "      <td>1.996313</td>\n",
              "      <td>1.540662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16507</th>\n",
              "      <td>16678.177943</td>\n",
              "      <td>-1.000162</td>\n",
              "      <td>3.527577</td>\n",
              "      <td>1.996313</td>\n",
              "      <td>0.990547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22399</th>\n",
              "      <td>26972.366988</td>\n",
              "      <td>0.999950</td>\n",
              "      <td>3.982350</td>\n",
              "      <td>3.336901</td>\n",
              "      <td>2.009444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Test_Time(s)  Current(A)  ...  Charge_Capacity(Ah)  Discharge_Capacity(Ah)\n",
              "22706  28576.552241    0.824601  ...             3.778262                2.009444\n",
              "18466  18658.375942   -1.000162  ...             1.996313                1.540662\n",
              "16507  16678.177943   -1.000162  ...             1.996313                0.990547\n",
              "22399  26972.366988    0.999950  ...             3.336901                2.009444\n",
              "\n",
              "[4 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnZATi--aPBy"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Td6WLxAAJcGp"
      },
      "source": [
        "\n",
        "\n",
        "#Creating Instance of StandarScaler\n",
        "#scaler = MinMaxScaler()\n",
        "#scaler = StandardScaler()\n",
        "#Perform scaling in X_train with fit_transform.\n",
        "#Here we are applying fit_transform because,\n",
        "        # fit will calculate mean and standard deviation of X_train\n",
        "        # transform will actually perform scaling with calculated mean and std.\n",
        "        # fit_transform method does this both thing in one line of code.\n",
        "X_train1 = scaler.fit_transform(X_train)\n",
        "\n",
        "# Here we will only use transform because we have already calculated mean and std.\n",
        "# Another reason is we don't want to know the mean and std of our test dataset As it\n",
        "# Lead to information leakage.\n",
        "X_test1 = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qt74xvs4IBGj"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87EBtoI4KO0r",
        "outputId": "29059ae2-21bb-4fe1-f94f-35106243389d"
      },
      "source": [
        "X_train1[1:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.35294118, 0.50008222, 0.62582368, 0.00954253, 0.70433413,\n",
              "        0.01020189, 0.725581  ],\n",
              "       [0.41176471, 0.50008222, 0.60076108, 0.00954253, 0.80495919,\n",
              "        0.01020189, 0.82212311],\n",
              "       [0.64705882, 0.50008222, 0.6355703 , 0.36398502, 1.        ,\n",
              "        0.349739  , 1.        ],\n",
              "       [0.35294118, 0.50008222, 0.62406014, 0.00954253, 0.70433413,\n",
              "        0.01020189, 0.725581  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 381
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDPopP5LU33b"
      },
      "source": [
        "a1=X_train1.shape[0]\n",
        "a2=X_train1.shape[1]\n",
        "a3=X_test1.shape[0]\n",
        "a4=X_test1.shape[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDWxggUWSRYt",
        "outputId": "edcc80e8-66b9-44c6-d15c-81d89e02c4ac"
      },
      "source": [
        "X_train1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.60495156, 0.52941176, 0.50008222, ..., 1.        , 0.12107558,\n",
              "        1.        ],\n",
              "       [0.38259242, 0.35294118, 0.50008222, ..., 0.70433413, 0.01020189,\n",
              "        0.725581  ],\n",
              "       [0.43491065, 0.41176471, 0.50008222, ..., 0.80495919, 0.01020189,\n",
              "        0.82212311],\n",
              "       ...,\n",
              "       [0.19091954, 0.11764706, 0.50008222, ..., 0.301847  , 0.01020189,\n",
              "        0.32665175],\n",
              "       [0.2913361 , 0.23529412, 0.50008222, ..., 0.50309009, 0.01020189,\n",
              "        0.52929068],\n",
              "       [0.99390948, 0.94117647, 0.50008222, ..., 1.        , 0.95138796,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 336
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-l_n8YAMSZo"
      },
      "source": [
        "# reshape input to be [samples, time steps, features]\n",
        "X_train2 = np.reshape(X_train1, (a1, 1, a2))\n",
        "X_test2 = np.reshape(X_test1, (a3,1, a4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7pbRcmrqABQ",
        "outputId": "86ba3ba5-5850-4081-d6d7-2bf7dce9800f"
      },
      "source": [
        "X_train2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(85271, 1, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 428
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHU_curtYGv5",
        "outputId": "83471f54-cd44-47f6-baa1-c83d1142aa88"
      },
      "source": [
        "y_train2=y_train.to_numpy()\n",
        "y_train2\n",
        "y_test2=y_test.to_numpy()\n",
        "y_test2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.63207216, 0.42418453, 0.70184814, ..., 0.64467682, 0.65214965,\n",
              "       0.61019394])"
            ]
          },
          "metadata": {},
          "execution_count": 429
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8kms4CWNi6R"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JckeJKvhNRrr",
        "outputId": "f1ba5a8c-aa82-4db9-badf-5aad792b6f9b"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(50, input_shape=(X_train2.shape[1], X_train2.shape[2])))\n",
        "model.add(Dropout(0.6))\n",
        "model.add(Dense(2))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "history = model.fit(X_train2, y_train2, epochs=20, batch_size=32, validation_data=(X_test2, y_test2), \n",
        "                    callbacks=[EarlyStopping(monitor='val_loss', patience=10)], verbose=1, shuffle=False)\n",
        "\n",
        "# Training Phase\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "2665/2665 [==============================] - 13s 4ms/step - loss: 0.0150 - val_loss: 3.9886e-04\n",
            "Epoch 2/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 0.0011 - val_loss: 2.8477e-04\n",
            "Epoch 3/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 6.2021e-04 - val_loss: 1.1893e-04\n",
            "Epoch 4/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.7525e-04 - val_loss: 3.4772e-05\n",
            "Epoch 5/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.4919e-04 - val_loss: 9.4581e-06\n",
            "Epoch 6/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.5217e-04 - val_loss: 2.1717e-05\n",
            "Epoch 7/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.3846e-04 - val_loss: 7.5175e-05\n",
            "Epoch 8/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.3855e-04 - val_loss: 6.7489e-05\n",
            "Epoch 9/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.2767e-04 - val_loss: 1.7911e-05\n",
            "Epoch 10/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.3578e-04 - val_loss: 1.6668e-05\n",
            "Epoch 11/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.3546e-04 - val_loss: 2.9912e-05\n",
            "Epoch 12/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.3473e-04 - val_loss: 4.3118e-05\n",
            "Epoch 13/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.2914e-04 - val_loss: 2.8471e-05\n",
            "Epoch 14/20\n",
            "2665/2665 [==============================] - 11s 4ms/step - loss: 4.2998e-04 - val_loss: 6.3201e-05\n",
            "Epoch 15/20\n",
            "2665/2665 [==============================] - 10s 4ms/step - loss: 4.3126e-04 - val_loss: 2.7037e-05\n",
            "Model: \"sequential_33\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_31 (LSTM)              (None, 50)                11600     \n",
            "                                                                 \n",
            " dropout_29 (Dropout)        (None, 50)                0         \n",
            "                                                                 \n",
            " dense_29 (Dense)            (None, 2)                 102       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11,702\n",
            "Trainable params: 11,702\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ac3hNOheevsV",
        "outputId": "343c44a0-b578-4941-ac51-2bdf8c982c48"
      },
      "source": [
        "X_train2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(85271, 1, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 432
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJNDVfzg5r3D"
      },
      "source": [
        "# make predictions\n",
        "train_predict = model.predict(X_train2)\n",
        "test_predict = model.predict(X_test2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euM66N1Or2xh",
        "outputId": "6499c8f4-bac2-4652-e0c3-7e43e22cc39f"
      },
      "source": [
        "train_predict.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(85271, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 434
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DG5VrwhL56Hu"
      },
      "source": [
        "train_predict.shape\n",
        "train_predict=np.delete(train_predict,1,1) #1,1 1st 1 represents 2nd(0,1) and 2nd 1 represents axis =1 ie vertical ie columns\n",
        "train_predict;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0w-Q4LFiiAQ",
        "outputId": "f0879299-eff5-4b80-e60d-340b947bb7ab"
      },
      "source": [
        "test_predict=np.delete(test_predict,1,1) #1,1 1st 1 represents 2nd(0,1) and 2nd 1 represents axis =1 ie vertical ie columns\n",
        "\n",
        "test_predict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.6305881 ],\n",
              "       [0.43137866],\n",
              "       [0.69799805],\n",
              "       ...,\n",
              "       [0.6452552 ],\n",
              "       [0.65107596],\n",
              "       [0.6093849 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 436
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ILQK7zD9Bvk",
        "outputId": "14668974-5db5-4858-a3a3-e103618179e1"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(85271,)"
            ]
          },
          "metadata": {},
          "execution_count": 437
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fen2ho2W8KMk",
        "outputId": "8c1d1f28-f4b9-472d-cded-f7c1de67ebcc"
      },
      "source": [
        "z = np.zeros((train_predict.shape[0],6), dtype=np.int64)\n",
        "train_predict2=np.append(train_predict, z, axis=1)\n",
        "train_predict2.shape\n",
        "\n",
        "z = np.zeros((test_predict.shape[0],6), dtype=np.int64)\n",
        "test_predict2=np.append(test_predict, z, axis=1)\n",
        "test_predict2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(56848, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 438
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBQ3E6wy-Qp-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "726fe269-9068-490d-f1d9-16d8db2da74f"
      },
      "source": [
        "# invert predictions\n",
        "\n",
        "train_predict3 = scaler.inverse_transform(train_predict2)\n",
        "#y_train = scaler.inverse_transform([y_train])\n",
        "test_predict3 = scaler.inverse_transform(test_predict2)\n",
        "#y_test = scaler.inverse_transform([y_test])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-cb57c0765f03>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# invert predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrain_predict3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_predict2\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      4\u001b[0m \u001b[0;31m#y_train = scaler.inverse_transform([y_train])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtest_predict3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_predict2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'scaler' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "LoIxxb3yhSyH",
        "outputId": "c24f46bd-45f1-4e9e-bf18-5fce138ea898"
      },
      "source": [
        "train_predict2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-cd4a143e7f4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_predict2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'train_predict2' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k169Hiiu-pRt",
        "outputId": "ad750869-27c4-4318-ca43-bb1b472e412f"
      },
      "source": [
        "train_predict3[:,0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11.46641475, 11.15107423, 11.46227348, ..., 13.5930441 ,\n",
              "       12.00150609, 14.48897624])"
            ]
          },
          "metadata": {},
          "execution_count": 440
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dmSiQAOZ5oR",
        "outputId": "5d37026b-3a2e-41ab-d7bb-d5f58f46e3a9"
      },
      "source": [
        "print('Train Mean Absolute Error:', mean_absolute_error(y_train, train_predict3[:,0]))\n",
        "print('Train Root Mean Squared Error:',np.sqrt(mean_squared_error(y_train, train_predict3[:,0])))\n",
        "print('Test Mean Absolute Error:', mean_absolute_error(y_test, test_predict3[:,0]))\n",
        "print('Test Root Mean Squared Error:',np.sqrt(mean_squared_error(y_test, test_predict3[:,0])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Mean Absolute Error: 11.483963278837287\n",
            "Train Root Mean Squared Error: 11.619462533194804\n",
            "Test Mean Absolute Error: 11.489873426364861\n",
            "Test Root Mean Squared Error: 11.62519924136153\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpbzmGfcDYsD",
        "outputId": "1ee96293-2911-4976-9f32-1ff4f611080f"
      },
      "source": [
        "y_test3[1:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.42418453, 0.70184814, 0.61028401, 0.74407392])"
            ]
          },
          "metadata": {},
          "execution_count": 442
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJU_dNSAJPV3",
        "outputId": "8c855dc4-c7dd-4386-f48f-05cb54c3a4d1"
      },
      "source": [
        "test_predict3.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(56848, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 443
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "F4FgvyxuZ8bK",
        "outputId": "4898cf87-0d0f-4bd8-ee11-82a5c0c04ace"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "0Z42pzCpZ_SO",
        "outputId": "6c986560-b84f-4f09-ee7a-20d112c1c9ca"
      },
      "source": [
        "aa=[x for x in range(40)]\n",
        "plt.figure(figsize=(18,15))\n",
        "plt.plot(aa, 100*y_test3[20:60], marker='.', label=\"actual\")\n",
        "plt.plot(aa, 100*test_predict[:,0][20:60], '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=8)\n",
        "plt.xlabel('Time step', size=15)\n",
        "plt.legend(fontsize=15)\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-3757578982d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0maa\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0my_test3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"actual\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtest_predict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"prediction\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# plt.tick_params(left=False, labelleft=True) #remove ticks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    }
  ]
}