GreatRes_att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_25deg.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_25deg.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1i7AAoUvs8slVYi7ARI5TC-oPVQIVDEbH",
      "authorship_tag": "ABX9TyMISEvoO/O7gNw4ohQx8Wlo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "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/GreatRes_att2dec17R3implementation_1_SOC_LSTM_CALCE_IC2_25deg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5wlvJX-tvun",
        "outputId": "0f5dc7ce-bda7-4cae-ee5c-3da15e610636"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tI6EDld8uL52"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import files\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ju4NGqAquQk9"
      },
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/calce_incremental_INR_18650-20R/Incremental_temp_25.xlsx')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vKmhVn9DO0Mo",
        "outputId": "f42daa3c-02c9-4b4f-9377-469d440363f3"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c34e3ffc-40c4-4b6a-b8a1-b39b5c42bf0e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.97</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0156</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0155</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0156</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0155</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>1048568</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048569</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048570</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048571</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048572</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1048573 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c34e3ffc-40c4-4b6a-b8a1-b39b5c42bf0e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c34e3ffc-40c4-4b6a-b8a1-b39b5c42bf0e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c34e3ffc-40c4-4b6a-b8a1-b39b5c42bf0e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Test_Time(s)  Cycle_Index  ...  TimeDelta       SOC\n",
              "0           10.000502          1.0  ...          1      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]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXneLTAPvFFs"
      },
      "source": [
        "df=df.dropna()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrVQ2ETgCrim"
      },
      "source": [
        "combined_data=df"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAjkmkUaqh0E"
      },
      "source": [
        "combined_data.to_csv('Incremental_Init_25.csv')\n",
        "combined_data=pd.read_csv('Incremental_Init_25.csv')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "oLqTe4j6GuNp",
        "outputId": "5a887edf-ba99-472a-c1e5-756b180c8155"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4b72f609-e465-4a23-8098-08ff17631c8b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</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>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>71059.000000</td>\n",
              "      <td>78705.582922</td>\n",
              "      <td>9.029032</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684208</td>\n",
              "      <td>0.412327</td>\n",
              "      <td>1.481353</td>\n",
              "      <td>1.058963</td>\n",
              "      <td>0.691114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>41026.365791</td>\n",
              "      <td>41479.490967</td>\n",
              "      <td>4.802561</td>\n",
              "      <td>0.303031</td>\n",
              "      <td>0.207378</td>\n",
              "      <td>0.539736</td>\n",
              "      <td>0.625663</td>\n",
              "      <td>0.660660</td>\n",
              "      <td>0.118811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000167</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>2.499903</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35529.500000</td>\n",
              "      <td>42831.169001</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.577119</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>0.999809</td>\n",
              "      <td>1.000083</td>\n",
              "      <td>0.633504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>71059.000000</td>\n",
              "      <td>78728.254274</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.661715</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>1.799699</td>\n",
              "      <td>1.015559</td>\n",
              "      <td>0.672399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>106588.500000</td>\n",
              "      <td>114616.556029</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.839495</td>\n",
              "      <td>0.815788</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015613</td>\n",
              "      <td>0.787366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>142118.000000</td>\n",
              "      <td>150507.800260</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.375753</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>10.017162</td>\n",
              "      <td>0.988512</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b72f609-e465-4a23-8098-08ff17631c8b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b72f609-e465-4a23-8098-08ff17631c8b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4b72f609-e465-4a23-8098-08ff17631c8b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          Unnamed: 0   Test_Time(s)  ...      TimeDelta            SOC\n",
              "count  142119.000000  142119.000000  ...  142119.000000  142119.000000\n",
              "mean    71059.000000   78705.582922  ...       1.058963       0.691114\n",
              "std     41026.365791   41479.490967  ...       0.660660       0.118811\n",
              "min         0.000000      10.000167  ...       0.000000       0.000000\n",
              "25%     35529.500000   42831.169001  ...       1.000083       0.633504\n",
              "50%     71059.000000   78728.254274  ...       1.015559       0.672399\n",
              "75%    106588.500000  114616.556029  ...       1.015613       0.787366\n",
              "max    142118.000000  150507.800260  ...      10.017162       0.988512\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nMfX0Z056Fe"
      },
      "source": [
        "###\n",
        "combined_data=combined_data.drop_duplicates()\n",
        "#combined_data.set_index('Data_Point',inplace=True)\n",
        "#cd2=combined_data.drop(63990)\n",
        "#cdcombined_data.drop(127979)\n",
        "\n",
        "#cd2=combined_data;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "6NHZKiuogMXL",
        "outputId": "e25edf04-e3aa-411b-f4c4-ea8d4ef0fab4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cb343849-f044-41fb-a352-6b32fb553e07\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</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>0</td>\n",
              "      <td>10.000167</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20.015713</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015546</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>30.031239</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015526</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>40.046793</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015553</td>\n",
              "      <td>0.980123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>50.062341</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015548</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142114</th>\n",
              "      <td>142114</td>\n",
              "      <td>150503.768645</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249022</td>\n",
              "      <td>1.691192</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015622</td>\n",
              "      <td>0.889110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142115</th>\n",
              "      <td>142115</td>\n",
              "      <td>150504.768729</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999364</td>\n",
              "      <td>4.249184</td>\n",
              "      <td>1.691470</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.000084</td>\n",
              "      <td>0.889249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142116</th>\n",
              "      <td>142116</td>\n",
              "      <td>150505.784258</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249508</td>\n",
              "      <td>1.691752</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015528</td>\n",
              "      <td>0.889390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142117</th>\n",
              "      <td>142117</td>\n",
              "      <td>150506.784411</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249671</td>\n",
              "      <td>1.692029</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.000153</td>\n",
              "      <td>0.889528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142118</th>\n",
              "      <td>142118</td>\n",
              "      <td>150507.800260</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015849</td>\n",
              "      <td>0.889669</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142119 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb343849-f044-41fb-a352-6b32fb553e07')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb343849-f044-41fb-a352-6b32fb553e07 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb343849-f044-41fb-a352-6b32fb553e07');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Unnamed: 0   Test_Time(s)  ...  TimeDelta       SOC\n",
              "0                0      10.000167  ...   1.000000  0.970000\n",
              "1                1      20.015713  ...  10.015546  0.980028\n",
              "2                2      30.031239  ...  10.015526  0.980028\n",
              "3                3      40.046793  ...  10.015553  0.980123\n",
              "4                4      50.062341  ...  10.015548  0.980028\n",
              "...            ...            ...  ...        ...       ...\n",
              "142114      142114  150503.768645  ...   1.015622  0.889110\n",
              "142115      142115  150504.768729  ...   1.000084  0.889249\n",
              "142116      142116  150505.784258  ...   1.015528  0.889390\n",
              "142117      142117  150506.784411  ...   1.000153  0.889528\n",
              "142118      142118  150507.800260  ...   1.015849  0.889669\n",
              "\n",
              "[142119 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lYfOvKWBml25",
        "outputId": "039994d7-1b43-4f97-c3f1-ebbb08ea1de7"
      },
      "source": [
        "combined_data=combined_data.iloc[:,1:] #drop first column\n",
        "combined_data.shape\n",
        "combined_data"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cefa03cb-7929-475f-90cd-1267ea512825\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>10.000167</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.015713</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015546</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30.031239</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015526</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40.046793</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015553</td>\n",
              "      <td>0.980123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.062341</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.015548</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142114</th>\n",
              "      <td>150503.768645</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249022</td>\n",
              "      <td>1.691192</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015622</td>\n",
              "      <td>0.889110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142115</th>\n",
              "      <td>150504.768729</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999364</td>\n",
              "      <td>4.249184</td>\n",
              "      <td>1.691470</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.000084</td>\n",
              "      <td>0.889249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142116</th>\n",
              "      <td>150505.784258</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249508</td>\n",
              "      <td>1.691752</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015528</td>\n",
              "      <td>0.889390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142117</th>\n",
              "      <td>150506.784411</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249671</td>\n",
              "      <td>1.692029</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.000153</td>\n",
              "      <td>0.889528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142118</th>\n",
              "      <td>150507.800260</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>1.015849</td>\n",
              "      <td>0.889669</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142119 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cefa03cb-7929-475f-90cd-1267ea512825')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cefa03cb-7929-475f-90cd-1267ea512825 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cefa03cb-7929-475f-90cd-1267ea512825');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Test_Time(s)  Cycle_Index  ...  TimeDelta       SOC\n",
              "0           10.000167          1.0  ...   1.000000  0.970000\n",
              "1           20.015713          1.0  ...  10.015546  0.980028\n",
              "2           30.031239          1.0  ...  10.015526  0.980028\n",
              "3           40.046793          1.0  ...  10.015553  0.980123\n",
              "4           50.062341          1.0  ...  10.015548  0.980028\n",
              "...               ...          ...  ...        ...       ...\n",
              "142114  150503.768645         18.0  ...   1.015622  0.889110\n",
              "142115  150504.768729         18.0  ...   1.000084  0.889249\n",
              "142116  150505.784258         18.0  ...   1.015528  0.889390\n",
              "142117  150506.784411         18.0  ...   1.000153  0.889528\n",
              "142118  150507.800260         18.0  ...   1.015849  0.889669\n",
              "\n",
              "[142119 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "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",
      "source": [
        "combined_data=combined_data.drop(columns=['Charge_Capacity(Ah)','Discharge_Capacity(Ah)','TimeDelta'])"
      ],
      "metadata": {
        "id": "AaFig_2fj2kU"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1gdmPPRs-kJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "014bf2cb-3767-4b23-a261-54f3e759b126"
      },
      "source": [
        "combined_data.hist()\n",
        "plt.figure(figsize=(20,20))\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data=combined_data.drop(columns=['Test_Time(s)'])"
      ],
      "metadata": {
        "id": "V47_5QPvkYg1"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0OIEihXABYQ",
        "outputId": "ad4c08bf-ada6-471a-a48c-69f7691d043d"
      },
      "source": [
        "  fig = plt.figure(figsize = (15,15))\n",
        "  ax = fig.gca()\n",
        "  combined_data.hist(ax = ax)"
      ],
      "execution_count": 13,
      "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 0x7ff4d0fc4650>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff4d20446d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ff4d248dc90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff4d27ca2d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "AG_E_pvAvEvy",
        "outputId": "eb763294-a13b-4f6d-dcae-bee326044996"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "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": "d6f6670a-6fab-4a9b-eee6-e28af4dbc3db"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3c135c09-4695-463f-8a39-5e9dcd54c649\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.029032</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684208</td>\n",
              "      <td>0.691114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.802561</td>\n",
              "      <td>0.303031</td>\n",
              "      <td>0.207378</td>\n",
              "      <td>0.118811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>2.499903</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.577119</td>\n",
              "      <td>0.633504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.661715</td>\n",
              "      <td>0.672399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.839495</td>\n",
              "      <td>0.787366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.375753</td>\n",
              "      <td>0.988512</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c135c09-4695-463f-8a39-5e9dcd54c649')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c135c09-4695-463f-8a39-5e9dcd54c649 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c135c09-4695-463f-8a39-5e9dcd54c649');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)            SOC\n",
              "count  142119.000000  142119.000000  142119.000000  142119.000000\n",
              "mean        9.029032      -0.007765       3.684208       0.691114\n",
              "std         4.802561       0.303031       0.207378       0.118811\n",
              "min         1.000000      -1.000233       2.499903       0.000000\n",
              "25%         5.000000       0.000000       3.577119       0.633504\n",
              "50%         9.000000       0.000000       3.661715       0.672399\n",
              "75%        13.000000       0.000000       3.839495       0.787366\n",
              "max        18.000000       0.999904       4.375753       0.988512"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jej3N8-4JDj4"
      },
      "source": [
        "Deleting 0 valued columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lA5X5VM0CvOI",
        "outputId": "52c056d9-61a1-491d-c6e8-77083a9863b4"
      },
      "source": [
        "#cd2=pd.read_csv('/content/drive/MyDrive/LSTM_SOC_Implementation/r1.csv')\n",
        "cd2=combined_data\n",
        "cd2.describe()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2fe2d06d-ec28-47eb-92b3-2e8579acfae0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.029032</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684208</td>\n",
              "      <td>0.691114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.802561</td>\n",
              "      <td>0.303031</td>\n",
              "      <td>0.207378</td>\n",
              "      <td>0.118811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>2.499903</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.577119</td>\n",
              "      <td>0.633504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.661715</td>\n",
              "      <td>0.672399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.839495</td>\n",
              "      <td>0.787366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.375753</td>\n",
              "      <td>0.988512</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fe2d06d-ec28-47eb-92b3-2e8579acfae0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2fe2d06d-ec28-47eb-92b3-2e8579acfae0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2fe2d06d-ec28-47eb-92b3-2e8579acfae0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)            SOC\n",
              "count  142119.000000  142119.000000  142119.000000  142119.000000\n",
              "mean        9.029032      -0.007765       3.684208       0.691114\n",
              "std         4.802561       0.303031       0.207378       0.118811\n",
              "min         1.000000      -1.000233       2.499903       0.000000\n",
              "25%         5.000000       0.000000       3.577119       0.633504\n",
              "50%         9.000000       0.000000       3.661715       0.672399\n",
              "75%        13.000000       0.000000       3.839495       0.787366\n",
              "max        18.000000       0.999904       4.375753       0.988512"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "FSzDs6JUPobf",
        "outputId": "d9b61c80-5cc1-440a-8afc-8fbb250580fc"
      },
      "source": [
        "cd2"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-84b3f34d-4fa0-4c82-9f1a-25ac4d174ae0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>SOC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.980123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.980028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142114</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249022</td>\n",
              "      <td>0.889110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142115</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999364</td>\n",
              "      <td>4.249184</td>\n",
              "      <td>0.889249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142116</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249508</td>\n",
              "      <td>0.889390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142117</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249671</td>\n",
              "      <td>0.889528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142118</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>0.889669</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142119 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84b3f34d-4fa0-4c82-9f1a-25ac4d174ae0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84b3f34d-4fa0-4c82-9f1a-25ac4d174ae0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-84b3f34d-4fa0-4c82-9f1a-25ac4d174ae0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Cycle_Index  Current(A)  Voltage(V)       SOC\n",
              "0               1.0    0.000000    4.166209  0.970000\n",
              "1               1.0    0.000000    4.166047  0.980028\n",
              "2               1.0    0.000000    4.166047  0.980028\n",
              "3               1.0    0.000000    4.166209  0.980123\n",
              "4               1.0    0.000000    4.166047  0.980028\n",
              "...             ...         ...         ...       ...\n",
              "142114         18.0    0.999544    4.249022  0.889110\n",
              "142115         18.0    0.999364    4.249184  0.889249\n",
              "142116         18.0    0.999544    4.249508  0.889390\n",
              "142117         18.0    0.999544    4.249671  0.889528\n",
              "142118         18.0    0.999544    4.249832  0.889669\n",
              "\n",
              "[142119 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVAxdxD_FSA2"
      },
      "source": [
        "y = cd2.SOC\n",
        "X = cd2.drop(['SOC'], axis=1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lWEv98nHWPCz",
        "outputId": "519fe37e-d1af-48e1-ac78-5e60a7168605"
      },
      "source": [
        "X.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a772eb4f-c77d-4ed2-ae56-7d2e3a70d241\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.029032</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.802561</td>\n",
              "      <td>0.303031</td>\n",
              "      <td>0.207378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000233</td>\n",
              "      <td>2.499903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.577119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.661715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.839495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.375753</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a772eb4f-c77d-4ed2-ae56-7d2e3a70d241')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a772eb4f-c77d-4ed2-ae56-7d2e3a70d241 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a772eb4f-c77d-4ed2-ae56-7d2e3a70d241');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Cycle_Index     Current(A)     Voltage(V)\n",
              "count  142119.000000  142119.000000  142119.000000\n",
              "mean        9.029032      -0.007765       3.684208\n",
              "std         4.802561       0.303031       0.207378\n",
              "min         1.000000      -1.000233       2.499903\n",
              "25%         5.000000       0.000000       3.577119\n",
              "50%         9.000000       0.000000       3.661715\n",
              "75%        13.000000       0.000000       3.839495\n",
              "max        18.000000       0.999904       4.375753"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7or5lAg9zv05"
      },
      "source": [
        "split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3KejRNIz8L6",
        "outputId": "4149ae74-ef7c-4bfb-c021-9c87d6816740"
      },
      "source": [
        "cd2.values"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.        ,  0.        ,  4.16620922,  0.97      ],\n",
              "       [ 1.        ,  0.        ,  4.1660471 ,  0.9800277 ],\n",
              "       [ 1.        ,  0.        ,  4.1660471 ,  0.9800277 ],\n",
              "       ...,\n",
              "       [18.        ,  0.99954402,  4.24950838,  0.88938957],\n",
              "       [18.        ,  0.99954402,  4.24967051,  0.88952842],\n",
              "       [18.        ,  0.99954402,  4.24983215,  0.88966944]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZ7aC4j11Rpw"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihBUmg5bzu8E"
      },
      "source": [
        "dataset = cd2.SOC.values #numpy.ndarray\n",
        "dataset = dataset.astype('float64')\n",
        "dataset = np.reshape(dataset, (-1, 1))\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "dataset = scaler.fit_transform(dataset)\n",
        "train_size = int(len(dataset) * 0.80)\n",
        "test_size = len(dataset) - train_size\n",
        "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]"
      ],
      "execution_count": 24,
      "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": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dw523Skl1al8"
      },
      "source": [
        "# reshape into X=t and Y=t+1\n",
        "look_back = 3\n",
        "\n",
        "X_train, Y_train = create_dataset(train, look_back)\n",
        "X_test, Y_test = create_dataset(test, look_back)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uk0IHjKKlUnK",
        "outputId": "09c4d11d-73dd-43f0-bbb3-53dc4f7cfbd0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.98127282, 0.99141706, 0.99141706],\n",
              "       [0.99141706, 0.99141706, 0.99151354],\n",
              "       [0.99141706, 0.99151354, 0.99141706],\n",
              "       ...,\n",
              "       [0.69930767, 0.69950048, 0.69930767],\n",
              "       [0.69950048, 0.69930767, 0.6992112 ],\n",
              "       [0.69930767, 0.6992112 , 0.69930767]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu8EmIUW13Ts",
        "outputId": "e370dba0-0326-4052-f50f-43682b4d144a"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(113691, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaHqs7FL2FKn",
        "outputId": "b96c5d69-67ea-4486-db95-69253c3e1afd"
      },
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(113691,)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "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": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06NjbtWi2Z6m"
      },
      "source": [
        "import math\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import *\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFJgXQVc2QAj",
        "outputId": "746c8e5b-13f7-4437-fbef-cc41883fb8ad"
      },
      "source": [
        "model = Sequential()\n",
        "#model.add(LSTM(,return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(LSTM(50, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(2))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "history = model.fit(X_train, Y_train, epochs=100, batch_size=64, validation_data=(X_test, Y_test), \n",
        "                    callbacks=[EarlyStopping(monitor='val_loss', patience=50)], verbose=1, shuffle=False)\n",
        "\n",
        "# Training Phase\n",
        "model.summary()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1777/1777 [==============================] - 9s 4ms/step - loss: 0.0203 - val_loss: 6.2112e-04\n",
            "Epoch 2/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 0.0014 - val_loss: 8.5071e-04\n",
            "Epoch 3/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 0.0010 - val_loss: 9.0748e-04\n",
            "Epoch 4/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 8.1428e-04 - val_loss: 0.0015\n",
            "Epoch 5/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 7.3410e-04 - val_loss: 0.0019\n",
            "Epoch 6/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 6.4870e-04 - val_loss: 0.0015\n",
            "Epoch 7/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 5.4244e-04 - val_loss: 0.0012\n",
            "Epoch 8/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 4.2461e-04 - val_loss: 8.1354e-04\n",
            "Epoch 9/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 3.1587e-04 - val_loss: 4.6097e-04\n",
            "Epoch 10/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 2.3784e-04 - val_loss: 4.1894e-04\n",
            "Epoch 11/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.9203e-04 - val_loss: 3.1500e-04\n",
            "Epoch 12/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6717e-04 - val_loss: 3.8866e-04\n",
            "Epoch 13/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5419e-04 - val_loss: 4.5512e-04\n",
            "Epoch 14/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5744e-04 - val_loss: 5.5094e-04\n",
            "Epoch 15/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5911e-04 - val_loss: 6.4178e-04\n",
            "Epoch 16/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5925e-04 - val_loss: 6.7594e-04\n",
            "Epoch 17/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6001e-04 - val_loss: 6.9575e-04\n",
            "Epoch 18/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5646e-04 - val_loss: 8.3831e-04\n",
            "Epoch 19/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6673e-04 - val_loss: 7.7248e-04\n",
            "Epoch 20/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5510e-04 - val_loss: 8.3698e-04\n",
            "Epoch 21/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.6003e-04 - val_loss: 7.3860e-04\n",
            "Epoch 22/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5181e-04 - val_loss: 8.4456e-04\n",
            "Epoch 23/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.5419e-04 - val_loss: 7.8135e-04\n",
            "Epoch 24/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4941e-04 - val_loss: 7.9714e-04\n",
            "Epoch 25/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4780e-04 - val_loss: 7.7416e-04\n",
            "Epoch 26/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4604e-04 - val_loss: 7.1146e-04\n",
            "Epoch 27/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.3923e-04 - val_loss: 7.6171e-04\n",
            "Epoch 28/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.4313e-04 - val_loss: 7.5858e-04\n",
            "Epoch 29/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.3801e-04 - val_loss: 7.1609e-04\n",
            "Epoch 30/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.3794e-04 - val_loss: 8.1362e-04\n",
            "Epoch 31/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.4225e-04 - val_loss: 7.1920e-04\n",
            "Epoch 32/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.2745e-04 - val_loss: 6.6229e-04\n",
            "Epoch 33/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.2718e-04 - val_loss: 6.4156e-04\n",
            "Epoch 34/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.2405e-04 - val_loss: 7.4012e-04\n",
            "Epoch 35/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.3101e-04 - val_loss: 6.8650e-04\n",
            "Epoch 36/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.2785e-04 - val_loss: 7.0383e-04\n",
            "Epoch 37/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.2567e-04 - val_loss: 6.7978e-04\n",
            "Epoch 38/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.2037e-04 - val_loss: 5.6783e-04\n",
            "Epoch 39/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1316e-04 - val_loss: 6.7110e-04\n",
            "Epoch 40/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.2440e-04 - val_loss: 6.6202e-04\n",
            "Epoch 41/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.2309e-04 - val_loss: 6.0287e-04\n",
            "Epoch 42/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1489e-04 - val_loss: 6.4375e-04\n",
            "Epoch 43/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1525e-04 - val_loss: 5.9420e-04\n",
            "Epoch 44/100\n",
            "1777/1777 [==============================] - 6s 3ms/step - loss: 1.1345e-04 - val_loss: 6.7770e-04\n",
            "Epoch 45/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.2013e-04 - val_loss: 5.9262e-04\n",
            "Epoch 46/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1292e-04 - val_loss: 6.0163e-04\n",
            "Epoch 47/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.1282e-04 - val_loss: 6.2880e-04\n",
            "Epoch 48/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1546e-04 - val_loss: 5.8597e-04\n",
            "Epoch 49/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1034e-04 - val_loss: 5.8773e-04\n",
            "Epoch 50/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1270e-04 - val_loss: 5.7510e-04\n",
            "Epoch 51/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1201e-04 - val_loss: 5.8043e-04\n",
            "Epoch 52/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.0878e-04 - val_loss: 5.3548e-04\n",
            "Epoch 53/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.0749e-04 - val_loss: 6.4672e-04\n",
            "Epoch 54/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1425e-04 - val_loss: 5.8008e-04\n",
            "Epoch 55/100\n",
            "1777/1777 [==============================] - 6s 4ms/step - loss: 1.1119e-04 - val_loss: 6.0992e-04\n",
            "Epoch 56/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1301e-04 - val_loss: 5.3873e-04\n",
            "Epoch 57/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1046e-04 - val_loss: 7.3475e-04\n",
            "Epoch 58/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.2027e-04 - val_loss: 5.3654e-04\n",
            "Epoch 59/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.0772e-04 - val_loss: 5.8441e-04\n",
            "Epoch 60/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1223e-04 - val_loss: 6.4094e-04\n",
            "Epoch 61/100\n",
            "1777/1777 [==============================] - 7s 4ms/step - loss: 1.1484e-04 - val_loss: 6.0513e-04\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 50)                10800     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 50)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 102       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,902\n",
            "Trainable params: 10,902\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9igKq7u2jvl",
        "outputId": "04f3419e-3351-428f-855e-7eea7fe3bab0"
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Mean Absolute Error: 0.012532890548755646\n",
            "Train Root Mean Squared Error: 0.020521774951570208\n",
            "Test Mean Absolute Error: 0.020227271912268977\n",
            "Test Root Mean Squared Error: 0.024317925676767955\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hADXDvCd2qzF",
        "outputId": "f608f33b-6c2f-40eb-f7e6-0d84c644dd6f"
      },
      "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": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAEWCAYAAAAZ9I+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dfZxcZX3//9d7Z2d3NvcQlgBZMAECGiKEkkIBUSBFEW+iCBoFDC0ttRXRrwqCrdTykH6ltaIIP5EKgtQKGAXiFyzlVrFqIEEEAqSEEMyG3G6Sze3ezO7n98c5sztZdpPdZGazw76fj8c89sy5ueY6Z2fmvOe6rjOjiMDMzMyGt6q9XQEzMzPb+xwIzMzMzIHAzMzMHAjMzMwMBwIzMzPDgcDMzMxwIDCzMpB0m6Sv9XPdZZL+fE/LMbM940BgZmZmDgRmZmbmQGA2bKVN9ZdJelbSVkm3SJog6ReSNkt6WNI+Ret/UNIiSRslPS7pbUXLjpX0dLrdXUCux2O9X9Iz6ba/kXT0btb5ryUtkbRe0jxJB6XzJek6SWskbZL0nKRp6bKzJL2Q1m2FpC/u1gEze5NzIDAb3j4CnAEcAXwA+AXwZaCe5P3hUgBJRwA/Bj6XLnsA+LmkGkk1wL3AHcC+wE/Sckm3PRa4FfgbYDzwPWCepNqBVFTS6cD/BT4KHAi8BtyZLn438M50P8am6zSly24B/iYiRgPTgEcH8rhmw4UDgdnw9p2IWB0RK4AngPkR8fuIaAHuAY5N1/sYcH9EPBQR7cA3gDrgJODPgCzwrYhoj4i5wFNFj3Ex8L2ImB8RHRFxO9CabjcQ5wG3RsTTEdEKXAmcKGkS0A6MBt4KKCJejIiV6XbtwFRJYyJiQ0Q8PcDHNRsWHAjMhrfVRdPbe7k/Kp0+iOQTOQAR0QksByamy1bEjr+U9lrR9FuAL6TdBRslbQQOTrcbiJ512ELSCjAxIh4FbgBuBNZIulnSmHTVjwBnAa9J+qWkEwf4uGbDggOBmfXH6yQndiDpsyc5qa8AVgIT03kFhxRNLweuiYhxRbcREfHjPazDSJIuiBUAEXF9RBwHTCXpOrgsnf9URMwC9ifp2rh7gI9rNiw4EJhZf9wNvE/STElZ4Askzf6/AX4L5IFLJWUlnQ0cX7TtvwOfknRCOvhvpKT3SRo9wDr8GPgLSdPT8Qf/TNLFsUzSn6blZ4GtQAvQmY5xOE/S2LSrYxPQuQfHwexNy4HAzHYpIhYD5wPfAdaRDED8QES0RUQbcDZwIbCeZLzBz4q2XQD8NUmT/gZgSbruQOvwMPAV4KckrRKHAbPTxWNIgscGkm6FJuBf02UXAMskbQI+RTIWwcx60I7dfmZmZjYcuYXAzMzMHAjMzMzMgcDMzMxwIDAzMzOgem9XYG/ab7/9YtKkSXu7GmZmZoNi4cKF6yKivrdlwzoQTJo0iQULFuztapiZmQ0KSa/1tcxdBmZmZuZAYGZmZg4EZmZmxjAfQ2BmZkNDe3s7jY2NtLS07O2qvCnkcjkaGhrIZrP93saBwMzM9rrGxkZGjx7NpEmT2PGHM22gIoKmpiYaGxuZPHlyv7dzl4GZme11LS0tjB8/3mGgBCQxfvz4Abe2OBCYmdmQ4DBQOrtzLB0ISuS5xma+8eBiNre07+2qmJmZDVhZA4GkMyUtlrRE0hW9LK+VdFe6fL6kSen8MyQtlPRc+vf0om2OS+cvkXS90hgkaV9JD0l6Of27Tzn3racXV23ihseWsHGbA4GZWaVpampi+vTpTJ8+nQMOOICJEyd23W9ra9vptgsWLODSSy8d0ONNmjSJdevW7UmVS65sgwolZYAbgTOARuApSfMi4oWi1S4CNkTE4ZJmA9cCHwPWAR+IiNclTQMeBCam23wX+GtgPvAAcCbwC+AK4JGI+HoaPq4AvlSu/eupLpsBoKW9Y7Ae0szMSmT8+PE888wzAHz1q19l1KhRfPGLX+xans/nqa7u/ZQ5Y8YMZsyYMSj1LKdythAcDyyJiKUR0QbcCczqsc4s4PZ0ei4wU5Ii4vcR8Xo6fxFQl7YmHAiMiYjfRUQAPwQ+1EtZtxfNHxS5rkDQOZgPa2ZmZXLhhRfyqU99ihNOOIHLL7+cJ598khNPPJFjjz2Wk046icWLFwPw+OOP8/73vx9IwsRf/uVfcuqpp3LooYdy/fXX9/vxli1bxumnn87RRx/NzJkz+eMf/wjAT37yE6ZNm8YxxxzDO9/5TgAWLVrE8ccfz/Tp0zn66KN5+eWX93h/y3nZ4URgedH9RuCEvtaJiLykZmA8SQtBwUeApyOiVdLEtJziMgstBxMiYmU6vQqY0FulJF0MXAxwyCGHDHSf+pTLJtlqu1sIzMz2yD/9fBEvvL6ppGVOPWgM//iBowa8XWNjI7/5zW/IZDJs2rSJJ554gurqah5++GG+/OUv89Of/vQN27z00ks89thjbN68mSOPPJK//du/7df3AXzmM59hzpw5zJkzh1tvvZVLL72Ue++9l6uvvpoHH3yQiRMnsnHjRgBuuukmPvvZz3LeeefR1tZGR8een3uG9PcQSDqKpBvh3QPZLiJCUvSx7GbgZoAZM2b0us7ucJeBmdmbz7nnnksmk7y/Nzc3M2fOHF5++WUk0d7e+5ix973vfdTW1lJbW8v+++/P6tWraWho2OVj/fa3v+VnP/sZABdccAGXX345ACeffDIXXnghH/3oRzn77LMBOPHEE7nmmmtobGzk7LPPZsqUKXu8r+UMBCuAg4vuN6TzelunUVI1MBZoApDUANwDfDIiXilav/ioFpe5WtKBEbEy7VpYU8qd2ZWcA4GZWUnszif5chk5cmTX9Fe+8hVOO+007rnnHpYtW8app57a6za1tbVd05lMhnw+v0d1uOmmm5g/fz73338/xx13HAsXLuQTn/gEJ5xwAvfffz9nnXUW3/ve9zj99NN3XdhOlHMMwVPAFEmTJdUAs4F5PdaZB8xJp88BHk0/3Y8D7geuiIj/KaycdglskvRn6dUFnwTu66WsOUXzB0UhELjLwMzszam5uZmJE5Ne6ttuu63k5Z900knceeedAPzoRz/ilFNOAeCVV17hhBNO4Oqrr6a+vp7ly5ezdOlSDj30UC699FJmzZrFs88+u8ePX7ZAEBF54BKSKwReBO6OiEWSrpb0wXS1W4DxkpYAnye5MoB0u8OBqyQ9k972T5f9HfB9YAnwCskVBgBfB86Q9DLw5+n9QVMYQ9DqQYVmZm9Kl19+OVdeeSXHHnvsHn/qBzj66KNpaGigoaGBz3/+83znO9/hBz/4AUcffTR33HEH3/72twG47LLLePvb3860adM46aSTOOaYY7j77ruZNm0a06dP5/nnn+eTn/zkHtdHyWD94WnGjBmxYMGCkpS1bksrM772MFfPOopPnjipJGWamQ0XL774Im9729v2djXeVHo7ppIWRkSv10j6mwpLpDCocHubuwzMzKzyOBCUiL+HwMzMKpkDQYlkqkQ2Iw8qNDOziuRAUEK5bMaXHZqZWUVyICihXDZDa96BwMzMKo8DQQnlslUeVGhmZhVpSH91caWpy2Y8qNDMrAI1NTUxc+ZMAFatWkUmk6G+vh6AJ598kpqamp1u//jjj1NTU8NJJ530hmW33XYbCxYs4IYbbih9xUvIgaCEctkMLe4yMDOrOLv6+eNdefzxxxk1alSvgaBSuMughHLZjLsMzMzeJBYuXMi73vUujjvuON7znvewcmXyg7rXX389U6dO5eijj2b27NksW7aMm266ieuuu47p06fzxBNP9Kv8b37zm0ybNo1p06bxrW99C4CtW7fyvve9j2OOOYZp06Zx1113AXDFFVd0PeZAgspAuIWghHLZDM3be//1KzMz66dfXAGrnittmQe8Hd7b/2+0jwg+85nPcN9991FfX89dd93F3//933Prrbfy9a9/nVdffZXa2lo2btzIuHHj+NSnPjWgVoWFCxfygx/8gPnz5xMRnHDCCbzrXe9i6dKlHHTQQdx///1A8vsJTU1N3HPPPbz00ktI6voJ5FJzC0EJ5aqraPVlh2ZmFa+1tZXnn3+eM844g+nTp/O1r32NxsZGIPkNgvPOO4//+I//oLp69z5X//rXv+bDH/4wI0eOZNSoUZx99tk88cQTvP3tb+ehhx7iS1/6Ek888QRjx45l7Nix5HI5LrroIn72s58xYsSIUu5qF7cQlFBdTcZfTGRmtqcG8Em+XCKCo446it/+9rdvWHb//ffzq1/9ip///Odcc801PPdc6VozjjjiCJ5++mkeeOAB/uEf/oGZM2dy1VVX8eSTT/LII48wd+5cbrjhBh599NGSPWaBWwhKKFftLyYyM3szqK2tZe3atV2BoL29nUWLFtHZ2cny5cs57bTTuPbaa2lubmbLli2MHj2azZs397v8U045hXvvvZdt27axdetW7rnnHk455RRef/11RowYwfnnn89ll13G008/zZYtW2hubuass87iuuuu4w9/+ENZ9tktBCXk7yEwM3tzqKqqYu7cuVx66aU0NzeTz+f53Oc+xxFHHMH5559Pc3MzEcGll17KuHHj+MAHPsA555zDfffdx3e+8x1OOeWUHcq77bbbuPfee7vu/+53v+PCCy/k+OOPB+Cv/uqvOPbYY3nwwQe57LLLqKqqIpvN8t3vfpfNmzcza9YsWlpaiAi++c1vlmWf/fPHJfr5Y4D/+4sX+cH/LON/v/bekpVpZjYc+OePS29I/fyxpDMlLZa0RNIVvSyvlXRXuny+pEnp/PGSHpO0RdINReuPlvRM0W2dpG+lyy6UtLZo2V+Vc996k6vO0JbvpLNz+IYsMzOrTGXrMpCUAW4EzgAagackzYuIF4pWuwjYEBGHS5oNXAt8DGgBvgJMS28ARMRmYHrRYywEflZU3l0RcUmZdmmXun4COd/BiBr3xpiZWeUoZwvB8cCSiFgaEW3AncCsHuvMAm5Pp+cCMyUpIrZGxK9JgkGvJB0B7A/07xsgBkFdNjmc/vpiM7OBG85d2KW2O8eynIFgIrC86H5jOq/XdSIiDzQD4/tZ/mySFoHivf6IpGclzZV0cG8bSbpY0gJJC9auXdvPh+qfrhYCX2lgZjYguVyOpqYmh4ISiAiamprI5XID2q6S27VnAxcU3f858OOIaJX0NyQtD6f33CgibgZuhmRQYSkrVFeTBAJ/F4GZ2cA0NDTQ2NhIqT+oDVe5XI6GhoYBbVPOQLACKP6U3pDO622dRknVwFigaVcFSzoGqI6IhYV5EVG83feBf9nNeu+22mq3EJiZ7Y5sNsvkyZP3djWGtXJ2GTwFTJE0WVINySf6eT3WmQfMSafPAR6N/rUXfRz4cfEMSQcW3f0g8OJu1XoP5DyGwMzMKlTZWggiIi/pEuBBIAPcGhGLJF0NLIiIecAtwB2SlgDrSUIDAJKWAWOAGkkfAt5ddIXCR4GzejzkpZI+COTTsi4s1771pc5jCMzMrEKVdQxBRDwAPNBj3lVF0y3AuX1sO2kn5R7ay7wrgSt3t66l4EGFZmZWqfxbBiVUCAQeVGhmZpXGgaCEursMPIbAzMwqiwNBCXUPKnQLgZmZVRYHghKq9RgCMzOrUA4EJeSrDMzMrFI5EJRQNiOq5DEEZmZWeRwISkgSddmMrzIwM7OK40BQYrlsxl0GZmZWcRwISiwJBO4yMDOzyuJAUGK5bJVbCMzMrOI4EJSYuwzMzKwSORCUWM6DCs3MrAI5EJRYnVsIzMysAjkQlFgyhsCDCs3MrLI4EJRYrVsIzMysAjkQlJi7DMzMrBKVNRBIOlPSYklLJF3Ry/JaSXely+dLmpTOHy/pMUlbJN3QY5vH0zKfSW/776yswZbLVtGSd5eBmZlVlrIFAkkZ4EbgvcBU4OOSpvZY7SJgQ0QcDlwHXJvObwG+Anyxj+LPi4jp6W3NLsoaVHXZDNvb3EJgZmaVpZwtBMcDSyJiaUS0AXcCs3qsMwu4PZ2eC8yUpIjYGhG/JgkG/dVrWbtf/d2Ty2ZoyXcQEYP90GZmZrutnIFgIrC86H5jOq/XdSIiDzQD4/tR9g/S7oKvFJ30+1WWpIslLZC0YO3atQPZn37JZTNEQFuHuw3MzKxyVOKgwvMi4u3AKentgoFsHBE3R8SMiJhRX19f8srlshkAWtocCMzMrHKUMxCsAA4uut+Qzut1HUnVwFigaWeFRsSK9O9m4D9JuiZ2q6xyyGWTQ9qS9zgCMzOrHOUMBE8BUyRNllQDzAbm9VhnHjAnnT4HeDR20vkuqVrSful0Fng/8PzulFUuueqkhcADC83MrJJUl6vgiMhLugR4EMgAt0bEIklXAwsiYh5wC3CHpCXAepLQAICkZcAYoEbSh4B3A68BD6ZhIAM8DPx7ukmfZQ2mupq0y8AtBGZmVkHKFggAIuIB4IEe864qmm4Bzu1j20l9FHtcH+v3WdZg6uoy8NcXm5lZBanEQYVDmrsMzMysEjkQlFjOXQZmZlaBHAhKrNBC0OrfMzAzswriQFBihUGF2x0IzMysgjgQlJgHFZqZWSVyICixQpeBfwLZzMwqiQNBibnLwMzMKpEDQYnVVrvLwMzMKo8DQYlJora6yl0GZmZWURwIyqCuJuNAYGZmFcWBoAxy1Q4EZmZWWRwIyiCXrWK7xxCYmVkFcSAog1zWLQRmZlZZHAjKwIHAzMwqjQNBGdQ5EJiZWYUpayCQdKakxZKWSLqil+W1ku5Kl8+XNCmdP17SY5K2SLqhaP0Rku6X9JKkRZK+XrTsQklrJT2T3v6qnPu2M7lslb+HwMzMKkrZAoGkDHAj8F5gKvBxSVN7rHYRsCEiDgeuA65N57cAXwG+2EvR34iItwLHAidLem/RsrsiYnp6+34Jd2dA3GVgZmaVppwtBMcDSyJiaUS0AXcCs3qsMwu4PZ2eC8yUpIjYGhG/JgkGXSJiW0Q8lk63AU8DDWXch91Sl834q4vNzKyilDMQTASWF91vTOf1uk5E5IFmYHx/Cpc0DvgA8EjR7I9IelbSXEkH97HdxZIWSFqwdu3a/u3JANVmM+4yMDOzilKRgwolVQM/Bq6PiKXp7J8DkyLiaOAhulsedhARN0fEjIiYUV9fX5b65bJVtLqFwMzMKkg5A8EKoPhTekM6r9d10pP8WKCpH2XfDLwcEd8qzIiIpohoTe9+HzhuN+u9x9xlYGZmlaacgeApYIqkyZJqgNnAvB7rzAPmpNPnAI9GROysUElfIwkOn+sx/8Ciux8EXtyDuu+RXDZDvjNo73C3gZmZVYbqchUcEXlJlwAPAhng1ohYJOlqYEFEzANuAe6QtARYTxIaAJC0DBgD1Ej6EPBuYBPw98BLwNOSAG5Iryi4VNIHgXxa1oXl2rddyWULP4HcQTZTkb0yZmY2zJQtEABExAPAAz3mXVU03QKc28e2k/ooVn2sfyVw5W5VtMTqshkAWto7GZ3by5UxMzPrB398LYParkDgcQRmZlYZHAjKoM6BwMzMKowDQRnkiroMzMzMKoEDQRl0DSrMu4XAzMwqgwNBGRS6DLa3ORCYmVllcCAog5zHEJiZWYVxICiD7i4DjyEwM7PK4EBQBl0tBO4yMDOzCuFAUAZdgcCDCs3MrEI4EJRBzoMKzcyswjgQlEGuuvBbBh5DYGZmlaFfgUDSZyWNUeIWSU9Lene5K1epqjNVZDNyl4GZmVWM/rYQ/GVEbCL5xcF9gAuAr5etVm8CuWzGXQZmZlYx+hsICr8weBZwR0Qsoo9fHbRELpuh1S0EZmZWIfobCBZK+m+SQPCgpNGAO8h3Ipet8hgCMzOrGNX9XO8iYDqwNCK2SdoX+IvyVavy1bnLwMzMKkh/WwhOBBZHxEZJ5wP/ADTvaiNJZ0paLGmJpCt6WV4r6a50+XxJk9L54yU9JmmLpBt6bHOcpOfSba6XpHT+vpIekvRy+neffu5bWeSyGQ8qNDOzitHfQPBdYJukY4AvAK8AP9zZBpIywI3Ae4GpwMclTe2x2kXAhog4HLgOuDad3wJ8BfhiH3X5a2BKejsznX8F8EhETAEeSe/vNbnqjH/LwMzMKkZ/A0E+IgKYBdwQETcCo3exzfHAkohYGhFtwJ3p9sVmAben03OBmZIUEVsj4tckwaCLpAOBMRHxu7Q+PwQ+1EtZtxfN3ytyNRm2ewyBmZlViP4Ggs2SriS53PB+SVVAdhfbTASWF91vTOf1uk5E5Em6IcbvoszGPsqcEBEr0+lVwITeCpB0saQFkhasXbt2F7uw+3LVVbS6hcDMzCpEfwPBx4BWku8jWAU0AP9atlrtobT1IPpYdnNEzIiIGfX19WWrQy6bYbsDgZmZVYh+BYI0BPwIGCvp/UBLROx0DAGwAji46H5DOq/XdSRVA2OBpl2U2dBHmavTLoVC18KaXdSvrOqyHkNgZmaVo79fXfxR4EngXOCjwHxJ5+xis6eAKZImS6oBZgPzeqwzD5iTTp8DPJp+uu9V2iWwSdKfpVcXfBK4r5ey5hTN3yv8PQRmZlZJ+vs9BH8P/GlErAGQVA88TDIQsFcRkZd0CfAgkAFujYhFkq4GFkTEPOAW4A5JS4D1JKGB9DGWAWOAGkkfAt4dES8AfwfcBtQBv0hvkHyV8t2SLgJeIwkue00yqNAtBGZmVhn6GwiqCmEg1UQ/Whci4gHggR7zriqabiFpdeht20l9zF8ATOtlfhMwc1d1Giy56gxt+U46O4OqKn/Ls5mZDW39DQT/JelB4Mfp/Y/R40RvO8plMwC05jupq8ns5dqYmZntXL8CQURcJukjwMnprJsj4p7yVavy1WWTBpTt7R0OBGZmNuT1t4WAiPgp8NMy1uVNpdBC4CsNzMysEuw0EEjaTO/X84vkcv8xZanVm4ADgZmZVZKdBoKI2NXXE1sfCoHAVxqYmVkl6O83FdoA5dIxBP4uAjMzqwQOBGXiLgMzM6skDgRlUudAYGZmFcSBoEy6WwjcZWBmZkOfA0GZ1HlQoZmZVRAHgjLpHlToQGBmZkOfA0GZ1HoMgZmZVRAHgjLxoEIzM6skDgRlks2IKnlQoZmZVQYHgjKRRC6bcQuBmZlVhLIGAklnSlosaYmkK3pZXivprnT5fEmTipZdmc5fLOk96bwjJT1TdNsk6XPpsq9KWlG07Kxy7lt/1GUzvsrAzMwqQr9/7XCgJGWAG4EzgEbgKUnzIuKFotUuAjZExOGSZgPXAh+TNBWYDRwFHAQ8LOmIiFgMTC8qfwVQ/DPM10XEN8q1TwOVtBC4y8DMzIa+crYQHA8siYilEdEG3AnM6rHOLOD2dHouMFOS0vl3RkRrRLwKLEnLKzYTeCUiXivbHuyhXLbKXQZmZlYRyhkIJgLLi+43pvN6XSci8kAzML6f284Gftxj3iWSnpV0q6R9equUpIslLZC0YO3atQPZnwHzGAIzM6sUFTmoUFIN8EHgJ0WzvwscRtKlsBL4t962jYibI2JGRMyor68vaz1z2QwteQcCMzMb+soZCFYABxfdb0jn9bqOpGpgLNDUj23fCzwdEasLMyJidUR0REQn8O+8sYth0NVlM2xvcyAwM7Ohr5yB4ClgiqTJ6Sf62cC8HuvMA+ak0+cAj0ZEpPNnp1chTAamAE8WbfdxenQXSDqw6O6HgedLtie7KRlD4EGFZmY29JXtKoOIyEu6BHgQyAC3RsQiSVcDCyJiHnALcIekJcB6ktBAut7dwAtAHvh0RHQASBpJcuXC3/R4yH+RNB0IYFkvywddrbsMzMysQpQtEABExAPAAz3mXVU03QKc28e21wDX9DJ/K8nAw57zL9jT+pZaXTZDi7sMzMysAlTkoMJKkctW0ZJ3l4GZmQ19DgRllKv2ZYdmZlYZHAjKqK4m+eriZJykmZnZ0OVAUEa5bIYIaOtwt4GZmQ1tDgRllMtmAGhpcyAwM7OhzYGgjHLZ5PD60kMzMxvqHAjKKFedthB4YKGZmQ1xDgRlVFeTBILtDgRmZjbEORCUUVeXgb++2MzMhjgHgjJyl4GZmVUKB4IyyrnLwMzMKoQDQRkVWghaHQjMzGyIcyAoI48hMDOzSuFAUEa+ysDMzCqFA0EZeVChmZlVCgeCMnILgZmZVYqyBgJJZ0paLGmJpCt6WV4r6a50+XxJk4qWXZnOXyzpPUXzl0l6TtIzkhYUzd9X0kOSXk7/7lPOfeuP2mqPITAzs8pQtkAgKQPcCLwXmAp8XNLUHqtdBGyIiMOB64Br022nArOBo4Azgf8vLa/gtIiYHhEziuZdATwSEVOAR9L7e5UkaqurfJWBmZkNeeVsITgeWBIRSyOiDbgTmNVjnVnA7en0XGCmJKXz74yI1oh4FViSlrczxWXdDnyoBPuwx+pqMu4yMDOzIa+cgWAisLzofmM6r9d1IiIPNAPjd7FtAP8taaGki4vWmRARK9PpVcCE3iol6WJJCyQtWLt27cD3aoBy1RkPKjQzsyGvEgcVviMi/oSkK+LTkt7Zc4WICJLg8AYRcXNEzIiIGfX19WWuavJdBB5DYGZmQ105A8EK4OCi+w3pvF7XkVQNjAWadrZtRBT+rgHuobsrYbWkA9OyDgTWlHBfdlsu6y4DMzMb+soZCJ4CpkiaLKmGZJDgvB7rzAPmpNPnAI+mn+7nAbPTqxAmA1OAJyWNlDQaQNJI4N3A872UNQe4r0z7NSC5rLsMzMxs6KsuV8ERkZd0CfAgkAFujYhFkq4GFkTEPOAW4A5JS4D1JKGBdL27gReAPPDpiOiQNAG4Jxl3SDXwnxHxX+lDfh24W9JFwGvAR8u1bwORy1bR6i4DMzMb4soWCAAi4gHggR7zriqabgHO7WPba4BresxbChzTx/pNwMw9rHLJ1WUzrNvStrerYWZmtlOVOKiworjLwMzMKoEDQZnVeVChmZlVAAeCMqvNZnzZoZmZDXkOBGWWDCp0C4GZmQ1tDgRl5i4DMzOrBA4EZZbLZsh3BvkOdxuYmdnQ5UBQZrls+hPIeQcCMzMbuhwIyqwum/xq8/Y2dxuYmdnQ5UBQZrVpIPB3EZiZ2VDmQFBmuTQQtOYdCMzMbOhyICiz7i4DjyEwM7Ohy4GgzFpbrOYAABkoSURBVLoHFbqFwMzMhi4HgjLzoEIzM6sEDgRllvOgQjMzqwAOBGXm7yEwM7NKUNZAIOlMSYslLZF0RS/LayXdlS6fL2lS0bIr0/mLJb0nnXewpMckvSBpkaTPFq3/VUkrJD2T3s4q5771V1cLgbsMzMxsCKsuV8GSMsCNwBlAI/CUpHkR8ULRahcBGyLicEmzgWuBj0maCswGjgIOAh6WdASQB74QEU9LGg0slPRQUZnXRcQ3yrVPu6MrEHhQoZmZDWHlbCE4HlgSEUsjog24E5jVY51ZwO3p9FxgpiSl8++MiNaIeBVYAhwfESsj4mmAiNgMvAhMLOM+7DGPITAzs0pQzkAwEVhedL+RN568u9aJiDzQDIzvz7Zp98KxwPyi2ZdIelbSrZL26a1Ski6WtEDSgrVr1w50nwYsV50cYn8PgZmZDWUVOahQ0ijgp8DnImJTOvu7wGHAdGAl8G+9bRsRN0fEjIiYUV9fX/a6VmeqyGbkLgMzMxvSyhkIVgAHF91vSOf1uo6kamAs0LSzbSVlScLAjyLiZ4UVImJ1RHRERCfw7yRdFkNCrjrjLgMzMxvSyhkIngKmSJosqYZkkOC8HuvMA+ak0+cAj0ZEpPNnp1chTAamAE+m4wtuAV6MiG8WFyTpwKK7HwaeL/ke7aZcjQOBmZkNbWW7yiAi8pIuAR4EMsCtEbFI0tXAgoiYR3Jyv0PSEmA9SWggXe9u4AWSKws+HREdkt4BXAA8J+mZ9KG+HBEPAP8iaToQwDLgb8q1bwOVy1bR0u4xBGZmNnSVLRAApCfqB3rMu6pougU4t49trwGu6THv14D6WP+CPa1vudRlM/7qYjMzG9IqclBhpcllM6UbVNj0Ctz7d/Bvb4X/+jJser005ZqZ2bBW1hYCS5RkUOG6JfDEN+DZuyGThbecBPNvgidvhumfgJM/C+MPK02Fzcxs2HEgGAS5mgzN29t3b+O1/wu/+ld4fi5kauHP/hZOuhRGT4ANy+A334Gn74Df3wFHfRje8Xk4YFpJ629mZm9+DgSDIFddxZqBthC0NMMDlyUtAtk6OPHTSRAYtX/3OvtMgvf9G7zzcvjdjfDUrfD8T+GIM+GUL8DBQ+bKSzMzG+IcCAZBLpth/dY2mre3M7Yuu+sNNr0OPzoX1r4EJ1+aBIGR+/W9/ugJcMbV8I7/A0/+O/zuu3DLGTDplCQYHHoqqNexmGZmZoAHFQ6KU4+sZ+2WVk77xuP85/w/0tEZfa+8+gX4/p/DhtfgE3cnJ/qdhYFidfvAuy6Hzz0H7/lnaFoCd3wIvj8TXrofOn3po5mZ9U7J9wANTzNmzIgFCxYMymMter2Zf5r3Ak8uW8/UA8fw1Q8exfGT991xpVd/BXeen3QRnPcTOPDoPXvQfCs88yP49bdg42uw/1R415dg6iy3GJiZDUOSFkbEjF6XORAMTiAAiAjuf24l/3z/i7ze3ML7jz6QK896GxPH1cGzP4F7/za5UuC8uTDu4F0X2F8d+WRQ4hP/Buv+Fw4+Ad59DRz8p6V7DDMzG/IcCPow2IGgYHtbBzf98hVu+uUrSMG3Jz7Oe1Z9j45DTibz8R8lTf/l0NkBv/8PeOwa2LIajjob/vwfk8GJZmb2pudA0IdBCwQd7cklglvXwbZ1sHUtbG1iy4ZVLH/5Wd629UnmdZzIl+PvmHHYAZx25P6cduT+HDJ+RHnq07oFfnM9/M/1EB1wwqeSwYd148rzeNa3CMi3QHXO3ThmVnYOBH0oayDIt8Grv4RF9yQD+lo2vnGd2jEwYl/y087ltwf/NY8uXsdjL61hWdM2AA6tH8mRE0Zz0Lg6DhpXx8RxOQ4cm0zvN6oG7ekJZNPr8OjX4Jn/TFolTvkC/OlFyRiG4aR9O6x6DlY8Da//Hl5/OglwI/ZLLvMcNSG5kmNUestkYcuapJVly2rYsjb9uwZqRibdPvse2n0bfxiMPThZ3vQyrEtvhemWjVBdB6Pqk/JH7p8+7v5QOzoJlB3t0NGW3tJpVUE2l4SJwi2bS8oase+O5VTXvnG/I6BtC2xbD9vXQ9tWQGkw6fG3Oge5scmtdgxU9RiP3LIJ1i9Nb6/A+leT59foA2CfybDv5O6/I8aXPvx05KEq41BltgsOBH0oeSDIt8HSx+GFe+Gl/5d8l0DtGHjr+5JL/0btn5xkRu6XvCn29iYNvLpuK4+9tIZfL1nHa01beX1jC9t7fI9BTXUVDePqmLhPHQ371NGwzwga9qlj4rg6Dtl3BPWja/sfGFY+Cw9dBUsfg1EHJMHguDl91m/I6WhPTmptW6B1c3Jia9uS3t+SfALPtyT/n47W7unWzbD6OVjzInTmk7JGTYCD/iQ5iW/fWHTSX5207ETRlRp1+6Qn8PREPmp/aN0ETemJccuqvus86gDYb0pyG3NQ8lhb1+4YMLY1kfxWV0oZyNSkt+qkLvnWJNCwi9dx7dikfnXjkv3eth62b4DO3fnCLCVBJTc2+btlTdLyVWz0gclt8yrY3OPrtWtGw7hDktfByP3S10Q9jByfTOfGJs+96trky7iqa5NAkqlJjktX6FiaHutXYPPKZJ0R45MwNGJ8960rVKWBKl8IVq1JfarruoNU8d/RB0H9kcn/aGchOSJp/WtaApvSX3ivyiT/r6oMVFUn09lcup/1kBv3xlDVU741ef7WjNh1SO/sTJ5vG5dD8/LkNTC2Aca9Jfmbze18+0rXtjV5HdTtu+vjujvyrfDab2DJw8mtpRkOOx2mnAGHnrZnraudndC+FbIjkudLmTkQ9KGkgeC5uXD/59MQMBbeehZM/RAcdtoen1gjgo3b2lmxcTsrm1t4feN2VmzczooN22ncuJ0VG7axbkvbDtuMqq3m0PqRHFY/ikP3G8lh+4/i0PqRTBo/kly2jyfdsv9Jxhe89j8wZiK884sw/Xyortmj+u+xiOQEtuHV5JP7DrfXYFPjjifqnUo/7VbXJC/A+rfCQcfCxD9JgsCYg/r+lNnZkbzxd7Ynb+q7+r+2bknqvH4pbPxj8ol9vykw/nDIjdl1VTvy0L4tDQDZvt8sItITXQu0tyTbbFsPW9ckJ+uta5KQsXVNEgJqxyRhZsS+yRtoYbpmVFpeJxBJuYW/+Zbkud3SnLQGFKZbNyUn3eIWkX0nJy0lBe3bk//ThleTloMNryYnrm3r0m60pqScgRpZD/umrTHjDune7+3rkzILt5ZN3UGquqYoVKXP6/z25LjlW5K6viEkCfZ5C+x3ZBIQ9j00CW9NS9KWnlegtXlgdVcmDUT13ZcVt2xKjkPh+BYCCyQhpet/tk9yAsqOTMLWxj9C84qdh7tRE5JjNPbg5LnXmU9ORNGRTnck0/lWaNuWHMv27d1/O1qTIJcbm2xfaC3KjU1eR13S80nhvCJBVbb7+VuVTQJSJv0+lujscUufc7Wjk9CUG5fsa25sMq2qpGVt7UvJt7iuWwxrFychCJKyR+6/Y4veqAnJdu1bu/etbWvyN9+aHM/RB8KYA7uD7OgDk22WPpYEgFd/lb4Wa2HSyUl9Xnksad1TJhmoPeXP4fAzkm23b0iWbd+Q3tLp4ud8oQt52/rk2Bf+z7WjoXZU8nqsHZME5Y/+cGDPr51wIOhDSQPBioXJlwKVKAQM1Pa2DlZs3Ebjhu281rSNpWu3sHTdVl5Zs4XXm1u61qsSTNpvJEfsP5ojDhjNkRNGc8SEUUzabyTZTFXyglz6eBIMGp9K3kTe8X+SE2ehuTg3NnmyljKJt26GzauTT3obliUn0cLJdP2rbzxhjJqQDIbcZ1LyKWj0AckLqWZkUreaUemLamTyhpWpSf4nVdVuVh6K8q3db5Ktm9OWnEJrTlGrzsjxaQiYnDwPy6GzIzkJNi9PTzyLu29NLyfhC2BMA+x3eBLwxh8O46ekVwcpOclGR1JW4WTbtjXdx7VFt3RMEaSvrTFFf8cmz+H2bd0nlm0buqfbtibP+3EHJyf6cQfD2EOSv9kR0NyY7MPGPyaXHW9Mp9u3FbVeFLVkFFoxsmmLRLYunR6RnMDbthaFwqJb29bu7iUoen0pOcl3tg8gsA9AdV0SsOuPTMJa7ejurrstq5L3ky2rkmNMpF1qI9L3h3S/qmuTE/LmlX2H0n0mJSf6KWfApHd0h92OfPK+//J/J7dVz+66znX77NhKXGghy41JnnOtm9NWzrR1s21L8r+Z8/NSHTUHgr7srasMBtu2tjyvrtvKK2u3smT1Zhav3szLq7ewrGkrhe9IymbEpPEjOXz/URxWP4rD6kcwvXUhh/zhm2RW/aGXUpU8iUfslzSvjy96Y9xvSpqwlSTyzSuT/uTC302vJy/ULWuSJuUta5L0XqyqOjnR7zs5+URW3A897pDkBW022DryyafyEfv5OTgQnZ1pS0Q+CQgd+eT9QUo+iRduhVDRujn5hN3SnHy6Lkx3tCfvMfVHJiGoPx9KCo+1q+b41i1pF9fK5Na+PQkA/f3RuM2r4JVHk3Lq9im6jUv+5sYOSpfArjgQ9GG4BIK+tLR3sGTNFl5es5nFq7bwytotvLJmC6+t31b0bYrBO0at4vBRrTTUtXFgro36bCv7ZrYzrmo7o9qbqNm4lKr1ryRNrwXZkcmnir4GU46akHy6KTTpjZ6Q9KuPTj/5j2lI+snNzKxkdhYIyvqOK+lM4NtABvh+RHy9x/Ja4IfAcUAT8LGIWJYuuxK4COgALo2IB3dWpqTJwJ3AeGAhcEFE7NixbjvIZTNMmziWaRN3bHpty3fyWtPWJCCs3coraxt4aeN2HmtuYeXyFtryb2z+y1XD4bnNTK1ZzZTMaibzOrWZTraO2Z9tuf1pqTuA1hEHkB85gUxuDDXVVVRXiepMFdmMyFSJ6qoqqlsEKyFWNhERaQtGEJH0Thbya6R9lbvKsz0X9xaAq6TkgwqiSt0tnh2dkO/spKMzyHcGnenfgKTuVaI6IzJVVV33C49ZqHtn+jciqFKyn1Wie7pKZKSubaKo0oV9lJTUMa1rUkd11bOrgTadkewLXdPQvW5h+6q0zKoqyEjpY9B1vDvToQOdEV3HuKpqx8fvmu56tJ7HPrrK6FkuQHVG1GSqqM6IbKYqvanr/9EXoa59LF43AjoiksfrTB63o7j+hePWdYyKjmHRfvQse4+v5jGrEGVrIZCUAf4XOANoBJ4CPh4RLxSt83fA0RHxKUmzgQ9HxMckTQV+DBwPHAQ8DByRbtZrmZLuBn4WEXdKugn4Q0R8d2d1HO4tBLsjImja2sbKjS283rydNZtb2bS9nU3b22nucdvamqelvZOWfAfb2zpo7SVImA11xQGhEBJ2CHdpqKuqSiJFZyTBsaMjaC8KlL291RYHtUxRQKuqSsrP7CIcpaXsUE+pEOYKYSzoSB+/ozO66l8l0n3oDqZ9PVYSyLuDeWEeRaG1t8BaqEvxvvfnnFMIYVVVOwbAwuMUdrs4DEca/t4QCjsLdXxjWTvuY/oho5eju0PYTvepozO6b5H8vzsiEFCdqer6wFBdVZV+cFD3sSz6wFC4X/jAEz0+AI3OVfPYF0/d5THrr73VQnA8sCQilqaVuBOYBbxQtM4s4Kvp9FzgBiVHfhZwZ0S0Aq9KWpKWR29lSnoROB34RLrO7Wm5Ow0ENnCS2G9ULfuNquXtDQMb1NXZGbR1dLK9rYP2zk7yHUG+6E2zvaOz+8VL9xtKb9PJOoU67bLWPfahezp6fHINous3oKq7Wi6SN83iF3VHZ1L3wpt9Pt2fQvldn8C7WhzU9SbQ0dn9Jt0RSctD8Sf5nm9Whe0Kbxqd6XbJwu4/vbWeFFoeutfpPjH0dsIo1L3rb9Hx6nmSKZSzM4VWBdix3Iik9aW9I/m/5zs6aevY8TnQl8I+FPa5Mz0uGXWfoJMTduEx1XUMOntsW3jz3WnZheOfPk86u/5/Ox6/wv+o8JzJpCeCQutR4X8a3Q/Wdb+4vMJJpvhk1uex2KGoHfelKg0shRagQoABeqn7rh+r+HVYHEIKj184dsWtYlFYs5cWmJ3tU+F/Q9H/d8cwEkXHcWf73P386+1kG0Sv9dphv4rrU/R4mfSEX6X0PaKota/4/ay9I31/6Iyu13bVDq+v4td9j/c7ibq+rgorg3IGgonA8qL7jcAJfa0TEXlJzSRN/hOB3/XYdmI63VuZ44GNEZHvZf0dSLoYuBjgkEMOGdge2R6pqhK5qkzflz2amdleM+x+/jgibo6IGRExo76+fm9Xx8zMbEgoZyBYART/ZF9DOq/XdSRVA2NJBhf2tW1f85uAcWkZfT2WmZmZ9aGcgeApYIqkyZJqgNnAvB7rzAPmpNPnAI9G0tk3D5gtqTa9emAK8GRfZabbPJaWQVrmfWXcNzMzszeVso0hSMcEXAI8SHKJ4K0RsUjS1cCCiJgH3ALckQ4aXE9ygidd726SAYh54NMRyXc79lZm+pBfAu6U9DXg92nZZmZm1g/+YiJfdmhmZsPEzi47HHaDCs3MzOyNHAjMzMzMgcDMzMyG+RgCSWuB10pY5H7AuhKWV+l8PHbk49HNx2JHPh478vHoVupj8ZaI6PVLeIZ1ICg1SQv6GqwxHPl47MjHo5uPxY58PHbk49FtMI+FuwzMzMzMgcDMzMwcCErt5r1dgSHGx2NHPh7dfCx25OOxIx+PboN2LDyGwMzMzNxCYGZmZg4EZmZmhgNByUg6U9JiSUskXbG36zPYJN0qaY2k54vm7SvpIUkvp3/32Zt1HCySDpb0mKQXJC2S9Nl0/nA9HjlJT0r6Q3o8/imdP1nS/PQ1c1f6C6bDgqSMpN9L+n/p/eF8LJZJek7SM5IWpPOG5WsFQNI4SXMlvSTpRUknDtbxcCAoAUkZ4EbgvcBU4OOSpu7dWg2624Aze8y7AngkIqYAj6T3h4M88IWImAr8GfDp9PkwXI9HK3B6RBwDTAfOlPRnwLXAdRFxOLABuGgv1nGwfRZ4sej+cD4WAKdFxPSi6+2H62sF4NvAf0XEW4FjSJ4ng3I8HAhK43hgSUQsjYg24E5g1l6u06CKiF+R/IR1sVnA7en07cCHBrVSe0lErIyIp9PpzSQv6IkM3+MREbElvZtNbwGcDsxN5w+b4yGpAXgf8P30vhimx2InhuVrRdJY4J3ALQAR0RYRGxmk4+FAUBoTgeVF9xvTecPdhIhYmU6vAibszcrsDZImAccC8xnGxyNtIn8GWAM8BLwCbIyIfLrKcHrNfAu4HOhM749n+B4LSMLhf0taKOnidN5wfa1MBtYCP0i7lL4vaSSDdDwcCGxQRHJ967C6xlXSKOCnwOciYlPxsuF2PCKiIyKmAw0kLWpv3ctV2iskvR9YExEL93ZdhpB3RMSfkHS5flrSO4sXDrPXSjXwJ8B3I+JYYCs9ugfKeTwcCEpjBXBw0f2GdN5wt1rSgQDp3zV7uT6DRlKWJAz8KCJ+ls4etsejIG3+fAw4ERgnqTpdNFxeMycDH5S0jKRr8XSSPuPheCwAiIgV6d81wD0kgXG4vlYagcaImJ/en0sSEAbleDgQlMZTwJR0pHANMBuYt5frNBTMA+ak03OA+/ZiXQZN2id8C/BiRHyzaNFwPR71ksal03XAGSTjKh4DzklXGxbHIyKujIiGiJhE8j7xaEScxzA8FgCSRkoaXZgG3g08zzB9rUTEKmC5pCPTWTOBFxik4+FvKiwRSWeR9A1mgFsj4pq9XKVBJenHwKkkP9W5GvhH4F7gbuAQkp+Z/mhE9Bx4+KYj6R3AE8BzdPcTf5lkHMFwPB5HkwyEypB8CLk7Iq6WdCjJp+R9gd8D50dE696r6eCSdCrwxYh4/3A9Ful+35PerQb+MyKukTSeYfhaAZA0nWTAaQ2wFPgL0tcNZT4eDgRmZmbmLgMzMzNzIDAzMzMcCMzMzAwHAjMzM8OBwMzMzHAgMLMhRNKphV8ANLPB5UBgZmZmDgRmNnCSzpf0ZPob9t9Lf7xoi6TrJC2S9Iik+nTd6ZJ+J+lZSfcUfstd0uGSHpb0B0lPSzosLX5U0e/B/yj95kckfV3SC2k539hLu272puVAYGYDIultwMeAk9MfLOoAzgNGAgsi4ijglyTfVgnwQ+BLEXE0ybc3Fub/CLgxIo4BTgIKv+Z2LPA5YCpwKHBy+s11HwaOSsv5Wnn30mz4cSAws4GaCRwHPJX+pPFMkhN3J3BXus5/AO9If999XET8Mp1/O/DO9PvrJ0bEPQAR0RIR29J1noyIxojoBJ4BJgHNQAtwi6SzgcK6ZlYiDgRmNlACbo+I6entyIj4ai/r7e73ohd/h38HUB0ReZJfwZsLvB/4r90s28z64EBgZgP1CHCOpP0BJO0r6S0k7yeFX+z7BPDriGgGNkg6JZ1/AfDLiNgMNEr6UFpGraQRfT2gpFHA2Ih4APg/wDHl2DGz4ax616uYmXWLiBck/QPw35KqgHbg08BW4Ph02RqScQaQ/FzrTekJv/DrbZCEg+9Jujot49ydPOxo4D5JOZIWis+XeLfMhj3/2qGZlYSkLRExam/Xw8x2j7sMzMzMzC0EZmZm5hYCMzMzw4HAzMzMcCAwMzMzHAjMzMwMBwIzMzMD/n8YA3h1lB0fewAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "stz_dngk2wpa",
        "outputId": "e7df4c6f-d482-4cce-dba3-dc516a4ccfab"
      },
      "source": [
        "aa=[x for x in range(20000)]\n",
        "plt.figure(figsize=(20,4))\n",
        "plt.plot(aa, Y_test[0][:20000], marker='.', label=\"actual\")\n",
        "plt.plot(aa, test_predict[:,0][:20000], 'r', label=\"prediction\")\n",
        "plt.tick_params(left=False, labelleft=True) #remove ticks\n",
        "#plt.tight_layout()\n",
        "#sns.despine(top=True)\n",
        "plt.subplots_adjust(left=0.07)\n",
        "plt.ylabel('SOC', size=15)\n",
        "plt.ylim((0,1))\n",
        "plt.xlabel('Time step', size=15)\n",
        "plt.legend(fontsize=15)\n",
        "plt.show();"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aa=[x for x in range(110000)]\n",
        "plt.figure(figsize=(20,4))\n",
        "plt.plot(aa, Y_train[0][:110000], marker='.', label=\"actual\")\n",
        "plt.plot(aa, train_predict[:,0][:110000], 'r', label=\"prediction\")\n",
        "plt.tick_params(left=False, labelleft=True) #remove ticks\n",
        "#plt.tight_layout()\n",
        "#sns.despine(top=True)\n",
        "plt.subplots_adjust(left=0.07)\n",
        "plt.ylabel('SOC', size=15)\n",
        "plt.ylim((0,1))\n",
        "plt.xlabel('Time step', size=15)\n",
        "plt.legend(fontsize=15)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "NQzm3UICyFYt",
        "outputId": "11725be1-fd61-4cf7-f6a4-49a14a992916"
      },
      "execution_count": 36,
      "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"
          }
        }
      ]
    }
  ]
}