implementation_1_SOC_LSTM_CALCE.ipynb

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      "name": "implementation_1_SOC_LSTM_CALCE.ipynb",
      "provenance": [],
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      "mount_file_id": "1i7AAoUvs8slVYi7ARI5TC-oPVQIVDEbH",
      "authorship_tag": "ABX9TyMmSnHn1HJlLl2ey5B1zNGD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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    {
      "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/implementation_1_SOC_LSTM_CALCE.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": "11e138b2-bbe4-4870-c6f0-f76bb9be99a2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\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",
        "sns.set_style('darkgrid')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ju4NGqAquQk9"
      },
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/12_2_2015_Incremental_OCV_test_SP20-1.xlsx')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
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          "height": 374
        },
        "id": "zXneLTAPvFFs",
        "outputId": "889b7acc-81ab-467c-83c8-daa7e4ee3734"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>Aux</td>\n",
              "      <td>SmartBty</td>\n",
              "      <td>Event</td>\n",
              "      <td>Grade</td>\n",
              "      <td>Internal_Resistance(Ohm)</td>\n",
              "      <td>Discharge_Capacity(Ah)</td>\n",
              "      <td>Concentration</td>\n",
              "      <td>Digital Input</td>\n",
              "      <td>Digital Output</td>\n",
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          "data": {
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              "(5, 23)"
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      "cell_type": "code",
      "metadata": {
        "id": "vaZMCdn-xvYC"
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      "source": [
        "ws1 = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/12_2_2015_Incremental_OCV_test_SP20-1.xlsx', sheet_name = 1)\n",
        "ws2 = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/12_2_2015_Incremental_OCV_test_SP20-1.xlsx', sheet_name = 2)\n",
        "ws3 = pd.read_excel('/content/drive/MyDrive/LSTM_SOC_Implementation/12_2_2015_Incremental_OCV_test_SP20-1.xlsx', sheet_name = 3)\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcf44841-1fa0-4c66-c59f-93a693d2ba18"
      },
      "source": [
        "combined_data=ws1.append(ws2,ignore_index=True).append(ws3,ignore_index=True)\n",
        "combined_data.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>142118</th>\n",
              "      <td>142117</td>\n",
              "      <td>150505.784258</td>\n",
              "      <td>2015-12-04 03:57:11</td>\n",
              "      <td>274.794963</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249508</td>\n",
              "      <td>1.691752</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.971949</td>\n",
              "      <td>6.646969</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142119</th>\n",
              "      <td>142118</td>\n",
              "      <td>150506.784411</td>\n",
              "      <td>2015-12-04 03:57:12</td>\n",
              "      <td>275.795116</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249671</td>\n",
              "      <td>1.692029</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.972039</td>\n",
              "      <td>6.648149</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142120</th>\n",
              "      <td>142119</td>\n",
              "      <td>150507.800260</td>\n",
              "      <td>2015-12-04 03:57:13</td>\n",
              "      <td>276.810965</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.972129</td>\n",
              "      <td>6.649329</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142121 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Data_Point   Test_Time(s)  ... AC_Impedance(Ohm)  ACI_Phase_Angle(Deg)\n",
              "0                1      10.000167  ...                 0                     0\n",
              "1                2      20.015713  ...                 0                     0\n",
              "2                3      30.031239  ...                 0                     0\n",
              "3                4      40.046793  ...                 0                     0\n",
              "4                5      50.062341  ...                 0                     0\n",
              "...            ...            ...  ...               ...                   ...\n",
              "142116      142115  150503.768645  ...                 0                     0\n",
              "142117      142116  150504.768729  ...                 0                     0\n",
              "142118      142117  150505.784258  ...                 0                     0\n",
              "142119      142118  150506.784411  ...                 0                     0\n",
              "142120      142119  150507.800260  ...                 0                     0\n",
              "\n",
              "[142121 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_t_G7McbGY1M"
      },
      "source": [
        "combined_data=combined_data.drop(columns=['SoC.1'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "oLqTe4j6GuNp",
        "outputId": "a469bf37-d020-4dd3-b80a-a8568217353d"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data_Point</th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Step_Time(s)</th>\n",
              "      <th>Step_Index</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>SoC</th>\n",
              "      <th>Charge_Energy(Wh)</th>\n",
              "      <th>Discharge_Energy(Wh)</th>\n",
              "      <th>dV/dt(V/s)</th>\n",
              "      <th>Internal_Resistance(Ohm)</th>\n",
              "      <th>Is_FC_Data</th>\n",
              "      <th>AC_Impedance(Ohm)</th>\n",
              "      <th>ACI_Phase_Angle(Deg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.000000</td>\n",
              "      <td>142121.0</td>\n",
              "      <td>142121.0</td>\n",
              "      <td>142121.0</td>\n",
              "      <td>142121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>71060.350750</td>\n",
              "      <td>78706.392361</td>\n",
              "      <td>3301.113778</td>\n",
              "      <td>7.770175</td>\n",
              "      <td>9.029130</td>\n",
              "      <td>-0.007765</td>\n",
              "      <td>3.684209</td>\n",
              "      <td>0.412341</td>\n",
              "      <td>1.481360</td>\n",
              "      <td>0.657894</td>\n",
              "      <td>1.589246</td>\n",
              "      <td>5.207026</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>41026.359223</td>\n",
              "      <td>41479.760320</td>\n",
              "      <td>2192.912020</td>\n",
              "      <td>1.984790</td>\n",
              "      <td>4.802599</td>\n",
              "      <td>0.303028</td>\n",
              "      <td>0.207374</td>\n",
              "      <td>0.539745</td>\n",
              "      <td>0.625662</td>\n",
              "      <td>0.115208</td>\n",
              "      <td>2.090087</td>\n",
              "      <td>2.114297</td>\n",
              "      <td>0.001058</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000167</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>1.000000</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>-0.000054</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.048326</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35531.000000</td>\n",
              "      <td>42831.669001</td>\n",
              "      <td>1251.397393</td>\n",
              "      <td>6.000000</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>0.598400</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>3.647907</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>71060.000000</td>\n",
              "      <td>78729.254299</td>\n",
              "      <td>3234.680500</td>\n",
              "      <td>6.000000</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>0.645397</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>6.317823</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>106590.000000</td>\n",
              "      <td>114618.079463</td>\n",
              "      <td>5217.744364</td>\n",
              "      <td>10.000000</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>0.744164</td>\n",
              "      <td>3.102921</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>142119.000000</td>\n",
              "      <td>150507.800260</td>\n",
              "      <td>7200.013696</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.972129</td>\n",
              "      <td>6.649329</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.082230</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Data_Point   Test_Time(s)  ...  AC_Impedance(Ohm)  ACI_Phase_Angle(Deg)\n",
              "count  142121.000000  142121.000000  ...           142121.0              142121.0\n",
              "mean    71060.350750   78706.392361  ...                0.0                   0.0\n",
              "std     41026.359223   41479.760320  ...                0.0                   0.0\n",
              "min         1.000000      10.000167  ...                0.0                   0.0\n",
              "25%     35531.000000   42831.669001  ...                0.0                   0.0\n",
              "50%     71060.000000   78729.254299  ...                0.0                   0.0\n",
              "75%    106590.000000  114618.079463  ...                0.0                   0.0\n",
              "max    142119.000000  150507.800260  ...                0.0                   0.0\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "hpknBqegD2f3",
        "outputId": "1ea1dcb7-823c-406b-97b5-7da18970ffe1"
      },
      "source": [
        "combined_data.to_csv('merged0.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_a701667c-fe3d-492a-b267-be1aea77901e\", \"merged0.csv\", 29806207)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXN7irT9lVh_"
      },
      "source": [
        "files.download('merged0.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9TT2zf97nvr"
      },
      "source": [
        "**63990, 127979 are repeated in datapoints**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nMfX0Z056Fe"
      },
      "source": [
        "combined_data=combined_data.drop_duplicates(subset=['Data_Point'])\n",
        "combined_data.set_index('Data_Point',inplace=True)\n",
        "#cd2=combined_data.drop(63990)\n",
        "#cdcombined_data.drop(127979)\n",
        "\n",
        "#cd2=combined_data;"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "lYfOvKWBml25",
        "outputId": "66a93150-121f-4a51-ba35-bd2b0880c9da"
      },
      "source": [
        "combined_data.shape\n",
        "combined_data"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Date_Time</th>\n",
              "      <th>Step_Time(s)</th>\n",
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              "      <th>AC_Impedance(Ohm)</th>\n",
              "      <th>ACI_Phase_Angle(Deg)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data_Point</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>1</th>\n",
              "      <td>10.000167</td>\n",
              "      <td>2015-12-02 10:08:48</td>\n",
              "      <td>10.000168</td>\n",
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              "      <th>2</th>\n",
              "      <td>20.015713</td>\n",
              "      <td>2015-12-02 10:08:58</td>\n",
              "      <td>20.015714</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
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              "      <td>0.925582</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30.031239</td>\n",
              "      <td>2015-12-02 10:09:08</td>\n",
              "      <td>30.031240</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.925582</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40.046793</td>\n",
              "      <td>2015-12-02 10:09:18</td>\n",
              "      <td>40.046793</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.925672</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50.062341</td>\n",
              "      <td>2015-12-02 10:09:28</td>\n",
              "      <td>50.062342</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.925582</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <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>142115</th>\n",
              "      <td>150503.768645</td>\n",
              "      <td>2015-12-04 03:57:09</td>\n",
              "      <td>272.779350</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249022</td>\n",
              "      <td>1.691192</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.971679</td>\n",
              "      <td>6.644591</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142116</th>\n",
              "      <td>150504.768729</td>\n",
              "      <td>2015-12-04 03:57:10</td>\n",
              "      <td>273.779434</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999364</td>\n",
              "      <td>4.249184</td>\n",
              "      <td>1.691470</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.971769</td>\n",
              "      <td>6.645771</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142117</th>\n",
              "      <td>150505.784258</td>\n",
              "      <td>2015-12-04 03:57:11</td>\n",
              "      <td>274.794963</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249508</td>\n",
              "      <td>1.691752</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.971949</td>\n",
              "      <td>6.646969</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142118</th>\n",
              "      <td>150506.784411</td>\n",
              "      <td>2015-12-04 03:57:12</td>\n",
              "      <td>275.795116</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249671</td>\n",
              "      <td>1.692029</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.972039</td>\n",
              "      <td>6.648149</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142119</th>\n",
              "      <td>150507.800260</td>\n",
              "      <td>2015-12-04 03:57:13</td>\n",
              "      <td>276.810965</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.972129</td>\n",
              "      <td>6.649329</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.000130</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142119 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Test_Time(s)  ... ACI_Phase_Angle(Deg)\n",
              "Data_Point                 ...                     \n",
              "1               10.000167  ...                    0\n",
              "2               20.015713  ...                    0\n",
              "3               30.031239  ...                    0\n",
              "4               40.046793  ...                    0\n",
              "5               50.062341  ...                    0\n",
              "...                   ...  ...                  ...\n",
              "142115      150503.768645  ...                    0\n",
              "142116      150504.768729  ...                    0\n",
              "142117      150505.784258  ...                    0\n",
              "142118      150506.784411  ...                    0\n",
              "142119      150507.800260  ...                    0\n",
              "\n",
              "[142119 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "65WCm6OYoFsX",
        "outputId": "62ec2cc8-7b84-47a0-d723-8a22989705bf"
      },
      "source": [
        "combined_data.to_csv('cd.csv')\n",
        "files.download('cd.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_67561a17-8732-4ee9-8805-347b2745beb9\", \"cd.csv\", 27699379)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "j8lLUYPgoehA",
        "outputId": "079531b6-48ea-4ddc-feb9-a2e63d9a0989"
      },
      "source": [
        "display(combined_data.loc[63990])\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Test_Time(s)                            71585.2\n",
              "Date_Time                   2015-12-03 06:01:47\n",
              "Step_Time(s)                            510.184\n",
              "Step_Index                                    5\n",
              "Cycle_Index                                   9\n",
              "Current(A)                            -0.999873\n",
              "Voltage(V)                              3.23388\n",
              "Charge_Capacity(Ah)                   0.0161462\n",
              "Discharge_Capacity(Ah)                  1.74142\n",
              "SoC                                    0.407708\n",
              "Charge_Energy(Wh)                     0.0678237\n",
              "Discharge_Energy(Wh)                    6.13017\n",
              "dV/dt(V/s)                         -0.000129652\n",
              "Internal_Resistance(Ohm)                      0\n",
              "Is_FC_Data                                    0\n",
              "AC_Impedance(Ohm)                             0\n",
              "ACI_Phase_Angle(Deg)                          0\n",
              "Name: 63990, dtype: object"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "D1gdmPPRs-kJ",
        "outputId": "cea5580d-422b-46e1-c944-84d86fef2ac7"
      },
      "source": [
        "combined_data.hist()\n",
        "plt.figure(figsize=(20,20))\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0OIEihXABYQ",
        "outputId": "fe6e5d58-a885-4a56-f64e-77541f4daaab"
      },
      "source": [
        "  fig = plt.figure(figsize = (15,15))\n",
        "  ax = fig.gca()\n",
        "  combined_data.hist(ax = ax)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb82ee8310>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb842cd890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb8483ad90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb84d2d350>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb85083890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb80056450>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb802e79d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb80719e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb80719e50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb80bd2490>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb81267d90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb8152d2d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb819527d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb821f5cd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb828d6190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb83043650>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "AG_E_pvAvEvy",
        "outputId": "89ba947a-72ec-48d1-fa0a-50d1d373be1f"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "IbtPgY8nDvbp",
        "outputId": "60d8f022-9d1b-40e6-df77-5588f370344e"
      },
      "source": [
        "combined_data.describe()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Step_Time(s)</th>\n",
              "      <th>Step_Index</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>SoC</th>\n",
              "      <th>Charge_Energy(Wh)</th>\n",
              "      <th>Discharge_Energy(Wh)</th>\n",
              "      <th>dV/dt(V/s)</th>\n",
              "      <th>Internal_Resistance(Ohm)</th>\n",
              "      <th>Is_FC_Data</th>\n",
              "      <th>AC_Impedance(Ohm)</th>\n",
              "      <th>ACI_Phase_Angle(Deg)</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",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.000000</td>\n",
              "      <td>142119.0</td>\n",
              "      <td>142119.0</td>\n",
              "      <td>142119.0</td>\n",
              "      <td>142119.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>78706.037752</td>\n",
              "      <td>3301.148810</td>\n",
              "      <td>7.770179</td>\n",
              "      <td>9.029081</td>\n",
              "      <td>-0.007758</td>\n",
              "      <td>3.684211</td>\n",
              "      <td>0.412337</td>\n",
              "      <td>1.481354</td>\n",
              "      <td>0.657895</td>\n",
              "      <td>1.589229</td>\n",
              "      <td>5.207008</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>41479.767284</td>\n",
              "      <td>2192.907274</td>\n",
              "      <td>1.984781</td>\n",
              "      <td>4.802597</td>\n",
              "      <td>0.303019</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>0.539741</td>\n",
              "      <td>0.625664</td>\n",
              "      <td>0.115206</td>\n",
              "      <td>2.090072</td>\n",
              "      <td>2.114306</td>\n",
              "      <td>0.001058</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000167</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>1.000000</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>-0.000054</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.048326</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>42831.169001</td>\n",
              "      <td>1251.741121</td>\n",
              "      <td>6.000000</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>0.598400</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>3.647907</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>78729.254299</td>\n",
              "      <td>3234.695696</td>\n",
              "      <td>6.000000</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>0.645397</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>6.317823</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>114617.571635</td>\n",
              "      <td>5217.752799</td>\n",
              "      <td>10.000000</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>0.744164</td>\n",
              "      <td>3.102921</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150507.800260</td>\n",
              "      <td>7200.013696</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.972129</td>\n",
              "      <td>6.649329</td>\n",
              "      <td>6.892068</td>\n",
              "      <td>0.082230</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Test_Time(s)   Step_Time(s)  ...  AC_Impedance(Ohm)  ACI_Phase_Angle(Deg)\n",
              "count  142119.000000  142119.000000  ...           142119.0              142119.0\n",
              "mean    78706.037752    3301.148810  ...                0.0                   0.0\n",
              "std     41479.767284    2192.907274  ...                0.0                   0.0\n",
              "min        10.000167       0.000005  ...                0.0                   0.0\n",
              "25%     42831.169001    1251.741121  ...                0.0                   0.0\n",
              "50%     78729.254299    3234.695696  ...                0.0                   0.0\n",
              "75%    114617.571635    5217.752799  ...                0.0                   0.0\n",
              "max    150507.800260    7200.013696  ...                0.0                   0.0\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jej3N8-4JDj4"
      },
      "source": [
        "Deleting 0 valued columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGcwcEaaItya"
      },
      "source": [
        "combined_data=combined_data.drop(columns=['Internal_Resistance(Ohm)','Is_FC_Data','AC_Impedance(Ohm)',\t'ACI_Phase_Angle(Deg)'])\n",
        "combined_data.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "e8j2W3tFJXVw",
        "outputId": "37946ec7-ce00-494c-814b-ca08b9d2115d"
      },
      "source": [
        "correlations = combined_data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UV6QQJwJomw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c897f35e-db20-4561-96d1-56d7b774ba18"
      },
      "source": [
        "  fig = plt.figure(figsize = (15,15))\n",
        "  ax = fig.gca()\n",
        "  combined_data.hist(ax = ax)"
      ],
      "execution_count": 21,
      "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 0x7ffb8440ab50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb7fd75350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb7fc42810>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb7fac7110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb7f6120d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb7fbe4450>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb7fc015d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb897f7890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb897f7990>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb81f76d50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb80143f10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb81e79450>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5QZfqs7XlqdsrTk+Xy25LnE3F0tG/o0vF0pY+W8vPvy+1NV94vF+5kBvPl5D/4ky9fn8iIj1Ya85hm/N/kf5/8X5K+lqhb28fvrY4x+P9lqyJ83i/6lN9errfWo/3KhfT5wpru/y70O9PRKTnas9zWP3/4t20vFNERERERKQbU9InIt3O8ePHmTlzJlOmTCEuLo7NmzcD8NxzzzFhwgSmTp3K1KlTyc/PN9+zceNGnE4nsbGxFBYWmu0FBQXExsbidDrJyMgw20tLS0lKSsLpdLJgwQLq6uo6boAiIiIiLaDlnSLS7VitVhYvXkxISAi1tbXMmDGDiIgIAGbNmsV9993XaPujR4+Sk5NDTk4OLpeL5ORkdu7cCcDy5ct56aWXsNvtJCYm4nA4uP7661m7di2zZs0iLi6O1NRUMjMzuffeezt8rCIiIiKXo2/6RKTbCQwMJCQkBAA/Pz+GDh2Ky+Vqcvu8vDzi4uLw9fUlODiYIUOGUFxcTHFxMUOGDCE4OBhfX1/i4uLIy8vDMAz2799PbGwsANOmTSMvL69DxiYiIiLSUvqmT0S6tWPHjvH+++8TGhrK22+/zSuvvEJ2djY333wzixcvJiAgAJfLRWhoqPkeu91uJolBQUGN2ouLi6msrMTf3x8fHx9zm0slledZrRZstis9PMKW6ez9f5XV2svrYmovGquIiHSWyyZ9S5YsYe/evQwcOJAdO3Y0eu3FF1/k6aefZt++fQwYMADDMFi1ahX5+fn06dOHNWvWmFfbs7KyeP755wGYM2cO06ZNA+Dw4cMsWbKE06dPEx0dzdKlS7FYLJ4ep4j0QCdPnmT+/Pk89thj+Pn5cc899zB37lwsFgs//elPWbNmDU899VSHxeN2G1RVnWrWtp5+XMN5zd1/R7HZrvS6mNqLxurd2uuYa6mamhoef/xx/v73v2OxWFi9ejVf//rXWbhwIZ9++inXXnst6enpBAQE6LxLRJrtsss7p0+fzqZNmy5oP378OK+//jqDBw822woKCigpKSE3N5cVK1aQlpYGQFVVFRs2bGDLli1s3bqVDRs2UF1dDUBaWhorVqwgNzeXkpISCgoKPDQ0EenJzp49y/z584mPjycmJgaAq6++GqvVSq9evUhKSuLdd98FGr7BKysrM9/rcrmw2+1Ntvfv35+amhrq6+sBKCsrw263d+DoRKS7WrVqFRMmTODPf/4z27dvZ9iwYWRkZBAeHk5ubi7h4eFmUSmdd4lIc1026RszZgwBAQEXtD/11FOkpKQ0ujqUl5dHQkICFouFsLAwampqKC8vp6ioiIiICGw2GwEBAURERFBYWEh5eTm1tbWEhYVhsVhISEjQfTEi0maGYbB06VKGDh1KcnKy2V5eXm7+fffu3QwfPhwAh8NBTk4OdXV1lJaWUlJSwi233MLIkSMpKSmhtLSUuro6cnJycDgcWCwWxo0bZxZ7ycrKwuFwdOwgRaTbOXHiBH/7299ITEwEwNfXF39/f/P8CiAhIYHdu3cDOu8SkeZr1T19u3fvJjAwkJtuuqlRu8vlanT/y/n7XL7afv5+maa2b47m3huj+wpEPKsrHE9vvfUW27dv54YbbmDq1KkALFq0iB07dvDBBx8AcO2117J8+XIAhg8fzuTJk5kyZQpWq5XU1FSsVisAqamp3H///bjdbmbMmGEmiikpKSxcuJD09HRGjBhBUlJSJ4xURLqTY8eOMWDAAJYsWcIHH3xASEgIS5cupaKigsDAQAAGDRpERUUF0LHnXSLStbU46fviiy/YuHEjL774YnvE02zNvTemPe4r8JZ1/yKdoTnHU2cfI6NHj+bDDz+8oD06OrrJ98yZM4c5c+Zc9D0Xe19wcDCZmZltC1RE5Evq6+t57733eOKJJwgNDWXlypWNng8KYLFYOvQePG8oQNUQR9e9iN+VY28JbxtjV/7c2yP2Fid9n3zyCceOHTOvnpeVlTF9+nS2bt16wf0v5+9zsdvtHDhwwGx3uVyMHTu2ye1FREREepqgoCCCgoLMasKTJk0iIyODgQMHUl5eTmBgIOXl5QwYMAC48H7k9jjvakkBqvbUFYsDnedNsbfnRVlvGeN53vS5t1RLYm/u77TFz+m78cYb2bdvH3v27GHPnj0EBQWxbds2Bg0ahMPhIDs7G8MwOHToEP369SMwMJDIyEiKioqorq6murqaoqIiIiMjCQwMxM/Pj0OHDmEYBtnZ2UycOLGlIYmIiIh0eYMGDSIoKIiPP/4YgH379jFs2DDz/ApodK6k8y4Raa7LftO3aNEiDhw4QGVlJVFRUTz44INN3rsSHR1Nfn4+TqeTvn37snr1agBsNhtz5841b0yeN28eNpsNgGXLlpmlg6OiooiKivLU2ERERES6lCeeeIKHH36Ys2fPEhwczFNPPcW5c+dYsGABmZmZDB48mPT0dEDnXSLSfJdN+tatW3fJ1/fs2WP+3WKxsGzZsotul5iYaE4+XzZy5MgLnv8nIiIi0hONGDGCbdu2XdC+efPmC9p03iUizdXi5Z0iIiIiIiLSdSjpExERERER6cZa9Zw+ERERERGRns7Pvy99e3s2pTp91u3R/kBJn4iIiIiISKv07e3D1xbneLTPkjVxnPBoj1reKSIiIiIi0q0p6RMREREREenGlPSJiIiIiIh0Y0r6REREREREujElfSIiIiIiIt2Ykj4REREREZFuTEmfiIiIiIhIN6akT0REREREpBu7bNK3ZMkSwsPDuf322822p59+mkmTJhEfH8+8efOoqakxX9u4cSNOp5PY2FgKCwvN9oKCAmJjY3E6nWRkZJjtpaWlJCUl4XQ6WbBgAXV1dZ4am4iIiIiISI932aRv+vTpbNq0qVFbREQEO3bs4A9/+ANf+9rX2LhxIwBHjx4lJyeHnJwcNm3axJNPPonb7cbtdrN8+XI2bdpETk4OO3bs4OjRowCsXbuWWbNmsWvXLvz9/cnMzGyHYYqIiIiIiPRMl036xowZQ0BAQKO2yMhIfHx8AAgLC6OsrAyAvLw84uLi8PX1JTg4mCFDhlBcXExxcTFDhgwhODgYX19f4uLiyMvLwzAM9u/fT2xsLADTpk0jLy/P02MUERERERHpsXza2sGrr77K5MmTAXC5XISGhpqv2e12XC4XAEFBQY3ai4uLqaysxN/f30wgg4KCzO0vx2q1YLNd2YztejVrOxFpHh1PIiIiIl1Lm5K+559/HqvVyh133OGpeJrN7Taoqjp12e1stiubtV1LDBrUz6P9iXQlzTmedIyIiLSOw+HgqquuolevXlitVrZt20ZVVRULFy7k008/5dprryU9PZ2AgAAMw2DVqlXk5+fTp08f1qxZQ0hICABZWVk8//zzAMyZM4dp06YBcPjwYZYsWcLp06eJjo5m6dKlWCyWThuviHSMVlfv3LZtG3v37mXt2rXmZGG3282lntDwzZ/dbm+yvX///tTU1FBfXw9AWVkZdru9tSGJiIiIdHmbN29m+/btbNu2DYCMjAzCw8PJzc0lPDzcLIhXUFBASUkJubm5rFixgrS0NACqqqrYsGEDW7ZsYevWrWzYsIHq6moA0tLSWLFiBbm5uZSUlFBQUNApYxSRjtWqpK+goIBNmzbx/PPP07dvX7Pd4XCQk5NDXV0dpaWllJSUcMsttzBy5EhKSkooLS2lrq6OnJwcHA4HFouFcePGsXPnTqDhqpTD4fDMyESkxzp+/DgzZ85kypQpxMXFsXnzZqDhRCg5OZmYmBiSk5PNkyDDMFi5ciVOp5P4+HiOHDli9pWVlUVMTAwxMTFkZWWZ7YcPHyY+Ph6n08nKlSsxDKNjBykiPUZeXh4JCQkAJCQksHv37kbtFouFsLAwampqKC8vp6ioiIiICGw2GwEBAURERFBYWEh5eTm1tbWEhYVhsVhISEhQLQWRHuKyyzsXLVrEgQMHqKysJCoqigcffJCMjAzq6upITk4GIDQ0lOXLlzN8+HAmT57MlClTsFqtpKamYrVaAUhNTeX+++/H7XYzY8YMhg8fDkBKSgoLFy4kPT2dESNGkJSU1I7DFZGewGq1snjxYkJCQqitrWXGjBlERESwbds2wsPDmT17NhkZGWRkZJCSktLoavk777xDWloaW7duNa+Wv/rqq1gsFqZPn47D4SAgIMC8Wh4aGsoPfvADCgoKiI6O7uyhi0g3cN9992GxWLjrrru46667qKioIDAwEIBBgwZRUVEBNKyc+nLNhPO1Eb7afr7GQlPbX0pzayi0t65co6Erx94S3jbGrv65ezr2yyZ969atu6DtUonZnDlzmDNnzgXt0dHRFz0hCg4O1mMaRMSjAgMDzRMkPz8/hg4disvlIi8vj5dffhlouFo+c+ZMUlJSmrxafuDAAfNqOWBeLR87dqx5tfx8X3l5eUr6RKTNfvvb32K326moqCA5OZmhQ4c2et1isXToPXjNraHQ3tqjRkNH8abY2/Oee28Z43kd9bm312fa3Nibu/82V+8UEfFmx44d4/333yc0NLRTr5aDd1wx7+z9f1VXvxLbEhqrNMf52gYDBw7E6XRSXFzMwIEDKS8vJzAwkPLycgYMGGBu++WaCedrI9jtdg4cOGC2u1wuxo4d2+T2ItL9KekTkW7r5MmTzJ8/n8ceeww/P79Gr3X01XJo2RXzzr5y2FG86Qp4e9NYvZs3VB0+deoU586dw8/Pj1OnTvH6668zd+5cHA4H2dnZzJ49m+zsbCZOnAg01FL49a9/TVxcHO+88w79+vUjMDCQyMhI1q1bZ963XFRUxKJFi7DZbPj5+XHo0CFCQ0PJzs5m5syZnTlkEekgSvpEpFs6e/Ys8+fPJz4+npiYGABdLRcRr1ZRUcG8efMAcLvd3H777URFRTFy5EgWLFhAZmYmgwcPJj09HWi4dSY/Px+n00nfvn1ZvXo1ADabjblz55KYmAjAvHnzzGXqy5YtMx/ZEBUVRVRUVCeMVEQ6mpI+Eel2DMNg6dKlDB061Cw4BehquYh4teDgYF577bUL2vv3729WIf4yi8XCsmXLLtpXYmKimfR92ciRI9mxY0fbgxWRLkVJn4h0O2+99Rbbt2/nhhtuYOrUqUBDJeLZs2frarmIiIj0OEr6RKTbGT16NB9++OFFX9PVchEREelpWvVwdhEREREREekalPSJiIiIiIh0Y0r6REREREREujElfSIiIiIiIt2Ykj4REREREZFuTEmfiIiIiIhIN3bZpG/JkiWEh4dz++23m21VVVUkJycTExNDcnKy+eBiwzBYuXIlTqeT+Ph4jhw5Yr4nKyuLmJgYYmJiyMrKMtsPHz5MfHw8TqeTlStXYhiGJ8cnIiIiIiLSo1026Zs+fTqbNm1q1JaRkUF4eDi5ubmEh4eTkZEBQEFBASUlJeTm5rJixQrS0tKAhiRxw4YNbNmyha1bt7JhwwYzUUxLS2PFihXk5uZSUlJCQUGBh4coIiIiIiLSc1026RszZgwBAQGN2vLy8khISAAgISGB3bt3N2q3WCyEhYVRU1NDeXk5RUVFREREYLPZCAgIICIigsLCQsrLy6mtrSUsLAyLxUJCQgJ5eXntMEwREREREZGeyac1b6qoqCAwMBCAQYMGUVFRAYDL5SIoKMjcLigoCJfLdUG73W6/aPv57ZvDarVgs13ZjO16NWs7EWkeHU8iIiIiXUurkr4vs1gsWCwWT8TSIm63QVXVqctuZ7Nd2aztWmLQoH4e7U+kK2nO8aRjRERERMR7tKp658CBAykvLwegvLycAQMGAA3f4JWVlZnblZWVYbfbL2h3uVwXbT+/vYiIiIiIiHhGq5I+h8NBdnY2ANnZ2UycOLFRu2EYHDp0iH79+hEYGEhkZCRFRUVUV1dTXV1NUVERkZGRBAYG4ufnx6FDhzAMo1FfIiIiIiIi0naXXd65aNEiDhw4QGVlJVFRUTz44IPMnj2bBQsWkJmZyeDBg0lPTwcgOjqa/Px8nE4nffv2ZfXq1QDYbDbmzp1LYmIiAPPmzcNmswGwbNkylixZwunTp4mKiiIqKqq9xioiIiIiItLjXDbpW7du3UXbN2/efEGbxWJh2bJlF90+MTHRTPq+bOTIkezYseNyYYiIiAHIWTIAACAASURBVIh0e263mxkzZmC329m4cSOlpaUsWrSIqqoqQkJCeOaZZ/D19aWuro5HHnmEI0eOYLPZWL9+Pddddx0AGzduJDMzk169evH4448zYcIEoOHRWqtWreLcuXMkJSUxe/bszhyqiHSgVi3vFBERERHP+9WvfsWwYcPMn9euXcusWbPYtWsX/v7+ZGZmArB161b8/f3ZtWsXs2bNYu3atQAcPXqUnJwccnJy2LRpE08++SRutxu3283y5cvZtGkTOTk57Nixg6NHj3bKGEWk4ynpExEREfECZWVl7N2711wZZRgG+/fvJzY2FoBp06aZzzPes2cP06ZNAyA2NpZ9+/ZhGAZ5eXnExcXh6+tLcHAwQ4YMobi4mOLiYoYMGUJwcDC+vr7ExcXp2cgiPUibH9kgIiIiIm23evVqUlJSOHnyJACVlZX4+/vj49Nwuvbl5xm7XC6uueYaAHx8fOjXrx+VlZW4XC5CQ0PNPs8/G/n8+7/cXlxcfNmYmvtc5PbWlZ+73JVjbwlvG2NX/9w9HbuSPhEREZFO9pe//IUBAwZw880388Ybb3R2OKbmPhe5vbXHc5c7ijfF3p7P0fWWMZ7XUZ97e32mzY29uftX0iciIiLSyd5++2327NlDQUEBZ86coba2llWrVlFTU0N9fT0+Pj6Nnmdst9s5fvw4QUFB1NfXc+LECfr379/ks5GBJttFpPvTPX0iIiIinezHP/4xBQUF7Nmzh3Xr1jF+/Hh+8pOfMG7cOHbu3AlAVlYWDocDaHg2clZWFgA7d+5k/PjxWCwWHA4HOTk51NXVUVpaSklJCbfccgsjR46kpKSE0tJS6urqyMnJMfsSke5PSZ+IdEtLliwhPDyc22+/3Wx77rnnmDBhAlOnTmXq1Knk5+ebr23cuBGn00lsbCyFhYVme0FBAbGxsTidTjIyMsz20tJSkpKScDqdLFiwgLq6uo4ZmIj0KCkpKbz00ks4nU6qqqpISkoCGh6FVVVVhdPp5KWXXuLhhx8GYPjw4UyePJkpU6Zw//33k5qaitVqxcfHh9TUVO6//36mTJnC5MmTGT58eGcOTUQ6kJZ3iki3NH36dL773e/y6KOPNmqfNWsW9913X6O2L5c4d7lcJCcnm1fWly9fzksvvYTdbicxMRGHw8H1119vllGPi4sjNTWVzMxM7r333g4bn4h0X+PGjWPcuHEABAcHm49p+LLevXvz7LPPXvT9c+bMYc6cORe0R0dHEx0d7dlgRaRL0Dd9ItItjRkzhoCAgGZt29IS55cqoy4iIiLibfRNn4j0KK+88grZ2dncfPPNLF68mICAgBaXOL9UGfVL8YbS5529/6/q6iW1W0JjFRGRzqKkT0R6jHvuuYe5c+disVj46U9/ypo1a3jqqac6bP8tKX3e2SWgO4o3lTJvbxqrd2vPUvYiIp1NyztFpMe4+uqrsVqt9OrVi6SkJN59912AJkucN9Xev39/s4w60KiMuoiIiIi3UdInIj1GeXm5+ffdu3ebletaWuLcYrE0WUZdRERExNu0aXnnL3/5S7Zu3YrFYuGGG27gqaeeory8nEWLFlFVVUVISAjPPPMMvr6+1NXV8cgjj3DkyBFsNhvr16/nuuuuAxpKpWdmZtKrVy8ef/xxJkyY4JHBiUjPtWjRIg4cOEBlZSVRUVE8+OCDHDhwgA8++ACAa6+9luXLlwONS5xbrVazxDlgljh3u93MmDHDTBRTUlJYuHAh6enpjBgxwiyjLiIiIuJtWp30uVwufvWrX/HHP/6RPn368NBDD5GTk0N+fv5Fy5hv3boVf39/du3aRU5ODmvXriU9Pb3JUunnT7hERFpj3bp1F7RdKjFraYnzpsqoi4iIiHibNn3T53a7OX36ND4+Ppw+fZpBgwaxf/9+fvKTnwANZcw3bNjAvffey549e3jggQcAiI2NZfny5RiG0WSp9FGjRrV9dIAb3ZwtIiIiIiI9V6uTPrvdzve//31uu+02evfuTUREBCEhIU2WMXe5XFxzzTUNO/XxoV+/flRWVl6yVPqlNLf0+RVXWPna4pzWDLFJJWviPNqfSFeiMuwiIiIiXUurk77q6mry8vLIy8ujX79+PPTQQxQWFnoytktqbulzfcsn4lk67kRERES6llZX7/zrX//Kddddx4ABA7jiiiuIiYnh7bffbrKMud1u5/jx4wDU19dz4sQJ+vfv32RJdBEREREREWm7Vid9gwcP5p133uGLL77AMAz27dvH9ddf32QZc4fDQVZWFgA7d+5k/PjxWCyWJkuli4iIiIiISNu1enlnaGgosbGxTJs2DR8fH0aMGMFdd93Ft7/97YuWMU9MTCQlJQWn00lAQADr168HLl0qXURERERERNqmTdU758+fz/z58xu1NVXGvHfv3jz77LMX7aepUukiIiIiIiLSNm1K+kREpOs4fdbt8SI7p8+66XNF21ZnfDUmT/T5Vd7S5+U+f2+J0xP9tubfWnvE+sWZemprvvBonyIiXY2SPhGRHqJPOz3CRn32vD7bq9/26rPWoz22nzNnzvCf//mf1NXV4Xa7iY2NZf78+ZSWlrJo0SKqqqoICQnhmWeewdfXl7q6Oh555BGOHDmCzWZj/fr1XHfddQBs3LiRzMxMevXqxeOPP86ECRMAKCgoYNWqVZw7d46kpCRmz57dmUMWkQ7S6kIuIiIiIuI5vr6+bN68mddee43s7GwKCws5dOgQa9euZdasWezatQt/f3/zNpqtW7fi7+/Prl27mDVrFmvXrgXg6NGj5OTkkJOTw6ZNm3jyySdxu9243W6WL1/Opk2byMnJYceOHRw9erQzhywiHURJn4iIiIgXsFgsXHXVVUDD463q6+uxWCzs37+f2NhYAKZNm0ZeXh4Ae/bsYdq0aQDExsayb98+DMMgLy+PuLg4fH19CQ4OZsiQIRQXF1NcXMyQIUMIDg7G19eXuLg4sy8R6d6U9ImIiIh4CbfbzdSpU7n11lu59dZbCQ4Oxt/fHx+fhjtygoKCcLlcQMOzja+55hoAfHx86NevH5WVlbhcLoKCgsw+7XY7LperyXYR6f50T5+IiIiIl7BarWzfvp2amhrmzZvHxx9/3MnxWLDZruzUGBri6OUVcbRGV469JbxtjF39c/d07Er6RERERLyMv78/48aN49ChQ9TU1FBfX4+Pjw9lZWXY7Xag4Zu648ePExQURH19PSdOnKB///7Y7XbKysrMvlwul/meptqb4nYbVFWdaocRtozNdqVXxNEa3hS7pys4f5m3jPG8jvrc2+szbW7szd2/lneKiIiIeIHPP/+cmpoaAE6fPs1f//pXhg0bxrhx49i5cycAWVlZOBwOABwOB1lZWQDs3LmT8ePHY7FYcDgc5OTkUFdXR2lpKSUlJdxyyy2MHDmSkpISSktLqaurIycnx+xLRLo3fdMnIiIi4gXKy8tZvHgxbrcbwzCYNGkSt912G9dffz0LFy4kPT2dESNGkJSUBEBiYiIpKSk4nU4CAgJYv349AMOHD2fy5MlMmTIFq9VKamoqVmvD8w9TU1O5//77cbvdzJgxg+HDh3faeEWk4yjpExEREfECN910E9nZ2Re0BwcHm49p+LLevXvz7LPPXrSvOXPmMGfOnAvao6OjiY6ObnuwItKlaHmniIiIiIhIN6akT0REREREpBtrU9JXU1PD/PnzmTRpEpMnT+bgwYNUVVWRnJxMTEwMycnJVFdXA2AYBitXrsTpdBIfH8+RI0fMfrKysoiJiSEmJsa8IVlERERERETark1J36pVq5gwYQJ//vOf2b59O8OGDSMjI4Pw8HByc3MJDw8nIyMDgIKCAkpKSsjNzWXFihWkpaUBUFVVxYYNG9iyZQtbt25lw4YNZqIoIiIiIiIibdPqpO/EiRP87W9/IzExEQBfX1/8/f3Jy8sjISEBgISEBHbv3g1gtlssFsLCwqipqaG8vJyioiIiIiKw2WwEBAQQERFBYWGhB4YmIiIiIiIira7eeezYMQYMGMCSJUv44IMPCAkJYenSpVRUVBAYGAjAoEGDqKioABoeABoUFGS+PygoCJfLdUG73W7H5XJddv9Wq8XjT6oXkcvrKsfdkiVL2Lt3LwMHDmTHjh1Aw8qChQsX8umnn3LttdeSnp5OQEAAhmGwatUq8vPz6dOnD2vWrCEkJARoWH7+/PPPAw3V8KZNmwbA4cOHWbJkCadPnyY6OpqlS5disVg6Z7AiIiIil9Dqb/rq6+t57733uOeee8jOzqZv377mUs7zLBZLu50Eud0GVVWnLvtHRDyrqxx306dPZ9OmTY3aPLn8PC0tjRUrVpCbm0tJSQkFBQUdOj4RERGR5mp10hcUFERQUBChoaEATJo0iffee4+BAwdSXl4ONDxkdMCAAUDDN3hlZWXm+8vKyrDb7Re0u1wu7HZ7a8MSEQFgzJgxBAQENGrz1PLz8vJyamtrCQsLw2KxkJCQQF5eXoePUURERKQ5Wp30DRo0iKCgID7++GMA9u3bx7Bhw3A4HOaDRbOzs5k4cSKA2W4YBocOHaJfv34EBgYSGRlJUVER1dXVVFdXU1RURGRkpAeGJiLSmKeWnze1/eWcX5benD8i4jk65kSkp2v1PX0ATzzxBA8//DBnz54lODiYp556inPnzrFgwQIyMzMZPHgw6enpAERHR5Ofn4/T6aRv376sXr0aAJvNxty5c82CMPPmzcNms7VxWCIil9aey8+bcn5ZenMMGtSvnaMR6Tmac9zpmBOR7qxNSd+IESPYtm3bBe2bN2++oM1isbBs2bKL9pOYmGgmfSIi7eX88vPAwMBmLz8/cOCA2e5yuRg7dmyT24uIiIh4ozY9p09EpCvx1PLzwMBA/Pz8OHToEIZhNOpLRERExNu06Zs+ERFvtWjRIg4cOEBlZSVRUVE8+OCDzJ4922PLz5ctW2Y+siEqKoqoqKjOGaiIiIjIZSjpE5Fuad26dRdt99Ty85EjR5rP/xMRERHxZlreKSIiIiIi0o0p6RMREREREenGlPSJiIiIeIHjx48zc+ZMpkyZQlxcnLkcvaqqiuTkZGJiYkhOTqa6uhoAwzBYuXIlTqeT+Ph4jhw5YvaVlZVFTEwMMTExZGVlme2HDx8mPj4ep9PJypUrMQyjYwcpIp1CSZ+IiIiIF7BarSxevJg//vGP/P73v+c3v/kNR48eJSMjg/DwcHJzcwkPDycjIwOAgoICSkpKyM3NZcWKFaSlpQENSeKGDRvYsmULW7duZcOGDWaimJaWxooVK8jNzaWkpISCgoLOGq6IdCAlfSIiIiJeIDAwkJCQEAD8/PwYOnQoLpeLvLw8EhISAEhISGD37t0AZrvFYiEsLIyamhrKy8spKioiIiICm81GQEAAERERFBYWUl5eTm1tLWFhYVgsFhISEsjLy+u08YpIx1H1ThEREREvc+zYMd5//31CQ0OpqKggMDAQgEGDBlFRUQGAy+UiKCjIfE9QUBAul+uCdrvdftH289tfitVqwWa70pNDaxWrtZdXxNEaXTn2lvC2MXb1z93TsSvpExEREfEiJ0+eZP78+Tz22GP4+fk1es1isWCxWDosFrfboKrqVIftryk225VeEUdreFPsgwb1a7e+vWWM53XU595en2lzY2/u/rW8U0RERMRLnD17lvnz5xMfH09MTAwAAwcOpLy8HIDy8nIGDBgANHyDV1ZWZr63rKwMu91+QbvL5bpo+/ntRaT7U9InIiIi4gUMw2Dp0qUMHTqU5ORks93hcJCdnQ1AdnY2EydObNRuGAaHDh2iX79+BAYGEhkZSVFREdXV1VRXV1NUVERkZCSBgYH4+flx6NAhDMNo1JeIdG9tXt7pdruZMWMGdrudjRs3UlpayqJFi6iqqiIkJIRnnnkGX19f6urqeOSRRzhy5Ag2m43169dz3XXXAbBx40YyMzPp1asXjz/+OBMmTGjzwERERES6krfeeovt27dzww03MHXqVAAWLVrE7NmzWbBgAZmZmQwePJj09HQAoqOjyc/Px+l00rdvX1avXg2AzWZj7ty5JCYmAjBv3jxsNhsAy5YtY8mSJZw+fZqoqCiioqI6YaQi0tHanPT96le/YtiwYdTW1gKwdu1aZs2aRVxcHKmpqWRmZnLvvfeydetW/P392bVrFzk5Oaxdu5b09HSOHj1KTk4OOTk5uFwukpOT2blzJ1artc2DExEREekqRo8ezYcffnjR184/s+/LLBYLy5Ytu+j2iYmJZtL3ZSNHjmTHjh1tC1REupw2Le8sKytj79695qRiGAb79+8nNjYWgGnTppmlgPfs2cO0adMAiI2NZd++fRiGQV5eHnFxcfj6+hIcHMyQIUMoLi5uS1giIiIiIiLy/7Xpm77Vq1eTkpLCyZMnAaisrMTf3x8fn4Zuv1wK2OVycc011zTs1MeHfv36UVlZicvlIjQ01OzzfFnhy/GWEsIiPY2OOxEREZGupdVJ31/+8hcGDBjAzTffzBtvvOHJmJqluSWE27M0rUhPpONOREREpGtpddL39ttvs2fPHgoKCjhz5gy1tbWsWrWKmpoa6uvr8fHxaVQK2G63c/z4cYKCgqivr+fEiRP079+/ybLCIiIiIiIi0natvqfvxz/+MQUFBezZs4d169Yxfvx4fvKTnzBu3Dh27twJQFZWFg6HA2goK5yVlQXAzp07GT9+PBaLBYfDQU5ODnV1dZSWllJSUsItt9zigaGJiIiIiIiIx5/Tl5KSwksvvYTT6aSqqoqkpCSgoYpUVVUVTqeTl156iYcffhiA4cOHM3nyZKZMmcL9999PamqqKneKiIiIiIh4SJsf2QAwbtw4xo0bB0BwcDCZmZkXbNO7d2+effbZi75/zpw5zJkzxxOhiIiIiIiIyJd4/Js+ERERERER8R5K+kRERERERLoxJX0iIiIiIiLdmEfu6RMR6UocDgdXXXUVvXr1wmq1sm3bNqqqqli4cCGffvop1157Lenp6QQEBGAYBqtWrSI/P58+ffqwZs0aQkJCgIYKxc8//zzQcG/ytGnTOnNYIiIiIhelb/pEpEfavHkz27dvZ9u2bQBkZGQQHh5Obm4u4eHhZGRkAFBQUEBJSQm5ubmsWLGCtLQ0AKqqqtiwYQNbtmxh69atbNiwgerq6s4ajoiIiEiTlPSJiAB5eXkkJCQAkJCQwO7duxu1WywWwsLCqKmpoby8nKKiIiIiIrDZbAQEBBAREUFhYWFnDkFERETkorS8U0R6pPvuuw+LxcJdd93FXXfdRUVFBYGBgQAMGjSIiooKAFwuF0FBQeb7goKCcLlcF7Tb7XZcLtcl92m1WrDZrmyH0YjIpei4E5GeTkmfiPQ4v/3tb7Hb7VRUVJCcnMzQoUMbvW6xWLBYLB7fr9ttUFV1qlnbDhrUz+P7F+mpmnPc6ZgTke5MyztFpMex2+0ADBw4EKfTSXFxMQMHDqS8vByA8vJyBgwYYG5bVlZmvresrAy73X5Bu8vlMvsVERER8SZK+kSkRzl16hS1tbXm319//XWGDx+Ow+EgOzsbgOzsbCZOnAhgthuGwaFDh+jXrx+BgYFERkZSVFREdXU11dXVFBUVERkZ2WnjEpGub8mSJYSHh3P77bebbVVVVSQnJxMTE0NycrJZMMowDFauXInT6SQ+Pp4jR46Y78nKyiImJoaYmBiysrLM9sOHDxMfH4/T6WTlypUYhtFxgxORTqWkT0R6lIqKCu69917uuOMOkpKSiI6OJioqitmzZ/P6668TExPDX//6V2bPng1AdHQ0wcHBOJ1OnnjiCZYtWwaAzWZj7ty5JCYmkpiYyLx587DZbJ05NBHp4qZPn86mTZsatXmysnBaWhorVqwgNzeXkpISCgoKOnR8ItJ5dE+fiPQowcHBvPbaaxe09+/fn82bN1/QbrFYzETvq84nfCIinjBmzBiOHTvWqC0vL4+XX34ZaKgsPHPmTFJSUpqsLHzgwAGzsjBgVhYeO3YstbW1hIWFmX3l5eURHR3dsYMUkU7R6qTv+PHjPPLII1RUVGCxWLjzzjv5r//6Lz3gWERERMRDPFVZuKntL8dbqg5brb28Io7W6Mqxt4S3jbGrf+6ejr3VSZ/VamXx4sWEhIRQW1vLjBkziIiIYNu2bYSHhzN79mwyMjLIyMggJSWl0TKEd955h7S0NLZu3WouQ3j11VexWCxMnz4dh8NBQECAJ8cpIiIi0qW1V2XhS2lJ1eH2ZLNd6RVxtIY3xd6eVWq9ZYznddTn3l6fqaerfbf6nr7AwEDzmzo/Pz+GDh2Ky+XSA45FREREPMRTlYWb2l5EegaPFHI5duwY77//PqGhoR3ygGMRERGRnsBTlYUDAwPx8/Pj0KFDGIbRqC8R6f7aXMjl5MmTzJ8/n8ceeww/P79Gr7XnMgRvWWMu0tPouBMRaR+LFi3iwIEDVFZWEhUVxYMPPsjs2bNZsGABmZmZDB48mPT0dKChsnB+fj5Op5O+ffuyevVqoHFlYaBRZeFly5axZMkSTp8+TVRUFFFRUZ0zUBHpcG1K+s6ePcv8+fOJj48nJiYG+L9lCIGBgc1ehnDgwAGz3eVyMXbs2Mvuu7lrzNtz7bJIT6TjTkSkfaxbt+6i7Z6qLDxy5Eh27NjRtiBFpEtq9fJOwzBYunQpQ4cOJTk52WzXA45FRERERES8R6u/6XvrrbfYvn07N9xwA1OnTgUaliV4chmCiIiIiEhP5Offl769e+4jtT0x/q+uPPriTD21NV+0qc+uqtWf5OjRo/nwww8v+poecCwiIiIi0np9e/vwtcU5Hu2zZE2cR/trT+01/lqP9th1eKR6p4iIiIiIiHinnvudsYiIiIiI9Binz7p7bLE5JX0iIiIiItLt9bnC2mOXzGp5p4iIiIiISDempE9ERERERKQbU9InIiIiIiLSjSnpExERERER6caU9ImIiIiIiHRjSvpERERERES6MSV9IiIiIiIi3ZiSPhERERERkW5MSZ+IiIiIiEg3pqRPRERERESkG/OapK+goIDY2FicTicZGRmdHY6IyGVp3hKRrkhzl0jP49PZAQC43W6WL1/OSy+9hN1uJzExEYfDwfXXX9/ZoYmIXJTmLRHpijR3tQ8//7707d30afWgQf06MBqRC3lF0ldcXMyQIUMIDg4GIC4ujry8PE1AIuK1NG+JSFekuat99O3tw9cW53i0z5I1cR7tT3o2i2EYRmcH8ec//5nCwkJWrVoFQHZ2NsXFxaSmpnZyZCIiF6d5S0S6Is1dIj2T19zTJyIiIiIiIp7nFUmf3W6nrKzM/NnlcmG32zsxIhGRS9O8JSJdkeYukZ7JK5K+kSNHUlJSQmlpKXV1deTk5OBwODo7LBGRJmneEpGuSHOXSM/kFYVcfHx8SE1N5f7778ftdjNjxgyGDx/e2WGJiDRJ85aIdEWau0R6Jq8o5CIiIiIiIiLtwyuWd4qIiIiIiEj7UNInIiIiIiLSjXXrpK+goIDY2FicTicZGRke6fP48ePMnDmTKVOmEBcXx+bNmwF47rnnmDBhAlOnTmXq1Knk5+eb79m4cSNOp5PY2FgKCwsvG19paSlJSUk4nU4WLFhAXV3dJWNyOBzEx8czdepUpk+fDkBVVRXJycnExMSQnJxMdXU1AIZhsHLlSpxOJ/Hx8Rw5csTsJysri5iYGGJiYsjKyjLbDx8+THx8PE6nk5UrV9LUiuCPP/7YHP/UqVP55je/yS9/+csO+2yWLFlCeHg4t99+u9nWEZ/DxfZxsViefvppJk2aRHx8PPPmzaOmpgaAY8eOccstt5ifz5efldSSfTY1rovF0hG/k7q6OhYsWIDT6SQpKYljx45d9N+NNNYe81ZHamqO9OSx6G3cbjcJCQn88Ic/BFp3TDR13HmTmpoa5s+fz6RJk5g8eTIHDx7s1r9X8Q5NzSldyVfniK7iYsd8V/HLX/6SuLg4br/9dhYtWsSZM2c6O6QmteT8tU2Mbqq+vt6YOHGi8cknnxhnzpwx4uPjjX/84x9t7tflchmHDx82DMMwTpw4YcTExBj/+Mc/jGeffdbYtGnTBdv/4x//MOLj440zZ84Yn3zyiTFx4kSjvr7+kvHNnz/f2LFjh2EYhvHEE08Yr7zyyiVjuu2224yKiopGbU8//bSxceNGwzAMY+PGjcYzzzxjGIZh7N2717jvvvuMc+fOGQcPHjQSExMNwzCMyspKw+FwGJWVlUZVVZXhcDiMqqoqwzAMY8aMGcbBgweNc+fOGffdd5+xd+/ey35O9fX1xq233mocO3aswz6bAwcOGIcPHzbi4uI69HO42D4uFkthYaFx9uxZwzAM45lnnjFjKS0tbbTdl7Vkn02N62KxdMTv5Ne//rXxxBNPGIZhGDt27DAeeuihi45R/k97zVsdqak50pPHord58cUXjUWLFhmzZ882DKPlx0RTx523eeSRR4wtW7YYhmEYZ86cMaqrq7v171W8Q1NzSlfy1Tmiq7jYMd8VlJWVGbfddpvxxRdfGIbRMCe/+uqrnRxV01py/toW3fabvuLiYoYMGUJwcDC+vr7ExcWRl5fX5n4DAwMJCQkBwM/Pj6FDh+JyuZrcPi8vj7i4OHx9fQkODmbIkCEU/z/27j0uqvvO//hrhOClXEYMzMSE2GpsmhoFm3hbCKTogIooKGw26dpKYtio1XoJrVbjXZukRm3q1g21TW227VapgJEkoGMUMRptU0Jiku2alFZTGX6LXMQbguf3Bw/OBgFF5TbD+/l45PGQ75z5nu/nnJlvzmfO93y/RUUtts8wDI4ePUpsbCwAiYmJt9Rup9NJQkICAAkJCezbt69RucViISwsjKqqKkpLSykoKCA8PByr1UpAQADh4eEcOnSI0tJSqqurCQsLw2KxkJCQ0Kr2HDlyhJCQEO6+++4OOzYjRowgICCgl8ULtwAAIABJREFUw49Dc/tori0RERF4e9dPmBsWFtZonaTm3Ow+W4prwIABTdrSEedk//79JCYmAhAbG8uRI0davEss9dqr3+pILfWRbfVd7GpKSko4cOAASUlJALf0nWjpe9eVnDt3juPHj5tx+vj44O/v77HnVbqOm73u6mqu7SPcRUvfeXdRV1fHpUuXqK2t5dKlSwQHB3d2k1p0M9evt8Njkz6Xy4Xdbjf/ttlsbd5JnD59mo8//pjQ0FAAfvOb3xAfH8+SJUvM27AttaOl8vLycvz9/c3kwG63t6rdTz31FFOnTuX3v/89AGVlZeYHPCgoiLKysmbb01B/a9vZ2vbk5OQ0uk3dWcemI45DS/u4nj/84Q9ERkaaf58+fZqEhAT+9V//lT/+8Y/XbeOtxNWc9j4nLpeLu+66C6ifItzPz4/y8vIbHpvurCP6rY70xT6yrb6LXc369etJS0ujR4/6/53eynfCHWI9ffo0gYGBLFmyhISEBJYuXcqFCxc89rxK13TtdZc7uLaPcBctfefdgc1m48knn+Sb3/wmERER+Pr6EhER0dnNuim3cm15I+71CexCzp8/z7x58/jhD3+Ir68vjz/+OHv37iU7O5vg4GCef/75DmvL7373OzIzM/n5z3/Ob37zG44fP97odYvFgsVi6bD21NTUsH//fsaPHw/QqcfmizriOLRmH1u3bsXLy4vJkycD9b9ivv3222RlZbF48WIWLVpEdXV1m+7zWl3lnIjnuraP/KKO7pPay9tvv01gYCAPPvhgZzel3dXW1vLRRx/x+OOPk5WVRe/evZs8c+op51W6puv1KV2VO/cRrfnOd1WVlZU4nU6cTieHDh3i4sWLZGdnd3azbllb9a0em/TZbLZGw+dcLhc2m61N6r5y5Qrz5s0jPj6emJgYAO688068vLzo0aMHycnJfPDBB9dtR0vlffv2paqqitraWqB+WMCN2t3wer9+/XA4HBQVFdGvXz9KS0uB+mGCgYGBzbanof7WtrM17cnPz2fIkCHceeednX5sOuI4tLSP5uzatYsDBw6wYcMG8wvs4+ND3759AXjwwQe59957+etf/3pL+2zt+eqIc2Kz2Thz5gxQ/z+Pc+fOmXFK89qz3+pIzfWRbfVd7Eree+899u/fT3R0NAsXLuTo0aOsW7fupr8T7hCr3W7Hbrebd1jGjx/PRx995JHnVbqe5voUd9BcH/Hss892drNapaXvvDt45513uOeeewgMDOSOO+4gJibGrSahgZu7tmwtj036hg4dSnFxMadOnaKmpoacnByio6Nvu17DMFi6dCkDBw4kJSXFLG84MQD79u1j8ODBQP3Mmjk5OdTU1HDq1CmKi4sZNmxYi+2zWCyMGjWK3NxcoH6Ws+u1+8KFC+ZdoQsXLnD48GEGDx5MdHQ0WVlZAGRlZTF27FizPVlZWRiGQWFhIX5+fgQHBxMREUFBQQGVlZVUVlZSUFBAREQEwcHB+Pr6UlhYiGEYjepqSU5ODnFxcZ1+bL4Yb3seh5b2ca38/Hy2bdvG1q1b6d27t1l+9uxZ6urqAMzjEBISckv7bCmua3XEOYmOjjZn5svNzWX06NG6C3AD7dVvdaSW+si2+i52JYsWLSI/P5/9+/ezceNGRo8ezUsvvXTT34mWvnddSVBQEHa7nc8++wyof2570KBBHnlepWtpqU9xB831ERs2bOjsZrVKS995d9C/f3/ef/99Ll68iGEYbtX2Bq29trwptz0VTBd24MABIyYmxhg7dqzxs5/9rE3qPH78uPHVr37VmDRpkjF58mRj8uTJxoEDB4xnn33WmDRpkjFp0iTj3/7t3wyXy2W+52c/+5kxduxYIyYmptHMly217+9//7sxbdo0Y9y4ccbcuXONy5cvt9iev//970Z8fLwRHx9vTJw40azn7Nmzxre//W3D4XAY3/nOd4zy8nLDMAzj6tWrxsqVK42xY8cakyZNMoqKisy6du7caYwbN84YN26ckZGRYZYXFRUZcXFxxtixY41Vq1YZV69ebbE958+fN0aOHGlUVVWZZR11bBYsWGCEh4cbX//6141HHnnE2LFjR4cch+b20Vxbxo0bZ0RGRpqfm4ZZ/N566y1j4sSJxuTJk42EhATD6XTe0j5biqu5tnTEObl06ZIxd+5cY9y4cca0adOMv//97y1+buT/tEe/1ZFa6iPb8rvYFR09etScme9WvhMtfe+6ko8++shITEw0Jk2aZMyaNcuoqKjw+PMqna+lPsXdfLGPcBfNfefdxU9+8hMjNjbWiIuLM5599tnrXkt3tpu5fr0dFsPQdHoiIiIiIiKeymOHd4qIiIiIiIiSPhEREREREY+mpE9ERERERMSDKekTERERERHxYEr6REREREREPJiSPhEREREREQ+mpE9ERERERMSDKekTERERERHxYEr6REREREREPJiSPhEREREREQ+mpE9ERERERMSDKekTERERERHxYEr6REREREREPJiSPhEREREREQ+mpE9ERERERMSDKekTERERERHxYEr6REREREREPJiSPhEREREREQ+mpE/aXHR0NO+8806n7X/69Ons3Lmz0/YvInIjhw4dYvbs2a3efu7cuRw8eLAdWyQiXd27775LZGRkp+1f/ZZ7U9InvP7660ydOpXhw4cTERHBzJkz+eMf/9hp7Tl9+jT3338/tbW1ndYGEXEvXa0fa7Br1y4ef/zxJuWbNm0iNTW1UZlhGIwdO5aJEyc22f7pp5/mJz/5Sbu1U0Q6zlNPPdXs93nfvn2Eh4e3+vqno39k/2K/NX78eDIyMppss337dqZOnQqo3+pqlPR1c6+++irr16/nmWee4fDhw7z99ts88cQTOJ3Ozm6aiEirtFU/1tyFVnv8+FRUVER1dTVhYWGNyo8fP87Zs2c5deoURUVFjV4bNmwY1dXVfPDBB23eHhHpWImJiezevRvDMBqV7969m/j4eLy9vTupZS27tt9KTEwkOzu7yXbZ2dkkJiYC6re6GiV93di5c+d4+eWXWb58OTExMfTp04c77riD6OhonnzySUJDQykvLze3P3HiBKNHj+bKlSsA7NixgwkTJjB8+HAmTpzIiRMnmuzj6tWrpKenM27cOEaNGsX3vvc9KioqbqqdixcvZtWqVaSmpjJ8+HCSk5P5+9//br5++PBhxo8fz0MPPcTq1aubdKIZGRlMmDCBESNG8NRTT/H5558DkJ6eTnJysnlR99vf/pa4uDguX758U+0Tkc5zvX7sBz/4AYsXL2bTpk3m9tcOj4qOjiY9PZ34+HjCwsL429/+xv3338/OnTt59NFH+c53vgO03I8A3H///fzud78jJiaGhx9+mFWrVmEYBp9++ikrVqygsLCQ4cOH8/DDDwOQn5/PiBEjmsSSmZlJdHQ0UVFRZGVlNXl95MiRGiol4gHGjRtHRUVFo9EIlZWVvP322yQkJLBu3ToiIiKIiIhg3bp11NTUNKkjLS2Nf/zjHzzzzDMMHz6cn//85wDMmzeP8PBwHnroIb71rW/xP//zP+Z7ysvLeeaZZ/jGN77BtGnT2LRpU6ORCJ9++ikpKSmMHDmS2NhY3njjDfO1a/utKVOm8Kc//alRX3jy5En+8pe/EBcXZ5ap3+o6lPR1Y3/+85+5fPkyDoejyWtBQUGMHDmSN9980yzLzs4mLi6OO+64gzfffJOf/vSnvPDCC7z33nts3boVq9XapJ7XXnuNffv28Z//+Z8cOnSIgIAAVq9efdNtfeONN/jud7/L8ePHuffee82LuLNnz/Ld736X+fPnc/ToUe69917ee+8983379u3jlVdeYcuWLRw5coSHHnqIRYsWATBz5kx8fHzYunUrxcXFbNq0iR//+Mf07NnzptsnIp3jev1Ya+Xk5JCens4f//hHvLy8gPq7bm+88Qa/+MUvrtuPNDhw4AAZGRns3r2bN998k0OHDjFo0CBWrVpFWFgYf/7zn80LvL/85S985StfafT+ixcvkpuby+TJk4mPjycnJ6fJhd6gQYP45JNPbjlOEekaevXqxYQJExr9uPPmm28ycOBAcnNzef/998nOzmb37t188MEH/OxnP2tSx49//GP69+/Pf/zHf/DnP/+Zp59+GoDIyEhyc3M5cuQIX//613n22WfN96xevZrevXtz+PBhXnjhhUb7v3DhAk8++SSTJk3inXfeYdOmTaxatYqTJ08CTfstu93OqFGjGt3ty87OJjIyksDAQLNM/VbXoaSvG6uoqKBv374tDiNoGH4AUFdXR05ODlOmTAHqf/WeOXMmw4YNw2KxMGDAAO6+++4mdfzXf/0XCxYswG634+Pjw3e/+11yc3NvesjUuHHjGDZsGN7e3kyePJmPP/4YqP/lafDgwYwfP5477riD73znO9x5552N9p+amsqgQYPw9vbmmWee4eOPP+bzzz+nR48evPDCC7z22mvMmjWLmTNn8vWvf/2m2iUinetG/VhrTJ8+nbvuuotevXqZZXPnzqVPnz706tXruv1Ig6effhp/f3/69+/PqFGjrnuRc+7cOb70pS81KsvLy8PHx4fw8HAeffRRamtrm/w6/qUvfYmqqqpbjlNEuo6EhARyc3PN0UVZWVkkJiby+uuvM2fOHPr160dgYCBz5swxr8VaIykpCV9fX3x8fJg7dy6ffPIJ586do66ujry8PObOnUvv3r257777SEhIMN934MAB7r77bqZNm4a3tzdf//rXiY2N5a233gKa77cSEhLMpO/q1au8/vrr5tDOBuq3uo6uN2hYOozVaqW8vJza2tpmL5jGjh3LihUrOHXqFH/961/x9fVl2LBhAJw5c4Z77733hvv4xz/+wZw5c+jR4/9+X+jRowdlZWXYbLZWt/WLiVyvXr24cOECAKWlpdjtdvM1i8XCXXfd1Wj/69ev54UXXjDLDMPA5XJx9913c8899zBq1CgOHjzIt771rVa3R0S6hhv1Y63xxT6jwRf7lRv1I1A/OqJB7969OX/+fIv78/f3b/J6VlYWEyZMwNvbG29vb2JiYsjMzGx0B/P8+fP4+/vffIAi0uU8/PDD9O3bl3379jF06FA++OADtmzZwsaNG+nfv7+5Xf/+/SktLW1VnXV1dWzatIm33nqLs2fPmtde5eXlXLp0idra2kb93Rf//fnnn1NUVGQOQ2+ob/LkyUDz/VZMTAyrVq2isLCQixcvcvHiRaKiohpto36r61DS140NHz4cHx8f9u3bx/jx45u83rNnTyZMmMDu3bv57LPPzLt8UN9RfPG5upbY7XbWr1/PQw891KZtbxAUFERJSYn5t2EYnDlzplE7n3nmGbPTutaBAwf485//zJgxY3jxxRdvaeipiHSeG/VjvXv35tKlS+bf//u//9tkG4vFct2yG/Uj19Nc3ffffz/FxcXm3yUlJRw9epSioiLy8vKA+uGeNTU1nD171hwq9emnn/K1r33tptsgIl3TlClTyMrK4q9//SsRERHceeedBAcH849//IPBgwcD9T+yBwcHt6q+119/HafTyauvvso999zDuXPnGDFiBIZhEBgYiLe3NyUlJeYwzWuvl0aMGMGrr77abN3X9ltQ37/GxsaSlZXF5cuXiYuLw8fHp9E26re6Dg3v7Mb8/PyYN28eq1evZt++fVy8eJErV65w8OBBXnzxRaC+Q8rMzGT//v2Nkr6kpCR++ctf8uGHH2IYBn/7298aDXVq8Pjjj7N582bztbNnz7Jv3742iyEqKor/+Z//IS8vj9raWn796183uqj7l3/5F9LT080Hmc+dO2c+p3j27FmWLVvGunXreP7559m/f78eNhZxMzfqxx544AEOHjxIRUUF/+///T+2b99+0/u4Xj9yI/369cPlcjV6Pi8qKorjx4+bf2dnZ/PlL3+Zt956i6ysLLKyssjNzcVms5GTk2Nud/z48U5do0tE2lZCQgJHjhxhx44d5lDLuLg4tm7dytmzZzl79iz//u//Tnx8fLPvv/POOzl16pT59/nz5/Hx8aFv375cvHiRjRs3mq95eXnhcDjYsmULFy9e5NNPP230PN6jjz5KcXExWVlZXLlyhStXrlBUVMSnn34KNO23GiQmJvLmm2+Sm5vbaLhoA/VbXYeSvm7uySefZPHixfzsZz9jzJgxPProo/zmN79h3LhxADz00EP06NGDIUOGNHpmb8KECTzzzDMsWrSIb3zjG8yZM4fKysom9X/72982ZwMdPnw4//zP/9xkKvLbERgYyE9+8hNeeuklRo0axd/+9je+8Y1vmK87HA5mzpzJwoUL+cY3vsGkSZPIz88HYPny5eZMeX379mXdunUsXbq00YylItL1Xa8fmzJlCl/72tfMfqi5NfBu5Hr9yI2MHj2a++67j4iICEaNGgXAkCFD8PX15f333wfqZ+184oknCAoKavTfv/zLv5CZmQnUT5fep08fc4i9iLi/e+65h+HDh3Px4kXGjh0LwOzZs3nwwQeZPHkykydPZsiQIS0uiJ6amsrWrVt5+OGH+cUvfkFCQgL9+/fnkUceIS4ursmyMMuXL+fcuXOEh4fz/e9/v9GdOV9fX37xi1/wxhtv8MgjjxAREcGGDRvMH6yu7bcajBgxAl9fX+x2e5P+Sf1W12Ixrp3fXuQa3/72t4mPjyc5ObmzmyIi4hEKCgr47W9/2+ysfM2ZO3cuSUlJTZ6XERG5VT/+8Y/53//930bPK1+P+i33pqRPrquoqIgnn3ySAwcO4Ovr29nNEREREZFb8Omnn3LlyhXuv/9+PvjgA55++mnWrVtnju4Sz6aJXKRFP/jBD9i3bx9Lly5t84Rv9+7drFixokl5//79Gz3DIiIiIiK37/z58yxatIjS0lL69evHk08+aQ4rFc+nO30iIiIiIiIeTBO5iIiIiIiIeDC3Hd559epV6uq6z01KLy9Lt4q3geJ2T3fc4dXZTeiSbqbfcvfPwLU8LR5QTO6itTF1lX6rqqqKZcuW8Ze//AWLxcL69ev5yle+woIFC/j888+5++672bx5MwEBARiGwbp16zh48CC9evXi+eefZ8iQIUD9rLBbt24FYNasWSQmJgLw4YcfsmTJEi5dukRUVBRLly5tdj3JBt2537qR7hYvdL+Y3SHe1vZdbju888qVOioqLnR2MzqM1dqnW8XbQHG7p6Agv85uQpd0M/2Wu38GruVp8YBichetjamr9Fs/+MEPePjhh0lOTqampoZLly7xH//xH1itVlJTU0lPT6eyspK0tDQOHjzIa6+9xs9//nPef/991q1bx86dO6moqGDatGn84Q9/wGKxMHXqVHbt2kVAQABJSUksW7aM0NBQnn76aaZPn37d2RW7c791I90tXuh+MbtDvK3tuzS8U0RERKQLOHfuHMePHycpKQkAHx8f/P39cTqd5sLXCQkJ7Nu3D8Ast1gshIWFUVVVRWlpKQUFBYSHh2O1WgkICCA8PJxDhw5RWlpKdXU1YWFhWCwWEhIScDqdnRaviHQctx3eKSIiIuJJTp8+TWBgIEuWLOGTTz5hyJAhLF26lLKyMoKDgwEICgqirKwMAJfLhd1uN99vt9txuVxNym02W7PlDdtfj5eXBau1T6va7+XVo9XbeoLuFi90v5g9KV4lfSIiIiJdQG1tLR999BHPPfccoaGhrF27lvT09EbbWCyW6z6D19bq6gwN72xBd4sXul/M7hCvhneKiIiIuBG73Y7dbic0NBSA8ePH89FHH9GvXz9KS0sBKC0tJTAwEKi/g1dSUmK+v6SkBJvN1qTc5XI1W96wvYh4PiV9IiIiIl1AUFAQdrudzz77DIAjR44waNAgoqOjycrKAiArK8tcULuh3DAMCgsL8fPzIzg4mIiICAoKCqisrKSyspKCggIiIiIIDg7G19eXwsJCDMNoVJeIeDYN7xQRERHpIp577jmeffZZrly5QkhICD/60Y+4evUq8+fPJyMjg/79+7N582YAoqKiOHjwIA6Hg969e7N+/XoArFYrs2fPNieEmTNnDlarFYAVK1aYSzZERkYSGRnZOYGKSIfSkg1uwh3GFN8OX//e9O7Ztr9BXLxcS3XVxTats6O4+/nuKlOfdzXdeepzT4sHwM/ah15tvLZbZ/dbnnie3G3Jhq6mO/dbN9Ld4oWWY/bU6zh3OMet7bt0p0+6hN49vfny4pw2rbP4+Tiq27RGEZH/0+sOL/VbIiLoOs4d6Jk+ERERERERD6akT0RERERExIMp6RMREREREfFgSvpEREREREQ8mJI+ERERERERD6akT0RERERExIMp6RMRt7VkyRLGjBnDpEmTzLIXXniB8ePHEx8fz5w5c6iqqjJfe+WVV3A4HMTGxnLo0CGzPD8/n9jYWBwOB+np6Wb5qVOnSE5OxuFwMH/+fGpqagCoqalh/vz5OBwOkpOTOX36dAdEKyIiInJrlPSJiNuaOnUq27Zta1QWHh7Onj17eP311/nyl7/MK6+8AsDJkyfJyckhJyeHbdu2sWrVKurq6qirq2P16tVs27aNnJwc9uzZw8mTJwHYsGEDM2bMYO/evfj7+5ORkQHAzp078ff3Z+/evcyYMYMNGzZ0bOAiIiIiN0FJn4i4rREjRhAQENCoLCIiAm9vbwDCwsIoKSkBwOl0EhcXh4+PDyEhIQwYMICioiKKiooYMGAAISEh+Pj4EBcXh9PpxDAMjh49SmxsLACJiYk4nU4A9u/fT2JiIgCxsbEcOXIEwzA6KmwRERGRm+Ld2Q0QEWkvf/jDH5gwYQIALpeL0NBQ8zWbzYbL5QLAbrc3Ki8qKqK8vBx/f38zgbTb7eb2LpeLu+66CwBvb2/8/PwoLy8nMDDwuu3x8rJgtfZpVdu9vHq0elt34GnxtKfOPE6eeJ48MSYRkZulpE9EPNLWrVvx8vJi8uTJnd0UU12dQUXFhVZta7X2afW27sDT4gEICvJrl3o78zh54nlqbUztdT5FRLoCJX0i4nF27drFgQMH+NWvfoXFYgHq7+A1DPWE+rt1NpsNoNnyvn37UlVVRW1tLd7e3pSUlJjb22w2zpw5g91up7a2lnPnztG3b98OjFBERESk9fRMn4h4lPz8fLZt28bWrVvp3bu3WR4dHU1OTg41NTWcOnWK4uJihg0bxtChQykuLubUqVPU1NSQk5NDdHQ0FouFUaNGkZubC0BmZibR0dFmXZmZmQDk5uYyevRoM7kUERER6Wp0p09E3NbChQs5duwY5eXlREZGMnfuXNLT06mpqSElJQWA0NBQVq9ezeDBg5kwYQITJ07Ey8uL5cuX4+XlBcDy5cuZOXMmdXV1TJs2jcGDBwOQlpbGggUL2Lx5Mw888ADJyckAJCUlkZaWhsPhICAggE2bNnXOARARERFpBSV9IuK2Nm7c2KSsITFrzqxZs5g1a1aT8qioKKKiopqUh4SEmMs0fFHPnj15+eWXb7K1IiIiIp1DwztFREREREQ8mJI+ERERERERD6akT0RERKSLiI6OJj4+nilTpjB16lQAKioqSElJISYmhpSUFCorKwEwDIO1a9ficDiIj4/nxIkTZj2ZmZnExMQQExNjTjwF8OGHHxIfH4/D4WDt2rUYhtGxAYpIp7hh0nfmzBmmT5/OxIkTiYuLY/v27YA6IBEREZH2sH37drKzs9m1axcA6enpjBkzhry8PMaMGUN6ejpQP1txcXExeXl5rFmzhpUrVwL112hbtmxhx44d7Ny5ky1btpjXaStXrmTNmjXk5eVRXFxMfn5+p8QoIh3rhkmfl5cXixcv5o033uD3v/89v/3tbzl58qQ6IBEREZEO4HQ6SUhIACAhIYF9+/Y1KrdYLISFhVFVVUVpaSkFBQWEh4djtVoJCAggPDycQ4cOUVpaSnV1NWFhYVgsFhISEnA6nZ0Zmoh0kBvO3hkcHExwcDAAvr6+DBw4EJfLhdPp5LXXXgPqO6Dp06eTlpbWYgd07NgxswMCzA5o5MiRZgfUUJfT6Wx2Jj0RERERT/fUU09hsVh47LHHeOyxxygrKzOvxYKCgigrKwPA5XJht9vN99ntdlwuV5Nym83WbHnD9tfj5WXBau3TqnZ7efVo9baeoLvFCx0fc2cfX086xze1ZMPp06f5+OOPCQ0N7dQOCG6uE/IEnvSh60juesx0vkVEuqff/e532Gw2ysrKSElJYeDAgY1et1gsWCyWDmtPXZ1BRcWFVm1rtfZp9baeoLvFCy3HHBTk1y776+zj6w7nuLXHvtVJ3/nz55k3bx4//OEP8fX1bfRaR3dAcHOdkCdwhw/d7fDUzuJWufv5bq/zKSLi6Ww2GwD9+vXD4XBQVFREv379KC0tJTg4mNLSUgIDA81tS0pKzPeWlJRgs9mw2WwcO3bMLHe5XIwcObLF7UXE87Vq9s4rV64wb9484uPjiYmJATA7IKDVHdAXy10uV7Pl6oBERESkO7pw4QLV1dXmvw8fPszgwYOJjo4mKysLgKysLMaOHQtglhuGQWFhIX5+fgQHBxMREUFBQQGVlZVUVlZSUFBAREQEwcHB+Pr6UlhYiGEYjeoSEc92w6TPMAyWLl3KwIEDSUlJMcvVAYmIiIi0nbKyMp544gkmT55McnIyUVFRREZGkpqayuHDh4mJieGdd94hNTUVgKioKEJCQnA4HDz33HOsWLECAKvVyuzZs0lKSiIpKYk5c+aYcyqsWLGCZcuW4XA4uPfee4mMjOy0eEWk49xweOef/vQnsrOz+epXv8qUKVMAWLhwIampqcyfP5+MjAz69+/P5s2bgfoO6ODBgzgcDnr37s369euBxh0Q0KQDWrJkCZcuXSIyMlIdkIiIiHQ7ISEh7N69u0l53759zSWzvshisZiJ3rUaEr5rDR06lD179tx+Y0XErdww6Xv44Yf57//+72ZfUwckIiIiIiLStbXqmT4RfGlSAAAgAElEQVQRERERERFxT0r6REREREREPJiSPhEREREREQ+mpE9E3NaSJUsYM2YMkyZNMssqKipISUkhJiaGlJQUKisrgfqZiNeuXYvD4SA+Pp4TJ06Y78nMzCQmJoaYmBgyMzPN8g8//JD4+HgcDgdr167FMIzr7kNERESkK1LSJyJua+rUqWzbtq1RWXp6OmPGjCEvL48xY8aQnp4OQH5+PsXFxeTl5bFmzRpWrlwJ1CdwW7ZsYceOHezcuZMtW7aYSdzKlStZs2YNeXl5FBcXk5+ff919iIiIiHRFSvpExG2NGDGCgICARmVOp5OEhAQAEhIS2LdvX6Nyi8VCWFgYVVVVlJaWUlBQQHh4OFarlYCAAMLDwzl06BClpaVUV1cTFhaGxWIhISEBp9N53X2IiIiIdEU3XLJBRMSdlJWVERwcDEBQUBBlZWUAuFwu7Ha7uZ3dbsflcjUpt9lszZY3bH+9fdyIl5cFq7VPK7ft0ept3YGnxdOeOvM4eeJ58sSYRERulpI+EfFYFosFi8XSZfZRV2dQUXGhVdtarX1ava078LR4AIKC/Nql3s48Tp54nlobU3udTxGRrkDDO0XEo/Tr14/S0lIASktLCQwMBOrv4JWUlJjblZSUYLPZmpS7XK5myxu2v94+RERERLoiJX0i4lGio6PJysoCICsri7FjxzYqNwyDwsJC/Pz8CA4OJiIigoKCAiorK6msrKSgoICIiAiCg4Px9fWlsLAQwzCarevafYiIiIh0RRreKSJua+HChRw7dozy8nIiIyOZO3cuqampzJ8/n4yMDPr378/mzZsBiIqK4uDBgzgcDnr37s369esBsFqtzJ49m6SkJADmzJmD1WoFYMWKFSxZsoRLly4RGRlJZGQkQIv7EBEREemKlPSJiNvauHFjs+Xbt29vUmaxWFixYkWz2yclJZlJ3xcNHTqUPXv2NCnv27dvs/sQERER6Yo0vFNERERERMSDKekTERERERHxYEr6REREREREPJiSPhEREREREQ+mpE9ERERERMSDKekTERER6SLq6upISEjg3/7t3wA4deoUycnJOBwO5s+fT01NDQA1NTXMnz8fh8NBcnIyp0+fNut45ZVXcDgcxMbGcujQIbM8Pz+f2NhYHA4H6enpHRuYiHQqj1+ywde/N717tm2YFy/XUl11sU3rFBEREfn1r3/NoEGDqK6uBmDDhg3MmDGDuLg4li9fTkZGBk888QQ7d+7E39+fvXv3kpOTw4YNG9i8eTMnT54kJyeHnJwcXC4XKSkp5ObmArB69WpeffVVbDYbSUlJREdHc99993VmuCLSQTw+6evd05svL85p0zqLn4+juk1rFBERke6upKSEAwcO8Mwzz/CrX/0KwzA4evQoL730EgCJiYls2bKFJ554gv379/Pd734XgNjYWFavXo1hGDidTuLi4vDx8SEkJIQBAwZQVFQEwIABAwgJCQEgLi4Op9OppE+km/D4pE9ERETEHaxfv560tDTOnz8PQHl5Of7+/nh711+u2e12XC4XAC6Xi7vuugsAb29v/Pz8KC8vx+VyERoaatZps9nM99jt9kblDcng9Xh5WbBa+7Sq/V5ePVq9rSfobvFCx8fc2cfXk86xkj4RERGRTvb2228TGBjIgw8+yLvvvtvZzTHV1RlUVFxo1bZWa59Wb+sJulu80HLMQUF+7bK/zj6+7nCOW3vslfSJiIiIdLL33nuP/fv3k5+fz+XLl6murmbdunVUVVVRW1uLt7c3JSUl2Gw2oP5O3ZkzZ7Db7dTW1nLu3Dn69u2LzWajpKTErNflcpnvaalcRDyfkr5bcOlKXbv8onHpSh297vBq8fVb2Wd7TDrTHpPjuIv2iL2l8347nzFNNiQi4l4WLVrEokWLAHj33Xf55S9/yUsvvcS8efPIzc0lLi6OzMxMoqOjAYiOjiYzM5Phw4eTm5vL6NGjsVgsREdHs2jRIlJSUnC5XBQXFzNs2DAMw6C4uJhTp05hs9nIyckxnxUUEc/XPa/cb1OvO7zafHIYqJ8gxh0mnWmvyXHcQXvF7g7nXUREOl5aWhoLFixg8+bNPPDAAyQnJwOQlJREWloaDoeDgIAANm3aBMDgwYOZMGECEydOxMvLi+XLl+PlVf/D4vLly5k5cyZ1dXVMmzaNwYMHd1pcItKxbpj0LVmyhAMHDtCvXz/27NkDwE9/+lN27NhBYGAgAAsXLiQqKgqoXxsmIyODHj16sGzZMh555BGgfm2YdevWcfXqVZKTk0lNTQXq159ZuHAhFRUVDBkyhBdffBEfH592CVZERESkqxs1ahSjRo0CICQkhIyMjCbb9OzZk5dffrnZ98+aNYtZs2Y1KY+KijKv10Ske7nh4uxTp05l27ZtTcpnzJhBdnY22dnZZgfyxbVhtm3bxqpVq6irq6Ouro7Vq1ezbds2cnJy2LNnDydPngT+b/2ZvXv34u/v32zHJiIiIiIiIrfmhknfiBEjCAgIaFVlLa0NU1RUZK4N4+PjY64N07D+TGxsLFC//ozT6by9iERERERERMR0y8/0/eY3vyErK4sHH3yQxYsXExAQcNNrw1xv/ZkbuZl1Y7q77nycFHv39atf/YqdO3disVj46le/yo9+9CNKS0ubHU5eU1PD97//fU6cOIHVamXTpk3cc889wM0PWRcRERHpam4p6Xv88ceZPXs2FouFn/zkJzz//PP86Ec/auu2XVdr141pr3VD3Elbry/iTsdUsXeOzj5OLpeLX//617zxxhv06tWL733ve+Tk5HDw4EFmzJhBXFwcy5cvJyMjgyeeeIKdO3fi7+/P3r17ycnJYcOGDWzevLnRkHWXy0VKSgq5ubkArF69mldffRWbzUZSUhLR0dHcd999nRq3iIiISHNuOLyzOXfeeSdeXl706NGD5ORkPvjgA4AW14Zpqbxv377m+jNAo/VnRERuR11dHZcuXaK2tpZLly4RFBTU4nDy/fv3k5iYCEBsbCxHjhzBMIybHrIuIiIi0hXd0p2+0tJSgoODAdi3b5855e/Nrg1jsVgYNWpUs+vPiIjcKpvNxpNPPsk3v/lNevbsSXh4OEOGDGlxOLnL5eKuu+4CwNvbGz8/P8rLy296yPqN3MywdC+vHh41RNfT4mlPnXmcPPE8eWJMIiI364ZJ38KFCzl27Bjl5eVERkYyd+5cjh07xieffALA3XffzerVq4FbWxumpfVnRERuVWVlJU6nE6fTiZ+fH9/73vc4dOhQZzer1cPSof7CvzOH6LY1T4sH2m8Yc2ceJ088T62NqbOHpYuItKcbJn0bN25sUna9xOxm14Zpaf0ZEZFb9c4773DPPfeYa4nGxMTw3nvvmcPJvb29Gw0nt9lsnDlzBrvdTm1tLefOnaNv374tDk0HWiwXERER6Wpu6Zk+EZGurH///rz//vtcvHgRwzA4cuQI9913nzmcHGg0nDw6OprMzEwAcnNzGT16NBaLhejoaHJycqipqeHUqVPmkPWhQ4eaQ9ZramrIycnR0HQRERHpsm55yQYRka4qNDSU2NhYEhMT8fb25oEHHuCxxx7j0UcfbXY4eVJSEmlpaTgcDgICAti0aRNwa0PWRURERLoaJX0i4pHmzZvHvHnzGpW1NJy8Z8+evPzyy83Wc7ND1kVERES6Gg3vFBERERER8WBK+kRERERERDyYkj4REREREREPpqRPRERERETEgynpExERERER8WBK+kRERERERDyYlmwQEREREZEu5dKVOoKC/Nq0zouXa6muutimdboLJX0iIiIiXcDly5f51re+RU1NDXV1dcTGxjJv3jxOnTrFwoULqaioYMiQIbz44ov4+PhQU1PD97//fU6cOIHVamXTpk3cc889ALzyyitkZGTQo0cPli1bxiOPPAJAfn4+69at4+rVqyQnJ5OamtqZIYu0qNcdXnx5cU6b1ln8fBzVbVqj+9DwThEREZEuwMfHh+3bt7N7926ysrI4dOgQhYWFbNiwgRkzZrB37178/f3JyMgAYOfOnfj7+7N3715mzJjBhg0bADh58iQ5OTnk5OSwbds2Vq1aRV1dHXV1daxevZpt27aRk5PDnj17OHnyZGeGLCIdREmfiIiISBdgsVj40pe+BEBtbS21tbVYLBaOHj1KbGwsAImJiTidTgD2799PYmIiALGxsRw5cgTDMHA6ncTFxeHj40NISAgDBgygqKiIoqIiBgwYQEhICD4+PsTFxZl1iYhnU9InIiIi0kXU1dUxZcoU/umf/ol/+qd/IiQkBH9/f7y965/IsdvtuFwuAFwuF3fddRcA3t7e+Pn5UV5ejsvlwm63m3XabDZcLleL5SLi+fRMn4iIiEgX4eXlRXZ2NlVVVcyZM4fPPvusk9tjwWrt08pte7R6W0/Q3eIFz4j5ZtrvCfE2UNInIiIi0sX4+/szatQoCgsLqaqqora2Fm9vb0pKSrDZbED9nbozZ85gt9upra3l3Llz9O3bF5vNRklJiVmXy+Uy39NSeUvq6gwqKi60qs1Wa59Wb+sJulu80HLMbT3LZnu6mXPmDue4tcdewztFREREuoCzZ89SVVUFwKVLl3jnnXcYNGgQo0aNIjc3F4DMzEyio6MBiI6OJjMzE4Dc3FxGjx6NxWIhOjqanJwcampqOHXqFMXFxQwbNoyhQ4dSXFzMqVOnqKmpIScnx6xLRDyb7vSJiIiIdAGlpaUsXryYuro6DMNg/PjxfPOb3+S+++5jwYIFbN68mQceeIDk5GQAkpKSSEtLw+FwEBAQwKZNmwAYPHgwEyZMYOLEiXh5ebF8+XK8vLwAWL58OTNnzqSuro5p06YxePDgTotXRDqOkj4R8UhVVVUsW7aMv/zlL1gsFtavX89XvvIVFixYwOeff87dd9/N5s2bCQgIwDAM1q1bx8GDB+nVqxfPP/88Q4YMAep/Vd+6dSsAs2bNMmfK+/DDD1myZAmXLl0iKiqKpUuXYrFYOi1eEXF/X/va18jKympSHhISYi7T8EU9e/bk5ZdfbrauWbNmMWvWrCblUVFRREVF3X5jRcStaHiniHikdevW8cgjj/DWW2+RnZ3NoEGDSE9PZ8yYMeTl5TFmzBjS09OB+sWKi4uLycvLY82aNaxcuRKAiooKtmzZwo4dO9i5cydbtmyhsrISgJUrV7JmzRry8vIoLi4mPz+/s0IVERERuS4lfSLicc6dO8fx48dJSkoC6hc89vf3x+l0kpCQAEBCQgL79u0DMMstFgthYWFUVVVRWlpKQUEB4eHhWK1WAgICCA8P59ChQ5SWllJdXU1YWBgWi4WEhAStdSUiIiJdloZ3iojHOX36NIGBgSxZsoRPPvmEIUOGsHTpUsrKyggODgYgKCiIsrIygCZrVzWsg9Xata6+uG7W9XTnqc89LZ721JnHyRPPkyfGJCJys5T0iYjHqa2t5aOPPuK5554jNDSUtWvXmkM5G1gslg5/Bq87T33uafFA+01R3pnHyRPPU2tjcqcp50VEbpaGd4qIx7Hb7djtdkJDQwEYP348H330Ef369aO0tBSonyUvMDAQoMmaVg3rYLW01lVL24uIiIh0RTdM+pYsWcKYMWOYNGmSWVZRUUFKSgoxMTGkpKSYExsYhsHatWtxOBzEx8dz4sQJ8z2ZmZnExMQQExNjrikD9TPgxcfH43A4WLt2LYZhtGV8ItINBQUFYbfb+eyzzwA4cuQIgwYNIjo62pwZLysri7FjxwKY5YZhUFhYiJ+fH8HBwURERFBQUEBlZSWVlZUUFBQQERFBcHAwvr6+FBYWYhhGo7pEREREupobJn1Tp05l27Ztjco0A56IdHXPPfcczz77LPHx8Xz88cc888wzpKamcvjwYWJiYnjnnXdITU0F6qcwDwkJweFw8Nxzz7FixQoArFYrs2fPJikpiaSkJObMmYPVagVgxYoVLFu2DIfDwb333ktkZGSnxSoiIiJyPTd8pm/EiBGcPn26UZnT6eS1114D6mfAmz59OmlpaS3OgHfs2DFzBjzAnAFv5MiR5gx4DXU5nU6tHyMit+2BBx5g165dTcq3b9/epMxisZiJ3rUaEr5rDR06lD179tx+Q0VERETa2S0909fZM+CJiIiIiIhI69z27J2dMQMe3NzU591ddz5Oil1EREREurtbSvoaZsALDg5u9Qx4x44dM8tdLhcjR468rRnwWjv1uaZgbvvpv93pmCr2zuFOx0lERETE093S8E7NgCciIiIiIuIebninb+HChRw7dozy8nIiIyOZO3cuqampzJ8/n4yMDPr378/mzZuB+hnwDh48iMPhoHfv3qxfvx5oPAMe0GQGvCVLlnDp0iUiIyM1A56IiIiIiLS5S1fqbno00o22v3i5luqqi7fTrA5xw6Rv48aNzZZrBjwREREREXEXve7w4suLc9q0zuLn46hu0xrbxy0N7xQRERERERH3oKRPRERERETEgynpExERERER8WBK+kRERERERDyYkj4REREREREPpqRPREREpAs4c+YM06dPZ+LEicTFxZkzpVdUVJCSkkJMTAwpKSlUVlYCYBgGa9euxeFwEB8fz4kTJ8y6MjMziYmJISYmhszMTLP8ww8/JD4+HofDwdq1azEMo2ODFJFOoaRPREREpAvw8vJi8eLFvPHGG/z+97/nt7/9LSdPniQ9PZ0xY8aQl5fHmDFjSE9PByA/P5/i4mLy8vJYs2YNK1euBOqTxC1btrBjxw527tzJli1bzERx5cqVrFmzhry8PIqLi8nPz++scEWkA91wnT4RcT+3svhoa7jLAqQiIu4oODiY4OBgAHx9fRk4cCAulwun08lrr70GQEJCAtOnTyctLQ2n00lCQgIWi4WwsDCqqqooLS3l2LFjhIeHY7VaAQgPD+fQoUOMHDmS6upqwsLCzLqcTidRUVGdE7CIdBglfSIeqD0WHwX3WYBURMTdnT59mo8//pjQ0FDKysrMZDAoKIiysjIAXC4XdrvdfI/dbsflcjUpt9lszZY3bH89Xl4WrNY+rWqzl1ePVm/rCbpbvNA9Y24NdzgmSvo8XHvd8XEH3Tl2ERFxX+fPn2fevHn88Ic/xNfXt9FrFosFi8XSYW2pqzOoqLjQqm2t1j6t3tYTdLd4oeWYu/v1Vmd+Dlp77JX0ebj2uONT/Hxcm9bXXrpz7FKvrq6OadOmYbPZeOWVVzh16hQLFy6koqKCIUOG8OKLL+Lj40NNTQ3f//73OXHiBFarlU2bNnHPPfcA8Morr5CRkUGPHj1YtmwZjzzyCFD/LM26deu4evUqycnJpKamdmaoIuIhrly5wrx584iPjycmJgaAfv36UVpaSnBwMKWlpQQGBgL1d/BKSkrM95aUlGCz2bDZbBw7dswsd7lcjBw5ssXtRcTzaSIXEfFYv/71rxk0aJD594YNG5gxYwZ79+7F39+fjIwMAHbu3Im/vz979+5lxowZbNiwAYCTJ0+Sk5NDTk4O27ZtY9WqVdTV1VFXV8fq1avZtm0bOTk57Nmzh5MnT3ZKjCLiOQzDYOnSpQwcOJCUlBSzPDo6mqysLACysrIYO3Zso3LDMCgsLMTPz4/g4GAiIiIoKCigsrKSyspKCgoKiIiIIDg4GF9fXwoLCzEMo1FdIuLZlPSJiEcqKSnhwIEDJCUlAfUXU0ePHiU2NhaAxMREnE4nAPv37ycxMRGA2NhYjhw5gmEYOJ1O4uLi8PHxISQkhAEDBlBUVERRUREDBgwgJCQEHx8f4uLizLpERG7Vn/70J7Kzszl69ChTpkxhypQpHDx4kNTUVA4fPkxMTAzvvPOOObIgKiqKkJAQHA4Hzz33HCtWrADAarUye/ZskpKSSEpKYs6cOeakLitWrGDZsmU4HA7uvfdeIiMjOy1eEek4Gt4pIh5p/fr1pKWlcf78eQDKy8vx9/fH27u+2/viBAYul4u77roLAG9vb/z8/CgvL8flchEaGmrW2TAZQsP7v1heVFR0wzZ15wkRPC2e9tSZx8kTz5M7xfTwww/z3//9382+1rBm3xdZLBYz0btWQ8J3raFDh7Jnz57ba6iIuB0lfSLicd5++20CAwN58MEHeffddzu7OabuPCGCp8UD7TdxQWceJ088T62NqbtPRCEink1Jn4h4nPfee4/9+/eTn5/P5cuXqa6uZt26dVRVVVFbW4u3t3ejCQxsNhtnzpzBbrdTW1vLuXPn6Nu3b5NJD1wul/melspFREREuholfSLicRYtWsSiRYsAePfdd/nlL3/JSy+9xLx588jNzSUuLo7MzEyio6OB+skQMjMzGT58OLm5uYwePRqLxUJ0dDSLFi0iJSUFl8tFcXExw4YNwzAMiouLOXXqFDabjZycHF566aXODFlEROSGfP1707vn7V3+6664e1LSJyLdRlpaGgsWLGDz5s088MADJCcnA/XPvqSlpeFwOAgICGDTpk0ADB48mAkTJjBx4kS8vLxYvnw5Xl5eACxfvpyZM2eay0IMHjy40+ISERFpjd49vdt8OSvQklbuQEmfiHi0UaNGMWrUKABCQkLMZRq+qGfPnrz88svNvn/WrFnMmjWrSXlUVBRRUVFt21gRERGRdqAlG0RERERERDyYkj4REREREREPpqRPRERERETEgynpExERERER8WBK+kRERERERDyYkj4REREREREPdltLNkRHR/OlL32JHj164OXlxa5du6ioqGDBggV8/vnn3H333WzevJmAgAAMw2DdunUcPHiQXr168fzzzzNkyBAAMjMz2bp1K1A/PXpiYuLtRyYiIiIiIiK3f6dv+/btZGdns2vXLgDS09MZM2YMeXl5jBkzhvT0dADy8/MpLi4mLy+PNWvWsHLlSgAqKirYsmULO3bsYOfOnWzZsoXKysrbbZaIiIiIiIjQDsM7nU4nCQkJACQkJLBv375G5RaLhbCwMKqqqigtLaWgoIDw8HCsVisBAQGEh4dz6NChtm6WiIiIiIhIt3RbwzsBnnrqKSwWC4899hiPPfYYZWVlBAcHAxAUFERZWRkALpcLu91uvs9ut+NyuZqU22w2XC7XDffr5WXBau1zu80XkZuk752IiIiIe7mtpO93v/sdNpuNsrIyUlJSGDhwYKPXLRYLFovlthrYkro6g4qKCzfcLijIr132L9Jd6XsnIiIi4l5ua3inzWYDoF+/fjgcDoqKiujXrx+lpaUAlJaWEhgYaG5bUlJivrekpASbzdak3OVymfWKiIiIiIjI7bnlpO/ChQtUV1eb/z58+DCDBw8mOjqarKwsALKyshg7diyAWW4YBoWFhfj5+REcHExERAQFBQVUVlZSWVlJQUEBERERbRCaiIiIiIiI3PLwzrKyMubMmQNAXV0dkyZNIjIykqFDhzJ//nwyMjLo378/mzdvBiAqKoqDBw/icDjo3bs369evB8BqtTJ79mySkpIAmDNnDlar9XbjEhEREXErS5Ys4cCBA/Tr1489e/YAtOlSWB9++CFLlizh0qVLREVFsXTp0nZ7DEdEupZbTvpCQkLYvXt3k/K+ffuyffv2JuUWi4UVK1Y0W1dSUpKZ9ImIiIh0R1OnTuVf//Vf+cEPfmCWNSyFlZqaSnp6Ounp6aSlpTVaCuv9999n5cqV7Ny501wK6w9/+AMWi4WpU6cSHR1NQEAAK1euZM2aNYSGhvL000+Tn59PVFRUJ0YsIh2lzZdsEBHpbGfOnGH69OlMnDiRuLg484eoiooKUlJSiImJISUlxVwT1DAM1q5di8PhID4+nhMnTph1ZWZmEhMTQ0xMDJmZmWb5hx9+SHx8PA6Hg7Vr12IYRscGKSIeZ8SIEQQEBDQqa6ulsEpLS6muriYsLAyLxUJCQgJOp7PDYxSRznHbSzaIiHQ1Xl5eLF68mCFDhlBdXc20adMIDw9n165d+sVcRNxKWy2F1dL2N3IzS2R5efXoVsv6dLd4pWXu8DlQ0iciHic4ONi8SPL19WXgwIG4XC6cTievvfYaUP+L+fTp00lLS2vxF/Njx46Zv5gD5i/mI0eONH8xb6jL6XQq6RORdtWeS2G1pLVLZEH9hW9rt/UE7hivllRqH535OWjtOdXwThHxaKdPn+bjjz8mNDS0038xFxG5WW21FFZL24tI96A7fSLisc6fP8+8efP44Q9/iK+vb6PXOuMX8+48TMrT4mlPnXmcPPE8uXtMDUtepaamNlkK6z//8z+Ji4vj/fffb7QU1saNG81nlgsKCli4cCFWqxVfX18KCwsJDQ0lKyuL6dOnd2ZocgO+/r3p3VOX6tI29EkSEY905coV5s2bR3x8PDExMcD//WIeHBzc6l/Mjx07Zpa7XC5Gjhx5y7+Yd+dhUp4WD7TfMKnOPE6eeJ5aG1NXGPa2cOFCjh07Rnl5OZGRkcydO5fU1NQ2WwprxYoV5pINkZGRREZGdk6g0iq9e3rz5cU5bVpn8fNxbVqfuA8lfSLicQzDYOnSpQwcOJCUlBSzXL+Yi0hXtnHjxmbL22oprKFDh5rr/4lI96KkT0Q8zp/+9Ceys7P56le/yv9n797joizz/4+/Rgg1OakLgxb5XU/peiQPqCjkGJAiiQrVVm5S5iGLTLN0PZta21qaubtFtq7tdtQCSyw1UNAyD6WRlpXuUloytCggKqfh/v3hw/nJijIlMMz4fj4ePR543dd98bnuYa7mc9+f+54RI0YA586g64y5iIiIXI2U9ImI2+nduzfffPNNtdt0xlxERESuNnp6p4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGPJ0dgIiIXMwGBAT41OqYZ0srKC46W6tjioiISMOnpE9EpAFqco0H/zcjrVbHzHk6hmk0CtkAACAASURBVOJaHVFERERcgZI+ERERaRC8fZvStHHtfjQpKbfV6ngiIq5ISZ+IiIg0CE0be9bJFe5TtTqiiIjrUdInIiIiIiLyK5SU21ziHnwlfSIiIiIiIr+Cq9yD32C+siErK4vo6GgiIyNJTk52djgiIjXSuiUirkhrl8jVp0EkfTabjYULF7Jq1SrS0tLYsGEDhw8fdnZYIiKXpHVLRFyR1i6Rq1ODKO/Mzs6mTZs2BAcHAxATE0N6ejrt27d3cmQiItXTuiUirkhrV92oiyfPitQmk2EYhrOD+PDDD9m+fTuLFy8GIDU1lezsbObOnevkyEREqqd1S0RckdYukatTgyjvFBERERERkbrRIJI+s9lMbm6u/d9WqxWz2ezEiERELk/rloi4Iq1dIlenBpH0devWjZycHI4ePUpZWRlpaWlYLBZnhyUicklat0TEFWntErk6NYg7Tj09PZk7dy7jxo3DZrMxevRoOnTo4OywREQuSeuWiLgirV0iV6cG8SAXERERERERqRsNorxTRERERERE6oaSPhERERERETempM8FWCwWYmNjGTFiBKNGjXJ2OHVm5syZ9O/fn+HDh9vbCgoKSExMJCoqisTERAoLC50YYd2obt4vvPACgwYNYsSIEYwYMYLMzEwnRij14YMPPiAmJoZOnTrx5ZdfXrJfVlYW0dHRREZGkpycXI8R/jKOvnc7d+5s/zufOHFiPUfpmJqOeVlZGVOmTCEyMpKEhASOHTvmhCh/mZrm9O6779KvXz/7a7N27VonROm46tbRCxmGwaJFi4iMjCQ2NpaDBw/Wc4Tu4/jx44wZM4Zhw4YRExPDmjVrLuqza9cuevXqZf/7WblypRMirR2lpaXEx8dz2223ERMTw4oVKy7q44prwKU4Ml9XWx8cZbPZiIuLY8KECRdtc4vX2JAGb/DgwUZ+fr6zw6hzu3fvNg4cOGDExMTY2/70pz8ZL730kmEYhvHSSy8ZzzzzjLPCqzPVzXvFihXGqlWrnBiV1LfDhw8bR44cMe655x4jOzu72j4VFRXGkCFDjB9++MEoLS01YmNjje+++66eI3WMo+/dnj171mdYv5gjx/xf//qXMWfOHMMwDGPDhg3GI4884oxQHebInN555x1jwYIFTorwl6tuHb3Qtm3bjPvvv9+orKw09u3bZ8THx9dzhO7DarUaBw4cMAzDME6dOmVERUVd9Pfz6aefGuPHj3dGeLWusrLSKC4uNgzDMMrKyoz4+Hhj3759Vfq42hpwOY7M19XWB0f9/e9/N6ZOnVrt3647vMa60icNRp8+ffDz86vSlp6eTlxcHABxcXF89NFHzgitTlU3b7n6tGvXjrZt2162T3Z2Nm3atCE4OBgvLy9iYmJIT0+vpwh/GXd57zpyzDMyMhg5ciQA0dHR7Ny5E6MBPyPNlf6OHFXTOnr+79FkMtGzZ0+KiorIy8urxwjdR2BgIF26dAHA29ubtm3bYrVanRxV3TGZTDRr1gyAiooKKioqMJlMVfq42hpwOY7M1x3l5uaybds24uPjq93uDq+xkj4Xcf/99zNq1CjeeustZ4dSr/Lz8wkMDAQgICCA/Px8J0dUf1577TViY2OZOXOmW5a1yi9ntVoJCgqy/9tsNjfYD1uOvndLS0sZNWoUt99+e4NMDB055larlVatWgHnHofv4+PDyZMn6zXOX8LRv6PNmzcTGxtLUlISx48fr88Qa93/zjkoKKjBvndcybFjx/j666/p0aPHRdv279/Pbbfdxrhx4/juu++cEF3tsdlsjBgxggEDBjBgwICL5utqa0BNapovuNf6ALBkyRKmT59Oo0bVp0bu8Bo3iO/pk8t74403MJvN5Ofnk5iYSNu2benTp4+zw6p3JpPpqjjbBPD73/+eBx98EJPJxPPPP8/TTz/NU0895eyw5AqNHTuW//73vxe1T5kyhVtuucUJEV2Zy83nQpd7727duhWz2czRo0e599576dixIzfccEOdxCuOGzx4MMOHD8fLy4s333yTJ554gldffdXZYUkDcvr0aZKSkvjjH/+It7d3lW1dunQhIyODZs2akZmZyeTJk9m8ebOTIr1yHh4erF+/nqKiIiZPnsy3335Lx44dnR1Wnalpvu62PmzdupUWLVrQtWtXdu3a5exw6oySPhdgNpsBaNmyJZGRkWRnZ181SV/Lli3Jy8sjMDCQvLw8WrRo4eyQ6sVvfvMb+88JCQkN9gEX8sv84x//uKL9zWYzubm59n9brVb7+uAMl5uPo+/d8/EHBwfTt29fvvrqqwaV9DlyzM1mM8ePHycoKIiKigpOnTpF8+bN6ztUhzkypwvjT0hI4M9//nO9xVcX/nfOubm5Tn3vuLry8nKSkpKIjY0lKirqou0XJoEREREsWLCAEydOuPz/w319fQkNDWX79u1VkiBXWwMcdan5utv68Pnnn5ORkUFWVhalpaUUFxfz2GOPsXTpUnsfd3iNVd7ZwJ05c4bi4mL7zx9//DEdOnRwclT1x2KxkJqaCkBqaipDhgxxckT148J7TT766KOr6jWXS+vWrRs5OTkcPXqUsrIy0tLSsFgszg6rWo68dwsLCykrKwPgxIkTfP7557Rv375e46yJI8fcYrGQkpICwKZNm+jXr1+DrkpwZE4XrkEZGRm0a9euvsOsVef/Hg3DYP/+/fj4+NjLj+WXMQyDWbNm0bZtWxITE6vt8/PPP9vvd8rOzqaystLlPiCfd+LECYqKigAoKSnhk08+uej+a1dbAy7Hkfm62/owbdo0srKyyMjI4LnnnqNfv35VEj5wj9dYV/oauPz8fCZPngycq7EePnw44eHhTo6qbkydOpXdu3dz8uRJwsPDefjhhxk/fjxTpkxh3bp1tG7dmuXLlzs7zFpX3bx3797NoUOHALjuuutYuHChk6OUurZlyxaefPJJTpw4wYQJE+jcuTOvvPIKVquV2bNn8/LLL+Pp6cncuXMZN24cNpuN0aNHN9gTApd673755Ze8+eabLF68mCNHjjBv3jxMJhOGYfDAAw80uKTvUsf8+eefp2vXrgwZMoT4+HimT59OZGQkfn5+LFu2zNlhX5Yjc/rnP/9JRkYGHh4e+Pn5Nfjy8urW0YqKCuBcuXxERASZmZlERkbStGlTlixZ4uSIXddnn33G+vXr6dixIyNGjADOHf+ffvoJOHe8N23axBtvvIGHhwdNmjThueeec7kPyOfl5eUxY8YMbDYbhmFw6623MnjwYJdeAy7Hkfm62vrwa7nba2wyXO3RMyIiIiIiIuIwlXeKiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJnwt54YUXeOyxx5wdhsv56aefCAkJwWaz/eoxnn32Wf7xj3843P/GG2/k+++/r3ZbRkYGU6ZM+dWxiDQkc+fO5S9/+cuv3v/dd9/l97//fS1GdPUICQnh6NGjv3r/N998k8WLFzvc32Kx8Mknn1S77dChQ9x5552/OhYREalbSvoaoPfff59Ro0YREhLCwIEDGTduHHv37nV2WA7Lzs7mgQceoHfv3vTt25f4+Hjeeecdp8XTunVr9u3bh4eHBwBjxoxh7dq1Du9/4sQJUlNTL/pAc/ToUTp16sS8efN+UTwWi4XDhw9z6NChX7SfiDNYLBa6d+9OSEgIvXv35s477+SNN96gsrISgIULFzJ58mQnR1n3/vOf/5CUlERoaCi9evUiNjaW1atXX9HJpCu1b98+goODAZgxYwbLli1zeN+ysjL+9re/MW7cuCrtp0+fJiQk5KL2mnTq1AkfHx8yMjJ+0X4iUnv27t3LnXfeSa9evejbty933nkn2dnZNe5XVlbGCy+8QFRUFD179sRisTBz5kyOHTtWD1FLfVHS18CsXr2aJUuWMHHiRD7++GO2bt3KXXfdRXp6eq3+noqKilod77x9+/Zx77330qdPHzZv3syuXbuYP38+WVlZdfL76sO7775LREQETZo0qdK+fv16/Pz8+OCDDygrK/tFY8bExPD222/XZpgidebFF19k3759bN26lQceeICXX36ZWbNmOTusi9TVuvbDDz9w++2306pVK95//30+++wznn/+eQ4cOMDp06fr5HfWtfT0dNq2bYvZbK7SvnnzZry8vPjkk0/4+eeff9GYsbGxvPXWW7UZpog4qLi4mIkTJ3LPPfewe/dusrKyeOihh/Dy8qpx36SkJDIyMli6dCl79+5l/fr1dO3alZ07d9ZD5FJflPQ1IKdOnWLFihXMnTuXqKgorr32Wq655hosFgtPPPEEAOXl5Tz++OOEhIQQExPDl19+ad8/OTmZW265hZCQEIYNG8aWLVvs2959913uvPNOlixZQmhoKC+88AInT55k4sSJ3HTTTYwePZply5ZVKbM6cuQIiYmJ9O3bl+joaDZu3FjjHJ555hni4uIYP348LVq0wGQy0bVrV55//nkACgsLmTBhAv369aNPnz5MmDCB3Nxc+/5jxozh2WefJT4+nptuuolJkyZRUFBg356UlERYWBi9evXi7rvv5rvvvrNvKykp4emnn2bw4MH06tWL3//+95SUlHDs2DFuvPFGKioqWLZsGXv37mXhwoWEhISwcOFCFixYwNNPP11lHhMnTrSXc2ZlZdGnT58q2w3DIDU1lUceeQRPT89qz25/8sknREVF0bt3bxYsWIBhGPZtffv2Zdu2bTUeT5GGxMfHhyFDhrB8+XJSUlL49ttvq1xhOnHiBBMmTLBf5b/rrrvsVwSPHz/OQw89RL9+/QgNDWXhwoVVxv7Tn/5Enz59sFgsZGZm2tvfeecdhg4dSkhICEOGDOHNN9+0b9u1axfh4eEkJycTFhbGzJkzKSkp4YknnqBPnz4MHTqUl19+mfDwcPs+VquVhx9+mH79+mGxWHj11VdrnPeKFSsICQlh5syZBAYGAtC2bVueffZZfH19gcuvTTNmzGDu3LkkJiYSEhLCPffcw48//mjfvmjRIiIiIrjpppsYNWpUlcoOm83Giy++aF/bR40axfHjx4H/X0b+1ltv8f777/PKK68QEhLCxIkTWbVqFQ8//HCVeSxatIhFixYB1a9rACkpKdx5553ceOONvPfeexdt//rrr4mNjaVXr15MmTKF0tJS+7bQ0FB27tz5i0+CiciV+89//gPA8OHD8fDwoEmTJgwcOJBOnTpRWVnJX//6VwYPHkz//v15/PHHOXXqFHDus8onn3zCX//6V7p3746npyc+Pj7cfffdJCQkOHNKUtsMaTAyMzONzp07G+Xl5dVuX7FihdG1a1dj27ZtRkVFhbF06VIjISHBvn3jxo1Gbm6uYbPZjLS0NKNHjx6G1Wo1DMMw3nnnHaNz587Gq6++apSXlxtnz541pkyZYkyZMsU4c+aM8d133xnh4eHGnXfeaRiGYZw+fdoIDw831q1bZ5SXlxsHDx40+vbta3z33XeXjP/MmTNGp06djJ07d16yz4kTJ4wPP/zQOHPmjHHq1Cnj4YcfNiZNmmTffs899xgDBw40vvnmG+P06dPGQw89ZEybNs2+fe3atcapU6eM0tJSY9GiRcZtt91m3zZ//nzjnnvuMXJzc42Kigrjs88+M0pLS42jR48aHTt2tB/Xe+65x3j77bft+33xxRdGWFiYYbPZDMMwjPz8fKN79+7Gzz//bBiGYYSGhhpffPFFlXns2bPH6NKli1FQUGAsXLjQmDBhQpXtHTt2NMaPH28UFhYaP/74oxEaGmpkZmbat588edLo2LGjcerUqUseK5GGYPDgwcbHH398UXtERITx2muvGU888YTx3HPPGYZhGEuXLjXmzJljlJWVGWVlZcaePXuMyspKo6KiwoiNjTUWL15snD592igpKTH27NljGMa5tel3v/ud8dZbbxkVFRXGa6+9ZoSFhRmVlZWGYRjG1q1bje+//96orKw0du3aZXTv3t04cOCAYRiG8emnnxqdO3c2nnnmGaO0tNQ4e/as8ec//9m4++67jYKCAuP48ePG8OHDjUGDBhmGYRg2m80YOXKk8cILLxilpaXGDz/8YFgsFiMrK+uyx2DAgAHGunXrLtvncmvTE088YfTs2dPYvXu3UVpaajz55JP2tdYwDCM1NdU4ceKEUV5ebrzyyivGgAEDjJKSEsMwDOPll182hg8fbhw5csSorKw0vv76a+PEiROGYZxbZ3Jycuy/4/zrYBiGYbVajR49ehiFhYWGYRhGeXm50a9fP+PLL780DMMwRo0aZWzcuLHKHI4dO2bceOONxnfffWe88sorxvDhw6tsHzx4sDF69GgjNzfXOHnypHHrrbcar7/+epU+ISEhxtdff33ZYyUite/UqVNG3759jccff9zYtm2bUVBQYN+2du1a45ZbbjF++OEHo7i42Jg8ebLx2GOPGYZh2NdMcX+60teAFBQU0Lx5czw9PS/Zp1evXkRERODh4cGIESOq3Bc2dOhQzGYzjRo1YtiwYbRp06ZKLXdgYCBjxozB09OTa665hs2bN/Pwww/TtGlT2rdvT1xcnL3vtm3buO666xg9ejSenp787ne/Izo6mg8//PCSsRUVFVFZWUlAQMAl+zRv3pzo6GiaNm2Kt7c3kyZNYs+ePVX6jBgxgo4dO3LttdfyyCOP8OGHH9rvm4mPj8fb2xsvLy8efvhhDh06xKlTp6isrOSdd95h1qxZmM1mPDw8uOmmmxwqa+jevTs+Pj72MoaNGzfSt29ffvOb3wDnrsA2a9asyj4pKSmEh4fj5+fH8OHD2b59O/n5+VX6PPDAA/j6+tK6dWtCQ0OrvFbnxysqKqoxPpGGKDAwkMLCwiptnp6e/Pzzz/z0009cc8019O7dG5PJRHZ2Nnl5eTz++ONce+21NG7cmN69e9v3a926NbfffjseHh6MHDmSn3/+mf/+978A3Hzzzdxwww2YTCb69u1LWFhYlSthjRo1IikpCS8vL5o0acIHH3zAhAkT8PPzIygoiD/84Q/2vl9++SUnTpywlzwFBwdz++2311jFUFBQcNl1DS69Np13880306dPH7y8vHj00UfZv3+//YrdiBEj7Gv/fffdR1lZmf2s/dq1a3nkkUdo27YtJpOJTp060bx588vGAuden969e9vX7O3bt9O8eXO6du0KVL+urV+/nhtvvJH27dsTExPD4cOH+eqrr6r0GTNmDGazGX9/fwYPHszXX39dZXuzZs2qzFtE6oe3tzevv/46JpOJOXPm0L9/fyZOnMh///tf3n//fcaOHUtwcDDNmjVj6tSpbNy4kYqKCofWN3EPl84upN75+/tz8uRJKioqLpn4nU9EAJo0aUJpaam9f2pqKqtXr7aXDZ05c4aTJ0/a+wcFBdl/PnHiBBUVFbRq1creduHPP/74I9nZ2VU+mNlsNm677bZLxu/r60ujRo34+eefadeuXbV9zp49y1NPPcX27dvtHxhPnz6NzWazP2jlwjhat25NeXk5J0+epHnz5ixbtowPP/yQEydO0KjRuXMWJ0+epKysjNLSUvtDDX6pkSNH8t577xEWFsZ7771X5YOir69vlft2SkpK+PDDD+1lUiEhIfZ7fcaOHWvvd+Ei2rRp0ypjnP/5fGmYiKuxWq34+flVabv//vtZuXIl9913HwB33HEH48eP5/jx47Ru3dqhda1p06bAufULIDMzk7/85S/k5ORQWVlJSUkJHTt2tPdv3rw5jRs3tv87Ly+vyhpy4br3448/kpeXd9G6duG/q+Pv73/Z+9tsNtsl1yYfH5+L4mjWrBl+fn72WF955RXWrVtHXl4eJpOJ4uJi+9qdm5vLDTfccNn4LmXkyJG88cYb3H777bz33nuMGDHCvu1/1zU4l/SdL+cym8306dOHlJQUfve739n7/O+6lpeXV2WM06dP2+csIvWrXbt29ttVjhw5wvTp01myZAl5eXlcd9119n7XXXcdFRUV5Ofn4+/vT05OjpMilvqkK30NSEhICF5eXnz00Ue/eN8ff/yR2bNnM2fOHHbt2sXevXvp0KFDlT4mk8n+c4sWLfD09KxyP935s85wLvHq06cPe/futf+3b98+FixYcMkYmjZtSs+ePdm8efMl+/z973/nP//5D2+//Taff/45r732GkCV+90ujOP48eNcc801NG/enPfff5/09HRWr17NZ599Zr+PzjAM+we/X/v48ttuu4309HQOHTrEkSNHuOWWW+zbbrzxxioL4pYtWyguLmbBggWEhYURFhaG1WolNTXV4d935MgRrrvuOry9vX9VvCLOlJ2djdVqpVevXlXavb29mTFjBunp6fztb39j9erV7Ny5k1atWnH8+PFf/KCVsrIykpKSuO+++/j444/Zu3cv4eHhVdaLC9c1OJeUXLiuXfhzq1atuP766y9a115++eXLxtG/f//LrmuXW5uqi+P06dMUFhYSGBjI3r17WbVqFcuXL2fPnj3s3bsXHx8f+75BQUH88MMPl42vuuMAcMstt/DNN9/w7bffsm3bNmJjY+3b/ndd+/zzz8nJybHfHxkWFkZ2djYbNmxw+HWzWq2Ul5fTtm1bh/qLSN1p164do0aN4rvvviMwMLDKfcQ//fQTnp6etGzZkgEDBpCdnV1ljRL3pKSvAfHx8SEpKYmFCxfy0UcfcfbsWcrLy8nMzOSZZ5657L5nz57FZDLRokUL4NzDDy58kMD/8vDwIDIykpUrV3L27FmOHDnC+vXr7dtvvvlmcnJySE1Npby8nPLycrKzszly5Mhl45g+fTopKSmsWrXKfqb60KFDPProo8C5DzuNGzfG19eXgoICVq5cedEY7733HocPH+bs2bM8//zzREdH4+HhwenTp/Hy8qJ58+acPXuW5557zr5Po0aNGD16NE899RRWqxWbzca+ffuqfaDAb37zm4uSw6CgILp168b06dOJioqq8qTOiIiIKiWoqampjB49mvfff5/U1FRSU1N54403OHToEN98881lj895e/bsqfJwCRFXUFxczNatW5k6dSq33XYbN954Y5XtW7du5fvvv8cwDHx8fPDw8MBkMtG9e3cCAgJ49tlnOXPmDKWlpXz22Wc1/r6ysjLKysrsJ6kyMzP5+OOPL7vP0KFDeemllygsLMRqtfKvf/3Lvq179+40a9aM5ORkSkpKsNlsfPvttzU+0jwpKYl9+/bxpz/9yX7F7/vvv+exxx6jqKjosmvTeZmZmezdu5eysjKef/55evToQatWrTh9+jQeHh60aNGCiooKVq5cSXFxsX2/hIQEnn/+eXJycjAMg0OHDlWp4DivZcuWFz1evXHjxkRHRzNt2jS6detG69at7duqW9fCwsJIS0uzr2vvv/8+JSUlDj99effu3fTr18+hsnoRqV1Hjhzh73//uz15O378OBs2bKBHjx4MHz6cNWvWcPToUU6fPs2yZcsYOnQonp6eDBgwgAEDBjB58mQOHDhARUUFxcXFvPHGG6xbt87Js5LapKSvgbnvvvuYMWMGf/3rX+nfvz8333wzr732WpUrT9Vp37499913H3feeScDBgzg22+/5aabbrrsPnPnzuXUqVOEhYXx+OOPExMTY/+ftbe3N6+88gobN25k0KBBDBw4kKVLl9b4VLabbrqJNWvW8Omnn3LLLbfQt29f5syZQ0REBAD33nsvpaWl9OvXjzvuuINBgwZdNMaIESOYMWMGYWFhlJWV2R8NHxcXR+vWrRk0aBAxMTH07Nmzyn5PPPEEHTt2JD4+nr59+7J06VL7kwMv9Ic//IFNmzbRp08fe4nm+fG//fbbKiVQ5+PJzMykpKQEq9XKzp07uffeewkICLD/17VrVwYNGuTw1b60tDR9kbG4jIkTJxISEkJERAQvvvgiiYmJPPXUUxf1+/777+1PqLzjjjv4/e9/T79+/fDw8ODFF1/k+++/Z/DgwYSHh/PBBx/U+Hu9vb2ZPXs2U6ZMoU+fPmzYsAGLxXLZfSZPnkxQUBBDhgxh7NixREdH29e183EcOnSIIUOG0K9fP2bPnl0lyarODTfcwJtvvsmPP/7I8OHD6dWrFw8//DBdu3alWbNmNa5NcO6Jen/5y18IDQ3l4MGD/PnPfwZg4MCBDBo0iOjoaCwWC40bN65SnpqYmMjQoUO57777uOmmm5g1a1aVJ2aeFx8fz+HDh+nduzcPPvigvf1S69rgwYP597//jdVqpbS0lA8++IB77rmnyroWHBzMiBEjHF7X3n//fa1rIk7i7e3NF198QUJCAj179uT222+nY8eOzJgxg9GjR3Pbbbdxzz33MGTIELy8vJgzZ4593xUrVhAREcGjjz5K7969iY2N5cCBAwwYMMCJM5LaZjIurD+Rq9qf//xn/vvf//KnP/3JaTGMGTOG2267zSmPCd6zZw/Tp09n69atF5VKPffcc7Ro0aLKPXu/VkZGBuvXr7d/jYWI1J3XX3+djRs3VrniV99mzJiB2Wy2VzzUp59++omhQ4fy8ccfX1RO/tZbb3H48OFa+c7FQ4cOMW/ePH1Pn4hIA6UHuVzFjhw5Qnl5OTfeeCNffvkl69atY/Hixc4OyynKy8t59dVXiY+Pr/bemKlTp9ba77JYLDVerRCRXycvL4+jR48SEhJCTk4Oq1ev5u6773Z2WE5RWVnJ6tWrGTZsWLX3D99xxx219rs6deqkhE9EpAFT0ncVO336NNOmTSMvL4+WLVty3333MWTIkBr3i4mJ4aeffrqofcGCR+wjFAAAIABJREFUBZd9umdDdeTIEUaPHk2nTp2qLVkTEddRXl7OvHnzOHbsGD4+PsTExHDXXXfVuN+4ceOqvc9wwoQJTJw4sS5CrVNnzpwhLCyM1q1bs2rVKmeHIyIiTqbyThERERERETemB7mIiIiIiIi4MZct76ysrMRmq/kipYeHyaF+7uhqnfvVOm9oOHO/5hoPZ4fQIDm6bkHDeS1rk+bkGq7WOTWUdauoqIjZs2fz7bffYjKZWLJkCb/97W959NFH+fHHH7nuuutYvnw5fn5+GIbB4sWLyczMpEmTJjz99NN06dIFgJSUFP72t78BMGnSJEaOHAnAgQMHmDlzJiUlJURERDBr1qxq72U/z13XLVeKFVwrXleKFVwr3upidXTtctnyzvJyGwUFZ2rs5+9/rUP93NHVOverdd7QcOYeEODj7BD4xz/+wdq1azGZTHTs2JGnnnqKvLw8pk6dSkFBAV26dOGZZ57By8uLsrIyHn/8cQ4ePIi/vz/Lli3j+uuvB+Cll15i3bp1NGrUiNmzZ9u/ZiQrK4vFixdTWVlJQkIC48ePrzEmR9ctaDivZW3SnFzD1TqnhrBuwbmvH+rduzcJCQmUlZVRUlLCiy++iL+/P+PHjyc5OZnCwkKmT59OZmYm//znP3n55Zf54osvWLx4MWvXrqWgoIDRo0fzzjvvYDKZGDVqFO+++y5+fn7Ex8cze/ZsevTowQMPPMCYMWPsX6tUHXddt1wpVnCteF0pVnCteKuL1dG1S+WdIuJ2rFYrr776Ku+88w4bNmzAZrORlpbG0qVLGTt2LFu2bMHX19f+xbNr167F19eXLVu2MHbsWJYuXQrA4cOHSUtLIy0tjVWrVrFgwQJsNhs2m42FCxeyatUq0tLS2LBhA4cPH3bmlEXEDZw6dYo9e/YQHx8PgJeXF76+vqSnpxMXFwec++7Fjz76CMDebjKZ6NmzJ0VFReTl5bFjxw7CwsLw9/fHz8+PsLAwtm/fTl5eHsXFxfTs2ROTyURcXBzp6elOm6+I1B+XLe8UEbkcm81GSUkJnp6elJSUEBAQwKeffsqzzz4LwMiRI1m5ciV33XUXGRkZPPTQQwBER0ezcOFCDMMgPT2dmJgYvLy8CA4Opk2bNmRnZwPQpk0bgoODgXNPtE1PT6d9+/bOmayIuIVjx47RokULZs6cyaFDh+jSpQuzZs0iPz+fwMBAAAICAsjPzwfOneAKCgqy7x8UFITVar2o3Ww2V9t+vv/leHiY8Pe/1qH4PTwaOdzX2VwpVnCteF0pVnCteK8kVoeSvoZYJiUicilms5n77ruPwYMH07hxY8LCwujSpQu+vr54ep5b9i78sGO1WmnVqhUAnp6e+Pj4cPLkSaxWKz169Kgy7vl9/vcD1flk8HLc9cOTozQn16A5OU9FRQVfffUVc+bMoUePHixatIjk5OQqfUwm02XvwattNpuh8s4GwJXidaVYwbXivZLyzhqTvvNlUhs3bqRJkyY88sgjpKWlkZmZydixY4mJiWHu3LmsW7eOu+66q0qZ1PlyquXLl1cpk7JarSQmJrJp0yYAFi5cyOrVqzGbzcTHx2OxWHTGXER+tcLCQtLT00lPT8fHx4dHHnmE7du3Ozsst/3w5CjNyTVcrXNqCPf0BQUFERQUZD/ZdOutt5KcnEzLli3Jy8sjMDCQvLw8WrRoAZw74ZSbm2vfPzc3F7PZjNlsZvfu3fZ2q9VK3759L9lfRNyfQ/f0nS+TqqioqFImFR0dDZwrkzpfE56RkWF/QlR0dDQ7d+68bJlUdna2vUzKy8vLXiYlIvJrffLJJ1x//fW0aNGCa665hqioKD7//HOKioqoqKgAqn7YMZvNHD9+HDh3pv3UqVM0b978og9IVqvV/oGqunYRkSsREBBAUFAQ//73vwHYuXMn7dq1w2KxkJqaCkBqaipDhgwBsLcbhsH+/fvx8fEhMDCQgQMHsmPHDgoLCyksLGTHjh0MHDiQwMBAvL292b9/P4ZhVBlLRNxbjVf6XL1MylVKOurC1Tr3q3XecHXP/UKtW7fmiy++4OzZszRp0oSdO3fStWtXQkND2bRpEzExMaSkpGCxWIBzH5xSUlIICQlh06ZN9OvXD5PJhMViYdq0aSQmJmK1WsnJyaF79+4YhkFOTg5Hjx7FbDaTlpZmv1dQRORKzJkzh8cee4zy8nKCg4N56qmnqKysZMqUKaxbt47WrVuzfPlyACIiIsjMzCQyMpKmTZuyZMkSAPz9/XnwwQftD4SZPHky/v7+AMybN8/+lQ3h4eGEh4c7Z6IiUq9qTPpcvUzKx/9amtTyd++cLa2guOhsrY5ZF9yxRMcRV+u8oeHM3dllUj169CA6OpqRI0fi6elJ586dueOOO7j55pt59NFHWb58OZ07dyYhIQGA+Ph4pk+fTmRkJH5+fixbtgyADh06MHToUIYNG4aHhwdz587Fw+PcejJ37lzGjRuHzWZj9OjRdOjQwWnzFXEX3r5Nadq4dp8xd7a0olbHq2udO3fm3Xffvah9zZo1F7WZTCbmzZtX7Tjx8fH2pO9C3bp1Y8OGDVceqMgF/ve9WxufA1zl87arqHFlvbBMCrioTMrT07PaMqmgoCCHyqSAOi2TanKNB/83I63WxgPIeTqG4lodUURqW1JSEklJSVXagoOD7V/TcKHGjRuzYsWKaseZNGkSkyZNuqg9IiList9tJSK/XNPGnnXy/+zyclutjikiVdXVe1eft2tPjff0XVgmZRgGO3fupH379vYyKaDaMingojKptLQ0ysrKOHr0qL1Mqlu3bvYyqbKyMtLS0uxjiYiIiIiIyJWp8UqfyqRERERERERcl0OF8yqTEhERERERcU0OfWWDiIiIiIiIuCYlfSIiIiIiIm5MSZ+IiIiIiIgbU9InIiIiIiLixpT0iYiIiIiIuDElfSIiIiIiIm5MSZ+IiIiIiIgbU9InIiIiIiLixpT0iYiIiIiIuDElfSIiIiIiIm5MSZ+IiIiIiIgbU9InIiIiIiLixpT0iYiIiIiIuDElfSIiIiIiIm5MSZ+IiIhIA2GxWIiNjWXEiBGMGjUKgIKCAhITE4mKiiIxMZHCwkIADMNg0aJFREZGEhsby8GDB+3jpKSkEBUVRVRUFCkpKfb2AwcOEBsbS2RkJIsWLcIwjPqdoIg4hZI+ERERkQZkzZo1rF+/nnfffReA5ORk+vfvz+bNm+nfvz/JyckAZGVlkZOTw+bNm3nyySeZP38+cC5JXLlyJW+//TZr165l5cqV9kRx/vz5PPnkk2zevJmcnByysrKcMkcRqV9K+kREREQasPT0dOLi4gCIi4vjo48+qtJuMpno2bMnRUVF5OXlsWPHDsLCwvD398fPz4+wsDC2b99OXl4excXF9OzZE5PJRFxcHOnp6c6cmojUEyV9IiIiIg3I/fffz6hRo3jrrbcAyM/PJzAwEICAgADy8/MBsFqtBAUF2fcLCgrCarVe1G42m6ttP99fRNyfp7MDEBEREZFz3njjDcxmM/n5+SQmJtK2bdsq200mEyaTqd7i8fAw4e9/rYN9Gznc19lcKVZwvXhrS33M2ZWO7ZXEqqRPREREpIEwm80AtGzZksjISLKzs2nZsiV5eXkEBgaSl5dHixYt7H1zc3Pt++bm5mI2mzGbzezevdvebrVa6du37yX7X47NZlBQcMah2P39r3W4r7O5UqzQ8OMNCPCpk3HrY84N/dheqLpYHT32Ku8UERERaQDOnDlDcXGx/eePP/6YDh06YLFYSE1NBSA1NZUhQ4YA2NsNw2D//v34+PgQGBjIwIED2bFjB4WFhRQWFrJjxw4GDhxIYGAg3t7e7N+/H8MwqowlIu5NV/pEREREGoD8/HwmT54MgM1mY/jw4YSHh9OtWzemTJnCunXraN26NcuXLwcgIiKCzMxMIiMjadq0KUuWLAHA39+fBx98kPj4eAAmT56Mv78/APPmzWPmzJmUlJQQHh5OeHi4E2YqIvVNSZ+IiIhIAxAcHMx77713UXvz5s1Zs2bNRe0mk4l58+ZVO1Z8fLw96btQt27d2LBhw5UHKyIuReWdIiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4i4paKiIpKSkrj11lsZOnQo+/bto6CggMTERKKiokhMTKSwsBAAwzBYtGgRkZGRxMbGcvDgQfs4KSkpREVFERUVRUpKir39wIEDxMbGEhkZyaJFizAMo97nKCIiIuIIJX0i4pYWL17MoEGD+PDDD1m/fj3t2rUjOTmZ/v37s3nzZvr3709ycjIAWVlZ5OTksHnzZp588knmz58PQEFBAStXruTtt99m7dq1rFy50p4ozp8/nyeffJLNmzeTk5NDVlaWs6YqIiIicllK+kTE7Zw6dYo9e/bYv6PKy8sLX19f0tPTiYuLAyAuLo6PPvoIwN5uMpno2bMnRUVF5OXlsWPHDsLCwvD398fPz4+wsDC2b99OXl4excXF9OzZE5PJRFxcHOnp6U6br4iIiMjlOPTl7EVFRcyePZtvv/0Wk8nEkiVL+O1vf8ujjz7Kjz/+yHXXXcfy5cvx8/PDMAwWL15MZmYmTZo04emnn6ZLly7AuTKpv/3tbwBMmjSJkSNHAufKpGbOnElJSQkRERHMmjULk8lUR1MWEXd37NgxWrRowcyZMzl06BBdunRh1qxZ5OfnExgYCEBAQAD5+fkAWK1WgoKC7PsHBQVhtVovajebzdW2n+9fEw8PE/7+1zo0Bw+PRg73dRWak2vQnERE3I9DSd/5MqkVK1ZQVlZGSUkJL774Iv3792f8+PEkJyeTnJzM9OnTq5RJffHFF8yfP5+1a9fay6TeeecdTCYTo0aNwmKx4OfnZy+T6tGjBw888ABZWVlERETU9dxFxE1VVFTw1VdfMWfOHHr06MGiRYvspZznmUymej+5ZLMZFBSccaivv/+1Dvd1FZqTa3DmnAICfOpkXJutssY51dXvFhFpCGos71SZlIi4mqCgIIKCgujRowcAt956K1999RUtW7YkLy8PgLy8PFq0aAGcu4KXm5tr3z83Nxez2XxRu9Vqrbb9fH8RERGRhqjGK33uUCZVF1yhTORqLWe5WucNV/fcLxQQEEBQUBD//ve/adu2LTt37qRdu3a0a9eO1NRUxo8fT2pqKkOGDAHAYrHwr3/9i5iYGL744gt8fHwIDAxk4MCBPPfcc/aHt+zYsYOpU6fi7++Pt7c3+/fvp0ePHqSmpjJmzBhnTllERETkkmpM+ly9TKquyjVcoZzHHcuOHHG1zhsaztwbQpnUnDlzeOyxxygvLyc4OJinnnqKyspKpkyZwrp162jdujXLly8HICIigszMTCIjI2natClLliwBwN/fnwcffNBe6TB58mT8/f0BmDdvnv1e5PDwcMLDw50zUREREZEa1Jj0VVcmlZycbC+TCgwMdLhMavfu3fZ2q9VK3759VSYlInWic+fOvPvuuxe1r1mz5qI2k8nEvHnzqh0nPj7envRdqFu3bmzYsOHKAxURERGpYzXe03dhmRRgL5OyWCykpqYCXFQmlZqaimEY7N+/v0qZ1I4dOygsLKSwsJAdO3YwcOBAAgMD7WVShmFUGUtERERERESujENP71SZlIiIiIiIiGtyKOlTmZSIiIiIiIhrqrG8U0RERERERFyXkj4RERERERE3pqRPREREpIGw2WzExcUxYcIEAI4ePUpCQgKRkZFMmTKFsrIyAMrKypgyZQqRkZEkJCRw7Ngx+xgvvfQSkZGRREdHs337dnt7VlYW0dHRREZGXvT1WyLi3pT0iYiIiDQQr776Ku3atbP/e+nSpYwdO5YtW7bg6+vLunXrAFi7di2+vr5s2bKFsWPHsnTpUgAOHz5MWloaaWlprFq1igULFmCz2bDZbCxcuJBVq1aRlpbGhg0bOHz4sFPmKCL1T0mfiIiISAOQm5vLtm3b7A+9MwyDTz/9lOjoaABGjhxJeno6ABkZGYwcORKA6Ohodu7ciWEYpKenExMTg5eXF8HBwbRp04bs7Gyys7Np06YNwcHBeHl5ERMTYx9LRNyfQ0/vFBEREZG6tWTJEqZPn87p06cBOHnyJL6+vnh6nvu4FhQUhNVqBcBqtdKqVSsAPD098fHx4eTJk1itVnr06GEf02w22/cJCgqq0p6dnV1jTB4eJvz9r3Uofg+PRg73dTZXihVcL97aUh9zdqVjeyWxKukTERERcbKtW7fSokULunbtyq5du5wdjp3NZlBQcMahvv7+1zrc19lcKVZo+PEGBPjUybj1MeeGfmwvVF2sjh57JX0iIiIiTvb555+TkZFBVlYWpaWlFBcXs3jxYoqKiqioqMDT05Pc3FzMZjNw7krd8ePHCQoKoqKiglOnTtG8eXPMZjO5ubn2ca1Wq32fS7WLiPvTPX0iIiIiTjZt2jSysrLIyMjgueeeo1+/fjz77LOEhoayadMmAFJSUrBYLABYLBZSUlIA2LRpE/369cNkMmGxWEhLS6OsrIyjR4+Sk5ND9+7d6datGzk5ORw9epSysjLS0tLsY4mI+9OVPhEREZEGavr06Tz66KMsX76czp07k5CQAEB8fDzTp08nMjISPz8/li1bBkCHDh0YOnQow4YNw8PDg7lz5+Lh4QHA3LlzGTduHDabjdGjR9OhQwenzUtE6peSPhEREZEGJDQ0lNDQUACCg4PtX9NwocaNG7NixYpq9580aRKTJk26qD0iIoKIiIjaDVZEXILKO0VERERERNyYkj4RERERERE3pqRPRERERETEjSnpExERERERcWNK+kRERERERNyYkj4RERERERE3pqRPRERERETEjSnpExERERERcWNK+kRERERERNyYkj4RERERERE3pqRPRERERETEjSnpExERERERcWNK+kRERERERNyYkj4RERERERE3pqRPRERERETEjSnpExERERERcWNK+kRERERERNyYp7MDEBERERERuVBJuY2AAJ9aHfNsaQXFRWdrdUxXoaRPRNyWzWZj9OjRmM1mXnrpJY4ePcrUqVMpKCigS5cuPPPMM3h5eVFWVsbjjz/OwYMH8ff3Z9myZVx//fUAvPTSS6xbt45GjRoxe/ZsBg0aBEBWVhaLFy+msrKShIQExo8f78ypiogbKC0t5e6776asrAybzUZ0dDRJSUlau+Sq1OQaD/5vRlqtjpnzdAzFtTqi61B5p4i4rVdffZV27drZ/7106VLGjh3Lli1b8PX1Zd26dQCsXbsWX19ftmzZwtixY1m6dCkAhw8fJi0tjbS0NFatWsWCBQuw2WzYbDYWLlzIqlWrSEtLY8OGDRw+fNgpcxQR9+Hl5cWaNWt47733SE1NZfv27ezfv19rl4hcMYeTPpvNRlxcHBMmTADg6NGjJCQkEBkZyZQpUygrKwOgrKyMKVOmEBkZSUJCAseOHbOP8dJLLxEZGUl0dDTbt2+3t2dlZREdHU1kZCTJycm1NTcRuYrl5uaybds24uPjATAMg08//ZTo6GgARo4cSXp6OgAZGRmMHDkSgOjoaHbu3IlhGKSnpxMTE4OXlxfBwcG0adOG7OxssrOzadOmDcHBwXh5eRETE2MfS0Tk1zKZTDRr1gyAiooKKioqMJlMWrtE5Io5XN55/ox5cfG5i6LnzzrFxMQwd+5c1q1bx1133VXlrFNaWhpLly5l+fLlVc46Wa1WEhMT2bRpEwALFy5k9erVmM1m4uPjsVgstG/fvm5mLCJXhSVLljB9+nROnz4NwMmTJ/H19cXT89yyFxQUhNVqBcBqtdKqVSsAPD098fHx4eTJk1itVnr06GEf02w22/cJCgqq0p6dnV1jTB4eJvz9r3Uofg+PRg73dRWak2vQnJzLZrMxatQofvjhB+666y6Cg4Oduna567rlSrGC68XbkP3vcXSlY3slsTqU9J0/Yz5x4kT+8Y9/2M+YP/vss8C5s04rV67krrvuIiMjg4ceegg4d9Zp4cKFlz3rBNjPOgH2s05K+kTk19q6dSstWrSga9eu7Nq1y9nh2NlsBgUFZxzq6+9/rcN9XYXm5BqcOafafmjDeTZbZY1zqqvf/Ut5eHiwfv16ioqKmDx5Mv/+97+dGo+7rluuFCs0/HgbyvvHEf97HBv6sb1QdbE6euwdSvpc/Yx5XXCFMwKudOaiNl2t84are+4X+vzzz8nIyCArK4vS0lKKi4tZvHgxRUVFVFRU4OnpSW5uLmazGTi37hw/fpygoCAqKio4deoUzZs3x2w2k5ubax/XarXa97lUu4hIbfD19SU0NJT9+/dr7RKRK1Zj0ufqZ8zr6syDK5wRcKUzF7Xpap03NJy5O/uM37Rp05g2bRoAu3bt4u9//zvPPvssSUlJbNq0iZiYGFJSUrBYLABYLBZSUlIICQlh06ZN9OvXD5PJhMViYdq0aSQmJmK1WsnJyaF79+4YhkFOTg5Hjx7FbDaTlpZmr3wQEfm1Tpw4gaenJ76+vpSUlPDJJ5/wwAMPEBoaqrVLRK5IjUmfzpiLiLuYPn06jz76KMuXL6dz584kJCQAEB8fz/Tp04mMjMTPz49ly5YB0KFDB4YOHcqwYcPw8PBg7ty5eHh4ADB37lzGjRtn/1qIDh06OG1eIuIe8vLymDFjBjabDcMwuPXWWxk8eDDt27fX2iUiV6TGpE9nzEXElYWGhhIaGgpAcHCw/VHnF2rcuDErVqyodv9JkyYxadKki9ojIiKIiIio3WBF5KrWqVMnUlNTL2rX2iUiV+pXfzm7zpiLiIiIiIg0fL8o6dMZcxEREREREdfi8Jezi4iIiIiIiOtR0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGlPSJiIiIiIi4MSV9IiIiIiIibkxJn4iIiIiIiBtT0iciIiIiIuLGPJ0dgIiIiIiISF0rKbcREOBzUXt1bY46W1pBcdHZKwmrXijpExERERERt9fkGg/+b0ZarY6Z83QMxbU6Yt1QeaeIiIiIiIgbU9InIiIiIiLixpT0iYiIiIiIuDElfSIiIiIiIm5MSZ+IiIhIA3D8+HHGjBnDsGHDiImJYc2aNQAUFBSQmJhIVFQUiYmJFBYWAmAYBosWLSIyMpLY2FgOHjxoHyslJYWoqCiioqJISUmxtx84cIDY2FgiIyNZtGgRhmHU7yRFxCmU9ImIiIg0AB4eHsyYMYONGzfy1ltv8frrr3P48GGSk5Pp378/mzdvpn///iQnJwOQlZVFTk4Omzdv5sknn2T+/PnAuSRx5cqVvP3226xdu5aVK1faE8X58+fz5JNPsnnzZnJycsjKynLWdEWkHinpExEREWkAAgMD6dKlCwDe3t60bdsWq9VKeno6cXFxAMTFxfHRRx8B2NtNJhM9e/akqKiIvLw8duzYQVhYGP7+/vj5+REWFsb27dvJy8ujuLiYnj17YjKZiIuLIz093WnzFZH6o+/pExEREWlgjh07xtdff02PHj3Iz88nMDAQgICAAPLz8wGwWq0EBQXZ9wkKCsJqtV7Ubjabq20/3/9yPDxM+Ptf61DMHh6NHO7rbK4UK7hevFeb+nptruTvQEmfiIiISANy+vRpkpKS+OMf/4i3t3eVbSaTCZPJVG+x2GwGBQVnHOrr73+tw32dzZVihYYfb0CAj7NDcKr6em2q+ztw9NirvFNERESkgSgvLycpKYnY2FiioqIAaNmyJXl5eQDk5eXRokUL4NwVvNzcXPu+ubm5mM3mi9qtVmu17ef7i4j7U9InIiIi0gAYhsGsWbNo27YtiYmJ9naLxUJqaioAqampDBkypEq7YRjs378fHx8fAgMDGThwIDt27KCwsJDCwkJ27NjBwIEDCQwMxNvbm/3792MYRpWxRMS9qbxTREREpAH47LPPWL9+PR07dmTEiBEATJ06lfHjxzNlyhTWrVtH69atWb58OQARERFkZmYSGRlJ06ZNWbJkCQD+/v48+OCDxMfHAzB58mT8/f0BmDdvHjNnzqSkpITw8HDCw8OdMFMRqW9K+kREREQagN69e/PNN99Uu+38d/ZdyGQyMW/evGr7x8fH25O+C3Xr1o0NGzZcWaAi4nJU3ikiIiIiIuLGlPSJiIiIiIi4MZV3ioiIiIhcBbx9m9K0sT7+X430qouIiIiIXAWaNvbk/2ak1fq4OU/H1PqYUrtU3ikiIiIiIuLGakz6jh8/zpgxYxg2bBgxMTH2p0cVFBSQmJhIVFQUiYmJFBYWAue+Y2bRokVERkYSGxvLwYMH7WOlpKQQFRVFVFQUKSkp9vYDBw4QGxtLZGQkixYtwjCM2p6niFxFtG6JiIiI/H81Jn0eHh7MmDGDjRs38tZbb/H6669z+PBhkpOT6d+/P5s3b+b/sXf3cVGV+f/HXyOI4YKgfmHQjXU3c10zA1JUFgMbd0BEE03bdlsryyg1jWyt/GreazdfS7d1tyQ3zWp3f2KJJZUoJIhZdiNR1q43u5SazOyqoKaIwPn9wcOzToDMJjfD8H4+Hj4ecJ0zZz7XzPBxPte5zrliYmJIT08HID8/n+LiYrKzs1m0aBHz588Har5srVy5kvXr15ORkcHKlSvNL1zz589n0aJFZGdnU1xcTH5+ftP1WES8nvKWiIiIyH80WPSFhobSt29fAAICArjqqqtwOBzk5OSQkpICQEpKCtu2bQMw2y0WC5GRkZw8eRKn00lBQQGxsbEEBwcTFBREbGwsO3bswOl0cvr0aSIjI7FYLKSkpJCTk9OEXRYRb6e8JSIiIvIf/9WNXA4fPsyXX35JREQEx44dIzQ0FICQkBCOHTsGgMPhICwszHxMWFgYDoejVrvVaq2z/cL+IiKNwZPylo+PheDgjm7F7ePTzu19Wwv1qXVQn0SfxMS2AAAgAElEQVREvI/bRd+3337L9OnT+d///V8CAgJctlksFiwWS6MHdyn/zZenptAa/vNoq//JtdV+Q9vue108LW9VVRmUlp5xa9/g4I5u79taqE+tQ0v2KSQksEmOW1VV3WCfmuq5RUQ8gVtF3/nz55k+fTqjRo0iISEBgK5du+J0OgkNDcXpdNKlSxegZiS8pKTEfGxJSQlWqxWr1cru3bvNdofDwcCBA+vdvyHufnlqqiTeGv6T98YvI+5oq/0Gz+m7J3x58sS8JSIiItISGrymzzAMZs+ezVVXXcXEiRPNdpvNRmZmJgCZmZkMGzbMpd0wDAoLCwkMDCQ0NJQhQ4ZQUFBAWVkZZWVlFBQUMGTIEEJDQwkICKCwsBDDMFyOJSLyfShviYiIiPxHg2f6Pv74YzZt2sRPf/pTRo8eDcCMGTNITU0lLS2NDRs20L17d1asWAFAfHw8eXl52O12/P39Wbp0KQDBwcFMmTKFcePGATB16lSCg4MBmDdvHrNmzaK8vJy4uDji4uKapLMi0jYob4mIiIj8R4NF34ABA/j73/9e57YLa19dzGKxMG/evDr3HzdunPnl6WL9+vVj8+bNDYUiIuIW5S0RERGR/2hweqeIiIiIiIi0Xir6REREREREvJiKPhERERERES+mok9ERERERMSLqegTERER8QCzZs0iJiaGkSNHmm2lpaVMnDiRhIQEJk6cSFlZGVCzNM3ixYux2+2MGjWKvXv3mo/ZuHEjCQkJJCQksHHjRrP9888/Z9SoUdjtdhYvXoxhGM3XORFpUSr6RERERDzA2LFjWb16tUtbeno6MTExZGdnExMTQ3p6OgD5+fkUFxeTnZ3NokWLmD9/PlBTJK5cuZL169eTkZHBypUrzUJx/vz5LFq0iOzsbIqLi8nPz2/W/olIy1HRJyIiIuIBoqOjCQoKcmnLyckhJSUFgJSUFLZt2+bSbrFYiIyM5OTJkzidTgoKCoiNjSU4OJigoCBiY2PZsWMHTqeT06dPExkZicViISUlhZycnGbvo4i0jAbX6RMRERGRlnHs2DFCQ0MBCAkJ4dixYwA4HA7CwsLM/cLCwnA4HLXarVZrne0X9m+Ij4+F4OCObsXq49PO7X1bWmuKFVpfvG1Nc703l/M5UNEnIiIi0gpYLBYsFkuzPmdVlUFp6Rm39g0O7uj2vi2tNcUKjRdvSEhgI0Qj39Vcn6W6Pgfuvqea3ikiIiLiobp27YrT6QTA6XTSpUsXoOYMXklJiblfSUkJVqu1VrvD4aiz/cL+ItI2qOgTERER8VA2m43MzEwAMjMzGTZsmEu7YRgUFhYSGBhIaGgoQ4YMoaCggLKyMsrKyigoKGDIkCGEhoYSEBBAYWEhhmG4HEs8U0Anf0JCAgkJCaR9ex/z58v5J22XpneKiIiIeIAZM2awe/duTpw4QVxcHNOmTSM1NZW0tDQ2bNhA9+7dWbFiBQDx8fHk5eVht9vx9/dn6dKlAAQHBzNlyhTGjRsHwNSpUwkODgZg3rx5zJo1i/LycuLi4oiLi2uZjopb/Dv48uNHsxr1mMVPJDfq8aT1UNEnIiIi4gGeeeaZOttfeumlWm0Wi4V58+bVuf+4cePMou9i/fr1Y/PmzZcXpIi0SpreKSIiIiIi4sVU9ImIiIiIiHgxFX0iIiIiIiJeTEWfiIiIiIiIF1PRJyIiIiIi4sVU9ImIiIiIiHgxLdnwPZSfr2qSBS7Lz1dxRXufRj1mYHDHRj9mU8R59lwlp0+ebdRjBnTyx79D437Em6LvjX3MkJDAJokTmuZ9kubjCX8T7uTO1vB3duGYFxZMbsxjekLfW+p9EhGRpqGi73u4or1Poy+WCTULZjbFIpyt5ZinG/WITbeoaVs95oXjNvb7JM1HfxM6pqcfVwtHi4g0DU3vFBERERER8WIq+kRERERERLyYij4REREREREvpqJPRERERETEi6noExERERER8WIq+kRERERERLyYij4REREREREvpqJPRERERETEi2lxdhERERERke+h/HwVISGBjXrMs+cqOX3ybKMeU0WfiIiIiIjI93BFex9+/GhWox6z+IlkTjfqET2o6MvPz2fJkiVUV1czfvx4UlNTWzokEZFLasq8VQWNPnIoIgL6ziXSFnlE0VdVVcXChQtZs2YNVquVcePGYbPZuPrqq1s6NBGROjV13mqqkUMRadv0nUukbfKIoq+oqIgePXoQHh4OQHJyMjk5OUpAbUhjz4fWGRJpaspbItIaKXc1jYBO/vh38Iiv1SJ1shiGYbR0EO+88w47duxgyZIlAGRmZlJUVMTcuXNbODIRkbopb4lIa6TcJdI2ackGERERERERL+YRRZ/VaqWkpMT83eFwYLVaWzAiEZFLU94SkdZIuUukbfKIoq9fv34UFxdz6NAhKioqyMrKwmaztXRYIiL1Ut4SkdZIuUukbfKIK059fX2ZO3cukyZNoqqqiptvvplevXq1dFgiIvVS3hKR1ki5S6Rt8ogbuYiIiIiIiEjT8IjpnSIiIiIiItI0VPSJiIiIiIh4Ma8p+vLz80lMTMRut5Oenl5re0VFBWlpadjtdsaPH8/hw4dbIMrG11C/X3/9dQYPHszo0aMZPXo0GRkZLRBl45s1axYxMTGMHDmyzu2GYbB48WLsdjujRo1i7969zRxh02mo7x988AH9+/c33/OVK1c2c4TiLm/MW96Yk7wx33hjHjl69CgTJkxgxIgRJCcn89JLL9XapzW+V56oNeWuhmJds2YNI0aMYNSoUdxxxx0cOXKkBaKs0VCsF2zZsoXevXvz2WefNWN0tbkT71tvvWX+TT700EPNHOF/NBTrN998w4QJE0hJSWHUqFHk5eW1QJQ1muz/HMMLVFZWGsOGDTO+/vpr49y5c8aoUaOM/fv3u+zzyiuvGI899phhGIaxefNm44EHHmiJUBuVO/1+7bXXjAULFrRQhE1n9+7dxueff24kJyfXuX379u3G3XffbVRXVxt79uwxxo0b18wRNp2G+v7+++8bqampzRyV/Le8MW95a07yxnzjjXnE4XAYn3/+uWEYhnHq1CkjISGh1uevNb5XnqY15S53Yt21a5dx5swZwzAM49VXX/XoWA2j5rP961//2hg/frxRVFTUApHWcCfef/7zn8bo0aON0tJSwzAM49///ndLhOpWrHPmzDFeffVVwzAMY//+/caNN97YEqEahtF0/+d4xZm+oqIievToQXh4OH5+fiQnJ5OTk+OyT25uLmPGjAEgMTGRXbt2YbTye9i4029vFR0dTVBQUL3bc3JySElJwWKxEBkZycmTJ3E6nc0YYdNpqO/SOnhj3vLWnOSN+cYb80hoaCh9+/YFICAggKuuugqHw+GyT2t8rzxNa8pd7sQ6ePBg/P39AYiMjHRZw7A5uZs/f/e733HPPffQoUOHFojyP9yJd/369dx2221mrunatWtLhOpWrBaLhdOnTwNw6tQpQkNDWyJUoOn+z/GKos/hcBAWFmb+brVaayV6h8NBt27dgJrbFQcGBnLixIlmjbOxudNvgOzsbEaNGsX06dM5evRoc4bYYr772oSFhdX52nirwsJCbrrpJiZNmsT+/ftbOhypgzfmrbaak7w137TmPHL48GG+/PJLIiIiXNq99b1qTq0pd7mbky7YsGEDcXFxzRFaLe7EunfvXkpKShg6dGgzR1ebO/EWFxfzz3/+k1tvvZVbbrmF/Pz85g4TcC/W+++/nzfffJO4uDhSU1OZM2dOc4fptu+bx7yi6JP63XjjjeTm5vLmm2/y85//nEceeaSlQ5Im1rdvX3Jzc3njjTeYMGECU6dObemQREzKSa1Da84j3377LdOnT+d///d/CQgIaOlwpJXYtGkTn3/+OZMmTWrpUOpUXV3NE0880apyZlVVFV999RUvv/wyTz/9NI899hgnT55s6bDqlJWVxZgxY8jPzyc9PZ2HH36Y6urqlg6rUXlF0We1Wl1OxzscDqxWa619LowoV1ZWcurUKTp37tyscTY2d/rduXNn/Pz8ABg/fnybuWj9u69NSUlJrdfGWwUEBPCDH/wAgPj4eCorKzl+/HgLRyXf5Y15q63mJG/MN601j5w/f57p06czatQoEhISam33xvequbWm3OVOrADvvfcezz//PM8995yZn5pbQ7F+++237Nu3j9tvvx2bzUZhYSGTJ09usZu5uPs5sNlstG/fnvDwcH784x9TXFzczJG6F+uGDRtISkoCICoqinPnznnszJrvm8e8oujr168fxcXFHDp0iIqKCrKysrDZbC772Gw2Nm7cCNTc9Wjw4MFYLJaWCLfRuNPvi+f45ubm0rNnz+YOs0XYbDYyMzMxDIPCwkICAwNbdH52c/rXv/5lXjtRVFREdXW1RxcKbZU35q22mpO8Md+0xjxiGAazZ8/mqquuYuLEiXXu443vVXNrTbnLnVi/+OIL5s6dy3PPPddi15xBw7EGBgbywQcfkJubS25uLpGRkTz33HP069fPI+MF+MUvfsHu3bsBOH78OMXFxYSHh3tkrN26dWPXrl0AHDx4kHPnztGlS5dmj9Ud3zeP+TZDbE3O19eXuXPnMmnSJKqqqrj55pvp1asXv/vd77j22msZNmwY48aNY+bMmdjtdoKCgli+fHlLh33Z3On3yy+/TG5uLj4+PgQFBfH444+3dNiNYsaMGezevZsTJ04QFxfHtGnTqKysBOBXv/oV8fHx5OXlYbfb8ff3Z+nSpS0cceNpqO9btmzhL3/5Cz4+PlxxxRU888wzHl0otFXemLe8NSd5Y77xxjzy8ccfs2nTJn76058yevRooKaf33zzDdB63ytP05pylzuxPvXUU5w5c4YHHngAqPny//zzz3tkrJ7EnXhvuOEGdu7cyYgRI/Dx8eHhhx9ukcEjd2J99NFHmTNnDmvXrsVisfDEE0+0WM5rqv9zLIYn3wpORERERERELotXTO8UERERERGRuqnoExERERER8WIq+kRERERERLyYij4REREREREvpqJPRERERETEi6noExERERER8WIq+kRERERERLyYij4REREREREvpqJPRERERETEi6noExERERER8WIq+kRERERERLyYij4REREREREvpqJPRERERETEi6noExERERER8WIq+kRERERERLyYij4REREREREvpqJPRERERETEi6noExERERER8WIq+lqx3//+9/z2t79t6TC8xo4dO5gyZcr3euzhw4fp3bs3lZWVdW5/+eWX+b//+7/LCU+kVZg7dy5/+MMfvvfjX3/9dX71q181YkRt24EDBxg7diyGYXyvx/fu3Zuvvvqqzm25ubmkpaVdTngiItJMVPS1Am+++SZjx44lKiqKIUOGMGnSJD766KOWDsstjz76KNdeey1RUVHmv5tuuqmlw6rT8uXLSU1NBWq+uM6bN8/cdv78eSIjI+tsKywsbPDYt9xyC2+++SbHjh1r/MBFmpHNZuO6664jKiqKAQMGcOutt/KXv/yF6upqABYuXMjUqVNbOMqm9fvf/56+ffu65LUBAwa0dFh1+t3vfsfdd9+NxWJh1apVTJo0yWV7QkJCnW1ZWVkNHttms3HgwAH+9re/NWrMIm3Zo48+yvLlyxvlWBMmTCAjI+OS+1zOgPelaFDI86jo83Br1qxh6dKl3HfffezcuZN3332XX//61+Tk5DTq89R3hqox3H333ezZs8f898YbbzT6c1xu/EVFRZw+fZrIyEgABgwY4FJYf/7553Tr1o2PP/7YpQ2gb9++DR6/Q4cOxMXFkZmZeVlxiniC559/nj179vDuu+9yzz338MILLzB79uyWDquWpsxrSUlJLnmtKQbiLjd+p9PJBx98wC9+8QugJq/t2bOHqqoqc3tlZSVffvmlS9tXX33ldhGbnJzM+vXrLytOEXHldDq55ppr+Prrr2ttmzp1Kk8++aT5+549e7j11lsbPGZ9syguHvAePnw4GzZsqLXPSy+9xNixY83fKyoqGDRoEN9++229z6dBIc+jos+DnTp1imeffZa5c+eSkJBAx44dad++PTabjUceeQSoOdv08MMPExUVRXJyMp999pn5+PT0dH7xi18QFRXFiBEj2Lp1q7nt9ddf59Zbb2Xp0qUMGjSI3//+95w4cYL77ruP66+/nptvvpnly5e7JIiDBw8yceJEBg4cSGJiIm+99dZl9e/ClMiNGzcydOhQBg0axHPPPWdur66uNvswaNAgHnjgAUpLS10em5GRwdChQ7njjjuoqqriiSeeYNCgQdhsNl555RVzyuXbb7/tkrCgpqCePHkyAPn5+URHR5vboqOjOXjwIMePHwfgo48+Ijk5mTNnzri0RUZG0r59e/Nxb775Zp19ARg4cCDbt2+/rNdMxJMEBgYybNgwVqxYwcaNG9m3b5/LKPXx48e59957GTBgAAMHDuTXv/61eUbw6NGj3H///QwePJhBgwaxcOFCl2M/+eSTREdHY7PZyMvLM9tfe+01kpKSiIqKYtiwYfz1r381t33wwQfExcWRnp5ObGwss2bNory8nEceeYTo6GiSkpJ44YUXiIuLMx/jcDiYNm0agwcPxmazsW7dust+XXr37s1f/vIXEhISGDBgAAsWLHCZXrlhwwaSkpKIjo7m7rvv5siRIy6PffXVV0lISCAhIQGAF154gSFDhjBkyBAyMjLMKZdFRUX8/Oc/Nws2gOzsbHM2xXvvvcc111xDhw4dAOjXr59Z5EFNDhs0aBA/+clPXNp+9KMfYbVazWO+99579fZFeU2k8YWGhhITE8OmTZtc2ktLS8nLyyMlJcVs2759u0tO+298d8B7zJgxtZ4TYNOmTYwZM8b8/aOPPqJPnz784Ac/uOTxNSjkWVT0ebA9e/Zw7tw57HZ7vfvk5uaSnJzMRx99hM1mY9GiRea28PBwXn31VT7++GPuv/9+Zs6cidPpNLcXFRURHh7Ozp07mTx5MgsXLsTf35+dO3fy5JNPupyVOnPmDHfddRcjR47kvffeY/ny5SxYsIADBw5cdj8//vhj3nnnHV566SX+8Ic/cPDgQaDmOrht27bxyiuvsGPHDoKCgmp9Mfzwww956623+NOf/sT69evJz89n06ZNbNy4kW3btpn7DRs2jMOHD5vHhpokdiFx7tu3j5/85Cfmtm7dutG9e3fzzN5HH31E//79iYqKcmn77mh4fX0B6NmzJ3//+98v+/US8TTXXXcdYWFhtc52rVmzBqvVyq5du9i5cyczZszAYrFQVVXFvffeS/fu3cnNzSU/P58RI0aYjysqKuInP/kJ77//PpMmTWL27NlmodG1a1dWrVrFJ598wuOPP87jjz/O3r17zcf++9//pqysjHfffZdFixaxcuVKjhw5wrZt21izZo3LTIPq6momT55M7969yc/P56WXXuKll15ix44dl/2abN++nQ0bNvDGG2/w9ttvm8fctm0bq1atYuXKlezatYv+/fvz0EMPuTx227ZtrF+/nrfeeov8/HzWrl3LmjVr2Lp1Kx988IHL6x4cHExBQYHZdnFe+/vf/+6S1/z8/LjuuuvM9+lCXuvfv79L23fzWn19gZq8duTIEU6fPn3Zr5lIW/TFF18wZswYoqKiSEtL49y5cwCkpKTUKsCysrK4+uqr6d27t9mWn59PfHw8ADt37mT48OH079+fhQsXmnnz4MGDzJs3j8LCQpfp6N8d8B49ejQff/yxy0DUgQMH2LdvH8nJyWZbXl6e+Zyvv/46w4YNIyoqCpvN5pJjNSjkWVT0ebDS0lI6d+6Mr69vvfv079+f+Ph4fHx8GD16tMtp9KSkJKxWK+3atWPEiBH06NGDoqIic3toaCgTJkzA19eX9u3bk52dzbRp0/D39+fqq6+uNZL0wx/+kJtvvhlfX1+uueYaEhMTeeeddxrsx4svvsiAAQPMfxfOUl5w//33c8UVV/Czn/2Mn/3sZ2Yf/vrXv/Lggw8SFhaGn58f999/P1u2bHGZ8jRt2jQ6duzIFVdcwdtvv83tt99OWFgYQUFB5nQFqPmyk5SUZCaj/fv3c+TIEW688Uag5qzqd0esBg4cyIcffkh1dTVFRUVERkYyYMAAs+2TTz5h4MCBbvUF4Ac/+AGnTp1q8PUSaY1CQ0MpKytzafP19eVf//oX33zzDe3bt2fAgAFYLBaKiopwOp08/PDDdOzYkQ4dOrgUGt27d+eWW27Bx8eHMWPG8K9//Yt///vfAAwdOpQf/ehHWCwWBg4cSGxsrEux2a5dO6ZPn46fn5+ZF+69916CgoIICwvj9ttvN/f97LPPOH78OPfffz9+fn6Eh4dzyy23uDWL4Z133nHJaxMmTHDZfs8999CpUye6d+/OoEGDXPJaamoqPXv2xNfXl/vuu48vv/zS5UtWamoqwcHBZvxjx46lV69e+Pv7M23aNJfnSUlJMfNaaWkpBQUFjBw5Erh0XoP/FHj9+/d3aftuXquvL4B5/JMnTzb4momIq4qKCqZOncro0aPZvXs3w4cPJzs7GwC73c6JEydc8tsbb7zh8t3M6XTy73//m2uuucbMZWlpabz//vv86Ec/4pNPPgFqBmcWLFhAZGSky3T07w54h4WFMWjQIJdic9OmTcTFxdGlSxez7ULRd+bMGRYvXswLL7zAnj17+Otf/0qfPn3M/TQo5FnqryakxQUHB3PixAkqKyvrLfz+53/+x/z5iiuu4Ny5c+b+mZmZrFmzxvwycebMGU6cOGHuHxYWZv58/PhxKisr6datm9l28c9HjhyhqKjI5YtZVVWVWzdlueuuu3jwwQfr3X5xH/z9/Tlz5gwA33zzDVOnTqVdu/+MTbRr187lZigX98HpdLrEfPE2qJm2MGPGDNLS0ti0aRNJSUn4+fkB0KlTp1pz0wcMGMCf//xn9u3bR3h4OP7+/vTv35+MjAz27dtHeXk5ERERbvUF4NtvvyUwMLDe10GkNXM4HAQFBbm03X333axcuZK77roLgF/+8pekpqZy9OhRunfv7lZe8/f3BzD/lvLy8vjDH/5AcXEx1dXVlJeX89Of/tTcv3PnzuZ0Rrh0Xjhy5AhOp7NWXnPnerbhw4ezbNmyereHhIS49OFCfvnmm29YunSpyzU5hmHgcDj44Q9/CLjmXqfTybXXXmv+fvE2qBmZT0pK4syZM7z99tsMGDCA0NBQoP689uqrr1JaWsrx48f58Y9/zP/8z//w6KOPUlpayv79+2v1v76+AObPnTp1qve1EJG6ffrpp5w/f5477rgDi8XC8OHDWbt2LVDznW748OFs2rSJAQMGUFxczN69e/njH/9oPj4vL48bbrgBi8VCfn4+vXr1Yvjw4QDccccdvPjii5d8/roGhlJSUvjjH//IlClTqK6u5s0333S5Zvvrr7+mqqqKq666ijNnztCuXTv2799P9+7dCQ0NNfMPuA4KBQQEXNZrJZdPRZ8Hi4qKws/Pj23btpl/xO46cuQIc+bMYe3atURFRZlnAi9msVjMn7t06YKvry8lJSXmqM/Ro0fN7d26dSM6Opo1a9ZcRo/+O2FhYSxdupT+/fvX2nb48GHAtQ8hISGUlJSYv1/8M2Bef/fRRx+xefNmly9svXv3pri42GX/6Oho5s6dy/bt280YevXqxdGjR9m+fTv9+vVz+XLZkIMHD7pMyRDxFkVFRTgcDvr37+8ymyAgIIBHH32URx99lH379nHHHXfQr18/unXrxtGjRy85oFWXiooKpk+fzpNPPsmwYcNo3749U6ZMcbnG7OKcAP/JC1dffTXgmhe6devGlVdeaY6sN4du3bpx3333XXLA7OI+hIaG4nA4zN8vzssAVquVqKgosrOz2bRpk8t12L17965186ioqChOnz7N+vXruf7664Ga9yk0NJT169cTGhpKeHi42/05ePAgP/zhD/WFTuR7cDqdWK1Wl7/57t27mz+PGTOGyZMnM2fOHDZt2sSQIUPo2rWruT0/P988s+90Ol0GtSwWS61Bou+qa2AoISGBBQsWUFhYyNmzZzl79qw5lRNqCs0L1xB27NiR5cuX8+KLLzJ79myuv/56HnnkEXr27AloUMjTaHqnBwsMDGT69OksXLiQbdu2cfbsWc6fP09eXh5PPfXUJR979uxZLBaLeTr+tddeY//+/fXu7+Pjg91uZ+XKlZw9e5aDBw+6nN4fOnQoxcXFZGZmcv78ec6fP09RUZHLNWuN7Ve/+hUrVqwwz1QeP37c5Tq970pKSmLdunU4HA5OnjzJCy+8UGuflJQUFi5ciK+vr8todnx8vDm96YIePXrQtWtX1q1bZ+5rsViIiIhg3bp1LvPg3fHhhx9+74utRTzR6dOneffdd5kxYwY33XRTrUGNd999l6+++grDMAgMDMTHxweLxcJ1111HSEgITz/9NGfOnOHcuXMud8atT0VFBRUVFeYgVV5eHjt37rzkY5KSkli1ahVlZWU4HA5eeeUVc9t1113HD37wA9LT0ykvL6eqqop9+/a5FK6N7dZbbyU9Pd3Mx6dOneLtt9+ud//hw4fz+uuvc/DgQc6ePesyyn/B6NGj+dOf/sS+ffvMm78AxMbG8sUXX5jXCEHN2YNrr72WtWvXuuTA/v3712pzh/KayPcXEhKCw+FwGbj65ptvzJ/79+9PUFAQOTk5taZ2nj9/nt27dxMbG2se6+JBLcMwXAaJvjsgBnUPePv7+5OYmEhmZiZvvPEGycnJ5qwocC36AG644QbWrFlDQUEBV111FY899pi5TYNCnkVFn4e76667ePTRR/njH/9ITEwMQ4cO5dVXXzVvwV2fq6++mrvuuotbb72Vn//85+zbt88c1a3P3LlzOXXqFLGxsTz88MMuf+gBAQH86U9/4q233uKGG25gyJAhLFu2jIqKigb78Kc//cllPXDA8JsAACAASURBVKtBgwa51ffbb78dm83GXXfdRVRUFLfccsslv4zdcsstxMbGctNNN5GSkkJ8fDy+vr74+PiY+4wePZr9+/fXGmXv27cvAQEBfPrppy7t0dHRHD9+3OW169+/P8eOHfuvvhydO3eOvLw8l7tfibRW9913H1FRUcTHx/P8888zceJEHn/88Vr7ffXVV0ycOJGoqCh++ctf8qtf/YrBgwfj4+PD888/z1dffcWNN95IXFzcJQufCwICApgzZw5paWlER0ezefNmbDbbJR8zdepUwsLCGDZsGHfeeSeJiYlmXrsQx9/+9jeGDRvG4MGDmTNnjlvXn7z99tsueS0qKsqtdTjtdjuTJk1ixowZXH/99YwcOZL8/Px694+Pj2fChAncfvvt2O12c0r5xV/C7HY7R44cwW63m9NhoWaa7KBBg2ot8RMdHc2xY8dcZlFcyGv/7WBWVlaWW7eLF5HaIiMj8fX1Zd26dZw/f57s7GyXu7BbLBZSUlJYtmwZp06dcsl3H3/8Mb179zYLqvj4ePbv3092djaVlZWsW7fOvBYaam6C5XA4XL631TXgDTVnGN9++222bNniUmiePXuWzz77jMGDBwM1N87atm0bZ86cwc/Pj44dO7pckqNBIQ9jiNTjqaeeMh5++OGWDuN72759uzF06FCXtrNnzxqRkZHGP//5z1r779ixw5g8eXKTxLJu3TrjySefbJJji4j7Xn31VeO2225r6TC+twMHDhg/+9nPjPPnz7u0Dxs2zNi5c2et/ffv32+MHTvWqK6ubvRYcnJyjOnTpzf6cUXakqKiImP06NFGZGSk8cADDxgPPPCA8cwzz5jbv/76a6N3797G3LlzXR73xBNPGKtXr3Zpy8vLMxISEozrr7/eWLBggXHbbbcZ69evNwzDMM6dO2fcc889RnR0tDFw4EDzMWPHjjUKCwtdjlNdXW3YbDYjKSnJpT03N9dITU01f3c4HMZtt91mXH/99Ub//v2N3/zmN8b+/fvN7SNHjjS+/PLL7/nKSGOzGMZF55SlTTt48CDnz5+nd+/efPbZZ9xzzz0sWbKkwbOKnqK8vJwPPviA2NhYjh07xrRp04iIiHC5AHnNmjW8++67jbIWl4h4PqfTyaFDh4iKiqK4uJh7772X2267jTvvvLOlQ3Pb1q1biY+P5+zZszzyyCO0a9fOZZrnli1bWLZsGVu2bHEZZRcR7zVixAieffZZ83rl76ugoIA///nPdU4d/6758+fTq1cvbrvttgb3zc3NZdOmTfzud7+7rPik8ajoE1NRUREPPfQQTqeTrl27mnfaq2se+MWSk5Nd5qBfsGDBArfu7tlYzp49y29+8xv+8Y9/cMUVVzB06FBmz55tTn2w2WwYhsEf/vAHrrnmmmaLS0RazpEjR7j33ns5fPgwgYGBJCcnM2PGDJfpkXWZNGlSndcZ3nvvvdx3331NFW6d7r77bgoLC/Hx8SE6Opp58+aZd8ibMGECBw4c4KmnnuKGG25o1rhEpGVUVFSwdu1al6WpmsP/+3//jxtvvNHlDp3SeqjoExERERER8WKaByIiIiIiIuLFWu06fdXV1VRVNXyS0sfH4tZ+3qot978t9x1atv/t2/s0vFMb5G7egrb5+VWf2w5P7LfyVt0aylue+F5+l6fHqPgun6fH2JTxuZu7Wm3RV1VlUFp6psH9goM7urWft2rL/W/LfYeW7X9ISGCLPO/F1q5dS0ZGBhaLhZ/+9Kc8/vjjOJ1OZsyYQWlpKX379uWpp57Cz8+PiooKHn74Yfbu3UtwcDDLly/nyiuvBGDVqlVs2LCBdu3aMWfOHPO6qfz8fJYsWUJ1dTXjx49369oKd/MWtM3Pr/rcdnhivz0hb3mihvKWJ76X3+XpMSq+y+fpMTZlfO7mLk3vFBGv43A4WLduHa+99hqbN2+mqqqKrKwsli1bxp133snWrVvp1KkTGzZsACAjI4NOnTqxdetW7rzzTpYtWwbAgQMHyMrKIisri9WrV7NgwQKqqqqoqqpi4cKFrF69mqysLDZv3syBAwdasssiIiIi9VLRJyJeqaqqivLyciorKykvLyckJIT333+fxMREoGbx2QuLVufm5jJmzBgAEhMT2bVrF4ZhkJOTQ3JyMn5+foSHh9OjRw+KioooKiqiR48ehIeH4+fnR3Jycq0FsEVEREQ8Raud3ikiUh+r1cpdd93FjTfeSIcOHYiNjaVv37506tQJX9+atBcWFobD4QBqzgx269YNAF9fXwIDAzlx4gQOh4OIiAiX4154TFhYmEt7UVFRg3H5+FgIDu7oVh98fNq5va+3UJ/bjrbabxGRluJW0eeJ18aIiNSnrKyMnJwccnJyCAwM5IEHHmDHjh0tHZau6WuA+tx2eGK/dU2fiHizBqd36toYEWlt3nvvPa688kq6dOlC+/btSUhI4JNPPuHkyZNUVlYCUFJSgtVqBWrO1B09ehSAyspKTp06RefOnbFarZSUlJjHdTgcWK3WettFREREPJFb1/Tp2hgRaU26d+/Op59+ytmzZzEMg127dnH11VczaNAgtmzZAsDGjRux2WwA2Gw2Nm7cCMCWLVsYPHgwFosFm81GVlYWFRUVHDp0iOLiYq677jr69etHcXExhw4doqKigqysLPNYIiINmTVrFjExMYwcObLWthdffJHevXtz/PhxAAzDYPHixdjtdkaNGsXevXvNfTdu3EhCQgIJCQlmDgP4/PPPGTVqFHa7ncWLF2MYNbeKLy0tZeLEiSQkJDBx4kTKysqauKci4ikanN7Z2q+NqaLxp2yUn6+itazm05avm2jLfYe23f+IiAgSExMZM2YMvr6+9OnTh1/+8pcMHTqUBx98kBUrVtCnTx/Gjx8PwLhx45g5cyZ2u52goCCWL18OQK9evUhKSmLEiBH4+Pgwd+5cfHxq/vrnzp3LpEmTqKqq4uabb6ZXr14t1l8RbxHQyR//Do17u4Gz5yo5ffJsox7zco0dO5bf/OY3PPLIIy7tR48eZefOnXTv3t1sy8/Pp7i4mOzsbD799FPmz59PRkYGpaWlrFy5ktdeew2LxcLYsWOx2WwEBQUxf/58Fi1aREREBPfccw/5+fnEx8eTnp5OTEwMqamppKenk56ezsyZM5u7+17pcj+7dX1X9cTPrrReDX46W/u1MSEhgfz40axGfe7iJ5L5179ONeoxm4onXjfRXNpy30Hr9E2fPp3p06e7tIWHh5tT0S/WoUMHnn322TqPM3nyZCZPnlyrPT4+nvj4+MYJVkQA8O/g2yT/Z59u1CNevujoaA4fPlyr/fHHH2fmzJlMmTLFbMvJySElJQWLxUJkZCQnT57E6XSye/duYmNjCQ4OBiA2NpYdO3YwcOBATp8+TWRkJAApKSnk5OQQHx9PTk4OL7/8stk+YcIEFX2NpK18dqX1anB6p66NEREREWla27ZtIzQ0lJ/97Gcu7Q6Hw2VG1IXZVd9tvzCDqr79AY4dO0ZoaCgAISEhHDt2rCm7JCIepMEzfRdfG3PFFVewa9curr32WvPamOTk5DqvjYmKiqp1bcxDDz3ExIkTcTgc5rUxhmGY18ZYrVaysrJ4+umnm7zjIiIiIp7g7NmzrFq1ihdffLHZntNisWCxWBrcr6HLaVrDpQStIcb6eELcreH18/QYPSG+Bos+XRsjIiIi0nS+/vprDh8+zOjRo4GaGVRjx44lIyOj1oyoC7OrrFYru3fvNtsdDgcDBw6sd3+Arl274nQ6CQ0Nxel00qVLlwZja+hymtZwKUVzxNhUlzV4wmur9/jyNWV87n723LriVNfGiIiIiDSN3r17s2vXLvN3m83Ghg0b6NKlCzabjVdeeYXk5GQ+/fRTAgMDCQ0NZciQITzzzDPmHTgLCgqYMWMGwcHBBAQEUFhYSEREBJmZmUyYMME8bmZmJqmpqWRmZjJs2LAW6a+INL/GvUWWiIiIiFzSjBkz2L17NydOnCAuLo5p06aZM6a+Kz4+nry8POx2O/7+/ixduhSA4OBgpkyZwrhx4wCYOnWqeVOXefPmMWvWLMrLy4mLiyMuLg6A1NRU0tLS2LBhA927d2fFihXN0FsR8QQq+kRERESa0TPPPHPJ7bm5uebPFouFefPm1bnfuHHjzKLvYv369WPz5s212jt37sxLL730X0YrIt7ArcXZRUREREREpHVS0SciIiIiIuLFVPSJiIiIiIh4MRV9IiIiIiIiXkxFn4iIiIiIiBdT0SciIiIiIuLFVPSJiIiIiIh4MRV9IiIiIiIiXkxFn4iIiIiIiBdT0SciIiIiIuLFVPSJiIiIiIh4MRV9IiIiIiIiXkxFn4iIiIiIiBdT0SciIiIiIuLFVPSJiIiINKNZs2YRExPDyJEjzbYnn3yS4cOHM2rUKKZOncrJkyfNbatWrcJut5OYmMiOHTvM9vz8fBITE7Hb7aSnp5vthw4dYvz48djtdtLS0qioqACgoqKCtLQ07HY748eP5/Dhw83QWxHxBCr6RERERJrR2LFjWb16tUtbbGwsmzdv5s033+THP/4xq1atAuDAgQNkZWWRlZXF6tWrWbBgAVVVVVRVVbFw4UJWr15NVlYWmzdv5sCBAwAsW7aMO++8k61bt9KpUyc2bNgAQEZGBp06dWLr1q3ceeedLFu2rHk7LiItRkWfiIiISDOKjo4mKCjIpW3IkCH4+voCEBkZSUlJCQA5OTkkJyfj5+dHeHg4PXr0oKioiKKiInr06EF4eDh+fn4kJyeTk5ODYRi8//77JCYmAjBmzBhycnIAyM3NZcyYMQAkJiaya9cuDMNorm6LSAvybekAREREROQ/XnvtNZKSkgBwOBxERESY26xWKw6HA4CwsDCX9qKiIk6cOEGnTp3MAjIsLMzc3+Fw0K1bNwB8fX0JDAzkxIkTdOnSpd5YfHwsBAd3vMT2dpfc7glaQ4z18YS4W8Pr5+kxekJ8KvpEREREPMRzzz2Hj48PN910U0uHAkBVlUFp6Zl6twcHd7zkdk/QHDGGhAQ2yXE94bXVe3z5mjI+dz97KvpEREREPMDrr7/O9u3bWbt2LRaLBag5g3dhqifUnK2zWq0AdbZ37tyZkydPUllZia+vLyUlJeb+VquVo0ePEhYWRmVlJadOnaJz587N2EMRaSm6pk9ERESkheXn57N69Wqee+45/P39zXabzUZWVhYVFRUcOnSI4uJirrvuOvr160dxcTGHDh2ioqKCrKwsbDYbFouFQYMGsWXLFgA2btyIzWYzj7Vx40YAtmzZwuDBg83iUkS8m870iYiIiDSjGTNmsHv3bk6cOEFcXBzTpk0jPT2diooKJk6cCEBERAQLFy6kV69eJCUlMWLECHx8fJg7dy4+Pj4AzJ07l0mTJlFVVcXNN99Mr169AJg5cyYPPvggK1asoE+fPowfPx6AcePGMXPmTOx2O0FBQSxfvrxlXgARaXYq+kRERESa0TPPPFOr7UJhVpfJkyczefLkWu3x8fHEx8fXag8PDzeXabhYhw4dePbZZ//LaEXEG2h6p4iIiIiIiBdT0SciIiIiIuLFVPSJiIiIiIh4MRV9IuKVTp48yfTp0xk+fDhJSUns2bOH0tJSJk6cSEJCAhMnTqSsrAwAwzBYvHgxdrudUaNGsXfvXvM4GzduJCEhgYSEBPOudwCff/45o0aNwm63s3jxYgzDaPY+ioiIiLhDRZ+IeKUlS5Zwww038M4777Bp0yZ69uxJeno6MTExZGdnExMTQ3p6OlBzq/Ti4mKys7NZtGgR8+fPB6C0tJSVK1eyfv16MjIyWLlypVkozp8/n0WLFpGdnU1xcTH5+fkt1VURERGRS3Kr6NOIuYi0JqdOneLDDz9k3LhxAPj5+dGpUydycnJISUkBICUlhW3btgGY7RaLhcjISE6ePInT6aSgoIDY2FiCg4MJCgoiNjaWHTt24HQ6OX36NJGRkVgsFlJSUsjJyWmx/oqIiIhciltLNlwYMX/22WepqKigvLyc559/npiYGFJTU0lPTyc9PZ2ZM2e6jJh/+umnzJ8/n4yMDHPE/LXXXsNisTB27FhsNhtBQUHmiHlERAT33HMP+fn5dd6CWETEHYcPH6ZLly7MmjWLv/3tb/Tt25fZs2dz7NgxQkNDAQgJCeHYsWMAOBwOwsLCzMeHhYXhcDhqtVut1jrbL+zfEB8fC8HBHd3qg49PO7f39Rbqc9vR3P1ui6+xiMjFGiz6LoyYP/HEE0DNiLmfnx85OTm8/PLLQM2I+YQJE5g5c2a9I+a7d+82R8wBc8R84MCB5oj5hWPl5OSo6BOR762yspIvvviCxx57jIiICBYvXmxO5bzAYrFgsViaNa6qKoPS0jNu7Rsc3NHtfb2F+tx21NfvkJDAJnk+d17jpnpuERFP0GDR5w0j5k2htYwattVRZGjbfYe23f+wsDDCwsKIiIgAYPjw4aSnp9O1a1ecTiehoaE4nU66dOkC1OSjkpIS8/ElJSVYrVasViu7d+822x0OBwMHDqx3fxERERFP1GDR19pHzFty1NATtNVRZGjbfYeW7X9Lj5iHhIQQFhbGP/7xD6666ip27dpFz5496dmzJ5mZmaSmppKZmcmwYcMAsNlsvPLKKyQnJ/Ppp58SGBhIaGgoQ4YM4ZlnnjGvWS4oKGDGjBkEBwcTEBBAYWEhERERZGZmMmHChJbssoiIiEi9Giz6NGIuIq3RY489xm9/+1vOnz9PeHg4jz/+ONXV1aSlpbFhwwa6d+/OihUrAIiPjycvLw+73Y6/vz9Lly4FIDg4mClTppg3hJk6dao5RX3evHnMmjWL8vJy4uLiiIuLa5mOioiIiDSgwaJPI+Yi0hr16dOH119/vVb7Sy+9VKvNYrEwb968Oo8zbtw4s+i7WL9+/di8efPlByoiIiLSxNy6e6dGzEVERERERFont4o+jZiLiIiINI5Zs2axfft2unbtan7/KS0t5cEHH+TIkSP88Ic/ZMWKFQQFBWEYBkuWLCEvL48rrriCJ554gr59+wI16x8/99xzAEyePJkxY8YANesfXxhMj4+PZ/bs2VgslnqfQ0S8n1uLs4uIiIhI4xg7diyrV692aUtPTycmJobs7GxiYmLMm+ZdvP7xokWLmD9/PoC5/vH69evJyMhg5cqV5iU0F9Y/zs7Opri4mPz8/Es+h4h4PxV9IiIiIs0oOjq61hm2C+scQ82axdu2bXNp/+76xwUFBeb6x0FBQeb6x06n01z/2GKxmOsfX+o5RMT7uTW9U0RERESaTnOsf1zfc1xKQ+sit4Y1YVtDjPXxhLhbw+vn6TF6Qnwq+kREREQ8SHOsf+zuczS0LnJrWBO3OWL05nWh9R5fvqaMz93PnqZ3ioiIiLSwC+sfA26vf3xxu8PhqLP94vWP63sOEfF+KvpEREREWpjNZiMzMxOg1vrHmZmZGIZBYWGhy/rHBQUFlJWVUVZWRkFBAUOGDCE0NNRc/9gwjDqP9d3nEBHvp+mdIiIiIs1oxowZ7N69mxMnThAXF8e0adNITU1t8vWP63sOEfF+KvpEREREmtEzzzxTZ3tTr3/cuXPnOp9DRLyfpneKiIiIiIh4MRV9IiIiIiIiXkxFn4iIiIiIiBdT0SciIiIiIuLFVPSJiIiIiIh4MRV9IiIiIiIiXkxFn4iIiIiIiBdT0SciIiIiIuLFVPSJiIiIiIh4MRV9IiIiIiIiXkxFn4iIiIiIiBdT0SciIiIiIuLFVPSJiIiIiIh4MRV9IiIiIh5i7dq1JCcnM3LkSGbMmMG5c+c4dOgQ48ePx263k5aWRkVFBQAVFRWkpaVht9sZP348hw8fNo+zatUq7HY7iYmJ7Nixw2zPz88nMTERu91Oenp6s/dPRFqGij4RERERD+BwOFi3bh2vvfYamzdvpqqqiqysLJYtW8add97J1q1b6dSpExs2bAAgIyODTp06sXXrVu68806WLVsGwIEDB8jKyiIrK4vVq1ezYMECqqqqqKqqYuHChaxevZqsrCw2b97MgQMHWrLLItJMVPSJiIiIeIiqqirKy8uprKykvLyckJAQ3n//fRITEwEYM2YMOTk5AOTm5jJmzBgAEhMT2bVrF4ZhkJOTQ3JyMn5+foSHh9OjRw+KioooKiqiR48ehIeH4+fnR3JysnksEfFuKvpEREREPIDVauWuu+7ixhtvZMiQIQQEBNC3b186deqEr68vAGFhYTgcDqDmzGC3bt0A8PX1JTAwkBMnTuBwOAgLC3M5rsPhqLddRLyfb0sHICIiIiJQVlZGTk4OOTk5BAYG8sADD7hcj9cSfHwsBAd3vMT2dpfc7glaQ4z18YS4W8Pr5+kxekJ8KvpEREREPMB7773HlVdeSZcuXQBISEjgk08+4eTJk1RWVuLr60tJSQlWqxWoOVN39OhRwsLCqKys5NSpU3Tu3Bmr1UpJSYl5XIfDYT6mvvb6VFUZlJaeqXd7cHDHS273BM0RY0hIYJMc1xNeW73Hl68p43P3s6fpnSIiIiIeoHv37nz66aecPXsWwzDYtWsXV199NYMGDWLLli0AbNy4EZvNBoDNZmPjxo0AbNmyhcGDB2OxWLDZbGRlZVFRUcGhQ4coLi7muuuuo1+/fhQXF3Po0CEqKirIysoyjyUi3k1n+kREREQ8QEREBImJiYwZMwZfX1/69OnDL3/5S4YOHcqDDz7IihUr6NOnD+PHjwdg3LhxzJw5E7vdTlBQEMuXLwegV69eJCUlMWLECHx8fJg7dy4+Pj4AzJ07l0mTJlFVVcXNN99Mr169Wqy/ItJ8VPSJiNe68KXGarWyatUqDh06xIwZMygtLaVv37489dRT+Pn5UVFRwcMPP8zevXsJDg5m+fLlXHnllUDNWlcbNmygXbt2zJkzhxtuuAGoWetqyZIlVFdXM378eFJTU1uyqyLiJaZPn8706dNd2sLDw81lGi7WoUMHnn322TqPM3nyZCZPnlyrPT4+nvj4+MYJVkRaDU3vFBGvtW7dOnr27Gn+rrWuREREpC1yu+irqqoiJSWFe++9F4BDhw4xfvx47HY7aWlpVFRUAFBRUUFaWhp2u53x48dz+PBh8xirVq3CbreTmJjocjeq/Px8EhMTsdvtpKenN1bfRKQNKykpYfv27YwbNw4AwzC01pWIiIi0SW4XfRoxF5HWZOnSpcycOZN27WrS3IkTJ7TWlYiIiLRJbl3Td2HE/L777mPt2rXmiPnTTz8N1IyYr1y5kl//+tfk5uZy//33AzUj5gsXLrzkiDlgjpgD5oj51Vdf3RT9FZE24N1336VLly5ce+21fPDBBy0djqmh9a5c9235NX2am/rcdjR3v9viaywicjG3ir4LI+bffvst8P1HzCMiIsxjXjwy/t0R8wvF4KX8N1+emkJr+Q+krX6hgLbdd2jb/f/kk0/Izc0lPz+fc+fOcfr0aZYsWdKia11Bw+tdXczT1xxqCupz21Ffv1tyrbOmem4REU/QYNHnqSPm7n558ubFMt3RVr9QQNvuO7Rs/1v6y9NDDz3EQw89BMAHH3zAiy++yNNPP8306dPZsmULycnJda51FRUVVWutq4ceeoiJEyficDjMta4MwzDXurJarWRlZZkzH0REREQ8TYNFn6eOmIuI/Ldmzpypta5ERESkzWmw6NOIuYi0ZoMGDWLQoEGA1roSERGRtul7L86uEXMRERERERHP918VfRoxFxERERERaV3cXqdPREREREREWh8VfSIiIiIiIl5MRZ+IiIiIiIgXU9EnIiIiIiLixVT0iYiIiHiIkydPMn36dIYPH05SUhJ79uyhtLSUiRMnkpCQwMSJEykrKwPAMAwWL16M3W5n1KhR7N271zzOxo0bSUhIICEhgY0bN5rtn3/+OaNGjcJut7N48WIMw2j2PopI81PRJyIiIuIhlixZwg033MA777zDpk2b6NmzJ+np6cTExJCdnU1MTAzp6ekA5OfnU1xcTHZ2NosWLWL+/PkAlJaWsnLlStavX09GRgYrV640C8X58+ezaNEisrOzKS4uJj8/v6W6KiLNSEWfiIiIiAc4deoUH374IePGjQPAz8+PTp06kZOTQ0pKCgApKSls27YNwGy3WCxERkZy8uRJnE4nBQUFxMbGEhwcTFBQELGxsezYsQOn08np06eJjIzEYrGQkpJCTk5Oi/VXRJrP916cXUREREQaz+HDh+nSpQuzZs3ib3/7G3379mX27NkcO3aM0NBQAEJCQjh27BgADoeDsLAw8/FhYWE4HI5a7Vartc72C/tfio+PheDgjpfY3u6S2z1Ba4ixPp4Qd2t4/Tw9Rk+IT0WfiIiIiAeorKzkiy++4LHHHiMiIoLFixebUzkvsFgsWCyWZoupqsqgtPRMvduDgztecrsnaI4YQ0ICm+S4nvDa6j2+fE0Zn7ufPU3vFBEREfEAYWFhhIWFERERAcDw4cP54osv6Nq1K06nEwCn00mXLl2AmjN4JSUl5uNLSkqwWq212h0OR53tF/YXEe+nok9ERETEA4SEhBAWFsY//vEPAHbt2kXPnj2x2WxkZmYCkJmZybBhwwDMdsMwKCwsJDAwkNDQUIYMGUJBQQFlZWWUlZVRUFDAkCFDCA0NJSAggMLCQgzDcDmWiHg3Te8UERER8RCPPfYYv/3tbzl//jzh4eE8/vjjVFdXk5aWxoYNG+jevTsrVqwAID4+nry8POx2O/7+/ixduhSA4OBgpkyZYt4QZurUqQQHBwMwb948Zs2aRXl5OXFxccTFxbVMR0WkWanoExEREfEQffr04fXXX6/V/v/bu/+Yqu77j+PPWygtCwpiuBdd0IYOs6moyRqFQDC75F7U2xuuFZoskUwyQ6MujGLpqk7Q+mPt5qrfxmaTNG62TdOqG7iIm1RQkU5rZ+sYzhnNRoQF7s2ooGiVH57vH8QbMofYNQAAFnpJREFUraAocO/l3tfjr/I55358fz7n9H0/73vOuXfPnj33tZlMJsrKygbsJycnx1v03S05OZmDBw8OP1ARGVN0e6eIiIiIiEgQU9EnIiIiIiISxFT0iYiIiIiIBDEVfSIiIiIiIkFMRZ+IiIiIiEgQU9EnIiIiIiISxFT0iYiIiIiIBDEVfSIiIiIiIkFMRZ+IiIiIiEgQU9EnIiIiIiISxFT0iYiIiIiIBDEVfSIiIiIiIkFMRZ+IiIiIiEgQU9EnIiIiEiD6+vpwuVy89NJLADQ3N5Obm4vNZqOoqIju7m4Auru7KSoqwmazkZubS0tLi7ePXbt2YbPZyMrK4sSJE972uro6srKysNlslJeX+3ZgIuJXKvpEREREAsR7773Hs88+6/1727ZtLFu2jE8++YTx48ezf/9+APbt28f48eP55JNPWLZsGdu2bQPg0qVLVFVVUVVVxbvvvsvGjRvp6+ujr6+P119/nXfffZeqqioOHjzIpUuX/DJGEfE9FX0iIiIiAaCtrY1jx46Rk5MDgGEYnDp1iqysLAAWL15MTU0NALW1tSxevBiArKwsTp48iWEY1NTU4HA4iIiIICEhgalTp9LQ0EBDQwNTp04lISGBiIgIHA6Hty8RCX7h/g5ARERERGDr1q2UlJRw/fp1AK5cucL48eMJD+9frsXHx+N2uwFwu91MmjQJgPDwcMaNG8eVK1dwu93Mnj3b26fFYvG+Jj4+/p72hoaGh8YUFmYiJuZbD9j+xAO3B4KxEONgAiHusTB/gR5jIMSnok9Egk5rayuvvvoq7e3tmEwmXnzxRX70ox/R0dHByy+/zH//+1++/e1vs2PHDqKjozEMgy1btnD8+HGefvpp3njjDWbMmAFARUUFv/nNbwBYsWKF95P1xsZG1qxZw82bN5k/fz7r1q3DZDL5bcwiMrYdPXqU2NhYZs6cyWeffebvcLz6+gw6Om4Muj0m5lsP3B4IfBFjXNy4Uek3EOZWx3j4RjO+oZ57D729s7W1lby8PBYtWoTD4WDPnj0AdHR0kJ+fj91uJz8/n87OTqD/VoTNmzdjs9lwOp2cO3fO21dFRQV2ux273U5FRYW3vbGxEafTic1mY/PmzRiG8UiDFRG5W1hYGK+99hqHDh3i448/5sMPP+TSpUuUl5eTmppKdXU1qamp3i8yqKuro6mpierqajZt2sSGDRuA/jy3c+dO9u7dy759+9i5c6c3123YsIFNmzZRXV1NU1MTdXV1/hquiASBL774gtraWqxWK8XFxZw6dYotW7Zw9epVent7gf7bPy0WC9B/pa61tRWA3t5erl27xoQJE7BYLLS1tXn7dbvdWCyWQdtFJDQ8tOjT4klExhqz2ey9UhcVFUViYiJut5uamhpcLhcALpeLI0eOAHjbTSYTc+bM4erVq3g8Hurr60lLSyMmJobo6GjS0tI4ceIEHo+Hrq4u5syZg8lkwuVy6dkYERmW1atXU1dXR21tLW+99RYpKSn8+te/Zt68eRw+fBjo//DcarUCYLVavR+gHz58mJSUFEwmE1arlaqqKrq7u2lubqapqYlZs2aRnJxMU1MTzc3NdHd3U1VV5e1LRILfQ2/vNJvNmM1m4P7F0/vvvw/0L57y8vIoKSkZdPF0+vRp7+IJ8C6e5s6d61083emrpqaG+fPnj9aYRSSEtLS0cP78eWbPnk17e7s3n8XFxdHe3g70f+J997Mud56b+Wb7nWdjBtv/YR72bMy9+/r//n9f05hDh6/HPZbnuKSkhJdffpkdO3bwve99j9zcXABycnIoKSnBZrMRHR3N9u3bAUhKSmLhwoUsWrSIsLAwSktLCQsLA6C0tJTly5fT19fHkiVLSEpK8tu4RMS3HumZvrG6eBoNY+UNJFQXFBDaYweNH+D69esUFhaydu1aoqKi7tlmMpl8/gzew56NuVugP58wGjTm0DHYuP35XNRo/duPY968ecybNw+AhIQE78803O2pp57i7bffHvD1K1asYMWKFfe1z58/Xx+qi4SoIRd9Y3XxFMwP1g5FqC4oILTHDv4dfyAsnnp6eigsLMTpdGK32wGYOHEiHo8Hs9mMx+MhNjYW4L5nXe48N2OxWDh9+rS33e12M3fu3EH3FxEREQlEQ/qdvgctnoAhL56G8mCxFk8iMlyGYbBu3ToSExPJz8/3tlutViorKwGorKwkMzPznnbDMDh79izjxo3DbDaTnp5OfX09nZ2ddHZ2Ul9fT3p6OmazmaioKM6ePYthGPf0JSIiIhJoHlr0afEkImPNmTNnOHDgAKdOnSI7O5vs7GyOHz9OQUEBn376KXa7nb/+9a8UFBQA/bc8JSQkYLPZWL9+PWVlZQDExMSwcuVKcnJyyMnJYdWqVd7nksvKyvj5z3+OzWZjypQpZGRk+G28IiIiIg/y0Ns77yyepk2bRnZ2NgDFxcUUFBRQVFTE/v37mTx5Mjt27AD6F0/Hjx/HZrMRGRnJ1q1bgXsXT8B9i6c7v3eVkZGhxZOIDMtzzz3HhQsXBtx252dn7mYymbyF3jfdKfi+KTk5mYMHDw4vUBEREREfeGjRp8WTiIiIiIjI2DWkZ/pERERERERkbFLRJyIiIiIiEsRU9ImIiIiIiAQxFX0iIiIiIiJBTEWfiIiIiIhIEFPRJyIiIiIiEsRU9ImIiIiIiAQxFX0iIiIiIiJBTEWfiIiISABobW0lLy+PRYsW4XA42LNnDwAdHR3k5+djt9vJz8+ns7MTAMMw2Lx5MzabDafTyblz57x9VVRUYLfbsdvtVFRUeNsbGxtxOp3YbDY2b96MYRi+HaSI+IWKPhEREZEAEBYWxmuvvcahQ4f4+OOP+fDDD7l06RLl5eWkpqZSXV1Namoq5eXlANTV1dHU1ER1dTWbNm1iw4YNQH+RuHPnTvbu3cu+ffvYuXOnt1DcsGEDmzZtorq6mqamJurq6vw1XBHxIRV9IiIiIgHAbDYzY8YMAKKiokhMTMTtdlNTU4PL5QLA5XJx5MgRAG+7yWRizpw5XL16FY/HQ319PWlpacTExBAdHU1aWhonTpzA4/HQ1dXFnDlzMJlMuFwuampq/DZeEfGdcH8HICIiIiL3amlp4fz588yePZv29nbMZjMAcXFxtLe3A+B2u4mPj/e+Jj4+HrfbfV+7xWIZsP3O/g8SFmYiJuZbD9j+xAO3B4KxEONgAiHusTB/gR5jIMSnok9EREQkgFy/fp3CwkLWrl1LVFTUPdtMJhMmk8lnsfT1GXR03Bh0e0zMtx64PRD4Isa4uHGj0m8gzK2O8fCNZnxDPfd0e6eIiIhIgOjp6aGwsBCn04ndbgdg4sSJeDweADweD7GxsUD/Fby2tjbva9va2rBYLPe1u93uAdvv7C8iwU9Fn4iIiEgAMAyDdevWkZiYSH5+vrfdarVSWVkJQGVlJZmZmfe0G4bB2bNnGTduHGazmfT0dOrr6+ns7KSzs5P6+nrS09Mxm81ERUVx9uxZDMO4py8RCW66vVNEREQkAJw5c4YDBw4wbdo0srOzASguLqagoICioiL279/P5MmT2bFjBwDz58/n+PHj2Gw2IiMj2bp1KwAxMTGsXLmSnJwcAFatWkVMTAwAZWVlrFmzhps3b5KRkUFGRoYfRioivqaiT0RERCQAPPfcc1y4cGHAbXd+s+9uJpOJsrKyAffPycnxFn13S05O5uDBg8MLVETGHN3eKSIiIiIiEsR0pe8x3OzpG5VvabrZ08fTT4aNeJ8jHetoxTkafY60qPGRRD41sv/bjMbYv77VO6L9SXAYC+dvXNy4MZVjRqLPu3N0IMc50v2O1rcdiojI/VT0PYannwzjmdeqRrzfpjccI95vqPd5bUR7hMinwsfE2P+1aQFPPhk24ouqr2/10nX16xHtU3xnrJy/6jPw+xytfpvecIxofyIi0k9Fn0gQGs0PJrpGvFcRERERGU16pk9ERERERCSIqegTEREREREJYir6REREREREgpiKPhERERERkSCmok9ERERERCSIqegTEREREREJYir6REREREREgpiKPhERERERkSCmok9ERERERCSIqegTEREREREJYuH+DuCOuro6tmzZwu3bt8nNzaWgoMDfIYmIPNBo5q0+IC5u3Ij1JyJyh9ZcIqEnIIq+vr4+Xn/9dX73u99hsVjIycnBarXyne98x9+hiYgMaLTz1tNPhvHMa1Uj0tcdTW84RrQ/ERl7tOYSCU0BUfQ1NDQwdepUEhISAHA4HNTU1CgBybDc7OnTlRIZNcpbIjIWKXeJhCaTYRiGv4P4y1/+wokTJ9iyZQsAlZWVNDQ0UFpa6ufIREQGprwlImORcpdIaNIXuYiIiIiIiASxgCj6LBYLbW1t3r/dbjcWi8WPEYmIPJjyloiMRcpdIqEpIIq+5ORkmpqaaG5upru7m6qqKqxWq7/DEhEZlPKWiIxFyl0ioSkgvsglPDyc0tJSli9fTl9fH0uWLCEpKcnfYYmIDEp5S0TGIuUukdAUEF/kIiIiIiIiIqMjIG7vFBERERERkdGhok9ERERERCSIBXXRV1dXR1ZWFjabjfLycn+H4zOtra3k5eWxaNEiHA4He/bs8XdIftHX14fL5eKll17ydyg+d/XqVQoLC1mwYAELFy7kyy+/9HdIMkShlrdCPV+FWp5Sbho7Ojo6yM/Px263k5+fT2dn54D7VVRUYLfbsdvtVFRUeNvz8vLIysoiOzub7Oxs2tvbAeju7qaoqAibzUZubi4tLS0+j+/rr7+moKCABQsW4HA42LZtm3f/P/7xj6SkpHjj3rdv3yPH9rA8/qA52LVrFzabjaysLE6cODHkPn0R36effsoLL7yA0+nkhRde4OTJk97XDHa8fRlfS0sLs2bN8sZw929PNjY24nQ6sdlsbN68meE+3fa4Mf7pT3/yxpednc13v/tdzp8/D4zsHA7ICFK9vb1GZmamcfnyZePWrVuG0+k0Ll686O+wfMLtdhuNjY2GYRjGtWvXDLvdHjJjv9vu3buN4uJio6CgwN+h+Nyrr75q7N271zAMw7h165bR2dnp54hkKEIxb4V6vgq1PKXcNHa8+eabxq5duwzDMIxdu3YZv/zlL+/b58qVK4bVajWuXLlidHR0GFar1ejo6DAMwzCWLl1qNDQ03PeaDz74wFi/fr1hGIZx8OBB46c//anP47tx44Zx8uRJwzD6z8Mf/vCHxrFjxwzDMIw//OEPxsaNGx8rJsMYWh4fbA4uXrxoOJ1O49atW8bly5eNzMxMo7e3d0TfG4YT37lz54y2tjbDMAzjwoULRnp6uvc1gx1vX8bX3NxsOByOAftdsmSJ8eWXXxq3b982fvzjH3uPt69jvNu//vUvIzMz0/v3SM3hYIL2Sl9DQwNTp04lISGBiIgIHA4HNTU1/g7LJ8xmMzNmzAAgKiqKxMRE3G63n6Pyrba2No4dO0ZOTo6/Q/G5a9eu8fnnn3vHHhERwfjx4/0clQxFKOatUM5XoZanlJvGlpqaGlwuFwAul4sjR47ct099fT1paWnExMQQHR1NWlraPVenBlJbW8vixYsByMrK4uTJk4911WU48UVGRpKSkgL0n4fTp08fsbwzlDw+2BzU1NTgcDiIiIggISGBqVOn0tDQMKLvDcOJb/r06d7fdExKSuLWrVt0d3c/VhyjEd9gPB4PXV1dzJkzB5PJhMvlGtZ760jFWFVVhcPheOw4HlXQFn1ut5v4+Hjv3xaLJWQWEndraWnh/PnzzJ4929+h+NTWrVspKSnhiSeC9hQfVEtLC7GxsaxZswaXy8W6deu4ceOGv8OSIQj1vBVq+SrU8pRy09jS3t6O2WwGIC4ubsBbzR6Ws9auXUt2djbvvPOOd8HrdruZNGkS0P/zEePGjePKlSt+iQ/6bzk+evQoqamp3rbq6mqcTieFhYW0trY+UlxD+TcHm4PBXjuS7w3Die9uhw8fZvr06URERHjbBjrevo6vpaUFl8vF0qVL+dvf/jZgn/Hx8cN6bx2pOTx06NB9Rd9IzOFgAuJ3+mR0XL9+ncLCQtauXUtUVJS/w/GZo0ePEhsby8yZM/nss8/8HY7P9fb28s9//pP169cze/ZsNm/eTHl5OUVFRf4OTWRQoZavQjFPKTcFnmXLlvG///3vvvZvHhOTyYTJZHqkvrdt24bFYqGrq4vCwkIOHDjgvTIXCPFB/zlZXFxMXl4eCQkJAPzgBz/g+eefJyIigo8++oif/exnvPfee4/cdzC7ePEi27ZtY/fu3d62kTjew2U2mzl69CgTJkygsbGRVatWUVVV5dMYhurvf/87kZGRTJs2zds22nMYtEWfxWKhra3N+7fb7fZekg4FPT09FBYW4nQ6sdvt/g7Hp7744gtqa2upq6vj1q1bdHV18corr9zzoHYwi4+PJz4+3nu1ZMGCBSHxhSDBIFTzVijmq1DMU8pNgef3v//9oNsmTpyIx+PBbDbj8XiIjY29bx+LxcLp06e9f7vdbubOnevdBv23bT///PM0NDTgcrmwWCy0trYSHx9Pb28v165dY8KECT6PD2D9+vU888wzLFu2zNt2dyy5ubn86le/GjSGgQwljw82Bw967Ui9Nwwnvjtx/OQnP+HNN99kypQp97wG7j/evozPZDJ5rzzOnDmTKVOm8J///Oe+Ptva2ob13jrcOYSBb+0cqTkcTNDeU5KcnExTUxPNzc10d3dTVVWF1Wr1d1g+YRgG69atIzExkfz8fH+H43OrV6+mrq6O2tpa3nrrLVJSUoJ6IfVNcXFxxMfH8+9//xuAkydP8uyzz/o5KhmKUMxboZqvQjFPKTeNLVarlcrKSgAqKyvJzMy8b5/09HTq6+vp7Oyks7OT+vp60tPT6e3t5auvvgL6P9Q5duwYSUlJ3n7vfIvm4cOHSUlJeayrdMOJD2D79u10dXWxdu3ae17j8Xi8/11bW/vI5+hQ8vhgc2C1WqmqqqK7u5vm5maampqYNWvWiL43DCe+q1evUlBQwOrVq/n+97/v3f9Bx9uX8X311Vf09fUBeOcvISEBs9lMVFQUZ8+exTCMQc8XX8QIcPv2bf785z/fU/SN5BwOJmiv9IWHh1NaWsry5cvp6+tjyZIlIz55gerMmTMcOHCAadOmkZ2dDUBxcTHz58/3c2TiK+vXr+eVV16hp6eHhIQEfvGLX/g7JBmCUMxbylehRblp7CgoKKCoqIj9+/czefJkduzYAcA//vEPPvroI7Zs2UJMTAwrV670fjnPqlWriImJ4caNGyxfvpyenh5u375NamoqL774IgA5OTmUlJRgs9mIjo5m+/btPo+vra2N3/72tyQmJnq/bGPp0qXk5uby/vvvU1tbS1hYGNHR0Y98jg6Wx//v//6PmTNnkpmZOegcJCUlsXDhQhYtWkRYWBilpaWEhYUBjNh7w3Di++CDD7h8+TLvvPMO77zzDgC7d+8mMjJy0OPty/g+//xz3n77bcLDw3niiSfYuHEjMTExAJSVlbFmzRpu3rxJRkYGGRkZjxXfcGO8E+ekSZO8txRD/088jNQcDsZkjPRTgiIiIiIiIhIwgvb2ThEREREREVHRJyIiIiIiEtRU9ImIiIiIiAQxFX0iIiIiIiJBTEWfiIiIiIhIEFPRJyIiIiIiEsRU9ImIiIiIiASx/wedQ4RPHEdvrgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 12 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "lA5X5VM0CvOI",
        "outputId": "af7b1418-94df-46eb-fac0-b9301e1b8be0"
      },
      "source": [
        "cd2=pd.read_csv('/content/drive/MyDrive/LSTM_SOC_Implementation/r1.csv')\n",
        "cd2=cd2.drop_duplicates(subset=['Data_Point'])\n",
        "cd2.describe()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data_Point</th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>SoC</th>\n",
              "      <th>Charge_Energy(Wh)</th>\n",
              "      <th>Discharge_Energy(Wh)</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",
              "      <td>142119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>71060.000000</td>\n",
              "      <td>78706.037752</td>\n",
              "      <td>9.029081</td>\n",
              "      <td>-0.007758</td>\n",
              "      <td>3.684211</td>\n",
              "      <td>0.412337</td>\n",
              "      <td>1.481354</td>\n",
              "      <td>0.657895</td>\n",
              "      <td>1.589229</td>\n",
              "      <td>5.207008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>41026.365791</td>\n",
              "      <td>41479.767284</td>\n",
              "      <td>4.802597</td>\n",
              "      <td>0.303019</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>0.539741</td>\n",
              "      <td>0.625664</td>\n",
              "      <td>0.115206</td>\n",
              "      <td>2.090072</td>\n",
              "      <td>2.114306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</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>-0.000054</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35530.500000</td>\n",
              "      <td>42831.169000</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>0.598400</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>3.647907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>71060.000000</td>\n",
              "      <td>78729.254300</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>0.645397</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>6.317823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>106589.500000</td>\n",
              "      <td>114617.571650</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>0.744164</td>\n",
              "      <td>3.102921</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>142119.000000</td>\n",
              "      <td>150507.800300</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.972129</td>\n",
              "      <td>6.649329</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Data_Point   Test_Time(s)  ...  Charge_Energy(Wh)  Discharge_Energy(Wh)\n",
              "count  142119.000000  142119.000000  ...      142119.000000         142119.000000\n",
              "mean    71060.000000   78706.037752  ...           1.589229              5.207008\n",
              "std     41026.365791   41479.767284  ...           2.090072              2.114306\n",
              "min         1.000000      10.000167  ...           0.000000              0.000000\n",
              "25%     35530.500000   42831.169000  ...           0.067824              3.647907\n",
              "50%     71060.000000   78729.254300  ...           0.067824              6.317823\n",
              "75%    106589.500000  114617.571650  ...           3.102921              6.892068\n",
              "max    142119.000000  150507.800300  ...           6.649329              6.892068\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "id": "B9hs5ar2DOBt",
        "outputId": "f5674707-0872-421c-b216-20b6b8ebcf9d"
      },
      "source": [
        "  fig = plt.figure(figsize = (12,12))\n",
        "  ax = fig.gca()\n",
        "  cd2.hist(ax = ax)"
      ],
      "execution_count": 34,
      "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 0x7ffb8368c6d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb80f3ead0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb8354a290>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb83bd9c10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb837bc710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb7ef4bd10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb8132f190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb81737910>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb81737950>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ffb81f373d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb82610ad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ffb82e8bf10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 12 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "qC51kWbhEywO",
        "outputId": "a795b488-7ecf-4acf-c5c6-df71adf1b841"
      },
      "source": [
        "correlations = cd2.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(correlations, cmap='RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVAxdxD_FSA2"
      },
      "source": [
        "y = cd2.SoC\n",
        "X = cd2.drop(['Data_Point','SoC'], axis=1)"
      ],
      "execution_count": 304,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "9upe3CIKFjui",
        "outputId": "e9942368-ec99-4523-eafd-78de4ec797d5"
      },
      "source": [
        "X"
      ],
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>Charge_Energy(Wh)</th>\n",
              "      <th>Discharge_Energy(Wh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.000167</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</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>1</th>\n",
              "      <td>20.015713</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</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>2</th>\n",
              "      <td>30.031239</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</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>3</th>\n",
              "      <td>40.046793</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166209</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>4</th>\n",
              "      <td>50.062341</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.166047</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>...</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>150503.768600</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249022</td>\n",
              "      <td>1.691192</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>6.644591</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142115</th>\n",
              "      <td>150504.768700</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999364</td>\n",
              "      <td>4.249184</td>\n",
              "      <td>1.691470</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>6.645771</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142116</th>\n",
              "      <td>150505.784300</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249508</td>\n",
              "      <td>1.691752</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>6.646969</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142117</th>\n",
              "      <td>150506.784400</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249671</td>\n",
              "      <td>1.692029</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>6.648149</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142118</th>\n",
              "      <td>150507.800300</td>\n",
              "      <td>18</td>\n",
              "      <td>0.999544</td>\n",
              "      <td>4.249832</td>\n",
              "      <td>1.692307</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>6.649329</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142119 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Test_Time(s)  Cycle_Index  ...  Charge_Energy(Wh)  Discharge_Energy(Wh)\n",
              "0           10.000167            1  ...           0.000000              0.000000\n",
              "1           20.015713            1  ...           0.000000              0.000000\n",
              "2           30.031239            1  ...           0.000000              0.000000\n",
              "3           40.046793            1  ...           0.000000              0.000000\n",
              "4           50.062341            1  ...           0.000000              0.000000\n",
              "...               ...          ...  ...                ...                   ...\n",
              "142114  150503.768600           18  ...           6.644591              6.892068\n",
              "142115  150504.768700           18  ...           6.645771              6.892068\n",
              "142116  150505.784300           18  ...           6.646969              6.892068\n",
              "142117  150506.784400           18  ...           6.648149              6.892068\n",
              "142118  150507.800300           18  ...           6.649329              6.892068\n",
              "\n",
              "[142119 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qt74xvs4IBGj"
      },
      "source": [
        "#split the dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size = 0.3, random_state = 11) "
      ],
      "execution_count": 330,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "1cFeJ6ZZLLL0",
        "outputId": "1273984e-7244-4f23-ad93-17a664395fb9"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test_Time(s)</th>\n",
              "      <th>Cycle_Index</th>\n",
              "      <th>Current(A)</th>\n",
              "      <th>Voltage(V)</th>\n",
              "      <th>Charge_Capacity(Ah)</th>\n",
              "      <th>Discharge_Capacity(Ah)</th>\n",
              "      <th>Charge_Energy(Wh)</th>\n",
              "      <th>Discharge_Energy(Wh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>83260</th>\n",
              "      <td>91053.26463</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.482638</td>\n",
              "      <td>0.216053</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>0.804928</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50137</th>\n",
              "      <td>57588.92842</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.596404</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>1.399749</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>5.000753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57930</th>\n",
              "      <td>65462.65505</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.552648</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>1.599724</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>5.666128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96499</th>\n",
              "      <td>104424.91820</td>\n",
              "      <td>12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.613421</td>\n",
              "      <td>0.615873</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>2.325117</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49181</th>\n",
              "      <td>56622.82568</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.593325</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>1.399749</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>5.000753</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>102327</th>\n",
              "      <td>110313.32980</td>\n",
              "      <td>12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.610504</td>\n",
              "      <td>0.615873</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>2.325117</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97617</th>\n",
              "      <td>105554.58220</td>\n",
              "      <td>12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.612124</td>\n",
              "      <td>0.615873</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>2.325117</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21584</th>\n",
              "      <td>28742.77721</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.839333</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>0.599871</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>2.251306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36543</th>\n",
              "      <td>43855.14643</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.664146</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>0.999809</td>\n",
              "      <td>0.067824</td>\n",
              "      <td>3.647907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141209</th>\n",
              "      <td>149590.18050</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.947428</td>\n",
              "      <td>1.615452</td>\n",
              "      <td>1.987336</td>\n",
              "      <td>6.324969</td>\n",
              "      <td>6.892068</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>99483 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Test_Time(s)  Cycle_Index  ...  Charge_Energy(Wh)  Discharge_Energy(Wh)\n",
              "83260    91053.26463           10  ...           0.804928              6.892068\n",
              "50137    57588.92842            7  ...           0.067824              5.000753\n",
              "57930    65462.65505            8  ...           0.067824              5.666128\n",
              "96499   104424.91820           12  ...           2.325117              6.892068\n",
              "49181    56622.82568            7  ...           0.067824              5.000753\n",
              "...              ...          ...  ...                ...                   ...\n",
              "102327  110313.32980           12  ...           2.325117              6.892068\n",
              "97617   105554.58220           12  ...           2.325117              6.892068\n",
              "21584    28742.77721            3  ...           0.067824              2.251306\n",
              "36543    43855.14643            5  ...           0.067824              3.647907\n",
              "141209  149590.18050           17  ...           6.324969              6.892068\n",
              "\n",
              "[99483 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 331
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnZATi--aPBy"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))"
      ],
      "execution_count": 332,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Td6WLxAAJcGp"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "#Creating Instance of StandarScaler\n",
        "#scaler = MinMaxScaler()\n",
        "#scaler = StandardScaler()\n",
        "#Perform scaling in X_train with fit_transform.\n",
        "#Here we are applying fit_transform because,\n",
        "        # fit will calculate mean and standard deviation of X_train\n",
        "        # transform will actually perform scaling with calculated mean and std.\n",
        "        # fit_transform method does this both thing in one line of code.\n",
        "X_train1 = scaler.fit_transform(X_train)\n",
        "\n",
        "# Here we will only use transform because we have already calculated mean and std.\n",
        "# Another reason is we don't want to know the mean and std of our test dataset As it\n",
        "# Lead to information leakage.\n",
        "X_test1 = scaler.transform(X_test)"
      ],
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87EBtoI4KO0r",
        "outputId": "52a273cc-99a3-4047-cabf-b9dea1d77568"
      },
      "source": [
        "X_test1.shape"
      ],
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42636, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDPopP5LU33b"
      },
      "source": [
        "a1=X_train1.shape[0]\n",
        "a2=X_train1.shape[1]\n",
        "a3=X_test1.shape[0]\n",
        "a4=X_test1.shape[1]"
      ],
      "execution_count": 335,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDWxggUWSRYt",
        "outputId": "edcc80e8-66b9-44c6-d15c-81d89e02c4ac"
      },
      "source": [
        "X_train1"
      ],
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.60495156, 0.52941176, 0.50008222, ..., 1.        , 0.12107558,\n",
              "        1.        ],\n",
              "       [0.38259242, 0.35294118, 0.50008222, ..., 0.70433413, 0.01020189,\n",
              "        0.725581  ],\n",
              "       [0.43491065, 0.41176471, 0.50008222, ..., 0.80495919, 0.01020189,\n",
              "        0.82212311],\n",
              "       ...,\n",
              "       [0.19091954, 0.11764706, 0.50008222, ..., 0.301847  , 0.01020189,\n",
              "        0.32665175],\n",
              "       [0.2913361 , 0.23529412, 0.50008222, ..., 0.50309009, 0.01020189,\n",
              "        0.52929068],\n",
              "       [0.99390948, 0.94117647, 0.50008222, ..., 1.        , 0.95138796,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 336
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-l_n8YAMSZo"
      },
      "source": [
        "# reshape input to be [samples, time steps, features]\n",
        "X_train2 = np.reshape(X_train1, (a1, 1, a2))\n",
        "X_test2 = np.reshape(X_test1, (a3,1, a4))"
      ],
      "execution_count": 337,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7pbRcmrqABQ",
        "outputId": "2de82ecd-d4cf-4bae-9b50-9c249deb7723"
      },
      "source": [
        "X_train2.shape"
      ],
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99483, 1, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8kms4CWNi6R"
      },
      "source": [
        "import math\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import *\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "execution_count": 339,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JckeJKvhNRrr",
        "outputId": "d5dcb9d9-74ac-4852-cd30-9265ca641a3f"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(50, input_shape=(X_train2.shape[1], X_train2.shape[2])))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "history = model.fit(X_train2, y_train, epochs=20, batch_size=35, validation_data=(X_test2, y_test), \n",
        "                    callbacks=[EarlyStopping(monitor='val_loss', patience=10)], verbose=1, shuffle=False)\n",
        "\n",
        "# Training Phase\n",
        "model.summary()"
      ],
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "2843/2843 [==============================] - 14s 4ms/step - loss: 0.0052 - val_loss: 1.6418e-04\n",
            "Epoch 2/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 6.2502e-04 - val_loss: 9.3767e-05\n",
            "Epoch 3/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 2.3598e-04 - val_loss: 2.0687e-05\n",
            "Epoch 4/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 6.7892e-05 - val_loss: 2.5704e-05\n",
            "Epoch 5/20\n",
            "2843/2843 [==============================] - 10s 3ms/step - loss: 4.1514e-05 - val_loss: 9.3065e-07\n",
            "Epoch 6/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.9876e-05 - val_loss: 1.1602e-06\n",
            "Epoch 7/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.8080e-05 - val_loss: 4.7470e-06\n",
            "Epoch 8/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.8246e-05 - val_loss: 2.9838e-06\n",
            "Epoch 9/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.7736e-05 - val_loss: 9.3321e-06\n",
            "Epoch 10/20\n",
            "2843/2843 [==============================] - 10s 3ms/step - loss: 3.6819e-05 - val_loss: 1.1370e-05\n",
            "Epoch 11/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.7475e-05 - val_loss: 9.2563e-07\n",
            "Epoch 12/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.6892e-05 - val_loss: 1.6328e-06\n",
            "Epoch 13/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.6434e-05 - val_loss: 1.3051e-05\n",
            "Epoch 14/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.7373e-05 - val_loss: 9.4942e-06\n",
            "Epoch 15/20\n",
            "2843/2843 [==============================] - 11s 4ms/step - loss: 3.6042e-05 - val_loss: 3.7337e-06\n",
            "Epoch 16/20\n",
            "2843/2843 [==============================] - 10s 3ms/step - loss: 3.6889e-05 - val_loss: 3.7861e-06\n",
            "Epoch 17/20\n",
            "2843/2843 [==============================] - 9s 3ms/step - loss: 3.6412e-05 - val_loss: 2.9800e-06\n",
            "Epoch 18/20\n",
            "2843/2843 [==============================] - 9s 3ms/step - loss: 3.6891e-05 - val_loss: 3.1126e-06\n",
            "Epoch 19/20\n",
            "2843/2843 [==============================] - 10s 3ms/step - loss: 3.5423e-05 - val_loss: 9.0410e-06\n",
            "Epoch 20/20\n",
            "2843/2843 [==============================] - 10s 3ms/step - loss: 3.6622e-05 - val_loss: 1.2458e-06\n",
            "Model: \"sequential_31\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_29 (LSTM)              (None, 50)                11800     \n",
            "                                                                 \n",
            " dropout_27 (Dropout)        (None, 50)                0         \n",
            "                                                                 \n",
            " dense_27 (Dense)            (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11,851\n",
            "Trainable params: 11,851\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJNDVfzg5r3D"
      },
      "source": [
        "# make predictions\n",
        "train_predict = model.predict(X_train2)\n",
        "test_predict = model.predict(X_test2)"
      ],
      "execution_count": 341,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DG5VrwhL56Hu",
        "outputId": "cceb7afe-37ec-4f49-9fe1-a93d4401af40"
      },
      "source": [
        "train_predict.shape"
      ],
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99483, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 342
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ILQK7zD9Bvk",
        "outputId": "8099d6a6-4fad-4f7c-b15b-a9862155aa0f"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99483,)"
            ]
          },
          "metadata": {},
          "execution_count": 343
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fen2ho2W8KMk",
        "outputId": "f6960365-2a0a-44b0-934a-88f43d6007a0"
      },
      "source": [
        "z = np.zeros((train_predict.shape[0],7), dtype=np.int64)\n",
        "train_predict2=np.append(train_predict, z, axis=1)\n",
        "train_predict2.shape\n",
        "\n",
        "z = np.zeros((test_predict.shape[0],7), dtype=np.int64)\n",
        "test_predict2=np.append(test_predict, z, axis=1)\n",
        "test_predict2.shape"
      ],
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42636, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 344
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBQ3E6wy-Qp-"
      },
      "source": [
        "# invert predictions\n",
        "\n",
        "train_predict3 = scaler.inverse_transform(train_predict2)\n",
        "#y_train = scaler.inverse_transform([y_train])\n",
        "test_predict3 = scaler.inverse_transform(test_predict2)\n",
        "#y_test = scaler.inverse_transform([y_test])"
      ],
      "execution_count": 346,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k169Hiiu-pRt",
        "outputId": "88bbaf33-d263-4fac-f35f-13f3c3d18ad6"
      },
      "source": [
        "train_predict3[:,0]"
      ],
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 82415.43274671,  91840.0323569 ,  88208.78017347, ...,\n",
              "       112183.10171414,  97552.58422523, 121015.51751338])"
            ]
          },
          "metadata": {},
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LT_pLNS7-wUt",
        "outputId": "cb3e83c7-db85-477f-e7a1-d221aac95ad5"
      },
      "source": [
        "y_train3=y_train.to_numpy()\n",
        "y_train3\n",
        "y_test3=y_test.to_numpy()\n",
        "y_test3"
      ],
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.63207216, 0.42418453, 0.70184814, ..., 0.69590595, 0.74965609,\n",
              "       0.70166813])"
            ]
          },
          "metadata": {},
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dmSiQAOZ5oR",
        "outputId": "26c078d1-a552-4ba6-ff9c-d632238ed561"
      },
      "source": [
        "print('Train Mean Absolute Error:', mean_absolute_error(y_train3, train_predict3[:,0]))\n",
        "print('Train Root Mean Squared Error:',np.sqrt(mean_squared_error(y_train3, train_predict3[:,0])))\n",
        "print('Test Mean Absolute Error:', mean_absolute_error(y_test3, test_predict3[:,0]))\n",
        "print('Test Root Mean Squared Error:',np.sqrt(mean_squared_error(y_test3, test_predict3[:,0])))"
      ],
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Mean Absolute Error: 99166.72884325754\n",
            "Train Root Mean Squared Error: 100669.46234696072\n",
            "Test Mean Absolute Error: 99180.0586294595\n",
            "Test Root Mean Squared Error: 100659.26117153373\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpbzmGfcDYsD",
        "outputId": "27e5ec7f-6046-44bc-e0ba-d9ac49c2db5a"
      },
      "source": [
        "y_test3[1:5]"
      ],
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.42418453, 0.70184814, 0.61028401, 0.74407392])"
            ]
          },
          "metadata": {},
          "execution_count": 357
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJU_dNSAJPV3",
        "outputId": "7918fa41-dc0d-4bed-e478-0204e2f86151"
      },
      "source": [
        "test_predict3.shape\n"
      ],
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42636, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 363
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "F4FgvyxuZ8bK",
        "outputId": "e688d9da-a364-4663-9824-df62b3439c01"
      },
      "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": 352,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "0Z42pzCpZ_SO",
        "outputId": "b1f31c41-2cbc-4cad-96ea-975f877b8392"
      },
      "source": [
        "aa=[x for x in range(100)]\n",
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(aa, y_test3[:100], marker='.', label=\"actual\")\n",
        "plt.plot(aa, test_predict[:,0][:100], '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.xlabel('Time step', size=15)\n",
        "plt.legend(fontsize=15)\n",
        "plt.show();"
      ],
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}