MilkyWay.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MilkyWay.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "XOukgp88eK_l",
        "xH-4ovD772js",
        "p5FHKI9iTswZ",
        "MVmL3FNHUWM2",
        "J51IgS8KLi7v",
        "B157wdZPQfQu",
        "FZhVc_ZTFOT8",
        "a5OReM0_lSgf",
        "QSr0e09B93k5",
        "SGvHETHRw1--",
        "H0C2RDAcZQiA",
        "fhEy6UiL_mtI",
        "1GigYWolvaee",
        "tsmxDY7NwvS_",
        "9sCGy8cPpifG"
      ],
      "authorship_tag": "ABX9TyNaJfaxy9JY+21sML3tTCuv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Amirosein/Variable-Stars-Classification/blob/main/MilkyWay.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjjCgvjYZTXa"
      },
      "source": [
        "# Milky Way Formation History\n",
        "* Classification part"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOukgp88eK_l"
      },
      "source": [
        "# Pre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xYXTtukc4Oe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1500aac-46af-48d6-b7fe-48fa8886fec1"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import io, requests, zipfile\n",
        "from zipfile import ZipFile\n",
        "import seaborn as sns\n",
        "\n",
        "!pip install tsfresh\n",
        "from tsfresh import extract_features\n",
        "\n",
        "!pip install pyunpack\n",
        "!pip install patool\n",
        "from pyunpack import Archive\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "# plt.rcParams.update({'font.size': 20, 'figsize':(8,6)})\n",
        "SMALL_SIZE = 12\n",
        "MEDIUM_SIZE = 15\n",
        "BIGGER_SIZE = 18\n",
        "\n",
        "plt.rc('font', size=BIGGER_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=BIGGER_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=MEDIUM_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
        "\n",
        "plt.rc('axes', linewidth = 5 )\n",
        "plt.rc('lines', linewidth = 5 )\n",
        "\n",
        "plt.rc('lines', markersize = 5 )\n",
        "\n",
        "plt.rc('figure', figsize=(8,4) )         # Image size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: entrypoint2, easyprocess, pyunpack\n",
            "Successfully installed easyprocess-0.3 entrypoint2-0.2.4 pyunpack-0.2.2\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pg9kRo7v7bY_"
      },
      "source": [
        "> * **Save file function**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0dtYscheD6j"
      },
      "source": [
        "def savefile(df,name):\n",
        "  t = df\n",
        "  t.to_csv(name, header=True, index=True)\n",
        "  return 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xH-4ovD772js"
      },
      "source": [
        "# Mount drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41fMnJI4lMll",
        "outputId": "e5377706-163c-435f-adc5-3eb8198423bc"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5FHKI9iTswZ"
      },
      "source": [
        "# Convert Dat. files to a CSV Function\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eppPUCj6QtAo"
      },
      "source": [
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "import csv\n",
        "from pathlib import Path\n",
        "\n",
        "f = open(\"/content/draft.csv\", \"x\")\n",
        "\n",
        "# function\n",
        "def data2csv(path, var, os):\n",
        "  files = Path(path).rglob(\"*.dat\")\n",
        "\n",
        "  i = 0\n",
        "\n",
        "  for file in files:\n",
        "    datContent = [i.strip().split() for i in open(file).readlines()]\n",
        "    id = os +'-'+ Path(file).stem\n",
        "    \n",
        "    # write it as a new CSV file\n",
        "    with open(\"/content/draft.csv\", \"w\") as f:\n",
        "      writer = csv.writer(f)\n",
        "      writer.writerows(datContent)\n",
        "\n",
        "    df = pd.read_csv(\"/content/draft.csv\", names=['time','value','e']) \n",
        "    df = df[['time','value']]\n",
        "    df['id'] = id\n",
        "    df['kind'] = var\n",
        "\n",
        "    if i==0:\n",
        "      data = df\n",
        "    else:\n",
        "      data = pd.concat([data,df], axis=0)\n",
        "\n",
        "    i = i+1\n",
        "    #print(i)\n",
        "\n",
        "  return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVmL3FNHUWM2"
      },
      "source": [
        "# Convert and Save data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lx3fKykQk6nB"
      },
      "source": [
        "Archive('/content/drive/MyDrive/LPV.rar').extractall('/content/drive/MyDrive')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wb6S2lj4Vt8G"
      },
      "source": [
        "> * **LMC**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wCY2HB2rJpB"
      },
      "source": [
        "LMC3_I1 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p1', 'I','III')\n",
        "LMC3_I2 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p2', 'I','III')\n",
        "LMC3_I3 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p3', 'I','III')\n",
        "LMC3_I4 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p4', 'I','III')\n",
        "LMC3_I5 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p5', 'I','III')\n",
        "LMC3_I6 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p6', 'I','III')\n",
        "LMC3_I7 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p7', 'I','III')\n",
        "LMC3_I8 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p8', 'I','III')\n",
        "LMC3_I9 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p9', 'I','III')\n",
        "LMC3_I10 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p10', 'I','III')\n",
        "LMC3_I11 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p11', 'I','III')\n",
        "LMC3_I12 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p12', 'I','III')\n",
        "LMC3_I13 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p13', 'I','III')\n",
        "LMC3_I14 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p14', 'I','III')\n",
        "LMC3_I15 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p15', 'I','III')\n",
        "LMC3_I16 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p16', 'I','III')\n",
        "LMC3_I17 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p17', 'I','III')\n",
        "LMC3_I18 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/I/p18', 'I','III')\n",
        "\n",
        "LMC3_I = pd.concat([LMC3_I1,LMC3_I2,LMC3_I3,LMC3_I4,LMC3_I5,LMC3_I6,\n",
        "                    LMC3_I7,LMC3_I8,LMC3_I9,LMC3_I10,LMC3_I11,LMC3_I12,\n",
        "                    LMC3_I13,LMC3_I14,LMC3_I15,LMC3_I16,LMC3_I17,LMC3_I18])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nx6csnmMak_E"
      },
      "source": [
        "LMC3_V1 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p1', 'V','III')\n",
        "LMC3_V2 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p2', 'V','III')\n",
        "LMC3_V3 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p3', 'V','III')\n",
        "LMC3_V4 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p4', 'V','III')\n",
        "LMC3_V5 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p5', 'V','III')\n",
        "LMC3_V6 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p6', 'V','III')\n",
        "LMC3_V7 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p7', 'V','III')\n",
        "LMC3_V8 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p8', 'V','III')\n",
        "LMC3_V9 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p9', 'V','III')\n",
        "LMC3_V10 = data2csv('/content/drive/MyDrive/LPV/IIILMC/phot/V/p10', 'V','III')\n",
        "\n",
        "LMC3_V = pd.concat([LMC3_V1,LMC3_V2,LMC3_V3,LMC3_V4,LMC3_V5,LMC3_V6,\n",
        "                    LMC3_V7,LMC3_V8,LMC3_V9,LMC3_V10])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DmiGHm_mHpSp",
        "outputId": "0593e880-c7fb-4d0c-e68b-c596fec41bfe"
      },
      "source": [
        "LPV_LMC = pd.concat([LMC3_I,LMC3_V])\n",
        "  \n",
        "LPV_LMC['class'] = 'LPV'  \n",
        "savefile(LPV_LMC,'LPV_LMC')\n",
        "LPV_LMC.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>value</th>\n",
              "      <th>id</th>\n",
              "      <th>kind</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2171.86025</td>\n",
              "      <td>14.956</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2183.81956</td>\n",
              "      <td>14.961</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2184.81907</td>\n",
              "      <td>14.961</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2188.86966</td>\n",
              "      <td>14.970</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2191.78070</td>\n",
              "      <td>14.955</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         time   value                      id kind class\n",
              "0  2171.86025  14.956  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "1  2183.81956  14.961  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "2  2184.81907  14.961  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "3  2188.86966  14.970  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "4  2191.78070  14.955  III-OGLE-LMC-LPV-00001    I   LPV"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udPcBSbIV39t"
      },
      "source": [
        "> * **SMC**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-gDT27VRaDd"
      },
      "source": [
        "SMC3_I1 = data2csv('/content/drive/MyDrive/LPV/III/phot/I/p1', 'I','III')\n",
        "SMC3_I2 = data2csv('/content/drive/MyDrive/LPV/III/phot/I/p2', 'I','III')\n",
        "SMC3_I3 = data2csv('/content/drive/MyDrive/LPV/III/phot/I/p3', 'I','III')\n",
        "SMC3_I4 = data2csv('/content/drive/MyDrive/LPV/III/phot/I/p4', 'I','III')\n",
        "SMC3_I = pd.concat([SMC3_I1,SMC3_I2,SMC3_I3,SMC3_I4])\n",
        "\n",
        "savefile(SMC3_I,'SMC3_I')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbRaVFRfTGSJ"
      },
      "source": [
        "SMC3_V1 = data2csv('/content/drive/MyDrive/LPV/III/phot/V/p1', 'V','III')\n",
        "SMC3_V2 = data2csv('/content/drive/MyDrive/LPV/III/phot/V/p2', 'V','III')\n",
        "SMC3_V3 = data2csv('/content/drive/MyDrive/LPV/III/phot/V/p3', 'V','III')\n",
        "SMC3_V4 = data2csv('/content/drive/MyDrive/LPV/III/phot/V/p4', 'V','III')\n",
        "SMC3_V = pd.concat([SMC3_V1,SMC3_V2,SMC3_V3,SMC3_V4])\n",
        "\n",
        "savefile(SMC3_V,'SMC3_V')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qx-HYboOJOwg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1c3ca412-8f91-4759-ba7b-cc71836a9f89"
      },
      "source": [
        "LPV_SMC = pd.concat([SMC3_I,SMC3_V])\n",
        "  \n",
        "LPV_SMC['class'] = 'LPV'  \n",
        "savefile(LPV_SMC,'LPV_SMC')\n",
        "LPV_SMC.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>value</th>\n",
              "      <th>id</th>\n",
              "      <th>kind</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2088.91535</td>\n",
              "      <td>14.977</td>\n",
              "      <td>III-OGLE-SMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2090.87535</td>\n",
              "      <td>14.978</td>\n",
              "      <td>III-OGLE-SMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2103.92958</td>\n",
              "      <td>14.980</td>\n",
              "      <td>III-OGLE-SMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2104.94812</td>\n",
              "      <td>14.986</td>\n",
              "      <td>III-OGLE-SMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2106.84596</td>\n",
              "      <td>14.998</td>\n",
              "      <td>III-OGLE-SMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         time   value                      id kind class\n",
              "0  2088.91535  14.977  III-OGLE-SMC-LPV-00001    I   LPV\n",
              "1  2090.87535  14.978  III-OGLE-SMC-LPV-00001    I   LPV\n",
              "2  2103.92958  14.980  III-OGLE-SMC-LPV-00001    I   LPV\n",
              "3  2104.94812  14.986  III-OGLE-SMC-LPV-00001    I   LPV\n",
              "4  2106.84596  14.998  III-OGLE-SMC-LPV-00001    I   LPV"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_B2zT1KVNUX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "acd74070-9a76-4794-f111-e577551a92b8"
      },
      "source": [
        "#SMC3 = pd.concat([SMC3_I,SMC3_V])\n",
        "#LMC3 = pd.concat([LMC3_I,LMC3_V])\n",
        "#LMC4 = pd.concat([LMC4_I,LMC4_V])\n",
        "#SMC4 = pd.concat([SMC4_I,SMC4_V])\n",
        "\n",
        "#LPV['class'] = 'LPV' \n",
        "\n",
        "LPV = pd.concat([LPV_LMC,LPV_SMC])\n",
        "\n",
        "savefile(LPV,'LPV')\n",
        "LPV.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>value</th>\n",
              "      <th>id</th>\n",
              "      <th>kind</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2171.86025</td>\n",
              "      <td>14.956</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2183.81956</td>\n",
              "      <td>14.961</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2184.81907</td>\n",
              "      <td>14.961</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2188.86966</td>\n",
              "      <td>14.970</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2191.78070</td>\n",
              "      <td>14.955</td>\n",
              "      <td>III-OGLE-LMC-LPV-00001</td>\n",
              "      <td>I</td>\n",
              "      <td>LPV</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         time   value                      id kind class\n",
              "0  2171.86025  14.956  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "1  2183.81956  14.961  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "2  2184.81907  14.961  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "3  2188.86966  14.970  III-OGLE-LMC-LPV-00001    I   LPV\n",
              "4  2191.78070  14.955  III-OGLE-LMC-LPV-00001    I   LPV"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvkpVdMBaWaf"
      },
      "source": [
        "zipObj = ZipFile('LPV.zip', 'w', zipfile.ZIP_BZIP2)\n",
        "\n",
        "zipObj.write('/content/LPV')\n",
        "zipObj.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J51IgS8KLi7v"
      },
      "source": [
        "# Feature Selection :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehFzcD8uowT5"
      },
      "source": [
        "Archive('/content/drive/MyDrive/Feature Selection Sample dataset.rar').extractall('/content')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVJGOurURdUc"
      },
      "source": [
        "from tsfresh.feature_extraction import EfficientFCParameters, MinimalFCParameters, ComprehensiveFCParameters\n",
        "\n",
        "def ftrs(X,cls):\n",
        "  m = extract_features(X, column_id=\"id\", column_sort=\"time\", n_jobs= 10,\n",
        "                       column_kind=\"kind\", column_value=\"value\",\n",
        "                       default_fc_parameters = ComprehensiveFCParameters())\n",
        "  m['class'] = cls\n",
        "  return m"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iKy4hEAoclb"
      },
      "source": [
        "def dat2feat(urlI,urlV,cls):\n",
        "  mI = data2csv(urlI, 'I','S')\n",
        "  mV = data2csv(urlV, 'V','S')\n",
        "  m = pd.concat([mI,mV])\n",
        "  \n",
        "  f = ftrs(m,cls)\n",
        "\n",
        "  return f"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6BRG6isV2OR1"
      },
      "source": [
        "> * Creating Sample Dataset for Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iid3_BMarR6Y",
        "outputId": "9cd51bba-dc78-4916-9969-90505bcbb603"
      },
      "source": [
        "ur11 = '/content/Anomalous-Cepheids/I'\n",
        "ur12 = '/content/Anomalous-Cepheids/V'\n",
        "ur21 = '/content/classical-Cepheids/I'\n",
        "ur22 = '/content/classical-Cepheids/V'\n",
        "ur31 = '/content/Double-Period-Variables/I'\n",
        "ur32 = '/content/Double-Period-Variables/V'\n",
        "ur41 = '/content/Eclipsing-and-ellipsoidal-binary-sys/I'\n",
        "ur42 = '/content/Eclipsing-and-ellipsoidal-binary-sys/V'\n",
        "ur51 = '/content/Long-period-variables/I'\n",
        "ur52 = '/content/Long-period-variables/V'\n",
        "ur61 = '/content/R-CrB/I'\n",
        "ur62 = '/content/R-CrB/V'\n",
        "ur71 = '/content/RR-Lyrae/I'\n",
        "ur72 = '/content/RR-Lyrae/V'\n",
        "ur81 = '/content/Type-II-Cepheids/I'\n",
        "ur82 = '/content/Type-II-Cepheids/V'\n",
        "ur91 = '/content/δ-Scuti/I'\n",
        "ur92 = '/content/δ-Scuti/V'\n",
        "\n",
        "\n",
        "ACEP_sf = dat2feat(ur11,ur12,'ACEP')\n",
        "CEP_sf = dat2feat(ur21,ur22,'CEP')\n",
        "DPV_sf = dat2feat(ur31,ur32,'DPV')\n",
        "ECL_sf = dat2feat(ur41,ur42,'ECL')\n",
        "LPV_sf = dat2feat(ur51,ur52,'LPV')\n",
        "RCB_sf = dat2feat(ur61,ur62,'RCB')\n",
        "RRLYR_sf = dat2feat(ur71,ur72,'RRLYR')\n",
        "T2CEP_sf = dat2feat(ur81,ur82,'T2CEP')\n",
        "DSCT_sf = dat2feat(ur91,ur92,'DSCT')\n",
        "\n",
        "data = pd.concat([ACEP_sf, CEP_sf, DPV_sf, ECL_sf, LPV_sf,\n",
        "                  RCB_sf, RRLYR_sf, T2CEP_sf, DSCT_sf])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature Extraction: 100%|██████████| 50/50 [01:09<00:00,  1.39s/it]\n",
            "Feature Extraction: 100%|██████████| 49/49 [01:16<00:00,  1.55s/it]\n",
            "Feature Extraction: 100%|██████████| 41/41 [00:57<00:00,  1.40s/it]\n",
            "Feature Extraction: 100%|██████████| 48/48 [01:04<00:00,  1.35s/it]\n",
            "Feature Extraction: 100%|██████████| 50/50 [01:29<00:00,  1.78s/it]\n",
            "Feature Extraction: 100%|██████████| 45/45 [00:09<00:00,  4.61it/s]\n",
            "Feature Extraction: 100%|██████████| 49/49 [01:11<00:00,  1.46s/it]\n",
            "Feature Extraction: 100%|██████████| 44/44 [01:19<00:00,  1.80s/it]\n",
            "Feature Extraction: 100%|██████████| 48/48 [00:41<00:00,  1.16it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjhoS7-yy6wk"
      },
      "source": [
        "X = data.iloc[:,0:1574]\n",
        "Y = data.loc[:,'class']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEP_6ajW2uXc"
      },
      "source": [
        "# Output Encoding\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "a = np.array(Y)\n",
        "OH_Enc = OneHotEncoder( )\n",
        "OH_Encoded = OH_Enc.fit_transform(a.reshape(-1,1)).toarray()\n",
        "OH_Y = pd.DataFrame(OH_Encoded)\n",
        "OH_Y.columns = ['OH1','OH2','OH3','OH4','OH5','OH6','OH7','OH8','OH9']\n",
        "\n",
        "from sklearn import preprocessing\n",
        "\n",
        "LE = preprocessing.LabelEncoder()\n",
        "y = LE.fit_transform(Y)\n",
        "\n",
        "O_Y = pd.DataFrame(y)\n",
        "O_Y.columns = ['OY']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CN8Kk6c3bPM"
      },
      "source": [
        "# Input Imputation\n",
        "\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "\n",
        "nc = X.columns[X.isnull().all()].tolist()\n",
        "X.drop(nc, inplace=True, axis=1)\n",
        "\n",
        "X.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "imp = IterativeImputer(n_nearest_features=30)\n",
        "imp_data = imp.fit_transform(X)\n",
        "X = pd.DataFrame(imp_data, columns= X.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-fs6DKEwHOR"
      },
      "source": [
        "> * **Relevant Features :**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4GiscpiKJMn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6eb1072-b0cc-4862-8744-2124325e8fc8"
      },
      "source": [
        "import tsfresh\n",
        "\n",
        "y = O_Y\n",
        "y = y.squeeze()\n",
        "\n",
        "# which are now filtered to only contain relevant features\n",
        "X_tsfresh_filtered = tsfresh.feature_selection.select_features(X, y, multiclass=True, n_significant=9,\n",
        "                                                               n_jobs=4, ml_task='classification')\n",
        "\n",
        "# construct the corresponding settings object\n",
        "kind_to_fc_parameters = tsfresh.feature_extraction.settings.from_columns(X_tsfresh_filtered)\n",
        "\n",
        "X_tsfresh_filtered.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1429, 53)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMdeoB-5HdjE"
      },
      "source": [
        "import pickle\n",
        "\n",
        "a_file = open(\"ktfcp.pkl\", \"wb\")\n",
        "pickle.dump(kind_to_fc_parameters, a_file)\n",
        "a_file.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "BELyKIRzzipj",
        "outputId": "62534372-3892-47fc-f9a3-41f1965210a2"
      },
      "source": [
        "a = np.arange(0,10)\n",
        "b = np.array([1489, 956, 861, 799, 734, 691, 599, 403, 199, 53])\n",
        "\n",
        "plt.figure(figsize=(11,6))\n",
        "plt.grid()\n",
        "plt.plot(a,b)\n",
        "\n",
        "plt.ylabel('Relevant_Features')\n",
        "plt.xlabel('Significant_Class')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Significant_Class')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B157wdZPQfQu"
      },
      "source": [
        "# Feature Extraction :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgD0dr6475kz"
      },
      "source": [
        "import pickle\n",
        "\n",
        "a_file = open(\"/content/ktfcp.pkl\", \"rb\")\n",
        "kind_to_fc_parameters = pickle.load(a_file)\n",
        "a_file.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUwPKkjJt7z0"
      },
      "source": [
        "def feat_ex(X,cls):\n",
        "  m = extract_features(X, column_id=\"id\", column_sort=\"time\", n_jobs= 14,\n",
        "                       column_kind=\"kind\", column_value=\"value\",\n",
        "                       kind_to_fc_parameters = kind_to_fc_parameters)\n",
        "  m['class'] = cls\n",
        "  return m"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZNsWT6BWv4s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80266e22-c281-4ad1-ddec-3786fc137ae5"
      },
      "source": [
        "Archive('/content/drive/MyDrive/RRLYR.rar').extractall('/content')\n",
        "RRLYR = pd.read_csv('/content/RRLYR', index_col=0, header=0)\n",
        "\n",
        "RRLYR.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42531784, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47UqPnBMNki7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7e913aa-b004-4cc7-d9f8-67f26ce94ae2"
      },
      "source": [
        "#Extracting features\n",
        "\n",
        "RRLYR_f = feat_ex(RRLYR,'RRLYR')  \n",
        "savefile(RRLYR_f,'RRLYR_f')\n",
        "\n",
        "RRLYR_f.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature Extraction: 100%|██████████| 70/70 [22:55<00:00, 19.64s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(74935, 54)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZhVc_ZTFOT8"
      },
      "source": [
        "# ALL Data Concatenation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VrneQbOHO65"
      },
      "source": [
        "Archive('/content/drive/MyDrive/features.rar').extractall('/content')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ie2nwKCxFYDN"
      },
      "source": [
        "c1 = pd.read_csv('/content/ACEP_f', index_col=0, header=0)\n",
        "c2 = pd.read_csv('/content/CEP_f', index_col=0, header=0)\n",
        "c3 = pd.read_csv('/content/DPV_f', index_col=0, header=0)\n",
        "c4 = pd.read_csv('/content/DSCT_f', index_col=0, header=0)\n",
        "c5 = pd.read_csv('/content/ECL_f', index_col=0, header=0)\n",
        "c6 = pd.read_csv('/content/LPV_f', index_col=0, header=0)\n",
        "c7 = pd.read_csv('/content/RCB_f', index_col=0, header=0)\n",
        "c8 = pd.read_csv('/content/RRLYR_f', index_col=0, header=0)\n",
        "c9 = pd.read_csv('/content/T2CEP_f', index_col=0, header=0)\n",
        "\n",
        "\n",
        "data = pd.concat([c1,c2,c3,c4,c5,c6,c7,c8,c9])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2Z09oklJ9gF"
      },
      "source": [
        "savefile(data,'data')\n",
        "\n",
        "zipObj = ZipFile('data.zip', 'w', zipfile.ZIP_BZIP2)\n",
        "zipObj.write('/content/data')\n",
        "zipObj.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbhaWA-bl9S2"
      },
      "source": [
        "> * X,Y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnh822zble17"
      },
      "source": [
        "Archive('/content/drive/MyDrive/data.rar').extractall('/content')\n",
        "data = pd.read_csv('/content/data', index_col=0, header=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sHJBgCMlxUO"
      },
      "source": [
        "X = data.iloc[:,0:53]\n",
        "Y = data.loc[:,'class']\n",
        "Y = Y.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5OReM0_lSgf"
      },
      "source": [
        "----\n",
        "# Preprocessing :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrR7m42aZ-NM"
      },
      "source": [
        "# One_Hot Encoding\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "a = np.array(Y)\n",
        "OH_Enc = OneHotEncoder( )\n",
        "OH_Encoded = OH_Enc.fit_transform(a.reshape(-1,1)).toarray()\n",
        "OH_Y = pd.DataFrame(OH_Encoded)\n",
        "OH_Y.columns = ['OH1','OH2','OH3','OH4','OH5','OH6','OH7','OH8','OH9']\n",
        "\n",
        "# Ordinal Encoding\n",
        "from sklearn import preprocessing\n",
        "\n",
        "LE = preprocessing.LabelEncoder()\n",
        "y = LE.fit_transform(Y)\n",
        "\n",
        "O_Y = pd.DataFrame(y)\n",
        "O_Y.columns = ['OY']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMBbA15tdpwH"
      },
      "source": [
        "> * **Encoding Keys :**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnOFQSAkdvz0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25c3e048-512a-4325-cb01-902b990702fc"
      },
      "source": [
        "a = OH_Enc.inverse_transform(np.array([[1,0,0,0,0,0,0,0,0],[0,1,0,0,0,0,0,0,0],\n",
        "                                       [0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0],\n",
        "                                       [0,0,0,0,1,0,0,0,0],[0,0,0,0,0,1,0,0,0],\n",
        "                                       [0,0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1,0],\n",
        "                                       [0,0,0,0,0,0,0,0,1]]))\n",
        "\n",
        "print('One-Hot Encoding: \\n')\n",
        "\n",
        "print('[1,0,0,0,0,0,0,0,0] =',a[0],'\\n')\n",
        "print('[0,1,0,0,0,0,0,0,0] =',a[1],'\\n')\n",
        "print('[0,0,1,0,0,0,0,0,0] =',a[2],'\\n')\n",
        "print('[0,0,0,1,0,0,0,0,0] =',a[3],'\\n')\n",
        "print('[0,0,0,0,1,0,0,0,0] =',a[4],'\\n')\n",
        "print('[0,0,0,0,0,1,0,0,0] =',a[5],'\\n')\n",
        "print('[0,0,0,0,0,0,1,0,0] =',a[6],'\\n')\n",
        "print('[0,0,0,0,0,0,0,1,0] =',a[7],'\\n')\n",
        "print('[0,0,0,0,0,0,0,0,1] =',a[8],'\\n')\n",
        "\n",
        "print('\\n')\n",
        "\n",
        "print('Ordinal Encoding: \\n')\n",
        "LE.inverse_transform(np.arange(0,9))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "One-Hot Encoding: \n",
            "\n",
            "[1,0,0,0,0,0,0,0,0] = ['ACEP'] \n",
            "\n",
            "[0,1,0,0,0,0,0,0,0] = ['CEP'] \n",
            "\n",
            "[0,0,1,0,0,0,0,0,0] = ['DPV'] \n",
            "\n",
            "[0,0,0,1,0,0,0,0,0] = ['DSCT'] \n",
            "\n",
            "[0,0,0,0,1,0,0,0,0] = ['ECL'] \n",
            "\n",
            "[0,0,0,0,0,1,0,0,0] = ['LPV'] \n",
            "\n",
            "[0,0,0,0,0,0,1,0,0] = ['RCB'] \n",
            "\n",
            "[0,0,0,0,0,0,0,1,0] = ['RRLYR'] \n",
            "\n",
            "[0,0,0,0,0,0,0,0,1] = ['T2CEP'] \n",
            "\n",
            "\n",
            "\n",
            "Ordinal Encoding: \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ACEP', 'CEP', 'DPV', 'DSCT', 'ECL', 'LPV', 'RCB', 'RRLYR',\n",
              "       'T2CEP'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwUt_EBP54nz"
      },
      "source": [
        "-----\n",
        "> * Iterative Imputer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWhounh13aRf"
      },
      "source": [
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "\n",
        "X.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "imp = IterativeImputer()\n",
        "imp_data = imp.fit_transform(X)\n",
        "X = pd.DataFrame(imp_data, columns= X.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "JpNAo9oDrl94",
        "outputId": "e03dfda4-7aa7-4f61-baea-bdc6479f20fb"
      },
      "source": [
        "final_data = pd.concat([X,Y,OH_Y,O_Y], axis=1)\n",
        "final_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>I__ar_coefficient__coeff_0__k_10</th>\n",
              "      <th>I__autocorrelation__lag_2</th>\n",
              "      <th>I__autocorrelation__lag_5</th>\n",
              "      <th>I__autocorrelation__lag_6</th>\n",
              "      <th>I__autocorrelation__lag_4</th>\n",
              "      <th>I__autocorrelation__lag_3</th>\n",
              "      <th>I__autocorrelation__lag_9</th>\n",
              "      <th>I__autocorrelation__lag_7</th>\n",
              "      <th>I__autocorrelation__lag_8</th>\n",
              "      <th>I__permutation_entropy__dimension_5__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_6__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_4__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_3__tau_1</th>\n",
              "      <th>I__cwt_coefficients__coeff_5__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_2__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_3__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_4__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_4__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_5__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_6__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_7__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_8__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_10__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_9__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_11__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_14__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_12__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_3__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_14__w_10__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_10__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"max\"</th>\n",
              "      <th>I__agg_linear_trend__attr_\"intercept\"__chunk_len_10__f_agg_\"max\"</th>\n",
              "      <th>I__quantile__q_0.7</th>\n",
              "      <th>I__quantile__q_0.6</th>\n",
              "      <th>I__median</th>\n",
              "      <th>V__variance</th>\n",
              "      <th>V__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0</th>\n",
              "      <th>V__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"min\"</th>\n",
              "      <th>V__agg_linear_trend__attr_\"stderr\"__chunk_len_10__f_agg_\"max\"</th>\n",
              "      <th>V__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"max\"</th>\n",
              "      <th>V__cwt_coefficients__coeff_3__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_4__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_2__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_5__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_5__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_1__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_2__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_3__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_4__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_6__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__quantile__q_0.6</th>\n",
              "      <th>class</th>\n",
              "      <th>OH1</th>\n",
              "      <th>OH2</th>\n",
              "      <th>OH3</th>\n",
              "      <th>OH4</th>\n",
              "      <th>OH5</th>\n",
              "      <th>OH6</th>\n",
              "      <th>OH7</th>\n",
              "      <th>OH8</th>\n",
              "      <th>OH9</th>\n",
              "      <th>OY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19.748969</td>\n",
              "      <td>0.009483</td>\n",
              "      <td>0.110762</td>\n",
              "      <td>-0.094961</td>\n",
              "      <td>-0.058667</td>\n",
              "      <td>-0.011547</td>\n",
              "      <td>0.083490</td>\n",
              "      <td>0.047766</td>\n",
              "      <td>-0.027308</td>\n",
              "      <td>4.524397</td>\n",
              "      <td>5.503021</td>\n",
              "      <td>3.122183</td>\n",
              "      <td>1.781072</td>\n",
              "      <td>2.604235</td>\n",
              "      <td>7.287752</td>\n",
              "      <td>10.708808</td>\n",
              "      <td>14.050479</td>\n",
              "      <td>5.861787</td>\n",
              "      <td>17.288430</td>\n",
              "      <td>20.399585</td>\n",
              "      <td>23.362441</td>\n",
              "      <td>26.157252</td>\n",
              "      <td>31.174052</td>\n",
              "      <td>28.766303</td>\n",
              "      <td>33.367295</td>\n",
              "      <td>37.069790</td>\n",
              "      <td>38.565095</td>\n",
              "      <td>35.335291</td>\n",
              "      <td>3.114119</td>\n",
              "      <td>10.604359</td>\n",
              "      <td>26.220393</td>\n",
              "      <td>27.872722</td>\n",
              "      <td>18.237648</td>\n",
              "      <td>18.273240</td>\n",
              "      <td>18.1797</td>\n",
              "      <td>18.1406</td>\n",
              "      <td>18.0445</td>\n",
              "      <td>0.076356</td>\n",
              "      <td>0.155135</td>\n",
              "      <td>0.043778</td>\n",
              "      <td>0.068507</td>\n",
              "      <td>19.101286</td>\n",
              "      <td>21.290027</td>\n",
              "      <td>23.161165</td>\n",
              "      <td>17.419322</td>\n",
              "      <td>23.098501</td>\n",
              "      <td>2.684836</td>\n",
              "      <td>10.556409</td>\n",
              "      <td>14.460544</td>\n",
              "      <td>11.560448</td>\n",
              "      <td>6.450865</td>\n",
              "      <td>21.462265</td>\n",
              "      <td>18.8650</td>\n",
              "      <td>ACEP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.068873</td>\n",
              "      <td>0.249417</td>\n",
              "      <td>-0.275014</td>\n",
              "      <td>-0.293618</td>\n",
              "      <td>-0.169613</td>\n",
              "      <td>0.011056</td>\n",
              "      <td>-0.101078</td>\n",
              "      <td>-0.268144</td>\n",
              "      <td>-0.147078</td>\n",
              "      <td>3.800394</td>\n",
              "      <td>4.683510</td>\n",
              "      <td>2.706102</td>\n",
              "      <td>1.605640</td>\n",
              "      <td>2.571258</td>\n",
              "      <td>6.598613</td>\n",
              "      <td>9.940941</td>\n",
              "      <td>13.209684</td>\n",
              "      <td>5.851413</td>\n",
              "      <td>16.381162</td>\n",
              "      <td>19.432895</td>\n",
              "      <td>22.343859</td>\n",
              "      <td>25.094735</td>\n",
              "      <td>30.048686</td>\n",
              "      <td>27.668113</td>\n",
              "      <td>32.223360</td>\n",
              "      <td>35.914477</td>\n",
              "      <td>37.416717</td>\n",
              "      <td>34.181399</td>\n",
              "      <td>3.029564</td>\n",
              "      <td>10.438170</td>\n",
              "      <td>25.327889</td>\n",
              "      <td>26.891578</td>\n",
              "      <td>17.770773</td>\n",
              "      <td>17.814498</td>\n",
              "      <td>17.7503</td>\n",
              "      <td>17.7108</td>\n",
              "      <td>17.6445</td>\n",
              "      <td>0.064929</td>\n",
              "      <td>0.134740</td>\n",
              "      <td>0.023825</td>\n",
              "      <td>0.083564</td>\n",
              "      <td>18.635107</td>\n",
              "      <td>20.379251</td>\n",
              "      <td>22.268491</td>\n",
              "      <td>16.554513</td>\n",
              "      <td>22.316570</td>\n",
              "      <td>2.171209</td>\n",
              "      <td>10.311997</td>\n",
              "      <td>13.958987</td>\n",
              "      <td>10.824435</td>\n",
              "      <td>5.644106</td>\n",
              "      <td>20.832429</td>\n",
              "      <td>18.4390</td>\n",
              "      <td>ACEP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16.856263</td>\n",
              "      <td>-0.025188</td>\n",
              "      <td>-0.009933</td>\n",
              "      <td>-0.022579</td>\n",
              "      <td>-0.051735</td>\n",
              "      <td>-0.014927</td>\n",
              "      <td>-0.039444</td>\n",
              "      <td>0.023149</td>\n",
              "      <td>0.025453</td>\n",
              "      <td>4.657379</td>\n",
              "      <td>5.916757</td>\n",
              "      <td>3.143452</td>\n",
              "      <td>1.782637</td>\n",
              "      <td>2.325580</td>\n",
              "      <td>7.030611</td>\n",
              "      <td>10.511738</td>\n",
              "      <td>13.914881</td>\n",
              "      <td>5.960914</td>\n",
              "      <td>17.215269</td>\n",
              "      <td>20.389391</td>\n",
              "      <td>23.415279</td>\n",
              "      <td>26.272741</td>\n",
              "      <td>31.411874</td>\n",
              "      <td>28.943598</td>\n",
              "      <td>33.663937</td>\n",
              "      <td>37.477286</td>\n",
              "      <td>39.023895</td>\n",
              "      <td>35.688616</td>\n",
              "      <td>3.212853</td>\n",
              "      <td>10.994730</td>\n",
              "      <td>26.553764</td>\n",
              "      <td>28.187842</td>\n",
              "      <td>18.492121</td>\n",
              "      <td>18.527015</td>\n",
              "      <td>18.4620</td>\n",
              "      <td>18.4298</td>\n",
              "      <td>18.3880</td>\n",
              "      <td>0.083495</td>\n",
              "      <td>0.153515</td>\n",
              "      <td>0.009420</td>\n",
              "      <td>0.015628</td>\n",
              "      <td>19.170495</td>\n",
              "      <td>18.840029</td>\n",
              "      <td>21.822026</td>\n",
              "      <td>13.892342</td>\n",
              "      <td>22.751140</td>\n",
              "      <td>2.627845</td>\n",
              "      <td>10.358207</td>\n",
              "      <td>14.437908</td>\n",
              "      <td>11.623109</td>\n",
              "      <td>6.440886</td>\n",
              "      <td>21.848849</td>\n",
              "      <td>18.9608</td>\n",
              "      <td>ACEP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19.255513</td>\n",
              "      <td>0.109938</td>\n",
              "      <td>-0.042236</td>\n",
              "      <td>-0.053299</td>\n",
              "      <td>0.047238</td>\n",
              "      <td>-0.006245</td>\n",
              "      <td>0.037559</td>\n",
              "      <td>-0.114417</td>\n",
              "      <td>-0.057879</td>\n",
              "      <td>4.289305</td>\n",
              "      <td>5.239870</td>\n",
              "      <td>2.975525</td>\n",
              "      <td>1.715220</td>\n",
              "      <td>2.196017</td>\n",
              "      <td>6.256552</td>\n",
              "      <td>9.477400</td>\n",
              "      <td>12.627239</td>\n",
              "      <td>5.509158</td>\n",
              "      <td>15.683202</td>\n",
              "      <td>18.623547</td>\n",
              "      <td>21.427972</td>\n",
              "      <td>24.077825</td>\n",
              "      <td>28.848598</td>\n",
              "      <td>26.556299</td>\n",
              "      <td>30.942115</td>\n",
              "      <td>34.493783</td>\n",
              "      <td>35.938309</td>\n",
              "      <td>32.826507</td>\n",
              "      <td>3.257107</td>\n",
              "      <td>10.150114</td>\n",
              "      <td>24.641670</td>\n",
              "      <td>26.129133</td>\n",
              "      <td>17.253431</td>\n",
              "      <td>17.306733</td>\n",
              "      <td>17.2010</td>\n",
              "      <td>17.1030</td>\n",
              "      <td>17.0430</td>\n",
              "      <td>0.096484</td>\n",
              "      <td>0.231728</td>\n",
              "      <td>0.040666</td>\n",
              "      <td>0.008811</td>\n",
              "      <td>18.128964</td>\n",
              "      <td>19.210782</td>\n",
              "      <td>20.928621</td>\n",
              "      <td>15.605192</td>\n",
              "      <td>20.848297</td>\n",
              "      <td>1.885335</td>\n",
              "      <td>10.274937</td>\n",
              "      <td>13.796137</td>\n",
              "      <td>10.693852</td>\n",
              "      <td>5.508701</td>\n",
              "      <td>19.303169</td>\n",
              "      <td>17.9048</td>\n",
              "      <td>ACEP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.232862</td>\n",
              "      <td>-0.280846</td>\n",
              "      <td>0.065069</td>\n",
              "      <td>-0.040802</td>\n",
              "      <td>0.119790</td>\n",
              "      <td>-0.106279</td>\n",
              "      <td>-0.009490</td>\n",
              "      <td>-0.060699</td>\n",
              "      <td>-0.015028</td>\n",
              "      <td>4.311274</td>\n",
              "      <td>5.365514</td>\n",
              "      <td>2.965344</td>\n",
              "      <td>1.703971</td>\n",
              "      <td>2.288207</td>\n",
              "      <td>6.842519</td>\n",
              "      <td>10.226231</td>\n",
              "      <td>13.533936</td>\n",
              "      <td>5.794111</td>\n",
              "      <td>16.741640</td>\n",
              "      <td>19.826575</td>\n",
              "      <td>22.767470</td>\n",
              "      <td>25.544807</td>\n",
              "      <td>30.540674</td>\n",
              "      <td>28.141020</td>\n",
              "      <td>32.730619</td>\n",
              "      <td>36.440819</td>\n",
              "      <td>37.946973</td>\n",
              "      <td>34.700100</td>\n",
              "      <td>3.065216</td>\n",
              "      <td>10.660442</td>\n",
              "      <td>25.734200</td>\n",
              "      <td>27.348026</td>\n",
              "      <td>17.953546</td>\n",
              "      <td>17.965266</td>\n",
              "      <td>17.9293</td>\n",
              "      <td>17.9094</td>\n",
              "      <td>17.8800</td>\n",
              "      <td>0.018139</td>\n",
              "      <td>0.041104</td>\n",
              "      <td>0.008410</td>\n",
              "      <td>0.010812</td>\n",
              "      <td>18.624750</td>\n",
              "      <td>19.743506</td>\n",
              "      <td>21.624846</td>\n",
              "      <td>15.900432</td>\n",
              "      <td>21.626409</td>\n",
              "      <td>2.477384</td>\n",
              "      <td>10.171427</td>\n",
              "      <td>13.977588</td>\n",
              "      <td>11.158619</td>\n",
              "      <td>6.151941</td>\n",
              "      <td>20.086791</td>\n",
              "      <td>18.6034</td>\n",
              "      <td>ACEP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   I__ar_coefficient__coeff_0__k_10  I__autocorrelation__lag_2  ...  OH9  OY\n",
              "0                         19.748969                   0.009483  ...  0.0   0\n",
              "1                         18.068873                   0.249417  ...  0.0   0\n",
              "2                         16.856263                  -0.025188  ...  0.0   0\n",
              "3                         19.255513                   0.109938  ...  0.0   0\n",
              "4                         30.232862                  -0.280846  ...  0.0   0\n",
              "\n",
              "[5 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXQsm_9b5zLa"
      },
      "source": [
        "> * Shuffle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckoF7pq6271w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "27028071-79d5-442f-b2b4-38fe904fb3af"
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "final_data = shuffle(final_data, random_state=42)\n",
        "final_data = final_data.reset_index(drop=True)\n",
        "\n",
        "savefile(final_data,'final_data')\n",
        "final_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>I__ar_coefficient__coeff_0__k_10</th>\n",
              "      <th>I__autocorrelation__lag_2</th>\n",
              "      <th>I__autocorrelation__lag_5</th>\n",
              "      <th>I__autocorrelation__lag_6</th>\n",
              "      <th>I__autocorrelation__lag_4</th>\n",
              "      <th>I__autocorrelation__lag_3</th>\n",
              "      <th>I__autocorrelation__lag_9</th>\n",
              "      <th>I__autocorrelation__lag_7</th>\n",
              "      <th>I__autocorrelation__lag_8</th>\n",
              "      <th>I__permutation_entropy__dimension_5__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_6__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_4__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_3__tau_1</th>\n",
              "      <th>I__cwt_coefficients__coeff_5__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_2__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_3__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_4__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_4__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_5__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_6__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_7__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_8__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_10__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_9__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_11__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_14__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_12__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_3__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_14__w_10__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_10__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"max\"</th>\n",
              "      <th>I__agg_linear_trend__attr_\"intercept\"__chunk_len_10__f_agg_\"max\"</th>\n",
              "      <th>I__quantile__q_0.7</th>\n",
              "      <th>I__quantile__q_0.6</th>\n",
              "      <th>I__median</th>\n",
              "      <th>V__variance</th>\n",
              "      <th>V__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0</th>\n",
              "      <th>V__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"min\"</th>\n",
              "      <th>V__agg_linear_trend__attr_\"stderr\"__chunk_len_10__f_agg_\"max\"</th>\n",
              "      <th>V__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"max\"</th>\n",
              "      <th>V__cwt_coefficients__coeff_3__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_4__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_2__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_5__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_5__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_1__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_2__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_3__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_4__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_6__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__quantile__q_0.6</th>\n",
              "      <th>class</th>\n",
              "      <th>OH1</th>\n",
              "      <th>OH2</th>\n",
              "      <th>OH3</th>\n",
              "      <th>OH4</th>\n",
              "      <th>OH5</th>\n",
              "      <th>OH6</th>\n",
              "      <th>OH7</th>\n",
              "      <th>OH8</th>\n",
              "      <th>OH9</th>\n",
              "      <th>OY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23.159766</td>\n",
              "      <td>0.058857</td>\n",
              "      <td>-0.192476</td>\n",
              "      <td>-0.018549</td>\n",
              "      <td>-0.201185</td>\n",
              "      <td>-0.217742</td>\n",
              "      <td>0.062612</td>\n",
              "      <td>0.012253</td>\n",
              "      <td>0.053790</td>\n",
              "      <td>4.590348</td>\n",
              "      <td>5.784798</td>\n",
              "      <td>3.115812</td>\n",
              "      <td>1.777689</td>\n",
              "      <td>2.467433</td>\n",
              "      <td>7.263619</td>\n",
              "      <td>10.834022</td>\n",
              "      <td>14.323661</td>\n",
              "      <td>6.152839</td>\n",
              "      <td>17.707255</td>\n",
              "      <td>20.960824</td>\n",
              "      <td>24.061984</td>\n",
              "      <td>26.990202</td>\n",
              "      <td>32.256228</td>\n",
              "      <td>29.727017</td>\n",
              "      <td>34.564049</td>\n",
              "      <td>38.473086</td>\n",
              "      <td>40.059611</td>\n",
              "      <td>36.639223</td>\n",
              "      <td>3.125351</td>\n",
              "      <td>11.288695</td>\n",
              "      <td>27.117744</td>\n",
              "      <td>28.846808</td>\n",
              "      <td>18.975268</td>\n",
              "      <td>19.006507</td>\n",
              "      <td>18.9381</td>\n",
              "      <td>18.9018</td>\n",
              "      <td>18.8675</td>\n",
              "      <td>0.026029</td>\n",
              "      <td>0.049539</td>\n",
              "      <td>0.002826</td>\n",
              "      <td>0.003705</td>\n",
              "      <td>19.519780</td>\n",
              "      <td>19.010092</td>\n",
              "      <td>21.957699</td>\n",
              "      <td>14.072427</td>\n",
              "      <td>22.848442</td>\n",
              "      <td>2.495199</td>\n",
              "      <td>10.936606</td>\n",
              "      <td>14.828420</td>\n",
              "      <td>11.699832</td>\n",
              "      <td>6.340944</td>\n",
              "      <td>21.924128</td>\n",
              "      <td>19.4190</td>\n",
              "      <td>RRLYR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.693699</td>\n",
              "      <td>0.297114</td>\n",
              "      <td>0.274560</td>\n",
              "      <td>0.278861</td>\n",
              "      <td>0.258145</td>\n",
              "      <td>0.195361</td>\n",
              "      <td>0.111471</td>\n",
              "      <td>0.232096</td>\n",
              "      <td>0.164473</td>\n",
              "      <td>4.683979</td>\n",
              "      <td>5.904097</td>\n",
              "      <td>3.154262</td>\n",
              "      <td>1.786278</td>\n",
              "      <td>2.022805</td>\n",
              "      <td>5.981635</td>\n",
              "      <td>8.908825</td>\n",
              "      <td>11.769654</td>\n",
              "      <td>5.088675</td>\n",
              "      <td>14.543354</td>\n",
              "      <td>17.210234</td>\n",
              "      <td>19.751907</td>\n",
              "      <td>22.151501</td>\n",
              "      <td>26.465630</td>\n",
              "      <td>24.393847</td>\n",
              "      <td>28.355518</td>\n",
              "      <td>31.554716</td>\n",
              "      <td>32.851946</td>\n",
              "      <td>30.054254</td>\n",
              "      <td>2.642013</td>\n",
              "      <td>9.312274</td>\n",
              "      <td>22.293482</td>\n",
              "      <td>23.701160</td>\n",
              "      <td>15.469872</td>\n",
              "      <td>15.472299</td>\n",
              "      <td>15.4660</td>\n",
              "      <td>15.4630</td>\n",
              "      <td>15.4610</td>\n",
              "      <td>0.000226</td>\n",
              "      <td>0.000376</td>\n",
              "      <td>0.001586</td>\n",
              "      <td>0.002198</td>\n",
              "      <td>17.137917</td>\n",
              "      <td>16.763443</td>\n",
              "      <td>19.420130</td>\n",
              "      <td>12.362662</td>\n",
              "      <td>20.266802</td>\n",
              "      <td>2.228590</td>\n",
              "      <td>9.526614</td>\n",
              "      <td>13.000720</td>\n",
              "      <td>10.303060</td>\n",
              "      <td>5.619679</td>\n",
              "      <td>19.508710</td>\n",
              "      <td>17.1184</td>\n",
              "      <td>LPV</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.075545</td>\n",
              "      <td>-0.010138</td>\n",
              "      <td>-0.051333</td>\n",
              "      <td>-0.054756</td>\n",
              "      <td>0.002645</td>\n",
              "      <td>-0.066083</td>\n",
              "      <td>0.045038</td>\n",
              "      <td>-0.058328</td>\n",
              "      <td>-0.050620</td>\n",
              "      <td>4.644219</td>\n",
              "      <td>5.814535</td>\n",
              "      <td>3.137554</td>\n",
              "      <td>1.788747</td>\n",
              "      <td>2.592135</td>\n",
              "      <td>7.548619</td>\n",
              "      <td>11.312388</td>\n",
              "      <td>14.991834</td>\n",
              "      <td>6.535771</td>\n",
              "      <td>18.560299</td>\n",
              "      <td>21.992500</td>\n",
              "      <td>25.264837</td>\n",
              "      <td>28.355645</td>\n",
              "      <td>33.917146</td>\n",
              "      <td>31.245447</td>\n",
              "      <td>36.356194</td>\n",
              "      <td>40.491474</td>\n",
              "      <td>42.172195</td>\n",
              "      <td>38.550675</td>\n",
              "      <td>3.307151</td>\n",
              "      <td>11.968065</td>\n",
              "      <td>28.783685</td>\n",
              "      <td>30.557072</td>\n",
              "      <td>20.288144</td>\n",
              "      <td>20.560041</td>\n",
              "      <td>19.8865</td>\n",
              "      <td>19.8360</td>\n",
              "      <td>19.8040</td>\n",
              "      <td>0.136006</td>\n",
              "      <td>0.314350</td>\n",
              "      <td>0.001790</td>\n",
              "      <td>0.064073</td>\n",
              "      <td>20.469228</td>\n",
              "      <td>19.672701</td>\n",
              "      <td>22.792197</td>\n",
              "      <td>14.502474</td>\n",
              "      <td>23.783316</td>\n",
              "      <td>2.644402</td>\n",
              "      <td>11.199386</td>\n",
              "      <td>15.262706</td>\n",
              "      <td>12.096694</td>\n",
              "      <td>6.620443</td>\n",
              "      <td>22.886799</td>\n",
              "      <td>20.1012</td>\n",
              "      <td>ECL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.563469</td>\n",
              "      <td>0.221679</td>\n",
              "      <td>0.198469</td>\n",
              "      <td>0.226109</td>\n",
              "      <td>0.128093</td>\n",
              "      <td>0.109930</td>\n",
              "      <td>0.154414</td>\n",
              "      <td>0.160429</td>\n",
              "      <td>0.154775</td>\n",
              "      <td>4.503569</td>\n",
              "      <td>5.604325</td>\n",
              "      <td>3.094803</td>\n",
              "      <td>1.771365</td>\n",
              "      <td>1.999820</td>\n",
              "      <td>5.923933</td>\n",
              "      <td>8.827997</td>\n",
              "      <td>11.666483</td>\n",
              "      <td>5.031938</td>\n",
              "      <td>14.418787</td>\n",
              "      <td>17.065368</td>\n",
              "      <td>19.587975</td>\n",
              "      <td>21.969858</td>\n",
              "      <td>26.253023</td>\n",
              "      <td>24.195951</td>\n",
              "      <td>28.129809</td>\n",
              "      <td>31.307792</td>\n",
              "      <td>32.596943</td>\n",
              "      <td>29.817097</td>\n",
              "      <td>2.631608</td>\n",
              "      <td>9.216900</td>\n",
              "      <td>22.111301</td>\n",
              "      <td>23.499921</td>\n",
              "      <td>15.329966</td>\n",
              "      <td>15.332412</td>\n",
              "      <td>15.3330</td>\n",
              "      <td>15.3290</td>\n",
              "      <td>15.3260</td>\n",
              "      <td>0.000436</td>\n",
              "      <td>0.001021</td>\n",
              "      <td>0.001368</td>\n",
              "      <td>0.003646</td>\n",
              "      <td>17.260822</td>\n",
              "      <td>16.851377</td>\n",
              "      <td>19.516705</td>\n",
              "      <td>12.429937</td>\n",
              "      <td>20.361645</td>\n",
              "      <td>2.226639</td>\n",
              "      <td>9.621720</td>\n",
              "      <td>13.116620</td>\n",
              "      <td>10.389279</td>\n",
              "      <td>5.659709</td>\n",
              "      <td>19.594358</td>\n",
              "      <td>17.2448</td>\n",
              "      <td>LPV</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.580594</td>\n",
              "      <td>0.537196</td>\n",
              "      <td>0.287874</td>\n",
              "      <td>0.238370</td>\n",
              "      <td>0.374481</td>\n",
              "      <td>0.429834</td>\n",
              "      <td>0.214728</td>\n",
              "      <td>0.214824</td>\n",
              "      <td>0.218350</td>\n",
              "      <td>4.459013</td>\n",
              "      <td>5.613660</td>\n",
              "      <td>3.047440</td>\n",
              "      <td>1.748917</td>\n",
              "      <td>1.802963</td>\n",
              "      <td>5.341492</td>\n",
              "      <td>7.958713</td>\n",
              "      <td>10.516652</td>\n",
              "      <td>4.545470</td>\n",
              "      <td>12.996739</td>\n",
              "      <td>15.381365</td>\n",
              "      <td>17.654086</td>\n",
              "      <td>19.799813</td>\n",
              "      <td>23.657668</td>\n",
              "      <td>21.804977</td>\n",
              "      <td>25.347747</td>\n",
              "      <td>28.208847</td>\n",
              "      <td>29.369052</td>\n",
              "      <td>26.866932</td>\n",
              "      <td>2.350618</td>\n",
              "      <td>8.322876</td>\n",
              "      <td>19.921824</td>\n",
              "      <td>21.178552</td>\n",
              "      <td>13.830976</td>\n",
              "      <td>13.834697</td>\n",
              "      <td>13.8340</td>\n",
              "      <td>13.8300</td>\n",
              "      <td>13.8270</td>\n",
              "      <td>0.001038</td>\n",
              "      <td>0.001894</td>\n",
              "      <td>0.002449</td>\n",
              "      <td>0.010331</td>\n",
              "      <td>15.890667</td>\n",
              "      <td>15.624232</td>\n",
              "      <td>18.086411</td>\n",
              "      <td>11.549153</td>\n",
              "      <td>18.875058</td>\n",
              "      <td>2.064307</td>\n",
              "      <td>8.816249</td>\n",
              "      <td>12.031768</td>\n",
              "      <td>9.540502</td>\n",
              "      <td>5.211766</td>\n",
              "      <td>18.180193</td>\n",
              "      <td>15.8690</td>\n",
              "      <td>LPV</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   I__ar_coefficient__coeff_0__k_10  I__autocorrelation__lag_2  ...  OH9  OY\n",
              "0                         23.159766                   0.058857  ...  0.0   7\n",
              "1                          4.693699                   0.297114  ...  0.0   5\n",
              "2                         33.075545                  -0.010138  ...  0.0   4\n",
              "3                          4.563469                   0.221679  ...  0.0   5\n",
              "4                          2.580594                   0.537196  ...  0.0   5\n",
              "\n",
              "[5 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "8XPZHUD8zrNh",
        "outputId": "85d736b1-b648-484e-ed30-84314e9d0399"
      },
      "source": [
        "final_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>I__ar_coefficient__coeff_0__k_10</th>\n",
              "      <th>I__autocorrelation__lag_2</th>\n",
              "      <th>I__autocorrelation__lag_5</th>\n",
              "      <th>I__autocorrelation__lag_6</th>\n",
              "      <th>I__autocorrelation__lag_4</th>\n",
              "      <th>I__autocorrelation__lag_3</th>\n",
              "      <th>I__autocorrelation__lag_9</th>\n",
              "      <th>I__autocorrelation__lag_7</th>\n",
              "      <th>I__autocorrelation__lag_8</th>\n",
              "      <th>I__permutation_entropy__dimension_5__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_6__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_4__tau_1</th>\n",
              "      <th>I__permutation_entropy__dimension_3__tau_1</th>\n",
              "      <th>I__cwt_coefficients__coeff_5__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_2__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_3__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_4__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_4__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_5__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_6__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_7__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_8__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_10__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_9__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_11__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_14__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_12__w_20__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_3__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_14__w_10__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__cwt_coefficients__coeff_13__w_10__widths_(2, 5, 10, 20)</th>\n",
              "      <th>I__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"max\"</th>\n",
              "      <th>I__agg_linear_trend__attr_\"intercept\"__chunk_len_10__f_agg_\"max\"</th>\n",
              "      <th>I__quantile__q_0.7</th>\n",
              "      <th>I__quantile__q_0.6</th>\n",
              "      <th>I__median</th>\n",
              "      <th>V__variance</th>\n",
              "      <th>V__change_quantiles__f_agg_\"var\"__isabs_False__qh_1.0__ql_0.0</th>\n",
              "      <th>V__agg_linear_trend__attr_\"stderr\"__chunk_len_5__f_agg_\"min\"</th>\n",
              "      <th>V__agg_linear_trend__attr_\"stderr\"__chunk_len_10__f_agg_\"max\"</th>\n",
              "      <th>V__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"max\"</th>\n",
              "      <th>V__cwt_coefficients__coeff_3__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_4__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_2__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_5__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_5__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_1__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_2__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_3__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_4__w_2__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__cwt_coefficients__coeff_6__w_5__widths_(2, 5, 10, 20)</th>\n",
              "      <th>V__quantile__q_0.6</th>\n",
              "      <th>OH1</th>\n",
              "      <th>OH2</th>\n",
              "      <th>OH3</th>\n",
              "      <th>OH4</th>\n",
              "      <th>OH5</th>\n",
              "      <th>OH6</th>\n",
              "      <th>OH7</th>\n",
              "      <th>OH8</th>\n",
              "      <th>OH9</th>\n",
              "      <th>OY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "      <td>286122.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.507051</td>\n",
              "      <td>0.183785</td>\n",
              "      <td>0.116116</td>\n",
              "      <td>0.107828</td>\n",
              "      <td>0.129021</td>\n",
              "      <td>0.148957</td>\n",
              "      <td>0.086275</td>\n",
              "      <td>0.099957</td>\n",
              "      <td>0.093139</td>\n",
              "      <td>4.456822</td>\n",
              "      <td>5.539335</td>\n",
              "      <td>3.056676</td>\n",
              "      <td>1.754849</td>\n",
              "      <td>2.212086</td>\n",
              "      <td>7.045371</td>\n",
              "      <td>10.263783</td>\n",
              "      <td>13.409215</td>\n",
              "      <td>5.579989</td>\n",
              "      <td>16.458784</td>\n",
              "      <td>19.390280</td>\n",
              "      <td>22.183019</td>\n",
              "      <td>24.818179</td>\n",
              "      <td>29.551665</td>\n",
              "      <td>27.279183</td>\n",
              "      <td>31.623170</td>\n",
              "      <td>35.125375</td>\n",
              "      <td>36.543406</td>\n",
              "      <td>33.483457</td>\n",
              "      <td>2.930556</td>\n",
              "      <td>10.228220</td>\n",
              "      <td>24.606883</td>\n",
              "      <td>26.151883</td>\n",
              "      <td>17.088845</td>\n",
              "      <td>17.125293</td>\n",
              "      <td>17.040662</td>\n",
              "      <td>17.016345</td>\n",
              "      <td>16.992066</td>\n",
              "      <td>505.389719</td>\n",
              "      <td>75.303527</td>\n",
              "      <td>0.038360</td>\n",
              "      <td>-0.157724</td>\n",
              "      <td>20.880963</td>\n",
              "      <td>20.519011</td>\n",
              "      <td>23.304415</td>\n",
              "      <td>15.682749</td>\n",
              "      <td>23.996867</td>\n",
              "      <td>2.807346</td>\n",
              "      <td>11.233701</td>\n",
              "      <td>15.144725</td>\n",
              "      <td>11.997754</td>\n",
              "      <td>6.624441</td>\n",
              "      <td>22.900598</td>\n",
              "      <td>18.772853</td>\n",
              "      <td>0.001220</td>\n",
              "      <td>0.061449</td>\n",
              "      <td>0.000479</td>\n",
              "      <td>0.009744</td>\n",
              "      <td>0.273810</td>\n",
              "      <td>0.389271</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>0.261899</td>\n",
              "      <td>0.002048</td>\n",
              "      <td>4.983392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.694786</td>\n",
              "      <td>0.304260</td>\n",
              "      <td>0.223004</td>\n",
              "      <td>0.208133</td>\n",
              "      <td>0.242378</td>\n",
              "      <td>0.267180</td>\n",
              "      <td>0.174787</td>\n",
              "      <td>0.195136</td>\n",
              "      <td>0.184527</td>\n",
              "      <td>0.359042</td>\n",
              "      <td>0.534112</td>\n",
              "      <td>0.185799</td>\n",
              "      <td>0.069587</td>\n",
              "      <td>0.283247</td>\n",
              "      <td>3.984579</td>\n",
              "      <td>4.096515</td>\n",
              "      <td>4.233359</td>\n",
              "      <td>0.652184</td>\n",
              "      <td>4.389217</td>\n",
              "      <td>4.556246</td>\n",
              "      <td>4.727421</td>\n",
              "      <td>4.897199</td>\n",
              "      <td>5.220143</td>\n",
              "      <td>5.062012</td>\n",
              "      <td>5.365435</td>\n",
              "      <td>5.609272</td>\n",
              "      <td>5.706350</td>\n",
              "      <td>5.495777</td>\n",
              "      <td>1.070098</td>\n",
              "      <td>1.178678</td>\n",
              "      <td>3.258882</td>\n",
              "      <td>3.415118</td>\n",
              "      <td>2.014632</td>\n",
              "      <td>2.040601</td>\n",
              "      <td>1.968160</td>\n",
              "      <td>1.957471</td>\n",
              "      <td>1.946528</td>\n",
              "      <td>9611.156393</td>\n",
              "      <td>1413.959570</td>\n",
              "      <td>0.598833</td>\n",
              "      <td>5.147174</td>\n",
              "      <td>47.530969</td>\n",
              "      <td>29.655364</td>\n",
              "      <td>35.223434</td>\n",
              "      <td>20.880494</td>\n",
              "      <td>37.387167</td>\n",
              "      <td>4.599357</td>\n",
              "      <td>18.031096</td>\n",
              "      <td>25.009651</td>\n",
              "      <td>19.938252</td>\n",
              "      <td>10.957653</td>\n",
              "      <td>36.508464</td>\n",
              "      <td>21.679613</td>\n",
              "      <td>0.034904</td>\n",
              "      <td>0.240153</td>\n",
              "      <td>0.021877</td>\n",
              "      <td>0.098230</td>\n",
              "      <td>0.445913</td>\n",
              "      <td>0.487586</td>\n",
              "      <td>0.008965</td>\n",
              "      <td>0.439669</td>\n",
              "      <td>0.045209</td>\n",
              "      <td>1.548102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-439.861981</td>\n",
              "      <td>-0.793005</td>\n",
              "      <td>-0.917665</td>\n",
              "      <td>-0.793043</td>\n",
              "      <td>-0.875278</td>\n",
              "      <td>-0.718652</td>\n",
              "      <td>-1.446503</td>\n",
              "      <td>-0.962559</td>\n",
              "      <td>-1.049972</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>1.304407</td>\n",
              "      <td>0.895389</td>\n",
              "      <td>0.461911</td>\n",
              "      <td>-0.782512</td>\n",
              "      <td>-0.658666</td>\n",
              "      <td>-1.677169</td>\n",
              "      <td>2.860194</td>\n",
              "      <td>-2.825155</td>\n",
              "      <td>-4.090479</td>\n",
              "      <td>-5.458613</td>\n",
              "      <td>-6.912820</td>\n",
              "      <td>-10.002751</td>\n",
              "      <td>-8.434358</td>\n",
              "      <td>-11.596064</td>\n",
              "      <td>-14.764500</td>\n",
              "      <td>-16.291620</td>\n",
              "      <td>-13.191252</td>\n",
              "      <td>-48.884054</td>\n",
              "      <td>5.525140</td>\n",
              "      <td>-7.329250</td>\n",
              "      <td>-3.371503</td>\n",
              "      <td>9.164088</td>\n",
              "      <td>9.171179</td>\n",
              "      <td>8.895800</td>\n",
              "      <td>8.824800</td>\n",
              "      <td>8.667000</td>\n",
              "      <td>-58311.361523</td>\n",
              "      <td>-344.439874</td>\n",
              "      <td>-2.952143</td>\n",
              "      <td>-124.893689</td>\n",
              "      <td>-130.440932</td>\n",
              "      <td>8.212921</td>\n",
              "      <td>-0.290036</td>\n",
              "      <td>5.662342</td>\n",
              "      <td>-9.313645</td>\n",
              "      <td>-14.116009</td>\n",
              "      <td>4.220412</td>\n",
              "      <td>2.393058</td>\n",
              "      <td>5.638090</td>\n",
              "      <td>0.742706</td>\n",
              "      <td>-15.590804</td>\n",
              "      <td>-882.705115</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.711044</td>\n",
              "      <td>-0.028569</td>\n",
              "      <td>-0.019070</td>\n",
              "      <td>-0.018263</td>\n",
              "      <td>-0.020758</td>\n",
              "      <td>-0.022758</td>\n",
              "      <td>-0.016998</td>\n",
              "      <td>-0.017261</td>\n",
              "      <td>-0.017474</td>\n",
              "      <td>4.422767</td>\n",
              "      <td>5.462506</td>\n",
              "      <td>3.048032</td>\n",
              "      <td>1.754218</td>\n",
              "      <td>1.968224</td>\n",
              "      <td>5.854477</td>\n",
              "      <td>8.722569</td>\n",
              "      <td>11.526420</td>\n",
              "      <td>4.969424</td>\n",
              "      <td>14.245302</td>\n",
              "      <td>16.859300</td>\n",
              "      <td>19.350669</td>\n",
              "      <td>21.702989</td>\n",
              "      <td>25.932993</td>\n",
              "      <td>23.901384</td>\n",
              "      <td>27.786084</td>\n",
              "      <td>30.922216</td>\n",
              "      <td>32.193830</td>\n",
              "      <td>29.451568</td>\n",
              "      <td>2.584349</td>\n",
              "      <td>9.110995</td>\n",
              "      <td>21.831320</td>\n",
              "      <td>23.204907</td>\n",
              "      <td>15.157839</td>\n",
              "      <td>15.165237</td>\n",
              "      <td>15.153000</td>\n",
              "      <td>15.145000</td>\n",
              "      <td>15.138000</td>\n",
              "      <td>0.000747</td>\n",
              "      <td>0.001182</td>\n",
              "      <td>0.000938</td>\n",
              "      <td>0.002384</td>\n",
              "      <td>16.872000</td>\n",
              "      <td>16.616662</td>\n",
              "      <td>19.199937</td>\n",
              "      <td>12.260068</td>\n",
              "      <td>19.875324</td>\n",
              "      <td>2.183259</td>\n",
              "      <td>9.375883</td>\n",
              "      <td>12.781548</td>\n",
              "      <td>10.119802</td>\n",
              "      <td>5.519840</td>\n",
              "      <td>19.014970</td>\n",
              "      <td>16.817400</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15.018787</td>\n",
              "      <td>0.075232</td>\n",
              "      <td>0.049531</td>\n",
              "      <td>0.048107</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.061496</td>\n",
              "      <td>0.041170</td>\n",
              "      <td>0.045785</td>\n",
              "      <td>0.043426</td>\n",
              "      <td>4.590470</td>\n",
              "      <td>5.665350</td>\n",
              "      <td>3.127675</td>\n",
              "      <td>1.780147</td>\n",
              "      <td>2.198103</td>\n",
              "      <td>6.677086</td>\n",
              "      <td>9.944148</td>\n",
              "      <td>13.133214</td>\n",
              "      <td>5.632524</td>\n",
              "      <td>16.224800</td>\n",
              "      <td>19.196004</td>\n",
              "      <td>22.028405</td>\n",
              "      <td>24.702015</td>\n",
              "      <td>29.504430</td>\n",
              "      <td>27.198971</td>\n",
              "      <td>31.603424</td>\n",
              "      <td>35.154174</td>\n",
              "      <td>36.593978</td>\n",
              "      <td>33.487590</td>\n",
              "      <td>2.908681</td>\n",
              "      <td>10.328045</td>\n",
              "      <td>24.769320</td>\n",
              "      <td>26.328267</td>\n",
              "      <td>17.246924</td>\n",
              "      <td>17.280727</td>\n",
              "      <td>17.201000</td>\n",
              "      <td>17.177200</td>\n",
              "      <td>17.156000</td>\n",
              "      <td>0.009678</td>\n",
              "      <td>0.014098</td>\n",
              "      <td>0.002650</td>\n",
              "      <td>0.006776</td>\n",
              "      <td>17.900903</td>\n",
              "      <td>18.233696</td>\n",
              "      <td>20.559726</td>\n",
              "      <td>13.822960</td>\n",
              "      <td>21.164817</td>\n",
              "      <td>2.293136</td>\n",
              "      <td>9.952704</td>\n",
              "      <td>13.539536</td>\n",
              "      <td>10.689909</td>\n",
              "      <td>5.830141</td>\n",
              "      <td>20.173477</td>\n",
              "      <td>17.717100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>20.674117</td>\n",
              "      <td>0.331038</td>\n",
              "      <td>0.182871</td>\n",
              "      <td>0.173235</td>\n",
              "      <td>0.204297</td>\n",
              "      <td>0.240419</td>\n",
              "      <td>0.142529</td>\n",
              "      <td>0.162323</td>\n",
              "      <td>0.153871</td>\n",
              "      <td>4.652623</td>\n",
              "      <td>5.860470</td>\n",
              "      <td>3.151808</td>\n",
              "      <td>1.787484</td>\n",
              "      <td>2.439325</td>\n",
              "      <td>7.297633</td>\n",
              "      <td>10.875986</td>\n",
              "      <td>14.372031</td>\n",
              "      <td>6.182538</td>\n",
              "      <td>17.761313</td>\n",
              "      <td>21.020423</td>\n",
              "      <td>24.126124</td>\n",
              "      <td>27.057958</td>\n",
              "      <td>32.329506</td>\n",
              "      <td>29.797954</td>\n",
              "      <td>34.639046</td>\n",
              "      <td>38.549405</td>\n",
              "      <td>40.135165</td>\n",
              "      <td>36.715198</td>\n",
              "      <td>3.203100</td>\n",
              "      <td>11.342187</td>\n",
              "      <td>27.186352</td>\n",
              "      <td>28.897197</td>\n",
              "      <td>19.010709</td>\n",
              "      <td>19.057495</td>\n",
              "      <td>18.939200</td>\n",
              "      <td>18.902000</td>\n",
              "      <td>18.860000</td>\n",
              "      <td>0.041554</td>\n",
              "      <td>0.076447</td>\n",
              "      <td>0.009487</td>\n",
              "      <td>0.021939</td>\n",
              "      <td>19.631740</td>\n",
              "      <td>19.490283</td>\n",
              "      <td>22.353647</td>\n",
              "      <td>14.793258</td>\n",
              "      <td>23.098689</td>\n",
              "      <td>2.530097</td>\n",
              "      <td>10.850407</td>\n",
              "      <td>14.771409</td>\n",
              "      <td>11.679229</td>\n",
              "      <td>6.367374</td>\n",
              "      <td>22.105726</td>\n",
              "      <td>19.468800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4775.443655</td>\n",
              "      <td>0.998748</td>\n",
              "      <td>0.995583</td>\n",
              "      <td>0.994289</td>\n",
              "      <td>0.996775</td>\n",
              "      <td>0.997823</td>\n",
              "      <td>1.409755</td>\n",
              "      <td>0.995681</td>\n",
              "      <td>1.787245</td>\n",
              "      <td>4.761095</td>\n",
              "      <td>6.390080</td>\n",
              "      <td>3.175780</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>12.896839</td>\n",
              "      <td>56.622355</td>\n",
              "      <td>60.377384</td>\n",
              "      <td>64.034896</td>\n",
              "      <td>20.511223</td>\n",
              "      <td>67.567551</td>\n",
              "      <td>70.947630</td>\n",
              "      <td>74.160656</td>\n",
              "      <td>77.178818</td>\n",
              "      <td>82.574011</td>\n",
              "      <td>79.990108</td>\n",
              "      <td>84.915110</td>\n",
              "      <td>88.841884</td>\n",
              "      <td>90.406991</td>\n",
              "      <td>87.007985</td>\n",
              "      <td>36.577848</td>\n",
              "      <td>22.751343</td>\n",
              "      <td>64.818902</td>\n",
              "      <td>65.275824</td>\n",
              "      <td>22.339626</td>\n",
              "      <td>22.844210</td>\n",
              "      <td>21.999400</td>\n",
              "      <td>21.821800</td>\n",
              "      <td>21.668000</td>\n",
              "      <td>236147.035896</td>\n",
              "      <td>58385.944358</td>\n",
              "      <td>35.026469</td>\n",
              "      <td>223.619872</td>\n",
              "      <td>1090.418892</td>\n",
              "      <td>717.461293</td>\n",
              "      <td>797.811179</td>\n",
              "      <td>569.361095</td>\n",
              "      <td>812.756979</td>\n",
              "      <td>99.032783</td>\n",
              "      <td>371.114703</td>\n",
              "      <td>509.504897</td>\n",
              "      <td>403.607663</td>\n",
              "      <td>225.906780</td>\n",
              "      <td>784.152472</td>\n",
              "      <td>793.352400</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       I__ar_coefficient__coeff_0__k_10  ...             OY\n",
              "count                     286122.000000  ...  286122.000000\n",
              "mean                          13.507051  ...       4.983392\n",
              "std                           13.694786  ...       1.548102\n",
              "min                         -439.861981  ...       0.000000\n",
              "25%                            4.711044  ...       4.000000\n",
              "50%                           15.018787  ...       5.000000\n",
              "75%                           20.674117  ...       7.000000\n",
              "max                         4775.443655  ...       8.000000\n",
              "\n",
              "[8 rows x 63 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6K3l8iv0eP1"
      },
      "source": [
        "zipObj = ZipFile('final_data.zip', 'w', zipfile.ZIP_BZIP2)\n",
        "zipObj.write('/content/final_data')\n",
        "zipObj.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7k4SiOwC0PuO"
      },
      "source": [
        "-----\n",
        "# Fitting Models :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QSr0e09B93k5"
      },
      "source": [
        "# data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "booBQzTB0w-v"
      },
      "source": [
        "Archive('/content/drive/MyDrive/final_data.rar').extractall('/content')\n",
        "final_data = pd.read_csv('/content/final_data', index_col=0, header=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q26tsAx8wupv"
      },
      "source": [
        "X = final_data.iloc[:,0:53]\n",
        "Y = final_data['class']\n",
        "OH_Y = final_data.loc[:,'OH1':'OH9']\n",
        "O_Y = final_data['OY']\n",
        "LPV_Y = final_data['OH6']\n",
        "categories = Y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FMoHWBuy7Ka",
        "outputId": "e96ec196-4343-490d-a3af-46eda8833d7b"
      },
      "source": [
        "Y.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LPV      111379\n",
              "ECL       78343\n",
              "RRLYR     74935\n",
              "CEP       17582\n",
              "DSCT       2788\n",
              "T2CEP       586\n",
              "ACEP        349\n",
              "DPV         137\n",
              "RCB          23\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Upjz3xkV2M3K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "44b699f4-a67a-41c6-d372-33a88632920a"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize = [20, 20])\n",
        "matrix = np.triu(X.corr())\n",
        "sns.heatmap(X.corr(), yticklabels=False, mask=matrix,\n",
        "            cmap=sns.diverging_palette(20, 220, n=200))\n",
        "\n",
        "plt.savefig('XCorr.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6YpErMMtnVT/8D74rXeOaKy912sufz6eP5T+mtGd1Hr/3sT9J17nliwY7ChcAAIiI+WzXLpLcMUw6AAAAACWEDgAAAEAJoQMAAABQQugAAAAAlBA6AAAAACW2Pb0CAAAALlezAU4yJMekAwAAAFBC6AAAAACUEDoAAAAAJYQOAAAAQAmLJAEAAJgkiyTHZ9IBAAAAKCF0AAAAAEoIHQAAAIASQgcAAACghNABAAAAKOH0CgAAACapOb1idCYdAAAAgBJCBwAAAKDEth+vuPkvXZ9+gv537kzd3+Y75BMgvY/dwTkTGQ/a+p33p2s89vAD6Rpf+NUvSddYffbV6RonDl4dn/jMI+k6L7w23wsAAMBQTDoAAAAAJXbIGAEAAAAMaz6bxqT45cykAwAAAFBC6AAAAACUEDoAAAAAJYQOAAAAQAmhAwAAAFDC6RUAAABMUmtOrxibSQcAAACghNABAAAAKCF0AAAAAEoIHQAAAIASFkkCAAAwSS0skhybSQcAAACghNABAAAAKCF0AAAAAEoIHQAAAIASQgcAAACghNMrAAAAmKT5zOkVYzPpAAAAAJTYdtLh9MZm/glOHE/dP9tzRbqHHWOxSJdoK/nhlN57ukbWfO++dI3Z2p58jSvy76/ZWr7G8dNn0jVe8+/fk65xZP1wugYAAMDnmXQAAAAASggdAAAAgBIWSQIAADBJs2aR5NhMOgAAAAAlhA4AAABACaEDAAAAUELoAAAAAJSwSBIAAIBJahZJjs6kAwAAAFBC6AAAAACUEDoAAAAAJYQOAAAAQAmhAwAAAFDC6RUAAABMktMrxmfSAQAAACghdAAAAABKCB0AAACAEkIHAAAAoIRFkgAAAEzSfGaR5Ni2DR3Obm2ln6CdOpkrsFyke9gp+iL/tcz2XJHvY2sz2cQ83cPK/oPpGrPVtXyNtfzrOVtdTdc4/tjZdI3sZt7ee9y+fne6jyPrh9M1AACAafDxCgAAAKCE0AEAAAAoIXQAAAAASggdAAAAgBJOrwAAAGCSssvWyTPpAAAAAJQQOgAAAAAlhA4AAABACaEDAAAAUMIiSQAAACZpFhZJjs2kAwAAAFBC6AAAAACUEDoAAAAAJYQOAAAAQAmhAwAAAFDC6RUAAABM0nzm9IqxmXQAAAAASggdAAAAgBJCBwAAAKCE0AEAAAAose0iyT944OH0E7zgN9+bun/tqueme4hZPlvpm5vpGsutjXSN1Sufna7Rs320/DKWrVe9MV3juq/8W+kaRx85lq6x94mtdI03vufX0zXe+g//bur+z508ne7h8VNn4tfv+8N0nZfc/KXpGgAA0Ab4tws5Jh0AAACAEkIHAAAAoITQAQAAACghdAAAAABKbLtIEgAAAC5XFkmOz6QDAAAAUELoAAAAAJQQOgAAAAAlhA4AAABACaEDAAAAUMLpFQAAAEzSzOEVozPpAAAAAJQQOgAAAAAlhA4AAABACaEDAAAAUMIiSQAAACZpPvNz9rH5DgAAAAAlhA4AAABACaEDAAAAUGLbnQ5ffM2z00+w/2tekrp/vu9Auoch9GUfoki6xGxtT76NxVauQGvpHvZ96iPpGmf+1yfTNb781q9P11g5uDdd4zXf8LXpGt/9s/85df9igPd47/kar/mGr413/c/fS9f5pq+5JV0DAADIMekAAAAAlHB6BQAAAJPUBpjSJsekAwAAAFBC6AAAAACUEDoAAAAAJYQOAAAAQAmLJAEAAJgkeyTHZ9IBAAAAKCF0AAAAAEoIHQAAAIASQgcAAACghNABAAAAKOH0CgAAACZp3vycfWy+AwAAAEAJoQMAAABQQugAAAAAE9Nau6q19u7W2snW2idba3c+xfVrrbU/aK19esg+7HQAAACA6XlzRGxExDURcUtEvK+1dl/v/eiTXP8DEfFIRBwcsgmhAwAAAJPUWhu7hVG01vZHxMsj4st67yci4t7W2nsj4hUR8YOXuP6GiPi2iHhtRPy7IXvx8QoAAACYlpsiYqv3fv8Fj90XES96kut/KiJ+KCJOD92I0AEAAAAuI621u1prH7rg110XXXIgIo5f9NjjcYmPTrTWXhYR8977uyt63fbjFRuLRfoJ9m6cTd3fVlbTPcQAIzV9gNcilgPUGEDfAX20tT3pGrO1Kwaoke+jrebfoyfObqRrrMznqft7z78vlukKEac28q/FW3/zd+Inf+3edJ0j64fTNQAAYGi993si4p5tLjkREYcueuxQRDxx4QPnP4bxpoj4xkEbvICdDgAAADAt90fESmvtxt77R88/dnNEXLxE8saIeGFEfOD8/ou1iLiytfaZiPjq3vsnso0IHQAAAJik2e7cIxm995OttXdFxBtaa6+Mc6dX3BERL77o0o9ExAsu+P2LI+LuiPiKOHeSRZqdDgAAADA9r46IvRHxcES8MyJe1Xs/2lq7rbV2IiKi977Ve//M539FxLGIWJ7//SCfyzfpAAAAABPTez8WES+9xOMfiHOLJi91z5GIeP6QfZh0AAAAAEoIHQAAAIASQgcAAACghJ0OAAAATNL5YyAZkUkHAAAAoITQAQAAACghdAAAAABKCB0AAACAEhZJAgAAMEnzmZ+zj813AAAAACghdAAAAABKCB0AAACAEkIHAAAAoITQAQAAACjh9AoAAAAmqbWxO2Db0GFrsUw/wXLjbOr+trqW7mEQy54u0Reb+T5m83yN5SJfI2m2tiddY4j3xiA1VlbTNc5s5N8bK8njgPo8/x4f4u+Mk2c30jWGcvv63ekaR9YPD9AJAABcnny8AgAAACghdAAAAABKCB0AAACAEhZJAgAAMEkzmyRHZ9IBAAAAKCF0AAAAAEoIHQAAAIASQgcAAACghNABAAAAKOH0CgAAACZp1vycfWy+AwAAAEAJoQMAAABQQugAAAAAlBA6AAAAACUskgQAAGCSZm3sDjDpAAAAAJQQOgAAAAAlhA4AAABACaEDAAAAUELoAAAAAJTY9vSKxbKnn2C5uZG6f75YpHsYwnKxla7Rt/I12mq+RmRf05bPqmZrV+RrrK4NUGM1XWMr8itxz2zmv6+r89z3ZdmX6R4W+RJxdoDXYj7Lv0cXywG+mIi4ff3u1P1H1g8P0gcAwG7UmuMrxmbSAQAAACghdAAAAABKCB0AAACAEkIHAAAAoMS2iyQBAADgcjWzSHJ0Jh0AAACAEkIHAAAAoITQAQAAACghdAAAAABKWCQJAADAJM1mFkmOzaQDAAAAUELoAAAAAJQQOgAAAAAlhA4AAABACaEDAAAAUMLpFQAAAEzSrDm9YmwmHQAAAIASQgcAAACghNABAAAAKLHtToet5SL9BMuzZ1L3L1ZPpXuINkC2shjgtVhspmu0lfwajr61lethln895/v274gafc/edI0zZ/Pf1ydOn03X2Lu2mq6RNRvgz9pjp3J/Z0RErMx3Rp667D11f+89bl+/O93HkfXD6RoAAPBMWCQJAADAJDWLJEe3M34cCAAAAEyO0AEAAAAoIXQAAAAASggdAAAAgBJCBwAAAKCE0ysAAACYpLnTK0Zn0gEAAAAoIXQAAAAASggdAAAAgBJCBwAAAKCERZIAAABMUrNIcnQmHQAAAIASQgcAAACghNABAAAAKCF0AAAAAEoIHQAAAIASTq8AAABgkmZOrxidSQcAAACghNABAAAAKLHtxyvmLZ9JzFbWUve3+QCfAJnlv46e72KQhGc2wOuxTN7fBhhR6psb6RrLAWq0AWqsDPA9WV2Zp2tsLhap+xfL/Lt8a5l9d0WsDfBaLAboY9nzr0cfoMYQbl+/O13jyPrhAToBAGC3MekAAAAAlLBIEgAAgEkaYkqbHJMOAAAAQAmhAwAAAFBC6AAAAACUEDoAAAAAJSySBAAAYJLmM4skx2bSAQAAACghdAAAAABKCB0AAACAEkIHAAAAoITQAQAAACjh9AoAAAAmqTWnV4zNpAMAAABQQugAAAAAlBA6AAAAACWEDgAAAEAJiyQBAACYpJlFkqMz6QAAAACU2HbSYWu5TD/B4szJ1P2ztT3pHobQe/616Jub6RptdTXfx2Ird3+6g4j5vgM7osbGLD/ss7G5SNc4vbGRrnFF8r0xxHFCLf8Wj1Mb+SJrK/N0jcUy/07fWuT+3lj2fA99gBoREbev352ucWT98ACdAABwOTHpAAAAAJQQOgAAAAAlhA4AAABACadXAAAAMEmzmdMrxmbSAQAAACghdAAAAABKCB0AAACAEkIHAAAAoIRFkgAAAEzSLCySHJtJBwAAAKCE0AEAAAAoIXQAAAAASggdAAAAgBJCBwAAAKCE0ysAAACYpNacXjE2kw4AAABACaEDAAAAUELoAAAAAJQQOgAAAAAlLJIEAABgkmb2SI5u29Bha7FIP0FfLFP3Lxeb6R4Gkfw6IiL6AF9LX2wNUCP/fc2are3ZETXObuZfi80BXs/TG/nv67611XSNneD02fyfk9X5PF1j1nq6RtZimf97Z4C/umI2wNbnxXIZt6/fna5zZP1wugYAAH9xfLwCAAAAKCF0AAAAgIlprV3VWnt3a+1ka+2TrbU7n+S6H2itfaS19kRr7eOttR8Ysg87HQAAAGB63hwRGxFxTUTcEhHva63d13s/etF1LSK+PSJ+PyL+ckT8t9bap3rv/3GIJkw6AAAAwIS01vZHxMsj4vW99xO993sj4r0R8YqLr+29v6n3/ru9963e+x9FxHsi4tahejHpAAAAwCTNZtP8OXtr7a6IuOuCh+7pvd9zwe9vioit3vv9Fzx2X0T8zaeo2yLitoj4maF6FToAAADAZeR8wHDPNpcciIjjFz32eEQcfIrS63HuExFve8bNXUToAAAAANNyIiIOXfTYoYh44sluaK0djnO7HW7rvZ8dqpFpzpoAAADA7nV/RKy01m684LGbI+LiJZIREdFa+66I+MGIeEnv/dNDNiJ0AAAAgAnpvZ+MiHdFxBtaa/tba7dGxB0R8Y6Lr22tfWtEvDEivq73/rGhe/HxCgAAACZp1trYLYzp1RHxcxHxcEQ8GhGv6r0fba3dFhG/2ns/cP66fxERz46ID7Y/e71+off+j4ZoQugAAAAAE9N7PxYRL73E4x+Ic4smP//7Gyr78PEKAAAAoITQAQAAACghdAAAAABK2OkAAADAJO3uPZI7g0kHAAAAoITQAQAAACghdAAAAABKCB0AAACAEtsuklz2AZ5hucjd34doIq9nv46I6EN8LQN8U4b4WrLaSn6HaZvN0zWWA3xPBqkxwPd1dSX3eqws8u+LrUU+x1z0ZbrGyjz/3ug9/3rMZ7nNRfPZPBbJ98ZiB/x5H9Lt63enaxxZPzxAJwAAPB1OrwDYobKBAwDAbjefGe4fm+8AAAAAUELoAAAAAJQQOgAAAAAlhA4AAABACYskAQAAmKTkYWIMwKQDAAAAUELoAAAAAJQQOgAAAAAlhA4AAABACaEDAAAAUMLpFQAAAEzSrPexW9j1TDoAAAAAJYQOAAAAQAmhAwAAAFBC6AAAAACUsEgSAACAaerLsTvY9Uw6AAAAACWEDgAAAEAJoQMAAABQYtudDqvzfCbRVnJrI9p8QmsntgaoMZ+nS+yEpGlx5ky6xnLjbLrG6kr+9RzC2mq+j9Mbm6n7z27m36Abi0W6xp7k3xkREZtb+T6G+Fq2kjV6T7cQfYAiU/sk5O3rd6drHFk/PEAnAADTtxP+/QkAAABM0ITGCAAAAODP9AGmV8kx6QAAAACUEDoAAAAAJYQOAAAAQAmhAwAAAFDCIkkAAACmaYgzyEkx6QAAAACUEDoAAAAAJYQOAAAAQAmhAwAAAFDCIkkAAACmqS/H7mDXM+kAAAAAlBA6AAAAACWEDgAAAEAJoQMAAABQQugAAAAAlHB6BQAAAJPUF06vGJtJBwAAAKBE670/6X987MEHnvw/Pk2PfuD92RIxW9uTrpG2zev0tEssFukas9W1fB9DnFWbfD323XBTuoXFqZPpGmtXX5uuMb9iX7rGyT0H0zU+eyL/emxu5d6jW8v8e+sL9u1N13j0xKl0jcUAX8tiOcDfG8k/a/NZS/cwwJcRe1bn6Rp7V1fTNVZX8n2cOruZrvFVX3JDugYADFNgJ40AACAASURBVCT/fxZ2sMcf+swA/09m57nymmsvm+/bjp902BGBA/+vAQIYdp5s4MDwsoEDAACMbceHDgAAAMDlySJJAAAAJmmQj5aTYtIBAAAAKCF0AAAAAEoIHQAAAIASQgcAAACghNABAAAAKOH0CgAAAKap97E72PVMOgAAAAAlhA4AAABACaEDAAAAUELoAAAAAJSwSBIAAIBp6suxO9j1TDoAAAAAJYQOAAAAQAmhAwAAAFBC6AAAAACUEDoAAAAAJZxeAQAAwCT1xWLsFna9bUOHX/0/D6af4AW//HOp+9eec026hyEM8WZdbpxN11i98ln5PrY2U/e3+Tzdw9ZfeWO6xnXXflG6xv2PHEvX2Lvs6Rpv+qX35mt8yzem7j924lS6hyfO5N/jP/SL70/X+Mdf/+J0jZNnNtI1Tm3kapw6m/uzGhFxYoDvyepK/s/8ybP51/PMxla6xtYyf2zWnuTr8YrbvjI+/IlPp/v48hc+P10DAKCaj1cAAAAAJYQOAAAAQAmhAwAAAFDCIkkAAACmqef3r5Fj0gEAAAAoIXQAAAAASggdAAAAgBJCBwAAAKCERZIAAABMUrdIcnQmHQAAAIASQgcAAACghNABAAAAKCF0AAAAAEoIHQAAAIASTq8AAABgkvpiMXYLu55JBwAAAKCE0AEAAAAoIXQAAAAASggdAAAAgBJPuUiytZZ+kvnBQ7n79+1P3b84dTLm+w+kamwdfzzme/elaiw3zqb7iMi/Hsvjj6W+luXG2ZhfkXsttiLixmufk6px4sxGHDz1WKpGRMR1X5B7fz526kwc2rtngD4Opmv81h9+7Bnf+2XPvzbu/aNPpJ7/RS+4Jj74x59K1YiIeN6zct+TiIjf/fgDqfu/5Lqr4/f/5MFUjS++9tlx9NMPPeP7b7j6qrj/wUdSPVz/rEPx8UeOpWrcdN3V8dHPfDbdxycf+VyqxjVXHowHHzueqnH1oQPx6BMnUzWuf9ah+NPP5fr4cPK99eVfeF18y7/++VSNd37vt6fuB4DLQl+O3cGut+2kwxQCh4gY5h/6ycBhsD6GeD2SX0s2cIjIBw4RsSMCh4iYROAQEenAISImEzhERDpwiIhU4BAR6cAhItKBQ0SkA4eISAcOEZEOHCIiHThExOiBQ0TEG//Lb6RrZEMLAICnw8crAAAAgBJCBwAAAKCE0AEAAAAoIXQAAAAASjzl6RUAAABwWep97A52PZMOAAAAQAmhAwAAAFBC6AAAAACUEDoAAAAAJSySBAAAYJJ6X47dwq5n0gEAAAAoIXQAAAAASggdAAAAgBJCBwAAAKCE0AEAAAAo4fQKAAAApmmxGLuDXc+kAwAAAFBC6AAAAACUEDoAAAAAJbbd6dB7zz/DMlejJ++PiGgDRCu9L/NFlvkag3xPkjX6Mv+5qCG+jNl8ni8ygCG+J1uL/HujtZa6fz7L3T+U5QCvZ/Kl4CKzAV7Q7PszQkp+ocUA/5v08OMn4vb1u9N1jqwfTtcAAKbLIkkAAAAmaZAf2pLiB0cAAABACaEDAAAAUELoAAAAAJQQOgAAAAAlhA4AAABACadXAAAAME1OrxidSQcAAACYmNbaVa21d7fWTrbWPtlau/NJrmuttR9trT16/tePttbaUH2YdAAAAIDpeXNEbETENRFxS0S8r7V2X+/96EXX3RURL42ImyOiR8R/j4iPR8RPD9GESQcAAACYkNba/oh4eUS8vvd+ovd+b0S8NyJecYnL/35E/Fjv/dO99wci4sci4juG6kXoAAAAANNyU0Rs9d7vv+Cx+yLiRZe49kXn/9tTXfeM+HgFAAAAk9QXi7FbKNFauyvOfSzi8+7pvd9zwe8PRMTxi257PCIOXqLcgfP/7cLrDrTWWu/5TZxCBwAAALiMnA8Y7tnmkhMRceiixw5FxBNP49pDEXFiiMAhwscrAAAAYGruj4iV1tqNFzx2c0RcvEQyzj9289O47hkROgAAAMCE9N5PRsS7IuINrbX9rbVbI+KOiHjHJS7/+Yh4bWvtea216yPi+yPi7UP1InQAAACA6Xl1ROyNiIcj4p0R8are+9HW2m2ttRMXXPczEfErEfHhiPhIRLzv/GODsNMBAACAaerLsTsYTe/9WES89BKPfyDOLY/8/O97RLzu/K/BmXQAAAAASggdAAAAgBJCBwAAAKDEtjsdhjiWc3HyUseAPn2zK65I99BaPltZbm6ka/StrXSN2dqedI3l1mbq/jbLv54fe/hYusZ1z7r42Nk/vwcfejRdY+/aWrrGoydOpmu8+MYXpu7/3MnT6R4eO5Wv8RtH/zhd42V//cvSNU6ezf+ZP3EmV+PMZu7PakTEidP5r2N1Jf9n/uTZ/NcyxOuxWOb/d21tPk/df9N1V6d7ePjxE0990VN47pUHnvqip9B7j7/3429L1/lPr/3OdA0AYGcy6QAAAACUcHoFAAAAkzTE9D45Jh0AAACAEkIHAAAAoITQAQAAACghdAAAAABKWCQJAADANPXl2B3seiYdAAAAgBJCBwAAAKCE0AEAAAAoIXQAAAAASggdAAAAgBJOrwAAAGCS+sLpFWMz6QAAAACUEDoAAAAAJYQOAAAAQAmhAwAAAFDCIkkAAACmqVskOTaTDgAAAEAJkw5ctmbR0jVay9eY5UsA7FqPHD8Zt6/fna5zZP3wAN0AAEMz6QAAAACUEDoAAAAAJYQOAAAAQAk7HQAAAJik3vvYLex6Jh0AAACAEkIHAAAAoITQAQAAACghdAAAAABKWCQJAADANC0WY3ew65l0AAAAAEoIHQAAAIASQgcAAACghNABAAAAKGGRJAAAAJPU+3LsFnY9kw4AAABACaEDAAAAUELoAAAAAJQQOgAAAAAlhA4AAABACadXAAAAME29j93Brrdt6NBa+4vqY5sedsYwRpvN0zV6bA3QSV6bJV/THfC+AIAL3b5+d+r+I+uHB+oEALjQzvgXPQAAADA5QgcAAACghNABAAAAKGGRJAAAANPUl2N3sOuZdAAAAABKCB0AAACAEkIHAAAAoITQAQAAACghdAAAAABKOL0CAACASeoLp1eMzaQDAAAAUELoAAAAAJQQOgAAAAAlhA4AAABACYskAQAAmKTeLZIcm0kHAAAAoITQAQAAACghdAAAAABKCB0AAACAEv+XvTsPk6UqDz/+PXfuveyrKCoKbqCICmo0LlERjRqNe6JRIybGEEHUmETjLuJuTFyCGy5EDWrigtFgXCJgRJP8IFEQxCiCuKEgy4XLeu/0+/vj1EAzd3q6Zs6p6bo938/z1DMzXV1vv3W6prv67VPn9H4gyRoDf6RUXluJwWxxjL6IQVmbpjXl7Tmo8LwOiOIYEeUxBuUhJEkTdvBRxxTHOOWoIytkIknSdOl90UGSJEmSpGWp8CWjynh5hSRJkiRJ6oRFB0mSJEmS1AmLDpIkSZIkqRMWHSRJkiRJUiccSFKSJEmSNJVidnpmIdxa2dNBkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJ5y9QpIkSZI0nWIw6QxWPXs6SJIkSZKkTlh0kCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcKBJCVJkiRJ0yli0hmsevZ0kCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcIxHbTVGgz6cX3WIAaTTkGS1AMHH3VMcYxTjjqyQiaSJPWHRQdJkiRJ0lQKB5KcOC+vkCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcKigyRJkiRJ6oRFB0mSJEmS1Alnr5AkSZIkTaWYnZ10CquePR0kSZIkSVInLDpIkiRJkqROWHSQJEmSJEmdsOggSZIkSZI64UCSkiRJkqTpFINJZ7Dq2dNBkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJ5y9QpIkSZI0nSImncGqZ08HSZIkSZLUiRSLVH4uPufM4rLQ4LprygKkntRF+jK/a1/ao9Ca9dsUx0gpFceIQfnzGrOzxTHWrFtXnkeFfemDtKb8GF/sdU1brxr/8zX04fjqS1v0xsxM2fYVXsf7ZI+73H3SKUjaukz1m8oFJ/7z5N+4O7DPY56y1Txv0/EJVpIkSZIk9Y5FB0mSJEmS1AkHkpQkSZIkTaUal0KrjD0dJEmSJElSJyw6SJIkSZKkTlh0kCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcLZKyRJkiRJ0yli0hmsevZ0kCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcKigyRJkiRJ6oQDSUqSJEmSplLEYNIprHr2dJAkSZIkaZVJKe2eUjohpXRVSumClNLTF7nvi1NKZ6WUrkwpnZ9SenHbx7GngyRJkiRJq8+7geuBPYGDgBNTSmdExNkL3DcBhwJnAncEvpJS+mlEfHLcg9jTQZIkSZKkVSSltAPwZOBVEbExIk4FPg88c6H7R8RbI+J/I2JzRPwf8C/AA9s8lkUHSZIkSZK2Iimlw1JKpw8thy0xxH7A5oj4wdBtZwAHtHjsBDwIWKhHxBa8vEKSJEmSNJ0iJp1BJyLiWODYghA7AlfMu20DsFOLbY8id2A4rs0D2dNBkiRJkqQpklI6JaUUI5ZTgY3AzvM22xm4ckzcI8ljOzwmIq5rk4s9HSRlKU06A3Ug+bxWV9qmMagwddfaCm/fs7PlMWqYmZl0BnX0ZD+e+olTgG8Ux/naq48ojiFJmpyIOHix9c2YDmtTSvtGxA+bmw9kkUsmUkrPBl4KPDgiftY2F3s6SJIkSZK0ikTEVcBngaNTSjuklB4IPB742EL3Tyk9A3gj8NsRcd5SHsuigyRJkiRJq88RwHbARcAngMPnpstMKT0opbRx6L6vB24GnJZS2tgs72vzIF5eIUmSJEnSKhMRlwJPGLHuG+TBJuf+vv1yH8eigyRJkiRpKkVfxjBaxby8QpIkSZIkdcKigyRJkiRJ6oRFB0mSJEmS1AmLDpIkSZIkqRMOJClJkiRJmk4xmHQGq549HSRJkiRJUicsOkiSJEmSpE5YdJAkSZIkSZ2w6CBJkiRJkjph0UGSJEmSJHXC2SskSZIkSVMpIiadwqpnTwdJkiRJktQJiw6SJEmSJKkTFh0kSZIkSVInLDpIkiRJkqROOJBkW8n6jEaIwaQzACA2b5p0ClX+T9K22xbHiGuvLY7RB2lmpjjGVA2elNKkM6giZmeLY6R168rz6Mmx0YtntcL/Wl+snSl/HZ4dDHj4695THOffX3VEcQxJKlbhfVdl/CQtSZIkSZI6YdFBkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpE85eIUmSJEmaSn2ZrWk1s6eDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJxxIUpIkSZI0nWIw6QxWPXs6SJIkSZKkTlh0kCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcKBJCVJkiRJUykGDiQ5afZ0kCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcKigyRJkiRJ6sSiA0lu3nhF8QOs23X3sgCzs8U5qLKZmeIQa3fatTyN7XcsjnHdr35WHKOGzVdtLI6x8fvfqZBJoVRex9zprgcVx9h4zhnFMWoc5yml8jyKk+hHbTnVaM815TGo8JzEoOx9aZs99yrOYWZ2U3GM6y+5uDhGDetvdvNJpzBV/uD+BxbH2DxbPuja5sGA9375G8VxDn/kg4pjSJImy9krJEmSJEnTqfDLApXrx1dgkiRJkiRp6lh0kCRJkiRJnbDoIEmSJEmSOmHRQZIkSZIkdcKBJCVJkiRJUykiJp3CqmdPB0mSJEmS1AmLDpIkSZIkqRMWHSRJkiRJUicsOkiSJEmSpE5YdJAkSZIkSZ1w9gpJkiRJ0nQaDCadwapnTwdJkiRJktQJiw6SJEmSJKkTFh0kSZIkSVInLDpIkiRJkqROOJCkJEmSJGkqxWB20imsevZ0kCRJkiRJnbDoIEmSJEmSOrHo5RXrdt29+AE2b7isLECaorpIVJgjdkraIzZvLo6x6bJfV8ikH2r8r+1634cUbZ/6cmytmSkOUdoWU6Uvz6tuENdfWxwjrd+2OMbanctfd9Q/H/ncl4tjzKwpf91YO1Me41kPujcf/4/TimI8/cH3Kc5DklTGs1FJkiRJktQJiw6SJEmSJKkTzl4hSZIkSZpOEZPOYNWzp4MkSZIkSeqERQdJkiRJktQJiw6SJEmSJKkTFh0kSZIkSVInHEhSkiRJkjSVYuBAkpNmTwdJkiRJktQJiw6SJEmSJKkTFh0kSZIkSVInLDpIkiRJkqROWHSQJEmSJEmdcPYKSZIkSdJ0isGkM1j17OkgSZIkSZI6YdFBkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUCQeSlCRJkiRNpRjMTjqFVc+eDpIkSZIkqROL9nRIa2aKHyA2by4LMFOeQ2/MVqiyTVN7FCo+toA169aV51EcAdas36Y4xuy1V1fIpFBKxSFmtt22OMbsNRXaIk2+Jpum6P89VTg2psXsbPnzWuP/JGq8J1UwTcd5H6ydmfxrF8C6Cs/rIMreYY/7+ul86JTTivP42quPKI4hSatZP96ZJEmSJEnS1LHoIEmSJEmSOuFAkpIkSZKk6VR4qZbK2dNBkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJ5y9QpIkSZI0lWIwmHQKq549HSRJkiRJUicsOkiSJEmSpE5YdJAkSZIkSZ2w6CBJkiRJkjrhQJKSJEmSpOk0mJ10BquePR0kSZIkSVInLDpIkiRJkqROWHSQJEmSJEmdsOggSZIkSZI6YdFBkiRJkiR1wtkrVtLMzKQz6I2UUnmQtVN0+EaUx5gtG5k3ahyeFXYjCvcjB6mQCIMKMcrEFA22XOMZSdPyGlrhGO/P/0m5KvuiG9R4Wmu8RdcwOyjbmUGFxogIDj7qmOI4pxx1ZHEMScsTPXm/W83s6SBJkiRJkjph0UGSJEmSJHXCooMkSZIkSeqERQdJkiRJktSJKRqJT5IkSZKkIYWD0qqcPR0kSZIkSVInLDpIkiRJkqROWHSQJEmSJEmdsOggSZIkSZI6YdFBkiRJkiR1wtkrJEmSJEnTKQaTzmDVs6eDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJxxIUpIkSZI0lWIwO+kUVj17OkiSJEmSpE5YdJAkSZIkSZ2w6CBJkiRJkjph0UGSJEmSJHVi8YEkU3lNonTgjlScQR0Rg0mnAECq8Jz0QQzK23Nw/bXFMWa23b44Rg1pbfmYrmnd+sIA5cdWmpkpj7F2XXGMNRHFMapIk38Fq/GcqK7YVH6M1/g/6Y0e/J9Mk7UzFV7LKzwnayrE2H6bsuN8Zk15Dptn67yfHHzUMcUxTjnqyAqZSKtP9OW8sIdSSrsDHwIeAfwaeFlEfHzMNuuBM4CdIuI2bR7H2SskSZIkSVp93g1cD+wJHAScmFI6IyLOXmSbFwMXAzu1fZDp+NpckiRJkiS1klLaAXgy8KqI2BgRpwKfB565yDa3B/4QeNNSHsuigyRJkiRJW5GU0mEppdOHlsOWGGI/YHNE/GDotjOAAxbZ5u+BlwPXLOWBvLxCkiRJkqStSEQcCxxbEGJH4Ip5t21gxGUTKaUnAjMRcUJK6eClPJA9HSRJkiRJmiIppVNSSjFiORXYCOw8b7OdgSsXiLUD8FbgBcvJxZ4OkiRJkqTpVGHWvK1RRBy82PqmkLA2pbRvRPywuflAYKFBJPcFbgd8o5nhaD2wS0rpl8D9IuLHiz2WRQdJkiRJklaRiLgqpfRZ4OiU0nPIs1c8HnjAAnc/C7jt0N8PAI4B7kWeyWJRXl4hSZIkSdLqcwSwHXAR8Ang8LnpMlNKD0opbQSIiM0R8cu5BbgUGDR/z457EHs6SJIkSZK0ykTEpcATRqz7BnmwyYXWnQLcpu3j2NNBkiRJkiR1wp4OkiRJkqSpFKt0IMk+saeDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJyw6SJIkSZKkTjh7hSRJkiRpOoWzV0yaPR0kSZIkSVInLDpIkiRJkqROLH55xWC2+AHSuvVl26dUnEMVEZPOAOhRexRKa8rrXWu22a5CJv0QmzcXxxhcf13R9jWOrRr/JTWOjdnrrinPI/WgJjsl/+/V9OE5qSA2b+pHjEE/upvW+J/XjTbPlj+vNV561lZ4XjdeW/a+Njvox7lbDSklHvradxfHOfk1z6uQjSQtje/0kiRJkiSpEw4kKUmSJEmaTlPU62lrZU8HSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJyw6SJIkSZKkTlh0kCRJkiRJnXD2CkmSJEnSVIoYTDqFVc+eDpIkSZIkqRMWHSRJkiRJUicsOkiSJEmSpE5YdJAkSZIkSZ1wIElJkiRJ0lSKweykU1j17OkgSZIkSZI6YdFBkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpE85eIUmSJEmaThGTzmDVs6eDJEmSJEnqxKI9HdK69cUPEJuuLwuwZqY4hxoiBpNOIUvTUSeavfbq4hiDa68tjrF2512LY5BSeYi15Z2O1u9+i7IcZvpxbA02bSqOUdoWUpc2b7yiOEZau644xpq+vL86f3pVO2+3TXGMdWvLj421a8rfU/bZY/ei7Wu0RY0vSCucJlTJY2ZN4ol/86HiOCe8+E/Kk5G0qvTjU4YkSZIkSZo6Fh0kSZIkSVInHEhSkiRJkjSdBg4kOWn2dJAkSZIkSZ2w6CBJkiRJkjph0UGSJEmSJHXCooMkSZIkSeqEA0lKkiRJkqZSDGYnncKqZ08HSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJyw6SJIkSZKkTlh0kCRJkiRJnXD2CkmSJEnSdIrBpDNY9ezpIEmSJEmSOmHRQZIkSZIkdcKigyRJkiRJ6oRFB0mSJEmS1AkHkpQkSZIkTaWImHQKq96iRYfYdH3xA6R168u2T6k4hxpqZFHjgO9Le5Ras8125THWb1ueSE/ac7BpU3GMuObqsgCpHx2f1qwtr4Vuvv66CpmUm5b/V9VV4701KhxbNWJUUeNksHRfpuiE9Ipryl//ahwa265bVxzjVxuuLNq+Rlv05cNKjfeTGvuy83bb8ri3fLAoxuf/+jnFeUjauvTjU4YkSZIkSZo6Fh0kSZIkSVInLDpIkiRJkqROWHSQJEmSJEmdcPYKSZIkSdJ0GgwmncGqZ08HSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJyw6SJIkSZKkTjiQpCRJkiRpKsVgdtIprHr2dJAkSZIkSZ2w6CBJkiRJkjph0UGSJEmSJHXCooMkSZIkSeqERQdJkiRJktQJZ6+QJEmSJE2niElnsOrZ00GSJEmSJHXCooMkSZIkSeqERQdJkiRJktQJiw6SJEmSJKkTDiS5glJKFYJUqBPFYOI51GmLCjEqiJ4MThObri8LMDNTJ5FSa8tflorbopLowzHah9eMWnlMidi8qTzI+m3K8xhUeF4rqPJ+0JPX4WnRl+a8fvPspFPojTUV/k9mKzyxpWlsuPpaDj7qmOI8TjnqyOIYWj1i0JMXtVXMs0BJkiRJktQJiw6SJEmSJKkTFh0kSZIkSVInLDpIkiRJkqROOJCkJEmSJGk6DRyUdtLs6SBJkiRJkjph0UGSJEmSJHXCooMkSZIkSeqERQdJkiRJktQJiw6SJEmSJKkTzl4hSZIkSZpKEYNJp7Dq2dNBkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpEw4kKUmSJEmaThGTzmDVs6eDJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpE47psBola03SqhGDSWcgScsysyZNOgX11MFHHVMc45SjjqyQiaQ2/PQpSZIkSZI6YU8HSZIkSdJUioG9PifNng6SJEmSJKkTFh0kSZIkSVInLDpIkiRJkqROWHSQJEmSJEmdcCBJSZIkSdJ0ciDJibOngyRJkiRJ6oRFB0mSJEmS1AmLDpIkSZIkqRMWHSRJkiRJUicsOkiSJEmSpE44e4UkSZIkaSpFOHvFpNnTQZIkSZIkdcKigyRJkiRJ6oRFB0mSJEmS1AmLDpIkSZIkqRMOJClJkiRJmk6DmHQGq549HSRJkiRJUicsOkiSJEmSpE54ecUKiijv2pOYjnlma7RFDSmlSadQz8zMpDMgpZ7UMaflee1Le/ZE6sExXkM/Xv2k0fryEjrbgy7R03SeUGNfrts8WyGTfjj4qGOKY5xy1JEVMpGmn2e0kiRJkiSpExYdJEmSJElSJ7y8QpIkSZI0lWIwPZcFba3s6SBJkiRJkjph0UGSJEmSJHXCooMkSZIkSatMSmn3lNIJKaWrUkoXpJSePub+90op/UdKaWNK6VcppRe2eRzHdJAkSZIkafV5N3A9sCdwEHBiSumMiDh7/h1TSnsAXwJeBHwaWA/cps2DWHSQJEmSJE2nGEw6g15KKe0APBm4W0RsBE5NKX0eeCbw0gU2+QvgyxFxfPP3dcA5bR7LyyskSZIkSVpd9gM2R8QPhm47AzhgxP3vB1yaUvpWSumilNIXUkp7t3kgiw6SJEmSJG1FUkqHpZROH1oOW2KIHYEr5t22AdhpxP1vAzwLeCGwN3A+8Ik2D+TlFZIkSZIkbUUi4ljg2FHrU0qnAA8ZsfqbwPOBnefdvjNw5YhtrgFOiIjTmvivBX6dUtolIjYslqtFB0mSJEmSpkhEHLzY+mZMh7UppX0j4ofNzQcCWwwi2TgTiOGHaJuLRQdJkiRJ0lSKaP3ZeFWJiKtSSp8Fjk4pPYc8e8XjZsp7XwAAIABJREFUgQeM2OQ44DMppXeRCxOvAk4d18sBHNNBkiRJkqTV6AhgO+Ai8vgMh89Nl5lSelBKaePcHSPiJODlwInN/e8EPL3Ng9jTQZIkSZKkVSYiLgWeMGLdN8iDTQ7f9l7gvUt9HHs6SJIkSZKkTlh0kCRJkiRJnbDoIEmSJEmSOuGYDpIkSZKk6TRw9opJs+igiUgpTTqFXqnRHrFpU2kSxTlUibF+m+IQsen68jxmZspjFEqpwptkjeekxlRTNY7xzYMKeVTo4BdleVSZuqsvz2sNvh9ohE2zs0Xbr6lwbM0OKrzuVFAjixqvPTtss75o++s2bS7OoS/TH6aUeOhr310c5+TXPK9CNlK/eXmFJEmSJEnqhEUHSZIkSZLUCYsOkiRJkiSpE47pIEmSJEmaSjEoGx9G5ezpIEmSJEmSOmHRQZIkSZIkdcKigyRJkiRJ6oRFB0mSJEmS1AmLDpIkSZIkqRPOXiFJkiRJmk4xmHQGq549HSRJkiRJUicsOkiSJEmSpE5YdJAkSZIkSZ2w6CBJkiRJkjrhQJKSJEmSpOk0iElnsOrZ00GSJEmSJHXCooMkSZIkSeqERQdJkiRJktQJiw6SJEmSJKkTFh0kSZIkSVInOp+9IqVUGKAndZEYFIcobgvoT3sUikF5e1KjPaN8NNuYnS2OMdi8uTjGmvXblAWo0Z49kdZvO+kUAEgzM5NOYapUeQ3tg568jqc1/chDdT3wzrcrjrHzdoXvJ8BFGzYWx7j9zXcv2v4h+9+hOIddt+/J+0mF17+ocM5zfeE5z7br1hXnUGM/asTYNFt+LrtuZg3vOvGU4jgveMzBxTGmWQzKz9VVxjMOSZIkSZLUCYsOkiRJkiSpExYdJEmSJElSJyw6SJIkSZKkTnQ+kKQkSZIkSZMQFSYEUBl7OkiSJEmSpE5YdJAkSZIkSZ2w6CBJkiRJkjph0UGSJEmSJHXCgSQlSZIkSdMpYtIZrHr2dJAkSZIkSZ2w6CBJkiRJkjph0UGSJEmSJHXCooMkSZIkSeqERQdJkiRJktQJZ6+QJEmSJE2ngbNXTJo9HSRJkiRJUicsOkiSJEmSpE5YdJAkSZIkSZ2w6CBJkiRJkroREUULcNgktzdGP2P0IQdj9C8HY/i8roYYfcjBGD6vqyFGH3Iwhs/raonh4lKy1OjpcNiEtzdGP2P0IQdj9C8HY9SP0YccjNG/HIxRP0YfcjBG/3IwRv0YfcjBGFJFXl4hSZIkSZI6YdFBkiRJkiR1okbR4dgJb2+MfsboQw7G6F8Oxqgfow85GKN/ORijfow+5GCM/uVgjPox+pCDMaSKUkRMOgdJkiRJkjSFvLxCkiRJkiR1wqKDJEmSJEnqhEUHSZIkSZLUibWTTkDSwlJKtwUOBHYFLgfOiIifttx2D+BQ4DFNjF2ADcAZwL8BH4mIi1cgxlrgcUMxbtiXJsbnImJzi/0paYviHPrSnvPi7Ti3LxGxse12zbbrgPuxZXv8V0RsarF9jfa4BfDIBXL4akT8suV+1IhR1BY18qh1bFR4XifeFtLWZDmvw315L+hLHkPbrwPuzI2vG//X9nWnhzGK2qJijLXA/s2f57Q535K6suyBJFNKuwE7AVdGxGVVs5JWqebN7s+a5Q7AucCV5P+1OwHnA+8Djo2I60fEeDPwDOCLwNeBc4Zi7A88BHg0cHxEvLTDGM8FXt5sOyrG/sAbI+J9HbVFUQ4V26I4RhPnbk17PAbYB0hAAD8mnyS+PyK+u8j2NwNeBjwLuBT4/rw8dgM+Arw5In7dxb6klPYHXgc8FPifBba/N3Ay8OqI+N6IHGrEqNEWNfKocXwV7Utf2kLdSindBfh94JYR8bzm7/URcWaLbfcDDqA57wPOjogftHzcved+j4ifLDP3Q4difHQ5MWopeR3u0XtBL/JoYjwGeC7wMGDTUB7rgJOA90XEv/Y9RqW2qPG8/mNEnNXE+hfgZs3qi4HHRcQ5i+UgdSYiWi/kf7w3AhcCs8Cg+fkL4A3AuqXEq7UAOwA7FWx/Z+D5zbJfYS4vBrZf5rZ7kKvN65e43e2BxwJPBR4I7LKEbe9Z6TnYA/hNYLvm73sAfwE8vELstcCHC2OsA06aZB5tcgC+BxwDPACYmbduBrh/s/7sRWIcCWwz5nG2BY7sOMbbyCe3i8W4FfC2DtuiKIeetecngTPJRZQHkD8Irm1+PoD8ofFM4JOLxDgH+CtgrxHrb928hn2vq30B/hv4vVExgG3IH4z+c5H4NWLUaIsaedQ4Nor2pS9t0dzvwXPLYvcbE+P8ZjmvIEaV98Z5MYvONYDdyedgXwT+Y3hpse3vAxeRC7VXNLf9BvDvY7bbG/hP4Grgu8CpzevMVcC3gL1X6Pk4rllK3oMHzTJbEKPodbjS/3uN94K+5PFN4F+BpwO3nrfu1sDTmvXf7HOMSm1RI8ZFNJ8hyIWSvxpa9yIKz4NdXEqWpd0ZPkj+puIQcuVsLflN8BDga8AHx2x/S3KlbgP5zeoB89Zf0SKHVwz9fjPgS9xY/Ph34BYtYpxCc0IBPIlczfw8uSK4gVwJHBfjkBHLr5uYh4zZfn9yF7aNzQvJo5ttrwZ+BtytRQ63avZl7o10M3BZsz+vo+nJMibGAPgh8Cpgn2UdRPB48gnIRcBPgN8hF6JOIFdW/7zoIM0nq8s+SehLjDbbtzl+m/vdvGRftobFtthiP3+39H60LGi2vd/WvExTW5TuS5/aojnHOLnk5Jj8DeE+y31Pa2LUeG8sPteYF+9LTdscTu6VcsPSYttzgAOb3y9rfq4DLh6z3deAtzLvyxTylz1vKXmelrjve88tK/F4i+RR/Do8DTnUygO4e8sYI+/XhxiV2qJGjCuAbZvfLwbWDq1bQ75Uo7NjwsVlsWVJl1eklC5v3nw3LLBuV+DHEbHrItv/E/lD9jHkrluvAp4fER9v1l8ZETuNyeGKiNi5+f3D5K5PRzar3wlcGxF/NCbGZcDuEREppTOaHP6jWfdAcvelu42JMSB/sJ5/nddtm9s3R8QdFtn+K8AXyCc272z24f3kb3D/Drh9RDx2TA5fJHe5eh35xeTVwHnAZ4FjyRXZV46JcRVwBPnE5UHAN4B/AD4TEVcttu1QjLOAl0TEF1NKTwD+ETg4Ik5PKd0T+FRE3GlMjJMWWT0D/FZEzIyJcd4iq9cAt20RoyiPGjnU0nSZfjLzusSSn9tLVjDGWnJBbn6Mk2KFri+skUNf2rOmksvkKrXHDsB+Q9v/oO3rTs0YTZyiSwZL86h5bFTYl4m2Ranm3ACAiHj2MmPUeG8sPteYF+8KcpH1uiXtTN72EmCPJpdLI2L35nXxFxFxi0W229jswxaXsKWUtgEujYgdxjz2+eQu4ix2XtQiRhNi2TFOHsrjkOXEqKUv7wV9yUN1pJS+QO699M7mM8I7IuIrzbpDyK85+040Sa1aSy06XAg8NCK+v8C6/YGTI+KWi2z/K3LR4trm73uQuwm+NiI+MFxQWCTGDYWJlNJPgHtHM9BN8+J5ZkTsNSbGxcCdI+LS5vdbzX3wSCnNkCuB44ofryZ3JX1JRHxp6PYLyd8mXDRm+0vIlyTMkHs37BIR1zTrdgZ+GBF7jolxBflkYC737cjdF2/VXEP5XxFx63Exhoo4+5AHFXomuRfFZ4B/iIhTxsTYEBG7NL+vAa6LiHULrV8kxlXAm8iX7sy3Dnh3i4LB5eQuwucvsHo98K8tYhTlUSOHJs5hwB+RTwR2JBfrzgaOi4gPtNj+YcCnyd1hzyB/q7Yz+fKduwNPjoiTVyDGgeRv9RK5W+BcjHuQT/4eH2OuJ67QFjVy6EV7NnH2J/+Pzj9J/Fi0uFazGSvjtcAfA7fgxmtGf0XuvnxUjBk0q3RfmiL1e8nf/l4/tP168uvO8yLi8jE51IhRoy1q5FHj+Cralx61xYAbPxguq0CbUnrW3O8R8ZFlxqjx3lh8rjEv3qnkXg0/Wsb+fIV8rfdHh4oOfwj8QUT87iLbnUPuYfrZBdY9kTwezv5bbnmT++0z93tEXLDU3GtJKT1kKI+vF8Za9utwj94LepFHE+MR3Pg+PxzjuIj46tYSo1JblBxbtwe+CvwcuIB82fWp5NfU+wF/GBGfa5OHVF0soVsE+XqgC4HXk68PfAT5g/fryN/uL9qNHrgE2HHebXMDwv0F7S6vuIJ8MjQD/BRYM7Qukb+ZGRfjPcDHyR9g/o7c4yKRv4l+BfCtlu1xB/LlIieQv8GmaZ82l3hcOvT7ZfPWrWnZFj8E7jL0993I3yjN/d2mLRZ8HPLYEMcO57lIjO8Bj2h+fyz5BXKuS+mB5ALKuBjfBH5vxLptgEGLGCcBzyiMUZRHpRzeTH6DeTZ58LU7Afdq/j4LeFPL5+RJI9Y9kTyK8UrEOI387d5C644ETl+BtijKoWft+TTyaNofBf4SeA75tfMj5EurntoiRtFlcjX2hdwb63jgDvNuvwPwMeCzLXKoEaNGW9TIo8axUXr5Yy/aoi8Ldd4bq5xrDMU7mvy+/3Lya+ANS4tt70K+/PHrwHXAl8nnXvuO2e5hzWvOqcC7yWNKHNP8fRljLiXt4HlZcCwM4IEVYrcal4zC1+FK/+813gv6ksfcZ4ujyb0uHk4uXL6W/NnihVtDjEptUSPGOuBPgQ81/+efa/apaMw6F5fSZekb5Kmwjge+Qx5N/jvN349sse1XyJXT+bfv08Qae708N47fMPfzXkPr9qPFQEXAduRLAOam8hoA1zbLD4D9l9gmv99s90ryuAZtig6nMeLaROCgli/2zyF/C/X35JOAi4DDm3UHkKcVHBdj0cIEYwYaau7zFPJJzC/JJ63PJo9P8ZkmpyNatuGCJy/kE7RntYhxMHCfEesS8JCu86iUw9w3YgutuzXw6xYxrhr13JGLH1evYIwFT+TI335etUJtsewcetae5zPi5Jr8YejHLWJczojBZrlxeq5O94VcmFxwwF3yteJtCqY1YtRoixp51Dg2ivalL20xdP+bjbj9jkuI8cH5+ZB7Knypzb6MWd/mvbH2ucbJI5ZW4yoA25Pfr18M/AHzvgRa7Lkgf4h5R9Om72j+3mOJ+S97IMyhGJfSnOc0f68jjy3xqyXE+Crz3lfIPd/GnjM19y16Ha70/17jvaAvefyCoS/Q5q27C3Dh1hCjUlsUx3Bx6euysg+WZzd41Ih1e5Gn0hoXY595yw5D6+5L7irYNp87k7swvQz4c/I3Omvbbj8v1o7kkfK/DezW4v77AzuPWPdwRnzbvsB9Dwb+plkeNnT7NrSYxQJ4eqXn9tZN+68Z2oe/pGAE8kUeq8Y3Gk+bdB6jciAXbBb7oH1Ji9gnNcfEDvNu34E8KNjJKxTjdOAFI9Y9n/E9HWq0RVEOPWvPjTQzxCywbntgY4sYFzL6xGp/4Jdd7wt5LJrFTqwuaJFDjRg12qJGHjWOjaJ96UtbzMvnd+bddjgtCo1D9/8k+QuN+zd//wG5kNmmh1SV98YmVrVzjYIc9mLeuQl5VPxbr2AOyx4IcyjGgeTL5E4k98I4g9zjdMH3iREx3kL+QuQp5C8DXkruifvcltsXvQ5X+n+v8V7Qlzw2MPp8eFdgw9YQo1JbFMcYE/82Jdu7uJQs9QLlF+5qHzCBE43Rnxx6FmPspSdbQ4xR2zcnROeQe7Lch9yD5zeAPyFfavDmFrH3Ic8Qc02zzbfIlyNcTftpzmrEOIj8QeSn5JPEj5OnnfoJuaJ/4Jjta7RFUQ49a88TgE8w79te4I7kHmcntIhRdJlcjX0hT0t2Bflb4BcDh5HHQvkY+VvhscXjSjFqtEWNPGocG6WXP/aiLYZi/Q75uuT3kC+r+jfyB8x7tI3RxHkG+QPmN8gFiN9ayvZ9XbjxMo01DF1musj9T2PeyPvk6/b/uzCP1gX85tgY20OkRZxtyYWHWVpc9jMixoPIr/8/bv7H7rSEbYtehyv9v9d4L+hLHh8mX/bzMODm5B6IezR/nwx8aGuIUaktimOMiV987uvistxlSQNJLqYZxfjqqDQyf5tBJVdLjD7k0LMYY2c52RpiLLZ9SunPyAOXzR888aMR8f4lPMZ+wF2HY0TED5eYZ1GMZoC6h86PAZwSYwama7YvbovSHIbiTLQ9m1kF3kO+znQT+SR+Z/L1958lD9Q3draBlNIjGd2mX16hfTmA/CF1fg7HR8T3VjBGcVvUyKOJU/q/VrQvfWqLJtbu5GknDyCfiD87FphFYUyMg8nXR8+Qr2F/ZkT8cikxRsR9WkR8YiVjpJT2Il9K+WDyN683GHfuNWpA5zYDPY+Je1a0nIGjZCDMoRh7ka9v35U8m8hrgA+Qe8q2ng0ppXQo8HbyjF/ryWMxndVy21qvwxN/L+hDHiml9eRxEw4lX/4098HkQnLB8jXj/u/7EKNSW1R7XkfEv21E/HS520slljp7xaGLrF5PnorFokPlGH3IwRj1Y9TIQatTSml7cq+PuZPEH0TE1ZPNSqonpbQj8C7yt4yfIo8V9NKIOHYJMd4G/CG5O/+/kscTeBb5xP1Thfm1/rBdK0bK0+FdTZ5h6evk4sNRwBdjzEw+KaVzyZe3njt0252Ar8Qyp6BcqpTS0eSB8o4jjwF1g4j48IIbbRnjYuB95FnPNjdFiOPIU4nes2WMT5MH3n5mRJyWUnoeuUfPmyLib5awPxN/He5DDjXzaGbA2ZF8GcGiM930NUaNtujL8yrVtNSiwyzwP+RBA7dYTb5u0qJD5Rh9yMEY9WN0XXRIKT2FfB31WeTp3TYNrXtPRByxQjHuB9wfOCvmTTmVUnppRLy57T4tV40c+tKetaSUdlvoG5OU0m0i4mctti/al+ak6s7AuRFx5bx1D4yIb7bIoThGc9/StqixL1WOjQr7MvG2aO57Hnk2oSMjYkNK6SDyt40XxCJTPM6LcSK5d8Svhm57MPCRiLh9mxh9kvJU23tHxFUppcsjYtemN8i3IuIuY7Z9OXn6vFeQv92/I/mD9j9HxBtbPv5uNFP4Lefb1pTSySNWRUQc0jLG/SPiPxe4/QUR8a6WMd4D/GU005Q3t+1HnpLwN9vEKNWX94K+5KF6UoWpQ6VOxBKuxQC+Dzx0xLptaTEd4BIea+LX3PclRh9yMMbKPa/ALcnXL28gX1f5gKU+Lvk66h8D7wT+lzzA6a0mEOOZ5NHG/4V8XfiJDI2YPi5GpbYoyqFn7bkGeEmzL28Edp+3fuyYKeRvT84hj6T/c+ApK318kQedvbh5Xq8GXrKMHGrEqNEWNfKocWwU7Utf2mLovk9Z4LZtgb9rG2OR2Dst8f67AXvTYpDojmNcRDMmQnO83Jw8aHSbGVLWkMfZ+D551oLvN8fdouNBkGeHeCO5i/nwzGG/AN5Ay2kmt4YFmGl5v6LX4Ur/7zXeC/qSxw7kSwrOII+5NH88g+9uDTEqtUXpsVU8daiLS1fL0u4M72doqqJ569bRYqTbJTxW66m1pj1GH3KYwhhnTTqPUTkA/0SeX/me5JHOL2FoJPU2jwv8iKE5mZs3nB8B+6xwjO8B921+3458Le5pwK5tYlRqi6IcetaebyHPxvEi8oBTPwHuOrS+zUnivzUnJLsBTwB+Ru62vmLHF3lQvz9tfj+IPCjc+5aYQ40YNdqiRh41jo2ifelLW9ReyJd+3p08psshc0uL7Yo/bNeIMS/eF4AnNr+/nzz7wIlUPPda4DE/SB5E7xDy1JlryVNfHgJ8jTEDOdL0qG1+XzNqWUI+a4EXkKfk/jrLmHazibMn8Fjgj8mX7jyb3CumzbZFr8OV/t9rvBf0JY9jydOoPpE8HeslwMFD69vkMfEYldqi9NgqnjrUxaWrZeIJjEwMXmaM/uSwUjGoc1Jyh1HLEmIU5VGSA/ArYNuhv+9B/gAwdzLf5o3rivm5Akc2b2B3XsEYW0wxRZ6i60zgFi3eQGu0RVEOPWvPn3DTb6KeTf5Ac+/m7zYnVr9m6Bs94DbkrpdvWEKMon0BLuOmH0Z2Jg8Y+NHm/6xNDjVi1GiLGnnUODaK9qUvbTEv3uOAvyUXCj86tyxh+99q/j8uBTY3PzcB57XYtujDdq0Y8+LtSvPNJ7mA+iryh5RW00U2x9JTGPqQzZgP2uRZRxacfrvJ5/Jxx/bQ73NFl+FlAMwuoQ3+vjkuX0i+1v2F5F4bRy0hxhOabb8NXN/83ETL4g2Fr8OV/t9rvBf0JY9fDB9jwG+Te/U8emuKUaktSo+t4qlDXVy6WtbSkZTSdyPi7iPWHT1is+vIHyi+FBFvGhN/KmL0IYc+xSCfGMaIdW3HCzm3iZGav4fjtY1RmkdJDmubJW8YcWYzAvtXU0ptZ8u4gPwB/TtDcY5JKV1N/hCwzQrF+FVKad8YGgk7Il7cxDiV/E3gYmq0RWkO0J/23IXcfX1u+w+nlC4FvphSejKjj9lhA/J1npc3MX7WtOmXV/D4uprcNfyiZtsrUkqPAj4HfJob/2+6jlGjLWrkUePYKN2XvrQFACml1wDPBT5JnsLz/eRZMf6pbQzy7ARvjYi3p5Qui4jdU0qvbvIc5/fI3/huGLrtUuCklNL/krulP2cFYtwghga0izwewevabtuM6fBqctfx4f0P8lSBo1xDHsl/wwLrbgVcO+ahDxj6vcY4Gk8ijx/2k5TSayPinSmlL5OPj6Naxng98McR8anmuLhnSumP5+W6mNLX4b68F/Qlj+0YGisuIr6aUnoc8LmU0vO3ohg12qI0xmeALzTn42fSFCGAA4FXkl+HpcnoqprBItU48knEdeSumB9vfl5H/mf4L/L1ho8aE38qYvQhh57F2Gfecj/yCeufFByLtwTey1C3/BbbVM1jKTkAXwGePCKnc2nxrRD5Ws2/GrHuGbT7pq9GjL8hTzO10LqXMWYcmEptUZRDz9rzdOCBC9z+KPI31de3iPE58gn3/Nt3Bf7fShxfzevDny9w+3pyF/I2OdSIUaMtauRR49go2pe+tMXQNhcAd2t+v7z5eV/g80uIsYHmm1zgsqFcft5i2wsZ3U15f+CXKxTjFUO/Hz1qaRHnIuAebdtuaLu5a8RfTy7+PIJcTHkd+ZvhLZ7vLheGetM0eW3f/L6U8UKGe1/MHRdrgItabl/0Olzp/73Ge0Ff8vgG8IgFbr8neZaTzVtDjEptUXpsrSfPbvNzbtqz6GfN7evbHOMuLl0s3QVe5A0A+Gea6xKHbns88E/N788CvjMm/lTE6EMOfYoxIu4u5OmCSo7HbcijnpfEKMqjbQ7AbzKiOAPsRZ6PfNn7sTUttsUW+3wo8Gcj1h0CnNQixr7AfUas2wk4dAX24+bA7UesWws8eIViFLdFjTwqtWnRvvStLRjqBkz+wLxu/u0tYvyEG8du+R5wV/K1/GNjUOHDdqUY7x36/bhRS4s4F7DMDxzAI4Hjyd+In9v8PB545BLj7EK+JOSz5ILyDcsSYnyLG8fo+QLwVvI3uOcsIca5wJ7N798mz2y0L3BJy+2LX4dLlz7kUCsP4NHAH4xYdzfgw1tDjEptUe15JReMbzP3GrjA+i2KGy4uXS5LmjJzKRabDjCltIF8XeLs0G0z5IrzzsO/LxJ/KmL0IYc+xRgR97bAmRGx21K2mxfjHsDXIuLmBTGK8qiRgyStBs3lB8+MiLNTSieRe2JcBrwuIm7XMsY7gP8XER9PKf0VefaGTcCXI+JPWmz/SPKHgAOAHcnjAJxNHlfiyy1zKI5RQ0rpUPLUiEeRx8q5QUQMKj3GotMPp5S+Qr688ATyZRvDOXyo5WPch9xj5n9TSvuSexDuRP7G/hstY/w1eUrXzzTtciz5W+G/jYhXtYkhbe0W+5wmdaGzMR3G+BFwOHDM0G3PbW4H2IPx11xOS4w+5NCbGCmlj3HTa9a2Bx4M/OOYxx6O8Y0FYhxA7obaNkZRHjVykKRV7JXkwRchXwZ1PPlD+xFtA0TEnw/9/raU0n83MVp92G+KAmPvu9iH7dIYKaU7tMz1vDF3+Yfm5/AYEon8PtV2rKNxXg6MLDqQL1PcIyKuX+4DRMRpQ7//kDwl4FJjvGXo94+mlE4BdoiIc5abl7QVaj3GjlRDlz0droyIBQefSindi9y9boZ83dFe5GuOntRUrx8M3DkiPrBI/KmI0YccehbjNfNuuop8Sca/j9pmgRjPWiDGGTE0kGCLGEV51MhBktR/Nb4xHBUjpTTgxkGJh0/YbvJ3RCxaOEgp7TNqXURcsOSEF36Mked9zfovkqdhPbPwcfYhD4y34/DtEfHxkrjSamJPB620ZRUdUkp3jYjvLXD7I+e6C6aUnr7YG0BKaR35Orpbka95/M+I2LTEPKYiRh9y6FMMSZJSStsDd2LLD5ffarn93sBryAPBzY+xX6U0x37YrhWjmWHh4eRLJC4gD6j7avJle/9Q8vg1jPsQk1K6BfBF4L/Z8hKPVr0AU0ovI48L8T1ueolGRMSDW8Y4kDyzyUHceFykJsb6NjGkrZ1FB6205RYdzgMeFhHnD932WODYiLhVxfy0CqWU9iSPUr4HQ92/ImKxab3mxzgIeNACMV69UnnUyKELTY+T70bEZROOsTd5FPnZsXfuSI0c+tKefTFN+9IHq7U9m2vtjwGuZ8sPl3u3jPHfwPeBT7HlGAJfq5Rqpz0d5t3nZ8C+kafLnLtte/IAx7dp8RiPAx7Clu9Jhy478ZvGH1d0+ADwOPIsAfOf01Y5pJR+TR6QdIsvvpaQ5/fIUwv+E1seFz9acKMV0pf/977koe5YdNCKi2WMPgk8GfgBcKvm7yeRR2K+d8vtdwb+DvgfcrX+J3PLEnKYihh9yKFnMZ5AHmjr2+STzW+TB/46eQkxDiNfznACeQ7xE5q/P75SedTIYUTcDwPPBmYKYgyAy4E39CDGecCfTrgtlp1Dz9rzUGC/wuPrJPLI+rvJtLCMAAAgAElEQVRNal+A84GPkS/FWm4ONWLUaIsaedQ4Nor2ZRJtQZ6i7reX+3hNjBumzOxyYQnTNZbEIJ9n7T/vtv2BC1ts+xpy78O3k8dWeju5t8G7KrbDyKnS59bTnDcWPMb/0UyTWRDjUpov3To4Fopeh3v0XtCXPF4NHLK1x6jUFsUx5sVb9P/VxaX2soZliIjPkOd7/WpKaW7gwEdFxP+0DPEe4F7kQfV2B55P/nD69iWkMS0x+pBDn2K8njxX/D2Bq5qfh5ELGW29hHw8PhG4pvn5e+SiwUrlUSOHhSTg6cCyr4mNiDXA3cndUycWA7g9+U30FsvcvrgtKuTQp/Z8LXBaSun4ghhfJ18atexvgSvsy1HkDxbvXG4OlWIUt0WNPCodG6X7Mom2uB44peDxIE+p+JDCGG3UGJCtTYy3AyellN6YUjo8pfRG8nPS5j322eQizouA65ufjwVut9yEFzBu9ojzKH8P/HPg2JTSb6SU9h5elhDjI+T3ji4UvQ735b2gL3kADyU/36du5TFqtEXrGCmlNeMW8hS20oppfXlFc4DO9yLgr8hzT58N7aZeSildRK7WX5JSujwidk0p7QV8ISLu1TKfqYjRhxx6FuOG7l4ppcsiYrfm2PtlRLT6YDgvxiXAzSNikFK6NCJ2X4k8auQwJv66cKwMwLYY1oypct+I+Oakc5FKNIPx/gbw2oj49TJj7A58izyD0vwxBJ5dnOSNj/PFiHj0SsRIKT0K+H3g1uSeC/8cEV9qsd2GiNil+f0iYK+I2DR8+wLbHNIm94g4qc39Up629EnA37Pl89E2xuOBD5AvEZkXYvHBNIdi7An8J/nSivl5tNrnMfEn/jrchxxq5pFS2isifr41x6jRFm1jpBsHoB15F5bwPyPVsJQpMzez5QE8V5n/Dixp6qU15G6PABtTSruQ3zzvtIR8piVGH3LoU4yLUkp7RsSvgB+nlO4P/JqlTen1s5TS7SLix+TLgB7fXAe6lGm6SvMoziGltAOwH3kO8ivJ1+1eBdDmQ3ZK6RHAH5Gn6pyLcTZwXER8tWUONWLsDzxzgRgfi5ZTlFVoixo59KI9F9O0RasTmpTSfvPziIgftH2s0n1JKa0FDllg+5MiYnPLHIpjNHFK26LGvlQ5Nirsy8TbovEDcq+5I1K6oRPAUk+UjyPPoHQO867db6vlB++3dR1jTlNgGFtkWMCPUkoHRMTZwFnA4Smly4DFrtn/UJuUgFbTegLPa36+sSDGe8hTc36SZT6nwKfJl/ucUBBjpDavw315L+hLHkP57DaXRwyNJ7GUD/p9iTHfUtuiMMaR5N61b+LGQWf/mjyWyRdLcpCWLVpeh0E+YMcuLWN9jTwQJcAnyPNvvx84fQn5TEWMPuTQsxh/DTy5+f1Q8ngIVwOvX0KMPwJ+p/n9d8hjKWwCjlipPEpyAHZt2u868gnAz4ArmhyOB3ZtEeNF5ILP0eQxWB5O/obpteTrgl+4QjGeRr4u9KPAX5LniP8LcvfWy4CnrkBbFOXQp/Zs4hxG/vZ2A/kD1Ybm71bjUgB7k7/luxr4LnAq+RKVq5o4e3e9L+Tp7n5MPhn6AvCPwOeb284H7tEihxoxarRFjTxqHF9F+9KXthiKdS55HIm7AXccXpYQ40pgp7b3HxHjfHKx+Lrmubiu+fu8Zt35wHldx2jibAO8odluQ3PbI4AjW2z7aPIAjAC/2bTvL8lTWi+7fVZ6IfdMWPY4PkPHxfrCGMt+Ha7x/16aQ8/yWEcuRF3YbD9ofv6iOd7XbUUxitqiYoxzmXd+BOwG/KjkuHdxKVm6C5xHvR217g40Jw7ka6k/SB5F+K5LiD8VMfqQQ59iLBBzb+YNnLWMGOuBHQtjFOWxlByAz5I/UN9hgfb9GPDZFjF+AdxlxLq70G7gsRoxzgceOGLdA4Efr0BbFOXQs/Z8M/lbqGcD9yb3IrpX8/dZwJtaxPga8FbmDcYG7AC8hfyNdKf7ApwGPH/EuiNpUaisFKNGW9TIo8axUbQvfWmLoftfRuFgf+RvBG9XGOPlwN/OtQuwPblXwstWMkaz3XuAfyFPS315c9te5N4oy97HrWkBXgy8ouTYIH/Te1DB9kWvw5X+32u8F/Qljw8CJ5N7SN2M3At79+bvrwEf3BpiVGqL4hhNnIuBW8+7bS/g18s97l1cSpdlTZnZRqowb7VWn5TSv0TE4xe4/bMR8aSWMb4deeDH+befHhG/sRJ5lOSQUroS2DMirl5g3Q7kcSXGzee+AbhtRFyxwLpdgQtixHW8lWNsJI9nsUUX1pSnersoInbccssb7lOjLYpyaO7Xl/a8mPxt8YULrLs1cGZEzL/Wef79NgK7R8QWl/qklLYBLo2IHbrcl5TSVeRvYba4NCaltB64rEUONWLUaIsaedT6X1v2vvSlLYbu/3fAdyLio23uPyLG64Cnki+zmH/tftupj+dO3jcN3bYO+EVE3HylYjTbXAjcKSKuGh4fKDVjKC1w/9tFvsSPlNLIyxci4rwWj/1TFr9GfC7WFgM6ppTOv3F1tL2MYrE8bknuKXLJuMceEePd5HExTmDL42LslNalr8M9ei/oSx6Xk3tKb1hg3a7kLwa2OL77FqNSWxTHaO77NnLvpncAPwVuC7wA+HJE/OW47aUuLGVMh6W6yZtTSqnVoE2LnQhMS4w+5NCnGPM8dMTtB7fcHhYYQyLli4KXcrJTmkdJDpcA92Th6/YOIk/3Nc5ngC+klI4md5HeQJ7S9EDgleRrWlcixleBD6eUXhlD85+nlO5I7tI57prRGm1RmgP0pz3HjXDfZgT8nwK/S+5FMt+jyTPOjFO6L+cAhwPvWmDdnzXrx6kRo0Zb1MijxrFRui99aYs59wWOTCm9gi0/GD64ZYzfAn5OvgThJiHIU+62cVWTy/Br0H3Il6G0VSMG5A/aNzlvSyndnHkfvod8l3xdOuTu1sGWrxFBu7GK3gk8i/zczl0jfiT5srXTx2x7cIv4bf1hhRjbAyeSeyDeduj2tt/Clb4O9+W9oC95XEOeHWeLD/vN7dduJTFqtEWNGJBnUDuXXHSdG3T2GPIgrNJEdNnT4YbR+5u/T26xWcQiIwdPS4w+5NCnGE2co5tfX0Lu4jvsDsABC/UcmBdj7huxp5Iv6xh2O/Lx/qAu86iUw9OB95GvhT6Dm54IPBZ4bkR8ckyM9eTrMg8lv1nO/aNfSL4s4TULfaPZQYzdyF2Cn0Qe0+KKZl/Wkj/cPC+GBmlaYPsabVGUQxOjL+35FuBx5K7a89vjL8gzxbx0TIyHkU82z5oX4yDyYGJPjjEjyZfuS0rpIOBz5A87wye79yBfw/qEiDhjTA41YtRoixp51Dg2ivalL20xFOtZo9ZFxEfaxKghpfRM8uvHF7jxG8PfJb9ufGylYjRx3kYuaL+IPH3zAeRvMs+NiFe0jbMcKaWzgEfG0EB6KaXbAF+KiLuN2faGAk9UnDVkUkpfh3v0XtCXPF5EPuf60AIx/gR4a0S8o+8xKrVFcYy2UkrviYgjasSS2lixosMytn9glE+xMxUx+pBD1zFSSsc1vz6DfA3/nCB/y/WhiDh3TOzXNL++nJuOjj0X41MRseg346V51MihiXMAeR7xA4AdgY3k6/yOj4glzZvddAvcEdgYEZcvZdtaMVK+jGE/btyXH8QCl0yM2LZKW5TkMC/ORNszpfRn5JPE+e3x0Yh4f8sYNyMXYebHOCGWOD3hcvcl5e7lBy+QwynRcgrUSjGK26JGHkOxSo6Non3pW1v0RUrpruSB9ua+Mfz0Ml6Ha8RYTx5f40/J39ZfTf7m8qURcd0i282QZwO562L3G/PYlwK3H+563hyr50fEbmO2vaGAtNyCUUrphsseIuLoxe67SIwbLr+IiDY9dxaLVfw63MSZ6HtBX/JIKT1ykRhf3lpiVGqLas/rmMcp+pwmLVVvx3So8c8wLTH6kMNKxUgp/WlELNr9K6X0tIj4xCLrHznuzSGl9NKIeHNXedTIQZJWm5TSof+fvfOOn6Oq2vj3BEhCaKF3QuhNigpKkaKIKGJHAZGmYsOuKIIIiMJrRV7FwktXEFRUug0ITZqFjkDoEGogoUnL8/5x7uY32ezszuzc3b37yzyfz/3s7J25zzz37N0pt5zT2FaX/hzMY9QHit7HoTezGyW9qp8cYVnF4yr4EGdmtwObVegoPQmYDByBRxFaGTgQuE9S7qyULs7TcvQ106GvCp0O0XxL1Kgx7Kj6nlajRlmM6SH3oxXLF123NC9wpKChLxydXvQD2vb0FuzR/lovdcTQ0ApmNib7UN4NzGycmb2SAMdYM+vowKxN+Ri2qKQhcCRhz8BTyJFam/LzZUcUu+SoVBczmz87HXuAHDFsEUNHjPZVqS59tsV2IW3b7bkkjQmp5x0OAav2i8PMJpjZhsCawOZmtoWZbVGg6NHAGWa2jZmtbmarNVJBfR/HQ6r+DPgn8FPg6pAfE3k+Gy4O6ZJuiSVNDqmnHQ5VrsMJ3QtS0WFmVtSHS+oclWwRiyOD3ow616iRg5SXVwzFyHw/OFLQkBhH5d7ZFDi6KW/uRf65Tg/THW5M44HbJLXtdIzB0YF/HPB8txxFbRFDwxDZM4Y9+tK+qmoYTRypt41B2aLbKfDmvoYUONr6E4qBft0bQyfrj3GHktloPFKHyA2Z2R/NiDYbJMbsvbz7oo34j1K3v2mY6dBoFz3peCjSzlP5v6eiY17gSEFDC756eUWNvqKX0Stq1OgVYvSUpcDRsnyHEcUFCnLfQ2tP5W3PHZujw0iJdeKIYYuqGgLuIQ17thtpGVdAAx1Gm4veE+6hQl3MrJ1DwkIPVJE4Ktsihg7itI1KdUnIFlPCpygXcSiLQ7sslzq+gzv0LBJxZw5UeXksga8BVZcMtmzrkvIiSs0Fy/c/tW23opr4q16H7yGBe0EqOqz9jMWxw8IRyRYxfteiiDF7uUaNwqg7HWrUSA9fB87FnQc1o+iD4zTcIVyrUGbj8QgO/eCYDuwLtHKYNg4P6dYOMWxRVQOkY89LAk/eqGUR7I57527l0LToi2HVurwOODLwNGMBPNRhJ8TgiGGLGDpitI2qdUnCFpImFzwXZraSpAdacExpdXwOx3mSdip6/IDxIhWWF/QBqbzEXIB7/J8Dku4tSmDtfWxcQrXrcCr3glR0nIhHY2nl5LRom0qB4xKq2yIGR1EUjpxTo0YMpNzpsGDNkZSGlDhSebDplY5bgZ+18gthZuOB3QpwXAdsIunqFhwvU0x7DI5/AEtJmtqCY1wBjhi2qKoB0rHnvcAHJV3ZgmM88GwBjhuBP0k6O4ejSDiuqnX5Nz51d6448OE3ObaAhhgcMWwRQ0eMtlG1LqnYogxuocXLZUm0DWFcEP3ymfR14AdmdpjKR5mZH/gksA2wVPZ8kiqtVc8gxgzCVPxPrdpmX9XrcCr3glR03AF8RdJcIdgDR5EoUylwxLBFDI6i2AO/JtSo0Rf0crpd1Yv+850PmWc4UtCQEkdVJ6UAC0XgqKojT8MfgGVy9r0MFAk59jHgrFY7JL1QcKptDI4vAi3DrMpDt3Ua1Yxhi6oaIB17Xge8NmffLKDIGviTyL/2v4THbe+EqnU5mtYj6g0N+xTQEIPjJKrbIoaOGG3jJKrVpWp5iGOLMkilAzrGPakIx+3AO4BHzOyVkGZ1WELWwA/xdnYp8Brgd/i1td2SmEHglxE4er18sup1OJV7QSo6pgDr5Ox7hZElV6lzxLBFDI6iSOX6WWMeQS8dSe4u6bQK5VNxWjhwjhQ0jEKOGI4kq/6udbiiGqVgZgsASHpp0Fpq1Bg0Erqf9MuR5J3A6cAZNHXgt5rJ1VT2QWBzSfeZ2VOSJprZOsDPJW1TRXvmHJ1CYu+bs+sFPAznVaEjuKc6qnKkcB1OQUNKOlJADFv00561I8ka/Ubp5RVmth+wN7A+sDC+1vpm4ERlwgxW6XCoUaMPSCFUUAoaagwR6ge7GjXmaSwJHKLuRosmAPeH7efNbIKk28xsk3jyOs4g3BPYHHgE72RYCVgWH91dFcDM3implY+BZJDCdTgFDZCOjhQQwxa1PWuMZpTqdDCzo4Cdge8D1wMz8PWUGwNfMLPVJB0YXWWNGjVq1KhRo0Z8DNMU4xOBDwGndFH2VmBT4Br8Jf9QM5sJPBhPXsc17zcDZ0k6ppFhZvvjU9q3Ag4C/hfvmKiCVPxC1KiRMuo2XqOvKDvT4cPAhpKaPVP/08wuBG4A6k6HGjVq1KhRo0avEeOh+dsROPqFzYD9zewgfLbAbBRwBvlZfF06wBeAnwKLAPtF1NdpBsbu+GyNLH4KPC5pfzP7LvDldgRm1tLPgKRZme0YyxY/FoGjRo2UEcN/So0ahVG206HTDT5mr1kqPdUpcKSgISWOGEhBRwoaatSoUWNYsV67nWZ2eM6uhg+BCyUdGUFHv+6Nx4VUGpKuzWzfAWzfDU9FPILPlv1jJm8nRpxojscdjrbDy7To3AjRFh7CHSN+Q1KrMMuNY09txcFIu/hDvUS4xjDDzAz4CB7haylJG5rZ1sByks4EkPSJQWqsMe+hbKfD8cBFZta8vGIjvOe81M3QzJbB/ULMhqS7wmehnurRwpGChpQ4OiCG997LInBU1RFDQ2mY2SHA5ZK69loeiWNP3HHY7d1yVEUMDanYMxVUrYuZrQI8KKmIR/6eccRApLqMirZR1hZmdj/tXwzPwkfJ22Et4N34koL7gZXx2QLn4C+/x5rZeyVdWEDPGGDZFjM9oeCoeFUOSR2j9ZjZsZLmCoNnZtfjI5u/lnT/3CX7gs8AvzGzmxj5PTYAdgn7X4cvr2iHTwPvAo4KHKsABwDnAf8BvoFHUflIG44Z+DKVszM6dgZ+DawLfMXMPi6pm2UslZDK/z0VHTW6xuHAm/H/ws9C3gN4FJszByWqxjwOSaUSfmO8AngK73F+Knz/WAmOHfF1hK/gIWAa6ZV5jSMFDSlxZLjG4M5KNwDmK1hmUWBs5vubcP8j3we2LauhifsYYJES2pfKfH8V8E68h7lrDYHrbuBUYO0KHBcDd+IPFIPkuBt/+PvVAG1RSUNi9jwB2Lfo/yWHYxbeGfbmQdUlaLgL+GjFesTgqGqLGDpitI1KdRmELfBp9tfjyzp3wF8i/wV8Dfg4cAfwnQ4cZwLvbsp7J3BG2N4L+HcHjonAacCLwLMh7x3AESXqXpmjxLlm5uS/O9jjGTz038eAJSKf++kCxyyFv/B/BXcsuWTJc0wFFmth36lhe0Xg4Q4cfwa2bMrbHPhL2N4RuK1LG1S6Did0L0hFx0XAN4HFh5kjki0Kc+CdaUuF7SfDpzW261SnQaTeEcNubfZNDQ8NC1bgHxUcKWhIgQP4RWZ7EnBjeDh6GneAtXoBjn8A64XtT+JTNo8BfoRP6/xIAY59c9KTwOeAfTuU3wJ4Au94+Rn+cHUL/oA8o/lBpws77QUcjE8L7poncK04aA5ggW5tEssWVTSkZE/cydxfgZsrcGwD7JH9P/a7LuH/vxVwUIVzx+CobIsYOiK1jUp1GYQtcKeDKzTboNG+gbWB+ztwzKDpAR2Yj/Bint1uw/FrfEbF8ow8vC8N3FGi7pU5Spyr7Ys/7sdhL+BC4Fng7IjnPj92fVqc4zFg+aa8FXC/EI3fdEaBdjF/U94CjXL4y9kzXeqrfB0OPAO9F6SiA5+58n/AP4eZI5ItCnPgS43Gh+3p4XORTtfMOtWpl8kk0Qt0iHE8He/d7vrko4UjBQ0pcGTbi5n9FpiGO74yfKbCKpLeU4LjNmAXSTeG7+sC50haowPHLOAmvOMgiy1wj98vSHpjm/JXAN/BR/X+AOwu6Yyw7yvAjpK2a6ehRo0qMLMFVIfdqjHkCPeTVSXNzORNBO6WtHhYszxTbZbsmdk/gRMk/TiT9ym8A3oTM1sWuF7Scm04HsM7P14ys+mSlgj5MyQtVrAulTmKot2zV+aYBYC34ffYrSW1XWprZksC78VnHy6CDwbcDPxOUvO9sh3PZXRYMiPpnA4c3wfegg8m3I+H3fws8GdJXzSztwKHSdqsDccU4Crc98N/zWw8cCiwhaStzWw14BJJqxStW4tzDPw6nIKGlHSkgBi2KMJhZv+Hz6z6PP48vSS+tGKsWiy/qlGjH2jpBTgS2jlFOh7YpyL/aOFIQUMKHNn2sgU+GjZLvv734JDXCU+b2UphezF8fWcDU/F44J2wNz7icYak7RoJmI5P083tcAhYV9IfgXPx5Ue/z+z7Eb7UohTMbIyZrW9mG5jZfAXLbJ3ZHmNmXzGza83sOjM7uAhPJI5Vm75/wMx+Y2a/NbM9itSlqXw3tqisIRV7FkHZBxozW9TMdjKzt5vZ4gXLRK2LmU02s0+Z2f5m1rZjsMccpW0RQ0cv2kbVugzKFhmcA/zRzLY3s3XMbHvgdyEffDr8PR04PgJ8yczuN7OrzP1EfBlfsgE+W+LrHThm4EsCZsPcP0Urvwy95KgEc7zJzI7HZ/4dClwATO5Q7k34NPs98GfGh/D79QeBO8ysTCf6JcCq+PKOX4bPSXiH/iPACWZ2QAeOLwM/BnbFX6J2B36C+3UAXxawTQeOvYA3ADPN7GFgJrB1yAdYAp8t2TXaXYdTuRekoiNH20JmVskHWCocWcTofCnI8QV8ZtUM/Hn4Gfy/9pWq569Ro2v0agoFbaYs4mtDXwBuBy7NphL8o4IjBQ0pcOAjJ5OB1fERj/kz+8ZQbK3oEfjUs8n4A8hx+CjISvjU1gsK1mNiOP4aYNOQNw1YpkDZxzPb1zftmz19swNHjKUmMzPbB+OjUh8I6UZ8hKffHB/HH1i/En6fB4BP9dkWpTUkZs8PZbYnhHb6KPA4PvVzoQIcF2a2Nwxt+1Z8GdAjwKt7XRfg1sz2NvhD/wXA+eG3fWMBDTE4Ytgiho4YbaNSXVKxRab8eNxZ4FTgedwfxFHAhLB/OXwGXCeeBfAXyg+EzwWKagjlvwpcCWyH+7DaHH+x/Vw/OUqcq+W9Mvyed+Dr0tctwXcL8J6cfe/O/uYFuK5uPjewDnB12N6M4JuhHwl3Qvm6Iu2oqVyl63Ck/3uMe0EqOg7KbC+JL/2ZhS9T/SvFnr0GzhHJFpU5mviWBTYlgk+xOtWpahrU8oq9WuVDMe/Mo4kjBQ0pcJgvaxAjMx62lnR52LcRPvNgnQ4cY3CPvZ/Fw25NZGQq52W4n5HCI0tmthk+gnI9/nC1rqRHO5S5HNhHHpKsed+WwE8kbdyBI8ZSk6cVph2bLzV5r6Sbw/d1gHPVealJbI4bgf0k/T183ww4SVJu2Lse2KK0hh7ZoluOrD1+ALyGkRG+I/GOrs+X4LgQn0Z8VPj+ZWAHSW/uZV2ayl8GHKfgKd7MPoh3BLWd3RSJI7YtYtQlRtsoXZdUbJEazMzwqAsfwzs+7wN+DvxIBR+gYnCU0JsXvWIzSdd0KLubpNOb8p7FHU6+0OL4cbiPigkFtc3AX9xeyOQtCEyTNDF8f0bSwm049s3Z1ViicVUrrU0cLWf5SprVoQqN8pWuwwndC1LRkeU4AV/Cs3/Y/SPgv5L2Tp2jB7YoxZHXrptRtJ3XqBEdvejJCPfQts6Z6lSnogmPxb5dieMn4lMnd8NDa61W4dxj8BBdvwcWLXD8ksC4nH2vwztTOnE8ndl+KHtePBRpW8/c4bjsCMaj7c7RR47HwTs6W+3vky1Ka0jMnll7TAUmZb6vDNxbUscjBGdT4fs44LFe16W5PJnRZ9wR3PSyGiJwRLFFjLpEaF+l65KKLTLHvzEnbZlt9x047sdf8JvTHfhMg0/T5FBw2FOR61mZsrjX/u/SNMIKLIT7L7q4BP85eBSPNfCZLGvgyyzODftfRQfnmvgSjRfC73hl+HwBj6L2YEiv7cDRGL1uTi/gEY2+Dyzcpnyl63Ck/3vse8EgdWQ57gOWznxfEg+1mzxHD2xRiqNNu26krqLJ1alOsVJb50FZmNn+Cs6YzGwNSXd2KHJfB759cM/+K+I3iVMlnVhUz2jiSEFDShzNkHQLPsWzcY4bJeX6RpD0FD6zoZ3OthwZrll43PC5Yoe34lAbp1qSri6qwcwm4x0es4DnMruewx/2OmGCmV0athc0s0mS7g3cy+Bey/vBMd7MGrHO58On+j0cOCbijo7aIoItKmsgHXtmR0UnNMoDSLrf3OFbJ8xvvg7bcJtmOWfhLwOdULUuC4RrhYXzj8VnJwHMj/9OnRCDI4YtYuiI0Taq1iUVWzRwPB6VANyxb6NtPwosZ2Y3ALuqxayyDI7BfREcg3dArAJ8CvgN7qfni/iD/Bx+BMysk+8eACRdlLcvBkeXaOdPq5uy+wCnA4+b2V34GvFFgdWAf+O+FYpiL+BY/H4+H+736CzclxL4tXi3Dhw34w4nj5kt2mx/fJnGVsBB+P168zYcn8YHJI5ipF0cAJyH+4L6BnA07hOkFapeh1O5F6SiQ2E20Bi8DWafo6bj7W0YOKLYogJHW/8sNWoMGoU7HYBv4c57AP5Jhz+fpA3y9pnZQXh85u8D9+LTDQ8wsxUkfauImNHCkYKGlDgKYtVRwpFXfiHccVfjAfD1wOVh+1V4Z04nfLjpe/Zh8tXAqX3iyP7uR+OzUB4O37fG46W3QwxbVNUA6dhzITNrdOgubmbrSLoNwNwx3VMFOB7F430D/Dec++/h+6b42vlOqFqXq/FrBfgLyHrAteH7NszpBLaXHDFsEUNHjLZRtS6p2KKB43EHaIdIej5Mwz8U9xNxNH6fORZot+Rjb+DNkh5qZJjZBXikg/XN7GJ8rXaz88LjC+gT/uLdTn9Vjm6gzocULxteerYwszXx6BUL4351bu7Q4TM3uTQd2DVMA18anz0zK7O/SPvYnZEOqAZ+ivtT2t/Mvos7m2yHL+A+SmaE73YNilYAACAASURBVLeb2XXAPyStbr4M7x9tyle9DqdyL0hFx8J4B1Sjs3Jj/D0DYE08TOowcMSwRdcc2Q6KGjVSRGGfDmb2L3ya3c34OvdPtTpO0gmt8pu47ga2zf5BzGwS7nBwUkE9o4IjBQ0pcRQ8T8eQYMPA0U15M1sPWFbSxd2ed7RgXrSFmW3TlHWrgp8RM9sW2ETSDyvwrwwsEmYXDQRmthg+Lf/xAXNUtkUMHTFQtS6DsIV5mMnlJb2cyVsAeEjS0ma2EPCApNzIGhYh7Oawocp9KcZ9seB5FsGjecx+yZVUpFOr4XvgK/IoUY28dwDflbR2aGdTJS3VhuMxYENlfDyZ2QrADZKWMo/aMF054Ux7fR0ughQ0xNIRnhOzeFzSs2HfZvgS2V+nzhHJFl1zmNkvJO0Xtk8lpwNS0p6t8mvU6DXKdDqshY8GTMI9MLeavi51DimImT2KPwg8l8lbGLhL0jIF9YwKjhQ0pMRR8DwD7zCIwdGvB7waNWrUGDaY2T2489+/Z/JejzsVnhRmPjzUodPhZHzq/LdwJ4MrAQfia7P3NLMtgJ+rwFK7YYFlnAP2o6zlOK7MOXY94FfARow4jhaApKIhkHfAl8fchC+NWBnYANhF0p/D/s0lHdaG4/vAW3DngPfj7eKz+AyYL5rZW4HDJG1WRFONGinAzA6UdGTY/kbece3+GzVq9BJlOh02knR92P6bpDd1fVJfX70IHkbqPrwj41vAc5I+NC9xpKAhJY6C5xl4h0EMjrrToUaNGjVaw8z2xGdVns3Ii+HOwKclnWJmbwfeKemjbTjG40sydsH9Q0wDzgQOl/ScmS0HjJV0X1O5WyWtG7bvJ3/EcJU2567MkeHqOJij4BvCzH4q6ROdjs85z1RJq5csU/h8ZnYJPl39cNxh46q4R/4rJf2yxDmXBN7GyG96ntr4U2pRfgywH3O3i+MkvRLajUl6vihnjRopwcyWk/Rw0fwaNfqBMp0O2TAud0has+uTmi2K+4f4AO5X4iX8gv8ZuRPAeYYjBQ0pcRQ8T9cjOSlxxNBQo0aNGqMVYWT8vYy8GP62H0t/zGwrjYRsbp7uPBuSpvSSI8N1d4dDJKmyb4hed4Sb2ZN4yMyXzOwpSRPDMpmbJNVO8GrUiIS8/7KZTZe0xCA01ahRptPhPuCTuHOo64ENYQ6nM0DxdXmBcwy+ru9xdRk3drRwpKAhJY4O/LtLOm3YOWJoqFGjRo0a+TCzscDazO1DoFDUCDPbRdJvWuS/T9Jv+8XRD/Sh02EasHqYZXInHgL1SXy5S6Hzmtn8+LPoNsz9m25dQsuywGYtODr6JatRI3W0GtQKA4N3qY2/kxo1eokynQ7vxmM1T2IknEwzlLcuz8xWlXRP2M7tkW/XaTFaOFLQkBJHE98SwJdwr8ELN3EUeqBIgSOGhl4hTFu+StLtA+bYGrhR0pPdclRFDA2p2DMVjKa6pIB52Z7mDgJbvVwWcoRmZlvh6//H4RG3ZuJLAO8vOjMgxojhsIw6lu10MLNx+PLJov4YzgTOl3SSmR2FL5d5AbhP0rsKcvwv3lnxC3z55kHAJ4BfSzq0IMe7gF8Cd+AROW7G/UJcLmm7Ihy9Qir/91R01CiHzFKuFYCHmnYvCZwuKS8UbI0aPUXhToc5CnXnbGh2GTNrxABv7rjI7bQYTRwpaEiJo4nvQvwB8Uzguew+SScPC0cMDTm8FwFXAD/o9kU5TNVdAjhX0gcHyDELfwn4iaSDuigfwxaVNASOVOx5CP7QXGgEt42Oy4EjVCx8XR5H13UJv8kV+Lr7v3SpIQZHDFvE0hGjfXVdl0HYwtwR2seBXwMfA36Oh0s8Q9JnCp7zWuA0ST80syflESsOwV+Uv9ehbKNT4gY8PG/2vrYacIqkFXrN0U+06nQwD9OXh/HAbZLGdHGuMfjvuQhuh2cLlnsQdxR5X2aJxjq4Q9DcZSxNHDfhjiJ/k2kX+wDrS/pS2bo0cVe6Did2L0hBxwn4tedkSa8MK0ckW3TkMF/KZcD5wFszuwQ80u31u0aNGOi202GspBfDTWNZZcIO1ahRBWY2E1ha0gvDzBFDQw7vN3Bv3a+W9OoKPAsAm0m6YsAcKwNbS/pVF2Vj2aJrDRmOgdvTzC7G7fGwpK265NgrcGwlacduOAJP13UJD02N32S/Ls8fg6OyLWLoCDxV20alugzCFmZ2L7CTpJsyL5ebAQdLekfBc84AFpc0K/NyORYPmblih7J5negADwOHSvpFrzmqwszGqOAyx5xOh3Z1gBKDCgU13Kg20UTM/UIsIUk253KNwrM0bE4fZY12MQa/dlaKtBXpOjzwe0EqOszsxMCxvKT1h5Ujki0Kc5jZBGUiydWokQQklU7AROA04EXg2ZD3DnwUpEj5Y3Lyjy6hYVRwpKAhMY7L8YeIrtpmKhwxNNSpTt0kYMVBa6hTnaomYEZm+1Fggeb8Ahz3ARPD9i3AesCyJTmmRKhLZY4K554GfAfYoMCxN7XIexB4HTBfi7QQ8EpkvU932H8l/iIMcE6o28HArSXOcSc+YAbwL2BzYE3giYj1GPh1OAUNsXQ0/v/DzhHJFh05gLF4hJZjgVOyadDtoU7zbio9JS7gZ8AM3L/DiyHv73jUgiLYOye/TFjF0cKRgoaUOC4CLjSzr5nZvtk0ZBwxNFSCmb3ezD5vHre8ed9X+8ixipm928zWarFvtyIcVRFDQyr27ARJD8bg6YRI9lg8J3+lEjoqc8RAVR39aBv9QqTfZKqZNUYUbwI+YWYfwh0PFsVZeGhFgBOAi4F/AIWdN6rglP1ec1TAx4HJwLVm9k8z+6yZLd3qQEkbtMi+DthE0ivNCXiZ/BkQ3aLT9NvPhvMCfAF4Ne4bosxMouOAxkjxD/F2cT3+ghYFna7DqdwLUtFRgOOl0cARyRZFOE4BPgc8DUxtSjVqDATdLq94DFhBHvZotiMkM5shabE25RovXD8G9m/avRqwi6S1O5x7VHCkoCEljgzXxTm7JKljrPJUOKqUNw8h9l1gS9zB1dclTc3sbzv9NBzzIeBHwGXApvhozgckPRP2d5yKGoljR9yvxd34SNJJwKfDA2tHjki2qKQhHJOEPcNx72fEHidmH4LM7FhJn+xQfjngRGCLwPElSVdm9ve8LqHz5494VIFpwOclnVlSQwyOGLaIoSNG+6pUl1RskTn2bcAzki41X1ZxGu6U95OSzirC0YJzK9yHwJ9UfMnBZNxhYSunwO38HUTlqAozm4gPDH0Ib2N/Ak4Gzm73IhXaxSxJj/ZJZ+UIGmb2VUlHlTh+FWAhSbdm8laS9ECbMl1fhxO6F6SiYwzueLvB8T1J0zP7z5O005BwVLJFRI4ngcmKGK6+Ro3K6GZ6BD41bfmwPT18roI7FGpX7uKQXs5sX4yPCp8OvL7AuUcFRwoaUuKo0xz2/AXuBOjdwNHAE8C2mf1tp5+GY25hZArqgvjD5bWMTDXuF8c/8XXZ4NOa/wKcC4wtwhHJFpU0JGbPLwH34A+K/8QfEpfP7J9ZgOMM4HhgE3wk5Alg9362L+AC4HBgceBdwAPAV0tqiMERwxYxdMRoG5XqkootyiRgtwgcbf8z+CzOX+JO2bbJphLnqMwR2W6rAYfgy08ej8h7bK9/jxQ4qHgdjvR/j3EvSEXH/+Azaj4P/D60y/WGjSOSLSpzhOOuJywhqlOdUkndFYKv4uvqtgOewtfDXQx8rmD5Qr4f5gWOFDSkxBF4Fgf2BA4Mn4sPI0e35fEwR4tlvr8ZX9P8tvC9yIPAXGuW8RkDNwDLFLz5RecA5se90f8VmNCpLr2wRVkNidlzKrBW5vthIW9SCXs8AozPfN8Qfzn8aPje87oAjwPzZb6vhI/qfKtEPWJwxLBFDB0x2kaluqRiizKpiKYCHJ2uQTOBMVV1VuWIaLOxwPuB8/BwlRcP0++RAkfV63Ck/3uMe0EqOu5jzhfrffGZUq8ZJo5ItqjMEY77InAVsBseYnZ2KlK+TnXqRequkK/h+yzeS/oscCs+MmJdco1ppAp6hp4jBQ2D5sA7sKbjnVqn4yHWpuMhsoaGo0p5fM3y+Ka81+Pezneh2IPA7cCaLfIPC/ue7xPHPcDKLdrGCcE2z/XBFpU0JGbPuV5e8CVN9+FT2ovY4wlg4aa8NfDlJ1/oR/vCO44mNuUtjY/sHEOxh7MYHDFsEUNHjLZRqS6p2KJMisFXwC7nEl46KpyjMkeEem6Fzxx7MrSprxNeZHr5ewAPZbZPKMCxewQdvZ7pUOk6HOn/HuNekIqOGcD8TXnvwjtCtxoWjki2qMwRytydk+4q8z+oU51ipsGcFFbApy49AbySTfMaRwoaEuO4Gti1Ke8DwLXDxFGlPL6+cocW+ZvgL9svF+D4LvCNnH0H4mt0+8Hxf8AhOft+1okjki0qaUjMnjcCG7fIb4zIvFCA48/Ae1vkT8KXznX8v1atC/AHYJ8W+ROBawpqiMERwxYxdMRoG5XqkootyiT6MBUf91X0GP7Cfng2lThHZY4K9Ts0/H5PhvNv2cNzzWXLUO8lY/1eKbSLqtfhhO4Fqei4rlW7BHbEZ0+9OAwckWxRmaNOdUo1dV8QtsVHCv8UPrcrUfYcfP3oxnjP4kb4y+pH5zWOFDQkxvEkc/fyzgc8OUwcVcrj3tZ3zdm3AQVGi1JJ+FTeCW32r9JrW1TVkFLC13t+KWffBykwioGHv9sxZ9+K5HTQRK7HmsCmOfsWAfbsE0dlW8TQEcmmleoyjLagP50OJ+alEueozFGhfhcAu9I0Y6xH52rV6XAE8Bw+Uvty+JwrRdbR0xkwMa7DEfQNXEMsHfjy04/l7HsjcNEwcESyRRK/a53q1IvUbfSKjwDfxkcQ78WdSH4Y9yx/XIHyT+AP+s+a2VOSJprZEsCVktYpqGFUcKSgITGOa4CjJZ2WydsVvwi/dlg4YmjoFmY2TtILVY6LwTFaUNtzToymuqSA2p7do5V3fTNbXFLhsJpm9rSkReKrm/eQZ0szWxmfMfNn3JnmXJA0pQ3vdyV9OWy/UdJFHXScL+ltTXkbSbq+QDUax/ekXaTyf09FR43ewMwWxWc5bQMsBSOhbdWniDk1asyFbnoq8LVeGzXlbQjcUbD8o8C4sH0PvuZzHCV6p0cLRwoaEuPYAvd9cBU+a+Lq8H2LYeLotjzwjoL8ucfhPlYOwMPattq/fNh/S485rsX9LozN2d9waHZ1D21RSUNi9izkibrdccBnGv/RNseMAz7Tq7oAPwCW66BhOeAHbfbH4Ihhixg6YrSNSnVJyBb7Z7bXaMcVjrmpRd7MzPZfC3Bs1emYcNwiwGQ88sNqwGpFysXmSDkBP+2w/00FOOaKgEHG4SFdzm5pahcdn1Vp8gOUya90HU7oXpCKjo0KcuQelwJHJFtU5sgc80vgEuCdwNPh83I8jHHHc9SpTr1I89MdlsSdSGbxH2CJguWvxqdN/x5fnnEG8Dy+nqooRgtHChqS4ZB0pZmtDuyE+4g4BzhfmVjJw8BRofyuZvZt4FfAFPx/9TT+sLoW3mu9B/Bv4OwcjjfgEWauD7GamzkmAicBW7fREYNjL3zd8k/N7J8tOF6Nh1XdO6d8DFtU1QDp2PMiM5sCnIp3ksxq7AjxxTfDp4dujS89aYXlgDvN7Hxa23RbfCTylB7W5T/ANWZ2axsNa+PTsvMQgyOGLWLoiNE2qtYlFVt8C/d/AO58ctE2xyKpVTt/zsw2wF+qNjMzIzPKlyk7K3xe3u4cZrYefg3aCFDgakwRna9d2ZgcKcDMdsCXTy6czZd0SPj8RLvykv5W4DR7AJ9syrvezH6LP3eOM7PDc/gPacP7lJm9PXAsb2aTad0u7gqf9+fwVL0Op3IvSEXHT8xsZuCYIumhDMfy+H1+z6DrDQlzxLBFDI4GdgDWlfSEmb0i6Y9mdh3+LPrDDmVr1OgJul1e8Ud8Hd5XJD1nZgsBRwKTJe1coPxEfL37dDNbEF/DtDA+HX1aQQ2jgiMFDSlx1AAzexXwMfwhfzIjD6dTgfOB4yTdXIBnLL5O+1X4A8STeCisayS9VFBLDI7l8FCXzRx/kfRoh7KxbNG1hgzHQO0Zyu4HfBy3xV2MPCROxp3F/Rw4XtKLbXiWwjtZ3tpCx/nAKZKe6HFdFsBHXvI0nCPp5T5wVLZFDB2Bp1L7qlqXFGxhZv/COwFvBn4CfKrVcZJOaMPxCeD7+MyMloc4hYp2GFyCd4Acjnt/XxV/3rlS0i/7xTFomNmP8VlhF+M+GhqQpH0jnmeuZQ1mtgx+7ZuEdyT/qkXRtjrM7N2448RJeFStuTocKNAuIl6HB34vSEVH6Az6OO434ZUMh+GhrX8h6fwOWgbKEcMWkX/Xx/GZZy+b2QPA+oHrKTUtSatRo1/ottNheXwUuxEWcAk89Nzukh6MqrDGqIeZXShpx7B9GSMvlnNAUm6PewocMTS04JyAPwg8Jem5Tsd3AzM7VlLzyNIgOL4q6ag2+/thi7YaCnL03J7ma6TneEjsxbXXzFaS9EBFjkr2MLMtJV1RUUMMjhi2iKEjRvuqVJde2cLM1sKndE8CtsOj1zRDkt7YgXt+fPbGbfjDdiuSewvqfBJYRtJLNuKraCF8acfkfnEMGmY2HZ9anjcDINZ55vLT0bT/REn7dOBo+z9r1bHRDfpxHU7oXtBzHaHTcs0Mx51FO/RT4ohki0ocZvY34NuS/mZmpwOzgGfw0L099StWo0Yeuup0mF3YbCV86vhDnR5ALGc6XDPUZnrcaOFIQUNiHLsrOFw0s73acJycMkcMDWXR6QFtXuJIQUNiHOdJ2ikBHfXvOso4+qHBzP4m6U0dONp2fpjZmpLu6MDR9n9iZtOA1eWzOu/ER0GfBB4saoMYHIOGmd2Ov7A83ePz9KNtjS0wUnyjpFdV0RF4Kl2HR9m9IIaOyr9LChyRbJHLYWar4e94U8NMoW/jMyYOk9S8PL5Gjb6gK58O5uv67pF0O/BAyFsbj1zwl5xiK3cncVRypKAhGQ5lIjx0+0KeAkcMDV2g1RTReZUjBQ0pceStXS2DFOqSgoaao88aOnU4BNxCG78PnTocAjr9Ty7DlxWcBPwWD0H5Ar4MpChicPQd4cWlge8DvzKzI4FHsscp+EGIddpec3TqcAhYNYIOqH4dHk33ghg6Vh0lHDFskcuR/U/Kl5B+JML5atSohG6XV9wBbK3MOn0zWwG4RNJaEfXVmMdgZrsB/5Z0a+jI+gU+LewTkm4bFo4YGgrqHPioZyocKWioOeJzpKCh5khPQ+CoPE2+jA5zZ24fxH0VnSLp2S7OV5mjXzCzWYw4vsyDVNA/RsFzxlg+lET7jMGTSl1qjrgcvdZgZrn+TdTGJ06NGr1Et9ErltHcjgGn4WsoC8HM1sFD2S0raf/wYjZO0g3zGkcKGhLiOAIPNwnwPTzk4TPAsfiU1GHhiKGhRo0aNWrko/v1od2czL3Jnzpojn5B0pgBnLZV9IoaNWqUw4eavi8HrA5cAdSdDjUGgm5vKHeZWfOL07a4V+aOMLNd8OmGK+LhX8DXGv2gqIDRwpGChpQ4gKUlPWJm44GtgINwj98bDxlHDA39QgrTtWNxVEUq9UjBFrEwmuqSAmp79hFmdqqZndIq9ZNjHkLdvudEKvZIRUeNApC0XVNaF4+KUTiEfY0asdHtTIdDgbPM7Hg8dN3qwD4hFcHhwPaSrjezD4S86/EY1kUxWjhS0JASx2NmtgbutfdaSS+YRy0oc8NLgSOGhiKIwRcjZFsMjlae6ssghi2qaoB07JlK50fVuqRSj1Q4UmgbqdiiH7iz6ftywPtoHbaxlxwDhbWJyJSFSkRnyqOoWB5gwQgcsdpnVZ4U/u+Qjo7RwjEIDScBjwNfjnDuGjXKQ1JXCdgM+BlwXvjctETZJxjxJzE9fM4PPDqvcaSgITGOvYEZeCjWN4e8d+D+QoaGI4aGFpyrAas25W3VocxuwLphe23gUjzO+jolzhuDYztgctheHjgZOBGPI90vW1TWkIo9C57nwJLHbwds05S3ci/rAiwNLBy258M7rvcCxpTQXZkjki1i1CV62+imLinYoqC2lyNwvNJFmdcC51Q8b2WOfiZ8UOEB4FvAfvgSwvuBw8JvuxewV4TzzEyEY2oku+Veh1O5F6Sio8XxC+LLc7N5uw8jR1VbdNG2xjSlhfGZDnfF/E3rVKcyqXfEcF6bfX8G9gzbjZfTPYBzS/CPCo4UNKTEEcpMACZkvi9DyZfTFDgilD8d2CJs7wM8DzwLfLgEx1TcvwbAObh/iUOBi/rMcSse3QbgtJCOB87uoy0qaUjMnl8ANg7brwfuw5e3bV6CYwqwZdj+Cu6R/kHga/2qC3A1sEnYPgq4Gfg38MMSGmJwxLBFDB0x2kaluqRii4LnidHp8HQXZean4ottDI5+JuAqYP2mvPWAqyKfp/Tv0YKjLx0XVa/Dkf7vMe4Fqej4HrBZ2N4Jv88/B+w8TByRbFG1bc0CXmlK9wFv6eb/UKc6xUi9I25zwQbWCY1/Ch426k/hz7RmCf5RwZGChpQ4MlzL4KPZs9MwclQpDzwKjA3bNwJbAusDd5TgmBk+x+Nx4cfhvd7TB8QxPz4jZmFgLPD4AGzRlYbE7Hk/sFjYvhj4HPBh4OoSHE8A84XtO/EXiJWB+/pVl1CmMTvqAWAVYAlgWgkNMThi2CKGjhhto1JdUrFFGXv1kgN3/JtNb8enmhd+2Y7BMeiEz9wb35S3IPBU5PP8dBjaRTim0nU40v89xr0gFR3TCAM1eMfle4HtgRuHiSOSLaq2rUlNaalu/gd1qlPM1K1Ph65hZga8CGwA7AicG/5c50p6Zl7iSEFDShyBZ0d89Hn5pl3Cp+kOBUcMDfhL9otmtiKwhKQrAveyBctDGv4tAGYG3RsAt0h6xszGAgsULB/DFlU1QDr2XEzSDDNbBPeZsr2kV8zs+yU4xgAys9XxF8RbAMxs8RIcVevyCjDWzNYCZki6L4QUXLiEhhgcMWwRQ0eMtlG1LqnYIhUc3/T9WXzWxm595hg0pgAnmdnX8Y6klfHR8NJ+cMxsMvA2vF1fKGm2zwtJn4iitj+oeh1O5V6Qio4Jkp4zsyXxAZrfAZjZpCHjiGGLShyS7i1xrho1+oK+dzpIkpndCCwi6cx5mSMFDSlxBPwE+CZwsqTnh5gjhoZ/m9mBeC/1eQDhpXtmCY5vAv/AXwIazj23xx189pPjf/GwoWPxHnvw2Qq3FSwfwxZVNUA69rzfzLbAZ3tcGh5GFg2cRXE58GO8Y+z3AOFF8/ESHFXrcgFwJrAk8OuQtx4+nb8oYnDEsEUMHTHaRtW6pGKLJCBpcgocCWBvPOTzzXjH+cvAWRRwIG5mt8q952Nm2+BT+K/AO+GPNLN3SrqoR7p7iarX4VTuBanouN3MPgisAfwFwMyWwpc3DBNHDFtU4uij49caNYqjV1MoaL+84nIqOqgZLRwpaEiMYzphWu4wc0TSsDrud+BkYJmQ9z7gf0ryDNy/RSizFrB60/dX9dkWXWtIyZ74KOFDwD3Aa0Le7sAFJTiWBL6NO4JrOP3bCfhcv+qCT+PdD39xmT/kbQvsWuL8MTgq2yKGjkhto1JdUrJFgfPEmEbf1ocAcztka5l6zZFKClqXbaUX2K2TjfGZEXtmvn8QuDKyxhh+ITpyEOc6PPB7QSo6gE2BK4FLCPfp0D5OHSaOSLaoxEGfHL/WqU5lUmPNZXSY2UxJi+bsOwJ3MHhS+BPMFiHphIL8o4IjBQ2JcXwXuLXo8alyxNAQE2a2DE1TmyXd1W+OwDOmiWNWWY6qqKohJXtm+BYIHC91y1Hh3FHrMq+jtmcxmNnTkhapyHGgpCPb7J9F+xFDwyf75S6bi8ExDMh77svmm9mjwIqN65SZzQc8JmmJgudYCx/9XQR4GrhZ0u2x6pA5z+6STuuiXOnrcCr3glR0jEbEsEUZDjO7Cne0fXMmbz3gBEmv71ZDjRpV0MtOh1fybqBmdnFOMUl6Y0H+UcGRgobEOC7Dw7HeCzzcRFJoGlgKHDE0BJ4dgI2Z+0HgkILlc31LFH3AjcTxanzJyYa4syoo+aAdwRYxNCRhz8CzGB7erNkehaYpm/uz2JvWNt2zIEelupjZEsCXcjQU/a/G4Ihhixg6YrSvSnVJxRYFz7OypPtz9u2A12OOF1TgREl/KXGOT+Kzqo7Er+eT8KgevwPObxynNmuoY3AMA/I6gczseeCT+PX2SHyd/LNh3zg8rPZiHbhXAc7A17dPxR1bLorPgrsen0VzXweOVSXdk/n+Afx3MeAPkn5ZsKpZzq6vwwndC5LQETjWxn/jZo7CAzgpcESyRZW2NQOPSPLfTN6CuDPfiUU11KgRE73sdMi7+cyHT+s5LftnKMk9KjhS0JASR+DZK2+fpJOHhSOShh8D78c9Fz/XxNFxHW3gmAp8lwq+JSJx3Iiv4z2VuevS8UE7ki0qaQgcqdhzb7wD5RnmrIskrVaQ43T8oeoc5rbHYQU5KtXFzC7Ep+Kf2UJD0f9JDI4YtoihI0bbqFSXQdvCzD4k6dSwPQH4Pu49fgzwB+CzjRfWNhyfBw4AjsNfSBsvqBsBH8WXZf2oYF3uBF4r6alM3uLAdZJW7xfHMKDNTIdLmHOmxwGSrg37dgCOkLRZB+6/4X4HDpX0XCZ/IeAQYNNOgxtNMy4+Hsr9KGj7DHCkpJ90rOgI395UuA4ndC9IRcfX8N/k+hYcRQeuBs4RyRaVOMzs7FCu2fHrIpJ2LqKhRo3YGNTyiqeq9rSNFo4UNKTEUWMEZjYd2ChvJK8Ex5Kq8EePxDET98bcFUckW1TSkNGRgj0fBD4i6YIKHE8Ck7MvQl1wVKpL+E2WlvRCBQ0xOGLYIoaOGG2jUl0GbYumF8MfAK/BOxDAR8mv+2DPUgAAIABJREFUl/T5DhwPAW+UNJeTWDNbB7hYUvOobh7XY/i156FM3opBx1L94hgGtHvua1NmMWABSY+H7ytJeqDFcc/gkYtebLFvHB7ecaEO55o9GBY6ofeT9PfwfTPgJEnrldBe6Tqc0L0gFR2P4lEabhhmjki2qNq2lsAdv76HOR2/frrxX6tRo98Y0/mQnuAcM6va0zZaOFLQkAyHOT5qZheZ2Q0hb2sze/8wccTQgHuL7/rBP+B4CngX7wPH74EdKpSPYYuqGiAde84P/Lkix334aHQVVK3LDcBKFTXE4Ihhixg6YrSNqnUZtC2yYfreiTsdvFrS1fhsuvcU4FgId8LWCg/jTvOK4mTgr2a2n5m91cz2A/4U8vvJMQwoE2IRAEkzml6Cbsk59H7g7Tn73oa3246ny2wvD1yV0XEN5dts1etwKveCVHQ8T7loUqlyxLBFJQ5J0yXtii8lXQFYUNJu2f+amQ1TyN4aowCDmunwG+AdwN+Z2+Fg0TWjo4IjBQ2JcXwTeDNwNPAzSRPNbDXgN5JeMywc3ZYPxzTwZtxr/JHAI9nj1Ma5k80ZKsmA1+EekAv7lojEcWqGYxywMx7hpJmjZduIZItKGgJHEvZs4vsCvk79myrhBNPMslNDNwF2wacXN9s0d81o1bqY2b6Zr6sCuwEntiifu3Y2EkcMW8TQEaN9VapLKrYIPNmZDtOaZySY2TOSFm5devYxJ+Br/Q/HO0CyyysOBu6S9OF2HBmuMbj3913wh/dp+LKR4yQVDV9XmWMYYGZTVXG5iOUvzX0T7gPjJuZcMrMx7rfjve3aZ+B4kZEQrjsD60p6OOybCNxZZuZJN9fhhO4FqejIDn7ugYexPpS5rz25nKlwZLi6skVsjgLnKD0zqUaNKui7T4ew7xt55VR8zeio4EhBQ2Ic9wObSHrczJ6UtLiZGT51cvFh4ei2vI14OW83YiS195Se60+iiSR3lC0SR257aOJo2TYi2aKShsCRij2zHXkGLAe8CDzRxLFKG467i8nIXzNatS6W73C2WUPu2tlIHDFsEUNHjLZRqS6p2CLwvAI8GL4uA2yssEzC3JnglZLajkibO8M8DNgTH9Fu/G+m4T5dvqEW0/S7hZkdK+mTg+YYNGK8xHQYsFoSn+myPu5c7xncOejvi0wZb3E/OCPTtt6BO6PcvQNHpetwQveCVHRkI7s07vXZF5Oy0WEGwhHJFpU5yqDde1qNGr1AV50OZraLpN+0yH+fpN+G7bZhqGrUaAXztbirSfqvmU2XtISZLQLcImnlYeGIoSEGzOx18mnJzfmbyaeT9otjucaIUpH8XiCGhkHa08y2KcIvaUqR42Ighj1qjKC2Z8t2fqukR8O+bfHO3B+W4JtIeEFVBT8VHc7R05ftYcFosoOZ7Sbp9Bb50a7DqdwLBqnDzCYV5GgXHWbgHJFs0dd7fCr/tRrzECSVTsDMnPzpJTi2BU7A1zWeAGzXhY5RwZGChlQ4gP/Dnd+MA6bjvb1HA8cOE0ckDcfk5B9dgiPGf3XgHLUt5jp2l5z895Xg+GNO/ll9/F3/lZN/XQkNMThi2CKGjhhto1JdUrFFwfMUvp72MgFPp8Ax6JTXfnvNgTvKO6Tfdal6HU7oXpCKji/l5H9hmDgi2aIyR7e/e53q1KtUypGkma1mvs56jJlNbnwPaXugUKhEM/sIvp7xYdyb6jTgdDP7aAkto4IjBQ0pcQBfwKfDzgAWw6dPTsJjmg8TRwwNe+fkf6hTQTMbYx7G1ALGZNKauCfjnnNk6VrwLwoUXau4d05+R1vE0JCgPY/Pyf9FCY7tcvK37VQwYl3WaMFtQKGwYhE5urZFDB2R20bVugzUFiWxR9kCZjbOfAlHTJSfMtobjtGA0s4ocad7hZbSRdbR1XU4lXtBKjoyOCQn/+Ah44hhixgcndDNf61Gja4xf8nj72RkjfXUpn0P405XiuAA4M2Srm9kmNkZuJOg4+YxjhQ0JMMhaSbwbjNbFlgFuF8lp9+nwFGlvI04ZJvf5nTOBv7QXiTc0cuMPMQ2PzjMAr7VDw4bWaO4oJk1exdfEphr+mpT+cq2qKohIBV7Nl7axpjZZOZ8aFiNAh2/ZnZ42Byb2c5y5E5BzaBSXczslIyGU5p2r4qv026LSByVbRFDB3HaRqW6JGSLMmj50Gzu+yEP4/PK1egfbE7HfWjEWV7LkJXmzkHzUPZZtghyO4EiXIeTuBekosNGHNnOZ2bbteB4ehg4Itkixu9aFI9G5KpRoyNKXagljQEwsymSCq09ysGSzB0W6T/AEvMgRwoaUuJo4HncmdgYM1sBQJkY50PE0U35xuj9WOYcyRfuSbmIA6jGzWoKkPU+LeAxSc/3iWOPwHE+Leoi6T8dysewRVUNkI49Y3T8NnyKjMlsN3TcX5Cjal2m5mwLuAKYy2dQjzhi2CKGjhhto2pdUrFFGeS9GN5Deye0sWcVxOjEGA0dIW3DVprZq4GfABvinT/g9Ra+RAJJ9+cU3x0f/Z3eYl+ug8Aeoep1OJV7QSo6GqP64/FluVkdDwOfHhKOGLaINbhbBEtH5KpRozMGsaYD+CPwv8CE8H0h4BjgnHmNIwUNiXFsD9wFvIL3tDfSK8PEEUnDEUWPTT012kSF8pVtUVVDSgmYEoHjownU4y2JcFS2RQwdkWxaqS7DZAvy16I/iIcAnK9FWqjMdbigjsq+JWJw9MHe6wBfB36S+b5hifI3At8G1sWXG85OBcpeC7wjZ994YFY/2lbTMZWvwxF0DlxDLB3AKaOBI5Itev67FmnjdapTzNRt9IrJ+LSrjXGv0LOhAqFczGx54Axgc7zXegngSmB3SQ+2KzvaOFLQkBjHvcA38Vjac/Syq3hM9IFzdFu+ecppHtQ+3vSpFBjJk7Rnjzmap2jncbRcPxnJFpU0BI4k7BkDmambnXTc1YajUl0yU1g7lb+ojYYYHDFsEUNHjPZVqS6p2KIsLCfkm5n9EbhA0s9a7BsHPK8wczOSjlETtSEPZrYLPkvhLPyevqiZvRY4StL2BTlmAoupiwdPM/sU8KCkP7TYNx9wsAqG5i54vpskbRCLL8Obyr0gCR01BofUrzk1Rh+6XQd3Gj7t54vAc2ULS5oGbG1mKwErAA9JemBe5EhBQ0oc+IjFiUU7BxLm6LZ8do1lO7SbTnpnZnspfAnCOfia7FWAnYHc2NsRObJTtMcD78VHqxocm+H+PvIQwxZVNUAi9jSzyyj2kLh1m93ZqZtZrubvvWxfWQdZAlYKn0/gS7QMeID2TgdjcMSwRSwdDVRpX1XqkootyuKXOfkfI8dBrKQXoJwT7QKYF5ZXHE7w2WRmHwh51wMbleD4PbADHt2qFCT9pM2+V4DZHQ5mtlKnZw8zWwxYm7kHzi4Kny07HCJch1O6FwxcR8bnUieO3AHNFDgi2SLG71qjRrLottNhfWDLdiOM7WBmOwD3SLodfwjBzNYGVpH0l3mJIwUNKXEAPwQOMLOjuhkNSYij2/KTM9s7Ae8DjsQfBCbh0S/aviRnR3vM7E/ATpIuy+RthU+R7TXHPpnjfw3sJul3mbz3ALu0oYhhi6oakrEnHoa1gdWBffGHwsZD4l7MuQ61lY7ZL1tmtg++DOhQRmx6CPC3XtZF0uTMsV/DX0i/Luk5M5uAv9w80UFDDI4YtoihI0b7qlSXVGzRjHBPaTWj8pDw+YkcLYWd/prZsZI+WUZXq1NWLB+Lo5dYBrghbCvzWUb3eOD3ZnY5vj59NiKPqN8C5I7gmtne+KyNZ5hz4Ex07hSrdB1O6F6QhA7mjECzaShzDCPXnv2BZqe0KXLEsEVlDjMb0+27WY0aPYe6WJMBnAu8ppuyofwdwPJNeSsAt89rHCloSIxjTdwXwszwOTsNE0ckDXcCE5vyFgemluCYASzQlLcAJdbyReSYrylvfmBGn23RtYbE7HkVsH5T3nrAVSU4HgAWbMqbADzQr7oAj+WUf6yEhhgcMWwRQ0eMtlGpLgnZ4se4d/UzgBMz6YSiHAXPU3ldcyocvUzAn4E9w/b08LkHcG4Jjm/kpchan+6w/0HgrRHOU+k6nNC9IBUdNwErNuWtBNw0TByRbNEVBx6q/jvABkXqWVRPneoUI3U70+Ee4EIz+z1z91bnro3OYBn5VPwspgHLldAwWjhS0JASx2+By3Av50U8J6fKEUPDYvjD/lOZvAkhvyj+BXzbzA6R9LyZLYhPQ/13nznuBD6Fjxw08Anm9s6chxi2qKoB0rHnusyt+27csVtRjMHDGN6ayZtEOU/wVevyLL7E5YpM3qaUW7YXgyOGLWLoiNE2qtYlFVvsDmyk/GgGsZDK0ojUl1d8BvizmX0YWCiMjq+FL5coBEX0udDpVB32z493olRF1etwKveCVHSsgM8+yeIZYMUh44hhi245Po53Bl5rZrfiMyVOk/RY84Hqgc+SGjXaodtOh4Xw2Q4LMOea6aK4y8zeqDkdSm2L/6HmNY4UNKTEMRnYRNWmh6XAEUPDycBfzexoPGTdyviDX6f13VnsjftgmWFmT+KzA67DH+j7yfERfFrtAfgo04q4z4b3FCwfwxZVNUA69pwCnGRmX8dHplfGp8Nf1q5QE34IXGRmJzJi071DflHsTbW6fB3vwD4no+HteOdQUcTgiGGLGDr2pnrbqFqXVGzxOHN2MvYKLV9QrYBjzMy9rqVviRgcqUDSbWa2Dv47nov/rudKan45awszezOwKz5IsbO5M8pFFdHBaAH8D3CwmX2z4j266nV4b9K4F6Si42zgbDM7IsNxYMgfJo4YtuiKQ9IfgT+a2UTgA3iY8O+ETsKTgbMlvVRCR40a0dBV9IrKJzV7J974j8d78lYH9gH2CX+YeYYjBQ2JcZwKnCzpr0WOT5UjkoYxwH64z4EV8FkjZwLHqaSDSjNbBVgemCapbSz1XnGY2QJ4ZJPl8br8vejNL5Ytqmho4hmoPc1sCeBYvMNkPrzz5Czg05IeL8GzI002lXRhGS2Bp0pd1sMdfDY0/FbSLQPgqGyLGDoCT9X/WqW6pGALM/sY7svlSOCR7D61iaJRFpbjwd3MOnWWS1Lb9f8xOFKFecSTWZLuKVHm08Bn8bXrB0pazMzWx6/jW0TU1tYrv7nTwOWAF2nyM6ICEdgyPLGuwwO/F6Sgw8zG4y/Wzff5wyQVmjGaAkckW8T8XVfDZz98BA8dvlSZ8jVqxELXnQ6hx3sXYFlJ+5s7DBwn6YYORRvlN8OdpKyM95gfL+nakhpGBUcKGlLhMLMz8ZGUy5j7QbOQo6kUOGJo6BZmZgp/bGsTdrLdCE8MjtGClO0ZuJbG18r35beo20Zc1PZsDTPLq68klVnq0ek8LcNu1pgTZnY68L+SrjR3NnosHiHkM5KOb196NsdU4E2S7jGzJyUtbh7u8lFJS0bU2vY3NbNt8vZJmtLF+Qpfh1O5F6SiYzQjhi2qcpjZWOBduBPK7YErJW3XjZYaNaqiq04H83jNx+Ke47uK11yjRiuY2Tfy9qngetAUOLotb2YfknRq2N63DUeuB+PsKE94cG/+kxsdHtwjcdwqad2wnRuOKm9kKZItKmkI5VKx56qNUcUwctES7UaBzewgSd8K24e34cj1zVO1Lmb2C0n7he3cWPHtOuciccSwRQwdMdpGpbqkYotBwMx+qpwoGDVGYGaPAitJetHMbsTXjj8F/EHSmiU4lpf0iplNl7REGFW+W9LyXWia42W58VJmZiurR75Aql6HE7oXpKJja0mXhu3c5Uhqs/wmBY5ItqjMkeHaCtgTHxx+DDgVOEXSvZ3K1qjRK3Tr0+FwYHt1Ga/ZfIrzwfhaoxWAh/A/xLckvTgvcaSgISWOop0CqXNUKL8bbjNwO7akp33YpPUz25Nzj2qPGBwfzWzvkXtUPmLYoqoGSMeeNwKN0bs78bo3O58T7R3+rZTZzvPH06knumpdslPO78w9qvccMWwRQ0eMtlG1LqnYYi6Y2cq4J/mruizfVdjNGnNhbOhwWBFYQtIVAGa2bAmOS4GvAt/K5H0GuLgogZm9Gg93uSEeghPCCzLh2tepw8HMxuFhYHcDlpQv89gBWEvSjztIqHodTuVekIqOY4GGQ8O8GTOifSjTFDhi2KIyh5kdij/rLIk7Mn97479ao8ag0e1MhyeApSQp01s9P/CQpGUKlP8h7tn6MEZi4H4duE7S5wtqGBUcKWgYNEcKvdQxOGJoiA0zmyipkjO2GByjBbU958RoqksKqO05AvP15afjHQaStLCZvQ/YUdJHCnL8GHg//lKbjZwhSbmzp2rMDTO7BPgTfl8fI2m/0AFxtaSV2hYe4VgeOAdYCnfkexfwNP5i9HC7shmOGwPHqTRFQyk6imtmx4bzHwVcIGliqMufJa3fvnQ8pPJ/T0VHjeowswtw32p/kPTfQeupUSOLbjsd/gz8UtIpmU6HPYBdJb29QPkH8FBYT2TylgKul1QoJM1o4UhBw6A5zOwmhdA9lu94S2rjcCsFjhgamvg+A1yign5ScjieB27DPSFPAS7N/j595HgCH+VqcPxbJS4+kWxRSUPgSMWeG1axReD4AXAJcJmkJ7vkqFQXMzurUVZSmfBssTli2CKGjhhto1JdErLFBbhPnKOAJ+Tr/xcDbpA0qSDHdPoTdnPUw8xWB74JvAR8WdKjoRNoU0lfKcFj+ODEKrjfp2tUYq26mc0EFit77W7imAasIenZxjNsyH9K0sQSPJWuwwndC1LR8Y5w7q47QFLgiGSLyhw1aqSGbjsd1sFjHN8NvB5/QFkL2EHSHQXKPwhs2OLl9AZJKxTUMCo4UtCQEkeNEZjZKcA2+HS7yxh5IPhn0Qcu8/WyWwBbB65N8f/tFEn795Fj9Uz5bYCJwOWB43sFysewRSUNgSMVe96Hhy7O2uJfJTtyDs+c/w7mfOCcK6Z3L+pi7quj8XssSvg9goZCDmgjccSwRQwdMdpGpbokZIsngKUlzer2xdDMbgdeI+npIsfXSB9mdjJwmqQ/VeC4F39WmZEZOFsauErS6iV4Kl2HE7oXpKLjUuC1wO3Med0pEwlk4ByRbFGZo0aN1FAlesUE3Dv/JErGazazoxmZhn9f4DgYn4b/uXmJIwUNKXHUmBtmtiojD/DvASgzGhM4JgBbAW/BwyY9L2m5fnMEnrVwT8r7AwtKGlui7KpUtEVVDaH8wO1p7miq8ZC4Nb6G83IVmG3WxDMO7zx+G+4cbmGVjA4QyR6T8LCo+3ejIQZHDFtE0hHDnpXqMmhbmNktwLsk3Z55MVwP+LWkDQty9CXs5miFtXHgm4XaO/PNdeDbxFEoVKWZnQHsjHdkzbEkQ8WjU30PWAP4PPAP3L/B0cCdkg4qwpHhqnwdTuheMHAdoQPk9RmO1wH3KMwiHRaOSLaI8rvWqJEKunUkiaTn8Li13eAA/GX0J7jDwQeBXwNHzIMcKWgYKEeMh5IUOGI/XAXOtfEbzrbAloz0vBct/z/4zWpF4Ep8ecHmkm7pM8cnAseWuIPRS3EnXpeX4KhqixgakrAn+EuTuS+dsSHtCHT0qZPRsTBui4ZNV8HXbPetfZnZuow8VG2Fv0T8vKSGGBwxbBFDR4z2VakuqdgC+B5wrpkdCcxvZrsBX8OXWxTFT8Nn80N6J4duNRx5DnyzEO2d+XbrwDcPt4RUBV8D/gd33DcBn9FzHD5gUgpVrsMJ3QuS0BEwXyg7DncU+hRw67BxxLBFJHvWqJEMCs90MLMLJe0Yti8jPxzW1gW4llMLp0F5+aOZIwUNg+awOWNmb4qPQB/DiDPK/fFQP99PmSOGhia+R3AnW7/FlzBdUXaasJk9gz/wHx84rpX08gA4ZgFTge/gs6KmlSwfwxaVNASOVOx5BrA53nlyCf6QeFkZm5jZS8A9eBv9i6TbymgIHJXqkvlNjgTOLDpbrgccMWwRQ0eMtlGpLqnYIvC8E/gYIzMqfybpD91w1RgOmNl5knbq4/mWBh7vZtp61etwQveCVHRcAywPXNHgKNvxkQJHJFtU5qhRIzWU6XTYXdJpYXuvvOMknVyAa3Z84Kb82es25xWOFDQkxnET8BZJD2byVgIuLDGtbeAckTQcB7whfG2s6ZuS5SzAMT/eAbJ1SBvjo0RTJBWaxRKJYwVGpgi+AViA4NRR0i8LlI9hi0oaAkcq9rwj6P8T/kAyRdJDRcpmOA4K598QuIERu14j6aV+1MXMPsjI7zGGEUefl6qg879IHDFsEUNHjLZRqS6p2CImrGLYzRpzwswMRkL5qYQjyALcLZ8jmo55M7ArsIyknc3stcCiah+dqpADZ5VYdlP1OpzQvSAVHX8BXgVc3+DArztlOrIHzhHJFpU5atRIDV37dKh0UrOnJS3SlLcocJekpeYljhQ0JMYxHZgsaUYmbyJwt6TFh4UjhoZMuWUZmaq8Bz4qs0ZJjsXxqdJvBPYExksa12+OwLMJ8D66WO8ewxZVNYTyA7enefi5xkPiVsCC+EtdoXCCGZ75gFcD7wY+BcwnaeGSHDHssRzwaar5dKjEEcMWkXTEsGelugzCFhbBh0ATX+WwmzUc5iElf4xfb+bwo9PNf7XNedp2OpjZp4HPAv8HHChpMTNbHzhO0hZtys3CZ+ha3jF4Gyn7X618HU7oXjBwHaED5DWMdFpuBtwkafth4ohkiyi/a40aqaArnw5mdgzuzOnKTN4WwPvVxmGgjax5X9DcM2sWS+IPB53OPSo4UtCQEkcGZwNnm9kRwAPAysCBIX+YOGJoaLwcbwNsh9/4ngWuKVH+mFB+TeA6fMTxffi6zX5yfB5/mNkKr8MU4EuUWyde1RYxNCRhTwBJ08zsP7j/lJVwu7y1hI4lGHHKuR2wNu5UrW/2CL/ptoHjDcDzwLklNcTgiGGLGDpitK9KdRmwLWL4EMji58B5QUMjqtJfgEJL3GrMgZ8BzwFvwn/HrYFDgfP7rONzwJsk3WNmjVCdt+HtNBeSxvRCTJXrcEL3giR0BCyKL21YCV9WNRF/2R4qjhi2iGTPGjWSQbchMx/Dpyq+mMkbB9wvKdfJifmad8NvUtk/joBHJP2nwLlHBUcKGlLiyHCNxx9kdsEvtA8BvwEOk/T8sHBE0vAkMIORqclTJN1ZpGyG49BQ9u+S/lumbGSOkxipQ2mv8ZFsUUlD4DiUNOx5Nt558nTgaiwT6RiyOMPxHN5p07DplUXbZobjUCrUxczuYWS96hRJUwfEEcMWMXQcSvW2Uakuqdii4Hm2lHRFm/2Vw27WcARbriLp2Yb9QgfVlZLWiXieTjMdHgWWl/SKjUQ1GY/PIly+XzrCMZWuwwndC1LRcQMeVeRa5rz+PDdMHJFsUZmjRo3kIKl0Ah7Fp11l8ybgU52LlJ/QzXlHI0cKGlLiKHier44Gjk7lgVULcOwWoR4zE+G4MQFb5GpIyZ7A3vjynXbHrNRh/7gCOmL8TyrZAzg2goa2HH20RYy6dGobleoyymxxC7BW2J4ePtcDbqh67nkthee+cWH7HmBp3LP/05HP05YPdyZ8UNNvegBwWj91hGMqX4cLnKPn94JUdOCzo8Z3OGbL1Dki2aIvv2ud6tTP1O1Mh98BdwMHyEcQxuBhrNaU9O4C5Q/P2yfpkIIaRgVHChpS4ih4no4jEMPAkYKGwDGXL45h5KhtMRfHqGhfCdUjFY6Bt41hsYW5j4iv4lE0foRHwvgacJSkX1U597wGMzsHOEHS783s5/hU/OfxwYbtCnKsoBbO8Mxs4/9n77zDZSmqtf9bBw7IISOSD0pGkSAqcgHhgAqIgBdFUES9oBiuCKiI6EdWggmFqxgJkkUBkSQgOQsoIAKSc845v98fq+fsPrMnVE/XzK49u97nqWf37u566+01XTNdq6vWknRtsf0dSft34FgYOBWYH0/xeAf+NnhjBWbaCtRa+/6MwZNCf09Mx1BwpKAhI2PQ6CmmAx7E5zTgQTO7G8/j/SCwSWD9qU3/L4SvJzu5goZh4UhBQ0ocIegUCGo8caSgAdqkvx2HHNkWM2JY7q9UriMVjhTujXFhC0mHFcsCvoSn3PwcsLty2s1g2EjK68/gmUjA4yp8E5gT+FkFurPNbG1JT5T434vHOloYoJPDoTj+YFFnNfzZ8148s0C0DBqRUfc+T6G/Qzo6hoUjBQ0ZGQNFT04HSfeZ2arA+/DgJpW+9CVt07zPzDYEPlVBw1BwpKAhJY7QpoaEIwUNw4RsixkxLPdXKteRCkcMTJjvLkmnAKcMoq0hxS14OsqnAMzsJEkfA4JSKTbhN7jjYZqk58wDkJ8IfL4KiXyK7pVFSR0p9PkUNEA63xspcKSgISNjoOh1pgOFg+HyiFrOBv6QOZLRkBJHRkZGRkZGV1jktJsZo96mTuuVSNLB5qmjzzCz/YEjgE9L+ltHASMZsrrxL96rtlbNRuTKyMjImPAIdjqY2U2S3l5st/0BCPnSN7Mlm3ZNAbbCZ0yE6hkKjhQ0pMSRURnD9GCUpytmtMIwfa6p6EgB/fpcY6fdnOiI+jZV0j5mNjf+MmJjSRcFVNs6poZiiceods1s25IzKqcmzMjIyIiIKjMdtitt1/0BuA3/IWs8MLwA/BNfbznROFLQkBJHCGYfEo4YGh6JwFE1f3W/OOpeSwxbDJM9p0TgiHGP1r2WoyJoiMERwxYxdMS4N1L47upoCzP7pqSfNO0z4FBJ2wK0CmwXGtSwxNkx7WYGM5vZuoz8rjf/j6Tz2lVu85JqUlGO9o+08wsrSRdWEWxmp0v6SIdTjjazzSRdU6rzZeDbFM4oSZdUabMD6n4Pp/JbMEw6UuCIYYsYHBkZA0Nw9gozu0LS6sX2npL27quyjIw2SCg7QN2o/DkadESOFDQkxvGapJ6X0BUcY36PJmTPMbdFRI7kv7vM7DrgYEmHFv8b7qhYQNKWJzNFAAAgAElEQVT6ddquomOiw8zuovNsB0lqnuVYrr9OSDtVHQudEHBvrQUcA2wo6SYz2wHYAfigpLti6SjaqvU9nNBvwTDpGHOOSLaozZGRMUhUuVmXNbM3SXoJj1qcnQ4ZY4VUAvCkEEgoI6MdXojAkcI9msryilSit8dACt9d3WyxIXCBmT2LBxs8Dp9hEZolK5aOCQ1Jbws918wWk3RfU/1ozoRYkHSJmX0Jjy1xArAZsK6kfiwFjfE9XBcpaIB0dKSAGLbI9swYV6jidDgFuKXwes9mZi3X4UlauxuRmc0F7IWnU5yfGafpBQUCGhaOFDSkxJGRkZHRhBQchLGQio4U0C3d5YNmtgFwPvA14DHgo5JeG6SOjEq4Eeg0w2AW4H+AVYA5ysckfbafwlrEnLoF+DWeAv4TwGQzW1LSHf3UkZGRkTFREex0kLRNMSXtbcB7gUNrtHsInmpzH+BoPEbEt/C3GRONIwUNKXFkZGRkZExAtMk8cRIekPho4LNmljNPpItus0Z+D6wMnAo83H85M6A55lQZFxb7Bcw0SFEZGRkZEwVVYjr8SNK3iu0vSvpNz42aPQK8XdLjZvaUpHnMbFHgVEmrTiSOFDSkxBHYzpjHY4jBkdAa8TG3RQyObItRHENhjxQ0ZI7BaDCz8wOqStJ6ddpuajPHdIiEgFgKTwJLSHqqzzpq35+xEGHt/zD9Fox5LIVUOFLQkJExaFRZXvFF/K01wI+Bnp0OeNTip4vt58zTJz0ILD0BOVLQkBJHCC4eEo4YGu7pdoKZzQvMCTwr6ckWp3RNDRaDIwBfqlm/qy1iaBhH9oyxVr3rPTqAazm6Zv1YHDH6awwdMe6NFL67RtlCFTNPREKO6TA43APMWofAzBaR9ECL/atIurb4d786bURG3fsrld+CVHQMC0cKGjIyBooqMx0uwtPJ3YinFfpBq/Mk7RHAdS6wn6Rzzew44A3gOeDdkt4TqGcoOFLQkBjHtcARwHGSepp+mQJHDA11YGaT8WCv2wALMDJ19GHgcGAvSa/2m6Pg+SK+jncFfB3vc8C/gcMl/baHy6uMuhpSsmfBNTuwLMVgH7hF0vNN50xtFxjNzN4MfBy3R4Pj38CJkh4PaD+GPd4OfKaFhqMk3dRNQ0SOWraIocPM1pZ0UbE9CXfwb47b9c/A/pJeD9Qybyvnj7UI8tfinDG3RQu+BRi9/j9o3b0FpN3MiIeAmQ7fxOMnHETT8gp1SLvZxHEDsLakJ0r73gv8RdLCgRxLAPvSOrZE1PhTrb6HY/b3XjWkpGMiIoYtsj0zxhsmVTh3c+BaYGH8C2lqmxKC7YC7iu0dgReBeYAqgYSGhSMFDSlx7AOsDdxhZmea2VZm9qYK9VPh6Lm+mS1U1HnazC4zszWajj8TQPNL4L+AT+ODwlmAt+BxNlYvjvedw8wOwO+F3wHrAcsB6xb/72hm+3epX9sWdTUUSMWe8xQOvSeAi4Bj8fXIj5vZMWY2T+PcDg6HD+Drm7fGfwMewL/TPw3camYhb5trXYuZfQq4HI8BU76ORYHLzGzLbgIicdS2RQwdwGml7e/i35k/Bn4EbAnsFqBjWTO7Cb8X7jezLZpOubFL/VRs0eDa0MzuBx4qdDXKraEceAyIz5c4G2k3F6vAkRGObm9etwcWxGciHFoqv6vQxm+As81sDoDiN+EvwOc71poRx+IvRb6JO8jKpSvM7DOl7Slm9ksze8TMHjOz3xVOYaDt93CM/l5XQywdk8xsFzM7xcz2M7P5mo6f3k2Hmc1uZoeY2XVmdqyZLdV0/F8BOsacI5ItYnyuGRlpQlLlgr8d7KluhTYOyRzpaBg0BzAf8GXgEuAp4DBgvYptjTlHL/WBP+APYu8CdgIeB7YqHX82oN2ngLnbHJsHeGpAHI8CC7c5tgjw2ABsUUtDYvY8Cc8vv2TT/iXxwdRJARw3Ah9rc2wz4KZ+XwtwJ7Bmm2NrAncFaIjBEcMWMXQ8W9q+GVih9P/ywG0BHGfizs55gf8G7gN2bdVGyrYonX87/t05W2idFhwLA/8BtsADBJ4AnA7M2itnLtNtuy6wTtO+qQNqew/cqfVhfMbEByvWfwaYVKP9Z0rbB+KOtfcV5Tzgp13qx+jvtTRE1PED4Grg68DJ+BKad7TS2YHjN8AZxffMz/Df+WmtdKbMEckWtT/XXHJJtfReEd6Me4W/Vfy/CLBYNGEBnXOicKSgYSw4gNnwt27X47EibsPTXAU/YKTAUbU+/hD1ptL/K+EDiO1CbYjH0li+zbG3Aw8NiOMxOg/4H+9SP4YtamlIzJ7PAlPaHJudsAer52kz6MLXW7/Q72vBl7e0HEwCU4DnAjTE4Ihhixg6yg+aj7T63AM4HgNmKv2/GL6sYd8QjlRsUTr/CYoloHUKnnHrTjwOxcnAzHU5J2LBBz9rFtvfLr6b7we+O0Z6foI7D9buoe5p+LLPXtsuD9ZvB95a+n8qcHeX+jH6ey0NEXXcQ+n3Fdi2+H14dwWOByg5sYEP4cu5NxpPHJFsUftzzSWXVEuVQJLTYWbr4GkQr8bfXvwIWAbYGdikF85WzWSOpDQMhKOY/ro+7tDaGJ+qewBwsqQXzezjeACyhVLmqFl/ZkpBXiVdb2bTgHPMLDR69A+B883sUOA63NkxF56u7POFlkFwHAqcZ2Y/acHxDaBbPIUYtqirAdKx5+P4rI9LWxxbBR+odcOVwPfNbC+V4kAU0zb3LI53Q91rOQc4zMx2k3R7ScNS+Nv6cwI0xOCIYYsYOqaYx00CmM3M3irp7oJnAdwh0A1v4DEUngKQdF/RV84K7Cup2KKBQ/GYIZXSY1pOu9kvvBO4otjeDp/p8Cz+XRQUuNHM7sVjv4yCOsRSaFNvUlGO9p/bSvEY7gL+amYn48t3yjq6xiVr0jKl0VeL+veax0bphBj9va6GWDrmxmcTNto+zMyeAM4onnNaft5NmA14ucRxjpltCvzZzL42jjhi2CLG55qRkSR6cjrg0462lAcMbASsuhJYLY4sIKxzThSOFDQMiuNB/I3dkcAuaopSLelEM9t+HHDUqX8NsAHu2Gucf1sxgDgXf6PdEZJ+amY34ms0N2bG4InbSDprQBzfNrM78MFDcxDHgyX9ugtFDFvU1ZCMPfF1t2ea2V8YPdjfBJ+O3g3bAMcBjxV2aXAsicft+eQArmVb4BDgRjN7FX9jORf+m3RScbwbYnDUtkUkHc3r0cvO2VXxpTPdcBk+Lfjwxg5Jj5rZesDZ+EyDTkjFFg2sDuxgZrsyemC4dod67dbl38zINYiKzowMJgEqHEgm6Uagkb0mFFs3/b8wHm/n+Ir16mJ2fLbDZMJjkc1Q38wa2ZLmNbPlJd0MYGaLUzj+OiBGf6+rIZaOW/Gp/9Md4ZL+bGYv4cEoQ2JZ3YDHwDq7xHGFmX0YXzbW7bsrFY4YtojxuWZkJIng7BUzVDJ7UtK8xfYTkuYzj3z7qKQoXjhLJIdtChwpaBgUh5m9R9LVNdsYc4469c3sfcC8kv7a4tiiwOcl7dOrtvGEbIvRMLMV8Le2zQ6UYxoDgUCeZYF3lDkkVQnSVxtmNgXPwtHQcIukF8aAo7YtYugIbGdXSaNmkpjZMsA8kq5qcWxOYDNJRwbwL0PTvTUWtjCzz7U7Jun3Vbgy6sPMTgXuxR0Ft0vauXBA/E3SEjV4FwL+KmmVSFL7DvPZvmXcJOmR4tg04F2SfhqprXb9fWAauuj4LL6kapTzvnB47iZpvS7cGwFzSRrlfDKzdwLfUJdsMylwRLLFQD/XjIxBolenw6XAPpLOKjkd1sfX9k2LIszsWUmh06eHmiMFDYPkKB56t8DX2z8A/FHSLRXbGXOOGBoC2jhd0kc6HO85fV5MjkGgmy0itTFh7BmCYbqW8YJIzt9/SVoxlqbUYTXSbmY4imnd3wReBX4k6Tkz+wiwjKSf1eCdFw8wOnfg+bPg6Y9bpbsMypRlZku2Oxb7vjCzQyT9b436Mfp7LQ0RdbR0XFTkiHEtY86Rii0yMgaJKikzy/gmcIyZ/R5fB/Zr4Ag8x28shEzrmigcKWgYCIeZbQX8Ew8Y+DywInBNsT8IKXDE0BCI97dpv1b6vIgcMdJ/hqKdLWKk3UzCngXPFDN7l7VYp29mawZybGFmB5nZdmY2uenYIQH166ZnDE4t1k+O4ry6toiiowJixNZ5W6UGzSab2Xl1GjSzWcyXbYSev227UoEjRtrNDEDS45K+K2lPSc8V+06v4nAws32ayo/xqehnVpDyezyT0bN4oL1yCUXjHmi+J/pxX9RdGhKjv8dYnhJDx3cjcMS4lhQ4UrFFRsbgoB4jUOJ5t3cBfgHsSsTMFQX/uMu20C+OFDQMigO4g6Zo1Phg8q4KbYw5RwwNdexJzfR5ETlqp7yMYIsYaTdTsedqeKCqp4EX8HghXW3QdM7OeCC1g4B/4M6xhSty1E3PGCO1WAyOGLaorSPGfd5PDjx7xes125wVeKPC+ec3lZuAV4DzK3DUTruZy3RbzlL0+VtxR/qtwPcoZRcK4Di8qfwc+AIVUpgCT+LLiGJe20LALyn9LkTkrvUbF6m/1/6dTUjHUHCkoCGXXAZd6lX2mRILUyPfcQfuJDpkChwpaBgUBz6gmty0bzIeLyS0jTHniKEhsJ12A+1a6fMictROeRnBFrHSbqZgz4tLulfB07D+qnQ8hON2YNnS/3tTSs01iGshTmqxGBwxbFFbR4z7vC4H7ihtV+4iwOkAvN6hvBHC0YV/W3xqf+j5UdJu5iJwx+0lwIfx+CcfLr6PDhuwjuuABfvAOyt9SEdYt7/2q79njvy55pLLoEuvKTPnwj3UW+JRqV8zs+OBHSQ93QtnCyhzJKVhUBwHAvuZ2e6SXjKz2fCBwIEV2kiBI4aGOqibPi8WR4yUl3URQ0Mq9nwn8Lui/rVmthbwFzM7El/nHIK34NOJKXj2NLNHgYvN7EOE9fO61xIjtVgMjhi2iKGjCmJMc26F+fCZH3e2ODYLHu2/G57AHQOtltfMCvyrZ3WOI3CHV+hSzp7Sbma0xH8DS0lqRM+/0cyuxPtPlSUvy+HZdprjMYR+RkcCp5jZQbhDucxRZwnQcoRlOBg0+tXfqyIVHRkZGeMUvabMPBhPObQicDfwVmDfYv/n4kjLmKD4X3yq447m6VjnxX/sHjSzrzROUud83ClwxNAQgnYPAnXT58XiqJ3ysgLa2SKGhlTs+QI+UH6kqP+MmW2Ip+P6E2EPhnfjsz2uLen4uZm9AFyADw77fS0xUovF4Ihhixg6quDiCByt7pN/AC9KOnfUyWaztqnTjGuA+SWNWl9fgaNxfnPMqSn4GuYqKeN6TbuZMRoP4Z9B2f6z4bN6gmBm3wX2wGcrlLOZiHDHUCPV9H5N+4Wndw3RcTEzOgOn4Blb+pEJqe5gvV/9vSpS0TEsHCloyMgYKHp1OmwILKmRFFi3mNk2VAvk0w2pdMgUOFLQMCiOFAL8xOAYVICf5gevBr4FzNO8U9JTZvYBfMDYDTE4dscdLs0cd5unhmrOE14H7WwRQ0Mq9rwQT5c5PXhbMZNmU9ypMlsAx++BD1IaaBc8h5nZy/g67W6oey0H47M2Li3vlPTXIijlbgEaYnDEsEUMHQCY2Un4Z3yhpGtbnSNpoy4c85TeRrfDl1rs2wdfq98KrwDrduGEkewGoyDpZTOrklrxNUbPErkf2K4Cx++KktEDCidiA0cBfzWz/8OXp00FvorPPAjFTsBqkq7vVZNqpOcsofmeeB64Tv1JGXx0uwMx+ntdDQPWEcNx0fFaxhFHKrbIyBgYek2ZeRewjqS7S/veBlzU6c1t0w9YSzSmx5nZLyV9pfn4sHCkoCEljozRMLPHgYsoHgaAa9VLh+3eTu30eZE42qa8HKAtaqfd7Kc9zewtwBySRk2BN7OZgTUkXVSn7SbOT0k6riZHLXtYnNRiMThi2KKjDvPMDOsUZS58Df2F+G/rVYFtvAjczEhfuUjS43V0t2hjELZ4a9Ou5yU9VqfNjGows1ZLbZohSaEzDO7GU2y+Uk9ZGjCzf+Izohr97ImK9WP091oaIuro6rgI4IhxLWPOkYotMjJSQ69Oh92Az+Lr0xvLK74OHCXp+x3qdfsB6/rjNSwcKWhIiaPENRl/M/gZYBHgAfwNy76hDyopcETSsBSwNiMPA/NQPAxI+nEIR2A7z0qqFV8hEkfbPOADtEWMXOTJ23PAHLWuJaHrGChHMej+Ij6dfA5JMwXWexOwBiP95b14nIYLJW3fqW4oUrFnQBttYw0oPIZARg00LZPZGlgT2IvR8RjeCOS7lzZxUjq99GrimAWPgbMKo2NLfDaEo+BZj5HfpNXw+BaNweafQnkKrl77ezQNNXXEcFzUvpYUOFKxRUZGaujV6WB4cKatGBlQHYdHMY7+9jFj4sDMfop/we7NiENrd+BqSV8fLxwxNDTxLYvHS9keT/82S1WODtxJDCBCObItgjmGwvmR0HX0ncPM3s6Is2AtfB39BfiD5ukV25pScGyApyV8UdJCPUpv5h6ELZbAY0W1GhiGDi7Pb9q1ELAUcKmkkOUiGTVhZm8w4iRoLK8sPyca/mIidHC7TtOuhYEdgeMlHRTIcRwezPJUZowtgaS9QzhacL4Z+AYVBusx+3uvGvqkoyfHRRNHT9eSGkcqtsjISAE9OR0yMvoFM7sPWLk8HdjM5sfXWy46XjgiafgK/iCwJu7Yuwh/ELhE0jMhHIHtpDJI7jTTIdtignKkoGFQHMUA7XZgf+AESc/10MYP8L6yKB7w8yLgAkmtMkr0hAHZ4nLcFscwemB4YY12twXeLik0A0YGYJ61bC98YDo/Iw6Ejk4gG71MpiVUWq7bg7aFgL9KWiXw/CeBJdQ99kk3ng8zMlifClzOyGD93wH1Y/T3Whoi6qjtuIh0LWPOkYotMjJSQ68zHQ7GvcqXlfatAWwhaaeI+jImGMzsfmClFoP16yUtMl44ImloPAj8EDhNUnCU8CoYDwOybIuJy5GChkFxmNmn8QfN9wOTGIljcpGkewPbeA5/yD0Uf0i9StJrdXS3aGMQtngGmEeB0+4rtDsJeEzSfDF5hx1mdjSwGPBTPIDd1nhA2RMl/TSQY1bgDUmvlvZNBiZJermGtnmBuyTNHXj+dcD6kh7uenJnnvJg/ciq/SxSf6+loU86enVcxLiWMedIxRYZGamhV6fDo8CiKq1NL35M7pW0QER9GRMMZvYzRpYl3IMvS9gNX5YQ5NBKgSOShkVwL3fjYWAyxcOApGhRi8fD1PNsi544Uhlo143pMEz2DNZRvL39GtWnSs+Mx3FYuyirADfifaVtzKUqGIQtzOw0YE9J19Roo13azV0UGGcow2Fmj+AzRB43s6ckzWNmiwKnSlo1kOMi3PZXlPatDhwgaVogR3NayynARrhD/5Md6pUDXr8L+ARwEKNjS5wXoqPgXJORfrYycAMjg/VK2Qlq9PdoGmrqiOG4qH0tKXCkYouMjNTQq9PhEWBxSS+V9k0B7pE0f0R9GRMM5gGedmMkXsj9wPHA90PfhKTAEUNDE9+7gM2p/iDQNX2emW0l6dh+coTAzL4jaf+A83qyRQwN48yeMQbaN0h6Z4fjfb8WMztDNdO1ReLoaIsYOop7exruYHs/8CIjwcN+U7GteQuu9fDAz2+SNGtvykdx98UWTQPK+YAtgZPxmRvTIWmPwDbK8QQauB/YTtJZlUVPYJjZY8BCkl4zXz64AvAs8FSoA8p8WcN8Kj14Fo6hxyWNSmvchuPwpl3P4ylvj+r022qRM3G04F8Ajy1RJaZDtP7eq4Y+6ejJcdHE0dO1pMaRii0yMlJAr06HE/Fo2LtIeqP40TgAT4UUkms+I2MUzGwm4DDgi71OtUyBI4aGgufr+IPAWviD1YWMvN2/JZCjdvq8SBy1Ul5GskXttJsJ2XMldcl1b2ZrSbqkw/EDS+0/WaX9Eketa7E4qcVicMSwRQwdd+FLIhr39u09cByMDx6WAa5m5J6/TNLzAfXHzBYtBpQtIWmbQL6cdjMSzOxcYD9J55oHYnwDeA54t6T3BHLcBawu6aHSvoXxJUCL9UF2X2FmmzEyWF8WuIaRvnt2QP27qN/fa2mIqKO24yLStYw5Ryq2yMhIDb06HRYDTsOjBt8NLA48CGwi6b6oCjMmFMzsQXwWzatdT06YI5KGIxj5obqjR47a6fMicdRKeRnJFrXTbiZkz3uA2YGLGRnw/7OiI2efUvu3lngukvToIK7F4qQWi8ERwxa1dQS2c4ik/+1wfK+i3cvLsxEr8I8bW2QMDma2JP7MeHvx5nV/PKvI3goMUmpmP8GXNuwA3IFnEjkQ+Jekb1TQshw+5bw5q0lQGlQzWwWfXXFvad9UfBbGdRV0XMBI/7hc0ouhdSu00a2/911DoI67qO+4uICa15ICRyq2yMhIDT1nryhmN6yGR1W9F/i7SgGfzGyx7IDIqAoz2wUfDO6lUsyQ8cYRQ0NgO/+StGLAebXT58XgKHj6kvIy1BYxNKRgz2IQ0Bjsrw28Gc/msXFFHbMCq+Pror9Mb1NIY9gjRmqxWhwxbBFDRxfugcTqGGtbmNln8dlI15f2rYwH6D0qkKN22s2MeCiclD/BU67PCryEzwrcOXRWoJl9F9gDuI4Zs5pI0nqta43iuAHYtOzALpzSJ0taKYQjFGZ2uqSP1Kgfo7/X0hBRR0fHRSBHjGsZc45UbJGRMUj0LWVmjC+ojIkHM7sXz6X+OvAopfW4oQ+JKXDE0BCos1swttrp8yJx9D3lZYAtamtIxZ4lrmUZmYK5IXC7pNUC686B22KdgmPxQs+Fkn4RyFHrWixOarEYHDFsETXXfYd2+h20NQlbmNndwCoqLfEws/nwGT1BaRitT2k3JyLM7FO4E+imYqbBb/AlFl+RdHNFLsPTbj7WPDvLzD4l6bgOdR8BPqguy8u6tN/y+bQfz611OSP19zEPCBxRx1BwpKAhI2PQ6KfTofYXVMbEg5mt0+5Y6ENiChwxNISg24+ORUifF4mj7ykvA2xRW0NC9vwD8F+48+QCfLB/saRnK3C8CtwFHAycU3XgUHDUuhaLn1qsV44YtqitI7CdfqenTcIW5kEH55f0emnfTMATCk+N2Je0mxMRZnY7sIakh83sVOA/eEyHtUNnGAS20+17/G48fljPMwjN7EZga0n/KO1bFThW0vK98rZpaygGp9lxEZcjFVtkZAwSeaZDRlIws09I+mOL/ZtL+tN44YihIVBntwe02unzInH0PeVlgC1qa0jInrcW+s9i5A3yAyF1Sxz/r2h/JeB6RtaP/l2BsUjqXovFSS0WgyOGLWrrCGyn306HJGxhZpcCB0k6obRvc3wq/uqBHLXTbmY4GveM+RKJB/GZfK/isxXmi9jOqMGYzZj6dGt8Js5ejE53GeRcMrPt8CUaP8SdY0sBOwP7qodsDV3ayk6HzJGkhoyMQSM7HTKSQodpj0+EPtikwBFDQx2dLc6rnT4vBkfB05eUl1W+c+pqSMGe5lHfG4P9tYDZ8EHdFyrqmAlYFdgM+Cowk6Q5OtcaxRHDHjFSi9XiiGGLGDq6cA/kDdlY28LM1gLOAM7BB4ZLAx8ANpJ0aYd6UdNuZjiKmQ4bACviSyrWN4/lcr8C010GtjPq3rQZU59a8bf88Gp4TIfgfmZmnwA+z0hcst/FfCFQamco3ognpGMoOFLQkJExaMw81gIyMgDMg+IBTDIP/mWlw0viAaeS54ihoSKs48HW6fM2x9dohzUQh6NVysudi7+x0M0WtTWkYk8ASQ+a2X+ARYDFgHWBD1fQMR8jGQbWBZbD03INzB7WOrXYaRU1xOCIYYvaOgIRFESxV6RiC0mXmNk7ga3wgeHfgR0DZkpMbfr/NHxWUPP+jGr4Hn4fvI47cgA+iAd07DeWiE1YzEQcNRuxAYsQ6C8S+trfKyCGjo6/0RMM2RYZEw69psyc1G0aWwyvaMbEQelNRqsv4ofwLBAdpz2mwBFDQxWY2e2SlupwfC9qpM+LyHEENVNeBrTRzRa1NSRkz7/gzpNnC67GMpFbK3C8gA/kGlPfL1P11GB7US89413UTy0WgyOGLWrrCGwnxhuyVyVNbnNsPNkiOGNNRn0UMxuQ9ELx/wLAJEkPdaxYrY1uAYFnBd4oL/Uxs8mFjqAMGIE6orxFNrPX68x0itTfa2mIqCNGxoYY1zLmHKnYIiNjkKjsdCimXD6HB2dq+wVvZlNjrmPNmBgwswsltQ3COF44YmgIbCeJKXqROGoNIFLQEFFHt/gU/4MP5u7scE7HtMVmNmu3h3Qz21XSASGaO3DUnYYa4+GsW475QdkixrX0dZrzMNnCIqTdnMgwM1PxkGgzxlWYAd1eQlVss5vz+CJgF0lXlPatDhwgaVpEHVFenNXlidTfU+FIZZnHmHOkYouMjEGi15kO1wEfVsXAZRkZGXGRyg9XChzZFqM4UnEmpfC5Zo6IHCloCOGwCGk3JzLK9rUZ4ypMP4WKsRSqtNnm+JPAfCo9vBYOkcfV59gSY8EzXvpaIMcw/a4NRayOjIxBoteYDscAp5nZQcB9lH6IJJ0XQ1jGxIR5HIR98Sj4MwQtk7T4eOGIoWGAiBFNNhWOukjlOmJwxFgzGoOj7rWkch2pcKSA8WKLuYBnmvY9DcwzgLaHASuUtqPHVegRTwMLMmNg0AXxOD0ZaSOF3/hUkG2RMeHQq9PhK8XfvZr2Cw+Wl5HRK47FI5V/E3hhHHPE0JCRUQepOD/qIpXrSIUjBcdFKrbohhuBjwMnlPZtBtw0gLbHPcpLZCXd3e38AcXYOBE41sx2AO7A010eyIyfcQyk0M8g68jIyBgS9OR0kJSKxztj+LACsGbNNaIpcMTQEIL8IDCCbIuMiYCjI3AMS1/pduVmS1wAACAASURBVB3fBs4wsy1pSrvZb2ETFG8bQBv/D/gJHux0Vjwj1GHAdyK3EyvmR92+lkp/T0XHsHCkoCEjY6BoGxgoI2OMcBHwriHgiKEhBF8aQBuDQvAPqJktbmbN58ewRf4RTw/D9HDWVoeZLWhm7yn9v7GZ7Whmq5TPk/SV0bUrIzi1ah8R4zN5pNNBSZcA7wSuAmbHB6rvlHRphLYzRqPvs1ckvSTpq/jnuRAwh6SvlYOfmtmnIjS1dQQOgP1CTzSzq4uYI9MRqb8HaTCzJczsq2a2vZkt3QcdMRwXwfZMnCMVW2RkDAy9BpKcC19asQ4wP6WHhwTXq2eMI5jZz/Ec4Ccz45pNJO0xXjjq1Dezt0m6q/T/lsDmeD/7s6QYP1bl9tqmzxswR8eo5U3nPgCsLOnROm3W0dCBIxV7viap1yV0DY4YqcVqXcsgslcEcsSwRUsdZrYp/hA6M3A+cA6wQfH/+4EtJP2lC/ckYAf8bf6vgYeBX+JLHv8G/D9Jr9TRX2qrb7aoyJFExpoMR6TP4wZJ70xAR7fMKO/Cl3acAbyMLzteCvibpNO7cB/Z5tDmwGnAS5I+G6BxceDdwL8l3dJ07FOSjutS/yZJby+21wFOBS7FnUfvBz5aNU5bEc9qI/x55a+SbqtSvwXfTPh31z4B59ayR3Fez59rB86rgfUlPRFw7prAHZIeNE8PuxsjM7NOBfaL9T2ekTFo9Op0OBpYDPgp/qC0NfAt4ERJP42qMGNCwcwOb3dM0jbjhaNO/aaI4V8G9gAOwh8EdgD2l/SLbhpCkUIk53YcZnZPm9MXwZ05b8R0dKZsix44YjgdxjzCdkL27JstzOyfjMzUuQLPDnVWcWxr4GuS3teF+yd44No3gJWAX+ExDSYDuwKnSdq1jv5SW2N+X0TkyGnnIqHD/T39O1pSu+/0mDr6nVb288D38d/kB4CTgKm4k/CTwI6SDuvA/SI+6+ZcZpzxszPeb5+TtHcXfRvicSzuBJYBjsC/J17vpr/EMd1OZnYx8FtJRxb/fxr4qqQ1unBEd1w08c8KvNDNyRnJHnU/19rOJDO7FVi7cDr8Hz5j9sBC09eBayR9vRNHRkaq6NXp8AjwdkmPm9lTkuYxs0WBUyWtGl1lRsYEQtODwL+AL0q6vPh/NeAISe+I2F4qD/+tnA434W9s98fX7oI/pJ0IfAF4QtKFddrtpiFzDEVqsVQGyS11NH5Hi+0X8SnjjYflmYFHJM3XXK+J4z58KcFMwKPAMpJuL469A3c6RAn0nMpgP4X7M2MEHe7vO4tNxboHu+jo631hZjcDm+K/RTcBa0m6rDi2AfBDSSt34F4G+DnwJPANFennzexBfAZfx2VDxbn/AHaXdLqZLYi/AHwZ+JikV0L6V9MLjkeARSW9Wvw/E/BowPdODMdF24E8PuD/dIDTIYY96n6uMZxJz0mao9i+B0/5+0Tx/7z4LI5FOnFkZKSKXmM6TMLTFgE8Z2ZzAw/i0zozMmrBzJY3s93NlyhgZsuZ2UrjjaNG/bIncGH8zacfkP6OzzKaKFgJn2p+IDC3pAslXQC8Alwa0+GQkSzGS6aEELTT8Yr58gjwabyvl47NjDsSumFOSU9Jehx4tuFwAJB0I/CWnhT3D6l8Jhnx0HKwLGmJooyn7GadYo4sLOkWSf8BXgQuLx07B3hrJ2JJt0raAPgzcL6Z7Vw4F6v0iaUa0/0lPYzHaXkOD5w6JZBjspltY2bbFm3PUjoW+r1T1rwcUF7CcDywfADHVrgd729R7guoD3HsUetzxZ9XXgLegTtf9i6cDM8BP+rmcChwj5m9t9h+mRkD/s8MzBbAkZGRJHp1OlyHx3MAuBg4BF87ekvbGhkZATCzT+D31KJAYxranPigc9xw1Kz/JjM7spiqNxOeg7zBOw8+4J4QkPSqpH2BjYHtzOx0M1uKPGDJGC7cgD+oImmTpmPTCEvx+Jh5vCWAL5cPmNlbgOdrakwRqQQIzXC0dGyZ2Z1mdoeZ3TEgHf3O1vC8mTXi1ByhGacMz4YvceoKSccD78VfJFyLPyOE4kkzm1rieg34FHAPHsMlxGFwJf588hl8KVZ5BuU6wH8COGI4Lv4FnCVp9+YC7EPY5xnDHrU+10jOpH2AE8xsG+B3wGlmtrX5MrvT8HTsGRnjEr2u992OkS+BHfEIqvMwMrjKyOgV+wAflHSdeQBFcCdX2yltiXLUqb9vaftneN9qBKNcGzg7UEMoUskO0JZD0p3AJma2GXAm8OYI7VXSMA45YiCFa0lBQ185JK3Xoc6dwP8EcP8f3i+e0eiAaZvigdFiIRV75ow14wPTBtxeyxkXZjb9GbWxBKAd1Dlbw7n4zN6b5Jk0ytgYuD5QJ5KeAXYyz1KzDvBMk+bFJLV62/83YBv8WaPBJWBbM/sVsHpA29M6HL6SUmrZDjoajgsYcVxcVfwf6rg4gvYvQV8FQmYI1LYHkT5XSceb2RmFlkrOpKLu43iw/vfgMXmOxGd8HA58L5QrIyM19BTTIYg4QmTqjImH4st2fkkysyckzVd4ih+QtMB44YihIVBnUETmLhxryVPLDYzDPLDYveU3CWa2laSuXvxiquQywL8kvVHaH8MWQRqKc+cEkPRs0/6B27MNR4w182dI2qj7mR05al2Lmf2yywBgUBwxbFFbRwzU7Sv9tIV5EF4BSNq2ThuBOmpnrMlwWOc4CDPhg7oNVEpvOUgdNhLgWf26t4pZRZL0WPF/u8F6KF+7a5kFmFnSC23qLa4iaGddDZ10dKkzNzA5li06cfTbHr1+riVn0q8lvVTa37V+seRuQeBFSU+1OF7bnhkZg0Q/nQ45OFNGZZjZ2cDRko4sDda3Bj4paePxwhFDQ6DOTg95WwBrAv8GDlcRIKo41tUpaH1MwWd9SHnZ4eGsdgpSM/sacLak/xRTOI8D1sAHR5cAW0m6vwvHdXh07aMUOYK7mc2npnRcZjZV0r1tzn8z8HFgBfwtzLP4fXKiPCZASJufA7bFgxdOwd/E/B34nqSba1zLJGDrbm8ii3MXBKZKurr4f2M8vdmFkq6toeFkYJtWD3ptzp+Cr2e+rYUTak1Jl9bQMhmfetxpNkRVzuDf5x5sUeszKe4rACT9PqTNOsjPKvHQzZZmdjewvKQXx0KHDTiLRictFeqnEnA1FR1DwZGChoyMQaOfToechiqjMsxseXz5wJ34dLgLgGXxHMe3jheOGBoCdbaLFr4zsD1wCp62yoCNJD1YHA9JH1U7BZ8NMOVlB1vUTkFqHlV8aUnPm9kpwF2MTPncHVhS0ke7cLwMXIZ/HhfhUyVPbPdmpg3Hwvg0+hXw6fLfA/4KrIYH3dqsMdjrwPEB4E/4Otrr8KDAc+FLf1YEPi7p/C4ce+LrgA/Fp8Vug681nQlffreVpL+FXlcTd2iKtE3xddczA+fjgb42KP5/P7CFpL904WiX+30n4LfA85L26MKxGnA6vpZ5MrCXpB+Wjtd9OA2yR0XOUX0lki1ifCbBU+BjID+4x0O35z7zdf9rA3viTsrpD6AqzVqLoKOd02GgWTSKNms9Cw+ZwyCVTERjzpGChoyMQSPPdMhIDsVbw43xSMH34oPb58YbRwwNAW20e7i6HfiwpFuK//cGtgbWk3R3yI+VRUjBZwNMednBFrVTkJrZs8C8kl4zs4fxN7mvFMcmAw9J6hhnoqHPzN7GSPCuBfFc4IeH2KFweDwF/AH4NB4Z/GTcEfFVYENJa3fhuBHYTdJJLY5tBuynIu96B46HgPdJurv4f2ngZEkrmtmHgX3VIX2ymXUavE4GvhvgdPgnI2v6r8Dv97OKY1vjOdrf14XjjaLuzTDD2v4tgb/g01q36cJxMXCkpN+aT6U9ErhM0peL4yF9rVOAvUn4/RbT6dAqPW0MW8T4TPo+Bb6pvfysEgnWZXlacY/BjIH1DP+s+3p/jxWG5Y145ojLkYKGjIxBIzsdMsYdUvmyTvlHx8yeAebRjDEPtgd2AT4EXBUw0+FpSXM3b5eOhwymJhdtbgV8p/GW0yrkIw9FJ1uUZjo8BrxFmiGWRMisjwuBX8qDPF2KD57+URxbCThDUsdUpm0GemvhDogtgCclLdGF4zFgEXne8Sm4A2J2Sa8Wtn5Y3fOqPw/Mpxbrqou36k9K6phirHC8LN7gMLM5gFslLWy+POIZFbnG29R/FY/E3coJNwlfhtTN6fCUpHmK7ReBOVSkmjSPn/JIgC3WwYO1ngXs05h1UuX+NLMncXuq+H8ufJB+Dx4A8umAfvIUnsv9zhaHZ8EdfP12OsSwRe3PZNDIzyqdYRFjbJhnHnqt1bGGAzMGun2mZnaKWsxMM7OTJH0slo4QLf2unznS5EhBQ0bGoNFr9ooQ5IjQGf1CKpHTU4jK3w5340sipq+hlvRzM3sBX+oxawDHY2Y2lzy6dk8p+ORxJPY1s2OBg83sS/iShkGmvHyTefpRGElB+hCAhacg/TpwZvEW/yrgb+Zr3QVsBnw3gGPU5y0PsHiJme0A/HcAx2v4UojHgLnx7/Ap+BKJ2fBI391wJfB9M9tL0vTP0Mxmx6c9XxnAcSZwpJn9AHcS7IoHiQPPtvJ0l/o3Ab9qvAUvw8zehKc664ZXzGxS4Vj7W2NwWyAoVZukC81zon8T+IeZ7SbpTwFtl/ECnirwkYLzGTPbEE+b9ifC+vk/8JkE5zYfKBxBff89jWSL2p9JA1bEwmmx/xFFDMZLflbphgtikJgHkrwBd4b3NZAk7vDrhHXb7J8WWccomNnKkq6rUmWYdWRkZEwctEtREwNH9ZE7Y2IjxoA1BY5R9YvZCI3tpQM42j1c/R744KgGpcPwN6odgx4WaKTgQzVT8Em6U9ImeN7poJSXEW2xL3B7URopSBsISkFazGp4B74OeUV8ucm78bfQm0v6dYC+izvwvyTP2d4NfwbOMbMDgD/iyywOM7NN8PgK5wRwbIMHGH3MzP5tZpeZ2Q34Na0FfK5jbceO+GD7LPzzfBGPIQJu350CrqPdwPE1/P7thhso8soX91YZ03DHRldIek3SD/DYA1ub2bm4AycUF+IzecqcL+F9ZHIg1z7ALW2OvUL7QRIQr69EsEWUz6TA5OYdxWyeaDM+CsRIuznMUKn0TuIOqFvoMd2xmS3eKAFtvbMNxz7msUtmaWyXytG4s76bjiqpt2H0YH3674CZhcR3GrX0L4KGZHQMC0cKGjIyUkdeXpEx7pDKtLR+TK9rWtIwkD5kcVJNBnNYYMrLiWCLqhzmacF2At6GOxluBQ7Bg0BeBeyspiwWHdpYBg9IOQe+zOHfihjktNRObXv00OZy+O9b5SwahQNnPTzuRdfZPMWsnzkkjVoaUSwpWEPSRVV1dGhvlD371Veq2qILV9BnYh4jQ8B/AZc3HV4Mv0+bHRrNHANNuznMsIgxNsxsF+CTeCDf5kCS53WpWzsIZOlaPg0cUzokPPbQoZJu68JRXq53q6Rlupw/QxYh8+DK/4sHZb4ed2K3mgXXNs5LXQ2J6RgKjhQ0ZGSkjpy9ImPcIQWHQQyONk6HfwLn4ekLf4EHBxyFYsZCFKRgi1Yc2Ra1OQY+2G+jIzugIyKV741+wTxlpuHpecvLuhoDw/NUSv/bgcMrDSDt5rDDPE7LNOAS9ZgmueBpFbMEBphNotCxnaTf9li31mDdPFjvj/AA05Na1aVLYM1IDoNUdAwFRwoaMjJSR57pkDHuEMOhlQJHq/pmtiweePGt+JTqVlPyJWm9XtsN0ZECR7bF4FOcFdPXzxpLm8bQMEwcLTgH/r0xFrYws+V7malS1B1o2s2JgLF+kVQ4LBqzV2o7KIqZXlvg6ZsfAE4ImekVY7Be4urJpjE1jLWOYeFIQUNGRurIToeMcQczW0segG9cc7Sqb6XgTmZ2rqQP1NEYqCOVN/PNMx2yLepxVH6QNA9a+ELMh5qq1xJDwzBxtOBsNdOhr31lrGxhZtviQUUbA8PjgcPU5cHFBpx2cyLAzE4Hvifpipo8k4HV8Sw8fzAPYIu6LN8xs7c2tlUz04WZbQX8Bjgdj+OwOPAR4EvqkPKzBU8v37EPSFqk2D5G0qer1I+hISUdw8iRgoaMjBSRs1dkJAUzWwIP/LcKvtZ8OiQtXvztONBPgaNG/Yvx7ATgD0ITGdkW9dByYGZmnaZm9jO4cFQNw8QRAbX7Smq2MLMfAh/Fg782BoY7A8vhszraQtI2VdrKCMLdeAafU4B7mTEewx4hBGa2Ip5O9mU8PscfgHXw4LVbdqor6W7zDBjnmtkGqpcB4/vARirFWjGz9+MB0Ds6HcqDdfxaqmKymb1Z0uNAx9gkfdSQjI5h4UhBQ0ZG6qjkdDCzrtMiNRIM6OieFGVMdByLZxn4Jh4df7xy9Fr/KTPbGF/Tt1DhvJioa/qyLfqD+fDBW6v11bMAp40TDcPEURcx+kpqtvgfYFVJ9zV2FG/b/0EXp0Pp/EGl3ZwImA3POgPuMOgFvwT2kHSUmT1Z7LsQCIqvIOn14t6u68ybk9FBSq8AZg+oW3ew/mvgXjN7DJhSrOMfhcbLiT5pSEnHsHCkoCEjI2lUnelwaJfjApYEkPSVnhRlTHSsAKypUlaDccrRa/0d8Td7jTV9t7c4R3RIG2dm20v6ebG9tLpE46ZF+rxEOLIt+oN/AC9KOrf5QDEFvuMstUjXUkvDMHFEsmftvkICtmjCs0Vp3vdMBY5Bpd0cekSaPbICIy+kGvEZnjezKmlZ9wZ+aWZ7MjoDRujv7YHAfma2u6SXivb3LvZ3Q63BuqTdzOzXeF89G/hMoOZoGlLSMUQcKWjIyEgafYvpkJHRC8zsNGBPSdeMZ45IGnpdq1k7fV4qHCWuCW+LqoNTM7tBLfLVm9k04HlJV7U4ZsDaki7swBvjWmppGCaOmP2k4Oi1r0wjAXuWzv8a8N/AAfjgcirwLeAU4IzGea1mb1iEtJsZrWFmcwLzU3Ighc42M8+ysp2kqxuzUMxsNeDnklYL5Gg4FsoPr0a1wIn3AgsVHE8C8xYcD5bPaze4M7OpjAzWP9zqnJD73Mw+0MpBF4JYGlLQMSwcKWjIyEgZ2emQkRTM7Of42s6TgYfKxxS+bnTMOSJpmEXSK+bpyhaU9GDXSkx/sKuVPi8VjhLXhLdF7MFpCKxF2s2Yn2uvGoaJI7Y9e+0rVTAIe5YGl53QcqBpEdJuZswIM3sHcAywMm5HY2S2Quhgf2N8xuyv8KWH++Kfz3aSzg7kWAp4rdUxBQaYNLN1Qs4LGCD2PFgv6hvwBTxY6vySVjKztYGFJJ0QyFFLQ2I6hoIjBQ0ZGSkiOx0ykoKNRB1vhhQYhTwFjkga5gEOATYHXpU0u5ltCqwmabcO9Wqnz0uFo8Q14W0x6MF+0WarTAkDTWUa6e1/shyx7dlrX6mCVOwZ0EbPaTczZoSZXYAvn9kHj9nxNmB/4DJJwTG8zOxdwHb4/X4v8FsFzgg0DyT5HDCP6gWSjIK6g3Uz+x7wIXxp1K8kzWNmSwJ/lPTuQWhITMdQcKSgISMjSUjKJZdcEix4erhfAgsDTxb73gLc2qXeyqXtc3tsOwmObIsZOJYFfgecg7/pO79FOS/yPfhsPz/XXjUME0dse/baV1KwRZvzpgKr12hn26LP/Lv4+3mKFy65VLLjk8DkYvup4u/swJ0D1nEdnm6zDses+CyLO4Cni33rA9tX5PkeHoDykyWbLAlcE1j/XnxASamvWmN7EBoS0zEUHCloyCWXFEue6ZCRHMxsGdy7uyhwP3CcpFvHG0eE+o/iD1evWikKe3mafZt6098gmtmtkpYJbTM1jhLXhLeFma0s6bpi+1xJH6jK0UObrd7MR/tce9UwTByx7dlrX6nYRt/taWaLA8fhaYclaQ4z2xzYUNIXAttolXZzR+BUSUEZMDIcZvYgsJSkF8zsNmA93BFxf5fPcZ8QfoUvO9wFH4gdxOhAkue1q9fEcQj+u3wAcKb8zf6iwNmSVgjhKHjuBd4l6TEze1LSvMUb6ickzRtQ/wFgSXkwy0aMizmBGyVNHYSGxHQMBUcKGjIyUkTV7BUZGX2FmW2Crxs9DX9IXA642sw+Iykob3EKHDE0AE/jAbumr8kuHsS7rdGOkT4vFY4Gsi182n3j4X4so1fnVKZxEduevfaV1PBr4HTg/cDjxb5zgJ9U4PgfaqbdzJiOi4EtgCOAPwFnAi/jS746oTxofRPwceAqRpxAqwEnVtDRyIy2V9P+6dnTArAZsLQ8c8YbAJLuLxwPVdBY7tFoH2CO0r5uOAM40My+DtOn1H8POHWAGlLSMSwcKWjIyEgPYz3VIpdcygX4F7Bu075pwA3jiSOShl2By/B13k/hkdjPB3bqUm8z4DbgVeB14I0W5fXxwJFtMQPHPcDG+IP1s8ASxfYMJXJ/fKafn2uvGoaJI7Y9e+0rKdii6fjjwKRi+4nS/qcqtHE7MHfTvnmA22PZYiIWPC3r1rgDYPYK9Y4HPt6072P4LMBB6r+7cV807i18CVKl+wJf7nYIvlzjCdxZ+DPgkMD6c+HBpl8q+v7zxf9zDkpDYjqGgiMFDbnkkmIZcwG55FIu+HTNmZv2zVzxQXPMOSJpMHwq8I3FQ8BNwE5UWI9MwmvZK9af8LYgjuNi+9L20gFtdnSS9XItMTQME0cf7q/KfSVRW9wILFtsNwaG7wCur2CLrwHn4kHy3o6v2z8H2J4+OeqGtQCrROB4Gpipad/MFHEVKvBMxmfAbFn8PzvVnB8/Bv6MO26fwOOf/AHYt6KO2oP1gmcB4L14kMCqNo2iIQUdw8KRgoZcckmx5JgOGUnBzM4H/irpB6V9uwAbSZo2XjhiaIgBi5A+LxWOukjlOiJxPCtpzh7qRU272cu1xNAwTBxNfGPSTxK1xbb4rI398fX7XwK+Cxwg6ZhAjp7TbmbMCDN7BHgUj7NxrHpYPmVm1wC/l3Rwad/XgG0krRrIsSLwF3xpx2LyWB8bAZ+TtGUgxyzAD/AsGlOAF4DfAruqh6wYZrYARTYOSQ91O7+p7rzAJozEfjpN0hOD1JCSjmHiSEFDRkZKyE6HjKRgZsvj6whnxyMqT8UfCDaRdNN44YihoeCZBnyWkQeBoySdX6F+7fR5CXFMI9uiwdHT4NQip93s5VpiaBgmjia+KOkuq/aVFG1RcH4Udza8FV9e9GtJfw6tnxEP5ukqN8SDI2+Kf87HAn+Q9Eggx7vwt7Uz4/flongmno8rPG3mJfh9cJSNBNibHbhFUtWYDJjZW4DH1OPDcJ3Bupn9Fx635GZGYly8HfiIpMsHoSElHcPEkYKGjIzUkJ0OGcnBzGbG1yEvDDwAXCnp1fHGEaH+F4D98LV9jQeBzwO7S/ptIMfx+FKPffBI1PMWD1mXKTBKfgoc2RajOHoanJrZsngAvbfia/4vbnGaJK3Xr2uJoWGYOJr4YtwblftKiraICTObCiwq6YpBtz2MMLPZ8KwgX8HTmc5aoe5kYHVgETy46eUVfxefBOaTJJsxO8v07QCOzwLXSrq+tG9lYCVJR1XQUmuwbmZXAj+VdHxp35bAzpLeOwgNiekYCo4UNGRkJAklsMYjl1xyGV2AW4CVm/atBNxageNRRnKrl4OxBa+hTYEj22IUx/HAL3GHViOv+lu62aNsQ+DcOvdnr9cSQ8MwcfTh3qjcVxK1xcHAGk371gB+VoFjceBSfD30c8W+zYHf1dU3UQuegeIT+IyF54G/Vah7Spv9J1Xg+CfwnmK7EetjNeDvFTjuBuZt2jcfcHdFW1wJfLJp35bAVYH1n6QIllraN1PjO30QGhLTMRQcKWjIJZcUyyQyMsYYZnZTafteM7unVUmdI4aGJrwZD6ZWxn/wh6NQNNLnlXVWTZ+XAke2xYz4ALCDfFmFACQ9igcC64Ty2+cYaTd7uZYYGoaJo4wY90YvfSVFW3wKuLpp3zXAVhU4Gmk358QDsIIHkvxQbXUTDGa2kZkdDTwCfBO4EFhK0gcr0KzbZv+0Chy7A6eb2d7ALGb2HeCPQPDyIzxI3zNN+57GM5tUwbLACU37/gQsHVj/VuCTTfs+gWddGZSGlHQMC0cKGjIyksPMYy0gIwMP5tTA1uOYI4aGMi7Bc2d/W9ILxbrV/fF0eKH4HXCimf0/YFIxZW8/4FfjjCPbYkY0BqfTB6OBg9OnzGxjfFC6kJktAVjzSQoPEtfLtcTQMEwcZcS4N3rpKynaQjDqxchMLfZ1wmr4dOQ3zKzhnHvazOauwJHh+DEew2FPSVUGo5jZPsXmLKXtBpbEZx4EQdJpZrYh/nt7Ib6c52MKjAlR4Ebg48w4qNsMz/RSBY3B+rGlfVUG6zsBp5nZDrgN3gYsg6dGHpSGlHQMC0cKGjIy0sNYT7XIJZdyAT7RZv/m44kjkoaFgYvwN3QPF38vxNcmh3LESDU55hzZFqM4dsUHkesCT+GxQ84HdupSr3bazbrXEkPDMHH04d6o3FcStcWJ+EB3UvH/JOCHwMkVOGqn3cylfgEOL8orpe3DgcNwh1jXFKuR9ayFz3Q4sbinTsIduWtW5FkDT7l5BZ5y88ri/zUqcMyLv6TYpfg736A1pKJjWDhS0JBLLimWHEgyIylYm1RrFYNEjTlHDA2lOovhQbcekHRflbrDhmwLh5kZsANNkf2BgxT4pW49pt2MiRgahokjJnrtK6nYotB/Gu5EaQRSexDPABR0PRYh7eZEh5nt0diW1DxLoSrXdgoM/NtUL6hdSXt0PwvMs/4shi/VmYpnmDpG0r09aJsX+AhFXwPOUHj2ikWBFyQ92cQ3m6QHBqEhJR3DxJGChoyM1JCdDhlJwMyWLDavB1Zkxmm5SwJHSlokdY4YGkpc6wN3SbqltG85YHFJ54RwFHWmUSPVZAoc2RbxYT2m3WzBM43eP9faGoaJo+CZRr3UsLX6zy4XRAAAIABJREFUSgq2KOpNw9/yrcTIwPDvkt6oyJXTbtaAme3Z2Ja0d02udwCPS3rYzOYAvoXPgPmRpBc61Du89O+b8KURVzHijFoNOFHSpwI0zAQ8B8wj6eWeL4b6g3UzuwrYVtK/SvtWxAOdvm8QGhLTMRQcKWjIyEgSYzG9IpdcmgvF1FtaT8l9APjieOCIoaHEdSuwcNO+RfB85KEcX8ADf+2HP3jvCzwEbDeeOLItWvJMw6cnn1X8Xbdi/Xnw9aKvAM8X+zYFvj/AzzWGhmHiiHF/1eorCdni2Sr3cy79KcDajRKB6zpguWL7V/iSsDNxx1oox/HAx5v2fQw4rqKORSJcz1XAik37VsTTY4fUb5mVpt3+fmhITMdQcKSgIZdcUixjLiCXXMoFuHAYOCJpGPWDj8+ceKYCR4xUk2POkW0xiiPG4LSntJuRP9cYGoaJI8a9UauvJGSL04HVQ89vw1E77eZEL7hj4HzgvAhcTxd/rfj+mh+YHXikCgcwU9O+mVvd9x04dgH+AXwOzwS0XqP0cj2h+1ucdxtN8Szw7AR3DEpDYjqGgiMFDbnkkmLJ2SsykoKkdYaBI4YG4A4zW0/SeaV904A7K3DESDWZAke2xYzYBfiQpOsaO8zsD3hgtNA10x/A3/a9Wors/6iZdUu7WUbda4mhYZg4YtwbdftKKra4GzjTzE7Bl1aocUCBa/fxtJs7N+27BvgzHqAzowsktUtzOQpmtqakSzuc8pKZzYkH87xH0mNmNjO+ZCIUtwFfxR1KDXyFalH9v1L83atpv/BlkKF41MyWlnRbY4eZLQ08Hlj/MEay1dwBLAV8D89iMygNKekYFo4UNGRkJIfsdMhICsUDyP8C6+BvQabHRJC09njhiKEBfyA6ycwOxR+olgK2KUooYqSaTIFjL7ItyogxOO017WYZda8lhoZh4ohxb+xFvb6Sii1mw50D4EH/eoGon3YzIxxnAqMCKJdwLHAeMCfw82LfqlRzHn8BONnMdsFjniwKvIbHeQiCpCUqtNcJdQfrB+DZXn6Mxy25BzgUOHCAGlLSMSwcKWjIyEgPYz3VIpdcygX4P+DfeNq454q/NwN7jSeOGBoKntXwta+nF3/fW7F+jFSTqXBkW4xwnFLcY1OK/2fH3/ydWoGjp7SbMa8lkoZh4qh9b9TtK6nYIkYhQtrNXCrZu2scDmB9SvFngPdQfVnDZOD9wJZ4vInJPWidzlH8Pzswe0WOSXgwzJsZSXG7c+N+i2TTXcdaw6B0DAtHChpyySXFMuYCcsmlXPA3F4sX208Vf5enQoyEFDhiaAhs5/TA8xbDByKL1WgrCY5si2iOC8OdYTeWHmp2oshqNIhriaFhmDhi3l9d+Nv2lZRsUXxn7g78vPh/OWClina8tugjf8fjnvyzX3ad6IUKMXZqtHFKm/0nVeBYEZ9dcTPwXLFvI+APfdDbcbA+CJvW1ZCYjqHgSEFDLrkMuuSUmRlJwcyeBOaTJDN7EFhKPs34GUmdpm0mxRFDQ6DOjnx10+elxBHQxoSzhZktRpHDW9J9VerGwCA+14mEQdkz9vdQP2BmnwAOwWcrbCVpLjN7D3CApA8G1I+WdjMjDO3uKzM7nyImh6T1+tTGE5KClpeZ2SV46tSjzOxJSfMWS5lukbRoHX2heivUf1bSnGOpITEdQ8GRgoaMjEEjx3TISA03Ae/F30pdDexlZs/gMwfGE0cMDTHwC3z6aRnPFvuXHWccdZHKddTmaBqc3lfsqzw4NbNpwGfxddH346nrzg+tT5xrqathmDhS6Cep2GIf4IOSrjOzLYt91wErh1SW9IaZnVIMlK4oSsbYYK+6BGa2T7E5S2m7gSXxwKOhWAE4uthuOEOeN7PZ6qlsCet+SkfEeDNYVwOko2NYOFLQkJExUORgShmpYUc8KBTAN/AgU5sAXxxnHDE0xMACkpqDtz0ILDQOOeoileuIwfELfDBaRmNwGgQz+wJwAj7l/KRCw3Fmtl0FHbWuJYaGYeIggX6Ski2A64ttlf5WGfxcZGarVzg/ox7aDYLWKZVeMbUok0rbU/ElNPcCn6jAdRfw7vIOM1sNz4wRGylMJ05BA8TRMSwcKWjIyBgsxnp9Ry65lAuwUJX9qXLE0BDYTsd1lvj65fWa9q0LXFehjSQ4si1mOH9Urm78gT943S1wC7By076VgFsHdS2RNAwTR9/7ScHZ9j5JyBZnA58ttp8o/m4NnFaB4xDgSeAIPPL7Po0S054ToeCD/VElsO6ejRJBx3YRODbG43zsjQd6/g4+U2L9PtitViyEuvUzR5ocKWjIJZdBl7y8IiM13ELrlFs3Ep4OMAWOGBpiYC/qp5pMhaMuYmhIheMOM1tP0nmlfdOolnouRtrNvah3LTE0DBPHXox9P0nFFjsAZ5vZ54HZzewsfInJ+hU4YqTdzHC8Ros3q2b2GvAAPqNlT0nPNZ8jae/QRsxsV0kHdDjlUjNbUNLDZjYHHuH/DeBHkl4IaUPSaWa2IbAdHoD3rcDHJF0TqnOAmH2sBRRIRUdGRsY4RQ4kmZEUWgUrMrO5gDskzT9eOGJoCNT5uqSZupyzGrAtI4HUDpV0VcV2kuDowj9hbGFmHwV+j+dSn2FwKumUQI5T8Hzs35YHOZ0d2B9YQtImg7iWGBqGiaPg6Ws/Kdpo21cSs8UU/K30W3FbnNZqUJvRf5jZV4H/Bg7AP4vFgV3wtKz/wWcy/FvSF2q20y0g8HXAFpL+Y2a/wjOavAQ8JukzgW18QtIfW+zfXNKfetXepq2uv0td6scI4FhLQ2I6hoIjBQ0ZGYNGdjpkJAEzuxd/i7II/takjDcDx3V7mEmBI4aGKoj0IHC6pI+Md46JZosIjouFgT8A/wU8gb+FvgzPFBAt4Gmna4mhYZg4AtuJcX+17Sup2MLMDpa0Q4v9P5O0UwhHcf7y+Hr/BSVtXwRcnVXS9V2qZpRgZrcDq0p6urRvHuAaSUuZ2aLFdq34I92+x83saUlzm5nhSyTeAbwI3ClpgcA2amfACEXd36VuTphBaEhMx1BwpKAhI2PQyE6HjCRgZuvga9LPAD5cOiTgYUn/GQ8cMTRUQaQHgaHgSEFDYhxBg1Prc9rNkGuJoWGYOLrwD+T+GmtbdBgYPi7pzYEctdJuZozAzB4FVlIp0KmZLQJcL2l+M5sJj70xd812us10eBhYGnc2/ELSe8xs5qLtbvf0ksXm9cCKzBj4ckngSEmL1NHfos38u5Y5ktSQkTFo5JgOGUlA0oUAZja/AtdlpsgRQ0NGRiS8v9NBi5R2sw5iaBgmjhQw1rYws22LzZlL2w0sCTwWfDE1025mzIAjgXPM7CB8ZtVieJam3xfH18eXWfQbxwLnAXMCPy/2rUpYPJvb8BcAhi9LK+MhIqT2zMjIyMhojex0yEgNu/qsydGQtMc44oihISOjn/gFsHbTvkbazWXHkYZh4kgBY22Lxrr8WUrbUMwWAz4XqAHipN3McHwLuBX4JD575UH88/xtcfx84III7bRLuwmApK8XTq1XJZ1f7H4D+Ho3YkmTAMzsQkl10ncOEh3tMUCkoiMjI2OcIjsdMlLD1Kb/F8Jze588zjhiaAhBfhAYQbZFNSxQnipd4EH8Xh1PGoaJY1Do1FfG1BaS1gUws+9L2q1Cm61wDe64OLK075PA32vyTjhIegP4VVFaHX+pU/1i+cXngGMkvdzh1IsDtJzd9P/V3eo0nT9Ih0Pd36Wu9hiABkhHx7BwpKAhI2OgyE6HjKQgaVRqOPPUVp8aTxwxNARiv8h84xnZFtUQI+1mChqGiWNQ6NRXkrBFw+FgZgsAczQduyOQJkbazQxmWPbSjJfxJTRXdHImSHrdzA6UdFindiRt1Kb98ylmqEhaL0x1a5jZEsC+wCqMvrcWr8PdAm0H62a2Lr4M6c4i+OoB+KyN70h6qNDT0h6xNKSkY4JxpKAhI2OgyIEkM5KHmU0CnqwToCoFjqr1zewbwHmSrjWz1YETgNfxgGiX96KhTTtJpG7qxJFt0RNHt4BstdNuBuro9LnGSP05NByB7XS8N+r2lVRsYWYbAIcBCzcdUpW+YTntZhSY2QV4NpKHcSfDYsCCwNXA24rTPtpp1oGZHQWcIOnUHtqfPjuhETupV5jZ5fh9eQwwQ+ylKtwhg/Uu9W8CNpB0j5kdW+x+EXiLpE0HoSExHUPBkYKGjIwUkZ0OGUmhFF26gSnAVsCmkt45XjgiabgXeKf0/9k78zBLiip9v6ehkUWEZl8baBYRVMAZUHTY3ZDFGQVHFFAUVBgX/I0iAkKLg6AOihuILAqogCAMIugwI8gy4I4gIEKz71sD3crWwPf7I/J2Zd26S+TNqFtRWed9nnjqdt6bX355KrP6RmTEOXqyeMpzAWFd9IckvTZGI/I4WZRu6qXhsRhIo+/AhdUsuxnpo18JvNoemqQRcYx+8ax9r+QQCwslGr8CnCbp6SrHLmkkKbvpgJl9G/irpG+Utn0U2BD4GHAosJOkLXtonAPsClxDuCYWfgGVtHef4x9e+uyRA55GS2sesGyxZKSOTq3Oemtg2EL1jYcIA2PPEaq9rDAMD5n5aIRGDh4cJ0d80MHJCjN7kZHs0hCeQlwLHCjpD5NFI5GH1heBpYG7CP/ZvGBmT0haNkajynFy1vBYDKSRYuAiquxmH426pcVSeGiSRr8ZLON+rwwjFmY2F1heNb6kdIuVVSi76QTM7HHC7+PF0rZFgEclzTCzlwAPq8dMPjM7ott7kj7f5/hHxH62H2b2M+CI2P+Le+jU6qyb2b3APwCvBGZL2srMFgMe6RXHlB4y89EIjRw8OE6OeE4HJytUZJee7BopPAD3mNnrgY2BK4qOw8sIU6WnGh6L6qQYUe5ZdnNIpPDQJI1+DONeGUYsTiEsyeiZA6ATlrbsphN4CNiFMHOmxU7Aw8XrxYEFfTRO7DQ13MxiEoxGDzSY2cGSjunxkTuBX5jZ+YRSmeXjVKkuNc/MViZ01m+S9Leisz49cv9vAr8jVGppzbx5A3DzED3k5KMpGjl4cJzs8EEHx8mXTwPnEka331ls25mpmXndY+E4cTTlXnkd8HEzO5ixHcP2cpztpCy76QQ+DpxjZjcQlkasSegQ7V68/1pC57UXtwCdZuncBCyXyCfAIYQ18N1YCvgZoQPXXmmqCrU665K+VAx8vCDptmLzfcAHh+UhJx8N0sjBg+Nkhy+vcLLCzDYBvsborNJGSB622GTRSOGhi+70QuP5QTU6aOaSg6CShseir0YjlnlkdB65aFSOZ+p7ZRixMLOuAwOSTos8Roqym06BmS0PvA1YjVAC9SJJj1XYf8y1W8zCuT3llPEUfz8rHGsDSp314t+LSbohYt8LJL29w/bzJL1jGB5y8tEkjRw8OE5u+EwHJzfOBH5CeKoyUPKwTDRqezCzuZJGPf2RtMDMHgZWGkSzCylKTY6rhsdiIHKp4e2lTNPSM55DvFfGldiBhT4aKcpuOgXFAMMZVfezkNxUwBJmdnfb28sT/r9MyZinaWa2tqQ7i9ftiZ5HdqxwXXTqrEu6xczOA2I669t12b7tED1k46MpGjl4cJwc8ZkOTlZYmuRhE66RyEOnp0LTgQcVmQTNEpSazEHDY1EdM/uspKNravR7Gj3u55LRDINhPN1PcX3VvlcijjEusTCzvSSdUbxuz8WwEElReR4sUdlNBywktDsA2AZYgdKgpvosd7FQ7tKAi4EdS28JeEjSXxN77XRtLbwvbGyi54V+qlwX3e6DTgN/be+3qm8cBHy57e1ZwMaSNhtPDzn5aJpGDh4cJ0d8poOTG6cRSkv+cJJrDLy/mV1J+EK0uJld0fb2GsDVFeQ+SUjIBnA08FVC+bzjCGtws9bwWHQmpnNad8ChYKk+76eIR10PTdIYOJ6J75V+jFcs9mDkSfpeHd6HcI6xySWPB75AjbKbzkK+BmwPfBc4ilAic3/grH47SrocwMw+0npdxsx2k3RuQq9jZnmVB+JUM9FzqbO+WOl1i1mEqjG9aOWRmMbonBIi5MuYPQQP2fhoikYOHhwnZ3ymg5MVFrL1XkNYkvBQ+T1J208WjTr7W1jLbMAJwEfKuxZal0rqlyW8pVW7fN5EangsumrcA7xS0pNmdhkho/x84EOSUnX2+66NTnEudT00SaNOPFPeK/3IJZ4Rx6g948wJmNl9wJaS7m5dj2a2IaEixTaRGnWfIC9CSAL6Q0nP9vjcxZLeFuNpEMzse8XL9zL6wULrXjtF0pwInf0knTSRHnLw0RSNHDw4Ts74TAcnN84F7gDOZ/B8DDloDLy/irXMZvZrSXUzFaconzdhGh6LrixTDDgsDWwCvLHQObaCRgz9OmvDKM+YosM4WTQGjmfie6Xv4TLR6MfAZTedMSxJePoN8LSZLSnpZjPrO/3eRnIoTDOzdRg9E2EW8EyMgeJ++Gq/5TXdBhyKAVoVn4l6ANFFf59C7+pBO+uFzklmtgzwcsbmHLl0GB5y8NEUjRw8OE7O+KCDkxubEp5MPTfJNWp7KL7QvZnRFTBa78XWEk9RPm/CNTwWYxhGZz+GppRnzIXa8Ux0r0wYFtbbQ5q8C3XKbjqj+QuwOeFa/D0w28zmEUor9mMOIzkUbmt770EipvGXuNDMdpF0YYV9WlQ5Tl/qdNYBzOz9wLeBvwFPlXcnDMaMu4ecfDRFIwcPjpMjvrzCyQozuxg4RNKfJrNGIg/fAt4FXEbbFwFJXZOsRejWLp83bA2PxZjPvw04maJzKukPZvYeYC9JO/beu5KvygkDU8SjrocmaQxwbYzLvdJ2jCziGXGM2mU3nYCZbU4o3/dHM1ufsIxnaeBTkq6M1Lg8dilGD41zgF0JyxdbVTEAkLR3n30PL322fb38IF7eT5fOuqS+nfViycq+kn4+UR4y89EIjRw8OE6O+KCDkxVm9m1gd8KyhPZcCFFP6XLQSORhLrCJpHv6friHhjqslTWzhyVFlc/LQcNjEaWbtLNfaPbLQTAu51LFQ5M0Ul1f1LxXIo4xbrFINQXeGT5mdrCkY8b5GEd0e0/S52P37ffZSC+1Outm9hCwmqSBZ6glGjDIxUcjNHLw4Dg54ssrnNxYErgIWIyx2ZQnk0YKD48CT1T4fCemt28oOqdVpi3noOGxGP35MZ1TSQvM7GEgSWe/4It93k8Rj7oemqSRIp4p7pV+jGcsZtcRtcRlN51KHAJ0HXSwGmU3S5wo6cH2jWa2Sr8dqww0RA6gLApcEqvZgS8Bh5nZFyS92PfT4+MhJx9N0cjBg+Nkh890cLLBQmbqI4Cj1CMzde4aKTwUOh8GdiKUzmufLXF7n31b5fO2JExDLbMGcKOkXSaDRqHjsRitNeYpcdE5fVDS8pEafctujve51PHQJI3E18bA90rd80ipEXmciyTt1LZtYeWCYtZEJ+SzKNITMZPnm3QpuylpduQxalXAiCVm+U9xnS8NDNRZt1CFaBXCMrnHyu9JmhmpUctDZj4aoZGDB8fJER90cLLCzB4BVq7zRzYHjUQeuu0r9UmwZgnK5+WiUeh4LEjeOR247GbC32vt0p9N0EgVz0Jr4Hul2D+LeEYeZ9zzQjjx9Pt9WJqym50GXF8G3C5phVon0Oc4HT5Tq7NuZl3PWdLlkT5TDBjk4qMRGjl4cJwc8UEHJyvM7KvAHEnHT2aNFB5SYGYbqmb5vFw06pLLedTRSNw5nSfpZRbKbt4FrKhQBeMJSctGatSKRyIPTdLI4T7JIhZVjpNKz6lHxKDD48BykmRmDwDrSnoqclZBK2nkasD9bW8vD5wpad+ap1A+Xoyn2p31uuTgIZWPpmjk4MFxcsRzOji5sQXwMTM7iLGZqWPXfOagkcIDAGa2JrC6pF9X2a84Vu3yeblogMdCRcZ9M/t1gs5p7bKbCeKRovRnYzRS3SdQ617JIhZ1sLRlN514rM/7dcpu7lnoXwzsVdou4CFJf61utx51O39m9hLgcGAPQontZYr7fwNJ3xqGh5x8NEUjBw+OkyM+6ODkxklFm+watT2Y2UzgTEIHRMBLzWw34K2xT3SsR/m8Cj4mXMNjMZpEndNPA+dSlN0stu1M6BBEkeBcantokkai66vuvZJFLOogadowjtN0zOwrkj5dvN5e0qV9dulXOvMTjAw8/T9Gym5+qJ+XVifMzD7SqUNmZrtJOrefTgX6DaCk6Kx/DVgdeC/QqlJwY7E9qrOfYsAgFx9N0cjBg+PkiC+vcJxMMbOfE77EHQM8JmmGmS0DXC9prUiNJKUmJ1rDYzFGo2vnVFLXjP0RupXKbqY4l7oemqSR6Nqofa900Mwinh00vezmOGJmT0papng9lKUs1qdqRDcfFplI0kKi5/cBP1SPRM9WSkja4zPHEzrrxwA/V8hRsTpwiaSNI7w8AKwn6e9l/xWXMtXykJmPRmjk4MFxcsRnOjhZYWYG7EsY3V1B0qvNbGtgFUk/niwaKTwQlmjsJOlFM2t9gX6y6EDEkqJ8Xg4aHovRvIcEgx/tX9JVvexmrXNJ4aFJGqS5NmrdKznEougYnkpIPNmrAtC4lN10FnKdmZ0L3AS8xMyO7PShCrOrYuhYdtPMZhUvp5nZOoyeiTALeCZGXGGpz1fVp2RqvwGHgn9hpLP+YrHffUXnMIbnaPsebmYr0pY4cJw95OSjKRo5eHCc7PApiE5uHAl8kFBSq5Wh917gM5NMI4WHh4D1yhvMbCPg7goaxwI/NLMtzWxWuU0yDY/FaFJ0Tqe3byieRldZA1/3XFJ4aJJGimuj7r0y4bGQ9ALwZqBn9R9JR3fZfnmr9TuWmV0U42mKshvwJ2BVQid/zQ5tjcTH7LasYQ5wK7AkcFvx71Y7nWoDTReaWVSVnz7U7ayfA5xWDKJgZqsSljOcNUQPOfloikYOHhwnO3ymg5Mb7wc2k/SomZ1QbLuD8CRjMmmk8PCfwM/M7GhgUTPbgy5PgXrQOvbObdtFfCciBw2PxWhandOjCZ3MERHp9l472kjZzcXN7Iq2t9cAro70AAOeSwoPTdIokeLaGOheyTAWXwM+b2ZHKLIiy4BsNY7akxpJDwP/AWBmi0rap9fnzewNkv6v7mG7eJlWHONyRZbX7MHiwLlmdg1jEz3vXUGn1Vn/ZOFtVeA44jvrhwBfAv5MGEy5lZAL6vND9JCTj6Zo5ODBcbLDczo4WWFm9wOzJD1jxRRdC2XXbpK05mTRSOGh0Hk78GFgLcKXo+9I+q/Y/ZuEx2IEG8nO347UJ1u/JSy7OSgpPDRJIzWD3Cu5xcJG6tS/ADzC6I5hsjr15mU3k5EilsP4fZjZEd3ekxTd0TazxQid9f0InfWnCJ31z0h6rqKnFYFHVfFLeUoPE+2jKRo5eHCcHPFBBycrzOxkwrSyTwIPEOpvfw1YTNIBk0UjhYeUWI1Sk7lp1CWX85joWJjZhqpfdrOlNdC5pPDQJI2S1oRdG7nEwoZUp94HHdJhXRJ7VtTo+fsws0WBA4BtgBUoLcdQZDlqM1tF0oOx2yM1ozrrZra2pDuL111nPvabsVbHQ04+mq6RgwfHyQUfdHCywkIt99OAHQnrgp8BLgH2ljR/smgMur+Z7SXpjOJ11yoE6pMEq6Q3qnyepEFKTU6IhsciSqtW59Rqlt1MFI+6pT8bozFoPMfhXskhFrtLOqfD9qSlEX3QIR2JZjq80Gu2lpl9E9iekC/pKOBQYH/gLEmz6/i0iAoYdTvr5YGZYsaaYEwei54z1lIMGGTkoxEaOXhwnNzxQQcnS8xsJYqpwTWePEy4RtX9rVSmy0LZt05IkWXgLE2pyQnR8Fj01EjR2a9ddrPuuSTy0CSNgeKZ8l7JKBa1SiPGkuLpvBNINOjQ8/dhZvcBW0q624qSjma2IXCiInM9dDpG8aDgdkkrxO47aGe9Ljl4SOWjKRo5eHCc3PFBByc7zGxZYCdgNeB+4GJJj082jRQe6mJmjwErKpTPG7T+dhYadcnlPBJppBi4mEv9spu1ziWRhyZp5HCfTGgsSk/4rgdeBWNKI54uabUInaiym2b2WXWpguFUI9GgQ7/lFY8Dy0mSmT0ArCvpqZhjW8gTIkb+Ty6zPHBm7KBtCiyUP3yq/L3AzGYAS0hq99d4H47jNBsvmelkhZltD9wJfBzYHPgYcIeZ7TCZNBJ5eLOZbdC2bQMze1OsBmlKTU64hsdiDFsAx0hqPQ1B0pPAMhU0UpTdrHsuKTw0SaP2tZHgXpnoWPQrjRiV6E81y246A7HEEI7xF8L/qQC/B2ab2WHAfRH77gnsTci3tFep7Qm8puqAg5mtXnTOy9tmmFnfQbGC/2JsydE1gPOH6CEbH03RyMGD42SJJG/esmnATcC72rbtDtw8mTQSebgVWLVt22rALRU0PgDcAuwDzAP2IJTFeu9k0vBYdLy+Nihezy1+bkSY6RCr8WHgp8CWhCfIC9sQf68pPDRJI8W1UeteySgWl8d+tofGQcAXgel1tbxFxXveeGsQBhxeU7xeH/hf4DfAVhWOsXeX7btV9Po74FVt214F/CZy/yerbB8PD5n5aIRGDh68ecux+fIKJyvM7AlgeYWnVK1tixIy98ZOPZ9wjUQenpS0TNs2I3wRiJ7CaglKTU60hsdizP4fAA4Gjga+XmgdQpj98MNIjYHLbrbp1Pm91vbQJI1Cp+61UeteySUWZvYNSR/vsP04SQdGagyl7KYTSLS8IkUFjIMlHdPj/ST5Qjrda722d/jcHEIenjmlbesBl0jqmkgwpYfMfDRCIwcPjpMji060Acdp4wzg34BvlLbtT5hWO5k0Uni43cy2l3Rpadu2wB0VNJB0AXBBlX0y1PBYjN7/VAvr/z9M6Ji+D/hclc6ppCTL6+qcSwoPTdIodOpeX7XulYxi8X7C8rR29gKiBh0I0+adycWVCTTdoOyvAAAgAElEQVQOIeS7GYWN5AuZZmbrMDZfyDMVj/OIma3XobP+WOT+pwI/MbNDgduBdYEvACcP0UNOPpqikYMHx8kOH3RwcmMz4CNmdhBhjebqwErAb8zsitaH1Lsedw4aKTzMBs4zs1MIa5vXJUy73qfHPliC8nm5aJSYjcei/bO1Bz8KP5XKbo7HuVT10CSNcYjnbAa4Vzr4mpB4lmKwaId4zCLki4hlJXUpu1lBw6mBhfxGfWkNkqmowlL3sF22z2GkIsBtbe89SLh3qlC3s34MsAD4T2BNwgDyycBXh+ghJx9N0cjBg+Nkhy+vcLLCzN4X8zlJp+WskcJDobMFYZ1364vAKZJ+12ef2uXzctFo05vSsUjdObUBy26m/L0O6qFJGqnvk0Kn8r1S9zxSaZRisBWjn3qLkGjz6xUGdIZSdtMJdIq3mbXPsFmd8Lt8jFAxwoB7FTmNf1Afbe9frsjymn2OMw34d+CDtHXWFZL8jjs5eEjloykaOXhwnBzxQQdn0mFmx0s6YLJrpPDgTB3GYRCndtnNuqTw0CSNHMglFmb2H5IOG/AckpTddKphffIxmNkhhIGGzymUuVwSOJJwjSSrItJv0GEiMbOtJV1RvO76t1qjl0c11ofjOFMHH3RwJh0pvlDkoNHlqdChko4qXh/ZbV9Jh0ce483AnZJuKW3bAFhL0v/krOGxGF8s5IRYUdKL5Se/ZvaE4pOd1jqXRB6apDFQPFPeK7nEok3PKA0c9HvSZyGRZWsafTsPAp+XdGJVH1MZM1sReFrS38xsEULpyReBMyo8vX0EWE3SgtK26cD9klZM6LXfTIdFgQOAbYAVGH1t9VryWLuzbmY3SHpl8bpbnhX1mvmRYsAgIx+N0MjBg+Pkjud0cCYj3dZrTjaNTvuXa2Wv2WW/KiOF3wbav0T9rdi+QeYaHosuJBq4eAhYj1CisaWxEXB35P5Q/1xSeGiSxqDxTHmvZBELC/XoW/FoH6joWQFDRSLLVNPoHQB+BnwEuBY4CtiFkAdgU+CTkRp/B7YA/q+0bXPgqXQ2AViqz/tfA7YHvks4l0MJiZ7PitA+Hnhl8fqULp8RYUZNJxYmN5W0TsTxxsNDTj6aopGDB8fJG2VQt9ObtyqNIdQCH4ZGp/2Bj5Zer5fA45g624TBjmjvE6XhseipcSuwatu21YBbKmh8gNAp3AeYB+wB/Bl477DOJZGHJmkMFM+U90pGsbgQOJvQqX0S2AQ4H9ivgsY3umw/rk6MpmIDHmdkduy9wExgOeCBChp7AfOBHwFfKn7OA/ZK7HV+n/fvA2YWr58ofm4IXB6hvWlNb/NKr28dUKOWh8x8NEIjBw/evOXeJtyAN29VW2yHJneNTvuXOx2JPF4LbN+2bTvgutw1PBY9NWoPXBT7vB24GLgR+AXwzxMQ01oemqQxaDzH4V7JIRaPAUsVr1sdw+WAmytodIwFIYdArRhNtUaoGvISQo6MG4tt0+jTwe+gsxHwOeAE4HBgo3Hw2m+QrjyA8gCwZMx+7Z9hgM46YbbPzoSn1X8D1ilej2rj6SEzH43QyMGDN2+5N19e4UxGclgakUKj0/63m9mxhC/q061LlQINt3zeRGl4LLpzu5ltr9FrO7cF7qiggeqX3ZxNzXNJ4KFJGrMZLJ5J75VMYvEC8Hzx+okip8A8QvWDnljasptO4OfAjwmJIFvLEDYizBqIRtJNwE1prVXmL4RlHb8Ffg/MNrN5xJ3LE2a2M+EcVjWzdejwf7mk27vs/wngOGAtwqBNe+lOCFPoey0hqushJx9N0cjBg+NkjSeSdCYdlkHliRQanfYv1uUfRPgisB2jS8a1kIZUPm8iNTwWPfd/O3AaYd3nqM5p0dnrtl/SspuFTtXfa20PTdLooDlIadha90qOsTCzC4FTJZ1vZicC6wNPE55Kb9dn38uKl7XLbjoBM3sJ8D5CHoczJD1vZtsCq0iKyYWAmS0HfIqwZOal5ffUJ4FjRa/9EkluDrwg6Y9mtj5h1sXSwKckdbp3yvv+C/AVRjrrnR4eSFLPvCOFVs9qH8PwMNE+mqKRgwfHyR0fdHAmHf2+UEwWjU77m9kmkq4rXv9S0g51PE5mPBa9GbBzmrTs5iCk8NAkjRTUvVdyjIWZLQtMkzTXzJYg1KxfmpCP4YFIjYHLbjqdMbNpwMqxv4O2fX9BWKLxY9qSR0o6LY3DwTvRbRoHSzom9XHM7H4V5VrN7IeS3lvT56ADBln4aKJGDh4cJ0d80MGZdOTyB308/mMqD0SY2a2S1h9At3b5vBw0PBZ50qRzyYFE11fte2UyYmYXSdop8rOVym46oykGgY4HdgMWSFrKzHYFtogd2LGwhGFFSc+Oo9VRA2A1NDo+VKjbWbdQNnRDSY8N+uAixYBBRj4aoZGDB8fJHc/p4ExGUoyU5aDRaf/ymr5VbLA1fSnK5+Wg4bEokbqzb4OX3UxWnrGGhyZppIhninsFmPBYVGWrPj4GLrvpjOE7hASMazGSk+Ea4FggdjbJ9YTrvVP+gK6YWdTsGBU5buoOOLQO22X7dDNbXtJjhLKhVTkRuMfMHgWWNLOOZWQlzeyhUddDTj6aopGDB8fJGh90cJy8SJHc6cbS66MkzRnARw4aHovRJOvsF7Q6Y2X+VmzfoMd+Kc6lrocmaaSIZ4p7pUUu8UzBiYRp/DsAlxeeZhOqajjV2AFYTdICMxOApEfMbKUKGpcCvzCz7wEPlt9Q71wfp7T9e3XC9fwYIbGlEcp4zqrgpR/d/pbW6qxLOsxCjpK1gEsIZUSrUnvAIBcfDdLIwYPjZI0vr3AmHTksjUih0W//Gms1n5S0TPF60GmTWWiUtKZ8LMzso5K+Vbxer2Znf5Sn0jYjlF/slYQt5e91IA9N0kgZz0Kj7t+lLOIZeZx+CQMfA2ZK+ruZPSFpWQvJDK+WtGEqH1MBM5sDbCXpATObK2k5M5sJXBIbS0uT6+MQwkDD5yQ9ZWZLAkcSyqAeHaMReZyu15aZrclIZ33HTp+RdHnEMXaQ9MsB/SXxkIOPpmjk4MFxcsZnOjhZEDN9UiPlAX+Qq0YKDyWWLzSrJu5KUT4vF40WHgs4CvhW8fqPQN3O26BlN1P+XlOU/pzsGqlLww56r5T95BDPFAxcdtMZw8nAT8zsUGCamW0JfJGw7CIK9ak6EsknKWZcFJpPmdlngfuBZIMOvZB0D+GJ9C41O4CXmtl+wB7ACpJebWZbEyqC/HhIHibcR1M0cvDgODnjMx2cLDCzfl9GJann1MkcNFJ4KGkNlLjLEpSazEWjpDXlY2Fm1xKmJ99ImKb+b50+F9s5tcHLbqb8vQ7koUkaKeNZ6NVK+JdLPCOP02+mw8BlN53RFDNVPg58mHCt3k2YDv51VfgiaWbLA28jdGi/YiHvxjRJ90bufyfwXkn/V9r2euBMSWvF+og4zgvqU5qwiMm+DNBZL/b/AvAmwtKo7xQzcWYB50j6h0iftTxk5qMRGjl4cJwskeTNm7cMG3AWoX74qsDjxbYVgVv77LdJ6fUvBzx2Fhoei1EaGxCeNv4P4entZR3apRU1tyA8qbyo+Ln5MH+vg3poksY4xHOgeyWHWAxwjBf6vL8ssFzxeglCwsMvAaum9uIt6ve1DfAo8AtgfmnbhRU09gLmAz8qfpc/Isxe2Sux1/kRn/kC8Gvg3cATxbZZwB8ij3EPoUNJ6V611utIjVoeMvPRCI0cPHjzlmPzmQ6OkykWSlq1EnfNlbRcsX3Meum2/VKUmsxCo6Q15WNhZptIuq54/UtJO1TVSEHK36uTPp6D3iuTEUuTmye67OZUwypWjYjQuxb4lKRfmtnjkmaY2eLAXZJWruBrI+CdwGrAA8C5km7qvVc1+s2iKT5zD7CZpEdL52PAXEkzIo5xPzBL0jM2kidjaeAmSd2SBSf1kJmPRmjk4MFxcsRzOjhOvjwJrED4UgWAhcRd/dZopyifl4tGC49FmHbf+hI8UPZqS1N2s9a5pPDQJA3S3icwwL2SUSyqkuKpSc+ym1Oc9qoRnRDxVSPW1kjCwtbv7jkqfhctBhiSDjIMyCKEqiwwcj4vLW3rx8XAV83sk7BwSv0XgAuH6CEnH03RyMGD42SHDzo4Tr4MmrgrRfm8XDRaeCzSdE5TlN2sey4pPDRJI+V9AoPdK7nEwskISesklrzJzN4i6b9L294I/DlWwEL1kU8BmxI6YQuR1F6mdbyp21n/f4TcJ08C0wkdykuAvYfoIScfTdHIwYPj5MdEr+/w5s1b50boUH6C0Mn8O/AX4ECKBLCRGn3XpU4GDY+FAP4FmAMsIGTlf7FD67fG/aOl1+tNxLmk8NAkjRTXRptG5Xslx1hEHnNeDhreomP9OkJOh9MICT1PJFSdiM73QcgHcRmwP/C+ckvsNSanw8uA84Fnir/Jfy/+vXTFY60EbE5IEljVZxIPOfhoikYOHrx5y7F5TgfHaTBmtpik52zw8nnZaNQll/NIpDHQWnYrreuPWbMcoVf5XFJ4aJJGm96E3Cc5xiLymClyOgzF62TEzP4i6RXF63voMlNFUtRyr+K6XgXYkzCz5x7gB4qsXFFozANWlPRs7D6DYGYXS3pb5GdXojgfSQ9WPM4MYBdCGdf7gJ9JmjuA34E95OSjSRo5eHCcnPDlFY6TMWa2LWGKY+uLwBmSLqsgsaSZfZ+ifB5QqXxeThoei1EsDwu/xFfpnN5uZscSym5ON7MPdPqQIstuMti5pPDQJI0yKa6NQe6VHGMRwxcTajlj2a/0es86QmbWWqO+rKQv15C6nrCUp9MypF7Hr5QUs8KAwwzgrRT3mplFd9aLpU8XATcDdwE7A8eZ2U6SronRqOshJx9N0sjBg+Pkhs90cJxMMbN9CV+qTyZ8EZgJfBD4nKSTIjXOAh4HjiRkop5hZisCVysyS34OGh6LMRrLAsdTdE4lRXVOzWwD4CDCk5PtCMkp25GkqC/og5xLCg9N0mjTS3FtVL5XcomF9UhA2SaSLBmlmb0gKTZfxpTFzHaXdE6H7btJOjdS4zpgR0n31/BxJLAH8D1g1JPfXgNaZnZH26bVCTM3HiMM4hpwr6TYpJidOuszgVcAUZ11M/sN8DVJZ5W2/Suhwsfmw/CQmY9GaOTgwXGyRBms8fDmzdvYBtwCbNK27dXArRU0HgGmF6/nlrY/OZk0PBZjNM4CTgBWZaSu+or94lGOIfDLOtfnoOeSwkOTNMbh2qh8r+QSC0JHstXOJFQ2+D/gR8BVxb/PrBvntmPWzqMxFRpdcl+Ur9MIjYOAPxJyMOwAbN9qFTQu69IuraBxCHAssGTx7yWB/wQ+WzEmvwHe3bbtX4HfRe7/ODCtbdsirb/pw/CQmY9GaOTgwZu3HNuEG/DmzVvnRngCM71t20uAxypozAFWLV7PLX7OBG6eTBoeizEaA3VOKXUcqDBgk/JcUnhoksY4XBuV75VMY3EW8M62be8g/aCDJ5LsHZ9ZRfsbsE7p37MIlSfur6B1R5d2+5DPaeHfz9K26cAjFXVqddaB3wLvadv2buD3w/KQmY9GaOTgwZu3HNs0HMfJlasIJZOWBDCzpYCvAFdX0GiVz9uOkfJ5p9G/1GRuGh6L0TwJrFDeYGYzgX65HZ4ws53NbBZF2U0zm9XexvlcUnhokkaZFNfGIPdKjrHYEfivtm0/BaLW2jvJmAPcSpgNcFvx71Y7HZgdKyRpnS6tynWBmS1vZnuZ2aeLf69mZmv026/E34Et2rZtDjxVxQchLu9u27Y78fkmDgS+ZWa/NrOzi2UOxwMfH6KHnHw0RSMHD46THxM96uHNm7fOjTB1/gpCQrmHip+XA6tX0EhRanLCNTwWYzQOJnQitwOeALYkTDE+sM9+tctu1j2XFB6apDEO10bleyXTWPwB+Hjbto8Bf4zViDyOz3SIi9PlCTQu6LL9vAoa2xDKbv6CYmlMse3CChp7AfMJy3a+VPycB+xV8XxeD8wFfg2cTZgSPxd4fQWNGYQknQcVP5cbtodcfDRFIwcP3rzl2DyRpONkTvEEZzXCNNbo0mJNxGMRMDMjPIX6MCFx392EmvdfV+QfdUtQbrAuKTw0SSMlg94rucTCzDYj1KVflFCBY3XgeeAdkv5YR7vtOFn93pqMdSlPamZzJS0XqXEtIcHhL83scYVkq4sDd0lauYKXjYB3Eu6RB4BzJd0Uu39JZwawU6FzP3Cx4qtXrA48JenxNr0lVCHZZh0POflokkYOHhwnN3zQwXEyxczeDNwp6ZbStpcDMyX9TwWdbalXanLCNTwW6TGzxSQ9Z9XLbrbrbMvgv9faHpqkUehsS41ro+69klksphNm8axK6BheI2nBIFo9jvFZSUen1GwiZrYOcBSwKfDS8nuSZvbZt1WV5CCgvVzmLGBjSZtF+nhc0ozi9VxJyxXX2SOSlo/RSEXdzrqZ/Q74gKQ/l7a9CjhZ0muH4SEzH43QyMGD4+SI53RwnHz5NmEKaJn5xfYoLJTP+zGhtNh5hC/uZ5rZfj13zE/DYzFWZ1szO9XM/rv4uV2V/YElzexHwDOEafGY2a5m9h8VPNQ9l9oemqSR6Nqoe69kEQsASQskXQGcQ8hV8ULRweyKmR0Z00rH8AGHOH5EWCLz74TlCeXWjzWLNq30ek1gDeAewlr1WG4ys7e0bXsj8OdOH+6EmS1nZl80s4vN7Ipyq+ADQs6R9lwSaxBm6MSwQbmjD1D8e8MhesjJR1M0cvDgOPkx0es7vHnz1rnRoRIBYc139Bpk0pSanHANj8UYjX2Bh4EvEpZYHEXoqO5XQWOgspuJf68pPDRJI8W1UeteySgWrwGuIeS2eIGRPBH9ckt8jyGX3ZwKjZDzYFpNjb5/n4A9+rz/OkJOh9OApwnLyu4HNq/g4xeEHDj7E8p3LmwVz6djtaBu2zt8bg6wXtu29ahQzaOuh8x8NEIjBw/evOXYFsVxnFy53cy2l3Rpadu2hBJjsSxPSEpX5q9A1PrZjDQ8FqM5CHiTpOtaG8zsbOAnwEmRGjsAq0laYGYCkPSIma1UwUfdc0nhoUkaKa6NuvdKLrE4DbgQ+AAVqgpI2qf12szOInRif1La9g6qPVl3AlcAmxESfA6EpJi/TScSBou68VvCQNyewKmEmRJbqFqOn9cDK0p6tsI+nXjEzNaTNKe1wczWI5StjeFUQrWaQ4HbgXWBLxCq2AzLQ04+mqKRgwfHyQ4fdHCcfJkNnGdmpxDKJK0L7FO0WFrl8z4j6SkL5fOOplqpyRw0ZuOxKJOic9oqu7lwvb3Fld0sU/dcUnhokkaKa2M29e6VXGKxFnCopDqJp3YE3tu27aeEWRBONe4EfmFm5xNmVS1E0uEJj2Nd3zBbBPgbsKyk9twQVbieMFW9bvnBup31YwjVXv6TsNzkbuAU4KtD9JCTj6Zo5ODBcfJjoqdaePPmrXsj1BL/DnBR8TN6Cmmxf4pSk7loeCxGNC4AvgksWfx7KeAbVCsbN1DZzZTnkshDkzRqXxt175WMYnEa8JYq591BYyhlN6dCY/SylVEt8XF6LgMCriPMoqlzjCOBW4FDCDNpFraKOtOATwM3M1Li9lPUXIbSdoyDJ9rDsHw0RSMHD9685di8eoXjTGLM7CJJO0V8rnapyVw0emhPmViY2aqE2t1bEmp3L0fo5L1H0n2RGrXLbpa0Bi3PmKL0Z2M0SlrjWhq2172SSyyK5UK7EGZ/tD9Z3ztSYyhlN510WJeymqX3DwLeDXwduBdYeD1p9JKiXse4rMtbkrR9BbsxxzpY0jE19u8Zj2F4yMxHIzRy8OA4w8YHHRxnEhPxBa12qclcNCKOMeViMd6d04jjj/vvdSoxrHim6ECMN2Z2RLf3JH2+gs64l92cSpjZ0oSlMwuXQUi6PaF+v7/j3XKTSNKsVD5SUfdeM7P5kpaeSA+Z+WiERg4eHGfY+KCD40xiIr6g3QpsLam8tno14FeSNog8RhYaEceYMrFI1Tk1s22BvQlPgO8DzpDU7Slgp/1TnEstD03SGMZ9Umj2u1e2JYN4psTaSm1KenGivExGzGwj4IfAJoTZBVb8RNIiEftPIyQ0vUrScz0+d4OkV6bw3MfP8sDbgFUkfaW4z6aNwwy8Wp31hg0YpPDRCI0cPDjOsOlZ79pxnEnPSuUOTMEDwCqTUKMuuZxHCo1vA/Pbts0vtkdhZvsCPyZMXT+v8HCmme1XwUetc0nhoUkaZHCfZBQLzOxNZnaKmV1Y/PsfzSx6+ruZvcbMrjGzvxPyYywgLK/wmQ7VOZ6Ql2M5QvnMGYQlM++L2bkY5Lmg14BD8bmeAw5mdkGX7efF+Cg+uw0h8e57gVYSzPUJZV5Tk8OTvRw8QBofTdHIwYPjDBdlkFjCmzdvgzX6J926Fti+bdt2wHUVjpGFhsdi1OfH1OomPHnsGYO2z98CbNK27dXArcM6l0QemqQx7vdJodn1OskoFh8D5hCSUj5ZbNsYuLqCxp+BLwKvIOSWWNhSxnMqNOBxYHrx+oni51LAHRU0LgJeV9NHx2sXmFtB41pgh9Z5FT8XBx4ah7hF/03usv/8ifaQmY9GaOTgwZu3YTcvmek4k5uu5cUKZlO/1GQuGv2YSrG43cy21+jEadsC3dY7dyJF2c3Z1DuXFB6apDGb8b9PoPe9kkssDiR0DO80s88U224GXl5BYy3ql910As8A0wmzRB61UAL1ccLvOpa7gJ8XsxXuYXQSyJ5lN83syOLlYqXXLWYV2rGsLemXrUMXP59jfMrI9yoBuoqkB/tsv3I8PeTkY4pp5ODBcYaKL69wnEwxs927bN+t9M8v9tKQdAHwZsITqZ2Kn28ptkeRg4bHYgyzCZ3TY83sADM7FvgJI1OFY7gK+KqZLQlgZksBXyFUwYgiwbnU9tAkjRTXRoJ7JYtYAEsTOqYw0jGcTugcxnI+IZ5Ofa4E3lW8Phf4OaGca1TFiIIlgP8i/D7XANYstX60Pjetbb81CNdJx+u+CzeZ2Vvatr2RMDMmGjPruOypbXuvzvotXbYvHLCT9LZx9pCNj6Zo5ODBcbJkoqdaePPmrXMjwTTSyONclLuGx6LjZ7YAvkOYsvwdYPOKx1gVuILw5PKh4uflwOrDimkKD03SSHFt1L1XcokFoWN7aNk7cBDwowoaZwNPAZcAp5dbyt/JVGuEjv9ewP7AUkM+9n4Rn9mjz/uvAx4FTgOeJuSmuH+Av6F177UxSxaAlwGPDstDZj4aoZGDB2/ecmy+vMJxMsPMWmW/ppnZOoyeQjeLMM01JVvlquGx6I6k3wK/7fa+mV0kaace+z8AbG3jX3az67mk8NAkjUg6xjPVvZJRLD4GXFgkn1zazP5KSJa6cwWNmxi7zMOpiUJSyDMG3d9qlN2UdFLEx04Ezuzx/m8JOUb2BE4lzJTYYoBrdMz0djN7GdCzMoqZtZaWLGFmd7e9vTy9vSfxkJOPBmrk4MFxssMHHRwnP+YwUpLstrb3HiRMrZ8qeCwGp+fAhY0uu3lvsa1y2c06pPDQJI2aJLlXcoiFhfKKryBcw68i5Ga4B/itKpS6lPT52M86vTGzM+iSLV/S3pEaXctuAn3LblagVx6FRYC/ActK+vJA4vU763sWHi8mzBhpIUIyy78OwUM2PpqikYMHx8kZkzy/kuPkiJldLmmbIRwnRf3tcdXwWKTXMLNbga1VKtFooVb9ryRtUOfYsT5SeGiSRuRx+v1ea90rucTCEtWgN7M3Ae8mlCPdxcz+EXiZRidhdfpgZke0bVoF2A34oaQDIzV+BfwROJKQ9HZt4GhCRZIfJPTa7x65DthR0v0D6m/DSGd9x9Jb0Z31QmcJSU9PpIccfDRFIwcPjpMzPujgOJliZq+WdP0QjpN9J9ljkV7DzJ6UtEzbNiOUJ6x17FgfKTw0SSPyOP1+r7XulVxiYWYXAV+Q9OtI6500PgZ8AjgZ+KykZcxsY+AkSa8fVNcJFAM4R0jaJfLzjxMGfxaY2ROSlrWQZPQGSesk9NXvHjmIMBD1dcJMnHIVjejBqDqd9WL/xwi5Ty4v2p9U8Ut5XQ+Z+WiERg4eHCdHfHmF4+TLz4ovZFcy8mXg2qpfBiLIpXRTLw2PRXpSlN2Mode5pPDQJI0Y+l0bde+VXGIxcHnFEinKbjrd+RNQZVZNirKbKdi/+Dm7bbsI+U9iudfM6nTWtwC2JsTwE8CyZnYVcLmk/xySh5x8NEUjBw+Okx1eMtNxMkXSTGBzQomxVwPnAI+b2c9iNSxBqckcNDwWA9GvczqbmmU3E5xLbQ9N0khxbSS4V2aTQSyoV16xRYqymw5gZtu3tZ2B71MtUWetsptmNq049mJ9Ptq+Fn4Uktbp0qoMOEDorP8U2BQ4D5hrZhea2adidpZ0m6TvSXo/8BbgeELnv8rf/1oecvLRII0cPDhOdvjyCsfJHDPbgPCUcBvgrcBtkraI3LfjNFMzmytpucmkUXzeYxE+u7ukczps303SucXrz0o6uo/OFsAHCB25e4BTJP0uxkOxf4pzqeWhSRqp7pNinzr3yoTHIgVmdi5hlsdRrRhamFq/qaT3DNPLZMfM2mep/J0w0+FzkirP5rGQLPQ9hIGh0yX9PXK/2rk+zOwCSW/vsP08Se8YUHMD4H3AR4ElJPUbGMHM9id07t9AKNl5BfAr4CpJ84bhIScfTdTIwYPj5IIPOjhOppjZ2cCWhC8BvyJ8EbhS0vyIfVtPbK4nZH9vL593uqTVJoNGoeOxGK2VrHPa5zgdy26mPJdBPTRJI/G1MfC9Essw4tntWjazhyWtFHmMVYELCeUZVwdupyi7KenBwZw7dSkGHFZWKdFohX1T5PpINQheq7NuZi8SKs18GfjZgPGoPWCQkY9GaOTgwXFyxAcdHCdTLGSAnw78N+E/m8sVmW27+BLRKknWzoPAbMPrhIoAACAASURBVEnfnQwahY7HguF29ovjdftynuT3WsdDkzRSxrPOvRLLMOLZ6Ym2mU0HHpTUNwdA0bHdFriGGmU3nUARz770iq2ZzQC+Tah6sUDSUma2K7CFpMMifRwP7AFUzvVhZkcWLw8idLDLzAI2lrRZjI9Cr1Zn3UJFl20IncutCPftFYR7NqqaR6IBg1x8NEIjBw+OkyM+6OA4GVM8qdu6aP9EWOd8haR9I/evXWoyI40pH4thdvaL441recYUHpqkkSqede+VCP3xHIC5knCNb0kYMCizBnCj4qslJCm76Yz629P1I4AkLdJD4yxC4sgjgZskzTCzFQklM9eP9PG9bu9J2idy3/cCPyzvCjxEWAI0J8ZHoVe7s17S2owwGPNR4KW94jheHibaR1M0cvDgODnigw6OkzlmtimwHeGp3XbAfEmrR+5bu9RkLhqFjseC4XT2i+OMa3nGFB6apJEynnXulQjt8Rx0eB+hA3sC8JHSW62O4aWSFkQeo/ZUfCdgZgcQOqNHEyqLrAV8hpAg9OLW5yTd1UPjEWA1hZKZC5cyWIcSq+OJme0n6aQ+n9lD0pkVNCt31s3sk4T7858IOTIuZ6RjeUvsset4yMlHEzVy8OA4ueCDDo6TKWb2U8KXgPmM/hJwawWNu4FapSZz0PBYjNEY985+cZx+neTa51LXQ5M0El0bte+ViGMMIxYbSrq5j8bxkg7o9T4DTsV3RmNmc4B/lPREadsM4PeS1q2gsZWkB2wksedM4BJJG1b0szQhV8fCWV+Sbq+i0Ue/7zVet7NuZt8v9rl8UO8pBgwy8tEIjRw8OE6O+KCD42SKmb2f8B9M5czgbTqzGKnBvTWhJvpVknaeLBoeizH7j3tnvzhO3+npKeJR10OTNBJcG+8nwb3S5xi5xLPfwMXAU/Gd0RSzFDZRKT+Ima0OXCdphUiNg4FdgUOB84EdCWUZL5B0XKTGRoSlEZswstRMACmfAEfeq9+nRmfdzF4CvFieuWMhb8k0Sc9GatTykJmPRmjk4MFxcmTRiTbgOE5X/gQ8X95QPBWaIem6WBFJt5vZosBiRXsrEJX9PSMNj8Xo/We2dU4/CixvZlU6p33LbhJRp73OuaTw0CQNSHJ91bpXcopFBJ1ymyzEBxaSchrwv2Z2HGHWyJrAx4vtsXwJeJqQTHI6cCpwIvD1ChrHA5cRlgzdAaxNWPJxdQWNGGIGcD9Mvc76/xCSWpaX//wDcAzhKXcMdT3k5KMpGjl4cJz8kOTNm7cMG3ADMKtt27rA9RU0zgbuJnyZOAZ4G7B0RR8TruGx6Kq1AfAhwpO/xwiZ+WP3nddl+9wh/l5TeGiSRorrq9a9kkss6hyn3/GAh1P6mAoNmEbIsfFL4C/ApcW/Fxmyj8eB6cXrJ4qfSwF3JD5Oz2ur+MwVwOvatr0O+FXkMZ6gmHHcFufHK/is5SEzH43QyMGDN285Np/p4Dj5MlNt0+ok3WZma1fQeA3wInBd0f4kaX5FHzloeCxKmNnZhOz+9xPKIv4Q+EiMjo2U3ZxmZuvAmLKbz1SwMtC5pPDQJI0SKa6vge6VDGORguntG4qnhZ6IrSIKpTC/U7SO9MuxUXzm5YSlES9t0z810sozhN/rAuDRYhbP44RlSMPm1cBv2rb9lnB+MTwBrEyoPNRiZcIa/mF5yMlHUzRy8OA42eGDDo6TL/ea2Wsk/bG1wcxeQ+hoRiFpfRtdPu9gM6tUPi8TDY/FaOp0Tucwshb6trb3HgRmR+rUOZcUHpqkASS7Nga9V7KKRSQdl1fYSNnNxc3sira31yD9VHwnsCfQK7HnIcDhhL9ZT5XeEmGpRQxXAu8Cvg+cC/wceJYw86IvZjaNsGTgKknP9fjo3RFydTvrPwF+ZGYfB24nzEj6KvDjyP1TeMjJR1M0cvDgONnhgw6Oky9fAy4wsy8TvsCvC3wKOKqKiEKm8L8CqxG+cG9HSOA1mTQ8FqP3H7hzKmkagCUquznIuaTw0CSNNr2619dA90pOsTCzRYAjgKPUe/1yt3r1JxMGJDYHTilbpCi7Oag3pyc9c2wABwJbqEblHUnvKv3zEMJyoqWB0yP3f9HMLlCfJJGSXhkhV7ezfihwLOEJ9ksIszhOBT4buX8KDzn5aIpGDh4cJz8men2HN2/eujdgd+AXwI3Fz90q7v9TYC6hrvrpwL7A+pNUw2MxVmtT4JOEkoDzgPsq7PvqBNdnrXNJ5KFJGkmujTr3SkaxeJSQNK2OxoYRnzm+rldvC2PZL8fGXcBiiY41DVh1wH0vom29/IA6ixOSYj4FvEB4Cv1N4CUVdQxYkba8CsV7ewzDQw4+mqKRgwdv3nJsXjLTcSYx/dbQWoLyebloRBxjysTCzH5KqN89n9H1u2+toFG77Gbdc0nkoUka72ec75PiOF3vlYxi8VVgjqTjY/cZBOtTdtOJp18szWxv4A2EZTYPld9TyBkRc4wZhM7YbsACSUuZ2a6EGRSHRWocD+xBGKy9h1KlCkmHx2i06RmwAvBo+zVuZntIOrOqZmn/qOtzPD0M20dTNHLw4Dg54YMOjjOJifiStynwmKR7StsqlZrMRSPiGFMmFqk6pza67ObWhGRsVcpupjiXWh6apDGM+6TQ7Hev5BCLq4DXAvcxtmO4dayPiOPMV5+p9k4c/WJpZq2BhfIXTwMkKSq5p5mdRUgceSRwk6QZZrYicLWk9SM1vtftPSUus1p3UCvF9ZliYC0jH43QyMGD4wwbz+ngOJObfmtofwDs2rZtOnAGITtyDLlo9GMqxeJPwPPlDYN0TiXdbmaLAosV7a3ASrH7k+BcEnhoksYw7hPoc69kEouTijbe+JOXdHTLsdFinQTH2AFYTdICMxOApEfMLPraSj2w0Id+/y/1I8X1WdcD5OOjKRo5eHCcoeIzHRxnEhPxxLLj+1VGyHPRGPQYKT1kpHEDsKtKpRHNbF3gfElRnVMbW3bzCuBKVSjRWPdcEnloksa43yf99HKJxbDwp4VxFNO89yUsS1hB0qvNbGtgFUlDS25nZnOArRQSrs6VtFwx4HqJpA0rai1NmLq+sPOmtnKzCfw24om4a6TVyMGD4wwbn+ngOM2mdqnJjDTqkst5pNCY2f7lWNJtZrZ2BY06ZTdb1D2XFB6apJHDfZJLLDCzlYEtGNsxjC2vGHWYhFpN5kjgTcBxwHeKbfcSqqV0HXQws+9K+lDx+gy6PDGXtHekj5OBn5jZocA0M9sS+GLJU1/MbCPgh8AmjJR4bfmKWuYxRHK5PnPx4TjOJMUHHRxnctPvi0CKUpO5aPRjKsWidudUNcpulqh1Lik8NEmD4dwn0ONeySUWZvbPhOn6twIbEypxvBK4ilDOr9/+dctuOqN5P7CZpEfN7IRi2x3ArD77lfPOzEng40vA04RkktMJ18KJwNcraBwPXEYoR3sHsDZwNHB1An/t1O2sP5yBB8jHR1M0cvDgOEPFl1c4ziTG+lRsKD6zO/BBYE1CQraTJZ1b8ThZaPTRnzKxMLP9gMOBMZ1TSd+t6GVTwpfvbYuf8yWtXmH/FPGo5aFJGuN9nxTHiLlXJjQWxRKiz0s6x8weLxIG7gNsLOlTkRqPAispsjKC0x0zux+YJemZ0rKGpQnJHNecaH9VMLPHCdfFAjN7QtKyZrYUcIOkFHknyse6TdK6NfZPMQ2/lofMfDRCIwcPjjNsfNDBcSYxib4I9O2ATAaNqRaLBAMXtctuRh6nV3nGFKU/G6MReZwU11evnA5ZxKLssTToMA14UFJU0kAbUtnNqYCZnQw8B3wSeIBQjeRrwGK9rkcz2z5GX9KlFby8nLA04qVtGlHLbszsAWBdSU9ZyBGxPaEixn2p18g3Ze2/a6TVyMGD4wwbH3RwnEmMeRmr8v4ei9EaPTunlqjsZoSPXh3c2h6apBF5nHEtf5dLLIrO4BskPWRm1wIHAI8Cv5a0fKTGUMpuTgXM7GXAacCOhGUNzwCXAHv3ytdhZjHXgCT1W6bR0juEMMvrOuCpNo2oAQ4z+zFwsaTvm9kxwC7As8Ddkv45RiOWpnROXSOtRg4eHGfYeE4Hx5nceBmrETwWo9mT0FHrRpKymxH0OpcUHpqkEcN4l7/LJRYnEWZL/ITwRP0yQnLKYyP3b2kMo+xm45E0D/gXC8k9ZwL3SHowYr9KyxXMbA1J9/b4yIHAFpKur6Lb5uldpX8eAtwALA2cPqim4ziO0xsfdHAcJ0VnPReNuuRyHsMYQPkBsGvbtunAGUBU2c1Iep1LCg9N0ohhvO+TLGIh6Uul16eb2a+ApST9JdIDkk6L/azTm2JpC8AjRcPMpo1DvoybgF5Pb58Gbk5xoOKcVpbkyUQdx3HGGR90cJzJjWcvHsFjMZp+ndMUZTfrksJDkzSGRa97JZtYmNl04HXAapLONrOlzGwpSX+voDGMsptTgefp8DfFzJ4nVM05DzhC0t9qHqff3/HPAd80s9nAQ+U3YgdAzGwGofrFbsACYCkz25Uwg+Kwyo4rUnGw5u6m+3AcZ2owrf9HHMfJmBRPaHJZDlBXw2NRjXstlNkcOWjFspuR9DqXFB6apBFDimuj172SRSzM7FXALYTlEacUm7cholxmSeOfCdVdjiSUVfxY8XOvWA1nIR8DLgXeDLwCeAvwS+AgYH/g9cBxCY7Tb7D0+8B+wL2EAYMFhAGRBRWOcQLwJLAWITkmwDXAv8bsXJr1EUOnzvp9ZvZlM3tlv50ldfxMAg/Z+GiKRg4eHCdnfKaD42RGTLbvVqZvSfsnOOQZuWp4LGrRr3P6NeACMxtTdjPBscv0OpcUHpqkEUPHeCa8V3KJxQnA4ZLOsFDiEEIljCo5Gv4D2EcjZTc3s6LsZgUNJ/D/gNdIerL49y1m9nvgD5LWNbM/A38Ygo8UJS13IMyeWWBmApD0iJlFVUUhdNbPAE6XdEOvD3bprH+EkHPnd2b2F0KCzh9JeiT+FGp7yMlHUzRy8OA42eLVKxwnMyKyfUdn+o48XhZZlDtpeCxqaYx72c1IHz3PJYWHJmlEHKNjPFPeKznEohhoWE6SzGyupOWK7QtfR2jULrvpBMzsEeDVkh4obVsNuF7SCma2CDBX0jI1jzPuGfktVEbZStIDreupSHR6iaQNI/Z/O6GzvjMwaGcdM1uWMLtiL2Bz4L8LrZ9K6jlzI5WHHHw0RSMHD46TNZK8efM2hRswvykaOXjISGNeAo3jJ/pcEnlokkYO98m4xwK4FvjH4vXc4ucWwG8rHGMOIVFgS29LYH3gsYmO4WRrhKohNxCWNrwV2Bf4M3Bs8f6OVX43PY4z5voGvlt6fQahysSYVuEYBwNXA9sBTxTXxWXAgRW9Lgt8GLiKUHLzp8A7gekDnPcsQinQu4FHJ8LDRPtoikYOHrx5y7H5TAfHmeJk9GR+wmtO53IeiTTmS1o6Ax+NqGfeJI26DOM8zGxnQi6H7wD/Tlia8RFgP0mXRB7jM8AcST8xs72B71KU3ZT0uTr+pxrFDJEPAbsDqwEPAD8GTpL0gpktTpg9+3TN46wp6Z62bZ+VdHTx+ohu+0r6fOQxDPg4oVO3FqGDfSLwdQ34pdjMZhGeUO8LLClphQr7Lgb8M/A+4I3A1ZK2G6aHnHw0SSMHD46TC57TwXEcp5kMo+zmMMglMWcuGjkw7rGQ9DMzeyvhyfrlhM7hOyRF5w1QgrKbTkChysF3itbp/Wf6aZjZPXT+u/QsITHkeYRcHu3aR5deRw0s9KIYWPh60WpTdNb/EXgtsDJhFkXMfv8E7E0YyHmEMIvjAEl3DctDTj6appGDB8fJCR90cBwnl85UDh2yXM4jh1hAHgMXKTw0SSOHa2MosZB0LdAzL0k/LEHZTSdgZm8GNgVeWt4u6fBIiW8Qnth+g5DnYybwb8A5wFzCjJY1CRUxysftmyS18HFppA/M7OXAJow9lyrVUQbqrFso97knsDzh3HeW9H+xx03hIScfTdPIwYPj5IgPOjiOk6LUZC4adcnlPHIp/5mCHH6vTWJKxLN4wvd+Ondy947UeBVhLfSzwBrA2YSym+8jsjyiEzCzbwHvIuQ+eKr0VpUBqPcDb5K0sHSqmf2ckMBxYzO7DPhf2gYdGCmZ2gsR8hH0xcwOIeQtuI6x59J30CFBZ/21wGHAf8XMEBknD9n4aIpGDh4cJ2c8p4PjNJCYJ0P9ngrlolGXXM5j2LEwsxNUs4xot7wQwzyXRLkpstbI4T6pwjDiaWZnEp5EX8jojmGVtftXASeqKLupUL1iKeAWSavXsD/lMLO5wCbt+RYG0Fhb0rzStmWBO4rfjRES4Na9ttaQdG+P9x8G3ijp+gH1f06oKjBwZ70uOXhI5aMpGjl4cJyc8UEHx2kglqB8Xi4adcnlPBJpDHvgomPZzWH+Xrt5aJJGDvdJFYYRTwslM9eR9ESNY9Quu+kEzOwW4B8kza+hcRphScVRhBwOawCfBe6TtLeZvZ4wSPSqml77JSm9C1hf0nN1juM4juPE44MOjuM4k4Rhd06nSqWEyaSRA8OIhZldB7xZ0kM1jnEtodrF71sDDWa2BfAtSVsMqjsVMbMPAzsBRwOjfieSbo/UWByYzdgKGEdKesrMVgEWk3R3Ta/9ZtHsDbyh8NJ+Li/WObbjOI7TGR90cBzHcTqSYhp9Dh6apJEDQ1pe8e+EzunXGdsxjJrNYwnKbjoBM+vWGZekRYZqpg8RA1qtcyl/ATYyPBfHcZym4IMOjuM4TkdyeDKfywyDXDRyYEgzHbrN6qk0m8fMNiOU3VyLUDHhJFUou+mko8fysGeBe5UoM3/EtbVWt/dSeXAcx3FG44MOjuM4Tkdy6CTn0tnPRSMHPBbOIBQDSasV/3yMkKEf4GFgFeB64N2Sbq15HL+2HMdxMsNLZjqO4zjdyKHsZgoPTdLIgUkRixRlN52AmS0KHEAoOboCpd+fpK0jZU4BlgEOl/S0mS1ByKswDzgOOBY4HnhTXbtjNph9V9KHitdn0KXUp18XjuM444MPOjiO4zjd+MFEGyCNhyZp5MC4xMLM/iLpFcXre+jeMZwZeYzTGCm7OXBCSgeArwHbA98l5MY4FNgfOKuCxieAVSU9D1AMPBwG3C/pqCKPR9dSlxXYqMO28nKdOQmO4TiO41TAl1c4juNMIYZddnO8PDRJIwdyiIWZ/ZOkq4rX2/TQuLzfcQqN2mU3nYCZ3QdsKeluM3tC0rJmtiGhxGXX31Wbxp3AHpKuKW17HXC2pLWKmQ/3S5rRQ6PbYNSzhAGL84ATWgMbjuM4Th74oIPjOM4UYthlN8fLQ5M0cqCJsUhRdtMJFAM4y0mSmT0ArFuUuYzOn1CUqvw28FNCUs81gF2Aj0k6vag28nZJ+/XQ+DSwJ/CNQmMm8G/AOcBcQpWS8yUd1LZf3wExmBwDhI7jOJMRH3RwHMdxHGfCMbMjYz4n6fBIvdplN52AmV0NHCjpt2Z2IfAXQi6G97aWxETqbAS8k5BQ8gHgXEk3Vdj/RuBNku4vbVsduETSxmb2cuB/Ja3Ztl+/ATGYJAOEjuM4kxEfdHAcx3EcZ8Ixs+/FfE7SPpF6ScpuOmBmmwMvSPqjma0PnAAsDXxK0pVD9DEXWFvSvNK2ZYE7JM0wMwPmSVq65nHWkJQiv4TjOI6DDzo4juM4jjMJMbM9JJ050T6ceMxsVzpXwIiqGmFmpxGWVBxFyOGwBvBZ4D5Je5vZ6wl5Jl5V06eX3XQcx0mIDzo4juM4jjPp8I7hcCmWLmzC2PKjp0bufwTwEULFiw8DJwLvISSS/HikxuKEMpu7M7JE48fAkUWOiVWAxSTdHaPX4zjz686WcBzHcUbwQQfHcRzHcSYdnTqG41B20wHM7BDgcOA64KnSW5IUlaTRzO4CdpJ0Q6kCxhbAYZJ2Te96cHxAy3EcJy0+6OA4juM4zqSjU8cwddlNJ2BmDwNvlHR9DY0nJS1T0ltd0oLy9giNbgMczwL3SrprUH9tx/FBB8dxnIQsOtEGHMdxHMdxUtAacChe+8BCOp4Gbq6pcZuZbSzpRuAGYP+iFOfjFTROISyrAHgMWL54/TCwipldD7xb0q01vTqO4zgJ8ZkOjuM4juNMOrrMdEhadnMqY2bTSv/cE3gDIZ9Ce/nRFyP13gb8TdIVxbKKHxHyQxwg6bxIjcOAZYDDJT1tZksUnuYBxwHHAutKelOMXo/jeE4Hx3GchPigg+M4juM4kw4zu0HSK9u2JS27OZUxsxcZyYnRqjRR/tJohJwOiyQ8Zs+KJGb2CLCqpOdL26YD90ta0cyWIiyzmFHTx5qS7qmj4TiO44zggw6O4ziO42SFmc3q8tazwAOxT9cjjuNlN7tgZmvFfC5VHoXimD1zKZjZncAekq4pbXsdoQLGWsXMh/t7DTr0SDD6LKEM53nACeWBDcdxHKcentPBcRzHcZzcmMPop+zlTuKLZvZTwrT8h8bsWY0TAR906EB5MMHMXgK8KGlBadt0YFqnfWtgfd4/HLik+P3fA6wB7AJ8rHh/B+DcPhrfICwX+UahMRP4N+AcYC7w78CawEED+Hccx3E64DMdHMdxHMfJCjP7ILAtYb1+q2N4GHANcDnwJeB5Se+seRxfux+BmV0BHCTp16VtrwOOkbRtwuP0rRphZhsB7yQklHwAOFfSTRWOcSPwJkn3l7atDlwiaWMzeznwv5LWHOgkHMdxnDH4oIPjOI7jOFlhZvcC60l6prRtSeAWSWuY2QzgVkkr1DyOl0aMoKgysZxKXxqLRJOP1c2f0Haccf99mNlcYG1J80rblgXukDTDzAyY54NRjuM46fDlFY7jOI7j5MY0YG1Gl2mcCbSSFv4d/w4zTJ4EVgYeLG1bmfB7SEm/5RWY2a7ANsD/b+/+Q/0syziOvz+LTFbqcDOz+UeZTCqFMm0Js5gRtvwRJDaTzSiUplBiBBqsXDojjH4QIbmo8Acz1GxugRroH4VmjIxyKZRTt1zhlriRmbbs6o/nWTudbef7Pe4553zP+b5fMM7N8zznvu8zDtt5rnNf1zVv5PNVdVGfa2wA7k5yHU0Nh2OBL7bXAU4Dnu5/y5KkXrrOxZMkSTpY3wYeSHJdkhVJVgP3t9cBPkKTaqHJ8RNgbZITk8xOchJwM3B7x+tsH+tmkqtp6nDMAs4HngPOBHaOY43PAL9u5/ktsAbYCKxo7z8JnDWuXUuSxmR6hSRJGjhJPkzzYrknd//2qrq34zX2abupfSU5FPgG8CngdcBLwA+BL1TVyx2u06t7xRbgrKralGRnVc1J8l5gZVWd29U+JEndMuggSZJmnMlquzlM2noH84C/1agfILtoP9pH0GFXVR3RjrcD86tq98jrfaxxxgFuvQw802ULUElSw6CDJEkaKEmuOcCtl2ny8O/t1S4zyX8Yo+0m0FXbTdFNEcg+gg6PAMur6g9JHgDWAc8D11bVW/pc4yma0zPQpGfMbcfbgTcBvwcuqKo/vbqvQpI0mjUdJEnSoFkAXAksBo5vP14JvBu4FHiyTb8YyyXA2nauQ4ETgFuAy4CTaApR3jARmx9SPYtAdmAle4MEVwGfA74OfH4cc/wA+A4wp6reDMyhqRXyvXa8Eb8vJKlTnnSQJEkDJcntwG1V9dMR1z4KXFhVS5N8Eriiqt41xhyT0nZTjQOdUkgyq99Ulo5OS4yZ5pFkB3BMVf17xLXXAn+pqqOSvJ4mzaKzVqCSNOw86SBJkgbNmTTpDyP9DFjSjm8FDlSzYY89bTdHsu3m5NuW5Pok/RTs3NrBejf2uP8P4NRR194DvNiOrfUhSR3zP1tJkjRoNtOkUXx3xLUV7XVoihm+OPqTRtnTdvNHwJ+BY2m6L9h2c2IcKL1iBbAM2JjkceAmYG1V7Rj9YEedRHqleXwZ+HmS9ez9vjgH+Gx7/4PAnR3sQ5LUMr1CkiQNlCQnA3fRnErYBswHXgE+VlWPJHk/cEJVfb/HPBPedlONJJur6m1j3J8DLAWW05w0uI8mALG+qnZ3uI+eKRpJ3gGcx97vizur6rGu9iBJ+n8GHSRJ0sBp8+xPA46heTH8VZcvp+rWeOoxtO1MlwEXA7O7rKvRRV0ISVK3TK+QJEkDpw0w/OLVfn4XbTfVvSSHAKcAC4GjgYe6XqKPPZwLfIAmTed/z1fVRR3vRZKEhSQlSdKASXJ4km8m+U2SLUm27vkzjmm6aLupjiRZlGQN8CywGngYWFBVizteanuPfVxNU2xyFk3qzXM0hUt3drwPSVLLoIMkSRo0NwAnA9cAR9IU+dsKfGscc8wCLqiq06vqwqo6Hfg48EpVvQ+4DPhat9vWaElWJXkC2NBeOruqFlTVtVW1ZQKWPKrH/U8DH6qqK4B/tR/PYd9OJ5KkjljTQZIkDZQk24G3V9VzSXZW1Zwk84ENVXVyn3PsAo6sqldGXHsN8HxVHT5yPCFfxAyQZFZV9dVC8kC1FJLcQ1Mwcl1VvdT1Hvvdx4j7u6rqiHa8HZhfVbtHXpckdcuTDpIkadDMAna14xeSHEFTTPL4ccyxp+3mSONtuznstiW5Pkk/rSz3m/pSVUuq6seTEXDo0+Yk72zHm4BLkywHnp/CPUnSjGYhSUmSNGh+R1Po737glzTpFi8AfxzHHBcDdyW5klFtN9v7JwBf6mrDM9QKmi4TG5M8TnNiYW1V7Rj9YFX1E5gYBCuBue34KmAt8AaadBtJ0gQwvUKSJA2UtqViqmpzkjcCXwUOA75SVY+NYx7bbnYgyRxgKbAcOBW4jyYAsX4y/j67SPMY53qfqKrbDmYOSdJeBh0kSdK0k+SGqvK305OsDQgtozlJMruq5k3Cmn8FbgFurqpNPZ7ddLCnLroIXEiS9rKmgyRJmo6WjXWzo7abGiHJIcApwELgaODRSVp6BfBWmjSPR5JcnmS/XSo6SvNIB3NIkloGHSRJsI73FwAAAptJREFU0nTU68Wwi7abApIsSrIGeBZYDTwMLKiqxZOxflXdXVXn06TJ3AicDzyTZH2S89o0mk6X7Hg+SRpqpldIkqRpp4/WiAfddnPYJVlFc6JkLnAHcFNVPTilm2pNZJqH6RWS1C27V0iSpJmoi7abw24hTbeHdQPU8nJ/aR4Pdb1Ex/NJ0lAz6CBJkqajXi+GXbTdHGpVtWSq9zBSkkXARTTpFTtoikteVlVbOl5qe8fzSdJQs6aDJEmajm7pcf8S4Ol2fDnwT2AOzUurppEkq5I8AWxoL51dVQuq6toJCDgA7LdIpSTp1bGmgyRJmna6yLu37eb0kOQe4CYmKc3Dmg6S1C2DDpIkadpJ8veqOuwg5/DlUvvw+0KSumV6hSRJmo66+K2JBQMlSZpgBh0kSdKw8rjnkEjiz7ySNEX8B1iSJE1HnlLQeGxLcn2SE/t4duuE70aShogtMyVJ0kBIckavZ6rqgXZ4axdLdjCHpocVwDJgY5LHaQpTrq2qHaMfrKp+AhOSpD5ZSFKSJA2EJE/1eKSq6rgO17N7xZBJMgdYCiwHTgXuowlArK+q3VO5N0maqQw6SJKkoWSXguGW5Dia0w8XA7Orat4Ub0mSZiRrOkiSpGFlesWQSnIIcAqwEDgaeHRqdyRJM5dBB0mSNKw87jlkkixKsgZ4FlgNPAwsqKrFU7szSZq5LCQpSZKkGS3JKppUirnAHcDZVfXglG5KkoaEQQdJkjSsTK8YHguBlcC6qnppqjcjScPEoIMkSZoxpqDtpqaBqloy1XuQpGFl9wpJkjRjTHbbTUmSNDaDDpIkSZIkaULYvUKSJEmSJE0Igw6SJEmSJGlCGHSQJEmSJEkTwqCDJEmSJEmaEAYdJEmSJEnShPgvaF9aiQoHxIcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGvHETHRw1--"
      },
      "source": [
        "-----\n",
        "# Problem Selection (with KNN)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65UeVgPIw68w"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "# problem 11\n",
        "X11 = X\n",
        "Y11 = O_Y\n",
        "\n",
        "# problem 12\n",
        "X12 = X\n",
        "Y12 = Y\n",
        "Y12 = Y12.replace({'ACEP': 'CEP', 'T2CEP': 'CEP'})\n",
        "LE12 = preprocessing.LabelEncoder()\n",
        "y = LE12.fit_transform(Y12)\n",
        "Y12 = pd.DataFrame(y)\n",
        "Y12.columns = ['Y12']\n",
        "\n",
        "# problem 2\n",
        "X2 = X\n",
        "Y2 = LPV_Y\n",
        "\n",
        "# problem 31\n",
        "X31 = X\n",
        "Y31 = Y\n",
        "Y31 = Y31.replace({'ACEP':'CEP','T2CEP':'CEP','DSCT':'OTHER',\n",
        "                   'RCB':'OTHER','DPV':'OTHER'})\n",
        "LE31 = preprocessing.LabelEncoder()\n",
        "y = LE31.fit_transform(Y31)\n",
        "Y31 = pd.DataFrame(y)\n",
        "Y31.columns = ['Y31']\n",
        "\n",
        "# problem 32\n",
        "X32 = X\n",
        "Y32 = Y\n",
        "Y32 = Y32.replace({'ACEP':'CEP','T2CEP':'CEP',\n",
        "                   'RCB':'OTHER','DPV':'OTHER'})\n",
        "LE32 = preprocessing.LabelEncoder()\n",
        "y = LE32.fit_transform(Y32)\n",
        "Y32 = pd.DataFrame(y)\n",
        "Y32.columns = ['Y32']\n",
        "\n",
        "# problem 41\n",
        "a = final_data\n",
        "a = a.drop(a[a['class'] == 'RCB'].index)\n",
        "a = a.drop(a[a['class'] == 'DPV'].index)\n",
        "a = a.drop(a[a['class'] == 'DSCT'].index)\n",
        "\n",
        "X41 = a.iloc[:,0:53]\n",
        "Y41 = a['class']\n",
        "Y41 = Y41.replace({'ACEP': 'CEP', 'T2CEP': 'CEP'})\n",
        "LE41 = preprocessing.LabelEncoder()\n",
        "y = LE41.fit_transform(Y41)\n",
        "Y41 = pd.DataFrame(y)\n",
        "Y41.columns = ['Y41']\n",
        "\n",
        "# problem 42\n",
        "a = final_data\n",
        "a = a.drop(a[a['class'] == 'RCB'].index)\n",
        "a = a.drop(a[a['class'] == 'DPV'].index)\n",
        "\n",
        "X42 = a.iloc[:,0:53]\n",
        "Y42 = a['class']\n",
        "Y42 = Y42.replace({'ACEP': 'CEP', 'T2CEP': 'CEP'})\n",
        "LE42 = preprocessing.LabelEncoder()\n",
        "y = LE42.fit_transform(Y42)\n",
        "Y42 = pd.DataFrame(y)\n",
        "Y42.columns = ['Y42']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEKMKPpu744e"
      },
      "source": [
        "> * function for testing problems :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oaaf0Xxw5vHM"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
        "\n",
        "def clf_reports(x,y,tn):\n",
        "  clf = KNeighborsClassifier()\n",
        "\n",
        "  x_train, x_test, y_train, y_test= train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "  clf.fit(x_train, y_train)\n",
        "  #clf.score(x_test,y_test)\n",
        "\n",
        "  y_pred = clf.predict(x_test)\n",
        "  print(classification_report(y_test, y_pred, target_names=tn),'\\n')\n",
        "\n",
        "  fig, ax = plt.subplots(figsize=(10, 10))\n",
        "  plot_confusion_matrix(clf, x_test, y_test,  ax=ax, display_labels=tn, normalize='true')\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J66lOTV34hxC"
      },
      "source": [
        "------\n",
        "> * problem 1.1 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "7wQdj2vz4fnc",
        "outputId": "a7ab7477-70fe-4c45-c49a-b239037b4267"
      },
      "source": [
        "tns = ['ACEP', 'CEP', 'DPV', 'DSCT', 'ECL', 'LPV', 'RCB', 'RRLYR','T2CEP']\n",
        "clf_reports(X11,Y11,tns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ACEP       0.21      0.05      0.08        65\n",
            "         CEP       0.84      0.88      0.86      3468\n",
            "         DPV       0.62      0.23      0.33        22\n",
            "        DSCT       0.68      0.46      0.55       604\n",
            "         ECL       0.86      0.87      0.87     15802\n",
            "         LPV       0.99      1.00      0.99     22288\n",
            "         RCB       0.00      0.00      0.00         4\n",
            "       RRLYR       0.90      0.89      0.89     14865\n",
            "       T2CEP       1.00      0.03      0.05       107\n",
            "\n",
            "    accuracy                           0.92     57225\n",
            "   macro avg       0.68      0.49      0.51     57225\n",
            "weighted avg       0.92      0.92      0.92     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ea5Q2PrW7KWA"
      },
      "source": [
        "------\n",
        "> * problem 1.2 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "id": "3aY7CUbZ7MfR",
        "outputId": "cb7e75bd-9c52-4739-92ca-a362efcd5584"
      },
      "source": [
        "tns = LE12.inverse_transform(np.arange(0,7))\n",
        "clf_reports(X12,Y12,tns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.86      0.86      0.86      3640\n",
            "         DPV       0.62      0.23      0.33        22\n",
            "        DSCT       0.68      0.46      0.55       604\n",
            "         ECL       0.86      0.87      0.87     15802\n",
            "         LPV       0.99      1.00      0.99     22288\n",
            "         RCB       0.00      0.00      0.00         4\n",
            "       RRLYR       0.90      0.89      0.89     14865\n",
            "\n",
            "    accuracy                           0.92     57225\n",
            "   macro avg       0.70      0.62      0.64     57225\n",
            "weighted avg       0.92      0.92      0.92     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSnpMX9b7M8_"
      },
      "source": [
        "------\n",
        "> * problem 2 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "kLcqpZqp9bJe",
        "outputId": "62f92740-6f2f-4428-86a0-930dedf5b93e"
      },
      "source": [
        "tns = ['Not-LPV', 'LPV']\n",
        "clf_reports(X2,Y2,tns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Not-LPV       1.00      0.99      0.99     34937\n",
            "         LPV       0.99      1.00      0.99     22288\n",
            "\n",
            "    accuracy                           0.99     57225\n",
            "   macro avg       0.99      0.99      0.99     57225\n",
            "weighted avg       0.99      0.99      0.99     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDBhuz1D7NMR"
      },
      "source": [
        "------\n",
        "> * problem 3.1 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "2Jkndxgl_S4p",
        "outputId": "7f521eba-5e5b-4f3d-d87a-98f8d22f6061"
      },
      "source": [
        "tns = LE31.inverse_transform(np.arange(0,5))\n",
        "clf_reports(X31,Y31,tns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.86      0.86      0.86      3640\n",
            "         ECL       0.86      0.87      0.87     15802\n",
            "         LPV       0.99      1.00      0.99     22288\n",
            "       OTHER       0.74      0.43      0.54       630\n",
            "       RRLYR       0.90      0.89      0.89     14865\n",
            "\n",
            "    accuracy                           0.92     57225\n",
            "   macro avg       0.87      0.81      0.83     57225\n",
            "weighted avg       0.92      0.92      0.92     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MTFsqhFAEfBG4NMZYHWN8EfgbcHILxUcBT8YYF8cY1wD3Ai1V3JqwQiZJkrRp2wP1McaZjeZNp+WK1m3A/iGEISGE7qSzaY9vbgcOeyFJknJaa/t7fRIhhH8CN8cYb240uwdQtUHRFUDPFjYxi/QTgxYASeAt0k8P2iQrZJIkSQ1ijHu3MLsa6LXBvF7AyhbK3gB0AYqBGtLP2n4c2HdT+7XJUpIk5bRUDBmZWmEmkB9CGNNo3u7AOy2U3QP4Q4xxeYxxLekO/fuEEEo2tQMrZJIkSZsQY6wBHgCuCCEUhRD2B44D7mih+D+Ar4cQeocQCoBJwMIYY/mm9mGFTJIk5bQkiYxMrTQJ6AYsAe4Gzo4xvhNCODCEUN2o3AXAGtJ9yZYCRwNf2NzG7UMmSZK0GTHG5cDnW5j/AulO/x+9Xkb6zspWsUImSZJyWiv7e22VbLKUJEnKMjNkkiQpp6U6Qf6o4x+hJElSjjNDJkmSclrSPmSSJEnKNCtkkiRJWWaTpSRJymkOeyFJkqSMM0MmSZJyWip2/PxRxz9CSZKkHGeGTJIk5bQk9iGTJElShpkhkyRJOc27LCVJkpRxZsgkSVJO8y5LSZIkZZwZMkmSlNNS3mUpSZKkTDNDJkmSclrSuywlSZKUaWbIJElSTvMuS0mSJGWcFTJJkqQss8myDfxu1x2yHUKHdNbbT2Y7hE7htzt6/WZa7AQdkrMtb8jAbIfQcZVmOwAfnSRJkqR2YIZMkiTlNAeGlSRJUsaZIZMkSTnNPmSSJEnKODNkkiQppzkwrCRJkjLODJkkScpp9iGTJElSxpkhkyRJOc1xyCRJkpRxZsgkSVJOsw+ZJEmSMs4MmSRJymlmyCRJkpRxVsgkSZKyzCZLSZKU02yylCRJUsaZIZMkSTnNDJkkSZIyzgyZJEnKaT46SZIkSRlnhkySJOU0+5BJkiQp48yQSZKknGaGTJIkSRlnhkySJOU0M2SSJEnKODNkkiQpp5khkyRJUsaZIZMkSTktmiGTJElSplkhkyRJyjKbLCVJUk7z4eKSJEnKODNkkiQppznshSRJkjLODJkkScppDnshSZKkjDNDJkmSclpn6ENmhWwrtddBlZz9o7kkEpEn7u3Pfb8d0mR5QWGKC37+IWN2qaGqMp9rzh3N4gVd+NQBKzjtonnkF0Tq6wK3XjOc6a/0AuDKP8yg34Ba8vLg7X/04IbLRpJKdfw3wZaY+3x3XryyhJiEHb9cxZ5nVjZZvnJhPs9cNIDaqjxSKfjMBcsYMX4VMx/qwRu39l1XbtmMQo7/6zxKdqpt70PISXsdvIKzL59HIg+euKeE+24c1GR5QWGKC35ZyphdV1FVkcc152zL4vld6Nmnnsm//YDtd1/F1PuLufGy4evWufL2WfQbUEdefuTt13pww+Thne463nt8FWddsYC8ROTxu4u574aBTZYXFKa48FdzG85rPlefPYLF87sA8JVzF3PkV5eRTAVuunQo/3qu17r1EonI/z4+k2VlBVx2yrYAfP9/5zBm91Uk6wIz3ujOr74/jGR95zrfAHt9Zgln/Pc7JPIiU/42nPvvGN1keX5Bku9d9gajd1jByhWF/HTyniwp605eXorzLn6T0WNXkJcXefrxbbj/9vS6RT3qOO+H0xmx3UqIgeuv2p333+7b0u7VAXSIJssQQmkIYXUIobrR9JuGZYNDCLeFEBaFEFaGEN4PIfw4hFDUsDyGEEZveg+5JZGInHPFHCZ/Y3vOOGJXxn9uGcNHr25SZuKXl1K9Io/TDtmdB28bxGk/mAdA1fJ8fvSt7Tn7qF257oJtufAXH6xb5+pzRzPp6F05c+Iu9O5Xz4FHL2/X48pVqSS8cHl/jrl1IV99fC6zH+nJ8lkFTcr868a+bHdUNcf/bR6H/7KMFy7vD8D2x1Xz5Yfn8eWH53HYdYvptU29lbEGiUTknCvnMvmUMZxx2E6M/9xyho/Z4Dr+Snn6Oj5oFx68dSCn/XABALVrA7f/fCi3XLVNs+1ePWlbJh25E2dO2Cl9HX+2ol2OJ1ckEpFzrprP5JO25fRDduCQz1cwfMyaJmUmnrCc6hV5nHrATjxwS3++eckiAIaPWcP44yo449AduORr23Lu1fNJJOK69T7/raXMm9WlybaeebAv3zpoB848bCyFXVMcdeKyzB9kjkkkImd/721+dP4+nH3CeA46fAHDRq5sUmbisfOoXlnA6ccfyl/vGcWp57wHwAGHLaKgIMU5Jx3Md75xIEd9fg4DBq0C4IzvvsO/pg3grK8ewrknH8S80h7tfmy5IsaQkSmXdIgKWYNjY4w9Gk3nhhD6Aa8A3YBxMcaewOFAH2C7bAb7SYzdvZpFc7pQNq8r9XUJnnu4mHGHN/3SGXd4BU/9pQSAFx7vxx77VQGRD94tYvmSQgDmzOxGl64pCgpTAKyqzgMgLz+SXxiJEQFL3uxK7xF19BpeT14hjP5sNaVPN/1gDEBddfrtVFudR/cB9c22M+uRHow+ZmWz+Z3V2D1qWFTalbK5XRqu476MO6Jp5nHcESt46s/FALzwWF/22D99Ha9dncc7/+hB3ZrmH6jrr2PS13HGjyS3jP3UKhaWdll3Xp99qC/jJq5oUmbcESuYen8/AF54tA97HLASiIybuIJnH+pLXW2CxfO6sLC0C2M/la4clAyuZZ/Dqnj87uIm2/rHM71IvwPSGbKSwXXtcJS5ZfudKlk4v4iyhUXU1yd4/qmhfOagxU3K7HvgYp5+bBgAL/59MLvvXQ5EiNC1Wz2JvBSFXZLU1yVYtSqf7kV17LLHMqY8nF6nvj5BTXXBhrtWB9KRKmQtOR9YCZwUYywFiDHOizF+J8b4ZlYj+wSKB9WxdNH6X6nlZYUUD2qadSkeuL5MKhmoWZlHr75NKwkHHFXB7LeLqKtdfxlc9cf3ueefr7O6OsGLj/fL4FFsPWrK8ihq9CVTNKiemsV5Tcrsfd5yZv6tJ7cfMJJHvzWYAy8rb7adDx7tyehjqjMe79aieFAdSxeu/4IpX1RI8cC6DcrUsnRh+gfE+us4udltX3XHLO55fXr6On60czXxND+vBZQManpeSxqVSSUDNVXp81rSwrrFDeue9eMF3HrlEGKq5f3m5UcO+2IF//x7zzY+otxX3H815Uu6rntdvqQrxf1Xb1BmDUsXp8ukkglWVRfQq3cdLz4zmDWr8/nTw0/xh78+zQN3bUt1VSGDhqxiRWUh3508nV//8XnO++F0unRt/kOvs0jFkJEpl3T0CtkE4IEYN/YR0nmNGLOK074/j19fMrLJ/EtO2YET9/kUBYWR3feryk5wW6HZj/Rk7H+t5OsvlvLZWxfx9AUDm3xxLX6jC/ndUhRvb3Nle7jk5DGcuPdu6et4f7OSn9S+E1ZQWZ7P7Le6b7TMt6+ex9uvFvH2a523We3j2H7nSlIpOPnYCZz2xUP5wgkfMmhIDYm8yOjtq3jsgRGcd8pBrFmdx/Ff/2DzG9RWqyNVyP4aQqhsNJ0OFAOL2moHIYQz2mpbn8SysgL6D1677nXJoFqWlRU2LbN4fZlEXqSoZ5Kqivx15S/9v1lc971tWTS3Kxuqq03wylN9mzWDdlZFg5LULFqfNagpy6doYNMszXv392T00ens16BPraF+bWB1xfos2myzY80sKyug/5D1mZuSwbUsW1ywQZlC+g9JV2LXX8dNs5MbU7c2wStT+zDu8MrNF+5Amp/XOsrLmp7X8kZlEnmRol7p81rewrrLygrYae8aPnNEFX+c9g4/vHEOu++/kot+PWddua99t4zexfX83+VDM3x0uWnZ0m6UDFjfT69kwBqWLe22QZmu9B+YLpPIS9G9Rx1VKwoYf8QC/jVtAMlkghUVXXj3rX6M3nEFy5Z0pXxpV2a8m87wvvT3wYzevmnTc2cSY2amXNKRKmSfjzH2aTTdAiwDBrfVDmKMN7fVtj6JGW/2YMjItQzcZi35BSkOPnYZ057q06TMtKf6MuGL6WazA49a3nAnZaCoZz1X/G4Gv//ZMN791/qmha7dk/Trv/6Lb59DKpn3QdMPlM5qwK5rqCwtoGpePslamP1oD0YeVtOkTI8h9cx/OX2+KmYXkKwNdOuXrrTFFHzweA/GfNZMTWMzphcxZNQaBg776DquYNrUDa7jqb2Z8KV0J/EDj65g+ssf9VdqWdfuSfoNWF/R2OfQFcz7oPmPjo5sxhvdGTpq7brzOv64CqZN6dWkzLQpvTj8+PRNOwd+tpLpL/UEAtOm9GL8cRUUFKYYOGwtQ0etZcbr3fn9T4dw0t47c8pnduaaSSOY/lJPrj1vBABHnrCMvcdXcc05I3Ouk3R7mfleb4YOq2Hg4FXk56c4aMICXn2h6Z2tr744kMOOTt9cdcAhi3jzXyVAYGlZN3bfK/1Z3aVrPTvsXMH80h5ULO/K0sXdGDo8/UNu973LmduJO/V3Bh192IungC+EEH7ckZotU8nAjT8awVW3v08iAVPu78+cWd05+bvzmfVWEdOe6ssT9/bnol9+wO/+Pp2VK/K55tvpexg+d8pihoxYy4nnLeTE8xYCcPHXxxICXH7LLAq6pAgBpk/ryaN3DsjmYeaMRD4c+KOlPHLaEGIysMOXqug3ppbXru9H/13XMOqwVez3g3KemzyAN/+QrlAc+tPFhIbvpoX/6EbRoHp6De+8/T9akkoGbrx0OFfdMSs9VMC9JcyZ2Y2Tz1/IrLe6M21qH564t4SLrv8Pv3v+bVZW5nHNuduuW/+PL71F955J8gsi4yZWcslJY6iqyOfy22ZTUBgJicj0l3vy6J/6Z/Eo218qGbhh8jZcfdeHJBKRKff2Y87Mbnz9gkXMnN6daVN788Q9xVz06zn8/sV3WVmZz9WT0pWrOTO78fzDfbj57++TTAZ+c8k2mx0y5LyfzmPx/EKu/9tMAF56rA93Xj9ok+t0NKlkgpt+vjM/uf5VEonI1EeGMfc/PTnp9BnMeq83r744iCkPD+OCH73BLfc/w8qqAq69dE8AHvnLSL47eTo33vksIcDUR4dR+kG6Av1/v9iZCy9/nfyCFGULunP9Vbtn8zCzKrWJH2IdRYi5lrP7GEIIpcC3YoxPbTC/H/Bv4AVgcoxxTghhKPA94A8xxjdDCBHYGfiw0ap1McYWew43lG/i8K5fa5sDURNnvf1OtkPoFH674w7ZDqHDi8nN34igTyZ/ePMhUNQ2nij9ZbN5MYPp0Ja+Z/d87OKM7OvfR1/dbF4mj21TOlKT5cMbjEP2YIxxObAfUAe8GkJYCTwNrABmN1r3HWB1o+nUdo5dkiR1Yh2iyTLGOHITyxYCp21iecfPg0qStBXrDF/VHSlDJkmStFXqEBkySZLUceXaIK6ZYIZMkiQpy8yQSZKknNYBBoTYLDNkkiRJWWaGTJIk5TTvspQkSVLGmSGTJEk5zQyZJEmSMs4MmSRJymmOQyZJkqSMM0MmSZJymuOQSZIkKePMkEmSpJzmXZaSJEnKOCtkkiRJWWaTpSRJymk2WUqSJCnjzJBJkqSc1glGvTBDJkmSlG1myCRJUk6zD5kkSZIyzgyZJEnKbZ2gE5kZMkmSpM0IIfQLITwYQqgJIcwJIZy4ibJ7hhCeDyFUhxAWhxC+s7ntmyGTJEk5LUf6kN0A1AIDgT2AR0MI02OM7zQuFEIoAZ4Avgv8GSgEttncxs2QSZIkbUIIoQj4InBpjLE6xvgi8Dfg5BaKnw88GWO8M8a4Nsa4Msb43ub2YYVMkiTltBgzM7XC9kB9jHFmo3nTgZ1bKPsZYHkI4eUQwpIQwsMhhOGb24EVMkmSpAYhhH+GEM7YYHYPoGqDeSuAni1sYhvgFOA7wHDgP8Ddm9uvfcgkSVJOa88+ZDHGvVuYXQ302mBeL2BlC2VXAw/GGP8BEEL4MVAeQugdY1yxsf2aIZMkSdq0mUB+CGFMo3m7A++0UPZNmg7UsUWNo1bIJElSboshM9OW7j7GGuAB4IoQQlEIYX/gOOCOFor/HvhCCGGPEEIBcCnw4qayY2CFTJIkaUtMAroBS0j3CTs7xvhOCOHAEEL1R4VijM8AFwOPNpQdDWx0zLKP2IdMkiRpM2KMy4HPtzD/BdKd/lthFDkAACAASURBVBvPuwm4qTXbt0ImSZJyWiuHqNgq2WQpSZKUZWbIJElSbjNDJkmSpEwzQyZJknJajjxcPKOskLWBuHZttkPokG4aMzrbIXQKTy78Z7ZD6PAmDtkj2yF0ePVz5mU7BOkTsUImSZJym33IJEmSlGkbzZCFEC77GNuLMcaffIJ4JEmSmujsfcgu/xjbi4AVMkmSpFbYVIVsVLtFIUmStDGdoA/ZRitkMcY57RmIJElSZ/Wx7rIMIXQBSoClMcbatg1JkiSpsY7fh6xVd1mGEPYMITwDrATmAgc0zB8QQng6hDAhAzFKkiR1aFtcIQsh7AG8AGwH3N54WYxxCdANOKVNo5MkSYoZmnJIazJkVwALgZ2BH9A8f/g0sE8bxSVJktRptKZCdiBwS4yxmpbrlXOBIW0SlSRJUifSmk79XYEVm1je6xPGIkmS1FyONS9mQmsyZB8Ae21i+aHAu58sHEmSpM6nNRWyu4CTN7iTMgKEEL4HHAnc0YaxSZIkQQyZmXJIa5osrwMOB54E3iddGftlCKE/MAiYCtzY5hFKkiR1cFucIWsYAPZw4AJgNbAG2B4oBy4CjokxpjIRpCRJ6rxizMyUS1o1Un+MsR74ZcMkSZKkNvCxHp0kSZLUbnIsm5UJrX10UtcQwkUhhFdCCIsbplca5nXLVJCSJEkd2RZnyBo67z9DeqT+KuDDhkU7AvsCXw8hHBJjXNrmUUqSpM4rx+6IzITWZMj+B9gJOB8YEGPcM8a4JzAA+B7pitn/tH2IkiRJHVtr+pAdC9wWY7y+8cyGuy9/GULYGfhCWwYnSZIU7EPWRCHw700s/2dDGUmSJLVCazJk/wD23MTyvYDXPlk4kiRJG+gEGbLWVMi+BzwdQngLuKlhTDJCCPnAOcB/AYe1fYiSJEkd20YrZCGEZ1qYvQy4HrgihPDRXZbbAr1IP3z851gpkyRJbakT3GW5qQzZtrScJJzb8G+/hn8rG6aChnUkSZLUChutkMUYR7ZjHJIkSZ2Wj06SJEm5rRN06m/Vo5MkSZLU9lqVIQshbAd8l/SjkvrSvEIXY4zbtVFskiRJZsgaCyHsSnpg2G+RHgB2W6AG6AqMBJKs7/AvSZKkLdSaJssrgFpgd9YPbfGdGOMQ4EygD+nxyCRJktpOzNCUQ1pTITsAuDnGOIP1hxEAYoy3AI8DP23b8CRJkjq+1lTIepIe/BXSmTKAokbLXyJdaZMkSWo7MWRmyiGtqZAtBgYBxBhXku4/tn2j5X2BvLYLTZIkqXNozV2WbwB7N3r9HPCdEMJrpCt25wLT2zA2SZIkQo7198qE1mTI7gJKQgjdGl5fCvQG/g48TbpT/8VtG54kSVLHt8UZshjjvcC9jV6/HkLYGfgC6SEvHo8xfrix9SVJkj6WTpAh+0SPTooxzgN+3UaxaDP2Hl/FWT9ZSF4i8vjd/bjvNwObLC8oTHHhr+cyZtfVVFXkc/VZI1g8vxCAr5y7mCNPWE4yFbhp8hD+9VwvAM7/xVz2nbCSyvJ8zjx0bLN9fvHMJZzxo0Ucv8vOVC3v+E/aautz3H9ILRf+ai59+tdDhMf+VMxfb+sPwMW/LWWb7dYCUNQrSU1VHpMOb/43UNrPvzuMV5/qRZ+Sem7++4xsh9Nhbe490Nll4nN4c9s8+ycLmPjV5Xx+zK4ADBhay/m/mEfv4npWVuZx7beHU76osB2OXpnUYR6dFEIoDSFM2GDe+BBCKoRQHUJYGUKYEUI4NYTQNYRQGUI4tIXt/DKE8Of2i3zLJBKRc65ewOSvjeL08WM55LhKho9Z06TMxBOWU12Zz6n778gDt5TwzckLARg+Zg3jj6vkjEPGcsmJozj3mgUkEumfG1Pu7cclXxvV4j77D6llz4NXsnh+QWYPLkdk4hwn6wM3XzGEM8bvwHeOGcOx3yhft82rzxrJpMPHMunwsbz0aB9eeqx3ux/z1uSIryznqjtNwmfSlrwHOrNMfEZsbptjdltFj97JJvs4/bKFPPXnvpw9YSx3/nIgp/6wLPMHr4zbaIUshPC7jzHd1p7Bb6GFMcYeQC/g+8AtpJ8ycC/w9cYFQwh5wAnAH9s7yM0Z+6lVLCwtpGxuF+rrEjz7UB/GTVzRpMy4iSuYen9fAF54pA97HFANRMZNXMGzD/WhrjbB4nldWFhayNhPrQLg7Vd7sLKi5czXmZcv5LYrhxA7QaoYMnOOly8pYPZb3QFYXZPHvNldKRlct8GeIwd9rpK//7VvOxzl1mvXz9TQs29y8wX1sW3Je6Azy8RnxKa2mUhETr90IbddObjJPkZsv4bpL/UAYPpLPfwbdRCbaoP6xsfYXgS++fFCyawYYwT+GkKoAHYiXel6MoQwKca4qqHYRNKV1MezFOZGFQ+qY+nC9Snp8kUF7LDnqiZlSgbVs3RhOpuVSgZqqvLo1S9JyeA63vtXUaN1CyketGGloKlxE1dQXlbAh+9222S5jiTT53jgNrVst8tq3v939ybzd9m3hoql+Sz8T5e2PiSpVbbkPdCZZeozYmPb/Nyp5bwypTfLlzRtpfjw3W7sf9QK/npbf/Y/agVFPVP07Fu/0R/XHUGnvssyxpj4GFPOjkMWQkiEEL5A+m7Qt2KMLwOLgP9qVOxk4K4YY302YswVXbql+Oq3l3D7/wzKdigdRtfuSS69tZTfXjaEVdVN3yaHfL6SZ//aJ0uRScpF/QbWceCxlTz0u5Jmy26+Ygi7jqvhhikz2HVcDUsXFpBK5tYgp2q9jludXm9ICKESSJF++PnJDY9/AriddLPln0IIvYDjgP03tqEQwhmZDnZjlpUV0H9I7brXJYPrKF/U9FdTeVk+/YfUUb6okERepKhXkqrleZQv2nDdWpaVbbxf2OARaxk0vJabnkqfpv6D67jhyZmcd/QYKpZ23P5kmTrHefmRS28t5ZkH+vLS400rXom8yP5Hr+DcI8dk8MikLbMl74HOLFOfES1tc/Quqxkyspbfv/wekP6h/PuX3uPU/Xdk+eICfvKtkUD6x94BR6+gpipn8yFtI8dG1c+EDtOpfxMWxhj7xBj7xRj3iDHe02jZHcAhIYQhwJeAD2KMr29sQzHGmzMd7MbMeKM7Q0fVMnDYWvILUow/rpJpU5p2Ap82pTeHH18BwIHHVDL9xR5AYNqU3ow/rpKCwhQDh61l6KhaZrzevYW9pJW+342v7LYzp+y7E6fsuxNLFxVwzsTtO3RlDDJ1jiPn/3we82Z15YGb+zfb554HrmTe7C7eIaWcsCXvgc4sE58RG9vma0/34oQ91n8Or12d4NT9dwSgV796QkMb3le/vYQp9/Zr1/OgzOgMGbKNijHOCSG8AJwEHEUOdub/SCoZuOGSoVx914ck8mDKPf2YM7MrX7+wjJnTuzFtSm+euLsfF/16Lr9/6T1WVuZx9dkjAJgzsyvPP9yHm5+dQTIZ+M3FQ0ml0r82fnDjHHYbV03vfvX86Z/vcsfPB/Lk3cXZPNSsycQ53nmfaiYcX8GH73blxqnpjOPvrxnMP55J3+5+8HE2V26pa84ewZuv9GDF8ny+ttdOnPy9Mo48cXm2w+pQNvYeUFqmPodbe853G1fNaT9cRIzw1qs9uOHioRk/dmVeiB3kFroQQilwNuknB3zkAOAPMcZtNrHeKcBPSD+nc0SMcdFm9tPshE0IX/o4IUs54cmFb2Q7hA5v4pA9sh2C9LE9FZuPBBVj5toQW/qeHfXLn2dkX//57veazcvksW1KR8uQPbbB65e2YJ2/ADcAT2+uMiZJkpQJHaZCFmMc+THXqwZ6tG00kiSpzXSMxrxN6gyd+iVJknJaqzNkIYSRwARgIHBnjLE0hFBIug9WWYyxdhOrS5IktUqnHhi2JSGEnwGzgJuBK0g/ggigK/AuMKlNo5MkSeoEtrhCFkI4E7iQdAf4I4B1dyHEGKuAvwHHtnWAkiSpk4sZmnJIazJkk4AHY4z/DbQ0eOqbwNg2iUqSJKkTaU2FbHtg6iaWLwWaP3RLkiTpkzBD1sQaoGgTy0cAlZ8sHEmSpM6nNRWy14AvtLQghNAVOJktG4hVkiRpi4WYmSmXtKZC9j/AuBDCHcBuDfMGhRAmAs8C2wDXtW14kiRJHd8Wj0MWY3wqhHA28CvgxIbZdzT8WwucHmN8pY3jkyRJnV12Hi/Zrlo1MGyM8eYQwt+A44EdSA99MQu4L8a4IAPxSZIkdXitHqk/xlgG/G8GYpEkSWoux/p7ZYLPspQkScqyLc6QhRCe2YJiMcZ42CeIR5IkqdNpTZPltjRPGuYDg0ln2sqBmjaKS5IkCci9ISoyoTV3WY5saX4IoQtwPnAqcHDbhCVJktR5fOI+ZDHGtTHGa4BXgV988pAkSZIa8dFJrfIiMLENtydJktQptHrYi00YBRS24fYkSZLsQ9ZYCGH4Rhb1AyYA55F+hJIkSZJaoTUZslI23uIagBmkK2WSJEltxwxZE1fQ/JREYDkwE3gqxphqq8AkSZI6i9YMe3F5BuOQJElqWSfIkG3RXZYhhB4hhA9CCP+d6YAkSZI6my3KkMUYq0MIxUB1huORJElqojPcZdmaccimAXtnKhBJkqTOqjUVsh8AXw4hnBpCCJkKSJIkqbPZZJNlw9hjS2OMq0k/FqkCuBW4NoTwAbBqg1VijPGwjEQqSZLUQW2uD9l/gJOAu4FtSd/nMLdh2cAMxiVJkpTWCfqQba5CFhomYowjMx6NJElSJ9SWDxeXJEnSx9CWDxeXJElqc51h2IstqZAdGEJozYj+t3+CeCRJkjqdLalondEwbU4g3e3OCpkkSWo7ZsgAuJn0oLCSJEnKgC2pkL0QY7wr45FIkiS1pBNkyLzLUpIkKcu8y1KSJOU077LUlknkZTuCDmnOj/bNdgidwpHD/RjItLmX7ZPtEDq8UXeXZTuEjmtmtgPoHDb5SRxjtElTkiRlVyfIkFnhkiRJyjLbKiRJUk7rDH3IzJBJkiRlmRUySZKU22KGplYIIfQLITwYQqgJIcwJIZy4mfKFIYT3Qgjzt2T7NllKkiRt3g1ALTAQ2AN4NIQwPcb4zkbKXwgsBXpuycbNkEmSpNyW5QxZCKEI+CJwaYyxOsb4IvA34OSNlB8FnARcs6X7sEImSZK0adsD9THGxqOyTQd23kj5/wUuBlZv6Q6skEmSJDUIIfwzhHDGBrN7AFUbzFtBC82RIYQvAHkxxgdbs1/7kEmSpJzWnsNexBj3bmF2NdBrg3m9gJWNZzQ0bV4LHN3a/VohkyRJ2rSZQH4IYUyMcVbDvN2BDTv0jwFGAi+EEAAKgd4hhDLgMzHG0o3twAqZJEnKbVkeGDbGWBNCeAC4IoTwLdJ3WR4H7LdB0beBYY1e7wf8BtiT9B2XG2UfMkmSpM2bBHQDlgB3A2fHGN8JIRwYQqgGiDHWxxjLPpqA5UCq4XVyUxs3QyZJknJbDjw6Kca4HPh8C/NfIN3pv6V1ngW22ZLtmyGTJEnKMjNkkiQpp/lwcUmSJGWcGTJJkpTbzJBJkiQp08yQSZKknGYfMkmSJGWcGTJJkpTbzJBJkiQp08yQSZKk3GaGTJIkSZlmhUySJCnLbLKUJEk5LWQ7gHZghkySJCnLzJBJkqTcZqd+SZIkZZoZMkmSlNN8dJIkSZIyzgyZJEnKbWbIJEmSlGlmyCRJUm4zQyZJkqRMM0MmSZJymndZSpIkKePMkEmSpNzWCTJkVsi2InuPX8FZP55PXh48fncx990wqMnygsIUF15fypjdVlNVkcfVZ49i8fwuAHzlnDKOPGEZySTcdNkw/vVcLwA+/80lHHVCOSHA43eV8OBtAwA46fyFHHXiMlYsS18iv//ZEP7xTO92PNrccsA2c7lk3IskQuTPM3bklul7tljuiJEf8OvDp/ClB7/I2+UD1s0fXLSSR46/hxv+9Wl+99Ye7RV2ztvr4BWcffk8EnnwxD0l3Hdj82v6gl+WMmbXVVRV5HHNOduyeH4XevapZ/JvP2D73Vcx9f5ibrxs+Lp1rrx9Fv0G1JGXH3n7tR7cMHk4qVRneDTxljlg2Fwu3r/hWn5vR259o+Vr+fBRH/DriVP40l++yDtLB7DrgMX8+KDngPSDnm/45948VbptO0a+9dhrn8Wc+e03SSQiTz46gvvvGttk+S67lXPGt99k1LZV/PSKT/PSc0PXLbvi2pfYYacK3n2rH5f/cL/2Dl1ZlPUmyxDCN0IIb4UQVoUQykIIN4UQ+oQQfhtCqG6YakMIdY1ePx5CGBlCiCGE/A2294cQwpUN//+oTPUG01cala1tmLc8hDA1hLBDNs7D5iQSkXOunMfkk0dz+iE7cshxFQwfs7pJmYlfXUb1inxOPWBnHrhlAN+8eAEAw8esZvxxFZxx6I5cctJozr1qLolEZMTY1Rx1QjnnHbMDZx2xI/tOWMGQkWvWbe/BWwYwaeKOTJq4Y6eujCVCisv2f4HTnziGY/78VT673Wy267O8WbmiglpO3uUt3lg8oNmyH3zmZV6YN7zZ/M4sfU3PZfIpYzjjsJ0Y/7nlza/pr5RTvSKP0w7ahQdvHchpP0xf07VrA7f/fCi3XLVNs+1ePWlbJh25E2dO2Ine/eo58LMV7XI8W4NESHHpAS9wxqPHcOy9X+Wzo2ezXd/m13L3glq+vutbTG90Lc9a3o/j//Il/uvPX+aMxz7L5Qc/R15ItWf4W4VEIjLpv6dz2UX7cdYpEzj4sPkMG1HVpMySJd34xTV78ezTza/fv9wzhuuu3qu9wt1qhJiZKZdktUIWQvge8DPgQqA38BlgBDAVOC/G2CPG2AO4Grj3o9cxxqNauas+jdbtEWO8t9Gyaxv2MRRYANz2SY8rE8buUcPC0i6Uze1CfV2CZx/qy7gjVjQpM+6ISqbe3w+AFx7tyx4HrAQi445YwbMP9aWuNsHieV1YWNqFsXvUMHz0Gt5/o4i1axKkkoE3p/Vg/6Mqs3B0uW23/kuYW9Wb+St7UZfK47EPRnPYiNJm5c7b6zVunf4papNNE8+HjfgP81f2ZHZFv3aKeOswdo8aFpV2XXdNP/dwX8Yd0fT6G3fECp76czEALzzWlz32rwIia1fn8c4/elC3pnnma1V1HgB5+ZBfGDtDS8cW221A82v50JGlzcp959Ovcesbn2Jto2t5TX0ByZj+yijMSxKjWceWbL/jchYuKKJsURH19Qmef2Ybxh2wqEmZJWVFlH7Yu8XM7fR/D2D1KhuvOqOsVchCCL2AHwPfjjE+EWOsizGWAl8GRgIntWc8McbVwH1ATrYnFQ+uY+miwnWvy8sKKBlc16RMyaD1ZVLJQE1VHr36JikZXMfSRQWN1i2keHAdpTO6sss+1fTsU0+Xrik+fWgV/Yes3+ax31jKTVPf5fzr5tCjd32GjzB3DSyqYVF10brXZTVFDCyqaVJmp+KlDO5RzXPzRjSZ3z2/jtN3f50b/v3pdol1a1I8qI6lCxtdl4sKKR5Yt0GZWpYubHRNr0xf05tz1R2zuOf16ayuTvDio33bNvCt2ICiGsoaXcuLq1u4lkuWMqhHNc/NHbHh6uw2YDEPf/keHvryvfz4+YPWVdC0XnHJGsqXdFv3unxpN4pL1mxiDSktm++m/YCuwAONZ8YYq4HHgMPbM5gQQhFwAjC7PfebTfNmd+O+GwdyzV2zuOpPs/nwnW6kGr7rHrm9P6fuvzOTjtiR5UvyOePSBdkNNocFIj/4zMv8bFrz/h7n7vUP/vD2bqyqL2hhTWXKJSeP4cS9d6OgMLL7/iuzHc5WIxD5/n4v87NXWu679OaSgRx731f58l++xOl7vk5hXuf9oaZ2FjM05ZBs5kVLgPIYY0vv6EXAljail4fQJO3bHbh2M2XGxRjfa/j/BSGEc4FewBzguI3tKIRwxhbG1OaWLSqg/+Dada9LBtVRvqjpl3x5WbpM+aJCEnmRol5JqiryKF9UQP9G2bSSQbUsa1j3yXtKePKeEgBO/f6CdRm2yvL12378rhKu+MMHGTu2XLe4pojBPdZnEQYV1bC4Zn2WoaigljH9lnP7MX8DoKTbKm484nEmTTmK3QYsZuKoD7lwn2n0LFxLKgbWJvO4891d2/04cs2ysoImGdmSwbUsW1ywQZlC+g+ppbys4Zrumb6mt0Td2gSvTO3DuMMref2FXm0a+9ZqSU0RgxpdywN7bHAtF9Yypu9ybv9co2v5yMeZ9MRRvLN0fX+yDyv7sqounzH9ljeZL1hW3pWSAev7Qpb0X82y8q5ZjEhbi2xmyMqBkg075TcY3LB8S5TEGPt8NAF3ba5Mo8oYwHUN640EVgNjW1gfgBjjzVsYU5ubMb2IoaPWMnDYWvILUow/roJpU5t2tJ82tQ+HH5/uoHvgZyuY/lJPIDBtam/GH1dBQWGKgcPWMnTUWma8kf4Q7l2c/kLsP6SW/Y+q5O9/TTfv9Buw/otyvyMrKZ3Rjc7qraUDGNGrkqE9qyhIJDl6u9k8M3fkuuXVdV0Yd8epHHbPSRx2z0lMXzKQSVOO4u3yAZz08BfWzb/97d24+Y09rYw1mDG9iCGj1qy7pg8+toJpU/s0KTNtam8mfGkZAAceXcH0l3uRvsevZV27J9ddu4m8yD6HrmDeB34ZfuStJQMY0bvptfz30pHrllfXdmG/P57KhDtPYsKdDddyQ2VsaM+qdZ34h/RYybZ9KlmwsmeWjiR3zXy/L0O2qWbgoBry81McdOh8pr00ONthbfU6Q6f+bGbIXgHWAv9Fuu8WACGEHsBRwMXtGUyMcW4I4TvAH0MIjzT0KcsZqWTghkuHcfWds0kkIlPuLWbOzG58/YKFzJzenWlT+/DEPcVc9KtSfv/iO6yszOPqSaMAmDOzG88/3Iebn3mXZDLwm8nD1nUmvezmD+nZN0myPvCbS4ZRU5W+JL55yQK223kVMcLieV349Q867x2CyZjgJy8fyG1HPUIiRP4yYwdmV/Tj23u9xttL+/P3uaOyHeJWKZUM3HjpcK66YxaJvMiUe0uYM7MbJ5+/kFlvNVzT95Zw0fX/4XfPv83KyjyuOXf9MAt/fOktuvdMkl8QGTexkktOGkNVRT6X3zabgsJISESmv9yTR//UP4tHmVuSMcGVLx7IrZ9NX8sPfHQt791wLc/Z+LW816BFnP6p16lLJYgxcMULB1G5pvP+UNuYVDLBTdfvzpXXvUQiAVMeG8Hc0l6cdNq7zHq/L6++PJgxO1Rw6U+m0aNnHfvut4iTTn2Ps78xAYBr//d5hg1fSddu9dx+/+Ncf+2e/PsfA7N8VGoPIcbsVRFDCBcB3wNOAZ4mfafjjcAg0s2KaxvKXQ6MjjGe1GjdkcB/gILGzZ4hhD8A82OMkzdWpqWyjeb9E7gjxvirjcTc7IRNyPvKlh+0tticH+2b7RA6hZE/eS3bIXR4cy7eJ9shdHij7i7Ldggd1pMzN+wFBDGDt9m29D37qTN/kZF9vf5/5zebl8lj25Ss3iITY7yWdCbsOqAKeBWYBxz2UWWsjVRuMA5Z87/Aev8DXBRC6NKG+5ckSdqorA92EmO8jc2M/RVjvLyFeaW00JkkxviNzZVpqWyjefcC9zYvLUmSsiLH+ntlgoPISJIkZVnWM2SSJEmbkmt3RGaCGTJJkqQsM0MmSZJymxkySZIkZZoZMkmSlNNCFsdMbS9myCRJkrLMDJkkScptHT9BZoZMkiQp26yQSZIkZZlNlpIkKac5MKwkSZIyzgyZJEnKbWbIJEmSlGlmyCRJUk6zD5kkSZIyzgyZJEnKbWbIJEmSlGlmyCRJUk6zD5kkSZIyzgyZJEnKbWbIJEmSlGlmyCRJUk6zD5kkSZIyzgyZJEn/3959x9lRlv0f/3w3vYcU0iQEQgIhSJOfEMpDqBHLk4AFqdKbKAqGBxQEFCMgoKgIojwEQRFUikhLQCIQCT4IBgiQEGBDSLLpZVM3u3v9/pgJnD05m2zKyZnd/b5fr3ltzsw9M/fcmZ1z7XXfM2PZFk0/ReYMmZmZmVmJOSAzMzMzKzF3WZqZmVmmeVC/mZmZmRWdM2RmZmaWbc6QmZmZmVmxOUNmZmZmmabaUteg+JwhMzMzMysxZ8jMzMws2zyGzMzMzMyKzRkyMzMzyzQ/h8zMzMzMis4ZMjMzM8u2ZvBycQdkllkD755d6io0C9XV1aWuQpPX/wf/LHUVmrxvTZ9S6io0WU8NLHUNmgcHZGZmZpZpHkNmZmZmZkXnDJmZmZllmzNkZmZmZlZsDsjMzMzMSsxdlmZmZpZpHtRvZmZmZkXnDJmZmZllWzN4MKwzZGZmZmYl5gyZmZmZZZrHkJmZmZlZ0TlDZmZmZtnmDJmZmZmZFZszZGZmZpZpHkNmZmZmZkXnDJmZmZllW23TT5E5Q2ZmZmZWYs6QmZmZWbY1/QSZM2RmZmZmpeYMmZmZmWWa77I0MzMzs6JzQGZmZmZWYu6yNDMzs2yLpt9n6QyZmZmZWYk5Q2ZmZmaZ5kH9ZmZmZoakbpIekrRC0gxJJ9ZTbrSkNyRVSnpf0uiGbN8ZMjMzM8u2bGTIbgWqgF7A3sBjkiZHxJS8cgJOBV4DBgLjJM2MiD9uaOPOkJmZmZltgKQOwBeBKyNieUS8APwVOCW/bETcEBGvRER1REwFHgEO2tg+HJCZmZlZpimiKNMmGAxUR8S0nHmTgaEbrLck4BAgP4u2HgdkZmZmZilJL0s6J292R2BZ3rylQKeNbO5qkljrro3t12PIzMzMLNtqt92uImK/ArOXA53z5nUGKuvbjqQLScaSHRIRaza2X2fIzMzMzDZsGtBS0qCceXtRT1ekpDOAy4AjIuLDhuzAGTIzMzPLtE0c77XVRcQKSQ8CP5B0FsldliOBA/PLSjoJGAMcFhHvNXQfzpCZmZmZbdwFQDtgHnAfcH5ETJF0iKTlOeWuBboD/ydpeTrdZ6o6NgAAGtlJREFUvrGNO0NmZmZm2ZaB55BFxCJgVIH5z5MM+l/3eafN2b4zZGZmZmYl5gyZmZmZZVuJx5BtC86QmZmZmZWYM2RmZmaWaWr6CTJnyMzMzMxKzRmyRmS/4Us575oPadECnrivOw/c2rvO8lataxn9s3IG7bmKZYtbMOb8nZj7YRsAjv96BZ85YSE1NXDb93fg3/9IHjh87FlzOeaEhUTA+2+346ZLdmTtmjIuubmcPQ9YzorKFgDc+O0dee/N9tv2gDPqU/vP5ZyLXqesDMb9rT9/undwneVD91rAOd98g50GLuP6q/dj4oS+JappNu03fBnn/XA2LcqCJ+7rxgO/7FVneavWtYz++QcM+uQqli1uyZjzdmTuh60BOP7CuXzmhEXU1Irbruj70Xl88c0fsP+RlSxZ0JJzD991vX1+8dx5nHPVHL68x1CWLWp+l72t3eY9+1Yx+pYP6NqzGgIev7c7D9/ZsxSHlknv/6Mjz17bm6iBPb6yhP3PW1Bn+bLZrXhydD9WLysjasUho+ey8/Dl1FSJ8Vf2Ye7r7VAZHHbFHHY4YGWJjsK2tUxlyCSVS1qVPrOjQtJYSR3TZWMlVaXLFkkaL2m3nHVPk/RCzuehkpZKGpy3j2ckXZf+OyStSLc5S9LNklpsq+PdFGVlwdevnckVp+zC2YcN4bCRi+k/aFWdMiO+upDlS1ty+sFDefA323Pmd2cB0H/QKoaPXMw5hw/heyfvwoU/+oCysqB77ypGnTGfCz+3G+ceuTstWgTD/3vxR9v7zY/6ccGIIVwwYoiDsVRZWXD+xa9x1XeGcf7Jh/NfR85ihwF1X282f257fjpmHyY83a9EtcyusrLg62NmccVJO3H28F05bOQS+g9aXafMiBMWsXxJS04/aAgP/qYHZ14xG4D+g1YzfOQSzjlsV7534k5c+ONZlJUl/Rjj7u/G904qfKd5z75V7HtoJXM/bFXcg8uoYrR5TbW44wd9OWf4blz0+UF84bQF622zuaqtgWeu7sNxd87gtCffZerfurDwnTZ1yky6tQeDP7uUUx99j8//7EOeuaoPAK/dvx0AX3v8Xb50dzkTftyb2IavDMq0iOJMGZKpgCz1hYjoSPIU3H2Ay3OW3ZAu6wfMAu6sbyMRMQW4Ebgzfds6ks5M1706p+he6TYPBY4Hzth6h7L17Lr3CmaXt6HigzZUry1jwiPbMezopXXKDDt6CeP/1A2A5x/bjr0PrgSCYUcvZcIj27G2qoy5M9swu7wNu+69AoAWLYM2bWspaxG0aVfLwrnN80uroQYPWczsDztQMbsD1dVlPPd0Pw44uKJOmXkV7Sl/twtRqxLVMrt23Wcls8tb55zHXRk2Iu88HrGU8X9Kvpie/1tX9j54ORAMG7GUCY90zTmPW7PrPkn24I2XOlK5uHDm69yrZ3PntX2zdu3dZorR5ovmtWL668kfaatWtGDm9Lb06LN2Wx9aJlVMbkfXHavo2n8tLVoHu35uKdOfrvv+aQmqlid/+6+pLKPD9tUALJzehv4HJNfm9t1raNu5horX223bA7CSyWJABkBEVABPkQRm+ctWAQ8UWpbnOpI3sV8gqRdwPXBGRKz3p1xETAcmNmCbJdG9z1rmz2n90ecFFa3WuwD26P1xmdoasWJZCzpvV0OPPmuZP6dVzrqt6d5nLQsrWvPnX/finpfe4L5XXmdFZQteee7jd6eedulsbhv/Jude9SGtWvvPNIDuPVezYN7HF8gF89vRvaczAw3Vvfda5s/OOY/nFDqPq5k/OzlfPzqPu6XncZ11W9O994aDgGEjlrKgohXvvdl8v9SK3ea9PlHFwD1W8fYrzqIDLJ/bik457dup91qWz637x8Kwb87nrUe68OuDBvPgWTtyxFVzANh+yGrefaYTtdWwdGYr5r7Rjso5za+LvRDVFmfKkswGZJI+ARwDTC+wrANwQqFluSJiLXA68EPgXuDeiPhnPfvbDThkY9tsSjp2qWbY0Uv42rChnPipT9K2XS2HH7cQgLuu68dZh+7ONz+3G526VvOVC+aWuLZmm6ZNu1q++o15/O4nvTde2DZL2/Y1XPnbcm7/fl9WLs/kaI9MevvRLgw9bgnnTpzGcb+dweOX9CNqYY8vLaZj72ruPXZnnr22N333XUk2B9FYMWQxIHtYUiUwk+R9UVflLPuOpCVAJXAwcMrGNhYRr5J0bQ4BvlugyCuSVgBvAROAX9W3LUnnNPAYtrqFc1rRs0/VR5979F7Lgjl1uxcXVHxcpqxF0KFzDcsWt2DBnFb0zPmLrUfvKhbOacU+B1dSMbMNSxe1oqZaTHyiK7t/KkmXL5rXChBrq8oY90D3j7o4m7uF89vSY/uPx+716LmKhfPblrBGjcvCilb07JtzHvcpdB63pGff5Hz96DxelJ7HddatYmFF/V3sfXZcQ+/+Vdz29FTufulNevZZy61PTWO7ns2ra61Ybd6iZXDlb8v5+4PbMfGJrtvgSBqHjr3WUpnTvpUVrejYq7pOmTf+1JXBn026jfvuu4qaqjJWLW5BWUs47IoKTn30PUb9eiarl7Wg24AqDI8hK5FREdEJGA7sBvTIWXZjRHQFBgCrgPVvpypsClAeEYVuV9mX5B1UxwP7Ax3q20hE3NHA/W11Uyd3oN9Oa+i1wxpatqpl+MjFTBrfpU6ZSeO7ctSXFwFwyOcWM3liJ0BMGt+F4SMX06p1Lb12WEO/ndYw9T8dmDe7NUP2WUGbtrVAsPfBlXwwPQkuum2/7ksrOHDEEsqnNt8un1zT3u5Kvx1W0KvPClq2rOW/jpzFSxOdgWmoqf9pT7+dqnLO4yVMGpd3Ho/rwlFfTm4uOeTzS5j8QkdATBrXheEjl+Scx1VMfbX+brLyt9tx/J5D+dr+u/O1/Xdn/pxWfH3EYBbPb17jJIvT5sHFN81k5jttefAO312Zq/eeq1gyozVLZ7aipkpMfawLA4+orFOmU9+1fPBi8urDhdNbU71GtOtWw9pVYu3KZOxp+QsdKGsZdB+0Zpsfg5VGZjunI+IfksaSDMwflbfsA0kXAXdL+ls6pmxL9hXAA5JGAt8HvrUl2yuG2hpx65U7MOb30ykrC8bd350Z09px6ndmM21yeyaN78qTf+zOpbeUc9cLU6hc0oIxFyR3nc2Y1o7nHu3KHX9/k5oa8csrdqC2Vkx9tQPPP96VW598i5pqMX1Ke574fRL//s8v3qdL92oEvPtmO35+Wf8SHn121NaUcdvNe/LDm1+krCwY/1h/Pni/Myef+RbvvN2Vlyb2YdBui7lizL/o2Gktnz6ogpPOfJsLTjm81FXPhNoacev3+jHmD+9R1gLG/bEbM6a15dTRFUyb3I5J47rw5H3duPTnH3DXxLeS8/j8HQGYMa1tch5PmJqcx9/tR21648Rlv5rBnsOW06VbNfe+/Cb33NSLp+7rXspDzYxitPnQTy/nyC8v5r032/Kr8VMBuOvHffi/v3feUFWahbKWcPhVc/jL6TtSWyP2+PJiegxew8Sf9aTXHqvZ5chKhl8+l3Hf68srd3UHBZ+5fhYSrFzYkr+cviMqSzJtn71xVqkPJzuylcwqCkWGUnaSyoGzIuLp9HNPoBw4EPg28GFEXJFT/mXgnoi4RdJpwNnAEbnbjIjV6bKzIuLgvP0FMCgd0I+kTwKTgIHpTQWF6rhegx3Z4vjNOVzbiJYDdih1FZqF6vfKS10Fsy12yfQppa5Ck3XMwLfWmxcRRbuNvOD37LAfFmVfT7945XrzinlsG5LFLsuPRMR84HckWatCfgJcKmndQ14OJOnK/GiS1OAsYES8DjwHjN7sSpuZmdlWpYiiTFmSqS7LiBhQYN75Gyh/P3B/+nFsOhVScFmhKDgijtlYPc3MzMy2pkwFZGZmZmbryVg2qxgy3WVpZmZm1hw4Q2ZmZmbZlrGn6heDM2RmZmZmJeYMmZmZmWVa1u6ILAZnyMzMzMxKzAGZmZmZWYm5y9LMzMyyzV2WZmZmZlZszpCZmZlZtjlDZmZmZmbF5gyZmZmZZZsfDGtmZmZmxeYMmZmZmWWaHwxrZmZmZkXnDJmZmZllmzNkZmZmZlZszpCZmZlZtjlDZmZmZmbF5gyZmZmZZZszZGZmZmZWbM6QmZmZWbb5Sf1mZmZmVmwOyMzMzMxKzF2WZmZmlml+dZKZmZmZFZ0zZGZmZpZtzpCZmZmZWbE5Q2ZmZmbZVusMmZmZmZkVmTNkZmZmlm0eQ2ZmZmZmxeYMmZmZmWWbM2RmZmZmVmzOkJmZmVm2OUNmZmZmZsXmDNlW8HTN/aWuQtP0bqkrYGaNxdMDS10DKyo/h8zMzMzMis0ZMjMzM8u2qC11DYrOGTIzMzOzEnNAZmZmZlZi7rLcRBGhUtdhc0h6OSL2K3U9mjq3c/G5jYvPbVx8buNN5MdeWBNyR6kr0Ey4nYvPbVx8buPicxtbHYpmEHWamZlZ4yBpvcDkMztcVJR9PTnzlvXmlaonzBkyMzMzsxLzGDIzMzPLtmbQm+cMWRMg6URJL0taLmmOpCckHSzpaklr0/nrpiU564WkFen8WZJultSilMeSRZLKJa3Ka8dfpsv6SLozbfdKSW9LukZSh3R5SNqltEfQOKTtfGTevOGSatM2r5Q0VdLpktpKWiLp8ALb+amkP2+7mmeHpNMkvS5ppaQKSbdJ6irp9pxztyrvuvCEpAHpudoyb3tjJV2b/ntdmeV50/E5ZavSeYskjZe0WynaodjyrgkV6bF3TJdtsB3S/6MXcj4PlbRU0uC8fTwj6br0375WNwMOyBo5SRcDPwPGAL2A/sCvgJFpkfsjomPO1DVvE3tFREfgCOBE4OxtVPXG5gt57XihpG7Ai0A7YFhEdAKOAroCfpHL1jM7PUc7A/8D/AbYGbgfODW3YPoldQJw97auZKlJugS4HhgNdAEOAHYExgPfXHfuklwrcq8Lx2zirrrm/S7kvjvuhnQf/YBZwJ1belwZ9oX0WPcG9gEuz1nW4HaIiCnAjcCdkgQg6cx03atziq67Vh8KHA+csfUOpRGIKM6UIQ7IGjFJXYAfAF+PiAcjYkVErI2IRyNi9KZsKyLeBp4H9ihGXZuoi4FK4OSIKAeIiJkRcVFEvFbSmjVBkXgYWAzsThJ0fVFS+5xiI0iua0+UoIolI6kzcA3wjYh4Mr0OlANfAQYAJ2/L+kTEKuABkmClSYuICuApChzrJrTDdUAn4AJJvUgC6zMiYnWBbU4HJjZgm9bIOCBr3IYBbYGHtnRDknYHDgFe3dJtNSNHAg9GNIN3emSApDJJx5JkIF+PiH8Cc4DjcoqdAvwhIqpLUccSOpDkWvBg7syIWA48TpK53WbSLvsTgOnbcr+lIOkTwDEUONaGtkNErAVOB34I3Avcm57fhfa3G8m1usm3bR3OkFnGdQcWbOTL5yvpWJt107N5y1+RtBh4FPgtcFexKtvIPZzXjmeTtP+cUlesGeibjn1cAFwFnBIRU9NlvyPttkyzRCNpht2VQA/qvxbMSZc3xILc85xkGMMGy0gakrPsO+l6lcDBJAFyU/WwpEpgJjCP5NxcZ5PbISJeJenaHAJ8t0CRVyStAN4CJpAMTbEmxAFZ47YQ6JE/EDfPAxHRNWc6LG/5vhGxXUQMjIgrnO2p16i8dvwNSfv3KXXFmoHZaZt3i4i9I+KPOcvuAQ6T1Bf4EvBu+sXW3Cyg/mtBn3R5Q/TIPc+BP2ysTES8lbPsxnS9AcAqYNdNOIbGZlQ6bnQ4sBt1g97NbYcpQHlErCywbF+gI8n4sf2BDptX7UaqtrY4U4Y4IGvcXgTWAKNKXZFm6mngWEn+PSqRiJhBMvbxZJIsRHPMjsHH14Lc7lvSO/+OAZ7ZlpWJiA+Ai4BbJLXblvve1iLiH8BYkoH5+cu2ajuk4ygfIPn//v6Wbs+yxV8kjVhELCX5pbxV0ihJ7SW1knSMpBtKXb9m4GaSO//ulrQjgKR+6S3pe+aUa50+pmHd5NvV69cqt61o2LMS7wYuBA4Cfl/U2mVUei24BviFpM+k14EBJAPKPyTJJG7rOo0HZgPnbOt9l8DPgKMk7ZW/oJ52UN41oe0m7u864GxJvTe/yo2Mx5BZ1kXETSR3+10BzCcZz3Ah8HBa5PgCzw3avkTVbcwezWvDhyJiEclg6rXAS+l4kmeApdQdcDuFpNti3XT6Nq57Y/I4ddvq6gas8xegG/BMRDTbMX0RcQPJ2KMbgWXASyTXgyMiYs1W3NWSvN+FizdQ9ifApZLabMX9Z05EzCcZz1hf1iq/HQ6k7nm+aiNDT/L39zrwHMkjTqyJ8LsszczMLDNU6F2W259XlH09Oe/29eb5XZZmZmZmzZQDMjMzM7MS88vFzczMLNtqm/7wKmfIzMzMzErMGTIzMzPLtObwzHJnyMzMzMxKzAGZmW0ySQMkhaSrNzQvSySNLXQ7fT1lyyVN2IJ9TZBUvrnrb2TbIWlsMbZtllm1UZwpQxyQmTUSkoanX8a503JJ/5Z0UWN+A0AazF0tae9S18XMrBQ8hsys8bmP5In2AvoCp5G8umUopX1NzQygHVC9GesOAK4CyoH/bL0qmVmT0AweYu+AzKzxeSUi7l33QdJtwFvAWZKujIi5hVaS1CkiKotVqUhe+7G6WNs3M2vK3GVp1shFxDLgRZKM2c7w8RgoSftIekrSUuC1detIGiTpHklzJFWl5X8iqUP+9iUdLGmipFWS5kr6JdCxQLl6x5BJ+mJanyWSVkqaKunnklpLOg14Ni16V0537ISc9SXp/LR7dmXaVfuspMMK7Ktteiyz0zr/S9LRm9aq65N0tKT7Jb2XbneJpHGSDt3AOjtLekTSUknLJD0kaecC5Rp8fGbNUm1tcaYMcYbMrJGTJGCX9OOCnEX9gb8DfyJ5AXfHtPyn0vlLgF8Ds4C9gG8CB0k6NCLWpmX3B54GKoHr03W+SvIi5YbW70ckL71+E/gpMAcYCHyR5GXMzwFj0jJ3AM+nq+Zm+u4BTgD+DNwFtAFOAsZLOi4i/ppT9j5gFPAo8FS6rweB9xta53qcRvIS898BHwL9gLOAZyQdFhHP55XvAEwgecn35cAg4ALgAEn7RETFZh6fmTVBDsjMGp/2knqQZMT6AN8gCagmRcQ7OeV2As6OiN/mrf+/JEHR/8vtwpT0DEngchIwNp39U5JM+kERMS0t9yvghYZUVNKnSQKtZ4HPRsTqnGWXAUTEEknj03Iv5nbHpuWOTet0bkTckTP/FmAScIukRyMi0kzYKODuiDgtp+xzwEMNqfMGnB0RK/LqdjswhSTgyg/IegC3RMS38urxIHA1cN6mHt8W1t+s8WoGp7+7LM0an2uA+cA8YDJwBvBXkkAk1yKSbMtHJH0S2BP4A9BGUo91E0mQtQI4Oi27PTAMeGRdMAYQEVUkgVpDnJT+vDw3GEu3Ew0MMk4mydA9nFffriRZsAEk2Sf4uA1+krevh4GpDaxzQbnBmKSOkroDNSQZsP3rWe26vG08lNYj9/9qU47PzJooZ8jMGp87SLohgySAmhYRiwqUezciavLmDUl/XpNOhfRKf64b6/R2gTJvNrCug9J6Tm5g+UKGAJ2o24WZrxcwjaTOtem/870F7Lq5lZA0EPgRMIIkWMpVKLBcktctmVuPUZI6pEHephyfWbMUGRvvVQwOyMwan3ci4ukGlFtZYJ7SnzcBT9az3uLNqlX9gsIBS0OJJCN44gbKvLEF2994BaSOJGPdOpA8YuR1kqxWLUl35eFbsnlKfHxmVnoOyMyal3VjzGoaENStGwS/W4Fluzdwf9OAY0jGuP1rA+U2FLC9AwwmGSO3fCP7e49kKMZgkrFduYasX7zBjiB55tsZEZHfDXxtPet0ldS7QJZsCDAvpwt0U47PrHnyGDIza2JeJcm2nFfP4xdaSuoGkD7PbBIwUtLgnDKtgW83cH9/SH+OSdfL39+6jN26QKRbgW38juRa9eNCO5DUK+fjI+nP0XllRrEF3ZUkY8Xg4wzjuu0eTf3jxwAuyyt/bFqPh3Nmb8rxmVkT5QyZWTOS3ol4CsljL16T9L8kmaT2JI/OOI6kC25susrFJI9umCjpVj5+7EWDrh0R8S9J1wP/A7wi6X6gguQO0C8Bn063+SZJF+AFklam8+ZFxN8j4s+S7gIulLQv8DeSx3t8guSmg11Ix7tFxFOSHgW+lgaWT5I89uJckkB0j01utMQLab1vkjSA5LEXewOnkHRffrLAOguA4yT1JWnDdY+9mEtyl+W6Nmrw8ZlZ0+WAzKyZiYj/SNqHJPD6b5LHL1SSvLZoLPBMTtkXJR1FcrfgZcBSkmdl3UYSiDRkf5dJmgxcCFxKkg2aSfL6p5VpmVWSvgpcSzJGqw3wD5LAkYg4Q9KzJK+GuhxoTRIgvZJ+znV8up2TgKPSeh5HMkZrswKy9NEcI4AbSB4z0hL4N/BZ4EwKB2QrSMaW/ZSk/UQSIF4SEXPytr8px2fW/GTsReDFID/axszMzLJC0nqBydFtTy7Kvsatvne9eRGhAkWLzhkyMzMzy7Zo+o+98KB+MzMzsxJzhszMzMwyLZrBGDJnyMzMzMxKzBkyMzMzyzaPITMzMzOzYnNAZmZmZpkWtVGUaVNI6ibpIUkrJM2QVPD9s0pcL2lhOl2f81aSernL0szMzGzjbgWqgF4kb+p4TNLkiMh/b+45wCiSd/gGMJ7k3cC3b2jjfjCsmZmZZUahB8NuS4UeDCupA7AY2CMipqXz7gFmRUT+O2v/CYyNiDvSz2cCZ0fEARvar7sszczMzDZsMFC9LhhLTQaGFig7NF22sXJ1OCAzMzMzS0l6WdI5ebM7Asvy5i0FOhXYRMd0WW65jhsbR+YxZGZmZmapiNivwOzlQOe8eZ2BygaU7Qwsj42MEXOGzMzMzGzDpgEtJQ3KmbcXkD+gn3TeXg0oV4cH9ZuZmZlthKQ/ktw1eRbJXZaPAwfm32Up6TzgIuBIPr7L8hcRscG7LJ0hMzMzM9u4C4B2wDzgPuD8iJgi6RBJy3PK/Rp4FHgdeAN4LJ23Qc6QmZmZmZWYM2RmZmZmJeaAzMzMzKzEHJCZmZmZlZgDMjMzM7MSc0BmZmZmVmIOyMzMzMxKzAGZmZmZWYk5IDMzMzMrMQdkZmZmZiX2/wGHVTkZicgtMwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wphgj6PN7NZd"
      },
      "source": [
        "------\n",
        "> * problem 3.2 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "JPTh6jGf_vb5",
        "outputId": "2e59fbcb-0cc9-4840-9f61-6d8f729e2019"
      },
      "source": [
        "tns = LE32.inverse_transform(np.arange(0,6))\n",
        "clf_reports(X32,Y32,tns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.86      0.86      0.86      3640\n",
            "        DSCT       0.68      0.46      0.55       604\n",
            "         ECL       0.86      0.87      0.87     15802\n",
            "         LPV       0.99      1.00      0.99     22288\n",
            "       OTHER       0.50      0.08      0.13        26\n",
            "       RRLYR       0.90      0.89      0.89     14865\n",
            "\n",
            "    accuracy                           0.92     57225\n",
            "   macro avg       0.80      0.69      0.72     57225\n",
            "weighted avg       0.92      0.92      0.92     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4fApgUX67Qn8"
      },
      "source": [
        "------\n",
        "> * problem 4.1 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "PKMDHENA_6EW",
        "outputId": "9c81a11b-2204-4001-cbc7-580c42f933c3"
      },
      "source": [
        "tns = LE41.inverse_transform(np.arange(0,4))\n",
        "clf_reports(X41,Y41,tns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.86      0.85      0.86      3685\n",
            "         ECL       0.87      0.88      0.88     15729\n",
            "         LPV       0.99      1.00      0.99     22284\n",
            "       RRLYR       0.90      0.89      0.90     14937\n",
            "\n",
            "    accuracy                           0.93     56635\n",
            "   macro avg       0.91      0.90      0.90     56635\n",
            "weighted avg       0.93      0.93      0.93     56635\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFQ3Fpru7Q63"
      },
      "source": [
        "------\n",
        "> * problem 4.2 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "co_2jYsfAG2N",
        "outputId": "aab91679-33f4-4852-ed63-199e3059ce76"
      },
      "source": [
        "tns = LE42.inverse_transform(np.arange(0,5))\n",
        "clf_reports(X42,Y42,tns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.86      0.86      0.86      3687\n",
            "        DSCT       0.63      0.43      0.51       523\n",
            "         ECL       0.86      0.87      0.87     15469\n",
            "         LPV       0.99      1.00      0.99     22481\n",
            "       RRLYR       0.90      0.89      0.89     15033\n",
            "\n",
            "    accuracy                           0.92     57193\n",
            "   macro avg       0.85      0.81      0.82     57193\n",
            "weighted avg       0.92      0.92      0.92     57193\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lj5JVJM4Yk7D"
      },
      "source": [
        "-----\n",
        "# Solving Problem 3.1 :\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0C2RDAcZQiA"
      },
      "source": [
        "# pre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJFJmutMZPxa"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
        "\n",
        "tn = LE31.inverse_transform(np.arange(0,5))\n",
        "\n",
        "def model_reports(clf):\n",
        "\n",
        "  x_train, x_test, y_train, y_test= train_test_split(X31, Y31, test_size=0.2, random_state=42)\n",
        "  clf.fit(x_train, y_train)\n",
        "  y_pred = clf.predict(x_test)\n",
        "  print(classification_report(y_test, y_pred, target_names=tn),'\\n')\n",
        "\n",
        "  fig, ax = plt.subplots(figsize=(10, 10))\n",
        "  plot_confusion_matrix(clf, x_test, y_test,  ax=ax, display_labels=tn, normalize='true')\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jFpVlHgunuB"
      },
      "source": [
        "np.save('classes.npy', LE31.classes_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhEy6UiL_mtI"
      },
      "source": [
        "# K-Nearest Neighbors (KNN)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfc4LcBza9DW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e96e9a41-a41d-4d73-831f-2bf02d8d7ca2"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "knn = KNeighborsClassifier(n_jobs=-1)\n",
        "\n",
        "weight_options = ['uniform', 'distance']\n",
        "algorithm = ['ball_tree','kd_tree','brute']\n",
        "hyperparameters = dict(algorithm=algorithm ,weights=weight_options)\n",
        "\n",
        "clf = GridSearchCV(knn, hyperparameters, cv=10, scoring='f1_micro', n_jobs=-1)\n",
        "clf.fit(X31,Y31)\n",
        "\n",
        "print(f'Best score : {clf.best_score_} \\n')\n",
        "print(f'Best parameters : \\n {clf.best_params_} \\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best score : 0.9201599319083282 \n",
            "\n",
            "Best parameters : \n",
            " {'algorithm': 'kd_tree', 'weights': 'distance'} \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9do7L9L3_lRB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "outputId": "59553183-d685-4172-e9b7-53adb1373356"
      },
      "source": [
        "# model evaluation\n",
        "\n",
        "clf = KNeighborsClassifier(n_jobs=-1,weights='distance',algorithm='kd_tree')\n",
        "model_reports(clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.87      0.85      0.86      3640\n",
            "         ECL       0.86      0.87      0.87     15802\n",
            "         LPV       0.99      1.00      0.99     22288\n",
            "       OTHER       0.72      0.44      0.55       630\n",
            "       RRLYR       0.89      0.89      0.89     14865\n",
            "\n",
            "    accuracy                           0.92     57225\n",
            "   macro avg       0.87      0.81      0.83     57225\n",
            "weighted avg       0.92      0.92      0.92     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GigYWolvaee"
      },
      "source": [
        "# Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4flprReved_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e6bb082-b17a-4c13-87f5-cfc87b29bee4"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier as DTC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "criterion = ['gini', 'entropy']\n",
        "splitter = ['best','random']\n",
        "max_features = ['auto','sqrt','log2',None]\n",
        "hyperparameters = dict(criterion=criterion,splitter=splitter,\n",
        "                       max_features=max_features)\n",
        "\n",
        "dtc = DTC()\n",
        "clf = GridSearchCV(dtc, hyperparameters, cv=10, scoring='f1_micro', n_jobs=-1)\n",
        "clf.fit(X31,Y31)\n",
        "\n",
        "print(f'Best score : {clf.best_score_} \\n')\n",
        "print(f'Best parameters : \\n {clf.best_params_} \\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best score : 0.9490077729926863 \n",
            "\n",
            "Best parameters : \n",
            " {'criterion': 'entropy', 'max_features': None, 'splitter': 'best'} \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PvO1wDShuUEp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "209e1fd0-c7b4-4454-fe11-c8ee6b15e0ee"
      },
      "source": [
        "# validation curve\n",
        "from sklearn.model_selection import validation_curve\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "clf = DTC(criterion='entropy',splitter='best',max_features='auto')\n",
        "\n",
        "cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
        "pn = \"max_depth\"\n",
        "pr = list(range(1, 30))\n",
        "\n",
        "train_scores, test_scores = validation_curve(clf, X31, Y31, cv=cv, n_jobs=-1,\n",
        "                                             param_name=pn, param_range=pr)\n",
        "\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "plt.title(\"Validation Curve\")\n",
        "plt.xlabel(f\"{pn}\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.ylim(0.0, 1.1)\n",
        "lw = 2\n",
        "\n",
        "plt.grid()\n",
        "plt.plot(pr, train_scores_mean, label=\"Training score\",\n",
        "             color=\"darkorange\", lw=lw)\n",
        "plt.plot(pr, test_scores_mean, label=\"Cross-validation score\",\n",
        "             color=\"navy\", lw=lw)\n",
        "\n",
        "plt.fill_between(pr, train_scores_mean - train_scores_std,\n",
        "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
        "                 color=\"darkorange\", lw=lw)\n",
        "plt.fill_between(pr, test_scores_mean - test_scores_std,\n",
        "                 test_scores_mean + test_scores_std, alpha=0.2,\n",
        "                 color=\"navy\", lw=lw)\n",
        "plt.legend(loc=\"best\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd0dc270cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXRALWcmihi1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795
        },
        "outputId": "8512e589-0481-45f4-9aff-ab3616df62c3"
      },
      "source": [
        "# model evaluation\n",
        "\n",
        "clf = DTC(criterion='entropy',splitter='best',max_features=None)\n",
        "model_reports(clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.88      0.89      0.89      3640\n",
            "         ECL       0.93      0.92      0.92     15802\n",
            "         LPV       0.99      0.99      0.99     22288\n",
            "       OTHER       0.58      0.60      0.59       630\n",
            "       RRLYR       0.93      0.94      0.94     14865\n",
            "\n",
            "    accuracy                           0.95     57225\n",
            "   macro avg       0.86      0.87      0.87     57225\n",
            "weighted avg       0.95      0.95      0.95     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsmxDY7NwvS_"
      },
      "source": [
        "# Linear Discriminant Analysis (LDA)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgX7bCWNwzQy",
        "outputId": "28f07b0a-4473-4c4a-a76d-ae3cd3da3277"
      },
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "shrink = np.arange(0, 1, 0.01)\n",
        "shrink1 = 'auto'\n",
        "solver = ['svd', 'lsqr', 'eigen']\n",
        "\n",
        "hyperparameters = dict(shrinkage=[shrink,shrink1], solver=solver)\n",
        "\n",
        "lda = LDA()\n",
        "clf = GridSearchCV(lda, hyperparameters, cv=10, scoring='f1_micro', n_jobs=-1)\n",
        "clf.fit(X31,Y31)\n",
        "\n",
        "print(f'Best score : {clf.best_score_} \\n')\n",
        "print(f'Best parameters : \\n {clf.best_params_} \\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best score : 0.8362551651683443 \n",
            "\n",
            "Best parameters : \n",
            " {'shrinkage': 'auto', 'solver': 'lsqr'} \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "XkLLYvJXuwUW",
        "outputId": "108ce190-19f1-4c86-ceba-ae02554f0dd5"
      },
      "source": [
        "# model evaluation\n",
        "\n",
        "clf = LDA(solver='lsqr', shrinkage='auto')\n",
        "model_reports(clf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         CEP       0.65      0.70      0.67      3640\n",
            "         ECL       0.78      0.65      0.71     15802\n",
            "         LPV       0.95      0.99      0.97     22288\n",
            "       OTHER       0.49      0.18      0.27       630\n",
            "       RRLYR       0.75      0.84      0.79     14865\n",
            "\n",
            "    accuracy                           0.83     57225\n",
            "   macro avg       0.72      0.67      0.68     57225\n",
            "weighted avg       0.83      0.83      0.82     57225\n",
            " \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4vpe7BcQY8A"
      },
      "source": [
        "> * Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "rREICiihvAAG",
        "outputId": "67a5696a-b277-4807-cf35-c988642d41b7"
      },
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6), dpi=80)\n",
        "plt.title('Learning Curve')\n",
        "plt.xlabel(\"Training samples\")\n",
        "plt.ylabel(\"Score\")\n",
        "\n",
        "clf = LDA(solver='lsqr', shrinkage='auto')\n",
        "cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
        "train_sizes = np.linspace(0.05, 1, 15)\n",
        "\n",
        "train_sizes, train_scores, test_scores = learning_curve(\n",
        "    clf, X31, Y31, cv=cv, n_jobs=-1, train_sizes=train_sizes)\n",
        "\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "plt.grid()\n",
        "\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                 train_scores_mean + train_scores_std, alpha=0.1,color=\"r\")\n",
        "plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                 test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "         label=\"Training score\")\n",
        "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "         label=\"Cross-validation score\")\n",
        "\n",
        "plt.legend(loc=\"best\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbda8c6d9d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sCGy8cPpifG"
      },
      "source": [
        "-----\n",
        "# Save the Best Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uE4yGNr7pqDv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f38a28fc-2660-4ab3-e8bd-65a644fe8d9c"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier as DTC\n",
        "model = DTC(criterion='entropy',splitter='best',max_features=None)\n",
        "model.fit(X31,Y31)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A97EPcd1h3cG"
      },
      "source": [
        "import pickle\n",
        "\n",
        "a_file = open(\"finalized_model.sav\", \"wb\")\n",
        "pickle.dump(model, a_file)\n",
        "a_file.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Binary Classification"
      ],
      "metadata": {
        "id": "azhhU4omP4lM"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBlK4hetQaHZ"
      },
      "source": [
        "final_data = pd.read_csv('/content/drive/MyDrive/final_data', index_col=0, header=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = final_data.iloc[:,0:53]\n",
        "Y = final_data['class']\n",
        "BY = final_data['OH6']"
      ],
      "metadata": {
        "id": "JsjI2984SYcJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
        "\n",
        "clf = KNeighborsClassifier()\n",
        "clf.fit(X,BY)\n",
        "\n",
        "import pickle\n",
        "a_file = open(\"Binary_model.sav\", \"wb\")\n",
        "pickle.dump(clf, a_file)\n",
        "a_file.close()"
      ],
      "metadata": {
        "id": "gg4N9OfsS393"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}