example2.ipynb

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  "metadata": {
    "colab": {
      "name": "example2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RaneemQaddoura/NPIR/blob/master/example2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHoo16PncaGh",
        "colab_type": "text"
      },
      "source": [
        "<h1>Nearest Point with Indexing Ratio (NPIR)</h1>\n",
        "An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsrUUsSEcsib",
        "colab_type": "text"
      },
      "source": [
        "Nearest Point with Indexing Ratio (NPIR) is a clustering algorithm which explores the characteristics of the data points to group similar data points into the same clusters and dissimilar data points into different clusters. It is based on the nearest neighbor search technique in finding a k-nearest neighbor to a certain point. The algorithm iterates to assign data points to the most suitable clusters. It performs Election, Selection, and Assignment operations to assign data points to appropriate clusters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7d3f6CVRcziX",
        "colab_type": "text"
      },
      "source": [
        "<h2>Installation</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koOrDAFPdCgE",
        "colab_type": "text"
      },
      "source": [
        "Python 3.xx is required"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1sAQ5tcdNeB",
        "colab_type": "text"
      },
      "source": [
        "<h2>GitHub</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYEW-MtEdWyS",
        "colab_type": "text"
      },
      "source": [
        "Clone the Git repository from GitHub:\n",
        "git clone https://github.com/RaneemQaddoura/NPIR.git"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9cptk_KxsuG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/RaneemQaddoura/NPIR.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXGpBJ2QyaFf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Change working directory\n",
        "import os\n",
        "os.chdir(\"NPIR/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xb2orhr8yg0N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Install NumPy, SciPy, sklearn, pandas, treelib, and matplotlib\n",
        "!pip install -r requirements.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxI2S08PzFuR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import metrics\n",
        "from NPIR import NPIR\n",
        "import numpy as np\n",
        "import datetime\n",
        "import warnings\n",
        "import statistics as stat\n",
        "import time\n",
        "import csv\n",
        "import plot_boxplot as box_plot\n",
        "import plot_surface as surface\n",
        "import plot_labels\n",
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "from IPython.display import Image\n",
        "import ipywidgets as widgets\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smGfQZTR0FxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#directories and flags\n",
        "datasets_directory = \"datasets/\"\n",
        "results_directory = time.strftime(\"%Y-%m-%d-%H-%M-%S\") + '/'\n",
        "Path(results_directory).mkdir(parents=True, exist_ok=True)\n",
        "Flag_avg=False\n",
        "Flag_best=False\n",
        "Flag_details=False\n",
        "Flag_details_Labels=False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWp2OzaU0MGX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Select data sets\n",
        "#\"aggregation\",\"aniso\",\"appendicitis\",\"blobs\",\"circles\",\"diagnosis_II\",\"flame\",\"glass\",\"iris\",\"iris2D\",\"jain\",\"liver\",\"moons\",\"mouse\",\"pathbased\",\"seeds\",\"smiley\",\"varied\",\"vary-density\",\"wdbc\",\"wine\"\n",
        "filenames = [\"vary-density\",\"iris2D\"]\n",
        "#IR: The indexing ratio to be used for generating the maximum index\n",
        "indexing_ratio = [0.1, 0.15]\n",
        "#The number of iteration i\n",
        "iterations = [30, 50]\n",
        "#Choose whether to Export the results in different formats\n",
        "export_flags = {'Export_avg':True, 'Export_best':True, 'Export_details':True, \n",
        "'Export_details_labels':True, 'Export_boxplot':True, 'Export_surface':True, 'Export_plot_labels':True}\n",
        "#Select number of runs\n",
        "NumOfRuns = 30"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkY7E5qExyEP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for filename in filenames:\n",
        "\tfor IR in indexing_ratio:\n",
        "\t\tfor i in iterations:\n",
        "\t\t    HS = [0]*NumOfRuns\n",
        "\t\t    CS = [0]*NumOfRuns\n",
        "\t\t    VM = [0]*NumOfRuns\n",
        "\t\t    AMI = [0]*NumOfRuns\n",
        "\t\t    ARI = [0]*NumOfRuns\n",
        "\t\t    \n",
        "\t\t    print()\n",
        "\t\t    print(\"dataset,\" + filename)\n",
        "\t\t    print(\"IR,\" + str(IR))\n",
        "\t\t    print(\"i,\" + str(i))\n",
        "\t\t    print(\"run,homo,compl,vmeasure,AMI,ARI,time\")\n",
        "\t\t    \n",
        "\t\t    for run in range(NumOfRuns):\n",
        "\t\t        \n",
        "\t\t        #Get current time (start time)\n",
        "\t\t        format = '%m/%d/%y %H:%M:%S'\n",
        "\t\t        tStart = datetime.datetime.now()\n",
        "\t\t        \n",
        "\t\t        data = np.genfromtxt(datasets_directory + filename + '.csv', delimiter=',')\n",
        "\t\t        points = data[:,:-1] #list of points\n",
        "\t\t        k = len(np.unique(data[:,-1]))#k: Number of clusters\n",
        "\t\t        labelsTrue = data[:,-1] #List of actual cluster of each points (last field)\n",
        "\t\t    \n",
        "\t\t        labelsPred = NPIR(points,k,IR,i)\n",
        "\t\t        \n",
        "\t\t        tEnd = datetime.datetime.now()\n",
        "\t\t        HS[run] = float(\"%0.2f\"%metrics.homogeneity_score(labelsTrue,labelsPred))\n",
        "\t\t        CS[run] = float(\"%0.2f\"%metrics.completeness_score(labelsTrue,labelsPred))\n",
        "\t\t        VM[run] = float(\"%0.2f\"%metrics.v_measure_score(labelsTrue,labelsPred))\n",
        "\t\t        AMI[run] = float(\"%0.2f\"%metrics.adjusted_mutual_info_score(labelsTrue,labelsPred))\n",
        "\t\t        ARI[run] = float(\"%0.2f\"%metrics.adjusted_rand_score(labelsTrue,labelsPred))\n",
        "\t\t        print(str(run + 1) +\",\" + str(HS[run]) +\",\" +str(CS[run]) +\",\" +str(VM[run]) +\",\" +str(AMI[run]) +\",\" +str(ARI[run]) +\",\" +str(tEnd - tStart))\n",
        "\n",
        "\t\t        if(export_flags['Export_details_labels']==True):\n",
        "\t\t        \tExportToFileDetailsLabels=results_directory + \"experiment_details_Labels.csv\"\n",
        "\t\t        \twith open(ExportToFileDetailsLabels, 'a',newline='\\n') as out_details_labels:\n",
        "\t\t        \t\twriter_details = csv.writer(out_details_labels,delimiter=',')\n",
        "\t\t        \t\tif (Flag_details_Labels==False): # just one time to write the header of the CSV file\n",
        "\t\t        \t\t\theader_details= np.concatenate([[\"Dataset\", \"IR\",\"i\"]])\n",
        "\t\t        \t\t\twriter_details.writerow(header_details)\n",
        "\t\t        \t\t\tFlag_details_Labels = True\n",
        "\t\t        \t\ta=np.concatenate([[filename, IR, i], labelsPred])  \n",
        "\t\t        \t\twriter_details.writerow(a)\n",
        "\t\t        \tout_details_labels.close() \n",
        "\n",
        "\t\t        if(export_flags['Export_details']==True):\n",
        "\t\t        \tExportToFileDetails=results_directory + \"experiment_details.csv\"\n",
        "\t\t        \twith open(ExportToFileDetails, 'a',newline='\\n') as out_details:\n",
        "\t\t        \t\twriter_details = csv.writer(out_details,delimiter=',')\n",
        "\t\t        \t\tif (Flag_details==False): # just one time to write the header of the CSV file\n",
        "\t\t        \t\t\theader_details= np.array([\"Dataset\", \"IR\",\"i\",\"HS\",\"CS\",\"VM\",\"AMI\",\"ARI\"])\n",
        "\t\t        \t\t\twriter_details.writerow(header_details)\n",
        "\t\t        \t\t\tFlag_details = True\n",
        "\t\t        \t\ta=np.array([filename, IR, i, float(\"%0.2f\"%(HS[run])), \n",
        "\t\t        \t\t\tfloat(\"%0.2f\"%(CS[run])),  float(\"%0.2f\"%(VM[run])),  float(\"%0.2f\"%(AMI[run])),  \n",
        "\t\t        \t\t\tfloat(\"%0.2f\"%(ARI[run]))])\n",
        "\t\t        \t\twriter_details.writerow(a)\n",
        "\t\t        \tout_details.close()\n",
        "\n",
        "\t\t    if(export_flags['Export_avg']==True):\n",
        "\t\t    \texport_to_file_avg=results_directory + \"experiment_avg.csv\"\n",
        "\t\t    \twith open(export_to_file_avg, 'a',newline='\\n') as out_avg:\n",
        "\t\t    \t\twriter = csv.writer(out_avg,delimiter=',')\n",
        "\t\t    \t\tif (Flag_avg==False): # just one time to write the header of the CSV file\n",
        "\t\t\t    \t\theader= np.array([\"Dataset\", \"IR\",\"i\", \"HS\",\"CS\",\"VM\",\"AMI\",\"ARI\"])\n",
        "\t\t\t    \t\twriter.writerow(header)\n",
        "\t\t\t    \t\tFlag_avg=True\n",
        "\t\t\t    \tavgHS = str(float(\"%0.2f\"%(sum(HS) / NumOfRuns)))\n",
        "\t\t\t    \tavgCS = str(float(\"%0.2f\"%(sum(CS) / NumOfRuns)))\n",
        "\t\t\t    \tavgVM = str(float(\"%0.2f\"%(sum(VM) / NumOfRuns)))\n",
        "\t\t\t    \tavgAMI = str(float(\"%0.2f\"%(sum(AMI) / NumOfRuns)))\n",
        "\t\t\t    \tavgARI = str(float(\"%0.2f\"%(sum(ARI) / NumOfRuns)))\n",
        "\n",
        "\t\t\t    \ta=np.array([filename, IR, i, avgHS, avgCS, avgVM, avgAMI, avgARI])\n",
        "\t\t\t    \twriter.writerow(a)\n",
        "\t\t    \tout_avg.close()\n",
        "\n",
        "\t\t    if(export_flags['Export_avg']==True):\n",
        "\t\t    \texport_to_file_best=results_directory + \"experiment_best.csv\"\n",
        "\t\t    \twith open(export_to_file_best, 'a',newline='\\n') as out_best:\n",
        "\t\t    \t\twriter = csv.writer(out_best,delimiter=',')\n",
        "\t\t    \t\tif (Flag_best==False): # just one time to write the header of the CSV file\n",
        "\t\t\t    \t\theader= np.array([\"Dataset\", \"IR\",\"i\", \"HS\",\"CS\",\"VM\",\"AMI\",\"ARI\"])\n",
        "\t\t\t    \t\twriter.writerow(header)\n",
        "\t\t\t    \t\tFlag_best=True\n",
        "\t\t\t    \tbestHS = str(float(\"%0.2f\"%(max(HS))))\n",
        "\t\t\t    \tbestCS = str(float(\"%0.2f\"%(max(CS))))\n",
        "\t\t\t    \tbestVM = str(float(\"%0.2f\"%(max(VM))))\n",
        "\t\t\t    \tbestAMI = str(float(\"%0.2f\"%(max(AMI))))\n",
        "\t\t\t    \tbestARI = str(float(\"%0.2f\"%(max(ARI))))\n",
        "\n",
        "\t\t\t    \ta=np.array([filename, IR, i, bestHS, bestCS, bestVM, bestAMI, bestARI])\n",
        "\t\t\t    \twriter.writerow(a)\n",
        "\t\t    \tout_best.close()\n",
        "\t\t    print(\"AVG,\" + avgHS+\",\" + avgCS +\",\"+ avgVM +\",\" +avgAMI +\",\" +avgARI)\n",
        "\t\t    print(\"Best,\" + bestHS+\",\" + bestCS +\",\"+ bestVM +\",\" + bestAMI +\",\" + bestARI) \n",
        "print()\n",
        "print('Experiments completed!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHpntNvL1FTT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import some useful packages to view the results' files in colab\n",
        "import pandas as pd\n",
        "from IPython.display import Image\n",
        "import os\n",
        "import datetime\n",
        "import ipywidgets as widgets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYZGAgGR0_IN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Select the experiments folder\n",
        "foldernames = [filename for filename in os.listdir() if filename.startswith(str(datetime.datetime.now().year))]\n",
        "drop_folder = widgets.Dropdown(options=foldernames, description='Select folder:')\n",
        "drop_folder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cF1WrT2V1gFM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get the selected folder\n",
        "results_directory = drop_folder.value"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TwgpdZ3_HSL2",
        "colab_type": "text"
      },
      "source": [
        "<h4>Average Results File</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6QIf2lsHWph",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ba3446ae-31f5-43f6-d1fe-1cf3f8d81fdd"
      },
      "source": [
        "#Show the average results file\n",
        "filename = results_directory +'/experiment_avg.csv' \n",
        "df = pd.read_csv(filename)\n",
        "df.head(4)"
      ],
      "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>Dataset</th>\n",
              "      <th>IR</th>\n",
              "      <th>i</th>\n",
              "      <th>HS</th>\n",
              "      <th>CS</th>\n",
              "      <th>VM</th>\n",
              "      <th>AMI</th>\n",
              "      <th>ARI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.10</td>\n",
              "      <td>30</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.10</td>\n",
              "      <td>50</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.15</td>\n",
              "      <td>30</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.15</td>\n",
              "      <td>50</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Dataset    IR   i    HS    CS    VM   AMI   ARI\n",
              "0  vary-density  0.10  30  0.83  0.92  0.86  0.86  0.82\n",
              "1  vary-density  0.10  50  0.88  0.96  0.91  0.91  0.87\n",
              "2  vary-density  0.15  30  0.91  0.94  0.92  0.92  0.90\n",
              "3  vary-density  0.15  50  0.98  0.99  0.98  0.98  0.98"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rtcts2x9HsTM",
        "colab_type": "text"
      },
      "source": [
        "<h4>Best Results File</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjSuMka6Hzv9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "1d32a3fc-dfdc-4901-e302-ad98c3577722"
      },
      "source": [
        "#Show the best results file\n",
        "filename = results_directory +'/experiment_best.csv' \n",
        "df = pd.read_csv(filename)\n",
        "df.head(4)"
      ],
      "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>Dataset</th>\n",
              "      <th>IR</th>\n",
              "      <th>i</th>\n",
              "      <th>HS</th>\n",
              "      <th>CS</th>\n",
              "      <th>VM</th>\n",
              "      <th>AMI</th>\n",
              "      <th>ARI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.10</td>\n",
              "      <td>30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.10</td>\n",
              "      <td>50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.15</td>\n",
              "      <td>30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.15</td>\n",
              "      <td>50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Dataset    IR   i   HS   CS   VM  AMI  ARI\n",
              "0  vary-density  0.10  30  1.0  1.0  1.0  1.0  1.0\n",
              "1  vary-density  0.10  50  1.0  1.0  1.0  1.0  1.0\n",
              "2  vary-density  0.15  30  1.0  1.0  1.0  1.0  1.0\n",
              "3  vary-density  0.15  50  1.0  1.0  1.0  1.0  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYSBA39CIGVq",
        "colab_type": "text"
      },
      "source": [
        "<h4>Detailed Results File</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFKWb-ImIL2U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "972d1110-ad8f-46e4-9bab-0652e24cd602"
      },
      "source": [
        "#Show the detailed results file\n",
        "filename = results_directory +'/experiment_details.csv' \n",
        "df = pd.read_csv(filename)\n",
        "df.head(12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "      <th>IR</th>\n",
              "      <th>i</th>\n",
              "      <th>HS</th>\n",
              "      <th>CS</th>\n",
              "      <th>VM</th>\n",
              "      <th>AMI</th>\n",
              "      <th>ARI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>vary-density</td>\n",
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              "      <td>30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
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              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Dataset   IR   i    HS    CS    VM   AMI   ARI\n",
              "0   vary-density  0.1  30  1.00  1.00  1.00  1.00  1.00\n",
              "1   vary-density  0.1  30  0.58  0.95  0.72  0.72  0.57\n",
              "2   vary-density  0.1  30  1.00  1.00  1.00  1.00  1.00\n",
              "3   vary-density  0.1  30  1.00  1.00  1.00  1.00  1.00\n",
              "4   vary-density  0.1  30  0.64  0.79  0.71  0.70  0.57\n",
              "5   vary-density  0.1  30  1.00  1.00  1.00  1.00  1.00\n",
              "6   vary-density  0.1  30  0.58  0.87  0.70  0.69  0.53\n",
              "7   vary-density  0.1  30  0.58  0.95  0.72  0.72  0.57\n",
              "8   vary-density  0.1  30  0.85  0.86  0.85  0.85  0.83\n",
              "9   vary-density  0.1  30  1.00  1.00  1.00  1.00  1.00\n",
              "10  vary-density  0.1  30  0.65  0.78  0.71  0.70  0.57\n",
              "11  vary-density  0.1  30  1.00  1.00  1.00  1.00  1.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1Qu4OXFIUdB",
        "colab_type": "text"
      },
      "source": [
        "<h4>Labels Results File</h4>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPSvsqtJIVPX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "c88e5b3a-5a81-4387-b216-3364d9fb4f57"
      },
      "source": [
        "#Show the labels results file\n",
        "filename = results_directory +'/experiment_details_Labels.csv' \n",
        "header_names=['Dataset','Optimizer','objfname'] + ['label' + str(i) for i in range(50)]\n",
        "df = pd.read_csv(filename,names=header_names,dtype=object)[1:]\n",
        "df.head(12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Dataset</th>\n",
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              "  <tbody>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>vary-density</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Dataset Optimizer objfname label0  ... label46 label47 label48 label49\n",
              "1   vary-density       0.1       30      2  ...       2       2       2       2\n",
              "2   vary-density       0.1       30      2  ...       2       2       2       1\n",
              "3   vary-density       0.1       30      0  ...       0       0       0       0\n",
              "4   vary-density       0.1       30      1  ...       1       1       1       1\n",
              "5   vary-density       0.1       30      0  ...       0       0       0       0\n",
              "6   vary-density       0.1       30      2  ...       2       2       2       2\n",
              "7   vary-density       0.1       30      1  ...       0       1       1       1\n",
              "8   vary-density       0.1       30      0  ...       0       0       0       0\n",
              "9   vary-density       0.1       30      1  ...       1       1       1       1\n",
              "10  vary-density       0.1       30      1  ...       1       1       1       1\n",
              "11  vary-density       0.1       30      1  ...       1       1       1       1\n",
              "12  vary-density       0.1       30      1  ...       1       1       1       1\n",
              "\n",
              "[12 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLZOxRRczYrs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Draw boxplot\n",
        "if export_flags['Export_boxplot'] == True:\n",
        "  ev_measures=['HS', 'CS', 'VM', 'AMI', 'ARI']\n",
        "  box_plot.run(results_directory, indexing_ratio, iterations, filenames, ev_measures)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHiu2aAi0hgV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Draw surface\n",
        "if export_flags['Export_surface'] == True:\n",
        "  ev_measures=['HS', 'CS', 'VM', 'AMI', 'ARI']\n",
        "  surface.run(results_directory, indexing_ratio, iterations, filenames, ev_measures)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIJMsuxOwmYv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Draw labels plot\n",
        "if export_flags['Export_plot_labels'] == True:\n",
        "  plot_labels.run(results_directory, filenames, ev_measures)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUFbWA961p6T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Select boxplot to show\n",
        "filenames = [filename for filename in os.listdir(results_directory) if filename.startswith('boxplot')]\n",
        "\n",
        "drop_boxplot = widgets.Dropdown(options=filenames, description='Select plot:')\n",
        "drop_boxplot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NENAnkgQ2rgk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "cb668c8d-5eae-43fd-dc79-4bd80b142337"
      },
      "source": [
        "#Show selected boxplot\n",
        "Image(results_directory +'/' + drop_boxplot.value)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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mJkaLFi1SXFyc1q9f7xnU+OGHH9aTTz6pKVOmKDU1Vc8884xnnjgAwLktmKZgy8nJkdPp1PLly3XkyBGf1dijsXnnz5+v+fPnd1j23fPWsbGxev/993u1YQBAcDs9BVtNTY0WLFjQYQq275/BPFunp2AbMWKEioqKdNNNN3UYsN6b757yXblypdatWyebzaZ169bpvvvu0/bt231SJwPdw6uJ9nAN+/0Kf5fRwcQe3CgBoG+dvmba3NysWbNmqbCwsFOI+mIKttPmz5+vu+++W83NzRo7dmyv6x03bpzn7zvvvFM33HCDT2qUGE4QPVBbXydjjE/+SfLJemrr67xUDaC/BMsUbAcPHvT8/corryg+Pt4nNUocmQJA0Jowwd7rO3C9re9sBcMUbPfff7/27Nkjm82myMhI/fnP386GdaYae4op2PpRsE7B5ktMG9Yzvr6Bjz4/NzAF2/8JtCnYODIFAhDhB5wZU7ABAHCOIUwBIIhw1sI/vPU7YQoAQWLo0KFqbW0lUPuZMUaHDx/W0KFDu23DNVMgyLjdbmVlZXluhsjMzFRqaqq/y0I/CA8P13vvvXfGL3X0jaFDhyo8vPvn2wlTIIi43W5lZmYqLy9PiYmJKi0tVVpamiQRqAPAoEGDtG3btj57EgJnj9O8QBDJyspSXl6ekpKSNGjQICUlJSkvL09ZWVn+Lg0Y0M7JI9MJ4RH6y/Lr/V1GJxPCI7w3As6gqqpKiYmJHZYlJiaqqqrKTxUBkM7RI9OGuk8Dbvg7Y4wa6j71c88g2MXExKi0tLTDstLS0m4fJAfQP87JMAXOVZmZmUpLS1NJSYna2tpUUlKitLQ0ZWZm+rs0YEA7J0/zAueq0zcZZWRkeO7mzcrK4uYjwM8IU/hEb8aS7UlbnqPrXmpqKuEJBBjCFD5B+AEYyLhmCgCARYQpEGTcbrfi4+MVEhKi+Ph4ud1uf5cEDHic5gWCCCMgAYGJI1MgiDACEhCYBvSRaU/vQOXuUwQKRkACAtOAPjL11chGBCn6CyMg+R7XoOELAzpMgWDDCEi+dfoa9LZt29TS0qJt27YpMzOTQEWvDejTvECwYQQk3/ruNWhJnmvQGRkZ9Cl6xWb8dI7S5XIxJx8AvwoJCVFLS4sGDRrkWdbW1qahQ4eqvb3dj5V1j+/OwMRpXgADFteg4SuEKYABi2vQ8BWumQIYsLgGDV8hTAEMaMzCA1/gNC8AABYRpgAAWNSjMC0uLlZ0dLQcDoeys7O7bPPiiy8qNjZWcXFxuvXWW31aJAAAgczrNdP29natWrVKb731lsLDwzVt2jQlJycrNjbW06a6ulpbt27V+++/r9GjR+vzzz/v06IBAAgkXo9My8rK5HA4FBUVpcGDByslJUUFBQUd2jz55JNatWqVRo8eLUkKCwvrm2oBAAhAXsO0oaFBERERntfh4eFqaGjo0Gbfvn3at2+fZs2apZkzZ6q4uLjLdeXm5srlcsnlcqmpqcli6QAABAafPBpz8uRJVVdX691331V9fb2uvvpq/fe//9WoUaM6tEtPT1d6erqkb4fEAgDgXOD1yNRut6uurs7zur6+Xna7vUOb8PBwJScna9CgQZo0aZIuv/xyVVdX+75aAAACkNcwnTZtmqqrq1VTU6PW1lbl5+crOTm5Q5ubbrpJ7777riSpublZ+/btU1RUVJ8UDABAoPEapqGhocrJydG8efMUExOjRYsWKS4uTuvXr1dhYaEkad68eRozZoxiY2OVlJSkhx56SGPGjOnz4gEACARMwQYAQYTvzsDECEgAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCART0K0+LiYkVHR8vhcCg7O7vbdi+//LJsNpvKy8t9ViAAAIHOa5i2t7dr1apV2rFjhyorK+V2u1VZWdmp3bFjx/Too49qxowZfVIoAACBymuYlpWVyeFwKCoqSoMHD1ZKSooKCgo6tVu3bp3Wrl2roUOH9kmhAAAEKq9h2tDQoIiICM/r8PBwNTQ0dGiza9cu1dXVacGCBWdcV25urlwul1wul5qams6yZAAAAovlG5BOnTqlX/7yl3r44Ye9tk1PT1d5ebnKy8t10UUXWd00AAABwWuY2u121dXVeV7X19fLbrd7Xh87dkwVFRWaPXu2IiMj9eGHHyo5OZmbkAAAA4bXMJ02bZqqq6tVU1Oj1tZW5efnKzk52fP+yJEj1dzcrNraWtXW1mrmzJkqLCyUy+Xq08IBAAgUXsM0NDRUOTk5mjdvnmJiYrRo0SLFxcVp/fr1Kiws7I8aAQAIaDZjjPHHhl0uF6eCAaCX+O4MTIyABACARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFhEmAIAYBFhCgCARYQpAAAWEaYAAFjUozAtLi5WdHS0HA6HsrOzO73/yCOPKDY2Vk6nU9dee60OHDjg80IBAAhUXsO0vb1dq1at0o4dO1RZWSm3263KysoObaZOnary8nL95z//0cKFC3X//ff3WcEAAAQar2FaVlYmh8OhqKgoDR48WCkpKSooKOjQJikpScOHD5ckzZw5U/X19X1TLQAAAchrmDY0NCgiIsLzOjw8XA0NDd22z8vL0/XXX++b6gAACAKhvlzZX/7yF5WXl+vvf/97l+/n5uYqNzdXktTU1OTLTQMA4Ddej0ztdrvq6uo8r+vr62W32zu1e/vtt5WVlaXCwkINGTKky3Wlp6ervLxc5eXluuiiiyyUDQBA4PAaptOmTVN1dbVqamrU2tqq/Px8JScnd2ize/durVixQoWFhQoLC+uzYgEACERewzQ0NFQ5OTmaN2+eYmJitGjRIsXFxWn9+vUqLCyUJK1Zs0Zff/21brnlFiUkJHQKWwAAzmU2Y4zxx4ZdLpfKy8v9sWkACFp8dwYmRkACAMAiwhQAAIsIUwAALCJMAQCwiDAFAMAiwhQAAIsIUwAALCJMAQCwiDAFAMAiwhQAAIsIUwAALCJMAQCwiDAFAMAiwhQAAIsIUwAALCJMAQCwiDAFAMAiwhQAAIsIUwAALCJMAQCwiDAFAMAiwhQAAIsIUwAALCJMAQCwiDAFAMAiwhQAAIsIUyDIuN1uxcfHKyQkRPHx8XK73f4uCRjwQv1dAICec7vdyszMVF5enhITE1VaWqq0tDRJUmpqqp+rAwYujky7wa9/BKKsrCzl5eUpKSlJgwYNUlJSkvLy8pSVleXv0oABjSPTLvDrH4GqqqpKiYmJHZYlJiaqqqrKTxUBkDgy7RK//hGoYmJiVFpa2mFZaWmpYmJi/FQRAIkw7RK//hGoMjMzlZaWppKSErW1tamkpERpaWnKzMz0d2nAgNajMC0uLlZ0dLQcDoeys7M7vX/ixAktXrxYDodDM2bMUG1tra/r7Ff8+kegSk1NVVZWljIyMjR06FBlZGQoKyuLyw+An3kN0/b2dq1atUo7duxQZWWl3G63KisrO7TJy8vT6NGj9fHHH+vee+/V2rVr+6zg/sCvfwSy1NRUVVRUqL29XRUVFQQpEAC83oBUVlYmh8OhqKgoSVJKSooKCgoUGxvraVNQUKCNGzdKkhYuXKh77rlHxhjZbLa+qbqPnf5yysjIUFVVlWJiYvj1DwDoltcwbWhoUEREhOd1eHi4du7c2W2b0NBQjRw5UocPH9bYsWM7tMvNzVVubq4kqampyXLxfSk1NZXwBAD0SL/egJSenq7y8nKVl5froosu6s9NAwDQZ7yGqd1uV11dned1fX297HZ7t21OnjypL7/8UmPGjPFxqQAABCavYTpt2jRVV1erpqZGra2tys/PV3Jycoc2ycnJevbZZyVJL730kq655pqgvV4KAEBveb1mGhoaqpycHM2bN0/t7e1avny54uLitH79erlcLiUnJystLU233XabHA6HLrzwQuXn5/dH7QAABASbMcb4Y8Mul0vl5eX+2DQABC2+OwOT38J07NixioyM9Meme6WpqYmbpXyI/vQd+tK3gqU/a2tr1dzc7O8y8D1+C9Ngwa9A36I/fYe+9C36E1YwNi8AABYRpgAAWBSy8fQ4gOjWVVdd5e8Szin0p+/Ql75Ff+Jscc0UAACLOM0LAIBFhCkAABYNmDD1NsH5e++9pyuvvFKhoaF66aWXulxHS0uLpk+frilTpiguLk4bNmzwvFdTU6MZM2bI4XBo8eLFam1t7bN98Tdf9KUkRUZG6oorrlBCQoJcLpdn+RdffKE5c+bosssu05w5c3TkyJE+2Q9/stKHISEhSkhIUEJCQqehPU9bt26dnE6nEhISNHfuXDU2NkqSjDFavXq1HA6HnE6ndu3a5fud84O+7s+NGzfKbrd72hUVFXne27p1qxwOh6Kjo/XGG2/4dscQPMwAcPLkSRMVFWX2799vTpw4YZxOp9m7d2+HNjU1Neajjz4yt912m/nrX//a5XpOnTpljh07ZowxprW11UyfPt188MEHxhhjbrnlFuN2u40xxqxYscL88Y9/7MM98h9f9aUxxkycONE0NTV1Wr5mzRqzdetWY4wxW7duNffff79vd8LPrPbhD37wA6/b+PLLLz1/P/roo2bFihXGGGNef/11c91115lTp06ZDz74wEyfPt0He+Rf/dGfGzZsMA899FCn5Xv37jVOp9O0tLSYTz75xERFRZmTJ09a2yEEpQFxZPrdCc4HDx7smeD8uyIjI+V0OnXeed13ic1m04gRIyRJbW1tamtrk81mkzFG77zzjhYuXChJWrZsmV599dW+2yE/8lVfnklBQYGWLVsm6dzsy/7owwsuuMDz9/Hjxz0TTxQUFGjp0qWy2WyaOXOmjh49qoMHD579zgSA/ujP7hQUFCglJUVDhgzRpEmT5HA4VFZW5tNtIDgMiDDtaoLzhoaGs1pXe3u7EhISFBYWpjlz5mjGjBk6fPiwRo0apdDQUMvrD3S+7Eubzaa5c+fqqquu8kwaL0mHDh3S+PHjJUkXX3yxDh06ZK3oAGO1D1taWuRyuTRz5swz/tDIzMxURESEnn/+eW3evNkn2w5E/dWfOTk5cjqdWr58uefSw7nYnzg7AyJMfSkkJER79uxRfX29ysrKVFFR4e+SglZpaal27dqlHTt26PHHH9d7773XqY3NZmM6v+85cOCAysvL9cILL+gXv/iF9u/f32W7rKws1dXVacmSJcrJyennKoNHT/pz5cqV2r9/v/bs2aPx48frvvvu80OlCGQDIkx7MsF5b40aNUpJSUkqLi7WmDFjdPToUZ08edJn6w9UvuzL058LCwvTzTff7Dk9Nm7cOM+px4MHDyosLMxi1YHFah+ebhsVFaXZs2dr9+7dZ2y/ZMkSvfzyyz7ZdiDqj/4cN26cQkJCdN555+nOO+/0/F89F/sTZ2dAhGlPJjg/k8mTJ0v6dlaJo0ePSpK++eYbvfXWW5o8ebJsNpuSkpI8dwk+++yzuvHGG32/IwHAV315/PhxHTt2zPP3m2++qfj4eEkdJ5s/F/vSSh8eOXJEJ06ckCQ1Nzfr/fffV2xsrCTp17/+tV555RVJUnV1teczBQUFnn5PTk7Wc889J2OMPvzwQ40cOdJzSj1Y9Ud/fve68iuvvNLh/2p+fr5OnDihmpoaVVdXa/r06b7cPQQLf98B1V9ef/11c9lll5moqCizZcsWY4wx69atMwUFBcYYY8rKyozdbjfDhw83F154oYmNjTXGGNPU1GQuv/xyY4wxH330kUlISDBXXHGFiYuLM5s2bfKsf//+/WbatGnm0ksvNQsXLjQtLS39vIf9xxd9uX//fuN0Oo3T6WjnLZkAAADuSURBVDSxsbGe9RhjTHNzs7nmmmuMw+Ew1157rTl8+HA/72HfO9s+fP/99018fLxxOp0mPj7ePPXUU551LliwwPzzn/80xhjz05/+1MTFxZkrrrjC3HDDDaa+vt4Y8+0d6XfffbeJiooy8fHx5l//+ld/7naf6ev+/NnPfmbi4+PNFVdcYX7yk5+YxsZGT7stW7aYqKgoc/nll5uioqL+2mUEGIYT9OK1117TJ598otWrV/u7lKBHX/atefPm8ZyjD9Gf6A3CFAAAiwbENVMAAPoSYQoAgEWEKQAAFhGmAABYRJgCAGARYQoAgEX/H3QNy7x4ZsiVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gu-d5ceC2yT5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Select surface to show\n",
        "filenames = [filename for filename in os.listdir(results_directory) if filename.startswith('surface')]\n",
        "\n",
        "drop_surface = widgets.Dropdown(options=filenames, description='Select plot:')\n",
        "drop_surface"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjurA61E26BG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "287f7b1a-a5de-43ee-8aff-6a37541c8c0a"
      },
      "source": [
        "#Show selected boxplot\n",
        "Image(results_directory +'/' + drop_surface.value)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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vvRdnzpzBSy+9hC9+8Yt47rnn8Nprr+FPf/oTrl27BgB4z3veg1deeaXwhZwCUOBUBqE4ffo0vvOd7+ATn/gELly4AIPBgPr6epw8eRJf+tKX+L7KF154AU899dQOn23+EAErc1KlC5eXl2G1WrFnzx50d3dvW+QkEglisSzlTTkQL4ROpxPj4+OoqakRtAF6t3ghxls6cUQiEXg8Hr5aLxgM8p8DZ48ll8tLdg5CR2CFjsjJhfg9Ns7xQ6VSwWw2o729HRcuXMD73vc+tLe346WXXsL73//+lMe5ePEiOjo6sHfvXgDAJz7xCfz2t79NEDCLxYJvfvObAIA777wTH/nIR/j3DQaDCIfDfF9kbW1tEVcl/h7YPffcg5dffhkbGxtoamrCV7/6Vb5Y5i/+4i9w6tQpPP/88+jo6IBGo8GPfvQjAIDJZMLjjz+O4eFhAMBXvvKVhCi5XCACVsYkpws9Hg/Gx8dhNBr5xuBUlDICC4VCmJmZQTgcRl9fH28kKxS7RcBSIZfLE9wvWJaFzWbD6upqQqNwqp6qQiiH9OScM4SLyzS6zSr01t74bmQrEqFpGkajEXfffTfuvvvutM9LVYxw4cKFhOf09fXh17/+NR555BH85je/gdfrxebmJm677TbceeedqK+vB8uyePjhh9Hd3V3E1aL0jcxZ+NnPfpbxcYqi8N3vfjflY2fPnsXZs2eFOC3RIAJWhqRKF05NTSEQCODgwYMJ5c6pkEqlRQsY12d1/fp17N+/H7W1teWz2S4SFEVBLpdDq9Wio6MDwI1Gco/Hg9XVVXi9Xr78nBO1+PLzTAgdgRVa4egLx/Dmih+XV2jY/Vvf0W7z9n3QTNfo8/myfo9z5Rvf+AYefvhhPPPMM7j99tvR2NgIqVSK6elpjI2NYWlpCcBWKfmrr76K9773vYW9kRB9YISMEAErI1LN6FpaWsLS0hLa29tzFpFiIzCPx4OxsTHEYjEcPHhQ1NRDuQ20BBIX6vi5Xw0NDQBulJ+ncpOPt8ZK/rflIm8hyHcPjGFZTG1uRVvjGwHEkj5OhTTxPLN93tyImGykK1KIp6GhAb/+9a/54/77v/87Kisr8YMf/ADHjh3jhfJDH/oQXn/99cIFDCACJjJEwMoEhmGwubmJQCCAmpoauFwuTExMoLq6GseOHcsrDVXoHhhnAeX1etHT04PFxcWbyiZop5w4UpWfc27ybrcbq6urfONvvKiJ1cicCVcwikvLNN5c9cMVTP+dUkjzO89cI7Dh4WFMTU1hbm4OjY2N+PnPf46f/vSnCc/Z2NiAyWSCRCLBU089xafNWlpa8IMf/ABf/OIXwbIsXnnlFXzuc5/L6zwTKX0VIiEzRMB2OfHpwmAwiM3NTd4KqtA9p3xTiKksoDhPQrFnc+3mPbBUFLpPlewmzxnvejweftQM52lpMpn4/bRSCVqmIo4Yw8JiD+DSCo2pzVBOfrPJEVg2chUwmUyG73znOzh58iRisRjOnj2LAwcO4Ctf+QqGhoZw+vRpvPzyy/jiF78IiqJw++2383tCd911F1566SUcOnQIFEXhgx/8IP7sz/4sr/NMgLjRiw4RsF1K8owulmVht9uxvr6OgwcP8uarhZCP8NA0jbGxMahUqm0WUKUqBuFgWRZWqxVLS0vQarUwGAwpPQvLiVKJbbzxLlcpd+3aNTQ0NCAUCiXYOcU7X+S6n5ZMqgjMTkdwaYXGlVU/fOH8/t3jBSyX6C7XFCIAnDp1CqdOnUr4uyeeeIL/77vuugt33XXXttdJpVL88z//c07vkRtkD0xsiIDtQpKrCzc3NzE5OQmDwYCampqixAvILVWWiwVUqaIhlmXh8/lgsVhgMBjQ39/PG/FardaEHiuZTJYw0qMcELK4RafTobq6mt/3iUaj/H7a7OxswaNmuD2wSIzFtXU/Lq3QmHeFCz7PeAHLVoEIbI3ayVXAdhUkhSgqRMB2EckzukKhEMbHxwEAAwMDiEajmJubE/w8crWAKlUENjExAZfLxTdcc/O+9Ho975jA9VjZbDY4HA5cvHgRarWaj9LytXcSC6FL3ZOPnWo6cyGjZuwhCqPzAYxuehCMFn+TEr8HlouA0TRdsipE0SBViKJDBGwXkFwWzzlarK+vJzha+P3+kjQgpyMYDGJsbCxnC6h0VlK5srGxAZqmUV9fj6NHj2aM6LgeK25w4v79+7fZOwHgU2cGg2FXTGreDb1auY6aUWgqsMLoMO6VYN2vA1B4xJVMvhGYz+crsql4Z6DKNNVdrhAB20HiZ3RxC53dbsf09DTq6+u3OVpIpVJBBKxQC6hCizi4yJKbz9Xc3JzzIh8/wiWVvRNXuccZxyoUCj5KK7UTRi7sRiup5FEzW83GPlxfDyDKAEBpC3MkFCCT5C9g5ZZCpCiAkuzsDdOtBhGwHSJ5n4uLfuRyOQ4fPpwy+hFCwDgLKLPZjKNHj+Zdjp9vNePS0hKsViv27duHmpoaXLx4sWSFDqnK0bnKvXgnDG4Qo8FgELxAZDdEYKlI1WwsFPKkRf2mTSGC2vGI/1aDCJjIpEoXzs3NYWNjA52dnRmbgkvhoMHBMAyuX79elAVUPhEYV6Sh0+kSbK7yLQTJ9/mcxx5X+MIwDGia5kel+Hw+yGQyvuqTM+EtFUKX/OezYG41GwdxcdmfstlYKJJL6G/WCAxA2VbLlitEwEQilemuzWbDzMwMmpqacpqXVYqqPy4K8vv9aG9vR11dXcF3jbk0RMdXM/b09CSY3wKpr0nIcSoSiYSfGswRiUQwOTmJUCiEiYkJBINBfv6XwWAoukBEyBRiLnDNxpdX/HCHhNtDTcctI2AkhSg6RMBEIDld6Pf7MTY2BrVajaGhoZzKmksBZwHF7QmZzeaiFleJRJIwJj6Zzc1NTExM8EUaqQR6NzQmy+VyviqvurqaL3Jwu90pC0Ty6a8SczhnPIU0GwtFNBTElStX+M8vEAjclGX0FEkhig4RMAHh0oUTExNob29HLBbDzMwMXC4Xurq6UvZWCQFnAeXxePhS9cuXLxedjkxXhRgOhzExMYFwOIyBgQF+jHw+x0iHGIIXX+SQXCAS71eoUCj4KC1dgYiQdk+pFstimo2FolKnxaFDTfyoGZvNhmg0CofDkdCfFv/50TQNvV6f8bjFDLK0Wq04d+4cFhcXQVEUnn/+ebS1tRV9rSSFKC5EwAQgOV24vr4OrVaLubk5tLS0oLOzU5Q7tXQWUEBpZoIl74GxLIuVlRXMz8/nbC6cSpB2KmrJRKoCkVAotK1AJN5BpKKiQpToMhxjcH09UHSzsVAopFTCqBnOVcRgMKT8/F566SVEIpGMYlDMIEsAuPfee/G3f/u3OHHiBHw+X2mEh6QQRYcIWIlJThfSNI1AIACn07nNiqlQclngM1lAAaVpQo4/Bk3TsFgs0Gg0OHLkSM7l6vmW4u+GlCOHUqmE2WyG2WwGcKNAhGsY9vl8iEQi0Gg0vJNIKlf5QnHGZPjNmBNvr/tL0mwsFKn2wGQy2bYCG27UDAA4HA4cP34cFEXhP/7jP1BXV5dwjGIGWVosFkSjUZw4cQIASlrtuNtuvG52iICVEIZhEAqFAGyl7aanp+H1eqHRaNDZ2VnwfKV4ONFIa7SagwVU/HGKgROfmZkZ2Gw2dHV1JThA5HqM3SJIxRJfIMJZO83NzSEWi4Gm6QRX+XgHkXy+F8Eog6urW9ZOKz4z4KOFupySkexEn66Igxs18/DDD+MXv/gF3njjDQQCgZRVocUMspycnERlZSU+9rGPYW5uDu9///vx9NNPF+3kQoEiKUSRIQJWQri7r5WVFSwsLGDPnj3o7u7GlStXEI1GSyJgXC9Yqh9brhZQ3HGKFTCaprGysoK2tracqihTkU7A0kWZ5SZ4EokEGo2Gd5VIdsGYmZkBy7IJDiKpCkS4ycYjtgAiTPlcP1BYFSJwYy+yUNINsoxGo3j11Vdx9epVtLS04O6778YzzzyDT3/60wW/19YJkxSi2BABKyEsy+LSpUswGAwJvU6lbEBOJTz5WkABxe2BRSIRTExMwOv1oqqqik/jFEKyIHFjWtKJVLkJWLIQJ7tgAFsLus/ng9vt5gtE5HI5FBUGLEQ0sDhZbATEL38vFfI8BSyXYZrFDLJsampCf38//739yEc+gjfeeKN4AQNJIYoNEbASIpFIMDAwsG2/qZQO6vFiWKgFFHeu+UZg8UUhe/bsQVNTEz+OvVDKTZDyJdeBlgaDAQaDgW82fmPRiwlrGAxbXs77qcg3AsvFhaOYQZbDw8NwuVyw2+0wm8146aWXMDQ0VODV3YCkEMWHCFiJUSqV2xbkUkdgsVisKAsoIH8B43rXFAoFXxTi9XpLso8W/3kxDIO1tTUolUro9fpt11VuglcuzcZCkq+Aeb3erAJWzCBLqVSKb3zjGzh+/DhYlsXg4CA+85nPFH+hJIUoOkTARKCUERjLspiamgKAgi2ggNz3wOKjvK6urgSrq1IVgnCLvMvlwtjYGCorK3lDXgB8n5DBYNh1xri5kO64u6nZWEhyLeLgyHUac6GDLAHgxIkTuHbtWtb3yBeSQhQXImAlJlWEUIoIjLOAstvtaG5uRkdHR9EuGtnOye12w2KxwGw2b3PGB0oTDVEUhWg0irGxMfh8PvT29kIul/OCEovF4PF44Ha7YbPZ4Pf7EYlEsLCwUBKbJ6FJJYw2OoJLyzSuru2eZmMhSY7Ast0slKeR7xYkAhMXImAiUGwEFm8B1dTUBL1eX/SdXqboKRqNYnJyEj6fD4cOHUq7mJQiAgsEAhgbG8PevXv5Rutw+EYzrlQqTRjQGA6Hce3aNSgUigSbp/goTaVS7Zo7YW6x3u3NxkKSLGDZKEcbKWDrZozsgYkLETARkEqlGT0D05HKAorrKyqWdBEYJwqtra3o7u7OKATFDLQMh8MYHx+H1+tFR0fHtgqydFAUBalUivr6et7mKRqN8nPA1tfXS27GWwz2IHDZGsa4a3VXNxsLSSECVrYR2C65cbpVIAJWYlJ9gWUyGQKBQM7HyGQBVaqCkGRRDQaDsFgskMlkORsMFzLQMv7a2tvboVKp8hoymSptKZPJEqK0ZDPeqakpUBSV4FsoZJSW0GzsVQEo/0rCYkjeA8sGTdNlGYEBJIUoNkTARCAf0clmAVVoNJcMl/5jWRYLCwtYXl5GZ2enoKX4nEjK5XL+2nw+X8mrClOZ8UajUd5Mdm1tjY/SuPL1UpzDrDOES2XabCwk8RFYLkM4y3KUCgBQxMxXbIiAiUAue2DxFlCZLJmkUilvV1UMEokEfr8fFy5cgMlkwrFjxwoqxc9l4WdZFouLi1hcXNwmkkIPtOSQyWQwmUx8FWV8lLa6uoqNjQ1sbGzwDuncXlo2xJxsXK7EC1ius8BaWlqEPq2SQwEgGURxIQJWYlKlpbJFYHa7HVNTU2hoaMjJAqrYFGI0GsXy8jKcTieGhoYKvtvNRUxomsbo6Cj0en2COwlHKhHciZEpMzMzvC+h2+3G2toaQqEQ71vI7aVJJJIdm2xcruQrYOVbhUhBQlKIokIETATSRWDxFlCHDx8W3AIKuOGXWF1dDblcLliqhmEYzM/PY319PaOpsFgRWC5IpdJtUZrf74fH48Hq6ireGp/GQkSLhYgGdIykinJlZnIC5kodDAYDJBJJThFYtllguxIKRMBEhvwKRSBZwBiGwdzcHK5cuYLm5mb09/fnJF5A4RFYKBTCW2+9heXlZQwODqK+vl4wIXC73bhw4QJYlsXRo0ezDu4sthS/FKTqTaIoCiq1BhsSA14LVOG8txaWYAURrzzp2NMKmUyG1dVVjI6OwuFwYHJyEmtra/D7/du+h7k2Mp8/fx6dnZ3o6OjA008/ve3xhYUFHD9+HL29vbjjjju22Z55PB40NTXh4YcfLu4C34ECIJVSJf1DyAyJwEpMthRisRZQ+QpY/P7Tvn37+NlL0Wi0ZPZWHLFYDNPT03C5XBn7x+LZjROZgRvNxldW/aAjOy+w5QoFwKCrgEFXgYaGBjidTthsNpjNZng8HszMzCAQCPDWYWtrazn1gRU70BIAHn/8cdx+++2lvV6yCSYq5FZSBLi038jICGZmZvDgsiIAACAASURBVNDb24uOjo6CepPyETCv14uLFy/C7/fj6NGjvHhx51TKyGdzcxMXLlyASqXCkSNHct7DKCSFKBQRhsWbKzS+f9mGb76+jletPiJeRZLKB1Eul8NoNKK1tRWHDh3C8PAwOjs7odVq8fzzz+PSpUu45557cP/99/N2YsnED7RUKBT8QMt4LBYL3ve+9wHYGmgZ//ibb76J9fV1fOADHyjZtVLvpBBL+ScXiolEpVIp+vv70d/fj9OnT5fssxALEoEJDMuyWF5eht/vR0dHB2pra4tahHPxMIzFYpiZmYHD4UBPT0/K/YRSzAMDtq5vZGQEoVAIAwMDUKvVeb2+kF6yUrPsCeOPmzLMLQYQiuXer0fITi5GvhRF8dOZv/a1r+Hy5cv49a9/jcXFxQTvzXiKGWhpNBrx+c9/Hj/5yU/w4osvluhKgZ0o4ig2ElWr1XjrrbdEPedSQgSsxMSLk9frhcVigcFggFar3TYWvRCyRWCbm5uYmJjgKxrTiWUpIrD19XXQNI09e/agvr6+IGHeKXf5xGbjCMhPQRgKGWZJ0zSMRmNCxqAQ0g20/N73vodTp06hqampqOOnQuwUYnwkCoCPROMFzGKx4Jvf/CaArUj0Ix/5iKjnKCTkVysAqSygXnvttZI4nqerQuSsmaLRaE6RUDHVjKFQiK+e1Gg0aGhoKOg4gPgCRpqNxSWVE322yeQMw2R9TjEDLV9//XW8+uqr+N73vgefz4dwOIyKioqU6bd8oHagCrGYSLSqqgrBYBBDQ0OQyWR47LHHyk7ciICVmGg0iosXL6KlpSXBAoqLeIr15EsueuBSlAsLC3yKMtfjFGIDxb0XVxBSrDCnEjCapvm0UinuaEmz8c6RKgLLZFOW681MMQMt//Vf/5V/zjPPPIPLly8XLV4cpRYwm92eMGzzgQcewAMPPJDXMdJFosDW/lhjYyNmZ2fxvve9D4cOHUJ7e3tJr0FIiICVGLlcnrK6kEv9ldJU1ufzYWxsDFqtNmWTcCbyFQa/3w+LxQKNRpPwXpwAlULAYrEYpqam4HK5IJVKEQwGodVq+UbiioqKnK16SLPx7qCQFCKQ/ftZzEBLISl1BtFsNuPy5ctpHy8mEuVeDwB79+7FHXfcgatXrxIBu9WRyWQpDWej0eg2b8NCYFkW09PTsNvtGZuESwHnlbiysoLu7u5tFlfFONIDNwTM6XRibGwMjY2NaG9v56NDv98Pt9uN5eVl+Hw+3kprY2MDBoNhmxHwzTzZuByR5ylg+dwMFTPQkuO+++7Dfffdl9P7ZUOIFGK2HEkxkajT6YRGo4FSqcTGxgb+9Kc/4dFHHy3p+QsNETCRKJWLvMPhAE3TkEqlWW2nioUrQjEajWl71rgqwkIjS5ZlYbfb4XQ60d/fD41Gwzd9UxQFrVYLrVbL77OFw2G8+eabcLvdsFqtiMVi0GgrsCHRY9Irxaw7etNONi5H8p3GTNN0wVPGdx7xqxCLiUTHxsbw2c9+lt9OeOyxxxKKP8oBImAiUexQy3A4jMnJSd5Fva2tTbCKJ4ZhMDMzg83NTRw4cCBjU2kx1YwOhwMTExPQarUYHBzM6XoUCgVkMhna29thoyO4uOTDlVU//FEWt/rYkt1IvinEXF04dis70chcaCT6rne9C9evXxf8/ISECJgApCpMKDQCY1kWq6urmJubw969e1FXV8fbNAnxY+FSefX19Thy5EjWCK+QKkJu4jPXG+fz+XK+lnCMwUJYjTcv2265ycblSCECVpajVLAzVYi3OkTARKKQCIwrnODcLbj9Hk4MS5k+5ESFpmk+lZcL+UZgm5ubGB8f5yc+OxwOeL3ebc9LFuglTxiXlmm8ve5HMFoJBIh4lQP5ClguNlK7GSJg4kIETCSkUmnOAsY5ua+traUsnOAELJ9Jxqng9q82NzcxOTnJi0o+kV2uAhaNRjExMYFgMIjBwUHevDhTEcj2ZmNCuZFKwDLdePl8Pmi1WqFPSzCIF6K4EAETgFRfYplMllMK0eVyYWxsDDU1NTh27FjKH3uxI1XiuXbtGliWTRCVfMglhbixsYGJiQm0tbWhoaFh2+eT/PpZRxAXlrwYsQVJs3GZI0/6/rIsm1XAyjUCoygyD0xsiICJhEwmyzhJORKJYGpqCjRNo7e3N+NdaLEVjdy+Gjf5NrlvJB8yRWCRSAQTExMIh8NpBZJ7vTcUw6VlLy4uemGjSbR1s5AcgWWjnAUMAKREwESFCJhIpBMdlmWxvr6OmZkZtLW15ZTCK0bAAoEALBYLlEoljEYjqqqqCjoORzoB4wZnZvJJZFgW084I/mhXYGlxgTQb34Qszs9A65bxzei5TPAu7yrEnT6DWwsyTkUA0qUQk/fAAoEArly5ApvNhuHhYTQ2NuaUQy9EwFiWhdVqxdWrV9HW1oaDBw/mnNbMRHIKMRKJ4Pr161haWsLQ0FDKlKEzEMX5SQee/H9W/KvFg4WAjIjXTUrXvg40NTXxDfGBQABXr17F7OwsNjY2EIkkRtterzfrNOZCx4e89dZbuO2223DgwAH09vbiF7/4RekuFFviJZVQJf1DyAyJwEQiXnQYhoHVasXKygo6OzvzjoLyHYXi8/lgsVig1+sTGpJL4UgffwybzYapqSm+3D9euGIMi5F1Gm8sejG5ESDNxrcISpkEer0eer0eDQ0NCAQCOHDgANxuN1wuF6xWK6LRKCoqKrCysoLV1VV0dHSkPV4x40M0Gg1+/OMfY9++fVhZWcHg4CBOnjxZUicbsgcmLkTARIKLwNxuNywWC6qrqwuayAzkXtHIMAzm5uZgs9nQ09MDg8GQ8HipBCwSieDtt98Gy7IYHh5OsMta94VxYdGLy8te+MJkOOStRrwTB1dCr1AoYDabYTabAWx9T30+H65cuYLXX38dv/vd7/Av//IvKScmFzM+ZP/+/fxzGhoaUFNTA7vdXjIBowBQIAImJkTABCBVGpBlWbjdbkxMTODQoUNF5flzicA4oaypqUlrOVWKoZY0TWN5eRldXV38vLNwjMHbqzTeWPRgzpm+cIVw8xNfxJGuB0wi2YrSPvnJT+LSpUv49Kc/jfb29pTPLXZ8CMfFixcRDodLa1xLkbSf2BABEwGbzYbJyUlQFIXh4WHBZoIBNxzd3W53VqEsphw/HA7DYrGApmm0t7ejrq4Oi+4QLix6cGWFRjBKoi1CbgIWD1eFWMzw10zjQwBgdXUVn/rUp/Dss8+W1AyAAqlCFBsiYAISDAYxNjYGiUSCoaEhXLlypSSNjjKZDOHwdicKbhpzU1MTOjs7s75XoSnEtbU1zMzMoKOjA04vjWtOFr9cWMKyh7hjEBJJFrBsgkHTdMYy+mLHh3g8Hnz4wx/G3//93+PYsWN5X082yB6YuBABE4iFhQUsLS1h//79fK6/VCRHTly/VSgUymkac/xx8hGwUCgEi8UCqVSKqo5e/O9aAG+tAFGWmOgSUpO8B5ZtZl22PrBixoeEw2F89KMfxb333pt1zEohUBQpoxcbImACEAqFEAqFcOzYsZIOsOSIr2jkoqFM/VaZjpOLgHGNz2MzC6D1zbA4WdiW7AWfP+HWgELiPLB8UojpKGZ8yC9/+Uv83//9HzY3N/HMM88A2JrI3N/fX/S1ckiJgokKxRYzjZCQEpZlU6b4XnvtNbzrXe8q+vhc+TG3IHR1dRU0KNNqtYKiqIRN8WQCwSD+9+oUZoJKLAVJvxYhdxRSCk/ceSO9t76+jkAggLa2trSvec973oO33nqrLD0Fq/b04INP/DT7E/Ng4tvnMk5kvtUhEZgAZPrxMQxT1MYxy7LY2NiAzWZDX19fUenJTEUcDn8EL46t4G1bGAG2fM1VCTtHKiPfTClEoUYEiQZFlff5lyFEwESEc74oVMD8fj9GR0ehVCphMpmK3lvjerg4uGbj1xbcmHYEwW7VVRX1HoRbF2Weo1SA8hcxUsQhLkTABCLTUMt8x6AwDIOFhQWsrq6iu7sbGo0GIyMjRZ8jtweWutmY/BAJxSGXJt6oxWIxKJXKtM8v990MUkYvPkTARKSQoZZerxejo6Ooqqrix6tEo9GiPQzDMQYjjhjeXAXWJpaKOhaBEE+VWoreWg366xKHomaLwILBYM4VtLsVIl/iQgRMRPIx4Y3FYpiZmYHD4cCBAwcSKrOKaUDe3mxMfnKE4tErpeitVaOvVoNmQ+qComzpc6/XW/ZO9CQCExciYAKRKoWYawTmdDoxNjaGhoYGHD16dNueQKYpxqkIRBhcWfHijUUvaTYmlAwlxaBBFsBebQz7qrUwGjWoqEgfYWUr4ij3USoA2QMTGyJgIpItAotGo5icnARN0+jv74dGo0n73FyYcQRwYdGLt1dpMtmYUBKUUgoHatToq1Wjw6SChNoaC+R2u7G6ugqv1wuJRMLP/zIYDHyLRy4RWDkPs6RAQVLGBSjlCBEwEckUgXGjSFpbW3MaapkObyiGS0teXFjywk4mGxNKgFxCoatahb46DTqrVAnNyQCg0Wig0WhQX18PYMsZxuPxwO12Y2lpCZFIBBUVFaBpGqFQCBqNJuX3u9xTiKBIBCY2RMAEItUPNNUYlHA4jLGxMbAsi6GhoYxVWulgWBYT9gDeWPTAYvOTZmNC0UgpYF+VCn21avSY1VDKcm/9kMvlqKqq4t3fGYYBTdMYGRnB8vIypqenoVAo+AhNr9dDJpPlnEI8f/48HnnkEcRiMZw7dw6PPfZYwuMLCws4e/Ys7HY7TCYTfvKTn6CpqQkA8Oyzz+LJJ58EAHz5y1/GmTNncr6ubFAgThxiQwRMRGQyGUKhrfEinD3T3NwcOjo6UFtbm/fxHP4ILi55cXHJC1ewuKpEAoECUKOIYKjJgMNNBmgVpekBlEgk0Ol0kMlkOHDgACQSCUKhENxuNzY2NjA7O4s33ngDr7zyCvR6PaxWK5qbm1PeBBYz0NLhcOCrX/0qLl++DIqiMDg4iNOnT8NoNJbkOgGABGDiQgRMRLi7zEAgAIvFAqVSiSNHjuTVFxZjWFxfp/Gqz4h/f3mRTDYmFE2zXoG+OjV6azWYnxhFZ2MDlCUSr3hYluX3wJRKJWpqalBTUwMA6OzsxObmJsbGxvDggw/izjvvxBe+8IVtxyhmoOXvf/97nDhxAiaTCQBw4sQJnD9/Hvfcc09Jro9UIYoPETCBSHX3KJFI4HQ6Ybfb0dnZmTBgLxvbm43zTzUSCBx1FXL01arRV6eBSX1jGSjGKSYbmfZ1dTodqqur8dGPfhTnzp1L+7xiBlqmeu3y8nIRV7QdqTAfHSENRMBEwufzYXJyEgzD4LbbbsvJpT4cY/DWKo0LZLIxoQRUqWXoq9vq1aqtSB31F+vVmYlsrR8+nw979uwp+n2yDbQUCgogVYgiQwRMYBiGwdzcHGw2G/bu3Yv19fWsPyYy2ZhQKgxcg3GdBk367BMLhBSwbGQbpQIUN9CysbERL7/8csJr77jjjpKdP7BV/EIQDyJgAkFRFNxuNywWC2pra3H06FGEw+GsKYtfj27gjwsekc6ScDOilUtw6B1XjLZKRd4tGUKY6TIMk/W4Pp8vaxViMQMtT548iS996UtwOp0AgBdeeAFPPfVUEVeVCEVRZA9MZIiACYTb7cbExAR6e3uh1W6NI8nFSiocIxEXIX8UEqBFFUEtPKhTRGGUGVARjSISMRQ0K67U5OJET9N01gismIGWJpMJjz/+OIaHhwEAX/nKV/iCjlJB9EtciIAJhF6vx/DwcMJdZ6o+sGQUZBeYkCNyCYVuswq9tRp0Vasge2f15BqJXS4XFhcXEYvFoNPp+L4rtVot+sgShmEy2kgBWxGYXq/PeqxTp07h1KlTCX/3xBNP8P9911134a677kr52rNnz/IRWakhbvTiQwRMICQSSUoPw2wQASNkQkoBbToJmhVB3HmwLWWDcapGYq/XC7fbjenpaQQCAWg0GlRWVsJgMKCiokLwfa9oNJr1PXJJIe5qKNLILDZktdxlKGXkB0BIhALQblTiY91G/O3tDfhYuxL7dGzO7hicN2FLSwt6e3tx5MgR7N27F1KpFEtLS7h8+TKuXr2Kubk5RKPRvEf+5EKuKcRcIrDdCgVAJintn1w4f/48Ojs70dHRgaeffnrb4wsLCzh+/Dh6e3txxx13YGnpxvikZ599Fvv27cO+ffvw7LPPluiTEA8Sge0ySARG4GgxKNBXu9VgrFPeWPzpIisFKYqCVquFVqtFQ0MDACAUCsHlciEajeKtt94CsJUG56K0QizO4slFwEKhEFQqVVHvs9OInZrd7c4kQkMETCAyfZEzjU0nEditTX2FnHfFiG8wjifT96dQlEolqqursbS0hMHBQUSjUd6Qd3l5mTfk5QQtnSFvOnIRsHJny4lD3Pfczc4kYkAETEBSzQSTSCRgGCbtj5lEYLce1RoZ74pRo81uKyZUr1b8cWUyGUwmE7+4cYa8LpcLc3NzoGkaarUaBoMBlZWV0Ol0Gc8p03ceuNHkLHYEU0q2Uojinv9udyYRGiJgIsONVEn3Y1aSTshbAoNSyrtiNObQYBxPLj1VhZBJZDhDXp1Oh+bmZrAsi2AwCJfLhdXVVUxOTkIqlSbMAYv3+Mz0nb+ZKPU/i91ux9DQEP//DzzwAB544IG8jrFTziRiQARMZLL1goUCtIhnQxCTCoUEB2vU6K/ToNWQf4MxR7wpbinJxweRoiio1Wqo1eqEOWButxsulwsLCwtgGAZ6vR4GgwHBYDDj/lYoFNoV/WrFQIEqeRWi2WzG5cuX0z6+251JhIbkqwQk1QKVbqhlLBbD2NgYbCvlFcITMiMDg24DcE+XFl98Tx0+0mVEW6WyqAhKiD0woPjUpFwuR3V1NTo6OjA4OIjDhw+jtrYWwWAQNpsNc3NzGB0dxdLSErxeb0J6PdcS+mwVd1arFXfeeScGBgbQ29uL559/HsCWuJ45cwaHDh1Cd3d3SR04eKitFGIp/2Qj3pkkHA7j5z//OU6fPp3wnI2NDTDMlkFCsjPJCy+8AKfTCafTiRdeeAEnT54s/eciICQCE5lUEZjD4cD4+DiamprQ17gXL/yRiFg5I5cAXdUqtGsZaAJ2VBkr4XKt4M2VKSiVSlRWVua0b5SOXJqCC6HUe2tSqZS/1lgsBr1eD7VaDbfbDavVCpqmoVAoIJVK8fbbb2d14cil4u7JJ5/Exz/+cTz44IOwWCw4deoU5ufn8atf/QqhUAjXr1+H3+9HT08P7rnnHrS1tZXseimUPoWYjd3uTCI0RMBEJj4Ci0ajmJychN/vx8DAANRqNRyByA6fIaEQpBSwv0qFvjoNOk1yyChsuWAoFaipqUFdXR2ArQncyftG3CJvMBhy2psQqohDyFEqsVgMMpkMFRUVqKio4NNcwWAQExMTOH/+PC5cuIB3v/vd+PjHP45HHnlk2zFyqbijKAoez5aXqNvt5tsEKIoCTdOIRqMIBAJQKBSC9JzthBPHbnUmEQMiYAKSKs3DRWCbm5uYmJhAc3Mzuru7+eeSKsTyQUIB7SYV+us0OFirgUa+JT7RaBRjY2OgKAr79+8HRVFgGAYsy0Imk6G6uhpmsxkSiQTRaBQulwubm5uYnZ0FAL6yr7KyMuWwUyFTiEJt7qcro1epVOjr68PDDz+M2tpafP3rX8fKykrKY+RScfd3f/d3+MAHPoB/+qd/Ak3TePHFFwFsLeK//e1vUV9fD7/fj29961sljzYo4sQhOkTARIaiKFitVkilUj7qiodUIe52WFRJI2hRhnCgWolGsxYGgwryd8TL6/VidHQULS0t/N0/BydiDMOAYRg+lVxZWcm7p0ejUXi93gQfQ64QorKyEiqVSpQy+lKTrQ/M6/Um+DUWys9+9jPcd999+PznP4/XX38dn/rUpzAyMoKLFy9CKpViZWUFTqcT733ve/H+97+fj+ZKBfn1igsRMBHZ2NiA1WpFZWUl+vr6Ut5Fy6USSCiAyTz7jyAyDTo5+uu06K/XwqiWIRKJwOVyweFwYHZ2FizLQiqVIhgM4uDBg6isrNx2DE4cuIU8XtBYlkUsFgNFUbxgcZEb1381Pj6OcDjMC6BKpcq7oTgTQgpYtugulyKOXCrufvjDH+L8+fMAgNtuuw3BYBAbGxv46U9/ig9+8IOQy+WoqanBu9/9bly+fLmkAkaBRGBiQwRMQLiFJRKJYGJiAuFwGO3t7QiHwxkXHYWUQjBKFGynMWtk6K/Xor9Oi5qkCcZyuRxmsxlmsxnRaBSjo6OIxWKora3F9PQ0otEo9Ho9jEYjjEZjyhLyZEEDwEdmnLAB4G2fuPTZ2NgYWJbF7Ows/H4/b8xbWVmJioqKggVNyD2wbH1guYxSyWUWWEtLC/73f/8X9913H8bGxhAMBmE2m9HS0oKXXnoJn/rUp0DTNN544w187nOfK8m13YCChLjRiwoRMIGx2+2YnJzEnj17UF9fj42NDfj9/oyvUUglCEYzzw0jCEOlSoq+Oi0G6rU5NRh7PB5YLBa0tbXxhRrAlhB5PB44nU5YLBaEQiHodDpUVlbCaDSmjZwkEkmCiHDRFveHE7aqqio+yuN8DBcXF+Hz+aBQKBIqHXPd19qJPTAOr9ebNXWYS8XdP/7jP+Izn/kMvvWtb4GiKDzzzDOgKAoPPfQQ7r//fhw4cAAsy+L+++9Hb29vSa+RAulLEhsiYAKyvr6OpaUlDA0N8WaoMpks61BLpVQCgAiYWFQoJOit1aK/XpNzjxbLslhcXMTa2hoOHTrEDy3lkEgkvIjs2bMHLMvC6/XC6XRienoafr8fWq2WF7R0kVO8oHGRnlKphF6vB8uyYFmWjwZra2tBURRf6bi+vo6pqSneIYOrdExXgi9UeT6Qvfna5/Ohqakp63GyVdz19PTgT3/607bXVVRU4Fe/+lUeZ1wYEpJCFBUiYAJSW1sLk8mUsDCla2SOR0EMfQVHLZPgYK0a/XVadFSp8lp4IpEILBYLlEolBgcHc4pauL0tvV6P1tZWsCzL723Nz8/D5/NBpVLxKcfkHjGfz4eRkRG0trbyzhfA9sIQLoqqqqpCdXU1JBIJYrEYXC4XnE4n5ufnwbIs7zRfWVnJO2AIuQeWjVxSiLsdUoUoPkTABISiqG131dmspABSSi8UcimFA2Y1+uu16KxWF2S86na7MTY2hj179qC2trbgc6Eoiu+Jampq4r0FnU4n71Qhl8tRWVkJhmGwsbGBQ4cObSt0yFQYEr+fZjAYYDQaeUHjKh05p3mdTodwOAyFQiFImX624/l8vrIXMKC8zYjLESJgIpNLBEZGqpQOKQV0VqsxUK9Fj1kNRa5TApNgWRZWqxXr6+vo7e2FRqMp6XnGewty5fd+vx8WiwXBYBAymQxjY2N8yrGysjJlui+XSkcA0Ol00Ov1/MQEmqYxPT2NlZUVLC0tQavV8mlHrVZb9MKcPJUhmbKfxvwOpIZDXIiACUimRuZMkAisOG40GGtxqFYDtby4zzMSiWB0dBRqtRpDQ0OipNkCgQBGRkZQV1eH5uZmUBSVsnQ/XtBSmeHmWumo0Wig0WhQV1cHvV6PQCDApzf9fj9UKhW/h5avBVYu7vnlPo0ZIGX0OwERMJHh5oFlgjQzF0aNIop6iQ97K1g0VFXCqFNBWWRRncvlwtjYGNrb21FTU1OaE82C3W7H9PQ0uru7E/rJ4kv3ga2iDrfbDafTCavVmlPpPpC60nFlZQU+nw9KpRIMw0CpVKKmpobfb+MKQ1ZWVhLSm1zjcaZ9wFyqG2+OFCIFirQyiwoRMJHJJRVDIrDcadQp0F+vQV/dVoMxsBW9xO8lKRQKflHX6/U5RQ8sy2JhYQF2ux39/f3bHFOEgGVZzMzMwOPxYHBwMOt4EZlMhqqqKlRVVQFIX7rPRWipSvcZhsHk5CTC4TCGh4chlUoT0o1cpSNngVVTU8NHg263GxsbG5idnQVFUQmVjvEWWLlMY+acOMoZEoGJDxEwASl030B+c8yaE4warQx977hipJpgnLyXxBVHrKysYHx8HHK5nBc0g8GwTdDC4TBGR0eh1WoxODgoSsowFAphZGQElZWVGBgYKOi7k1y6zzAMfD7fttJ9TtDkcjlGRkZQXV2Nzs5O/j2zFYZw1YpGo5G3wGIYJuUssMrKSiiVyqwCFgwGRblJEBQK2KEizlsWImACw22S54rP58PG2ioAbdbn3koY32kw7s+xwTgelUqF+vp6Ph0WCoXgdDqxtraGiYkJyGQyXtBYlsXk5CQ6Ojr4VJ3QOJ1OjI+PY9++faiuri7ZcSUSScrSfafTicnJSbhcLj7q8Xg8afe2cq105LwM9+zZg1gsBp/PxzdYcwUpnMCq1eptIp3tRuH8+fN45JFHEIvFcO7cOTz22GMJj1utVpw5cwYulwuxWAxPP/003zN27do1fPazn4XH44FEIsGlS5cyDtgsBBKBiQ8RsB2A87iL/8FyKavV1VU01O7ByHxmt45bARXFoL9Bi8FGPVqLHAIZj1KpRF1dXcKIE4fDgampKdA0Da1WC7fbzUc0QrlTcJWNNpsNAwMDJV9Qk6EoClqtll/gb7vtNlAUta10n4vQ0u1t5VrpWFFRwVc72u12NDQ0wOVyYWZmJsECa21tLeu5FzMLLBqN4pOf/CSee+459PX1YXNzM6XLfykge2DiQgRsB+AqEbmFwO/3Y2RkBAaDAUePHsUbSz4At6aAqWUU6qR+HDIr8a7uVkhFyMmwLIuVlRVUVVVheHiYH3GysbGB6elpXsgyla/nS7yrhlhpylgshvHxcQBIaMCOT7dy0anNZsPU1BSfKuQip1xK94EblY4Mw8Bms0GlUvGp3fhZYBsbG/j6178Oq9WKD3/4w7jzzjvx13/919veo5hZYC+88AJ6e3vR19cHAPyeoRAQJw5xIQImMKlS8plR/AAAIABJREFUiFwvmEwmw+LiIpaWltDT08NXnN1qVYgKKYUesxqtqjAkjkX0dHeldHMXAofDgYmJCezfv59f2BSKrSGUXNVhcvk6gARBy/dunhu5kuyfKCTBYBDXr19HXV0dmpqa0kazydFpqmvnmqLTle4D4BumLRYL9Ho92traEtKOwNbnXF9fjx//+Mc4efIkvv/97+P69espj1fMLLDJyUlQFIWTJ0/CbrfjE5/4BB599NE8Pr3coECRFKLIEAHbAaRSKfx+P18ocPTo0YS711uhClEm2Wow7q/TYr9JjtmpSbBhFl1Dg4Kld+Lh3NxdLlfW9F1y+Tq3qLtcLszNzQG4sagbjcaM57+ysoLFxcWU/olCwYl0cll+LuRSus9VH8aX7tM0jevXr6d1LIlPN/7oRz+C0+lES0sLWlpaCr7OdLPAotEo/vjHP+LSpUvQaDQ4fvw4BgcHcfz48YLfKyXUlp0UQTyIgIkMy7IIhUIYHR3FwYMHU06FvVmdOCQU0GFSob9ei4M1Ww3Gbrcb165eR0tLC+rr60Wx4omv+Dt8+HDe75luUXc4HHwFXrygKRQKxGIxTExMIBaLYXBwUDDT3HiE2GNLVbrPVR9ypfsKhQJ+vx9dXV1pi1K44Z2PP/44VldXMTIykvF9i5kF1tTUhNtvv50/l1OnTuHKlSslFzBSxCE+RMAEJn5x5IQrEomgq6sr7UjzmysC25pg3KYK42CNCo1mBQyGrX6k+fl52Gw2QayZ0rG5uYnJyUl0dnaWbKR88qIeb567uLiISCSCcDiM6upq7N+/XxTx4tJ3MplM0D02bo/MaDSira0NCwsLWFtbQ2NjI5aXlzE9PZ1Qus+57rtcLpw9exZHjx7Ft7/97aznV8wssJMnT+If/uEf4Pf7oVAo8Morr+Av//IvBfk8iH6JCxEwkVhdXcXs7Cw6OzvhcrkyltbfDALWpFegr06D/jotKtUyhMNhvnR9fHwcoVAIWq0WHR0dglffAVuRwuzsLDweDw4fPsyPtxECzg2+qqoKdrsdU1NT2Lt3L8LhMEZGRhCJRBIitFKfC1cU1NjYuC1KEYpYLIaxsTFIJBIcOXKEF6T40v35+XlcuXIFzz33HGw2G+6//348+uijOYlrMbPAjEYj/uqv/grDw8OgKAqnTp3Chz/8YUE+B1KFKC4Um0+TEiFv/H4/rl+/DolEgu7ubsjlcszPz0OhUPBVUsls+iP4+5cXUz62m6nRytFfvyVa5hQNxsANm6Q9e/YA2Nqf8Xg8kMvlMJlMebll5EowGMTIyAhMJhP27NkjSpqSYRjMzMzA5/PhwIEDCcUOXNrN6XTC6XQiEonkZAGVC1yE2dPTk3VAZKkIhUK4du0a79uYiZdeeglPPPEEPvShD2FxcRE1NTV4+umnRTlPoek+NIAf//blkh7zwY8fx+XLl0t6zJsJEoEJDNf/Eu+jJ5VKMzrSl1MEZlRL0V+nRX+dFg0ZGowZhsHU1BT8fn+CTRJX7ZbslsHZP5lMprzNY+PhBLOrqwtGo7GgY+QLt8dmNBrR39+fsmGXEysgtQVUvKDl4lDBsizm5+fhcDhysqEqFW63GxaLJWtKlmVZ/PCHP8QvfvEL/OY3vxEtMhQTCmScitgQAROYpqambWIlk8kQDAbTvma3F3HoFNKt9GC9Fq2V2dNfNE1jdHQUdXV12L9/f8ofebJbRvxsLI/HA6VSyUdouQgawzCYnp4GTdOiLuicq0Z8WX42UllAcdObx8fHEQwGeU9DTtDiP0Oup0ylUmFgYEC0oZSrq6uwWq3o6+vLuIcZiUTwN3/zN3C73XjxxRfL3zIqHaQKUXSIgO0A2SIwuWQrk76bcrsauQQHazQYqNdgrym3CcYsy2J5eRlLS0s4cOBAXmatyYIWb9Dr8Xj46cVchBa/oHOjSMxmM/bt2yfKXXG8+W+xFX8SiYR3eef6pzhBm5ycRCAQQEVFBZ9unJ6e3japWUhYlk24OchUlOJwOHDmzBnceeed+N73vrdjE5/FggiYuBABE5hUi2e2oZYURUEhpRCK7ayEKaUUemo06K/ToLNaDWke0/oikQjGxsYgk8l4l/NiSDboDQQCcDgcsFqt8Hq9UKvVfEpueXk5oTFcaCKRCCwWC1QqlSAVfxRFbfM09Pl8sFqtmJqagkKhgN1uRzQahdFoLMkAynREo1Fcv34dOp0OfX19Gd9nfHwcn/70p/HlL38ZH/vYx26B9BpFnDhEhgjYDpDrUMtQlucIgUwCdFWr0f/OBGN5AftxXOpr7969KZtYSwFnSdTY2AiWZeH3+zE+Pg6apiGTyTA/P8+nHLnSbSHgXDXSNewKhc1mQygUwnve8x7IZDK+0m92dhY0TUOj0fApx1JdP1eQ1NramtVB5MUXX8Tjjz+OZ555BgMDA0W/dzlAvfOHIB5EwHaAbBEYAChkFBAW53y4BuOBei0O1mqgkhUWQTAMg7m5OTidTtFmaAFb+2UWiwU1NTU4fPgwgK3Flivd9vl8/IJuMplKFqFw6VExXTUikQhGRkag0+kSxq5UVFSgoqICzc3NvKA7HA7Mzc2Bpmk+QuX2EPO9fq668cCBAxknJzMMg+9///v4r//6L/z+978XzSprt3DzR5m7CyJgApPqC51rBCYsLBo1Egw0aDHYXIkKRXEpvkAggNHRURiNRhw+fFi0vY719XXMzc2hu7s7oWxcq9VCq9WiqamJX9DjIxSuubaQlBtnisuyLIaGhgRzq0/G5/NhZGQEe/fuzTgdmnOd12q1CYLG2T95vV5+DzFbUQzLslhcXITNZsvaPxcOh/H5z38ekUgEv//970Xp79tt5JFlJ5QAImA7QLYILBwOIxr0Q4h/Hq7BuKOCRZR2wem0YuTKFPR6PZ9yy7exdn19HbOzs6KWqsdiMUxNTSEUCmFwMLN/YvyCzglacsqNEzSTyZRycjEH1yTc0NCAxsZG0e6419bWMD8/X1C0l+r6uaKYxcXFtILGMAzGxsYAIOtNycbGBs6cOYMPfehD+MIXvnDTF2ukhOQQRYcI2A6QKQKz2+2YnJyEVtUIe4QpyfvVxjUYV8c3GJsr0dbWxvchORwOLC8vIxKJoLKykhe0dOLA+ftFo1EMDQ2JYsIL3BCRurq6hEnCuUJR1LaUG03TcDgc/ORirsrPaDTygmaz2TA7O4uenp6MabRSwrUD+P1+DA0NlcSGiqIoaDQaaDQavh8rvsrT6/VCJpMhEAigpqYGHR0dGQXJYrHg3LlzeOKJJ3D69Omiz69coUDGqYgNETCBSbW4pvo7TgwCgQAGBwcxMeIEvIGC39eklqG/ToO+ei0adJl7oOL7kLhziTenZVmWFzRuJhRXvNDc3IyGhgbRI5FSiki8oLW0tPBVfk6nE1NTUwgEAmBZFhRF5d0OUAyc9VRlZWXWir9iia/y9Hg8/OiVSCSCS5cu8YMujUYjDAYDL2i/+93v8LWvfQ3PPfccDh06JNj5lQtEv8SFCNgugHPybm5uRldXFxiGgbyAmWB6pRS9tRoM1GvRkkODcTqkUilMJhPvrMANeHQ4HJiZmUEkEgHDMOjo6EBtba0o4pUc7QlpiEtRFHQ6HXQ6HWpra3Ht2jXodDqoVCrMzs4iEAjwjcUmk0mQYhWPxwOLxYKOjo60ju5CsLa2hoWFBQwMDCQ0J3ODLtfW1jAxMYHvfve7CAaDWF5exn/+538WNQblZmInvBDPnz+PRx55BLFYDOfOncNjjz2W8LjVasWZM2f4SdxPP/00P626u7sbnZ2dAIBjx47h+9//vujnXwxEwEQg1VBL4IZfnsPhQG9vL9RqNWKxGCiKgjLHSkC1jELvO1ZOe01KQVIYMpkM1dXV0Ov1oGkaOp0OJpMJLpcLVqtVUB9DYMvJI96cVqxoj5ujlWyTxDUWOxwO3ikjX+unTHAzw8R06WdZFjMzM/B6vSmbk+MHXQaDQahUKgSDQbzrXe/Cn//5n+Pf/u3f0NraWpJzaWtrg06ng1QqhUwmw+XLl+FwOHD33Xdjfn4ebW1t+OUvfynafms+iF3EEYvF8NBDD+EPf/gDmpqaMDw8jNOnTydMqn7yySfx8Y9/HA8++CAsFgsvXgDQ3t6Ot956S9yTLiFEwHaIWCyGixcvwmw2Y2hoiJ9WS1HUloBlqEKUgUGHQYrb9lbn3WBcKFwZ9b59+/iIgCuRDoVCcDgcvI8hZ/tkMpmK7kFaXf3/7d17VNRl/gfw9zAgg4BcRRAQA0HuDDdR1wtYaLdlJV0jTTErLNssq7NqbqzbaRNXc+tsF2vXlLXEtbazmrko4Jo3jBRU7oggAnKfGbkOA995fn/4+34DRS4y84XRz+uczvECMw9q3w/P83wuNaioqBD16I7vK9jU1NRnV42ehcX8HSLfKaOwsFDoZcgH9cFm42m1WpSUlECj0Yia3djd3Y28vDyYm5v32buxp7q6OqxYsQKLFi3C2rVr9Zas8b///a/XzjMpKQkPP/wwNmzYgKSkJCQlJWHr1q16ee97NRK9ELOysjBlyhS4u7sDAOLi4nDw4MFeAUwikaC5uRnArd6Vd2sibogogImMHzKoVquFbhH8iPWeD4Mxtx0hGhtJMGmsFhPYTcwL9ID1OAtR1ssnEbS2tt41jdrU1PSOtk/8/VlLSwvGjh0rBLT+Mvx64lPVtVqt3o8Me+rq6kJ+fj7Gjh076HKA21s/8QFNoVCgoKAAGo1mwG7znZ2dyM3Nhb29/T0lptwrvjiZHyjan8uXL2P16tVISkrCY489Jsr6eAcPHsSJEycAAPHx8YiMjBx1AQwS8Xdg1dXVvSYAuLi44Keffur1MZs3b8b8+fPxt7/9DW1tbUhPTxd+r7y8HMHBwRg3bhzee+89zJ49W7S16wIFMBHwR4j8WA9zc3NYW1tDJpP12nX1NEYqgZEE8LSTwc/OBKgvx3hbK7i7B4iWosw34XVwcOhVNDuQvrpk3J7hxwe0vo7bWltbkZ+fDxcXF1ETRPh7p+F21egZ0PjmvHy3eX6gac8dWkdHBwoLC3U6ZHMw+CPSgUavMMbw/fffY+vWrdi/fz98fHz0ui6JRIL58+dDIpFg9erVSEhIQF1dnRBgHR0dUVdXp9c13DMd/1NtaGhAWFiY8POEhAQkJCQM6TVSUlKwcuVKvPnmm8jMzMTy5cuRl5cHJycnXL9+HXZ2drhw4QIWLlyI/Px80TJsdYECmEh6DrS0sbHBtWvXkJOTAwsLC9jZ2cHW1rbXd+aBE8YibKI5mpvqcf16Oby9vUXr7ccYEzqNDzfbr6+i2tbW1jvuj/iHeVNTk9D818JCnF0m33S4urpaL101+uo239zcjKamJly9ehVqtRoODg7QaDTQaDR675zPGENVVRVqa2sHLE7WarXYvn07zpw5g/T09EF32B+O06dPw9nZGfX19YiOjoa3t3ev3+/rG77RQfe9EMePH9/vPDBnZ2dUVv4yO7CqquqOUTW7du1CamoqAGDGjBlQq9VobGyEg4OD8HcfGhoKDw8PlJSU9AqYox0FMBEUFxejvb1daGqr1Wrh5uYGNze3O46a+HR1CwsLlBaXQiqVin6EVlRUBCMjI728b88MPzc3N+Fh3tjYiJKSEjDG4ODggLa2Npiamuq9tow/qgQg2r2TkZERLC0tUV1dDWtra3h5eQlBvaqqCt3d3bC2thaOHHUZ0LRardBFZKDGwx0dHVizZg3s7Oxw5MgR0er8+Aewg4MDYmNjkZWVhQkTJqCmpgZOTk6oqanptxPJSLl1Bybue4aHh+PKlSsoLy+Hs7Mz9u/fj3379vX6mEmTJiEjIwMrV65EYWEh1Go1xo8fj4aGBtja2kIqlaKsrEyYHG5IKICJwMPDQ0jSuP3I8PZkAJVKherqauTm5kImk8HBwQHNzc2wsrLS+8NVpVKhqKgIkydPFq2HnZGREaRSKRobG+Hp6YkJEyYINWjXr1/vswZNV/gjUv64UyxqtVqos3JxcRHG3vNZdXwdHt8pg+M4WFlZCX8G9xrQNBoNLl++jPHjx2PSpEn97mJqamqwfPlyLFu2DGvWrBFtx9PW1gatVgtLS0u0tbXh2LFjSExMRExMDJKTk7FhwwYkJyfjN7/5jSjrGSqx0+iNjY3x8ccfY8GCBeA4DqtWrYKfnx8SExMRFhaGmJgYfPDBB3jxxRfx17/+FRKJBHv27IFEIsHJkyeRmJgIExMTGBkZYefOnaIeYeuChPWV30106vPPP4e3tzeCg4P7fQBzHCfMWfL19YVUKoVSqYRCoYBKpRLS1W1tbTFu3DidPVT4rLvGxkb4+/uL1oS359Gdv79/n0d3PWvQVCqV8LC3tbUdVlDneyiK2VUD+OXeycfHZ9BHwj0Ly5VKJbRaba8d2mB2Rnzhuaen54DHgNnZ2VizZg127NiBRx55ZFBr1JWysjLExsYCuPV3v3TpUmzatAlNTU1YsmQJrl+/Djc3Nxw4cGDUPWwD5aH44fhZnb5m7Pxf9XuE+KCjACaCPXv2IC0tDZcuXYKXlxciIyMRFRUFd3d3IQi1tLSgoKAATk5OcHV17TM4qdVqKBQKKBSKe87u6+s18/PzYWVlBXd3d9ESRLq7u1FQUABjY2NMnTp10IGoq6vrjqDOB7TB1KBptVqhu4afn59ox2J89ml9fT0CAgKG1eiW4zioVCoolcpeAY3fod3+NfHBeqD7PcYYvvvuO3z44YdISUmBl5fXPa/xQRQYHIojxzN1+poLo2dSAOsHBTARabVa5OfnIy0tDRkZGaisrERISAg4jsO4ceOQmJg46Fqnntl9CoUC7e3tQjKEra3toBry1tfX4+rVq6Jnv/HZfrqYIsx3iFAoFGhuboapqeldJzXzWaD29vZwc3MT7ViM47hewVrX3yTwu1Q+oAEQdmgqlQqtra3w9/fvN1hrtVps2bIF2dnZ2Ldv36gsEh7tgoJDceR/ug1gv3mEAlh/KICNoIqKCixevFj4blytVuNXv/oVoqKiMHPmzCFlwzHGhIa8CoVCaMhrZ2cHGxubXkeXHMcJBbM+Pj56z3rrucaqqirU1NTAz89PLzO0+Ka0t+9SjYyMUFFRAW9vb1GDdUdHB3Jzc0W9Z+vu7kZTUxNKS0vBcVyvTvN93SO2tbXhpZdegqurK7Zv3y5awtD9Jig4FKkndBvAfv0wBbD+UAAbQY2NjcjPz8fcuXMB3NqZ/Pjjj0hLS8OZM2dgaWmJuXPnIioqCiEhIUN6sPDHTPy9CX93ZGZmhsrKSjg7OwsJBGLo6upCQUEBxowZAy8vL1Gy/fgu86Wlpbh58yZMTEyENlj66mHYE9+9ZKA6K13jg6arqyucnJzQ1dUl7NBUKhUAwMbGBtXV1XBycsLLL7+M559/Hi+88MIoTU83DEHBoTj64zmdvuaT82ZQAOsHBbBRiq/FSk9PR3p6OnJycvDQQw8J92eenp5DOorSaDQoLS1FfX09xowZA5lMJjzI72VC71DcvHkThYWFwy4QHip+erGFhQU8PDwgkUiEdHWFQnFHDZquBjDySTEKhQIBAQGi7XABQKlUoqioqN+gyQe0t99+G+np6ZgwYQJiYmLwxhtv6HR3ynEcwsLC4OzsjMOHD2PlypX48ccfhXXt2bMHcrlcZ+830oKCQ3FMxwHsCQpg/aKzglFKIpFg4sSJWLFiBVasWAGtVovi4mKkpaVh8+bNKCsrg1wuFwJaf13hNRoNCgoKIJPJMHv2bEilUiEhpKKiAq2trTA3N++VEKILfOJCXV2dqI1pgV+C5u3Ti2+vQePr8PgOGXy6+r3WX3V3dyM/Px8ymQzBwcGiDnbkj2f76t/Yk7GxMTIyMlBSUoKzZ8/CxsYGp06dGvIg04F89NFH8PHxEfrwAcC2bduwePFinb7PaCGR0DwwsVEAMxBGRkbw8fGBj48P1q5di66uLpw/fx5paWlYtWoVmpubMXPmTERFRWHWrFlCMkhDQwNKS0vh4eHR60Euk8kwceJETJw4sddAx+LiYqjVauFBbmtre08Pcr6noJmZGcLCwkR7kPOp+Tdu3BgwaPbV8olPV+frr3oO9hzoCJfvmj+YvoK6xH9zw3EcQkJC+j2e5TgO7777LoqKipCeni7shnRdV1VVVYUffvgBmzZtwo4dO3T62qMZxS9x0RHifaK1tRWnTp1CWloaTp06BRMTE5ibm8Pe3h4ffvjhkDq595zQrFAohvwgV6lUKCwsvCNo6hvHcSgsLIREIoG3t/ew79nudo/IJ0P0fH1+WrOYXfOBW7vr3Nxc2NnZDZhZ2dLSghdffBFTp05FUlKSXu8hFy9ejI0bN6KlpQXbt28XjhAzMzNhamqKhx9+GElJSTrf9Y0keUgoMk79NPAHDsGCudPpCLEftAO7T1hYWOCxxx7DY489hrq6OixcuBB2dnaQyWSIjo6Gi4sLIiMjMW/ePHh7e/e7I+rZu8/d3R0cxwmZfWVlZTAyMoKtrS3s7Ox61V4xxlBRUYGGhgbI5XLRCqKBX3Y/Li4uOsv2k0qlsLOzEwp/+Rq0xsZGlJbeavNlY2MDtVoNtVqN0NBQ0erKgF+Kkz08PDB+/Ph+P7aiogIrVqzAK6+8gvj4eL3eeR4+fBgODg4IDQ0VOsgDwJYtW+Do6AiNRoOEhARs3boViYmJeluH2CR66IVI+kc7sPsQn/EXFBQE4FZgKS0tFerPiouLERAQIAS0oXZ812g0UCqVaGpqEmqvrKys0NTUhHHjxg05wWS4+EJdsXc/bW1tyM3NBXDrz7jnHDR9J8bwO77BNB8+e/Ys1q1bh88++wyzZs3S25p4GzduxN69e2FsbAy1Wo3m5mY89dRT+Oqrr4SPOXHihLAzu18Eh4Thf2d0uwN7ZHYE7cD6QQHsAcRxHLKzs4WAplAoEBERgaioKMyePRtWVlZDevjW1taipKQEZmZm6O7uHnBciq7wXTX42Wpi7n5aW1uRl5fXK0nk9ho0MzMz4c/B3NxcJwGNMYby8nKoVCoEBAT0+zUzxrB3717s3r0bBw4c0NnE5KHoGaj4ZryMMaxbtw4ymQxJSUmir0lfgkPCcOJMlk5f8+HZ0yiA9YOOEB9AUqkU4eHhCA8Px9tvv42Ojg6cPn0aaWlpQsPPOXPmICoqChEREXe9p+g5uXjatGmQyWR9jkuxsrISCqp1lVLON8QdP348vLy8RK1fqq2txbVr1+7Y/ZiZmcHMzExIjGlvb4dSqURZWRna2tpgYWEhdAkxMzMb8po5jkN+fj5MTU0hl8v73eV2d3cjMTER169fR0ZGhmijafqzbNkyNDQ0gDEGuVyOnTt3jvSSdEoyAgMtH3S0AyO9MMbQ1NSE48ePIyMjA+fOnYOjo6OQru/v7w8jIyO0traipKQElpaW8PDwuOvDtGdmn0KhELrL29nZ3ZEIMVh8gbC3t7eoLY/46dTt7e3w9/cfUmE5H9j5HVpHR8eQatD44mR+yGd/bt68ieeffx7BwcF49913RSkaJ0BIaBhOnv1Zp68Z+atw2oH1gwIY6Rd/ZMUXVBcUFMDR0RHXrl1DcnIy5HL5kHYS3d3dvZrxSqXSXh32+9tV8GtRKpXw9/cXNYNNo9EgLy9PGEo53B1fzxo0pVIJjUZz1xo0vjh5MB3sy8rKEB8fjzfeeANLly6lzhoiCgkNw6lM3QawuTMpgPWHAhgZkm3btuHbb79FdHQ0fv75Z9TW1mLatGmIjIzE3LlzYWNjM6SHZmdnp7A7a25uvuu9kUajQX5+vtBVQ8wkEb758JQpU2Bvb6+X9+i5U1UqlULpAmMMN2/eRFBQ0IC7tJMnT+L3v/89/v73vyMiIkIv6yR3FxIahtPndBvA5sygANYfCmBkSPLz83vVWHV2duLs2bNIS0vDiRMnwHEcZs+ejaioKEyfPn1ISRyMMXR0dEChUKCpqQnt7e2wtLSETCZDfX09PD09B0wX17UbN26gsrISAQEBonYS4QvB29vbhaNK/v7s9qNXxhi+/PJLpKSk4MCBA3BxcRFtneQXjz76KBobG3X6mvb29khNTdXpa95PKIARnWGMQaVS4cSJEzh27BgyMzNhZ2eHqKgoREVFITAwcEj3MVqtFlevXkVtbS1kMtkdBdX6zDrUarVCx34/Pz9R75G6urqQm5sLGxsbTJ48GRKJROhf2PPo9ebNm1Cr1cjIyIBSqcTu3btFDbKEjDTxzmHuY6mpqZg6dSqmTJnSZ1rwyZMnhW7y3377ba/fS05OhqenJzw9PZGcnCzWkvWC71QRGxuLzz77DDk5Ofjyyy/h5OSETz75BDNmzMDy5cuxa9culJWVob/vnfiMu66uLsycORPh4eGYNm0aHBwc0NzcjIsXLyIrKwtXrlxBU1MTOI7T2dfR2dmJ7OxsmJmZISAgQNTg1draigsXLsDV1bXXXZuJiQnGjx+PqVOnIiIiAoGBgVCr1di2bRsOHjwIlUqFH374QS9r4jgOwcHBePLJJwEA5eXliIiIwJQpU/D0009Do9Ho5X0JGQjtwIaJ4zh4eXkhLS0NLi4uCA8PR0pKCnx9fYWPuXbtGpqbm7F9+3bExMQIzUwVCgXCwsJw/vx5SCQShIaG4sKFC/ftMEGtVou8vDyh/qyqqgphYWGIiorCnDlzYG9vD4lEgubmZhQWFsLV1bXfjLu+pjP3TAi5lwQGvg2W2EM+gVt9K69evQp/f/8B095LSkqwatUqbNy4EYsXL0Z1dTVUKhX8/f11vq4dO3bg/PnzaG5uxuHDh7FkyRI89dRTiIuLw0svvYSgoCC8/PLLOn9fQgZCdWDDlJWVhSlTpsDd3R0AEBcXh4MHD/YKYJMnTwaAOxIPjh49iujoaOFBGR0djdTUVDzzzDPiLF5kRkZGCAwMRGBgIN58801oNBpkZmYiPT0dn3/+OTo7O+Hq6oqWpt6PAAAOVElEQVSamhrs379/wPsuExMTODg4CIXEarUaSqUSlZWVvYZZ8h32+wtoPZsAi90Gi6+nUyqVg2pHlZGRgU2bNmH37t0IDQ0FALi4uOjl7uv2pryMMRw/fhz79u0DAMTHx2Pz5s0UwMiIoAA2TNXV1XB1dRV+7uLigp9+Glw7mb4+t7q6WudrHK3GjBmDuXPnYu7cuWCM4eWXX0Zubi7kcjmeeuopWFpaCvVnwcHBA9ZdyWQyODk5Cd0e2tvboVAohNotvu7K1ta2Vwo+x3EoKioCAISGhop6ZMhxHAoKCmBiYjJgcTJjDF988QW+++47HD16VJSO96+//jr+8pe/oKWlBcCtGryeU50ftH+zZHShAEZGBYlEgqVLl2L27NmQSCRgjOHGjRtIT0/HP/7xD+Tk5MDDw0MIaFOmTOn3YS+RSGBubg5zc3O4urqCMSZ02M/Ly0NXVxesra1haWkpTCYWc0I1cGvHePnyZTg7Ow/YgFij0eCtt96CWq1GWlqazoZv9uduTXkJGS0ogA2Ts7MzKisrhZ9XVVUNuhu6s7NzrwdDVVUVIiMjdbxCwzFnzhzhxxKJBM7OzoiPj0d8fDy0Wi2KioqQlpaGxMRElJeXIzg4GJGRkYiMjOx3oCf/ej1nf3Ech8rKSly5cgVjxoxBbW0tOjs7hTR1fdeZ8XdtgylObmpqQnx8PKKjo7F+/XrRauDOnDmDQ4cO4ciRI0JT3tdeew0qlQrd3d0wNjYe0r93QnSNshCHKTw8HFeuXEF5eTk0Gg3279+PmJiYQX3uggULcOzYMSiVSiiVShw7dgwLFiwQfn842Y2PPvoorK2thcwxQ2dkZARfX1+89tprOHToELKzs7F69WpUVlbiueeew+zZs7F+/XqkpqYKx113wxhDVVUVGhoaMH36dMycORNyuRyWlpaoq6tDVlYWsrOzheQbXec53bhxAyUlJZDL5QMGr4KCAvz617/G2rVrsXHjRlELuLds2YKqqipcu3YN+/fvx7x58/D1118jKipK+PeWnJys82GYhAwWZSHqwJEjR/D666+D4zisWrUKmzZtQmJiIsLCwhATE4Off/4ZsbGxUCqVkMlkcHR0RH5+PgDgyy+/xPvvvw8A2LRpE5577jkAw8tuBG5d9Le3t+Pzzz+/r0ZW3E1LS4sw0PP06dOQyWTC7iwsLExIjODvnIyNjTF16tS7BgS1Wi10CGlpaYG5uXmvDvv3ctTId8/v7OwcVG3Z0aNHsXnzZvzzn/8URuOMlJ5d5cvKyhAXFweFQoHg4GB89dVX99VgSmI4KICNUpmZmdi8eTOOHj0K4NZ3w8CtWUu3W7lyJZ588sleAQy4P2cuDQZjDPX19UL/xvPnz2PSpEmQy+U4ceIEvvjiCzz00ENDer22tjahQ4harca4ceNgZ2cHW1vbQXXY54uTB9NLUavV4pNPPsGRI0dw4MABTJgwYdBrJeRBQndgo9RwshsfdBKJBBMmTMCyZcuwbNkyaLVa7N69G3/84x8RHByMp59+GoGBgcJATycnpwHvzywsLGBhYYFJkyZBq9UKCSFVVVV3dAi5PVuSH3zZc3bY3XR2dmLdunWQSCQ4duwY7WwI6QcFMHLfMzIygqmpKbKzs+Hg4IDu7m5hoGdCQgKUSiWmT58uDPQcqAjayMgI1tbWsLa2hru7OziOEwqqy8vLIZFIhOPG7u5uXL16dVDTouvr6xEfH4+YmBisW7dO1PsuQgwRBbBRajjZjeROzz77rPBjY2NjTJs2DdOmTcOmTZvQ3t4uDPT84IMPIJVKhYGe06ZNG3AXJJVKYW9vL3Sq12g0UCgUKCkpQVtbG6ysrKBQKAAAFhYWfQbHvLw8vPjii3j//ffxxBNP6PArJ+T+Rd/ijVLDyW4cyL1mN168eBEzZsyAn58fAgMD8a9//Usn6xlpY8eOxfz587Ft2zacO3cOhw4dQnBwMP79738jMjISsbGx+Oijj5CbmwutVjvg60mlUjQ2NsLKygqRkZHw9fWFsbExysvLce7cOeTm5qK6ulrIcDx8+DASEhKwb98+Cl6EDAUjo9YPP/zAPD09mbu7O3vvvfcYY4y988477ODBg4wxxrKyspizszMbO3Yss7W1Zb6+vsLnzpo1i9nb2zOZTMacnZ1ZamoqY4yx7u5u5u7uzq5evco6OztZYGAgy8/P7/W+5eXl7NKlS2z58uXsm2++EX69uLiYlZSUMMYYq66uZo6OjkypVOr1z2CkabVaVlpaynbu3MkWL17M/Pz82G9/+1v26aefsqKiItba2sra2tqE/5qamtiJEydYcXFxr1/n/2ttbWW1tbWsoKCAvfHGG8zNzY05OzuzXbt2scbGRp2uvaOjg4WHh7PAwEDm6+vLEhMTGWOMxcfHs8mTJ7OgoCAWFBTEcnJydPq+hIiFshAfMLrIbuQFBQXh22+/haenp/4WPMpwHIdLly4JDYnr6uoQERGByMhIjBkzBsXFxVi5cuWADZk7Ojrw6quvwtzcHM888wxOnjyJhx56CPHx8TpbK/v/7EkLCwt0dXVh1qxZ+Oijj7Bz585+/14JMRR0B/aA0VV2Y1ZWFjQaDTw8PHS5vFFPKpUiJCQEISEhWL9+PdRqNc6cOYMPP/wQ586dg7e3N1QqlTDQs6+WT7W1tVi+fDmefvppvPrqq5BIJJg3b57O18pnTwK30vi7urpEbZVFiL7RHRgZspqaGixfvhy7d+9+4DPlZDIZfH19YWVlhbKyMhw8eBDTp0/HoUOHMG/ePMTExGDHjh3IyckBx3G4ePEiFi5ciMTERKxdu1bvAYXjOMjlcjg4OCA6OhoREREAbhXNBwYGYt26dejs7NTrGgjRF9qBPWCGm93Y3NyMJ554An/+858xffp0fSzR4Dg5OeGrr74Sfr5o0SIsWrQIjDFUVFQgPT0dH3/8MbKystDV1YVjx47B29tblLVJpVJcvHgRKpUKsbGxyMvLw5YtW+Do6AiNRoOEhARs3boViYmJoqyHEF16sL99fgANJ7tRo9EgNjYWK1asuOv9yb1mOFZUVCAkJARyuRx+fn7YuXPnvX2Bo4hEIsHkyZPxwgsvICUlBcXFxcIxo9isra0RFRWF1NRUoXDb1NQUzz33HLKyskRfDyE6MbI5JGQk3Gt24969e5mxsbGQvXZ7BttwMhw7OzuZWq1mjDHW0tLC3NzcWHV1tV7/HO539fX1QpZoe3s7mzVrFvv+++/ZjRs3GGO3Mixfe+01tn79+pFcJiH3jI4QH0CPP/44Hn/88V6/9u677wo/Dg8PR1VV1R2f9+yzz/YqCL7dcKZT9+wn2NnZOah6K9K/mpoaxMfHg+M4aLVaLFmyBE8++STmzZuHhoYGMMYgl8vvi90ueTBRACM6M9wMx8rKSjzxxBMoLS3Ftm3bMHHiRH0s84ERGBiInJycO379+PHjI7AaQnSP7sDIqOHq6orLly+jtLQUycnJqKurG+klEUJGMQpgRGd01b9x4sSJ8Pf3x6lTp3S5PELIfYYCGNGZ4WQ4VlVVoaOjAwCgVCpx+vRpTJ06tdfHDGdCNXCrBMDFxQW/+93v7uGrI4SMNhTAiM4YGxvj448/xoIFC+Dj44MlS5bAz88PiYmJOHToEADg559/houLC7755husXr0afn5+AIDCwkJEREQgKCgIc+fOxVtvvYWAgADhtTmOwyuvvIL//ve/KCgoQEpKCgoKCnq9/6RJk7Bnzx4sXbq0z/W98847mDNnjp6+ekKI2CiJg+jUvWY4RkdH4/Lly3d93eFkOALAhQsXUFdXh0cffRTnz58f0tc0ktRqNebMmYPOzk50d3dj8eLF+NOf/oTy8nLExcWhqakJoaGh2Lt376AmQxNyP6EdGDEIfWU4VldXD+pztVot3nzzTWzfvl1fy9MbU1NTHD9+HJcuXcLFixeRmpqKc+fOYf369Vi3bh1KS0thY2ODXbt2jfRSCREdBTBy3/v000/x+OOPw8XFZaSXMmR3a8h7/PhxoRtKfHw8/vOf/4zkMgkZEXSESAzCcDIcMzMzcerUKXz66adobW2FRqOBhYVFn4kgoxHHcQgNDUVpaSleeeUVeHh4wNraGsbGt/73HcpulJD7CQUwYhB6Zjg6Oztj//792Ldv36A+9+uvvxZ+vGfPHpw/f95gghdwZ0PeoqKikV4SIaMCHSESgzCcDMfBGk6avlQqhVwuh1wuH3TpwFDxDXkzMzOhUqnQ3d0N4N7r7QgxdDSRmRDcOqbz8vJCWloaXFxcEB4ejpSUlF5ZjteuXUNzczO2b9+OmJiYXh35LSws0NraqvN1NTQ0wMTEBNbW1ujo6MD8+fOxfv16JCcnY9GiRYiLi8NLL72EwMBArFmzRufvT8hoRkeIhGD4afr6creGvL6+voiLi8Mf/vAHBAcH4/nnnxdtTYSMFhTACMHwGxGr1WqEhYXB2NgYGzZswMKFC3Wyrrs15HV3d6c5XuSBRwGMEB2oqKiAs7MzysrKMG/ePAQEBMDDw2Okl0XIfY2SOAjB8BsR8x/r7u6OyMjIPndNhBDdogBGCIbXiFipVKKzsxMA0NjYiDNnzvS6OyOE6AcFMEIw/EbEYWFhCAoKQlRUFDZs2EABjBARUBo9IYQQg0Q7MEIIIQaJAhghhBCDRAGMEEKIQaIARgghxCBRACOEEGKQKIARQggxSBTACCGEGCQKYIQQQgwSBTBCCCEGiQIYIYQQg0QBjBBCiEGiAEYIIcQgUQAjhBBikCiAEUIIMUgUwAghhBgkCmCEEEIMEgUwQgghBokCGCGEEINEAYwQQohBogBGCCHEIFEAI4QQYpAogBFCCDFIFMAIIYQYJApghBBCDBIFMEIIIQaJAhghhBCDRAGMEEKIQfo/hvZbOaIn0rQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEiSsUUq3AQh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Select boxplot to show\n",
        "filenames = [filename for filename in os.listdir(results_directory) if filename.startswith('plot')]\n",
        "\n",
        "drop_plot_label = widgets.Dropdown(options=filenames, description='Select plot:')\n",
        "drop_plot_label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ec6-MnCAUd5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "22c9e372-1a9c-44cc-dfee-eed1161683b5"
      },
      "source": [
        "#Show selected boxplot\n",
        "Image(results_directory +'/' + drop_plot_label.value)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfPyY8LBpD7k",
        "colab_type": "text"
      },
      "source": [
        "<h2>Citation Request</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbXrAcqOo-kQ",
        "colab_type": "text"
      },
      "source": [
        "Qaddoura, R., Faris, H., & Aljarah, I. (2019). An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio. International Journal of Machine Learning and Cybernetics, 1-40."
      ]
    }
  ]
}