MCDA Portfolio Selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot\n",
    "import fpdf\n",
    "import math\n",
    "import pandas as pd\n",
    "from matplotlib import rc\n",
    "import matplotlib.pylab as plt\n",
    "rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})\n",
    "rc('text', usetex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> After importing all necessary libraries we should define the input .csv file that includes the financial indexes. This file is formulated from data fetched by the investing.com database and it contains the evaluation table for all criteria for each alternative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputname = \"/home/elissaios/Documents/Thesis/1) MCDA/3.TOKYO/TOKYO_FINANCIAL.csv\"\n",
    "file =  open(inputname, \"rt\")\n",
    "list1 = list(csv.reader(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now, list1 contains all the information of the experiment of MCDA portfolio selection. Therefore, the next step involves taking all the useful data from this list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "criteria = int(list1[0][1])\n",
    "alternatives = int(list1[1][1])\n",
    "weights = [0 for y in range(criteria)]\n",
    "optimizationType = [0 for y in range(criteria)]\n",
    "preferenceThreshold = [0 for y in range(criteria)]\n",
    "criterion = [0 for y in range(criteria)]\n",
    "indifferenceThreshold = [0 for y in range(criteria)]\n",
    "criterionName = [0 for y in range(criteria)]\n",
    "vetoThreshold = [0 for y in range(criteria)]\n",
    "for i in range(criteria):\n",
    "    optimizationType[i] = int(list1[2][i+1])\n",
    "    weights[i] = float(list1[3][i+1])\n",
    "    vetoThreshold[i] = float(list1[4][i+1])\n",
    "    criterion[i] = int(list1[7][i+1])\n",
    "    criterionName[i] = list1[9][i+1]\n",
    "    preferenceThreshold[i] = float(list1[5][i+1])\n",
    "    indifferenceThreshold[i] = float(list1[6][i+1])\n",
    "decisionMatrix = [[0 for y in range(criteria)] for x in range(alternatives)]\n",
    "companyName = [\"\" for i in range(alternatives)]\n",
    "for i in range(alternatives):\n",
    "    companyName[i] = list1[i+10][0]\n",
    "    for j in range(criteria):\n",
    "        decisionMatrix[i][j] = float(list1[i+10][j+1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After importing all necessary libraries we should define the input information such as the criteria, the alternatives, the decision matrix etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "criteria = 8\n",
    "alternatives = 6\n",
    "weights = [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125]\n",
    "optimizationType = [1, 0, 0, 1, 0, 0, 0, 0]\n",
    "criterion = [5, 5, 5, 5, 5, 1, 5, 5]\n",
    "vetoThreshold = [15, 15, 50, 0.8, 3, 3, 30, 30]\n",
    "preferenceThreshold = [10, 10, 20, 0.4, 2, 2, 15, 15]\n",
    "indifferenceThreshold = [4, 4, 8, 0.2, 1, 0, 5, 5]\n",
    "criterionName = ['P/E Ratio', 'EPS', 'Revenue (B)', 'Beta', 'Dividend Yield', 'Monthly', 'YTD (%)', '1 Year']\n",
    "companyName = ['Accenture', 'Northrop Grumman', 'IBM', 'Motorola', 'MSCI', 'Oracle']\n",
    "decisionMatrix = [[25.05, 7.36, 43.22, 1.05, 1.74, 5, 30.37, 15.3],\n",
    "                  [18.15, 20.26, 32.89, 0.8, 1.44, 5, 50.57, 21.95],\n",
    "                  [11.75, 12.01, 77.86, 1.36, 4.59, 4, 24.16, 0.2],\n",
    "                  [31.39, 5.45, 7.63, 0.59, 1.33, 5, 47.78,41.39],\n",
    "                  [33.64, 6.62, 1.48, 1.12, 1.22, 5, 51.02, 44.54],\n",
    "                  [18.17, 3, 39.53, 1.15, 1.76, 5, 22.9, 17.12]\n",
    "                 ]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the following cell, we print the decision matrix that we have obtained as input, after transforing it to a dataframe using pandas function DataFrame as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Decision Matrix ===============\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P/E Ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>Revenue (B)</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Dividend Yield</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>YTD (%)</th>\n",
       "      <th>1 Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>25.05</td>\n",
       "      <td>7.36</td>\n",
       "      <td>43.22</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.74</td>\n",
       "      <td>5</td>\n",
       "      <td>30.37</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>18.15</td>\n",
       "      <td>20.26</td>\n",
       "      <td>32.89</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.44</td>\n",
       "      <td>5</td>\n",
       "      <td>50.57</td>\n",
       "      <td>21.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>11.75</td>\n",
       "      <td>12.01</td>\n",
       "      <td>77.86</td>\n",
       "      <td>1.36</td>\n",
       "      <td>4.59</td>\n",
       "      <td>4</td>\n",
       "      <td>24.16</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>31.39</td>\n",
       "      <td>5.45</td>\n",
       "      <td>7.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.33</td>\n",
       "      <td>5</td>\n",
       "      <td>47.78</td>\n",
       "      <td>41.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>33.64</td>\n",
       "      <td>6.62</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.22</td>\n",
       "      <td>5</td>\n",
       "      <td>51.02</td>\n",
       "      <td>44.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>18.17</td>\n",
       "      <td>3.00</td>\n",
       "      <td>39.53</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.76</td>\n",
       "      <td>5</td>\n",
       "      <td>22.90</td>\n",
       "      <td>17.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  P/E Ratio    EPS  Revenue (B)  Beta  Dividend Yield  \\\n",
       "Accenture             25.05   7.36        43.22  1.05            1.74   \n",
       "Northrop Grumman      18.15  20.26        32.89  0.80            1.44   \n",
       "IBM                   11.75  12.01        77.86  1.36            4.59   \n",
       "Motorola              31.39   5.45         7.63  0.59            1.33   \n",
       "MSCI                  33.64   6.62         1.48  1.12            1.22   \n",
       "Oracle                18.17   3.00        39.53  1.15            1.76   \n",
       "\n",
       "                  Monthly  YTD (%)  1 Year  \n",
       "Accenture               5    30.37   15.30  \n",
       "Northrop Grumman        5    50.57   21.95  \n",
       "IBM                     4    24.16    0.20  \n",
       "Motorola                5    47.78   41.39  \n",
       "MSCI                    5    51.02   44.54  \n",
       "Oracle                  5    22.90   17.12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"=============== Decision Matrix ===============\\n\")\n",
    "df = pd.DataFrame.from_records(decisionMatrix, index=companyName ,columns=criterionName)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> After we have translated all the data from the .csv file that was imported, we present the source code which implements the 4 MCDA ranking methods for portfolio selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAUT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Initially, we should calculate the normalised decision matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Normalised decision Matrix ===============\n",
      "\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P/E Ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>Revenue (B)</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Dividend Yield</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>YTD (%)</th>\n",
       "      <th>1 Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.392417</td>\n",
       "      <td>0.252607</td>\n",
       "      <td>0.546478</td>\n",
       "      <td>0.402597</td>\n",
       "      <td>0.154303</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.265647</td>\n",
       "      <td>0.340550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.707629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.411233</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.065282</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.983997</td>\n",
       "      <td>0.490528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.522016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044808</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.102787</td>\n",
       "      <td>0.141947</td>\n",
       "      <td>0.080518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032641</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.884780</td>\n",
       "      <td>0.928958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.209733</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.311688</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.706715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.498167</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.160237</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.381597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  P/E Ratio       EPS  Revenue (B)      Beta  Dividend Yield  \\\n",
       "Accenture          0.392417  0.252607     0.546478  0.402597        0.154303   \n",
       "Northrop Grumman   0.707629  1.000000     0.411233  0.727273        0.065282   \n",
       "IBM                1.000000  0.522016     1.000000  0.000000        1.000000   \n",
       "Motorola           0.102787  0.141947     0.080518  1.000000        0.032641   \n",
       "MSCI               0.000000  0.209733     0.000000  0.311688        0.000000   \n",
       "Oracle             0.706715  0.000000     0.498167  0.272727        0.160237   \n",
       "\n",
       "                  Monthly   YTD (%)    1 Year  \n",
       "Accenture             1.0  0.265647  0.340550  \n",
       "Northrop Grumman      1.0  0.983997  0.490528  \n",
       "IBM                   0.0  0.044808  0.000000  \n",
       "Motorola              1.0  0.884780  0.928958  \n",
       "MSCI                  1.0  1.000000  1.000000  \n",
       "Oracle                1.0  0.000000  0.381597  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxValue = np.max(decisionMatrix, axis = 0)\n",
    "minValue = np.min(decisionMatrix, axis = 0)\n",
    "\n",
    "normalisedMatrix = [[0 for y in range(criteria)] for x in range(alternatives)]\n",
    "for i in range(alternatives):\n",
    "\tfor j in range(criteria):\n",
    "\t\tif optimizationType[j] == 0: \n",
    "\t\t\tnormalisedMatrix[i][j] = (decisionMatrix[i][j] - minValue[j])*1.0 / (maxValue[j] - minValue[j])\n",
    "\t\telif optimizationType[j] == 1:\n",
    "\t\t\tnormalisedMatrix[i][j] = (maxValue[j] - decisionMatrix[i][j])*1.0 / (maxValue[j] - minValue[j])\n",
    "\n",
    "print(\"=============== Normalised decision Matrix ===============\\n\")\n",
    "df = pd.DataFrame.from_records(normalisedMatrix, index=companyName ,columns=criterionName)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It is obvious that the values of the initial decision matrix have been normalised to 0-1. After the normalisation process the utility score is calculated, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Utility Score ===============\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.4193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.6732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.4459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.5215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.4402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.3774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Score\n",
       "Accenture         0.4193\n",
       "Northrop Grumman  0.6732\n",
       "IBM               0.4459\n",
       "Motorola          0.5215\n",
       "MSCI              0.4402\n",
       "Oracle            0.3774"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utilityScore = [0 for x in range(alternatives)]\n",
    "utilityScorePer = [0 for x in range(alternatives)]\n",
    "\n",
    "for i in range(alternatives):\n",
    "\ttempSum = 0\n",
    "\tfor j in range(criteria):\n",
    "\t\ttempSum += normalisedMatrix[i][j] * weights[j]\n",
    "\tutilityScore[i] = round(tempSum,4)\n",
    "\tutilityScorePer[i] = round(round(tempSum,4) * 100,2)\n",
    "\n",
    "print(\"=============== Utility Score ===============\\n\")\n",
    "df = pd.DataFrame(utilityScore, index=companyName, columns=[\"Score\"])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, the results should be sorted in order to find the ranking of the method. The following shell describes this process and offers a visualisation of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== MAUT Ranking ===============\n",
      "\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Northrop Grumman</td>\n",
       "      <td>67.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Motorola</td>\n",
       "      <td>52.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBM</td>\n",
       "      <td>44.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSCI</td>\n",
       "      <td>44.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Accenture</td>\n",
       "      <td>41.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oracle</td>\n",
       "      <td>37.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Company  Score\n",
       "1  Northrop Grumman  67.32\n",
       "2          Motorola  52.15\n",
       "3               IBM  44.59\n",
       "4              MSCI  44.02\n",
       "5         Accenture  41.93\n",
       "6            Oracle  37.74"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tupledList = list(zip(companyName,utilityScorePer))\n",
    "tupledListSorted = sorted(tupledList, key=lambda tup: tup[1], reverse=True)\n",
    "\n",
    "print(\"=============== MAUT Ranking ===============\\n\")\n",
    "df = pd.DataFrame(tupledListSorted, columns=[\"Company\", \"Score\"])\n",
    "df.index = df.index + 1\n",
    "display(df)\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.bar(companyName, utilityScorePer, color = 'lightblue', edgecolor = 'black', width=0.6)\n",
    "plt.xlabel(r\" Alternatives\", fontsize=22)\n",
    "plt.ylabel(r\"Utility Score\", fontsize=22)\n",
    "plt.title(r'MAUT Method Result', fontsize=28)\n",
    "plt.xticks(fontsize=18, rotation=0)\n",
    "plt.savefig(\"barplot1.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOPSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In TOPSIS method we initially calculate the normalised decision matrix as you can see in the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Normalised decision Matrix ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P/E Ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>Revenue (B)</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Dividend Yield</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>YTD (%)</th>\n",
       "      <th>1 Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.421</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.305</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.197</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.527</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.565</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.305</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  P/E Ratio    EPS  Revenue (B)   Beta  Dividend Yield  \\\n",
       "Accenture             0.421  0.280        0.419  0.411           0.305   \n",
       "Northrop Grumman      0.305  0.771        0.319  0.313           0.252   \n",
       "IBM                   0.197  0.457        0.755  0.533           0.805   \n",
       "Motorola              0.527  0.207        0.074  0.231           0.233   \n",
       "MSCI                  0.565  0.252        0.014  0.439           0.214   \n",
       "Oracle                0.305  0.114        0.383  0.450           0.309   \n",
       "\n",
       "                  Monthly  YTD (%)  1 Year  \n",
       "Accenture           0.421    0.312   0.223  \n",
       "Northrop Grumman    0.421    0.520   0.320  \n",
       "IBM                 0.337    0.248   0.003  \n",
       "Motorola            0.421    0.491   0.603  \n",
       "MSCI                0.421    0.524   0.649  \n",
       "Oracle              0.421    0.235   0.250  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalisedDecisionMatrix = [[0 for i in range(criteria)] for y in range(alternatives)]\n",
    "for j in range(criteria):\n",
    "    sumOfPows = 0\n",
    "    for i in range(alternatives):\n",
    "        sumOfPows = sumOfPows + math.pow(decisionMatrix[i][j],2)\n",
    "        sqSumOfPows =  math.sqrt(sumOfPows)\n",
    "        for i in range(alternatives):\n",
    "            normalisedDecisionMatrix[i][j] = round(decisionMatrix[i][j]*1.0 / sqSumOfPows,3)\n",
    "            \n",
    "print(\"=============== Normalised decision Matrix ===============\\n\")\n",
    "df = pd.DataFrame.from_records(normalisedDecisionMatrix, index=companyName ,columns=criterionName)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the following step we incorporate the offset, computing the weight decision matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Weighted decision Matrix ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P/E Ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>Revenue (B)</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Dividend Yield</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>YTD (%)</th>\n",
       "      <th>1 Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.053</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.038</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.025</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.066</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.071</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.038</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  P/E Ratio    EPS  Revenue (B)   Beta  Dividend Yield  \\\n",
       "Accenture             0.053  0.035        0.052  0.051           0.038   \n",
       "Northrop Grumman      0.038  0.096        0.040  0.039           0.032   \n",
       "IBM                   0.025  0.057        0.094  0.067           0.101   \n",
       "Motorola              0.066  0.026        0.009  0.029           0.029   \n",
       "MSCI                  0.071  0.032        0.002  0.055           0.027   \n",
       "Oracle                0.038  0.014        0.048  0.056           0.039   \n",
       "\n",
       "                  Monthly  YTD (%)  1 Year  \n",
       "Accenture           0.053    0.039   0.028  \n",
       "Northrop Grumman    0.053    0.065   0.040  \n",
       "IBM                 0.042    0.031   0.000  \n",
       "Motorola            0.053    0.061   0.075  \n",
       "MSCI                0.053    0.066   0.081  \n",
       "Oracle              0.053    0.029   0.031  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weightedDecisionMatrix = [[0 for i in range(criteria)] for y in range(alternatives)]\n",
    "for j in range(criteria):\n",
    "    for i in range(alternatives):\n",
    "        weightedDecisionMatrix[i][j] = round(normalisedDecisionMatrix[i][j] * weights[j],3)\n",
    "\n",
    "print(\"=============== Weighted decision Matrix ===============\\n\")\n",
    "df = pd.DataFrame.from_records(weightedDecisionMatrix, index=companyName ,columns=criterionName)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Subsequently, we calculate the positive and negative ideal solutions as well as the distance of each alternative from them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Positive Ideal Solution ===============\n",
      "\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P/E Ratio</th>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS</th>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revenue (B)</th>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dividend Yield</th>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly</th>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YTD (%)</th>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 Year</th>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Score\n",
       "P/E Ratio       0.025\n",
       "EPS             0.096\n",
       "Revenue (B)     0.094\n",
       "Beta            0.029\n",
       "Dividend Yield  0.101\n",
       "Monthly         0.053\n",
       "YTD (%)         0.066\n",
       "1 Year          0.081"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============== Negative Ideal Solution ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P/E Ratio</th>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS</th>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revenue (B)</th>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dividend Yield</th>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly</th>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YTD (%)</th>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 Year</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Score\n",
       "P/E Ratio       0.071\n",
       "EPS             0.014\n",
       "Revenue (B)     0.002\n",
       "Beta            0.067\n",
       "Dividend Yield  0.027\n",
       "Monthly         0.042\n",
       "YTD (%)         0.029\n",
       "1 Year          0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============== Distance from Positive Ideal Solution ===============\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.119415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.098122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.104269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.138025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.144319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.132102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Distance\n",
       "Accenture         0.119415\n",
       "Northrop Grumman  0.098122\n",
       "IBM               0.104269\n",
       "Motorola          0.138025\n",
       "MSCI              0.144319\n",
       "Oracle            0.132102"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============== Distance from Negative Ideal Solution ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.068169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.114381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.133825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.091848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.092298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.067469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Distance\n",
       "Accenture         0.068169\n",
       "Northrop Grumman  0.114381\n",
       "IBM               0.133825\n",
       "Motorola          0.091848\n",
       "MSCI              0.092298\n",
       "Oracle            0.067469"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idealSolution = [0 for i in range(criteria)]\n",
    "nonIdealSolution = [0 for i in range(criteria)]\n",
    "for j in range(criteria):\n",
    "    maxValue = float('-inf')\n",
    "    minValue = float('inf')\n",
    "    for i in range(alternatives):\n",
    "        if weightedDecisionMatrix[i][j] < minValue:\n",
    "            minValue = weightedDecisionMatrix[i][j]\n",
    "        if weightedDecisionMatrix[i][j] > maxValue:\n",
    "            maxValue = weightedDecisionMatrix[i][j]\n",
    "    if optimizationType[j] == 0:\n",
    "        idealSolution[j] = maxValue\n",
    "        nonIdealSolution[j] = minValue\n",
    "    elif optimizationType[j] == 1:\n",
    "        idealSolution[j] = minValue\n",
    "        nonIdealSolution[j] = maxValue\n",
    "\n",
    "sPlus = [0 for i in range(alternatives)]\n",
    "sMinus = [0 for i in range(alternatives)]\n",
    "for i in range(alternatives):\n",
    "    sumPlusTemp = 0\n",
    "    sumMinusTemp = 0\n",
    "    for j in range(criteria):\n",
    "        sumPlusTemp = sumPlusTemp + math.pow(idealSolution[j]-weightedDecisionMatrix[i][j],2)\n",
    "        sumMinusTemp = sumMinusTemp + math.pow(nonIdealSolution[j]-weightedDecisionMatrix[i][j],2)\n",
    "    sPlus[i] = math.sqrt(sumPlusTemp)\n",
    "    sMinus[i] = math.sqrt(sumMinusTemp)\n",
    "    \n",
    "print(\"=============== Positive Ideal Solution ===============\\n\")\n",
    "df = pd.DataFrame(idealSolution, index=criterionName, columns=[\"Score\"])\n",
    "display(df)\n",
    "print(\"\\n\")\n",
    "print(\"=============== Negative Ideal Solution ===============\\n\")\n",
    "df = pd.DataFrame(nonIdealSolution, index=criterionName, columns=[\"Score\"])\n",
    "display(df)\n",
    "print(\"\\n\")\n",
    "print(\"=============== Distance from Positive Ideal Solution ===============\\n\")\n",
    "df = pd.DataFrame(sPlus, index=companyName, columns=[\"Distance\"])\n",
    "display(df)\n",
    "print(\"\\n\")\n",
    "print(\"=============== Distance from Negative Ideal Solution ===============\\n\")\n",
    "df = pd.DataFrame(sMinus, index=companyName, columns=[\"Distance\"])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, we calculate the relative closeness of every alternative to the ideal solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Relative Closeness ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.363404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.538255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.562067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.399558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.390074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.338068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Distance\n",
       "Accenture         0.363404\n",
       "Northrop Grumman  0.538255\n",
       "IBM               0.562067\n",
       "Motorola          0.399558\n",
       "MSCI              0.390074\n",
       "Oracle            0.338068"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = [0 for i in range(alternatives)]\n",
    "C2 = [0 for i in range(alternatives)]\n",
    "for i in range(alternatives):\n",
    "    C2[i] = round(round(sMinus[i]*1.0 / (sMinus[i] + sPlus[i]),4) * 100,2) #percentage\n",
    "    C[i] = sMinus[i]*1.0 / (sMinus[i] + sPlus[i])\n",
    "    \n",
    "print(\"=============== Relative Closeness ===============\\n\")\n",
    "df = pd.DataFrame(C, index=companyName, columns=[\"Distance\"])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The final ranking of TOPSIS method is provided if we sort the relative closeness. In the following cell we describe this procedure as well the visualisation of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== TOPSIS Ranking ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IBM</td>\n",
       "      <td>0.562067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Northrop Grumman</td>\n",
       "      <td>0.538255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Motorola</td>\n",
       "      <td>0.399558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSCI</td>\n",
       "      <td>0.390074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Accenture</td>\n",
       "      <td>0.363404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oracle</td>\n",
       "      <td>0.338068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Company     Score\n",
       "1               IBM  0.562067\n",
       "2  Northrop Grumman  0.538255\n",
       "3          Motorola  0.399558\n",
       "4              MSCI  0.390074\n",
       "5         Accenture  0.363404\n",
       "6            Oracle  0.338068"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tupledList = list(zip(companyName,C))\n",
    "tupledListSorted = sorted(tupledList, key=lambda tup: tup[1], reverse=True)\n",
    "\n",
    "print(\"=============== TOPSIS Ranking ===============\\n\")\n",
    "df = pd.DataFrame(tupledListSorted, columns=[\"Company\", \"Score\"])\n",
    "df.index = df.index + 1\n",
    "display(df)\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.bar(companyName, C, color = 'lightblue', edgecolor = 'black', width=0.6)\n",
    "plt.xlabel(r\" Alternatives\", fontsize=22)\n",
    "plt.ylabel(r\"Relative Closeness\", fontsize=22)\n",
    "plt.title(r'TOPSIS Method Result', fontsize=28)\n",
    "plt.xticks(fontsize=18, rotation=0)\n",
    "plt.savefig(\"barplot2.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ELECTRE III"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ELECTRE III belongs to the ELECTRE family methods which are based on a different concept than the previous ones. This method is based on pairwise comparisons between the alternatives. Firstly, we calculate the agreement and the disagreement tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sumOfWeights = sum(weights)\n",
    "agreementTable = [[0 for i in range(alternatives)] for y in range(alternatives)]\n",
    "for k in range(criteria):\n",
    "    if optimizationType[k] == 0:\n",
    "        for i in range(alternatives):\n",
    "            for j in range(alternatives):\n",
    "                if i!=j:\n",
    "                    if decisionMatrix[j][k] - decisionMatrix[i][k] <= indifferenceThreshold[k]:\n",
    "                        agreementTable[i][j] = round(agreementTable[i][j] + 1.0 * weights[k],2)\n",
    "                    elif decisionMatrix[j][k] - decisionMatrix[i][k] <= preferenceThreshold[k]:\n",
    "                        agreementTable[i][j] = round(agreementTable[i][j] + ((decisionMatrix[i][k] - decisionMatrix [j][k] + preferenceThreshold[k])*1.0 / (preferenceThreshold[k]-indifferenceThreshold[k])) * weights[k],2)\n",
    "                    else:\n",
    "                        agreementTable[i][j] = round(agreementTable[i][j] + 0.0 * weights[k],2)\n",
    "    elif optimizationType[k] == 1:\n",
    "        for i in range(alternatives):\n",
    "            for j in range(alternatives):\n",
    "                if i!=j:\n",
    "                    if decisionMatrix[i][k] - decisionMatrix[j][k] <= indifferenceThreshold[k]:\n",
    "                        agreementTable[i][j] = round(agreementTable[i][j] + 1.0 * weights[k],2)\n",
    "                    elif decisionMatrix[i][k] - decisionMatrix[j][k] <= preferenceThreshold[k]:\n",
    "                        agreementTable[i][j] = round(agreementTable[i][j] + ((decisionMatrix[j][k] - decisionMatrix [i][k] + preferenceThreshold[k])*1.0 / (preferenceThreshold[k]-indifferenceThreshold[k])) * weights[k],2)\n",
    "                    else:\n",
    "                        agreementTable[i][j] = round(agreementTable[i][j] + 0.0 * weights[k],2)\n",
    "\n",
    "disagreementTable = [[[0 for k in range(criteria)] for i in range(alternatives)] for j in range(alternatives)] \n",
    "for k in range(criteria):\n",
    "    if optimizationType[k] == 0:\n",
    "        for i in range(alternatives):\n",
    "            for j in range(alternatives):\n",
    "                if i!=j:\n",
    "                    if decisionMatrix[j][k] - decisionMatrix[i][k] <= preferenceThreshold[k]:\n",
    "                        disagreementTable[i][j][k] = 0\n",
    "                    elif decisionMatrix[j][k] - decisionMatrix[i][k] <= vetoThreshold[k]:\n",
    "                        disagreementTable[i][j][k] = round(((decisionMatrix[j][k] - decisionMatrix [i][k] - preferenceThreshold[k])*1.0 / (vetoThreshold[k]-preferenceThreshold[k])),2)\n",
    "                    else:\n",
    "                        disagreementTable[i][j][k] = 1\n",
    "    elif optimizationType[k] == 1:\n",
    "        for i in range(alternatives):\n",
    "            for j in range(alternatives):\n",
    "                if i!=j:\n",
    "                    if decisionMatrix[i][k] - decisionMatrix[j][k] <= indifferenceThreshold[k]:\n",
    "                        disagreementTable[i][j][k] = 0\n",
    "                    elif decisionMatrix[i][k] - decisionMatrix[j][k] <= vetoThreshold[k]:\n",
    "                        disagreementTable[i][j][k] = round(((decisionMatrix[j][k] - decisionMatrix [i][k] + preferenceThreshold[k])*1.0 / (vetoThreshold[k]-preferenceThreshold[k])),2)\n",
    "                    else:\n",
    "                        disagreementTable[i][j][k] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the following step we are able to calculate the reliability and dominance tables based on the above tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "reliabilityTable = [[0 for i in range(alternatives)] for y in range(alternatives)]\n",
    "for i in range(alternatives):\n",
    "    for j in range(alternatives):\n",
    "        if i!=j:\n",
    "            reliabilityTable[i][j] = agreementTable[i][j]\n",
    "            for k in range(criteria):\n",
    "                if agreementTable[i][j] < disagreementTable[i][j][k]:\n",
    "                    reliabilityTable[i][j] = round(reliabilityTable[i][j] * ((1 - disagreementTable[i][j][k]) / (1 - agreementTable[i][j])), 2)\n",
    "\n",
    "d = 0.8\n",
    "dominanceTable = [[0 for i in range(alternatives)] for y in range(alternatives)]\n",
    "for i in range(alternatives):\n",
    "    for j in range(alternatives):\n",
    "        if i!=j and reliabilityTable[i][j] >= d:\n",
    "            dominanceTable[i][j] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Finally, the proposed version of ELECTRE III suggests the calculation of the positive and negative flow for each alternative. Based on these two, we can compute the final ELECTRE III flow, as described in the following cell: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Positive Flow ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Flow\n",
       "Accenture         2.31\n",
       "Northrop Grumman  3.59\n",
       "IBM               1.71\n",
       "Motorola          2.56\n",
       "MSCI              1.63\n",
       "Oracle            1.77"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============== Negative Flow ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Flow\n",
       "Accenture         4.21\n",
       "Northrop Grumman  0.90\n",
       "IBM               0.37\n",
       "Motorola          2.55\n",
       "MSCI              2.13\n",
       "Oracle            3.41"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============== ELECTRE III Flow ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>-1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>-1.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Flow\n",
       "Accenture        -1.90\n",
       "Northrop Grumman  2.69\n",
       "IBM               1.34\n",
       "Motorola          0.01\n",
       "MSCI             -0.50\n",
       "Oracle           -1.64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phiPlus = [round(sum(x),6) for x in reliabilityTable ]\n",
    "phiMinus = [round(sum(x),6) for x in zip(*reliabilityTable)]\n",
    "phiEl = [round(x1 - x2,6) for (x1, x2) in zip(phiPlus, phiMinus)]\n",
    "\n",
    "print(\"=============== Positive Flow ===============\\n\")\n",
    "df = pd.DataFrame(phiPlus, index=companyName, columns=[\"Flow\"])\n",
    "display(df)\n",
    "print(\"\\n\")\n",
    "print(\"=============== Negative Flow ===============\\n\")\n",
    "df = pd.DataFrame(phiMinus, index=companyName, columns=[\"Flow\"])\n",
    "display(df)\n",
    "print(\"\\n\")\n",
    "print(\"=============== ELECTRE III Flow ===============\\n\")\n",
    "df = pd.DataFrame(phiEl, index=companyName, columns=[\"Flow\"])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The final ranking of ELECTRE III method is provided if we sort the ELECTRE III flows. In the following cell we describe this procedure as well the visualisation of the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== ELECTRE III Ranking ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Northrop Grumman</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Motorola</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSCI</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oracle</td>\n",
       "      <td>-1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Accenture</td>\n",
       "      <td>-1.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Company  Score\n",
       "1  Northrop Grumman   2.69\n",
       "2               IBM   1.34\n",
       "3          Motorola   0.01\n",
       "4              MSCI  -0.50\n",
       "5            Oracle  -1.64\n",
       "6         Accenture  -1.90"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tupledList = list(zip(companyName,phiEl))\n",
    "tupledListSorted = sorted(tupledList, key=lambda tup: tup[1], reverse=True)\n",
    "\n",
    "print(\"=============== ELECTRE III Ranking ===============\\n\")\n",
    "df = pd.DataFrame(tupledListSorted, columns=[\"Company\", \"Score\"])\n",
    "df.index = df.index + 1\n",
    "display(df)\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.bar(companyName, phiEl, color = 'lightblue', edgecolor = 'black', width=0.6)\n",
    "plt.xlabel(r\" Alternatives\", fontsize=22)\n",
    "plt.ylabel(r\"ELECTRE III Flow\", fontsize=22)\n",
    "plt.title(r\"ELECTRE III Method Result\", fontsize=28)\n",
    "plt.xticks(fontsize=18, rotation=0)\n",
    "matplotlib.pyplot.axhline(0, c=\"k\")\n",
    "plt.savefig(\"barplot3.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PROMETHEE II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, the PROMETHEE method is also based on pairwise comparisons of the alternatives. IT differs from ELECTRE III because it gives the opportunity to customise the comparison function based on the decision-maker's profile. Therefore, we must begin the presentation of this method with the different functions for pairwise comparisons. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def usualCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType):\n",
    "    for i in range(alternatives):\n",
    "        for j in range(alternatives):\n",
    "            if i!=j and decisionMatrix[i][k] >= decisionMatrix[j][k]:\n",
    "                if decisionMatrix[i][k] > decisionMatrix[j][k]:\n",
    "                    evaluationTable[i][j] = evaluationTable[i][j] + 1.0 * weights[k]\n",
    "\n",
    "def quasiCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType):\n",
    "    for i in range(alternatives):\n",
    "        for j in range(alternatives):\n",
    "            if i!=j and decisionMatrix[i][k] >= decisionMatrix[j][k]:\n",
    "                if decisionMatrix[i][k] - decisionMatrix[j][k] > indifferenceThreshold[k]:\n",
    "                    evaluationTable[i][j] = evaluationTable[i][j] + 1.0 * weights[k]\n",
    "\n",
    "def linearPreferenceCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType):\n",
    "    for i in range(alternatives):\n",
    "        for j in range(alternatives):\n",
    "            if i!=j and decisionMatrix[i][k] >= decisionMatrix[j][k]:\n",
    "                if decisionMatrix[i][k] - decisionMatrix[j][k] > preferenceThreshold[k]:\n",
    "                    evaluationTable[i][j] = evaluationTable[i][j] + 1.0 * weights[k]\n",
    "                else:\n",
    "                    evaluationTable[i][j] = evaluationTable[i][j] + ((decisionMatrix[i][k] - decisionMatrix [j][k])*1.0 / preferenceThreshold[k]) * weights[k]\n",
    "\n",
    "def levelCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType):\n",
    "    for i in range(alternatives):\n",
    "        for j in range(alternatives):\n",
    "            if i!=j and decisionMatrix[i][k] >= decisionMatrix[j][k]:\n",
    "                if decisionMatrix[i][k] - decisionMatrix[j][k] > preferenceThreshold[k]:\n",
    "                    evaluationTable[i][j] = evaluationTable[i][j] + 1.0 * weights[k]\n",
    "                elif decisionMatrix[i][k] - decisionMatrix[j][k] <= indifferenceThreshold[k]:\n",
    "                    evaluationTable[i][j] = evaluationTable[i][j] + 0.0 * weights[k]\n",
    "                else:\n",
    "                    evaluationTable[i][j] = evaluationTable[i][j] + 0.5 * weights[k]\n",
    "\n",
    "def linearPreferenceAndIndifferenceCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType):\n",
    "    if optimizationType[k] == 0:\n",
    "        for i in range(alternatives):\n",
    "            for j in range(alternatives):\n",
    "                if i!=j and decisionMatrix[i][k] >= decisionMatrix[j][k]:\n",
    "                    if decisionMatrix[i][k] - decisionMatrix[j][k] > preferenceThreshold[k]:\n",
    "                        evaluationTable[i][j] = evaluationTable[i][j] + 1.0 * weights[k]\n",
    "                    elif decisionMatrix[i][k] - decisionMatrix[j][k] > indifferenceThreshold[k]:\n",
    "                        evaluationTable[i][j] = evaluationTable[i][j] + ((decisionMatrix[i][k] - decisionMatrix [j][k] - indifferenceThreshold[k])*1.0 / (preferenceThreshold[k]-indifferenceThreshold[k])) * weights[k]\n",
    "                    else:\n",
    "                        evaluationTable[i][j] = evaluationTable[i][j] + 0.0 * weights[k]\n",
    "    elif optimizationType[k] == 1:\n",
    "        for i in range(alternatives):\n",
    "            for j in range(alternatives):\n",
    "                if i!=j and decisionMatrix[i][k] >= decisionMatrix[j][k]:\n",
    "                    if decisionMatrix[i][k] - decisionMatrix[j][k] > preferenceThreshold[k]:\n",
    "                        evaluationTable[j][i] = evaluationTable[j][i] + 1.0 * weights[k]\n",
    "                    elif decisionMatrix[i][k] - decisionMatrix[j][k] > indifferenceThreshold[k]:\n",
    "                        evaluationTable[j][i] = evaluationTable[j][i] + ((decisionMatrix[i][k] - decisionMatrix [j][k] - indifferenceThreshold[k])*1.0 / (preferenceThreshold[k]-indifferenceThreshold[k])) * weights[k]\n",
    "                    else:\n",
    "                        evaluationTable[j][i] = evaluationTable[j][i] + 0.0 * weights[k]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">After the definition of the necessary functions we can calculate the evaluation table between all the alternatives. The criterion used must be defined by the user in the .csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluationTable = [[0.0 for i in range(alternatives)] for y in range(alternatives)]\n",
    "\n",
    "for k in range(criteria):\n",
    "    if criterion[k] == 1:\n",
    "        usualCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType)\n",
    "    elif criterion[k] == 2:\n",
    "        quasiCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType)\n",
    "    elif criterion[k] == 3:\n",
    "        linearPreferenceCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType)\n",
    "    elif criterion[k] == 4:\n",
    "        levelCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType)\n",
    "    elif criterion[k] == 5:\n",
    "        linearPreferenceAndIndifferenceCriterion(evaluationTable, k, alternatives, decisionMatrix, indifferenceThreshold, preferenceThreshold, weights, optimizationType)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> After the calculation of the evaluation table, we can compute the positive and negative flows just like in ELECTRE III method. The net flow of PROMETHEE method is the difference between these two flows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== Positive Flow ===============\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.158179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.423917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.385125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.301250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.205000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.163250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Flow\n",
       "Accenture         0.158179\n",
       "Northrop Grumman  0.423917\n",
       "IBM               0.385125\n",
       "Motorola          0.301250\n",
       "MSCI              0.205000\n",
       "Oracle            0.163250"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============== Negative Flow ===============\n",
      "\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>0.287167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.116104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.415733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.245417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>0.289917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>0.282383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Flow\n",
       "Accenture         0.287167\n",
       "Northrop Grumman  0.116104\n",
       "IBM               0.415733\n",
       "Motorola          0.245417\n",
       "MSCI              0.289917\n",
       "Oracle            0.282383"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============== PROMETHEE Net Flow ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accenture</th>\n",
       "      <td>-0.128988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northrop Grumman</th>\n",
       "      <td>0.307813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>-0.030608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola</th>\n",
       "      <td>0.055833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSCI</th>\n",
       "      <td>-0.084917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oracle</th>\n",
       "      <td>-0.119133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Flow\n",
       "Accenture        -0.128988\n",
       "Northrop Grumman  0.307813\n",
       "IBM              -0.030608\n",
       "Motorola          0.055833\n",
       "MSCI             -0.084917\n",
       "Oracle           -0.119133"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sumOfLines = np.sum(evaluationTable, axis=1)\n",
    "sumOfColumns = np.sum(evaluationTable, axis=0)\n",
    "\n",
    "phiPlus = sumOfLines*1.0 / (alternatives - 1)\n",
    "phiMinus = sumOfColumns*1.0 / (alternatives - 1)\n",
    "phi = phiPlus - phiMinus\n",
    "phi2 = phiPlus - phiMinus\n",
    "\n",
    "for i in range(alternatives):\n",
    "    phi2[i] = round(round(phi[i],4) * 100,2)\n",
    "\n",
    "print(\"=============== Positive Flow ===============\\n\")\n",
    "df = pd.DataFrame(phiPlus, index=companyName, columns=[\"Flow\"])\n",
    "display(df)\n",
    "print(\"\\n\")\n",
    "print(\"=============== Negative Flow ===============\\n\")\n",
    "df = pd.DataFrame(phiMinus, index=companyName, columns=[\"Flow\"])\n",
    "display(df)\n",
    "print(\"\\n\")\n",
    "print(\"=============== PROMETHEE Net Flow ===============\\n\")\n",
    "df = pd.DataFrame(phi, index=companyName, columns=[\"Flow\"])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The final ranking of PROMETHEE II method is provided if we sort the net flows. In the following cell we describe this procedure as well the visualisation of the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== PROMETHEE II Ranking ===============\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Northrop Grumman</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Motorola</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSCI</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oracle</td>\n",
       "      <td>-1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Accenture</td>\n",
       "      <td>-1.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Company  Score\n",
       "1  Northrop Grumman   2.69\n",
       "2               IBM   1.34\n",
       "3          Motorola   0.01\n",
       "4              MSCI  -0.50\n",
       "5            Oracle  -1.64\n",
       "6         Accenture  -1.90"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACD9tquC+hTVTeTnCa51lr7YMb0292vV1tr9y60OAAAAC6FvW3xraprSdJaO05yOr4/Mf0oyXFr7WGSw+4+AAAAvGJvg2+SdzJq7U2SkyTTwfZw4rGT7j4AAAC8Yp+7Oh8keTlx//XJiV1L79i1JB9dRFEAAABcLvvc4ruUrgv0k9ba8xnTblfV06p6+vvf/34H1QEAALBr+xx8T5Nc6X4/SPL5nOcdzRr4Khm1CrfWrrfWrn/ve987jxoBAADYc/scfD/K1+ftHiY5TpKqOhg/oapuj0Ovwa0AAACYZW+D77jrchdoTye6Mn8y8fj9qnpRVV/sqEwAAAD23D4PbjU9gNX4sbe6n8dJvnvhRQEAAHCp7G2LLwAAAGyD4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKCtFHyr6k/PqxAAAAA4D6u2+D46lyoAAADgnKwafK9W1UdV9S+r6s1zqAcAAAC26rUVn/9Ba+3dJKmqn1XVjSQtyXFr7XfbLg4AAAA2tVLwHYfe7vdPknySjM79rap/meQgyWMhGAAAgH2xaovvGd2AV3+e5O0kL5OcJvmbTZcLAAAA27DqqM4fdT//tKr+dVW9zGjAqxdJ3mqt/ePWmtALAADA3li1xfdWVR1l1KX5g4zC7m+3XxYAAABsx6qjOp8kOUpyJcnTJD+rqp9uvSoAAADYklVbfO+31v6u+/3XSVJV3+kGtkqSl621f7O16gAAAGBDK7X4ttY+nPHwd5P84yTvJnlcVe9vozAAAADYhlUHt/pV9/PNqnq/qv5zRgNb/TDJvdbat1pr751DnQAAALCWVc/xvVNVn2cUdq9mFHb/h9baO621X2+/PAAAANjMquf4nia5LeQCAABwWaza4vtQ6AUAAOAyWanFt7X27vj3qvqjJNe7u09ba3/YZmEAAACwDau2+Kaq/qiqPs6o2/Nxd/uiqv6vqnpzu+UBAADAZlYd1fk7SR4neZLku90ozt9K8j8m+STJo64lGAAAAPbCqi2+v0hyq7X2YWvty/GDrbWT1toHSd5J4nJGAAAA7I1Vg++Xk4F3WmvtJMnJZiUBAADA9qwafNsSz/nOOoUAAADAeVg1+H637xzebtofb1YSAAAAbM/K1/FN8riq/ufJANyN9PwvMxrg6v/cZoEAAACwiVWv4/tlVd1J8iDJr6tqsuvz8yRvu54vAAAA+2Sl4JskrbXfJvlnVfXDJIdJDpI87x4HAACAvbJy8B3rgu6ZsFtVf9Fa++uNqgIAAIAtmRl8q+qnGbXkruNOEsEXAACAvTCvxffdJEcZnbe7ioMkP9yoIgAAANiiecH3NMnVdc7braqPNysJAAAAtmfe5YzubTBY1b11iwEAAIBtmxl8J0Nvd43eP+vO+13I6M4AAADsk3ktvqmqz6vq8yTvJWlJnl1YVQAAALAlfZcz+m6St1prf3dRxQAAAMC2zW3xTfJc6AUAAOCy62vxPZm8U1U/THIzyY2MLll0kuRJksettd+dV4EAAACwib7g+3LyTjdo1S+r6sOMQu9brbU/nGdxAAAAsKm+rs5t5oOtnSY5nhd6lx39GQAAAC5CX/Dt83nPtDtrLhMAAAC2rq+r8/Wq+kmSmjHtcM60gyTXtlUcAAAAbKov+L6V5Dizg28yGuRqlpldpAEAAGAX+oLv8yS3VlxeJfl4/XIAAABgu/qC73E3kvNKqup4g3oAAABgq+YObtVae3edBa47HwAAAJyHdUd1BgAAgEtB8AUAAGDQBF8AAAAGTfAFAABg0ARfAAAABk3wBQAAYNAEXwAAAAZt68G3qv5o28sEAACAdc0NvlX1qzWXeX/N+QAAAGDrXuuZdlRVP0lSKy7zaIN6AAAAYKv6gu/VJMdZPfi29csBAACA7eoLvs+T3FpxeZXk4/XLAQAAgO3qC75PW2u/XXWBVfV0g3oAAABgq+YObtVa+/N1FrjufAAAAHAeXMcXAACAQXMdXwAAAAbNdXwBAAAYNNfxBQAAYNBcxxcAAIBBcx1fAAAABs11fAEAABg01/EFAABg0FzHFwAAgEETfAEAABg0wRcAAIBBE3wBAAAYNMEXAACAQdso+FbVm9spAwAAAM7H3Ov4dqH2sLslyXFr7XdTT7taVTeSXE3yZ0lOkrxorf311isFAACANcwNvhmF2CdJ7swIvEmS1tonST5Jkqr6TpK/SXI7ieALAADAXugLvqdJbrXW/rDMglprXya5VVX/fSuVAQAAwBb0neP7dNnQO+X5usUAAADAtvUF35PJO1X1l1X176rqH6rqN1X1F1X100XzAQAAwC71dXVur9xp7ZdJfllVL1trP+6Z7+VWKgMAAIAt6GvxrTmPH59HIQAAAHAe+oJvm/P45wuWOW8+AAAAuHB9XZ2vV9VPcrbl93DO42NHW6kMAAAAtqAv+L6VUbfmWQH3Rs98WnwBAADYG33B93mSWysur5J8vH45AAAAsF19wfe4tfbbVRdYVQa/AgAAYG/MHdyqtfbuOgtcdz4AAAA4D32jOi9UVW9upwwAAAA4H3O7Oneh9rC7JaOuz7+betrVqrqR5GqSP0tykuRFa+2vt14pAAAArKHvHN+TJE+S3JkReJMkrbVPknySJFX1nSR/k+R2kq0E36q6meQ0ybXW2gdznnOttfZ8G68HAADA8PR1dT5Ncmte6J3WWvuytXYr86/vu5KqutYt9zjJ6fj+1HOOkny4jdcDAABgmPqC79PW2h/WWOa2Wl/fySh8J6PW56PpJ3Sh+OWWXg8AAIAB6gu+J5N3quovq+rfVdU/VNVvquovquqni+bbwEFeDbWvb2m5AAAAfIP0nePbXrnT2i+T/LKqXrbWftwznxZYAAAA9kZfi++8c3WPz6OQGU6TXOl+P0jy+aoLqKrbVfW0qp7+/ve/32pxAAAAXA59wbfNeXxRAJ0336o+yteXUjpMF7ir6mDZBbTWHrbWrrfWrn/ve9/bUlkAAABcJn1dna9X1U9ytuX3cM7jY2cGoVpHa+15VV3vRm4+nbhk0SdJ3kq+utzR9aq62Vp7vI3XBQAAYFj6gu9bGbWyzgq4N3rm21aLb1prD2c89tbE74+TCLwAAADM1Rd8nye5teLyKsnH65cDAAAA29UXfI9ba79ddYFVdVGDXwEAAMBCcwe3aq29u84C150PAAAAzkPfqM4AAABw6c0NvlX1q3UWuO58AAAAcB4WXc7of8r8yxbNnW+DegAAAGCrFl3O6HnP9FkqW7ycEQAAAGxq0eWMfjH1WCX5qyT35szzepJ/vYW6AAAAYCv6gu/T1trfTT9YVS9mPT4x3eWMAAAA2Bt9lzP683UWuO58AAAAcB5czggAAIBBE3wBAAAYNNfxBQAAYNAWXcf3Jzl7Hd/DOY+PHW2lMgAAANiCRdfxPc7sgHujZz7X8QUAAGBvLLqO760Vl1dJPl6/HAAAANiuvuB73Fr77aoLdB1fAAAA9knfdXzfXWeB684HAAAA58HljAAAABi0uV2du8sSXUnyMsmLJCettX9zUYUBAADANvSd43snyU1hFwAAgMusr6vzc6EXAACAy64v+J6ss8Cq+umatQAAAMDW9QXfl2su886a8wEAAMDW9QXf7665zMM15wMAAICt6xvc6lZV3bywSgAAAOAc9AXfkyQPVlxeJbm3fjkAAACwXX3B97i19stVF1hVRxvUAwAAAFt1Huf4Pl9zPgAAANi6vuB7sM4CW2vvrlkLAAAAbF1f8L1eVf/owioBAACAc7Coq/PfVtU/r6o3L6YcAAAA2K65g1u11r5VVT/MqMvz1aq61lr7NxdXGgAAAGyub1TntNZ+2ze9qt5srf1uqxUBAADAFs0Nvl335sPulowub/S7qaddraobSa4m+bOMrv37orX211uvFAAAANbQ1+J7kuRJkjvzWnVba58k+SRJquo7Sf4mye0kgi8AAAB7oS/4nia51Vr7wzILaq19meRWVf33rVQGAAAAW9A3qvPTZUPvlOfrFgMAAADb1hd8TybvVNVfVtW/q6p/qKrfVNVfVNVPF80HAAAAu9TX1bm9cqe1Xyb5ZVW9bK39uGe+l1upDAAAALagr8W35jx+fB6FAAAAwHlYusV3wucLljlvPgBg4N74kz/J33/22a7LGKQfvPFGPvv0012XAXAp9QXf61X1k5xt+T2c8/jY0VYqAwAunb//7LP8+j/+l12XMUg//9H3d10CwKXVF3zfyqhb86yAe6NnPi2+AAAA7I2+4Ps8ya0Vl1dJPl6/HAAAANiuvuB73Fr77aoLrCqDXwEAALA35o7q3Fp7d50FrjsfAAAAnIe+yxkBAADApTc3+FbVm+sscN35AAAA4Dz0tfjeX3OZ99acDwAAALaub3CrG1X1qzWW+XaS/23NegAAAGCr+oJvJbme5IsVlnclycFGFQEAAMAW9QXfN5Mcdb8/X/bSRlXlOr4AAADsjbnBt7X2ZZJfJ0lV/VlV/fNu0nFr7Q89y3ywxfoAAABgI30tvl9prf1dkr9Lkqr6WVUdJPmitfa3M577yXZLBAAAgPUtFXwnTQbbqvp5kpbkpLX2H7ZZGAAAAGzDysF3Umtt3BX6j7qu0IdJHrfWfreF2gAAAGBjGwXfJKmqP01yp7slydW4nBEAAAB74lvrzFRVb1bV+1X1eZJnGYXdW621b7XWhF4AAAD2xtItvlX1R0nezqhl91pGg139VZKH3QjQAAAAsHcWBt/u3N07GV3T98skD5O8vex1fQEAAGCX5nZ1rqpfVdU/JHmU5CTJP2utXWmtvdsXeqvqo3OoEwAAANbS1+J7J8njJOMg+52u9bfP1SQ3t1EYAAAAbENf8H2e5G6SL1ZY3h8y6g4NAAAAe6Ev+B6vcT3e46p6uEE9AAAAsFVzz/Ftrb27zgLXnQ8AAADOw1rX8QUAAIDLYtXr+B4l+XGSgySnSX6TUZfoP5xPeQAAALCZpYJvVf1Vkr9MUjMmt6p60Fr737daGQAAAGxBb1fnqvpOVT3NqKX3zzO6XNF3W2vfSvLdJDeS/HWSf1FV/29V/aPzLhgAAABWsegc34+TPGitXW+tfdha+21r7cskaa192Vr7pLV2L8kPk/zbJH97zvUCAADASuYG36r6eUbn7364aCFdCL6X5JOq+ufbLBAAAAA20dfie7219stVFtZdyujGZiUBAADA9vQF35drLvNkzfkAAABg6/qCb1tzmevOBwAAAFvXF3xfX3OZ684HAAAAW9cXfKuq/nSVhVXVTzP7Wr8AAACwE6/1THs/yXFV/bS19v8tWlBVvZnkQZK3tlMaAAAAbG5ui293vd73kvyuqv7XqvqjWc+rqjer6qMkL5Lcaa394XxKBQAAgNX1tfimtXZcVTeSHCd5WOD4LRIAABgzSURBVFUnGY3afJrkIMn17ueXSf5Za+1vz7leAAAAWElv8E2S1trzJFeq6naSm0l+nFHYPU3yNMmj1tqH51olAAAArGlh8B1rrT1M8nDe9Kr6TpLbSZ601v7DFmoDAACAjfWN6ryS1tqXrbVfZtT9GQAAAPbCUsG3qv60G7V5GVfXrgYAAAC2rLercxd2nyf5Tnf/UWvtX0xNP8oo7B4mudY9HwAAAPbConN8nyf5OMmzjLow/6KqTlpr/6qq/jLJ/annP2yt/fk51AkAAABrmRt8q+ovktxqrX3SPfRhVT1L8quqepjRqM5Xk7zsrvkLAAAAe6fvHN+rE6E3yVcjO3+ZUdj9sLX2W6EXAACAfbbOqM4PW2t/2HolAAAAcA76gm/Nefw351EIAAAAnIe+4NtWfDxJUlW/Wr8cAAAA2K6+UZ2vV9VPcrbl98dVddoz39HmZQEAAMB29AXft5IcZ3aX53s98/W2CAMAAMBF6gu+z5PcSvJyheW9ntF1fwEAAGAv9AXf49bab1dc3pdVdbxJQZOq6mZG1wu+1lr7YNXpAAAAMHdwq9bau+sscN35plXVtW55x0lOx/eXnQ4AAADJetfxvSjvZNSamyQnOTto1qLpAAAA0NvVOUlSVX+aUcj8r0l+3Vr73XkX1TnIq+cXv77i9Ff8p//0n/JP/+k/3U5lsMf+j//l57suYbDsQ2A59kPnx34IYD29wbeq3s+rIzh/UFU3W2v/9nzL2o6qup3kdpJ8+9vf3nE1i/37f//v89/+23/bdRmD9O1vfzv/5J/8k12Xce6+/e1v5//5zf+96zIG6TLsQ7bBfuj82A+xqW/Cfsg+6Px8U/ZBtqHzc9m3oWpt9tWHqurPkjxKcifJ0+7hG0neT/JWa+0P51pY1f0kT1prx90gVoeTA1gtmj7t+vXr7enTp/Mm74Wqyq//43/ZdRmD9PMffT/ztnXga/ZD58d+CBazDzo/35R9kG3o/FyGbaiqnrXWrs+a1neO77tJbrTWPmmtfdndHif583StqOfsoySH3e+HGV1TOFV10DcdAAAAJvUF35p1OaPW2idJ/vj8SvrqdZ4nSVUdJTkd30/yyYLpAAAA8JW+c3xf9kz7fNuFzNJaezjjsbf6pgMAAMCkvhbfvg7c+925GwAAADpbv45vVf1q28sEAACAdfV1db5eVT9JUjOmXa2qn854/CDJ0VYqAwAAgC3oC75vZTRS8qzgm4wuczSLbtAAAADsjb7g+zzJrfQPcjXt9SQfb1QRAAAAbFFf8D2edTmjBb6sKtfTBQAAYG/MHdyqtfbuOgtcdz4AAAA4D1sf1RkAAAD2ieALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAM2mu7LgCA/fGDN97Iz3/0/V2XMUg/eOONXZcAAN9Ygi8AX/ns0093XQIAwNbp6gwAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKC9tusCAAAgSX7wxhv5+Y++v+syBukHb7yx6xJgpwRfAAD2wmeffrrrEoCB0tUZAACAQRN8AQAAGDTBFwAAgEETfAEAABg0wRcAAIBBE3wBAAAYNMEXAACAQRN8AQAAGDTBFwAAgEHb++BbVTer6qiq7vY859pF1gQAAMDlsdfBdxxoW2vHSU5nBdyqOkry4UXXBgAAwOWw18E3yTtJTrvfT5IcTT+hC8UvL7IoAAAALo99D74HeTXUvr6rQgAAALic9j34AgAAwEZe23UBVXV7xsMn4/N6k1zpHjtI8vkay76dJG+88cYmZQIAAHBJ7Tz4ttYe9kz+KMn17vfDJMdJUlUHrbXTuXO9uuyHSXL9+vW2YakAAABcQnvd1bm19jz5auTm0/H9JJ+Mn1NVN5Nc734CAADAK3be4rvIrBbh1tpbE78/TvL4QosCAADg0tjrFl8AAADYlOALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADNpruy4AAABgG37wxhv5+Y++v+syBukHb7yx6xI2IvgCAACD8Nmnn+66BPaUrs4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoL226wL42g/eeCM//9H3d13GIP3gjTd2XQIAALAjgu8e+ezTT3ddAgAAwODo6gwAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMmuALAADAoAm+AAAADJrgCwAAwKAJvgAAAAya4AsAAMCgCb4AAAAMWrXWdl3Dhaiq3yf5dNd1DMgfJ/mvuy6CS802xKZsQ2zKNsSmbENsyja0XX/SWvverAnfmODLdlXV09ba9V3XweVlG2JTtiE2ZRtiU7YhNmUbuji6OgMAADBogi8AAACDJviyroe7LoBLzzbEpmxDbMo2xKZsQ2zKNnRBnOMLAADAoL226wKA81FV15K8k+Tz1toHu64HYMjsc4HzZj+zGV2dL7GqOqyq+7uuY56qOtx1DbtWVXer6kVVPZsx7XY37Um3I9v0tV5Z362150leJLmx6bIvQlXd7253u3Vzu3v8dlUd7Lo+tq+qDrrP/Ivub+HuxLTx48+6bWJ8u19VD6a3ie7xF1X1YsFr3q+q1v20Xe3YeX9uF/l/qNvn/iaXZJ/7TbXpNjexL7rd/TyY3HfNWMbc/2t9+0D21/gzr6qbE7cL+ezsZzbUWnO7pLckR0m+2HUdPfXd3nUN+3BLcjPJsyR350w7OK/1neQwyZNdr4MFdV9L8iTJ0az3lOSLba0jt/28JXmU5P4Kj99O8mLG4zcz+rLnWs9r3Z81r9tOP/9z+9wu+v/QZdjnuq2/zSV5kORw6rEz+6NV/6/N29e57d+t+2wfTR+XJDnoHj+8gBrsZ9a8afG93E6SPK2qm7suZA7fRn3tF0nuz2h9OG2tnW7pNS7r+n6U5E5r7Xh6QmvNgA98Pv1At10cVtXRjOc/SHJn1oK65z/ZbnlsyXl9bpd1v8j5W2ebO2ytnUw+0O2PTqae5//aAHUt/4+S/GL62K27/378j9lrgu8lVVXXup3v3B33rnRdQB5l9O3XzOkznj/oLodt1DXlYUaf11YtWt/7rKoeJDmePpCYsvRBwjdx2/ommvhMX86Y/DjJ2xdYDtux1c/tMu8XuTArbXPdfmde1/lHE8/b6v819sr9jD7bmQ0W3bHeiS7r+8vgVpfXlSRprT2uqkdVdTDrD7H74xvvfK9MftO4xLTnGR00HLbWPujOQ/2wm+f9roaDJD9urd2beNmjjA5Ir0388T/M6B/Gh939t7rXuZ3RjuRekocTr/E0XZeRJDdaa7fm1bXCOtup1tqd7jyem621x/Oe162T8QH9V+9x3rpJ8lFmrO/J7aGb98zntWh9L1nP+AuYdPNfndoe+hxl9Nn3eTB+Lz3r4P0sv23N3H4nWg+vJTkZf0brzjc2sf4OV1nu4lX3jfZhRtv48+kJrbWTqno6/XfWHbSeZP6BKzu07uc2b/+UOf+HJvYlK+1nJ/4HzXu9uebtA9itVbe51tppVZ10X6i80uI31Yq70v81LpW3s/izfZ7R4FMfrLA/mblv6DtOn3aZj48v1K77Wrutfstoo742cf9RZp8/ej/Jzan5bi4x7UkmzlFIcjfdeVIZHdw/m5r+KFPnsXTPO3P+wXj+GXXenrh/NPkaE689t659vk2t55uZOLdnxnp7MPUeDybXY8+6mbe+DzM6j2nu5zVvmSvU8yKvnqt0NKuWOdtxS885VnPm61sHi7atedvv/an5vpi6v+58t6fuT2/DS/09Df02a11OPP6o+8yPur+fmfu77vnjfdjN6W1wvE7H2+yu37Pb5p/bEvunefvFdfezvfN1jx1OLat3H+B26ba5cVfXltH/vvt59Xhs3f9rzvHd89uyn223TbWJ+/P2J4uOD/qO06f3M5fy+HgXN12dL6fr7dWWjjPdnbtvLG+3V789up3kxoJp13L2HJbHE8s/nTF9lVaUZb7hfJlRkDpJRt+kLlHXpdCt86cZ7dBe0b3H65PvsY2+ET4ZjwSZGetmiZe9suDzmrnMFeo5aa9+832c+edfbsO8dbDMtjVv+z3zGlNdpNed79bEukpG38ZOrpdN/56+CX7TWjvubo/b6Jvyk5oxUvpY93d2XTf3y2WVz23J/dO68837H7Ty62XxPoAdWnVf0Vo77fZB382o5e8gybMF2wDDcmXF6fOOWebuG/qO06dfbCjHxxdFV+fLaRxQJx125/2OA/FRpg7M26i78sGCaW8nOZ0RWiYHaOg7b2Vbpl/jehbXdVncSvJFdx7QpOuZvW5fpOu+21l1/S/z/FnPWbaeWZ5n1Noy9/Npo25jpxmFvFe6rI53+kmudtNPMvo2fFzPJtvgrHnPDKC0pfnG3Zlupgu5M+a5iL+nQWmjUzzuVNWD1tq8f+4fZ7QNjbubPb24CtnAsp/buvundfez677eMvsAdmvlfUX3pcfjJI9rdFnJZ1X1cfd/bfzl5ZlTMcbmnZ7G/po6ZulzNWc/+1n7jr59Q99x+rQhHR+fO8H3kulGBX4w9c1OqupqRt/u9H7D0/3hLpp20s6ORLjRH1BVnRkJcYFZ/xC2XtcudOv5XkZdm5Y9l3PyG8SF/yy3tL6XrWcTH2d0Lswr57V0BwTjnfwXGZ03PPl+tn3AsO7yFs13lOSd9vX5PBtfr5mvPM+oS9k8DzL6G/sgo14Pcw9C2Svb+Nxm7p9q8TV9V9rPLnq9jn3A/ltqm+u2n8Pp45DWnSucUQA57m5n/q9NOVownf30cUatrn297Y4yGntl0qz9ycr7hp4vSwZxfHwRdHW+fK7NCTQP8urohMeZ/61U37SnGbXUvWILXQb7/qCXWfZ51bUTbTTowGmS9yYefprZn8vVrD48/jYOrjapp7e1d6xrrTtaYoc/a/TeZexs++i2zUdtYqCwJK+Ppy1xEE6/3haV8cGroHG5rPC5rbN/GrfmrbNfW3k++4DLYcV9xbzLY52ma6Hr/q9dW/D5buvLYy7WvYyOWeYNtneUUbfm3oGlFu0bkvznLH/K06COj8+b4HuJdBvxzJ3lxI77Znf/NMnH0+edVNXRgmnPkzyf0WXievdz3h/S9OPT5ymednWdzHju4YzHXrm/RF37bN7O6xeZ2Fm1r4fB/+qx7jO/PnUu76zPYOb6nvPcWY+fed4K9bxyflS3DT5foaXmZ0k+rNnXo347s78pnVXvMtvWsutj2el9813J2doPJ6ZtUs83yevTD3Tb5M2MRsOeNP239iDJhzO+CWe/rPy5Lbl/OrNfXHc/u8J8k5bZB7Ab6+4rbk4fh4xD0FSjxK0kD2aNddEde328esnsWnf8PP5spy+feJhRMJ715cj0//RF+4Y/ZM5x+oyaLvPx8cXbZGQst4u75etR4V5kxohyGZ2f8kV3uzvx+N1u2s1MjA63zrSMQtp4NMO73WM3u5qezVnG3RmPj5c7HqX1blf37e41noxfIxOjBS+qeR9vGQ1i1TL6p3owa/qMx+6O39/kOlhy3Xy1vpf5vBYts6+eqZomR92dOeLukuvqQXcbf87jgLPsOli0bS3cfic+s/GlB9aab2Ld3Z9YPwdd/UvXM+Rbtz7ud5/Ri7y67xo//mxi277bbR9PMrUfzIy/te7zuz/xnJsT28/9Wdu724VvAxt9bn37p4nps/4PbbKfnbc/POz+pr/6P9y3D9j1uv+m3tbd5rrnjD/HyX3S3NGYu3nv5+v/adMj+c7dB7rt76373Cb3BTOPffr2J8vsGya2m8lj8TP7mXnPdTt7q25lAaysa/2431qb1/0LAAB2TldnAAAABk3wBQAAYNAEX2At427OGY1weHfX9QAAwDzO8QUAAGDQtPgCAAAwaIIvAAAAgyb4AsAGquqwqh5V1ZOqOtp1PQDAWYIvAPSoqid901trJ0nuJTlKcnAhRe1QVV2rqi+q6nDXtQDAsgRfAJijG738+qKW3C78Pp8x/+FlbAVeUPdpkpOLrAcANiX4AsB8V5J8nOTOmvNf22ItF2lu3a21k9baW13YB4BLQfAFgH6PktysqnW6Mb+z7WIuyGWtGwBmEnwBYIaqOmqtHbfWjjPq3vv2CvMeVNXdJDfPrcBzcFnrBoBFXtt1AQCwpyZbeMfdnR8uOe/tJFe73+9V1a3u9wetta/OBe4GiLqT5DcZdas+aK190E27luTDJIdJftb9vJLkVmvtRjfvo+7x8fIPuufcSPKL1trpZFHjc5a7u28ledQF+6Xq7lq9P+le8xettcddUH6ve+3HrbXxPOmm3c/o/OdfjN973/vupo8HCjvtXutlkh+31u7NXNsAsEC11nZdAwDslS7gXR+Hwi4wPktydd65rVX1LMn7rbXHE4+9SHJnKlyOp11L8mFr7a2Jx+4nyWTAq6qW0ajRjzMKgL9N8sNxqK2qL5K8n1HoPOkeu50uIE8s5zDJ4WQtXX23JsP4oronXvMX4/faLftFku/OCNuPpsJw7/vulnVzKggfZhS+bwQA1qCrMwCcdTQZ+rpgeJrtdgH+MKPAOun9JHenzic+SfJ6N6jUaWttOlye5Gwgf5rR5ZUm3UzyYOqxBxm1yK7qlfA/Mar1K93Bx4F1at5F7/vMaNLd8nsvKwUAfQRfADjryozHNhnd+RVdILyWqUsgdYH2NF93Rx57sWCRz6buz2qVfpyzIfQks9/rOh7k7Po5mmphXuZ9Hye5X1V3p64VvGw3cwA4wzm+ADChC1u3quqtqUlXkhxW1bXprsFrGF8u6FrX9XfSxxl1aZ606NJB088/o2s1/aBrVX27m+fHefVc5rW11h5W1YOqOuy51NHC991aO+nOLb6fUQA+yaib8wcBgDUJvgDwqqN555KOz33Nmi2/VXUw2U158nzgCbMe21gXeB9l1Nr6fmvttKqSJbpvT9fd43FG6+ZeF2xnniO86H130x93X0Icdcu74RxfANalqzMAvKqvBfRxVris0QzjeSdHN74oj5KctNbuzQqxC65TvOx7fpDRyNDJaHCw6Zbfhe+7qm6Pa+nOa37YWruaUWv7VlqnAfjmEXwBoNPXStl5kOSgu9zOsg6mf+8C4XFmtLZW1eGMbsDbcJRR+J00GUCnw+2Zuhfpzud9WVU3M6P79ZLve+YAV9182zofGYBvGMEXAL72Xt/5u11wO8nsrs6zwuFxRufRjk22tN5JcmdG6+fNqRoWhb2DGc+5kpxpxR1fE3farOX31T1+zXkeZzRy87wvEJZ53+/NqrPn3GEA6OU6vgB843UtuPczGnzpuOcc38muvONRkk+6eW92v78yEFM3z7NkNADU1PIOunlfdPNeGT+nC4aTyz1urd2ZmHd6+oPW2gdda+t74/eS5H5r7Xji+b/JqMvxQWvtcVdfkjyaGoH5TN0zXvP+vPc0WeuM9dj3vm929R1l1Gp8JaOg/VjwBWBdgi8AAACDpqszAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACDJvgCAAAwaIIvAAAAgyb4AgAAMGiCLwAAAIMm+AIAADBogi8AAACD9v8Dqclfoyf6hXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tupledList = list(zip(companyName,phiEl))\n",
    "tupledListSorted = sorted(tupledList, key=lambda tup: tup[1], reverse=True)\n",
    "\n",
    "print(\"=============== PROMETHEE II Ranking ===============\\n\")\n",
    "df = pd.DataFrame(tupledListSorted, columns=[\"Company\", \"Score\"])\n",
    "df.index = df.index + 1\n",
    "display(df)\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.bar(companyName, phi, color = 'lightblue', edgecolor = 'black', width=0.6)\n",
    "plt.xlabel(r\"Alternatives\", fontsize=22)\n",
    "plt.ylabel(r\"PROMETHEE II Flow\", fontsize=22)\n",
    "plt.title(r\"PROMETHEE II Method Result\", fontsize=28)\n",
    "plt.xticks(fontsize=18, rotation=0)\n",
    "matplotlib.pyplot.axhline(0, c=\"k\")\n",
    "plt.savefig(\"barplot4.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The final ranking is the weighted sum of the rankings of the four MCDA methods. Therefore, in the following cell we describe the calculation of the cumulative score of each alternative as well as the final ranking and a visualisation of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    Northrop Grumman\n",
       "2                 IBM\n",
       "3            Motorola\n",
       "4                MSCI\n",
       "0           Accenture\n",
       "5              Oracle\n",
       "Name: Name, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZZRFmAQDggVp0+O2oarUajx49ujY/tlQqzTV38fz8fOYQ2t3d3Wg2m3FwcBCdTufWTu+4crl8bb7ppODzrixSy13q7vV68fjx47nrmfS9RXweZlut1vA7HL/uPEO9B1syNRqNa53eWW677tra2rWh1NOGVZdKpVt/kbIIc2YBAOA9tba2dqOTuLu7e2t3rN1uX9v7dNzoqsaLzAcdNRrSl70w0KIWqeVd1D3pe2u327G3txfVajW2trbiyZMnd7p2t9uN3d3dqNVqS1sxebRj3e12Y3Nzc6E51HclzAIAwHuqVCrFq1evrj3XaDSi3W5PDWLtdjt6vd7EBYgGBt3IebYHmseqw+yoRWqZ59hSqTRz/u60c0a/t8F2SoPubMTnc4IHCzfNq1KpRKlUioODgzg7O1vKcO9Bx7rVakW73Z76y5Jer7fUlasNMwYAgPfU5ubmMHB2u91YW1uLUqkUJycnUavV4uTk5FpXdbD40KQwMh6YarVa7O3txevXr9+qxmnzWt/WXYazLlLLvMfOmrc6zfj39vOf/3zigl+PHz8efs5p86NnbfNzfHwcOzs7cX5+PvWYea776tWruRbG6na7sb6+futx8xJmAQDggTk8PIx2ux1ra2vx+PHjmV3SWcc2Go3Y29uLVqsVl5eXwzmSW1tbsbm5Gc1m89pw0G63OzHI1uv1ODw8jPPz82g0GlEqlaJarV7rOLZarTg6Ooputxv1ej2ePXsWpVIp6vX6tfoGzs/P4+zsLDqdzo33HHT3Bnvalkql2N7evrEK7rTP3m63h8Ogd3Z24tmzZ1GpVBaq5S51T1IulycGwkW/t2fPnkW9Xh9umzMIoqVSKTY3N+P58+fR6XTi8PAwDg4OhtvjlEqlKJfLUS6Xo16vx9nZ2fCYiKvvfGNjIxqNxrVjms3mXNetVquxvb0djx49Gv5dKpfL8fTp0xvzvS8vL5e2knFERMo5L+1iq7C5uZlH93p6CD5588mqSyiEjx99vOoSAABu+PGPfxzf/va3V10G75GdnZ04Pj6+09zih67b7Uar1RqujNzr9eLy8jIajUasr69fC+njQ6XHTfq3l1J6nXOeuMKYObMAAAD3qFarDbvM75vBnsOjK2aXy+U4Ojq61rle9nzZCGEWAADgXm1tbc2cl1pkT548mbiI1Pi2RS9fvpxrXu0izJkFAAC4Z7VaLVqt1p33DX6oqtVqtNvta/OvR4caDx4PFh9bJmEWAADgng0Wgup2u+9kD9Z36bZFndrt9r2EeGEWAADgHRgddvtFcl/daHNmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAABgDp1OJ7rd7sreu16vx+Hh4Urev9VqreR9Z7E1DwAAPDD1ej2azWasra1FrVYbPn9xcRHtdjs6nU7knG8cW61Wo1arTdzHdJFrjp8XEfH48eMolUoREbG/vx/NZjN2d3ej2+3GxsZGVKvVKJfL8fjx4zg6OoqIiFqtFhcXF9HtdqPVasX5+XmUy+VhLeVyOZ4+fXqtlu3t7Rv7lt619kmazWb0er2IiDg/P496vT7Xvq+DPWIH28ws+hneVqVSibOzszg9PZ15XL1ej16vN/wOlvn+rVbr3rbZuQthFgAAHphGoxHdbjfK5XIcHBzceL1erw9fHz220Wgs5ZoRn3cC6/X6jWDWbDajXq/H7u5uXF5exsnJybWQc35+HhFx7X2azebEmsdr2d7ejm63G/v7+3eufZrDw8Nr57dardje3h7WO0uz2bx27qKfYRk2NzdvDbOjwXqZyuVytNvt6PV6w19qrJphxgAAUDDPnj1b+nDX8Wvu7OzE0dHRxA7jaEjr9Xpzdev29/eHHdFZ6vX6te7rPOa9H0dHR9eGy1Yqleh2u7fW1W635+reDtzlMyxLpVKJSqVyL9fe3d0dduofAmEWAAAKotPpREREqVSKtbW1e7tmrVaLra2tmQFu2V3HgXkC78Ci9+P09PRa8O52u1EqlW7tNJ6cnCw0bHiRz1AkpVJpZXOGJxFmAQCgALrdbpydnQ0fL6P7Nu2a7XY7tre3Z55bq9WiVCotNIfytmN7vV48f/584lDicXe5H+PhvNFoxMnJya3nXV5ezj20dtZnaDab0Wq14vDw8FqHuNPpxMbGRuzs7ESn04l2ux2tVuvWLujh4WGklKJWqw3n9G5vbw+/u0Wu2+v1ol6vX6uv1Wrd6DBvb29Hu92e617cN3NmAQBghv/8n//zUq/3ne98Z+5j2+12HB4exsXFRTSbzZlzYpd1zUEoum1Y7SLDbqfpdrvDYHRychLdbjcajcbULuiy7ker1YrT09OJ84En1biMz9BsNq91s7e3t6NSqUS5XI5KpRLHx8ext7cXpVJpGMxfvHgR7XZ7Zo2DRbUirjqnjUZjGFYXue7e3t6wIx8Rsb6+Hufn5zd+AVEul+P09HTpC1zdhc4sAAA8UFtbW3FwcBCNRiNev379YK95V+VyOba2tmJrayuOjo6GHcZpllV7tVqNo6Oj4SJXs9wW7Of9DCcnJ9FsNoePK5XKtQ7nYAjv6HuVy+WpYXoQjsdrG+8gz3vddrsdm5ub154bDOMe/7wPZRi1MAsAAAVQLpevzQudFDQmmdVZnHTNUqk0M0QN3EegqVarsbe3N9e173o/Rh0cHESz2Zw5bLbX68Xjx4/nvua0z3BychK7u7vRarWi3W5PXHhq3i2CBl3peYc+z3PdtbW1uLy8HD6eNrS6VCpdO26VhFkAACiI8cWL5nFbyJt0za2trXjx4sXM8+5r3uTa2trc117kfnQ6nXj06NGN5wfDZpdp0mdot9uxt7cX1Wo1tra24smTJ3e6drfbjd3d3ajVaktdMblWqw1r7na7sbm5uZSh5PdJmAUAgAKaN8wusvrs4NjBENxZ595Xd65UKsWrV68WPu+2z3l5eTlxBeZutxvr6+sz67m4uFiolvHP0Ov1Ymdn59piU4NrDuYoz6tSqUSpVIqDg4M4Oztb2i8VBp3uQed4WsDv9XpLW0n7bQmzAABQMIeHh3MdN1hx+C7XPDk5udatG9VsNmN3d3eu6y5qc3Nz2E2eZw/YiPnux9bW1o3hwoP3mfVZ5hlyPW78M/zkJz+58T0Mrjn4pcC0zznr8x8fH9/anZ33uq9evYpqtRrVanXmtku3hf93yWrGAADwwNTr9Wi327G2tnYtgJ2fn8fZ2Vl0Op1h52zSsaPHDbqBi1wz4qoDOFjx9/T0NB4/fjwMZNPCzqCj9/Lly4i46lBub2/fWPn28PDwWi2j29g0Go3Y29uLVqs17KYuWvs0+/v714Lv+fl5vH79embgn7bg0aKf4dmzZ1Gv14fb5hwfH8fOzk6USqXY3NyM58+fR6fTicPDwzg4OBhujTOYw1wul6Ner8fZ2dnwmIircLmxsRGNRuPaMc1mc67rDoZqb29vx6NHj4ZDi8vlcjx9+vTGasaXl5cPYiXjiIiUc151DW9lc3Mzj+4v9RB88uaTVZdQCB8/+njVJQAA3PDjH/84vv3tb6+6DB6QnZ2dOD4+nrvLXTTdbjdarVbs7+9HqVSKXq8Xl5eX0Wg0Yn19/VpQHx8uvUyT/u2llF7nnDcnHW+YMQAAwAy1Wm3YbX4fHR0dRbVaHYb1Qdf26OjoWsf7Ic2XjRBmAQAAZtra2orz8/NVl3Fvnjx5MnFudKfTiUqlMnz88uXLaDQa77K0mcyZBQAAuEWtVotWq3VjDun7oFqtRrvdjmazOZwzOzrUePB4bW3tQQ21FmYBAABuMVgIqtvtPvj9V+/itkWd2u32gwvywiwAAMAcRofcftE8tCAbYc4sAAAABSTMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAPDgdTqd6Ha7K3vver0eh4eH7/y9W63WO3/PorCaMQAAPCCdTic2NjaiWq1GuVyOx48fx9HRUURc7XV6cXER3W43Wq1WnJ+fX9smptfrXdsrNCKi2+3GwcHBjfep1+vRbDZjbW0tarVaRERcXFxEr9eLjY2N2N/fv3b8IMiVSqXo9Xqxv78fzWYzDg4OotfrxfPnz69db/Q9Z507j8GWOIMVdQe1l8vlePr06fC4i4uL2N7evnWbmUVVKpU4OzuL09PTmcfV6/Xo9XrD72tZ7/2+7m/7toRZAAB4QC4vL+Pk5ORaeDk/P4+IuBb+ms3mtT1PO51OPH/+PI6Pj6NUKg2P6/V6sbOzE41G41rIbTQaw/PHQ2W9Xo+dnZ04OTmJiKsQXa/Xr53fbDbj6OgoDg4OolQqTb3ebefOYzz4zqp9e3s7ut3ujTD+tjY3N28Ns6PBelnK5XK02+3o9XrXvlcMMwYAgAel1+vN1YXb39+PXq83PGdnZ+dGkI246oY+e/Ystre3567h2bNn0Wq1otPpRERcC82j7z/+3CRvc25ERLvdnvvYiKsgPug0v2uVSuVe9qLd3d2Ner2+9OsWnTALAAAFV6/XY2tra2rnrlKpRLlcnnvO5+A63W53OMR3kp2dnZnXeZtzB05OThYaNjwI+O+TUqm0svnCD5kwCwAAD8gicyMHx758+TI2NjZmHlupVOLFixdzXXcQnAYBuVwux87Ozo2geNtQ3rc5d+Dy8nLu4bWDubuThi83m81otVpxeHh4bVGlwRzlnZ2d6HQ60W63o9Vq3doJPTw8jJRS1Gq1YWjf3t4edsDnvW6v14t6vX6ttlardaO7vL29He12e6778EVhziwAAMzQ+d3fXer1Kj/84VKv1+v1otfrxebm5szjnjx5MndntlarxdHR0TBEnpycxN7eXjx69CjK5XJUq9V4+vTpXENq3+bc27qR3W53GPBOTk6i2+1Go9G40cltNpvXwvP29vawW12pVOL4+Dj29vaiVCoN63rx4kW02+2ZXeHRBbgG84YHYXXe6+7t7UWtVhs+Xl9fj/Pz8xu/1CiXy3F6err0xa2KTJgFAID3wOXl5Z1e73Q6w07l5eXlcDXe0XmqpVIpTk5OotfrRbvdjtPT09jY2Iijo6O5urN3PXfSfNtR5XJ5GO62trai1WpNHGY8WMhq8H6VSiXa7fbw8WAY7+h7lcvlqWF6EI4nzU8ef3zbddvtdhwfH187r9Pp3Aj7gzDL54RZAAAosFKpNNecyvPz84nd0EqlMvfQ5lKpFNVqNarVatTr9djY2Ijd3d25hgHf5dxerxePHz+eq7aIq2HXjx49ujF/eBBmW63W8F6NX3eeRaYGWx81Go25h0nfdt21tbVrQ6mnDasulUq3/sLii8acWQAAKLjd3d1bu3btdvtOW8eMDuUdVS6XY3NzM87Ozu7l3LtaW1u78Z7tdjv29vaiWq3G1tZWPHny5E7X7na7sbu7G7VabWkrJtdqtWG93W43Njc3F1q9+YtMmAUAgIJrNBrRbrendmcH+5TOu6/ruGlBebDA032dWyqV4uLiYr4iR8559erV8PFg26JBdzYihtectdryJJVKJUqlUhwcHMTZ2dlSFmQql8uxtrYWrVZrOAx7kl6vF2tra2/9fu8TYRYAAApuMC91sLLuqMGiSG8z33IQtMavG3H7MNq3OXfWvNVpNjc3r+2P+5Of/OTGsN3BNQfDdqdt5zNrm5/j4+Nbu7PzXPfVq1fD4dezhi53u91YX1+f+X5fNObMAgDAAzXo1L18+TIirkLr9vb2xBVtt7a2YnNzM5rN5rWQ2O12JwbZw8PDaLfbsba2Fo8fP57ZtT06OhqeM3BxcTHsdg62xJl0vdvOnaVcLk8MhLNqbzQasbe3F61WKy4vL2N/fz+ePXsW9Xp9uG3O8fFx7OzsRKlUis3NzXj+/Hl0Op04PDyMg4OD4fY4g+5xuVyOer0eZ2dnw2MG93ZjYyMajca1Y5rN5lzXrVarsb29PVzpefCZnz59emMe8+XlpZWMx6Sc86preCubm5v5Psbav41P3nyy6hIK4eNHH6+6BACAG3784x/Ht7/97VWXQd/Ozk4cHx/PvddskXS73Wi1WsOVkXu9XlxeXkaj0Yj19fVrIX18qPT7aNK/vZTS65zzxH2nDDMGAAAerFqtNuxMv2+Ojo6iWq0Og/qgY3t0dHStm26+7GTCLAAA8GBtbWS/9AEAACAASURBVG3F+fn5qsu4F0+ePJm4iNT4PrMvX76MRqPxLksrBHNmAQCAB61Wq0Wr1Zp7P9yiqFar0W63r81zHh1qPHi8trb2Xg6zflvCLAAA8KANFoLqdrvv3R6sty3q1G6337sQvyzCLAAA8OCNDrv9IhFkpzNnFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAonJWH2ZTSfv+nMfJcNaW0lVI6WGVtAADA/et0OtHtdlf23vV6PQ4PD9/5e7darXf+nu+TlYbZlNJWRLRzzs2IKPcDbCUiIufcjoje4DEAAHxR1Ov1WF9fj/X19VuPSylFvV6PXq838Zhutxv1en3mNR49ehTr6+s3Al2z2Yz19fXY3t6OTqcTnU4nUkqxs7MzDICDOg8PD6Ner8fOzk6klOYOp+Nb7gzq2djYiMPDw+FPvV6Pdrs91zUXUalUYn19PV69ejXzuHq9HrVabenvLdDe3aq35in3f5oR0e3/eTsiTvuvdyNiKyI6K6kOAABWoNFoxJMnT6Jer0en05m5LU25XI5GozH19W63G81mc+oxjUZjGCYPDq4PjNzf34+IiN3d3SiVStFut+Pk5OTadjHn5+cREdfObTabc+8J22w2r507q57t7e3odrvDupZlc3MzTk9PZx7z9OnTpb5nxNV31263o9frRalUWvr133cr7czmnJv9rmxERCUiziKiFBGXI4c9fueFAQDAA1Cr1eLo6Gjia+12O7a3t2+9Rrlcjs3NzTt3ANfW1oZBq9frzbXv6f7+/tRO8ah2uz1X4B24j+7ovCqVyr3sdbu7uzuzc850K58zGxHRH0p8mnOeqwPbn2N7llI6+7u/+7t7rg4AAFajWq3Gy5cv73x+p9OJcrk8MxSv0snJSWxtbc19/DwBuWhKpdLK5gsX3YMIsxGxlXMeDNDvRcRa/8+liLgYP7jf0d3MOW9+7Wtfe1c1AgDAOzWtq9rr9ebqaF5eXg14rFarw+Gsb2Oeruwix15eXs49vLbX68Xz589vDD2OuBqq3Gq14vDw8Nq96nQ6sbGxETs7O9HpdKLdbker1bq1E3p4eBgppajVasM5vdvb28NO+LzX7fV6Ua/Xr9XWarVudJe3t7fvZT7w+27Vc2YjpbQ/CLL9BaFeRMRm/+VyRPhWAQD4whp0VUfD4dnZWWxtbc3s6PV6vVhbWxs+rlarN+anrtJt3chutzsMeCcnJ9HtdqPRaNzo5DabzWtzaLe3t6NSqUS5XI5KpRLHx8ext7cXpVJpOEz4xYsX0W63Z3aFz8/Ph78wKJVK0Wg0hmF13uvu7e1FrVYbPl5fX4/z8/MbQb9cLsfp6elCXWpWHGb74bWRUqrHVTd2J+fcSSlt9l/rzTv0GAAA7sMnbz5Z6vU+fvTxQsdXq9XY29tbeJGgQeAdqNVqUavVHlSYndVdLpfLw/q3trai1WpN7CyfnJxExOeLVVUqlWi328PHg2G8o+9VLpenhulBOB6/15Me33bddrsdx8fH186btKDXIMyymJWG2f72O48mPN+ccDgAAHwh7e7uDruqnU4nNjc3bz3n9PQ0Op3rfaFut3vr6sjvSq/Xi8eP51/rtVqtxqNHj2Jra+tasByE2VarNQyY49edZ0h2r9cbrvo872rJt113bW3t2lDqacOqS6XScEg481v5MGMAAGC2Wq0WOzs7cXBwEJeXl7eG0W63G7Va7UbYOj8/j6OjoxuLQZXL5anzaR9SyFpbW4t2u31tmG673Y4XL14MQ+14gJ9Xt9uN3d3d6PV6S1swq1arDbvE3W43Njc3F1q9mdkeygJQAADAFIPwOm9QG6xiPK5Wq01cHXkVCxCVSqW4uLix1uut57x69Wr4uNfrxc7OzjDIRsTwmoOFm+ZVqVSiVCrFwcFBnJ2dLeV+lMvlWFtbi1arFe12e+pQ4vH5zcxHmAUAgAdoPIjVarXY29u7dZGgXq83tZs6CMXjqyNvbW1FpVKJZvP6bL9Wq3VvixLNmrc6zebm5jDQd7vd+MlPfnJj2O7gmoN7MK3jPGtl5+Pj41v3s53nuq9evYpqtRrVanXm0OVutxvr6+sz34+b/tH3v//9VdfwVprN5vfnHdP+rvzXX/zXVZdQCP/sl//ZqksAALjh7//+72N0+8dl/7/dPP8PVK/X4w//8A/jb//2b+Ojjz6KDz/8MH71V3813rx5M9weptVqxSeffBKvX7+O//E//kc8efIkfvSjH8Xv/u7vxqeffhofffRR/Oqv/uq16zabzWi32/GXf/mX8cEHH8Rv/uZvDl/b3d2NTz/9NP7iL/4i/vqv/zr+y3/5L/Hd73536rDYdrsdR0dH8ad/+qfxN3/zN/HmzZtIKc09jPbRo0dxfHwc/+pf/atrzx8eHsaf/umfxmeffRb/83/+z2s1fvTRR3F6ehoffPBB/M3f/E38i3/xL+KDDz6Iv/iLv4iUUnS73fjX//pfxx/90R/FL//yL8fa2lo8f/48Pv300/jH//gfx2/+5m9Gq9WKf//v/32cn5/H48eP4xe/+EXU6/X40Y9+NLwnf/u3fxuNRiP+8i//chgyB8esra1FSunW6/76r/96pJRiY2MjTk5Ohvf+gw8+iF//9V+/9pl/9KMfxT//5//8xvf1RTP+by8i4g//8A//9vvf//7ENZVSzvmdFHZfNjc389nZ2arLuGbZK969rxZdyQ8A4F348Y9/HN/+9reHj1e9mvH7bGdnJ46Pjxdapbkout1utFqt4crIg455o9GI9fX1a6tKjw+V/qIa/7cXEZFSep1znrjimQWgAABgBuHz/gzm8D60kZbLcHR0FLVabRjUS6VSlEqlODo6iu3t7WGYNV/27syZBQAAVmJrayvOz89XXca9ePLkycRFpMa3Rnr58mU0Go13Wdp7Q2cWAABYmVqtFq1W69p2O++DarUa7XY7ms3mcB7x6FDjweO1tbX3cpj1uyDMAgAAKzPY47bb7b53e7DethL0+J65LEaYBQAAVmp02O0XiSD7dsyZBQAAoHCEWQAAGFP07SuhaO7yb06YBQCAER9++GFcXFwItPCO5Jzj4uIiPvzww4XOM2cWAABGfP3rX4+f/vSn8Xd/93erLgW+MD788MP4+te/vtA5wiwAAIz4pV/6pfjWt7616jKAWwizUACfvPlk1SUUwsePPl51CQAAvCPmzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACF86VVFwDAu/XJm09WXUJhfPzo41WXAABMoTMLAABA4QizAAAAFI4wCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4QizAAAAFI4wCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4QizAAAAFI4wCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4Xxp1QW8jz76vbNVl1AMP1x1AQAAQFHpzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhSPMAgAAUDjCLAAAAIUjzAIAAFA4wiwAAACFI8wCAABQOMIsAAAAhfMgwmxKqTL2uNH/7/5qKgIAAOAhW3mYTSltRcTx2NP7KaXziOiuoCQAAAAeuC+tuoCcczuldDn29E7Oub2SggAAAHjwVh5mp6iklCIiKjnnw1UXAwAAwMOy8mHGk+ScD/ud2cf9YcgAAAAw9OA6symlakREzrkVERcRUZ5wzH5E7EdEfOMb33in9cEqfPR7Z6suoRh+uOoCAAB4Vx5iZ7YbEYP5susRceP/4nPOzZzzZs5582tf+9o7LQ4AAIDVW3mY7XdiN0c6sp2I2O0/Pu8/BgAAgKGVDzPuDydujT3XXFE5AAAAFMDKO7MAAACwKGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCuVOYTSl9OaX0Wyml3xh57jdmnQMAAADLsnCYTSn9UUT0IuIkImojL/0spfT7yyoMAAAAplkozKaU/m3/j49yzo8joj14Lef8Wc75j1NK31tmgQAAADDuSwse38s5/2DkcZ5wzJu3qAcAAAButegw44s5jvnWXQoBAACAeS0aZv+Pscfp2oOUvhkRv/YW9QAAAMCtFh1mfJRSehUR/y4i/jr6w4z7IXYnIvYjYmOJ9QEAAMANC4XZnPNnKaVaRDQjohIROaVhc7YdEb+dc/75cksEAACA6xbtzEbOuRMRmymlb8VVoI2I6OScP1tqZQAAADDFwmE2pfTlnPPP++FVgAUAAOCdW3Sf2f87BFgAAABWbNHVjE8ionwfhQAAAMC8Fg2zlxHxaNYBKaU/uXs5AAAAcLtF58yeR0Q1pfS4/+fuhGO23roqAAAAmGHRMPufIqIUk0NsRMRaRHzlrSoCAACAWywaZrs5581ZB6SUXr5FPQAAAHCrRefM7s1xTP0uhQAAAMC8FurM5pz/evDnlNKXI2LQpT3LOf+8f4ytewAAALhXi3ZmI6X05f5Q4l5EtPs/b1JK/zGl9M3llgcAAAA3LRRmU0pfiYhWRJxGxKOc8wc55w8i4p9ExKcRcdLv2AIAAMC9ucuc2Z2c83HO+WeDJ3PO3ZzzYUQ8jYhnyywQAAAAxi0aZn82GmLH5Zy7MX3bHgAAAFiKRcNsnuMY+8wCAABwrxYNs49mzYntv/bVtysJAAAAZls0zDYjopVS+pejoba/wvH34moRqH+3zAIBAABg3KL7zP4spVSLiKOI+LOU0uiw405E7A72mwUAAID7slCYjYjIOX8WEb+dUvpWRJQjohQRnf7zAAAAcO8WDrMD/fA6DLAppd/IOf+3pVQFAAAAMyw0Z7Y/N/bfppT+v5TS74+9/LMJzwEAAMDSLboA1G7O+QcR8dsRcTz6Qs75s5zzH6eUfmdp1QEAAMAEiw4zThEROedP76EWAAAAmMuiYTbffkiU71IIAO/GR793tuoSiuOHqy4AAJhm0WHGv5ZS+t+mvZhS+o2I+LW3KwkAAABmW7Qz+zwiXqeUTiLiNCK6/efLEbETEVsRsbG88gAAAOCmhTqzOeefRcRmRKxHRDsizvs/7YhYi4jNnPPPl10kAAAAjFp4n9mccy8idiMiUkqVq6fyXy+7MAAAAJhm4TA7KufcWVYhAAAAMK+pYTal9G/jaujwwEXO+Y9HXv+diGjE56sXtyOilnP+yT3UCQAAAENT58zmnH8QV3Nj1yPi5ViQ3YuIk4j4s4j4tZzzP4qIZxFxklL65n0WDAAAAFPDbH+bnVc5590Jc2IbEdHMOf9BzvmziOGQ4+2IqN9btQAAABCzVzN+2u/OXpNS+m5EfCUijsZf6y8OlZZXHgAAANw0K8xOC6XbEdHLOf+3Ka/ntysJAAAAZpsVZqeF0q24WuxpGp1ZAAAA7tWsMPsopfQro0+klP5pRFQi4sWkE1JKvxURr5dXHgAAANw0K8w2IuKvUkr/Z0rpy/2teD6NiNOc85+PH9xfMGon53x8T7UCAABARMzYZzbn/FlKaTci/q+I2Ow/3cw5/8HgmJTSV+JqS55KXA0/7qWUXuec/8M91gwAAMAX3NQwGxGRc+5GxG/PeP1nEfEH014HAACA+zBrmDEAAAA8SMIsAAAAhSPMAgAAUDjCLAAAAIUzcwEo7uYfvve9VZcAAO/MJ28+WXUJhfHxo49XXQLAe0NnFgAAgMIRZgEAACicpYfZlNLvL/uaAAAAMGrinNmU0m9FROmO16xFxB/fuSIAAAC4xbQFoP4gIrYiorPg9UoR8a23qggAAABuMS3M9iJiPef82aIXTCm9fLuSAAAAYLZpc2brdwmyg3PvWgwAAADMY2JndtEgm1L6SkTs9h+eR8RdgzAAAADcatow44XknH8WEccRESmlP4mI/7SM6wIAAMAkC4fZlNJvREQjItbi5orHaxFhziwAAAD3aqEwm1L6bkQc9X+6EfEkIl71Xy5HROScf7DMAgEAAGDcop3Zas751wYPUko55/znoweklH5n/DkAAABYpmmrGU8zvu/s45TSl8ee+9lb1AMAAAC3WjTMjnsZEc/Gnvunb3lNAAAAmGnRMHuWUvpuSun/SSn9fn8V442U0p+klH4rpfS9uJpHCwAAAPdmoTmzOee/Til9KyKaEfFn/ad3IqIVEe2IeBMRG0utEAAAAMYsvDVPzvmziPjByOOfRcT2MosCAACAWd52ziwAAAC8c4vuM/sbOef/NvbcVyJiNyJKEfEm5/wfllgfAAAA3LDoMONaRPyb0Sf6w4yPB49TSt8TaAEAALhPhhkDAABQODM7syml70ZEHnmqnFL6rSmHl+JqW55SROjMAgAAcG9uG2bcjYhKRDyNiGpcBdv1Gcef5pz/zYzXAQAA4K3NDLP9bXg+i4g/SykdRMS3hFUAAABWbZE5s0dx1akFAACAlZp7NeP+qsU/GDxOKX05Ijb7D89yzj9fcm0AAAAw0cKrGaeUvpxSehkRvYho93/epJT+Y0rpm8stDwAAAG5aKMymlL4SEa2IOI2IRznnD3LOH0TEP4mITyPipN+xBQAAgHuzaGd2LyJ2cs7H/WHHERGRc+7mnA/jatXjZ8ssEAAAAMYtGmZ/Nhpix+Wcu2GRKAAAAO7ZomE2z3HMV+5SCAAAAMxr0TD7aNac2P5rX327kgAAAGC2RcNsMyJaKaV/ORpq+yscfy+uFoH6d8ssEAAAAMbNvc9sxNVesymlWkQcRcSfpZRGhx13ImLXfrMAAADct6lhNqX0JxGxFhGXEXEeEd2c85/nnD+LiN9OKX0rIsoRUYqITv95AAAAuHezOrO1iKjmnP980ov98LqUAJtSquScOyOPqxHRi4hKf8sfAAAAGJo1Z7YzLcguU0ppKyKORx5XIiJyzu2I6A0eAwAAwMCsMHun/WJTSr+1yPH90Ho58tTTuOrKDmrYuksdAAAAvL9mhdnLGa/NUrvjeQOlsfd+/JbXAwAA4D0zK8w+uuM1y3c8DwAAAOYyawGonf5CTO9aL65WUY646tJejB+QUtqPiP2IiG984xvvrjIAAAAehFlhthtX+8kuIkVE/e7lRETEi4jY7P+5HBHt8QNyzs2IaEZEbG5u5vHXAQAAeL/NCrPtnPMPFr1gf3XiRY6vRsRmSqmac27lnDsppc3+dXqjW/YAAABAxOwwe9c5swuFz5xzKyJaY8817/jeAAAAfAHMWgCqdJcL5pz/4I61AAAAwFxmhdnNlNKvvLNKAAAAYE63bc3zn1JKv5NS+ua7KQcAAABuN3XObM75g5TSt+JquPF6SqmSc/7zd1caAAAATDZrAajIOX/2rgoBAACAec0aZgwAAAAPkjALAABA4QizAAAAFI4wCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4QizAAAAFI4wCwAAQOEIswAAABTOl1ZdAHC7f/je91ZdAu8Rf59Yto9+72zVJRTHD1ddAMD7Q2cWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAonC+tuoD30V9956urLqEQvrPqAgBYin/43vdWXQLvmU/efLLqEgrh40cfr7qEwvB3aj5F+zulMwsAAEDhCLMAAAAUjjALAABA4QizAAAAFI4wCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4QizAAAAFI4wCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4TzIMJtSavT/u7/qWgAAAHh4HmSYjYj9lNJ5RHRXXQgAAAAPz5dWXcAUOznn9qqLAAAA4GF6qGG2klKKiKjknA9XXQwAAAAPy4MMs4MAm1LaTiltjXdp+3Np9yMivvGNb6ygQoDi+qvvfHXVJRTGd1ZdAABL8dHvna26hGL44aoLWMyDmzObUqqmlKr9hxcRUR4/JufczDlv5pw3v/a1r73bAgEAAFi5Bxdm42rRp0Endj0i/BoFAACAax7cMOOccyeltJ9SuoyI85xzZ9U1AQAA8LA8uDAbcTWMeNU1AAAA8HA9xGHGAAAAMJMwCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4QizAAAAFI4wCwAAQOEIswAAABSOMAsAAEDhCLMAAAAUjjALAABA4QizAAAAFI4wCwAAQOEIswAAABTOl1ZdAHC7v/rOV1ddQiF8Z9UFALAUH/3e2apLKIYfrrqA4viH731v1SVwD3RmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgMIRZgEAACgcYRYAAIDCEWYBAAAoHGEWAACAwhFmAQAAKBxhFgAAgML50qoLAACK7a++89VVl1AY31l1AQDvEZ1ZAAAACkeYBQAAoHCEWQAAAApHmAUAAKBwhFkAAAAKR5gFAADg/2/vXnLbSLY0jn8HqAXwqqrHjaYHPadd4xpcege0awPX8g5keAUGvQPaK7iWdmA17gLK0ryBFnsDfS2iewGnBxlZDgUjH6QoJoP8/4CExHwxmBmMzJPxYHEIZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFAcglkAAAAAQHEIZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFAcglkAAAAAQHEIZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFAcglkAAAAAQHEIZgEAAAAAxflp6AQAAAAAsf/729+GTgKOzD9++2XoJBTht6ETsCFqZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFAcglkAAAAAQHEIZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFAcglkAAAAAQHEIZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFAcglkAAAAAQHEIZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFAcglkAAAAAQHEIZgEAAAAAxSGYBQAAAAAUh2AWAAAAAFCcn4ZOAAAAABD7x2+/DJ2EIvw2dAKAgVEzCwAAAAAoDsEsAAAAAKA4BLMAAAAAgOIQzAIAAAAAikMwCwAAAAAoDsEsAAAAAKA4BLMAAAAAgOIQzAIAAAAAikMwCwAAAAAoDsEsAAAAAKA4BLMAAAAAgOIQzAIAAAAAikMwCwAAAAAoDsEsAAAAAKA4BLMAAAAAgOL8NHQCcsxsJmklaeLuH4dODwAAAADgsBxczayZTSTJ3a8lrerXAAAAAADUDi6YlfS7qlpZSVpKmg6YFgAAAADAATrEYHYk6Xv0+uehEgIAAAAAOEzm7kOn4QEzW0hauPutmU0lvXT3d8k655LOw8t/l/Sfe05miX6R9D9DJwJHhTyFXSNPYZfIT9g18hR2jTzVz7+6+7/kFhziAFArSWfh/5Gkf6YruPsnSZ/2majSmdk3d38xdDpwPMhT2DXyFHaJ/IRdI09h18hTj3eIzYz/Lmkc/h9Luh4wLQAAAACAA3Rwway730pSaGK8ql8DAAAAAFA7xGbGdTNi7BbHFLtGnsKukaewS+Qn7Bp5CrtGnnqkgxsACgAAAACALgdZM1saM5uo+n3cf7r7x6HTAwCnjnIZAHBMuK7lHVyf2W2Z2YWZ3ZnZTWbZeVj2NWSEx77XOH4d+vXeSXr52H3vg5nNw3QRjs15mH9uZqOh04enZ2ajkAfuw3fjIlpWz78JeaSe5ma2SPNImH9nZncd7zk3Mw9/yWcH6KnPZVp2PqVQLv+hQsplPD7/RWXVefg7isu2zD4ar4NtZSSGZWZjM5sPnY4m+yznsH91uWJms2jaS/nAda2Bux/NJGkm6UbSRcOy0Y7e5zwzbyzp69DHoCPdE0lfJU1zn0nS/a6OEVMZk6RLSfMN5p9LusvMn6l6oDNpea95blumw5qe8lzmys4n/iwHXy4zrZ2zrfKfpIWkcTJvrbza9DrYVBYyDZpHppLuh05HS/r2Ws4x7fXcTkKZMErmj8L88R7SwHUtmY6mZjbyRtI882Rs5e6rHb1HqU9ELiW9dfe1nztyBt3CuqbfeB6H0cZTC0lvczsK63/dbfLwhJ7qXJZadmK/tsl/Y3dfxjNCebVM1uM6WL6lpG9mNhs6IQ0o545QaAVyKelNGk+E1x/Efc4gji6Y9aoK/pOqi+FOhaYFl6qewBTF2YG17AAADI5JREFUzBaSrtOLfaL3hTzT1HRE09HjF53j75nFV5Je7zE5eDo7PZcll50YxEb5L5RLTU07L6P1dnodxP6Z2SScv8YHHkPpKue4byreXFX5ka0YC/HHki4J+3eUA0C5+9vQz2Xm7ldN64U+MvVN+dhDZ+rQr/azpG8KzQZUPWn7e1h/EmXWT3HGDtueqSrMfnX3d237dPdXG6SnLsAVtn9W77+HqaSudRf1Z2k5Bh/CfEl6HqV7Hvb/KUnvh/R4RLV6E0nL+hxtu10tOn7jTfbbfegQ+awqz6/9/rO7L83sW/q9CxfrpZpvNnFgtj2XTWWYqvKnsezctCyuy82W92vUVE7gcGya/9x9ZWbLEEg8qDVJals3ug7iIJ1JkrtfmdmlmY1y5yuUM/VDi7M4H/RYdqvqHmHs7h83uIfIlnOq8mvf+6amsm4tXRscM+zGa3WXH7eqBmj6uMH1K3stasunqZPPH0O3c97lJGkW/6+o74uS/jFK+teoygBfo9dTVf1vx+H1efg7UaatuqrMeJfs8zJ+36Z9bpCeOz3syzPNpSWTtpEkV0sfpIbt2o7BTbLuPPk8k3jb6HjMk+3uk9fbbneevP6a7KNpv2v9pk5pyh3baP5lyAPT8H26VKY/elh/Vv9N82R9jOs8PPRnZurME1udyx5lWFPZuW1Z3LpdmDdO9tVaTjANPz0i/9VNAF3VtXIeX/O0/XWQPrMHMoVzGJ/T7DUpnPtZst2sx7L0vuFCyX1P1z1ESznX576pqaxrTBfTXvNeZ/kRyi3vcU677lnb8ml6XTv5/HF0zYxrXj3h+KYqQzwQnpa88KipkVdP9pb1iIaqnpaM6nW8X1+aM3/YfCl9gpzd5wbpWfrDJ87Xau6/uAtNx6DPU+uV1vsw5Zp2fU+a2Wy73avoWEnVE6r4uDTtl9rCZn+4+3WYrrx6mri0zIjhtfC9e0HTqfJtci57lmHbbrdWDm37fuouJ3AgNi1L3H0Vyqi/qKo9GUm66cgPKMsLf9gqaK2pccgv5/6wlutc0suOZROt3yNcRft/7D1En/umprKuLV3Yr7MNlzfdRzdei9ryafpm5I/KUTYzjrySdB/6ycReKB8g3Sk0AQna+tXk9Fk/t07f9OTcqnritzaYRc2rJlgrVYXug+ah9ZdG0rOwfKnqKXSdnk2PQSy37dqgQjvarm62MVO46GS2ecxngf5s2vXWzBbu3lRYflGVp+pmNt/2l0LsWN9zuW0Ztm1ZvO379SkncDg2LkvCQ40rSVfh51tuzOxLuA7WwcdaN4laU7NVHIQ66IyNQz/a+pxOlZQNXjUVHnUsey1plakciO+t9nEPkSvrutKFJ5bcR7d5pvXyJZdv2q5Fbfk0Rf7QkQezIfO9U9UUpG/fyPipSucFzczWRlDssOlFsuspUF9fVLXjf9AmP1y06y/Jvap+uPHn2fVFfdv9dW03lfS7/+iL8OjfE0ajW1VNaZosVH3nPqpqrdB444iDt4tzmS3DMiPOt223SbnRVmZSTpSlV/4LeWnsyQjFHvreqrrhuw7T2nUwMe1YjgGEc7xI77fM7JmqWqjWmqhwP9i1bJnmIT0yKNjRPeLO04WtfFFVO9rWUnOqanydWO6cbnwtannIdvL542ibGde86gS9kvQ+mv1N+acrz7T5sNq7uBl6THpaa2VroRZt2uMLkxulto/BmpWGQPzSo8G0JP1cL+tx04zNtNZs1DecBArl2+BcblOG1TVt25R9G29HOVGeDcuSpp9DWSnUcoTr4KTjXO/qATJ2a9IQFC70cOTrazXXnrUt+6bM/dwOusy05d0++36qdGFz71TdRzcNgDhV1aS4dfClrmuRpP9S/+br5A8dXzDbdPLfKDrZ/mP47D/nhRP/Iukbm8sMaR+JVcu6uflr622Qngf9h0LzhNsNakv+Kumz5X+b7bXyT49y6V1m5o+TeX2PR9/lbdudaT3t42jZY9Jzyn5OZ4Q8OlM1omMs/e4tJH3OPC3E4dv4XPYsw9bKzm3L4g22i/UpJzC8bcuSWdrUrr7pTIKgV5IWubEmQh+2L5snGU8pfLez39HogccsvF5J+pL2lTazaceyW0m3mXzxIvztew+RvUfsed+0tr8e6cKehPxTlx/pzyyNVQW7uYdq6Tnuuhb9rxryaSZN5A/peEYzVjXQk6u68I1yyzPzLlTdmM/C//XIxxNVT/c9np9sd6EfI4tN9GMUxYswb6aq/9ZN+L91n23pSdIUjy6bHVm257FahOlCVZ+kOkjpewzOw/rTKL330b5aj0dyzuohy7faLjp28+j4jEL6e6fnlKZwfObhnN3FeSmafxPl9YuQX74qGc1Pme9eOJ/zaJ1ZlJ/mufzPNPz02HPZVoZFy/8sO7u261EOtZWZ4/C9v4++943lxNDHnmn7/BfWqc9pXGY1jkIctp3rxzUwHV20sYxk2mueqEeDvUuvPWH5eThH93p4HavP66yhvOm9TBveQ7SUc133TV1lXTbNTHvPk6Pk2pO9H287p32uRQ15ce26Rv5wWTgIOHChBmLu7k1NqQAAAADgZBxbM2MAAAAAwAkgmAUAAAAAFIdgtgB1E2NVo6hdDJ0eAAAAABgafWYBAAAAAMWhZhYAAAAAUByCWQAAAABAcQhmAQDIMLOxmV2a2dfcD9YDAIBhEcwCAE6SmX1tW+7uS0nv9ONH7Y+amU3M7N7MxkOnBQCAPghmAQAnJ4wS/6KrxjUEtLeZ7ccl1tZ2pHslabnP9AAA8BgEswCAU3Qm6Yukt1tuP9lhWvapMd3uvnT35yGABwDg4BHMAgBO1aWkmZlt04T4910nZk9KTTcAAGsIZgEAJ8XMpu5+7e7XqprWvt5g25GZXUiaPVkCn0Cp6QYAoM1PQycAAIA9i2ti66bGn3puey7pWfj/nZm9Cv8v3P3PvrVhEKW3kv5Q1aR55O4fw7KJpM+SxpL+Gv6eSXrl7i/Dtpdhfr3/UVjnpaQ37r6KE1X3AQ4vn0u6DMF6r3SH2un/CO/5xt2vQvD7Prz3lbvX2ygsm6vqT/ym/uxtnzssrwfTWoX3+i7pV3d/lz3aAAC0MHcfOg0AAOxFCNpe1IFeCAJvJD1r6itqZjeSPrj7VTTvTtLbJGCsl00kfXb359G8uSTFQZuZuarRkq9UBXX/Lenf6kDVzO4lfVAVSC7DvHOFoDfaz1jSOE5LSN+rOMDuSnf0nm/qzxr2fSfpL5kA+jIJcFs/d9jXLAlux6oC6pcCAGBDNDMGAJySaRzIhWBvpd02v/2sKgiNfZB0kfTPXUr6OQy8tHL3NGBcaj3I/qbqp4JiM0mLZN5CVc3pph4E9NFozg+aYtdBaLJt1+deG0U57L/1J5IAAGhCMAsAOCVnmXmPGdX4gRDkTZT8nE8IUlf60RS4dtexy5vkda72+ErrgeVS+c+6jYXWj880qQnu87mvJc3N7CL5Ldu+TbwBAHiAPrMAgJMQAqhXZvY8WXQmaWxmk7RZ7hbqn76ZhGa3sS+qmhPHun4GJ11/Tajd/BhqP1+HbX7Vw77BW3P3T2a2MLNxy8/2dH5ud1+GvrpzVUHtUlUT448CAGALBLMAgFMxbeqbWfcl1ZY1tGY2ipsIx/1rI7l5jxaC2EtVtaIf3H1lZlKPptNpultcqTo270Kwmu1z2/W5w/Kr8GBhGvb3kj6zAIBt0MwYAHAq2moqr7TBT/Rk1NvGo/ruy6Wkpbu/ywWmHb+j2/czL1SNiCxVA2ilNbSdn9vMzuu0hH7Cn9z9mapa8Z3UIgMATgvBLADg6LXVJgYLSaPw0zF9jdL/Q5B3rUytqJmNM01wd2GqKqCNxUFlGrCupbtL6B/73cxmyjR97vm5s4NAhe121b8XAHBCCGYBAKfgfVt/2BCMLZVvZpwL+K5V9UutxTWibyW9zdRSzpI0dAVwo8w6Z9JabWv9m62p3P7b0l2/Z5MrVSMWNz0U6PO53+fS2dIXFwCARvzOLADgaIWa1rmqAYquW/rMxs1o69GBl2HbWfj/wWBFYZsbqRokKdnfKGx7F7Y9q9cJwV6832t3fxttmy5fuPvHUCv6vv4skubufh2t/4eq5r4jd78K6ZOky2Tk4bV0Z95z3vSZ4rRmjmPb556F9E1V1e6eqQqerwhmAQDbIJgFAAAAABSHZsYAAAAAgOIQzAIAAAAAikMwCwAAAAAoDsEsAAAAAKA4BLMAAAAAgOIQzAIAAAAAikMwCwAAAAAoDsEsAAAAAKA4BLMAAAAAgOIQzAIAAAAAikMwCwAAAAAozv8Dae6QOQav6IQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decisionMatrix = np.array(decisionMatrix)\n",
    "data = {'Name':companyName, 'P/E Ratio':decisionMatrix[:,0],'EPS':decisionMatrix[:,1],'Revenue (B)':decisionMatrix[:,2],\n",
    "\t    'Beta':decisionMatrix[:,3],'Dividend Yield':decisionMatrix[:,4],'Monthly':decisionMatrix[:,5],'YTD':decisionMatrix[:,6],\n",
    "\t    '1Year':decisionMatrix[:,7], 'ELECTRE 3':phiEl, 'MAUT':utilityScorePer, 'PROMETHEE':phi, 'TOPSIS':C2} \n",
    "\n",
    "df = pd.DataFrame(data) \n",
    "df[\"ELECTRE 3 (Ranking)\"] = df[\"ELECTRE 3\"].rank() \n",
    "df[\"MAUT (Ranking)\"] = df[\"MAUT\"].rank() \n",
    "df[\"PROMETHEE (Ranking)\"] = df[\"PROMETHEE\"].rank() \n",
    "df[\"TOPSIS (Ranking)\"] = df[\"TOPSIS\"].rank() \n",
    "df[\"RankSum\"] = df.iloc[:,13:17].sum(axis=1)\n",
    "df.sort_values(\"RankSum\", inplace=True, ascending=False) \n",
    "# display(df)\n",
    "display(df['Name'].iloc[:20])\n",
    "\n",
    "# plt.figure(figsize=(16,9))\n",
    "ax = df.iloc[:50].plot(x ='Name', \n",
    "\t\ty={'ELECTRE 3 (Ranking)','MAUT (Ranking)','TOPSIS (Ranking)','PROMETHEE (Ranking)'}, \n",
    "\t    color={'lightgreen','silver','aquamarine','indianred'}, \n",
    "\t    kind = 'bar', \n",
    "\t    figsize=(16, 9), \n",
    "\t    stacked=True, \n",
    "\t    rot=0)\n",
    "# ax.tick_params(axis='x', colors='white', length=10, labelsize='xx-large')\n",
    "# ax.tick_params(axis='y', colors='white')\n",
    "legend = plt.legend()\n",
    "plt.setp(legend.get_texts(), color='black', size='18')\n",
    "plt.xlabel(r\"Alternatives\", fontsize=22)\n",
    "plt.ylabel(r\"Total Score\", fontsize=22)\n",
    "plt.title(r\"Cumulative Score\", fontsize=28)\n",
    "plt.xticks(fontsize=18, rotation=0)\n",
    "# matplotlib.pyplot.axhline(0, c=\"k\")\n",
    "plt.savefig(\"barplot5.png\", dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ELECTRE 3</th>\n",
       "      <th>MAUT</th>\n",
       "      <th>PROMETHEE</th>\n",
       "      <th>TOPSIS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accenture</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>41.93</td>\n",
       "      <td>-12.90</td>\n",
       "      <td>36.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Northrop Grumman</td>\n",
       "      <td>2.69</td>\n",
       "      <td>67.32</td>\n",
       "      <td>30.78</td>\n",
       "      <td>53.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBM</td>\n",
       "      <td>1.34</td>\n",
       "      <td>44.59</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>56.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Motorola</td>\n",
       "      <td>0.01</td>\n",
       "      <td>52.15</td>\n",
       "      <td>5.58</td>\n",
       "      <td>39.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MSCI</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>44.02</td>\n",
       "      <td>-8.49</td>\n",
       "      <td>39.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oracle</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>37.74</td>\n",
       "      <td>-11.91</td>\n",
       "      <td>33.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  ELECTRE 3   MAUT  PROMETHEE  TOPSIS\n",
       "1         Accenture      -1.90  41.93     -12.90   36.34\n",
       "2  Northrop Grumman       2.69  67.32      30.78   53.83\n",
       "3               IBM       1.34  44.59      -3.06   56.21\n",
       "4          Motorola       0.01  52.15       5.58   39.96\n",
       "5              MSCI      -0.50  44.02      -8.49   39.01\n",
       "6            Oracle      -1.64  37.74     -11.91   33.81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'Name':companyName, 'ELECTRE 3':phiEl, 'MAUT':utilityScorePer, 'PROMETHEE':phi2, 'TOPSIS':C2} \n",
    "\n",
    "df = pd.DataFrame(data) \n",
    "df.index = df.index + 1\n",
    "\n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also\n",
    "    display(df)\n",
    "# print(df.to_latex(index=False))"
   ]
  }
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