{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data-analyysi levyraadin tuloksista\n",
    "\n",
    "Esittelen tässä Jupyter-muistiossa Python-ohjelmointikielen data-analyysityökaluja. Muistion esimerkit pohjautuvat Facebook-ryhmässä järjestettyyn musiikkikilpailuun, joka on mukailtu televisio-ohjelmasta Levyraati.\n",
    "\n",
    "### Analyysissä käytetyt menetelmät:\n",
    "- laatikkodiagrammi, joka sisältää kvartiilit ja mediaanin\n",
    "- keskihajonta\n",
    "- aineiston normalisointi\n",
    "- euklidisen etäisyyden mittaaminen\n",
    "- pääkomponenttianalyysi\n",
    "\n",
    "### Aineisto\n",
    "Aineisto on kerätty Facebook-ryhmästä keväällä 2016. Mukana olleet raatilaiset ovat arvostelleet jokaisen kappaleen, kirjoittaneen sanallisen arvioinnin sekä antaneet numeerisen arvosanan. Tässä analyysissä hyödynnän vain arvosanoja.\n",
    "- 10 kappaletta\n",
    "- 7 arvostelijaa\n",
    "- arvosanat asteikolla 0,5 - 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hae aineisto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data-analyysin perustyökalut\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Kuvaajat\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sami</th>\n",
       "      <th>Joe</th>\n",
       "      <th>Mikko</th>\n",
       "      <th>Taneli</th>\n",
       "      <th>Arttu</th>\n",
       "      <th>Ville</th>\n",
       "      <th>Kirmo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kappale</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tungevaag &amp; Raaban – Parade</th>\n",
       "      <td>4.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asa – Mä haluun olla hippi</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chubby Wolf – You are the Description that brings me out of Myself</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supperheads – Easy</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dream Theater – The Silent Man</th>\n",
       "      <td>2.25</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tony Bennett &amp; Lady Gaga – It Don't Mean a Thing</th>\n",
       "      <td>3.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elina Born – Miss Calculation</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caravan Palace – Lone Digger</th>\n",
       "      <td>0.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avril Lavigne – When You're Gone</th>\n",
       "      <td>4.50</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Halestorm – Here's to US</th>\n",
       "      <td>3.50</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Sami   Joe  Mikko  Taneli  \\\n",
       "Kappale                                                                         \n",
       "Tungevaag & Raaban – Parade                         4.50  0.50      3       2   \n",
       "Asa – Mä haluun olla hippi                          1.00  2.25      4       1   \n",
       "Chubby Wolf – You are the Description that brin...  0.50  1.00      1       3   \n",
       "Supperheads – Easy                                  2.00  2.75      5       3   \n",
       "Dream Theater – The Silent Man                      2.25  2.25      1       3   \n",
       "Tony Bennett & Lady Gaga – It Don't Mean a Thing    3.00  1.25      3       3   \n",
       "Elina Born – Miss Calculation                       2.00  2.50      4       4   \n",
       "Caravan Palace – Lone Digger                        0.50  2.50      5       5   \n",
       "Avril Lavigne – When You're Gone                    4.50  2.75      2       3   \n",
       "Halestorm – Here's to US                            3.50  4.00      4       4   \n",
       "\n",
       "                                                    Arttu  Ville  Kirmo  \n",
       "Kappale                                                                  \n",
       "Tungevaag & Raaban – Parade                           2.0   2.00   4.50  \n",
       "Asa – Mä haluun olla hippi                            2.0   2.00   2.50  \n",
       "Chubby Wolf – You are the Description that brin...    4.0   1.00   2.00  \n",
       "Supperheads – Easy                                    2.0   5.00   3.50  \n",
       "Dream Theater – The Silent Man                        2.5   4.50   3.75  \n",
       "Tony Bennett & Lady Gaga – It Don't Mean a Thing      4.0   1.50   4.00  \n",
       "Elina Born – Miss Calculation                         1.0   3.00   3.75  \n",
       "Caravan Palace – Lone Digger                          4.0   1.75   4.75  \n",
       "Avril Lavigne – When You're Gone                      1.0   3.75   4.25  \n",
       "Halestorm – Here's to US                              0.5   3.50   4.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('levyraati-2016.csv', index_col=0, decimal=',')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysoi musiikkikappaleet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mikä kappale voitti levyraadin?\n",
    "Levyraadin voittaa eniten pisteitä kerännyt kilpailija. Raadissamme oli kaksi tasavertaista kappaletta: *Here's to US* ja *Lone Digger*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2af6cc3da0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxIAAAFgCAYAAAAvsU8oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4Tef6//H33kl2DIlGTGlLqoIaEmNLjW0JQlFKRZEc\nqqWU6GQIiSJiaDmpobRHqUiiql9Fi0PLOWj9WiHRVrRKDTHWmJCIZCeyfn/4Zn+FJM1WkZTP67pc\n185az3qee907WPd+1rO2yTAMAxERERERETuYizsAERERERH5+1EhISIiIiIidlMhISIiIiIidlMh\nISIiIiIidlMhISIiIiIidlMhISIiIiIidlMhISIiJc6aNWvo2rUr2dnZubYPHjyYBQsWcPLkSerX\nr5/nsVarlTVr1hRJXAWNWxgBAQF89dVXdh2zYcMGrly5csfa3Wjs2LFs3br1jvd7o6J8PwqyefNm\nJkyYcNfHzc93333HH3/8ccf6GzRoEL/++muxHX+zcePGsXfvXmJiYoiJicm1r0uXLsD137e9e/fe\nsTGl+KmQEBGREqdHjx64ubkRHR1t27Z582ZOnDjBkCFDADCZTHke+8svv7B27doiiy2/cYvKvHnz\nSE1NvWPtbjRz5kyefvrpO97vjYr6/ciPr68v4eHhd33c/CxdupSTJ0/esf4++eQT6tatW2zH32zG\njBn4+PjQv39/+vfvn2vfhg0bgOu/bz4+PndsTCl+KiRERKRECg0NZeHChSQlJWG1Wpk5cyYTJ07E\n0dERAMMwWLVqFd27d+eZZ55hw4YNXLhwgREjRvDjjz8yYMAAAOLi4ujduzcdO3akb9++HD9+3DbG\n/Pnz8fPzo127dkybNo2c72gNCAhg6dKl9OvXj7Zt2/LWW2/dEp9hGEyePBk/Pz98fX0ZM2YM165d\nAyA2Npbnn3+erl278uyzz7Jp06Zbjt++fTt+fn4kJSWRkpLCmDFj6NSpEx06dGD16tUAjB8/niNH\njhAYGEhcXNwtMxo5P9/YLj4+Ptc4q1evZsiQIYwZM4YOHTrQrVs3jh07lut4gIiICPz8/OjUqRMD\nBw7k7Nmzt/R7c5xffPGFbZyb83zixIk8348b7dq1K8885XeeN88I3fjz6tWrGTlyJAMHDmTWrFms\nXr2aQYMGARAcHExERASBgYG0bNmSiIgIPv/8c7p160b79u1JSEiwq92FCxd4+eWX6dy5M76+vixd\nutQWU3R0NF26dKFz58706dOH33//nTlz5vDDDz8wevRo/v3vf5Oens7rr79u+92ZOXPmLbmB67+f\nwcHBvPrqq7Rr145+/fpx8eJFANq1a2d7rzdv3ky3bt3o0KEDgwcPJjk52XZ8WFgYI0aMwNfXlz59\n+nD+/Plcx588eZLWrVvz8ccf4+fnh5+fHz/99BNDhw6lbdu2jB8/Hrj+O929e3dmzpxpi/vnn38G\nKPB8Dh8+TL9+/ejSpQsdO3Zk/fr1tn116tRh7dq19OzZkzZt2uTKo/xNGCIiIiVUeHi4ERoaaixc\nuNAYOXKkbfuJEyeMOnXqGJ999plhGIaxceNGw9fX1zAMw/jiiy+MQYMGGYZhGKmpqUazZs2M//f/\n/p9hGIaxfv164/nnnzcMwzBWr15tdO3a1UhNTTWuXbtmDB061IiOjjYMwzAGDBhgBAYGGhkZGUZa\nWprRsmVLIz4+3jhx4oRRv359wzAMY9OmTUb37t2Na9euGRkZGUaXLl2ML7/80jAMw+jVq5exa9cu\nwzAMIzEx0Xjrrbds/X755ZfG4cOHjXbt2hmHDx82DMMwgoODjXHjxhmGYRgXL140nn76aePgwYOG\nYRjGY489Zpw5cybX8Tlu/PnGdjf64osvjPr16xs//fSTYRiGERERYYwYMSLX8QcPHjQ6duxoXLt2\nzTAMw4iOjjbWrFlzS7/5xVlQnm98P272Z3m6+TxvzL9hGLl+/uKLL4zGjRsbx44du2XccePGGT17\n9jTS09ONAwcOGPXq1TM++ugjwzAMY8aMGcaYMWPsahcWFmZMnjzZMAzDOHbsmOHt7W388ccfRmpq\nqvHEE08YaWlphmFc/738+OOPDcMwjGeeecaIj483DMMwlixZYgwZMsQwDMO4fPmy0bx5cyMuLu6W\n/MybN89o2rSpcfLkScMwDGP06NHG9OnTbf3FxcUZx44dM5o0aWL8/vvvhmEYxkcffWT7uzJv3jyj\nVatWxunTpw3DMIyhQ4caH374Ya7jc3K4du1awzAMY+TIkcYzzzxjJCcnG0lJSYaPj49x7NgxY+fO\nnUa9evWMf//734ZhGMbKlSuNHj16/On5DB061Fi0aJFhGIaxa9cuo2HDhkZWVpZhGNd/t2bPnm0Y\nhmH8/PPPRoMGDYzs7Oxb8iAll2YkRESkxAoKCmLr1q0sW7Ysz/vdn3vuOQDq1avHmTNnbtm/e/du\nPDw8aNGiBXD9Xu1jx47xxx9/sHXrVnr16kXZsmUxm8307t2bb775xnZsp06dsFgslC5dmurVq3P6\n9OlcfXfs2JFVq1ZhNpuxWCz4+PjYZjsqVKjAmjVrOHz4MJ6ensyaNct23OXLlwkKCiI8PJxHH30U\ngK1btxIYGAhA+fLl6dChA19//bXtGON/Z0r+TH7tatasSYMGDWzntWfPnlz7XV1dSU5OZu3atVy+\nfJn+/fvbcntjv/nFWVCeC1KxYsV883Q7qlevTrVq1fLc16pVK5ydnalVqxbZ2dm0b98egMcee4yz\nZ8/a1S4kJMT2+1itWjUqVarE8ePHcXZ2xmQy8fnnn3PhwgU6derE4MGDbX3n5HHQoEF88MEHwPXc\n16pVK9dM2Y2aN2/OQw89BFz/nbv5vfv2229p3rw5Xl5eAPj7+/Of//zHNtbjjz+Oh4cHAHXr1uXU\nqVO3jHHt2jX8/PwAqF27Nj4+PjzwwAO4ublRqVIl23mXLVvW1q5Tp07s37+fjIyMAs/nww8/5KWX\nXgKgSZMmZGRkcO7cOdvYOb9n9evXx2q1cuHChTzzICWTY3EHICIikh8XFxd69uzJ2bNnqVKlSq59\nDg4OODs7A2A2m29ZmA2QkpLCsWPHbIs9DcPA2dmZixcvkpKSwpIlS1i5ciWGYZCdnY27u7vtWFdX\nV9trs9lsu20px8WLF5k6dSr79u3DbDZz4cIF20X29OnTWbBgAYMGDaJUqVK8+eabdOrUCYC5c+di\nGAaVKlWy9XX58mVef/11HBwcMAyDjIwMOnfu/FdSl8sDDzxge12uXDkuXbqUa3+VKlWYN28eixcv\nJiwsjGbNmjF58uRbcp5XnH5+fgXmuSDTpk3Llae33nqLjh073vZ5urm55buvbNmyttdms5nSpUvb\nXt/43ham3c8//8w///lPTp8+jdls5ty5cxiGgaOjI5GRkSxcuJC5c+dSp04d3nnnHWrVqpUrlsTE\nRKZPn86RI0cwm8388ccf9OrVK8+4/+y9S0lJYdeuXbly/8ADD5CUlATk/j12cHDI8++Jg4MDFovF\n9rpMmTJ5HlOuXLlcsRiGweXLl0lLS8v3fLZt28aHH35IUlKSbX3RjTG4uLjY8msYxi1/z6RkUyEh\nIiIlmpOTk21dhL0qV66Ml5cX//M//5Pnvnbt2t2yMLSwIiIicHJyYv369Tg6OvL222/b9rm7uxMS\nEkJISAg7duxgxIgRtG3bFrh+v3/lypUZM2YMn3/+OWazmSpVqvDBBx9Qs2bNAsd0cHDIdaF1+fLl\nQsWac1EJcOnSpVwXpzmaNWtGs2bNSE9PZ8aMGcyaNYv33nsP+L8F5vnFGRsbm2+ef/vtt3zjyitP\nbdq0yfc8b95+80X13TJ69Gheeukl/P39AWzvLVy/73/OnDlkZWWxaNEi3nnnHZYvX57r+ClTpuDt\n7c2HH34IwIsvvpjvWDe/dzcXS5UrV6Zly5bMmTPnL5/Xn8lZe5ETi8lk4oEHHmDcuHF5nk9WVhav\nv/46c+fOpU2bNlitVho2bHjXH1ggRUe3NomIyN9SfrfxODo62p4y1LBhQ86dO2dbFHr8+HHGjBkD\nQPv27fnyyy9JT08H4LPPPrPrMaUXL16kdu3aODo6sn//fuLj40lLSyMrK4uAgADb7Rv16tXDYrFg\nNl//L/eRRx6hT58+uLm5sWDBAuD6wtdPP/0UuH7xNX36dNujOR0dHUlJSQGgUqVKtgvzPXv2cPTo\n0VznndPuZkeOHGH//v0AbNy4kSeeeCLX/h07djBlyhQMw6BUqVLUqVPHdrHn6Ohou5Bv3759nnEW\nlOcb348bFZSn/M6zfPnyODg4cODAAYBieRoUXL+4r1evHnB9kXd6ejppaWkcOHCAUaNGkZmZiaOj\nI/Xr17fl0cnJyfb+XLhwwfbEpB07dnDs2LF8H7EbHx9vu21v48aNPP7447n2t27dmri4ONutRD//\n/LPdT6sq7K1zV69eZcuWLbZYvL29sVgst5zP0aNHuXLlClevXiU9Pd22ID4yMhInJ6e/9DhhKVk0\nIyEiIn9L+X2q2bRpU2bNmkWbNm3Yvn07c+bMISwsjLS0NJycnBg1ahRw/fGgv//+Oz179sRkMuHp\n6Wm7ALu577zGGjRoEOPGjeOLL76gadOmBAcHM2HCBBo2bEifPn0YOHAgJpMJk8lEaGio7f75HOHh\n4fTs2ZP27dszatQopkyZgp+fHyaTidatW/PYY48B4OfnR9++fZk6dSqDBg3izTffZPv27TRr1ozW\nrVvb+ruxXc597DkaN27M0qVL2bVrF2XKlLF9cpwTzxNPPMG6devo1KkTzs7OuLu723JxY7+jRo2y\nPanqxjjNZjNz587NM883vx83Fij55Sm/83R2diYoKIjBgwdTpUqVPJ8EVRiF/UQ8v3ZBQUG89tpr\nlC9fHn9/f/z9/QkJCWH58uVUrVqVrl27YrFYKFu2LO+88w5wfU3BG2+8QVBQEMOHD2fatGl88MEH\n+Pr68tprrzF37lzq1atH48aNc43VsmVLJk2axK+//srDDz9MaGhortgqVapkezJTVlYWZcuWtT1p\nqbDnVlA+btz38MMPExcXx7vvvktWVhZz584FYNiwYUyfPt12PiNGjGDevHnUq1ePl19+mR49elCx\nYkWGDRtGhw4dGDp0KOvWrSvU3zMp2UxGYctQERER+dtZvXo1X331FUuWLCnuUMRO8+fP58yZM4SF\nhRV3KMTGxhIaGprno4zl/qVbm0RERERExG4qJERERERExG66tUlEREREROymGQkREREREbGbntok\nIpKPuLi44g5BRETkrmratGmh26qQEBEpgD3/oIr94uLilOMiphzfHcpz0VOOi569H6Dp1iYRERER\nEbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbntokIlKAAwcOFHcI97TExERc\nXV2LO4x7mnJ8dyjPRe9+zbGXlxcODg7FHUaeVEiIiBRg6IzNxR3CvW/dH8Udwb1POb47lOeid5/l\nOO3SWaKm96N27drFHUqeVEiIiBTApfzDxR2CiIhIiaQ1EiIiIiIiYjcVEiJ/wcmTJ+nVq1eubfPn\nzycmJibfY4KDg9m2bZtd4/z2228kJibeVox/VV7n065dO65evXrbfZ48eZLg4OA/bZeamsqOHTsK\n1WdsbCxBQUG5tt0Y+86dO+nfvz8BAQE8//zzLF261O64RURE5P+okBD5i0wmU5GP8c0333DkyJEi\nH6ew7sY5A+zbt4/vvvuu0O0LimvixInMmTOHqKgoVqxYwddff8358+fvRJgiIiL3Ja2REPmLDMPI\nd9+MGTPYu3cvGRkZ9O3bl969e9v2ZWdnExoayokTJ8jKyiIoKIjmzZuzZs0aYmJisFgs1KlTB39/\nf1asWIG7uzsVKlTg6tWr/POf/8TJyQkPDw/Cw8NZv34927dv59y5c7z55pvMnj0bT09P9uzZQ9++\nffntt9/Yu3cv/fr1o1+/fnfsnK9cuUJwcDApKSlkZWURGhpK7dq16dSpE0899RQVKlSgZ8+eTJgw\ngaysLMxmM+Hh4VSpUoW33nqL1NRURo0aRWZmJlarlXfeeYe6devaxgkLC+PKlSvUqFGDtm3bMn78\neKxWKw4ODoSHh/Pww4Vfv3D58mVSU1OpWLEiFouF5cuX/+U8iIiI3M9USIj8RUeOHCEwMBC4foF9\n6tQpXnrpJaxWK1WrVmXcuHFkZGTg6+ubq5D46quvqFy5MuHh4SQlJfGPf/yDL7/8kiVLlrBo0SKq\nVKnC6tWreeSRR2jTpg1+fn74+PjQuXNnli5dSpUqVZg6dSrr1q3DZDJx+vRpVqxYwcmTJ9m/fz8L\nFy4kKSmJZ599lv/+97+kp6czcuTI2yokli1bxqZNm2zneOHCBQAiIyNp27YtvXv35tChQ4SHh7Nk\nyRIyMzN56qmnaNWqFRMmTOCll16iRYsWbNu2jQ8++ICwsDAqVqzIN998w4MPPsjUqVM5ceIER48e\nzTXu4MGDOXjwIC+88ALjx4+nd+/edO7cmU2bNjFv3jxmzJhR6HMICgqid+/eNG/enJYtW9KtWzfK\nlStndy5ERETkOhUSIn9RjRo1WLZsme3n+fPnA2CxWEhOTqZv3744OTmRlJSU67g9e/YQFxdHXFwc\nhmFgtVrJysqia9euDB8+nO7du9O1a1ecnZ1tx1y6dAmz2UyVKlUAaNasGbGxsdSvXx8fHx9bO09P\nT8qVK4ejoyMVK1akUqVKpKWlkZqaelvnGBgYSP/+/W0/t2/f3nYOSUlJrF27FgCr1WprkxPPnj17\nOHr0KAsWLMAwDNzd3W1tGjVqxJw5c5g0aRIdOnSgdevW+caQkJDA22+/DUDz5s1ZsGBBoWLPud3p\nxRdfpGPHjnz77bds3ryZDz/8kNWrV1OxYsVC9SMiIlIcEhISSElJKe4w8qRCQqSI7Nq1i507d7J8\n+XLMZjNNmjTJtd/JyYlhw4bRpUuXXNuHDBlC9+7d2bhxIwMHDiQqKsq2z2QykZ2dbfs5MzPT9iU1\nTk5Otu03fnHNja9vvg1r2rRp/Prrr7nWFjzyyCOEhYUV6hydnJwIDQ2lYcOGee6D6wXVnDlz8rxg\nr1SpEmvXrmXnzp18+umn/Pjjj7z22mt5jmUymWzxZ2ZmYjbnXuLl7u7O5cuXc227ePEiderUASAj\nI4MKFSrQo0cPevToQXBwMN999x09evQo1LmKiIgUB29v77v2PRJxcXF2tddia5G/KL81EklJSXh4\neGA2m9myZQvZ2dlkZmba9jds2JDNm69/2dmFCxeIiIjAMAwiIiKoWLEiAwcOpFGjRpw+fRqTyURW\nVhblypXDbDbzxx/Xv5AnNjYWb2/vAmPK7zXA+PHjiYqKYtmyZbY/hSkicvpp2LAh33zzDQC///57\nnk9CatCgga3N999/z/r16237vv/+e3bs2EHLli0JCQlh3759uY41mUxcu3bN1s8PP/yQ73lXr16d\nM2fOcPz4ceB6EREbG0uTJk1ITEzk+eefJy0tDbi+PuXs2bNUq1btT89VRERE8qYZCZG/KL8nBbVs\n2ZJFixYREBCAr68vTz/9NJMnT7bt79y5Mz/88AN9+/bFMAxGjBiByWSibNmy+Pv7U65cOapVq0bd\nunV5/PHHCQ8Pp2zZsoSFhfHmm2/i6OiIp6cnzz77rO3Worxiyu/1nTjnAQMGEBwcTP/+/cnOziYk\nJOSWcUaMGEFwcDDr16/HZDLlWtfg6enJ6NGj+fjjjzGbzYwcOTLXOPXr12f27Nl4eHgQFBTE+PHj\nWblyJRaLhfDw8FxtHR0dmTVrFiEhIRiGgWEYhISE4O7ujru7O6+88goDBw6kdOnSWK1W2rdvT9Om\nTe9IPkRERO5HJqOgR86IiNzH4uLimLT8RHGHISIi96nUpJN8NM73rt7aZM+HbLq1SURERERE7KZC\nQkRERERE7KY1EiIiBUhNOlncIYiIyH0q7dLZ4g6hQCokREQK8NE43+IO4Z6WkJCQ55PH5M5Rju8O\n5bno3a859vLyKu4Q8qVCQkSkAHdrgdv9KiUlRTkuYsrx3aE8Fz3luOTRGgkREREREbGbCgkRERER\nEbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGb\nCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbY3EHICJSkh04cKC4Q7inJSYm4urq\nWtxh3NOU47tDeS5692uOvby8cHBwKO4w8qRCQkSkAENnbC7uEO596/4o7gjufcrx3aE8F737LMdp\nl84SNb0ftWvXLu5Q8qRCQkSkAC7lHy7uEEREREokrZEQERERERG7aUZC7nmJiYlMmzaNpKQkrl27\nRuPGjRkzZgwWi6W4QwOgXbt2PPTQQ5hMJjIzM2nZsiVBQUEFtl+/fj2lS5e+azGePHmSoKAgVq1a\nVaRjdOvWDW9vbwzDwNHRkSFDhtCiRQvOnz/PvHnzmDx5cpGNLyIiIvZRISH3tOzsbEaOHMnEiRN5\n/PHHAZg6dSoLFizg9ddfL+borjOZTHz88ceUKlUKwzAYNGgQ8fHxNGnSJN/2xeFujFujRg2WLVsG\nwPHjx3n11VeJiIigdu3aKiJERERKGBUSck/bsWMHXl5etiICYMyYMbaL4hkzZrB3714yMjLo27cv\nvXv3Jjg4GCcnJ5KTk5k2bRpvv/02V69eJT09nQkTJnDu3Dm2bNnCtGnTAAgODqZjx46kpqYSHR2N\ng4MDNWvWZMqUKaxevZq4uDguXLhAYmIigwcPplevXrliNAwDwzCA6xfrPj4+JCYmUrt27Vxjh4SE\n4OPjY2u7f/9+pkyZgpOTE2azmTlz5lCuXDkWLVrE119/jYODA2+++SbNmjUjJiaGdevW4eDggK+v\nLwMHDrwj+T1w4ABTpkzBbDZTtmxZZs6cyf79+4mOjsZkMnHkyBE6duzIiBEjOHToUK62M2bMwMXF\nJd++q1WrxrBhw4iJiWHIkCG2GZE1a9awePFiHnroIdzc3GjRogW+vr4EBQWRkZFB27ZtWblyJVu2\nbGH37t1ERETg5OTEgw8+SFhYGPHx8SxZsoS0tDTGjRtHvXr17kguRERE7jdaIyH3tMOHD1O3bt1c\n2ywWC05OTlitVqpWrUpMTAwxMTHMmTPH1sbNzY25c+dy/vx5XnjhBSIjI3njjTf4+OOPadOmDbt2\n7QKuz3jExcXRunVr0tPTWbx4McuXL+fw4cMcPHgQgIMHD7Jw4ULmz59PVFRUgfGmp6ezc+dOfHx8\nbhl70aJFudpevHiRiRMnEhkZSePGjfnqq69ITEzkm2++4fPPP+fdd9/lq6++4sSJE2zatIlPP/2U\n6OhoNm7cyB9/3JmnXoSHhzN27FiWLVvGE088QWRkJAAJCQm89957rFixgpiYGADCwsIICwvjk08+\noWXLlkRHR/9p//Xr1+fQoUPA9SLLMAwiIiKIjIzk/fffZ/fu3QCsWbOGmjVrEhMTk+vRgOHh4Sxc\nuJClS5fi7u7Oxo0bgesF0JIlS1REiIiI/AWakZB7mslk4tq1a3nus1gsJCcn07dvX5ycnEhKSrLt\na9CgAQAVK1ZkwYIFLF68GKvVSpkyZbBYLNSvX5+ff/4Zq9VKgwYNcHJyoly5cgwbNgy4XsAkJycD\n0KhRIwA8PDxITU3NM5ZXXnkFk8mEyWTC39+fmjVrkpqaesvYN6pQoQKzZs0iPT2ds2fP0q1bN375\n5Rdb7J6enoSFhbFhwwYSExMJDAzEMAzS0tI4ceIEHh4efyGz1x06dAgfHx8AmjdvzgcffEDz5s2p\nV68eFosl1zqUn3/+mZCQEAzDIDMz03ZcQa5cuYLZ/H+fdyQlJeHq6oq7uzsALVu2tMXRvHlzANq3\nb8/ixYu5cOECR48eZcSIERiGQXp6Ou7u7lSuXJk6derg6Kh//kRERP4K/U8q97QaNWrc8sm31Wol\nMTGR5ORkdu7cyfLlyzGbzbnWJDg5OQGwdOlSPDw8ePfdd0lISODdd98FoEOHDmzZsgWr1Yqfnx+Z\nmZlMmTKFr776Cnd3d1599VVbXzd+iUzObUk3unGNxI3yGzvntqzw8HCGDh1Kq1atbLfqODo6kp2d\nnasfi8XC008/ne8ag2+//ZZ//etftn4Nw8BkMhEeHk61atUKyG5umZmZtov+vL44p3Tp0rb1D4WV\nkJCQa9YgJ7abGYaRq+CA6+ddpUqVW8aMjY21vb8iIiIlXUJCAikpKcUdRp5USMg9rVWrVrz33nts\n3bqVp59+muzsbGbNmoWLiwt16tTBw8MDs9nMli1byM7OJjMzM9fxycnJPPbYYwB88803tv1PPfUU\ny5cvx2q18sYbb3DlyhUcHR1xd3fn9OnTJCQkYLVab4knr0LixjUShRk7p31ycjLVqlXDarWybds2\nGjVqRL169ViwYAHZ2dlcvHiRSZMmERISwnvvvUd6ejrOzs5MmzaN0aNH22YL2rRpQ5s2bf40l3nF\nWLt2bX766ScaNmxIbGws3t7e+batU6cO27dvp23btmzYsAF3d3eefPLJfMc4duwYS5cuZenSpbZZ\nJTc3N5KTk0lJScHJyYnY2FiaNm3KI488wt69e+nYsSPbt28HwNXVFZPJxKFDh/Dy8iI6OppmzZr9\n6XmKiIiUJN7e3nftC+ni4uLsaq9CQu5pJpOJxYsXExISwvz583FycqJVq1aMGDGC1NRUFi1aREBA\nAL6+vnl+av/cc88xduxYNm7cyIABA9iwYQOrV6+mZ8+elCtXznark8VioWXLlrzwwgvUqVOHl19+\nmRkzZhAYGHhLPHnFmJe8xv7iiy9st0ANGDCA4cOH4+npSUBAAGFhYXTp0oXnnnuOfv36AfDWW2/h\n4eHBwIED6d+/P46OjrRv3/62Hn179OhR2+1RJpOJ0aNHM2HCBCZPnozZbKZcuXJMnz6dffv25XlO\n48ePZ+LEiSxatIhSpUoxe/bsfMewWq1kZ2fzzjvv4OHhwcmTJ4HrMx3Dhg2jX79+VK9eHW9vb8xm\nMz169GCYOmWCAAAgAElEQVT48OEEBgbSqlUr24zI1KlTCQ4OxmKxULlyZfz9/dmzZ4/d5y4iIiK3\nMhl5fXQoIlJCbdq0iRYtWlCuXDkGDx7MyJEjqVy5MkeOHKFVq1b8+OOPzJs3j8WLF//lseLi4pi0\n/MQdiFpERMR+qUkn+Wic712dkWjatGmh22tGQkT+Vq5evUpgYCBlypShbt26NGrUiJSUFJYsWcL8\n+fMBCAkJKeYoRURE7n0qJETkb6VHjx706NEj1zZXV9c7MgMhIiIihafvkRAREREREbtpRkJEpACp\nSSeLOwQREblPpV06W9whFEiFhIhIAT4a51vcIdzTEhISbI8NlqKhHN8dynPRu19z7OXlVdwh5EuF\nhIhIAe7WkzLuVykpKcpxEVOO7w7luegpxyWP1kiIiIiIiIjdVEiIiIiIiIjdVEiIiIiIiIjdVEiI\niIiIiIjdVEiIiIiIiIjdVEiIiIiIiIjdVEiIiIiIiIjdVEiIiIiIiIjdVEiIiIiIiIjdVEiIiIiI\niIjdVEiIiIiIiIjdVEiIiIiIiIjdHIs7ABGRkuzAgQPFHcI9LTExEVdX1+IO456mHN8dynPRu19y\n7OXlhYODQ3GHUSgqJERECjB0xubiDuHet+6P4o7g3qcc3x3Kc9G7x3OcduksUdP7Ubt27eIOpVBU\nSIiIFMCl/MPFHYKIiEiJpDUSIiIiIiJiN81IiJQwMTExfPnll1gsFjIyMnjjjTdo0aLFXRk7NjaW\n6Oho5s6de0f627p1K5s2bWL69Ol/ua+AgADS09MpXbo0hmFgMpnw9/fn2WefvQORioiIiL1USIiU\nICdPnuTzzz/niy++wGw2c+zYMSZMmHDXCgkAk8l018ay14wZM/Dy8iruMERERAQVEiIlSkpKClar\nlYyMDEqXLo2npydRUVHA9U/k33nnHWrWrElMTAxJSUk0a9aMRYsWYbFYOH36NJ06dWLo0KEEBATg\n4+NDQkICVquViIgIHnzwQSIiIoiPj+fatWsMGDCALl26EBwcjJOTE8nJyQwYMIArV64wevRo9u/f\nj5+fH6+99hqHDh1iypQpmM1mypYty4wZM3BxcWHGjBns3buXjIwM/P39eeGFFzhw4ABjx47Fzc2N\natWqAZCVlcXo0aM5f/48VquVkSNH0rp1a7vzYxjGLduuXbvG2LFjOXPmDFevXmXkyJE89dRTrFmz\nhpiYGCwWC3Xq1KFfv35MnDiRmJgYAD788ENcXFwYMGDAX3jHRERE7l9aIyFSgtSpUwcfHx/at29P\ncHAw//73v7l27VqBx+zbt4/Zs2ezYsUKVq5cSXJyMgDly5dn2bJldO3alaVLl7J7925OnTpFVFQU\nS5cuZcGCBVitVgDc3NxstzMdPnyY8PBwPvvsM9tFd1hYGGFhYXzyySe0bNmS6OhorFYrVatWJSYm\nhpiYGNvxCxYsICgoiE8++QSz+fo/MQcOHCApKYmoqCgWL17MpUuX7ljOLl26ROvWrYmKiiIiIoI5\nc+YAsGTJEubPn09MTAze3t5UrVqVzMxMzpw5A1y/7apLly53LA4REZH7jWYkREqYmTNncvjwYb77\n7js+/vhjVqxYQWRkZL7tGzRoQKlSpQCoVasWx44dA6Bly5YANGrUiO3bt7Nnzx5++uknAgMDbZ/s\nnz171tZHjnr16mGxWLBYLLZ2P//8MyEhIRiGQWZmJj4+PlgsFpKTk+nbty9OTk4kJSUBcOjQIRo1\nagRAs2bN+Pbbb/Hy8iItLY2xY8fi6+t72+sagoODc62RmD59OlWqVGHv3r189tlnmM1mW5HStWtX\nhg8fTvfu3enatSvOzs50796df//733Tp0gVXV1fc3d1vKw4RERFRISFS4litVmrUqEGNGjUYMGAA\nnTt35vTp07nWLmRmZtpeZ2dn214bhmGbBcjZnnPRbbFY6N27N0OGDLllTCcnJ9vrvL4Ep3Tp0ixb\ntizXtl27drFz506WL1+O2WymSZMmt8SQU4g4OzuzcuVK4uPjWb16Nf/973+ZNm2ara+AgADb+eXE\n26lTJ/r3759rzLzWSKxZs4ZLly7x6aefkpSURO/evQEYMmQI3bt3Z+PGjfzjH/8gJiaGLl26EBQU\nROnSpbVIW0RESqSEhARSUlKKO4xCUSEhUoJ8/vnn7N69m5kzZwJw+fJlDMOgQoUKuLi4cO7cOWrW\nrEl8fLzty2p++eUXMjIygOuzAdWrVwcgLi4OHx8ffvzxR2rWrEmDBg2YOXMmr7zyClarlffee4+Q\nkJBCxVWnTh22b99O27Zt2bBhA+7u7ly+fBkPDw/MZjNbtmwhOzubzMxMatSowd69e2ndujU7d+4E\n4Ndff+XgwYN0796dBg0a3LIuIWcdyJ+5sWjKkZSURNWqVQH4+uuvyczMxDAM3n//fUaOHMnAgQP5\n/fffOXXqFHXr1uWBBx7gyy+/ZNGiRYUaU0RE5G7y9vYuti+ki4uLs6u9CgmREqRXr14cPnyYF154\ngTJlynDt2jVCQkKwWCz4+/szadIkHn30UdsiZgAvLy/Gjx/PkSNHePHFF3FxcQHg1KlTvPzyy6Sm\npjJ37lwqV67Mk08+ib+/PwD9+vUrdFzjx49n4sSJLFq0iFKlSjF79mzMZjOLFi0iICAAX19fnn76\naSZPnsywYcMYN24cUVFRVKtWjczMTKpWrcrs2bP57LPPcHBwYPDgwbeVn/Hjx+e6tenJJ5+kR48e\nvPrqq/z000/06tULDw8PFi5ciIuLC/7+/pQrV45q1apRt25dADp16sTWrVspU6bMbcUgIiIi15mM\nvB6DIiJ/C7GxscTExNgWGOe48QlPktu4ceN4/vnnadas2Z+2jYuLY9LyE3chKhEREUhNOslH43yL\ndUaiadOmhW6vpzaJ3INK8ndBFBer1Yq/vz+urq6FKiJERESkYLq1SeRvrFmzZnleFN+8MFrAYrHw\n2WefFXcYIiIi9wwVEiIiBUhNOlncIYiIyH0i7dLZ4g7BLiokREQK8NE43+IO4Z6WkJCAt7d3cYdx\nT1OO7w7luejdLzm++THnJZkKCRGRAhTXgrf7RUpKinJcxJTju0N5LnrKccmjxdYiIiIiImI3FRIi\nIiIiImI3FRIiIiIiImI3FRIiIiIiImI3FRIiIiIiImI3FRIiIiIiImI3FRIiIiIiImI3FRIiIiIi\nImI3FRIiIiIiImI3FRIiIiIiImI3FRIiIiIiImI3FRIiIiIiImI3FRIiIiIiImI3x+IOQESkJDtw\n4EBxh3BPS0xMxNXVtbjDuKcpx3eH8lz0iirHXl5eODg43PF+7wcqJERECjB0xubiDuHet+6P4o7g\n3qcc3x3Kc9G7wzlOu3SWqOn9qF279h3t936hQkJEpAAu5R8u7hBERERKJK2REBERERERu6mQkL+F\ndevW4e3tTXJycqGPWb16NZs3byY2NpagoKBb9j/55JO3Hc/58+d55513bvv42zV16lRWrVpl+3ny\n5Mm89957tp+XLVvGP//5T+bPn09MTMwdGTMuLo5+/frl2jZ58mSWL19uVz/BwcGcOnXK7vGvXr3K\nxIkT6d27N/379ycgIIB9+/bZ3Y+IiIjcWSok5G9h3bp1eHp6smnTpkIf07NnT3x9fQEwmUy37M9r\nW2FVrFiRyZMn3/bxt6t58+bs2rXL9vPvv//OL7/8Yvt59+7df6lAykvTpk2pVKkS33zzDQCHDx9m\nz5499O3b946Ok5/p06fzyCOP8D//8z/ExMTwxhtv8Oabb3Lt2rW7Mr6IiIjkTWskpMS7dOkSCQkJ\nhIeH8/HHH+Pv78/+/fuZPn06kZGRAMyfP58HHniAr7/+mtq1a2MymXBzc6N8+fLUqlWr0GPt37+f\nKVOm4OTkhNls5v3332fBggXUq1eP5557DoBOnToRERFBaGgoq1atYs2aNSxevJiHHnoINzc324V8\nXFwcFy5cIDExkcGDB9OrVy92795NREQETk5OPPjgg4SFheHoWPi/hk888QTvvvuuLS/Ozs5YrVYy\nMjJwdnZm7969vPvuu8THx3PgwAFeffVVEhMTmTBhAq1bt+abb75hyZIlODo64u3tzdixY1m9enWe\nsd7o7bff5rXXXqNdu3bMnj2b0aNHYzabSU1NZdy4cVy+fJlr164REhJC3bp1efLJJ/nhhx8ACAoK\nIiAggLfeegs3NzcCAgJs79Ebb7xBcHAwKSkpZGVlERoammvB25UrV/j++++ZMmWKbVuTJk3YsGED\nDg4OtmNOnDhBZmYmQUFBtGzZko4dO9KnTx+2bt1KZmYmn3zyCaVKlbK1zcrKYuTIkXe86BIREbmf\naEZCSryNGzfyzDPP0KZNGxITEzl79ix16tTh3LlzpKamAvCf//yHTp06AVC7dm1CQkJua6yLFy8y\nceJEIiMjady4MevWraNjx4785z//AeC3336jatWqPPDAA5hMJgzDICIigsjISN5//312795tm+k4\nePAgCxcuZP78+URHRwMQHh7OwoULWbp0Ke7u7mzcuNGu+Nzc3HBxceHMmTPEx8fTqFEjGjRoQHx8\nPEeOHOGhhx6iVKlSACQnJ/Phhx8yYcIEVqxYQVpaGgsXLmTZsmVERUVx+vRp9uzZc0usUVFRt4xb\nrVo1WrduTXBwMJmZmbRq1QqAyMhIGjVqxLJlywgODmbatGlA3rM9FStWtBVNOe9RZGQkbdu25ZNP\nPmHSpEnMmDEj1zHHjx+nevXqt/SV85i+devWUapUKaKiopg7d65tligrK4tatWoRHR1N1apV+f77\n7/nqq6+oXLkykZGRzJ8/3xariIiI3B7NSEiJt27dOoYPH47ZbKZjx45s2LCBgQMH8swzz7B9+3Ya\nN25MqVKlqFy5MgANGjS47bEqVKjArFmzSE9P5+zZs3Tr1o0mTZowYcIEsrKy2LJlC35+frb2SUlJ\nuLq64u7uDkDLli1t+xo1agSAh4cHKSkpXLhwgaNHjzJixAgMwyA9Pd12nD2aN29ObGws+/fvp02b\nNly9epXdu3fj4eFB8+bNbe2aNm0KQJUqVUhJSeH333/n1KlTDB48GMMwuHLlim3Nwo2x5hRnNxs+\nfDhPPfUUK1assG1LSEhg2LBhAHh7e3Ps2DEADMMo8Bxy3qM9e/aQlJTE2rVrAbBarbnamUwmsrKy\nbD/PmzeP2NhYkpOTmTBhAgkJCTRr1gyAypUr4+zszKVLl3Kdf+XKlUlJSeHHH38kLi6OuLg4DMPA\narWSlZVl14yQiIjcexISEkhJSSnuMP6W9D+olGhnzpzhp59+YubMmQCkp6dTrlw5Bg4cSIcOHYiO\njiYpKYmOHTvajnFycrrt8cLDwxk6dCitWrViyZIlpKWlYTKZbBfv27Zt48MPPyQtLQ24fsGc31qL\nG7/cxjAMLBYLVapUYdmyZXm2P3/+PG+88Yatv5y+BwwYkOv8mjVrxrfffsvBgwcZOXIk6enpREdH\nU7lyZXr06FHg+N7e3nz88ce5xl29evUtbfPi4uKCm5sbVatWtW27+dzzWrdwYyGQI+c9cnJyIjQ0\nlIYNG+Y5pqenJ4mJiWRmZuLk5MTIkSOB6wu3MzIybLNCOTIzMzGbzbecP4DFYmHYsGF06dIlz7FE\nROT+5O3tre+R+F9xcXF2tdetTVKirVu3jv79+7NmzRrWrFnDxo0buXTpEsePH6dRo0YcOnSIbdu2\n5ZolKKy8LpiTk5OpVq0aVquVbdu2kZmZCUCHDh1Ys2YNZcqUoXz58rb2bm5uJCcnk5KSQnp6OrGx\nsfmO5+rqislk4tChQwBER0fn+tbkihUrEhUVxbJly2y3Hy1btixXEQHX10n8+OOPmEwmSpUqhZub\nG1euXOG3336jcePG+Y5fvXp1Dh8+zMWLF4Hrn+6fPXu2UHnJb1+DBg1sayF+/PFH2z/EZrOZjIwM\nrl69yq+//ppvfw0bNrQt4v79999ZunRprv2lS5emffv2zJkzx7btwoUL/Pbbb1gsFnx8fGzjnz59\nGrPZnO+3njZs2JDNmzfb+oiIiMg3LhEREflzmpGQEm39+vW2xcU5evTowYYNGxg6dCiNGzfm119/\nxcPDA7DvSUypqakEBgbaPvkfNGgQAQEBDB8+HE9PTwICAggLC6NLly48+eSTjB49mlGjRuXqw8HB\ngWHDhtGvXz+qV6+Ot7c3ZrM53ycKTZ06leDgYCwWC5UrV8bf39/OjFwvSBwcHPD29rZtq127NkeP\nHsViseR7XKlSpQgODuaVV17B2dmZevXq2W4Hu1FBObx5X0BAAMHBwfzjH//AMAzbI3FffPFFXnjh\nBWrWrJkrzpv7GDBgAMHBwfTv35/s7Ow817YEBwfz/vvv07NnT1xcXMjMzCQgIIAWLVpw7do1YmNj\nCQwMJCsry7Yo+8Yxcl537tyZ77//nr59+2IYBiNGjMj3PEVEROTPmYw/u5lZRAq0adMmWrRoQbly\n5Rg8eDAjR460rTmQv7e4uDgmLT9R3GGIiEgRSU06yUfjfHVr0/+Ki4uzrTEsDM1IiPxFV69eJTAw\nkDJlylC3bl0VESIiInJfUCEh8hf16NEj1yJnERERkfuBCgkRkQKkJp0s7hBERKSIpF269aEjUngq\nJERECvDRON/iDuGelpCQcMuCfLmzlOO7Q3kuekWVYy8vrzve5/1ChYSISAG0AK9opaSkKMdFTDm+\nO5Tnoqcclzz6HgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkR\nEREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkREREREbGbCgkRERER\nEbGbY3EHICJSkh04cKC4Q7inJSYm4urqWtxh3NOU47tDeS56N+fYy8sLBweHYoxIVEiIiBRg6IzN\nxR3CvW/dH8Udwb1POb47lOei9785Trt0lqjp/ahdu3YxB3R/UyEhIlIAl/IPF3cIIiIiJZLWSIiI\niIiIiN1USMh96+TJkzRp0oTAwEACAwMJCAggMDCQy5cvExwczLZt2/j2229ZsWLFHRujb9++xMfH\n38Gz+HN16tRhw4YNubYFBQURGBgIwLRp0zh58uRt979mzRp69epFv3796NOnD5s2bSqw/ZNPPmlX\n/7GxsQQFBeW7PzU1lR07dgDwr3/9i59++smu/kVEROT26NYmua/VqFGDZcuW5bu/TZs2d3SM3bt3\n88EHH7B48eK/3G9hVatWjfXr19OlSxcArly5wpEjRyhfvjwA48ePv+2+4+Li+PTTT4mMjMTFxYWL\nFy/y4osv8thjj1G9evU8jzGZTHaPU9Ax+/bt47vvvqNVq1YMGTLE7r5FRETk9qiQECnA6tWrOXDg\nAAMGDGDcuHFUrVqV3377jXr16jF16lT279/PlClTcHJywmw2M2fOHMqVK5erD8MwbK/PnTuHh4cH\nAGfOnGH8+PFYrVYcHBwIDw8H4O2338bFxYX+/fszbdo0+vTpw9atW8nMzOSTTz6hTJkydp2Dh4cH\nf/zxBykpKbi6urJlyxYef/xxDh06BEBAQADvvPMOmZmZTJ48GYvFgsVi4f333+f48eO3bHNxcbH1\nHRMTw2uvvWbb5u7uzqpVq3BxceHMmTOMHj0ak8lEVlYWM2bMoFq1arZjf/nlF6ZMmYLZbKZx48aM\nHj3aFkvNmjWJiYkhKSmJZs2a2Y755JNP2LRpE4Zh0LZtW1577TXCwsK4cuUKNWrUID4+Hj8/P1q1\nakVoaCgnTpwgMzOToKAgWrZsSceOHf9yPkVEROS6Qt3adPLkSYKCgggICABg5cqVHD16tCjjErkr\nbrzIz0/Op+H79u1j9OjRrFq1im3btpGamsrFixeZOHEikZGRNG7cmK+++uqW448cOUJgYCD+/v68\n++67vPTSSwDMmTOH3r17ExUVxYsvvsi8efMA2L9/P7Nnz+bpp58mKyuLWrVqER0dzcMPP8z3339/\nW+fZrl072y1HGzZsoGPHjre0WbVqFf369WPZsmW88sornD179pZt586dy3XM4cOHqVu3bq5tOUXF\n2bNnGTFiBJGRkTz//PMsX748V7vw8HDCwsJYvnw5Fy5c4NSpU396HiaTiU8//ZTPPvuML774gitX\nrjB48GA6d+7MCy+8YGu3bt06SpUqRVRUFHPnzmXy5MkAdyyfIiIiUshCIjQ0lOeee8520fXoo48S\nGhpapIGJ3A05F/kBAQG2T8Pz88gjj+Du7o7JZKJKlSqkpKRQoUIFZs+eTUBAAOvXrycpKemW43Ju\nbfrss89YvHgxr7/+OteuXSMhIYHmzZsD0Lx5c3799VcAPD09c81qNG3aFMA2pr1MJhN+fn6sW7eO\ny5cvc/HiRTw9PW9p1759ez744APmzp2Lu7s7NWrUuGXbo48+ekvf2dnZeY5bqVIlli1bxoABA4iM\njCQ5OTnX/iNHjlCrVi0AZsyYwUMPPfSn5+Ls7Ez//v0JCAggOTmZS5cu5dkuISHBNpNRuXJlnJ2d\nbW3/aj5FRETkukLd2pSZmUn79u1ZunQpAE888URRxiRy1/zZGokb3fylN4ZhEB4eztChQ2nVqhVL\nliwhLS3tT8crXbo0p0+fxmw224rzzMxMzObrdb2Tk1OB4+bYv38/4eHhthkTwzAwmUy8/vrrNGnS\nJFdbLy8vLl68yMqVK2nXrl2e/bVo0YJVq1bx3//+l3HjxjF27Ng8t914q1GNGjX46aefcs1wHD58\nGA8PD+bMmUObNm3w9/dn06ZNbN26Ndd4Oed7oxvXQmRmZubad+rUKZYuXcratWspVaoU3bp1y/M8\ncvq5cbbpxvzqy4tERO4NCQkJ+kComBV6jcTly5dt/8kfPHiQjIyMIgtK5G4pzK1NebU1DAPDMEhO\nTqZatWpYrVa2bdtGo0aNCjwuOTnZtk7Cx8eHH374gWeffZbY2Fi8vb3tiqlOnTpERUUVOu6OHTuy\naNEiPv300zzbxcTE8NRTT9GtWzcMw+CXX37h4MGDt2y7sZAIDAwkNDSUxx9/HHd3d86dO8frr79O\nREQEycnJtpmPLVu22GYucuKpWbMmP//8Mw0aNGDChAm89NJLuLq6cu7cOWrWrEl8fHyuLxpKSkqi\nQoUKlCpVin379nHq1CmsVismk4lr167lOpcGDRrwww8/0KVLF1vRpm+cFRG5t3h7e+sL6e6wuLg4\nu9oXqpB47bXX6NOnD+fOnaNbt24kJSXx3nvv3VaAIiXJ0aNHbY9BzflEf/To0Xm2vfHTcpPJhMlk\nYsCAAQwfPhxPT08CAgIICwujS5cuPPbYY7eMYRgGmZmZTJw4EUdHR0aOHMmECRNYuXIlFouF8PBw\nMjMzbxknr9f2yDnOz8+PTZs2UaNGjVyPe83Z7+npyahRo3B1dcXZ2Znp06ezb9++W7bdqGHDhrz5\n5psMHjyYMmXK4ODgQGhoKF5eXvj7+zNlyhSqVq3KgAEDmDhxIjt27LCNN378eCZNmoTJZKJhw4Z4\neXnRp08fJk2axKOPPpprYTZAvXr1KF26NP369aNp06a2/sePH8/s2bNti9gBunTpws6dOwkMDCQr\nK4spU6bcsXyKiIjIdSajkB9/pqenc+DAASwWC48++ijOzs5FHZuISLGKi4tj0vITxR2GiIjcJDXp\nJB+N89WMxB0WFxdnW0tYGAXOSMyZM6fAg0eNGlXogURERERE5N5RYCGhRYkiIiIiIpKXAguJESNG\n5Ltv5syZdzwYERERERH5eyjUYusdO3bwz3/+0/YceKvVipubG2PHji3S4EREiltq0sk/byQiIndV\n2qWzxR2CUMhC4v333yc0NJRp06YRHh7Ohg0bePzxx4s6NhGRYvfRON/iDuGelpCQYHv0sRQN5fju\nUJ6L3s059vLyKsZoBApZSLi4uNCoUSOcnJyoVasWo0aN4uWXX6ZVq1ZFHZ+ISLHSE0GKVkpKinJc\nxJTju0N5LnrKcclTqEIiKyuL3bt3U65cOVavXo2XlxcnTuiRiCIiIiIi96tCFRKTJ0/m/PnzjBkz\nhrCwMM6fP8+rr75a1LGJiIiIiEgJVahCokaNGtSoUYMLFy4wa9Ys3N3dizouEREREREpwQpVSKxf\nv55p06ZhMpkAMJvNTJw4EV9fLUIUEREREbkfFaqQ+Oijj/j000/x9PQE4MiRI4waNUqFhIiIiIjI\nfcpcmEaVKlWyFREAjz76KFWrVi2yoEREREREpGQr1IxErVq1mDp1Km3atCE7O5sffviBBx98kO+/\n/x6AFi1aFGmQIiIiIiJSshSqkNi3bx8Av/32W67tBw4cwGQyqZAQEREREbnPFKqQiIqKKuo4RERE\nRETkb6RQayRiY2N5/vnnadiwIY0aNcLf358ff/yxqGMTEREREZESqlAzEtOmTWPs2LE0bdoUwzDY\nvXs3kyZNYs2aNUUdn4iIiIiIlECFmpFwc3OjRYsWWCwWnJ2dadWqFVWqVCnq2EREREREpIQq1IxE\nw4YNWbp0Ka1bt7Y9tcnLy4vjx48DUK1atSINUkSkuBw4cKC4Q7inJSYm4urqWtxh3NOU47tDeb6z\nvLy8cHBwKO4w5E+YDMMw/qxRu3bt8u/AZGLLli13NCgRkZIgLi6Otz/YWdxhiIjcV9IunSVqej9q\n166da3tcXBxNmzYtpqjuD/bmuFAzEv/5z39u2RYfH0+TJk0KH5mIyN+QS/mHizsEERGREqlQhURq\naipr164lKSkJgMzMTFatWsV3331XpMGJiIiIiEjJVKhC4vXXX+ehhx7iu+++o1OnTuzYsYNJkyYV\ncWj3tpkzZ5KQkMD58+e5evUqnp6euLm5MXfu3Ds2xvfff8//b+/OA6qq1v+Pv89hcEJSwllJxYEU\nTRuc0uuQqTlXmgNCVjeHLqmlhoqpieRIiJpjToBKlle7DmlaN9McUPSqWGoO4YCJAyA4IXB+f/Bj\nf5nQoYoAACAASURBVEEGOYZS+Hn9E+yz91rPWntj+9lrrX2GDx9OjRo1ALBYLPj7++Pi4pJvddwr\nISGByMhImjZtyoULF4iNjcXd3T3LfnPnzmXHjh0UK1aMWbNm4eDgkOnzr776irNnzzJ8+HCr6j92\n7BhTp05l6dKl9933ypUr+Pv7c+HCBcxmM46OjowfP55KlR79E+iRI0fSrVs3mjdvzpYtW2jfvn2m\nzzOey9TUVBwcHBg2bBhubm5W1fPVV19RtGhRWrVqZZyne+NISEhg/vz5xrZt27bh7e3N9u3bH+lL\nFq5cucIHH3wAwK+//kq1atUoVqwY3bp149y5c1SoUIFevXoZ+6empjJs2LB8/RsSERGRnOXprU13\n7txh4sSJVKpUCR8fH4KDg/n2228fdmyFmo+PDyEhIQwYMICOHTsSHBz8UG6AmjZtSnBwMMHBwXTv\n3p3ly5fnex0ZHTlyhF27dgGwa9cuIiMjs93vp59+YtWqVTRt2pR9+/Zlu4/JZHqgGPJ63PDhw+nU\nqROrV68mLCyMLl26MGrUqAeqMz8tXLgw2+3p5zI0NBRvb2/ef/99rl27ZlXZ6X0TGRlpnKd7nTt3\njuvXrxu/f/vttw81+cyJs7MzISEhhISEULt2baZPn05wcDCvv/56tvubzWYlESIiIo9QnkYk7t69\ny82bN0lNTSU2NpbSpUtz9uzZhx3bY2vq1KkcOnSIlJQUvLy86NSpE3379uUf//gHu3fvJj4+ngUL\nFuDv74+XlxfPP/88d+7coXPnznz33XeZbqQzrqW/evWq8UT5t99+w8/PDxsbG0qWLMmnn37KtWvX\n+Pjjj6lQoQInTpygfv36TJgwgZEjR1KxYkUiIyO5dOkSM2bMwM3NjZCQEDZt2oSNjQ3t2rXDy8uL\niRMnkpSUhIODA19++SX29vZUqFCBli1bZmqjyWTi6tWr7Nq1i65du+a5b7744gu2bdtGamoqbdq0\nYdCgQVy8eJFhw4ZRpEgRY2HWihUriI2NxdvbGwAvLy/Gjx+Pq6ur0f7U1FTatm1rlN2lSxc6duwI\nwMWLF/noo48wm80kJyczbdo0KlWqxPz589m8eTMuLi7cuXOHgQMHUqFChWz3fRALFy7k+PHjDBs2\njJkzZ+a4n7u7O927d2fdunW8/fbbeb5mOnToAECPHj1ISkqiatWqWW7MmzZtyrfffkuvXr24desW\nFy5coEyZMgCkpKQwduxYoqOjSU5O5oMPPuD555/n559/Zvbs2djZ2VG6dGkCAwMJDw/nyy+/JDU1\nlTNnztCpUycGDRp03/OZHYvFwr3vhfj1118ZNGgQUVFRjBs3jkaNGtGyZUt27tyZbdudnJwYPnw4\nMTExPPvss2zcuJHt27dbfY5EREQkTZ5GJLp168bq1avp2bMnHTt2pFOnTjg5OT3s2B5Le/bsISoq\nipUrV7J06VICAwO5desWkPZ9HsuXL6dZs2Zs27aNbt26sXHjRgB+/vln2rRpk+Vp/J49e/Dy8uK1\n117jm2++oUePHgBMnDiRyZMns3TpUl544QXCwsKAtCfVPj4+rFmzhm3bthl1p6amsnjxYvr06cN/\n/vMfzp49yw8//MCqVasICQlhw4YNxMTE8M4779C5c2cGDhxI165deeutt7IkETdu3OD27dt88MEH\njBo1itKlS+e5f2xtbQkLC+PLL79k9erV3L59m+XLl9OtWzeCg4NxdnYGoGPHjmzduhWA+Ph4bt26\nZSQRAKdPn6Z27dpZyk9/1dzly5cZOnSoUXZYWBjXrl1j9erVfPXVV4wdO9YYSclu3wf1zjvvUKpU\nqVyTiHTu7u6cPHkyT9dM06ZN2bZtGw4ODjg4OBjnKbun+x06dGDDhg0A/Pe//6V58+bGZ9988w2V\nKlVi+fLlzJ49m0mTJgFpfTxz5kxCQkKws7MzRjsiIyOZMWOGcZ3cK7vzmVfpU7BGjRqVbZ/f+/eS\nnjSEhYXx7LPPWj2aIyIiIpnlOiLx448/0qpVK/r06WNsa9q0KZcuXWL69OkPPbjHUWRkJI0aNQKg\nePHiVK9e3Rj9ef755wEoV64ciYmJtGzZksDAQCwWC99//32m85SuadOmBAQEALB3716GDRtGcHAw\nR44cYfTo0VgsFu7evUuDBg0AqFatmnFjX6ZMGRISErLUffz4cQ4dOsSZM2fw8vLCYrFw+/ZtLly4\ncN/2WSwWBg0axEsvvcTRo0epXbs2U6ZMoWvXrtSpU+e+x9vZ2dGnTx9sbW2Ji4sjPj6ekydP0r17\ndwAaNWpEeHg4pUuXplKlSpw4cYJff/2VV155JVM56aMH6caOHUtUVBRXrlxh4cKFODs74+/vz8yZ\nM4mPj6dBgwZERUXh5uaGnZ0dZcuWNdZ+ZLfvo3Djxg1sbGzydM2UL1+exMTE+5ZpMpmoUqUKt27d\n4tKlS2zcuJEPPviA3bt3A3Dw4EEOHTpEeHg4FouFO3fukJqaipOTEz4+PqSmpnLu3DlatWqFs7Mz\ndevWxd7eHnt7e1JTU7PUl935LFq0aJ7an/7WuPS/h3vd+/eSmJhoHNO6dWvM5jw9RxEREZEc5JpI\nBAYGkpiYSOfOnY1t165d48MPP8xxnrL8OSaTKdMUjqSkJOMpecYvZrFYLNja2tKkSRN2795NVFRU\ntouaM2rcuDEffvghACVKlCA4ODjT52fPnsXWNvMlkR7LvXXb29vz0ksv8fHHH2fa/+TJk7nGcPTo\nUZycnPD29mbixIl88803REZG5mltwrlz5wgNDWXdunUUKVLEmIZksViMkZiMN6vdu3fn22+/5fff\nf89Sfo0aNTItKE5/st63b1+Sk5OZPXs2rVu3pkePHmzatIndu3dnqiejmTNnZtk3o6FDhxpvPEsv\no1GjRsa0qwd15MgRnn76aW7fvp3nayav2rVrx9q1a7lw4QI1atQwjrWzs+Nf//pXlsXgY8aMYdmy\nZbi4uDB+/Hhj+73XU0b3ns97k737yVh2dm27t+0mkylT8vCga3BEROThi4yMNB5mZhQREVEA0UhO\nck0kli9fzqBBg4iPj8fDw4P169cTEBCAn58fLVq0eFQxPlbq1avHkiVLeOutt0hMTCQ6OjrXha5d\nu3bFz88vy/ShdBlvsM6cOWNMSatZsya7du2iWbNmrF+/nnLlylG+fPlM++d241m3bl1mzpzJnTt3\nsLOz49NPP8XHxwez2czdu3eBrE/9Ie2JeUxMDAAffvghbdu25e23375v7ACxsbE4OztTpEgRDh8+\nzKVLl0hKSqJ69eocOXKE2rVrs3fv/315WKtWrVi6dClPPPFElrcNVatWDWdnZ1avXs0bb7wBpH0r\n6cWLF7G3tycuLg4XFxdjtMfOzo7KlSsbayuuXbvGr7/+CpDtvhkFBQXl2I/3yq7PsuuPw4cP89//\n/pd169Zx+PBhq66Z9HrSz1N2dXTo0IGePXvSt2/fTJ/Xr1+frVu30r59ey5fvsyKFSsYNmwYN27c\noHz58sTHxxMeHs4zzzyTa/yQ9XzGxMRkG1N+qVKlCv/9738B2LlzJykpKQ+tLhER+XPc3d31hXQF\nwNpELddEolSpUixdupShQ4eydetWrl+/TmhoKJUrV/5TQUrOGjVqxI4dO+jXrx/Jycn4+Phgb2+f\n49PT+vXrc+3aNbp06ZLt53v37jWmH6WmphpP3n19fRk/fjzz5s2jePHiBAQEcO3atUz1pP+cXd2V\nK1emb9++eHh4GIut7ezsjASjfPnyNGzYEF9fX0qXLm2MHlSvXp2mTZsaN+89evTgxx9/5NixY3z2\n2WeZ6ti4cSOHDh0yniYvXrwYW1tbPDw8eP755+nRowcTJ05k/PjxDBs2jC1btlCzZk3jeHt7e6pW\nrZrjFycGBgbi7+/Pl19+SbFixTCZTMbbyXr16sWECROoXLkyHh4efPzxx3Tv3p127drRo0cPXF1d\nqV+/PjY2Ntnuu2fPHpo0aZJtvdnJ2Nc1a9akT58+rFq1KttzeevWLYoVK8bMmTMpWrSo1dcMkOk8\nvfnmm1nicHFxoVKlSsbIQ/r2zp07Ex4eTp8+fUhNTWXo0KEA9O7dm169elG9enUGDBjA3Llzs7y6\n99546tatm+V8fvLJJyxatCjH/snp9/vtD/DSSy/x73//Gw8PD5o2bYqjo2OuZYiIiEjuTJY8zHdI\nSUlhzJgx1KpVi3feeedRxCV5dOrUKSZPnswXX3xR0KH85dy+fZt+/foRHBxM8eLF86XMtWvX0rVr\nV0wmE126dCE4OJgnn3wyX8qWhys2Npb9+/fz8ssvEx0dzcCBA1m/fn2ux0RERDBh5flHFKGIiAAk\nxl5gwai2GpEoANb2ca4jEi1btsw093z9+vWEhIQYT4h//PHHPxWs/DkrVqxgzZo1WviejQMHDvDJ\nJ58wcODAfEsiAC5dukSPHj0oUqQIr732mpKIvxEHBwc2bNhgJN2jR48u4IhERET+3nIdkbjfW3gK\n4huARUQeFY1IiIg8ehqRKDj5OiKhREFEHneJsfd/rbGIiOSfm/ExBR2C5FGevtlaRORxtWBU2/vv\nJA8sMjLyvq+ulj9HffxoqJ/zV8YvkZW/LiUSIiK5uHdoXfJXQkKC+vghUx8/GupneRzpq11FRERE\nRMRqSiRERERERMRqSiRERERERMRqSiRERERERMRqSiRERERERMRqSiRERERERMRqSiRERERERMRq\nSiRERERERMRqSiRERERERMRqSiRERERERMRqSiRERERERMRqSiRERERERMRqSiRERERERMRqtgUd\ngIjIX9mJEycKOoRCLSoqipIlSxZ0GIWa+vjRUD/nnaurKzY2NgUdhuQDJRIiIrkYOGVbQYdQ+G34\no6AjKPzUx4+G+vm+bsbHEDK5L7Vq1SroUCQfKJEQEcmFQ+lKBR2CiIjIX5LWSIiIiIiIiNU0IiF/\nWxcuXKBLly64u7tjsViwtbVlwIABNG3a9JHUf+3aNYYOHQrAsWPHeOqppyhevDhdunTh0qVLlC5d\nGg8Pjwcuf//+/VSvXh0nJ6f8ChmAXbt2MW/ePAAOHjzIs88+C8CIESOYPn0648ePp0aNGlaVeenS\nJT7++GNu3brF7du3qVWrFp988gknT55k27ZteHt706RJE/bs2WNVuRcvXuTy5cvUr18/03ZPT09c\nXV2ZMGGCsS00NJRJkyZx7Ngxq+oQERGRB6NEQv7WqlevTnBwMADnzp1j0KBBBAYGPpK5l05OToSE\nhADg5eXF+PHjcXV1BWDOnDl/uvw1a9bw9ttv53si0axZM5o1awZA06ZNjf77M2bOnMnrr79O+/bt\nAZgwYQI7duygdevWuLm5AWAymawud8+ePdy8eTNLIgHw66+/kpKSYizY++9//0vZsmX/RCtERETE\nGkokpNCoUqUKgwcPJjQ0lIEDBzJixAgcHBzo168fJUqUIDAwEDs7OypUqICfnx8mkwkfHx8uXbrE\nrVu3eP/992nZsiWenp40btyYXbt2YTab6d69O2vXrsXGxobly5dne0NssViwWCyZtp04cYJBgwYR\nFRWFr68vzZs3Z+vWrSxZsgRbW1vc3d3x8fEhMTGRESNGGE/zx44dS0JCAtu2bePkyZPMnj2bI0eO\nZDlu7dq1/PTTT1y+fJnPPvss326iN23axIEDB4iPj2fevHmUL1+ewMBADhw4QEpKCh4eHnTq1CnT\nMQkJCVy/ft34PX2kIDw8nNDQUGbNmmX0z8mTJ/Hz88NsNlOiRAmmTJlCfHw8o0aNonLlyhw/fpy6\ndevywQcfMHv2bOzs7KhYsSKtW7fOVGe9evXYuXMnLVu25I8//sDW1hY7OzsgbYRk5MiRmEwmkpOT\nmTJlClWqVKFdu3a89NJLHDx4EEdHRxYuXJgvfSYiIvI40hoJKVTq1q3L6dOngbTpRgEBAbRs2RJ/\nf3/mzZvHsmXLcHJyYvPmzcTHx9O8eXNCQkIIDAwkKCjIKKdcuXKsXLmSlJQUrl+/zooVK0hJSeH4\n8eN5jiUuLo758+fj6+tLWFgYN2/eZN68eQQHBxMSEsLFixc5ePAgV65coWfPnixfvpwPPviARYsW\n0axZM9zc3JgyZQqOjo7ZHgdpU39CQ0Pz9Un8k08+ybJly2jRogXfffcd+/fvJzo6mpCQEJYtW8a8\nefNISkrKdMy7777LzJkz8fDw4PPPP+fs2bPGZ+mJV/p/J02ahJ+fH0uXLqVZs2aEhoYCcPToUUaO\nHMmaNWvYvn079vb2vPbaa3h5eWVJIgA6dOjAhg0bgLTk5+WXXzY+i4mJwdvbm+XLl/Paa6+xcuVK\nIG3U6rXXXiMsLIz4+HhNgxIREfkTNCIhhcqNGzcwm9PyYxcXFxwdHbl69Sq///473t7eWCwWbt++\njZOTE46Ojhw5coQvv/wSs9lMfHy8UU69evUAKFOmDE8//TSQdoOdmJiY51iee+45IC0pSUhI4OTJ\nk0RHR/POO+9gsVi4ceMG0dHR1KxZk7lz57J48WKSkpIoXry4UYbFYsnxuIxx5qeMccfFxXHw4EEO\nHz6Ml5eXMaoQExND5cqVjWOeeeYZfvjhB37++We2b99Oz549CQwMxNY26z8xhw8fZuzYsVgsFu7e\nvWu04amnnjKmcZUtW5aEhIQcYzSZTDz33HP4+vpy584dvvvuO+bPn8/cuXOBtPM2adIkZs2axfXr\n16lbty4ADg4O1KxZ02ifNedTRETyR2RkZK7/xucmIiIin6ORP0OJhBQqkZGR1KlTB8CY5mJnZ0f5\n8uWzrAVYt24d8fHxrFq1itjYWHr06GF8lvEGOOPP905fyk3GL9uxWCzY29vj7u7OF198kWm/OXPm\nUL58eaZNm0ZkZCTTpk3L9LmdnV22x61du9ZoY0aenp7G03+LxYLJZKJ9+/Z5Xvh9b3vt7e15/fXX\nGTBgQI7H3LlzhyJFitCmTRvatGlDw4YN2bBhA927d8+yb7FixbKciwsXLmT5cqL79bXJZKJ58+as\nWLGC4sWLU6pUKaPdQUFBtGjRgl69erFlyxZ+/PFHAKvrEBGR/Ofu7v5AaxkjIiKMh13ycFibqGlq\nk/ytZbwRPHv2LMuWLaN///6ZPnN0dATg1KlTQNrbfY4fP05sbKzxVP27777j7t27DzXWqlWrcvr0\naa5duwbA7NmzuXTpEnFxcVSpUgWArVu3GnGYzWaSk5OpVq1aluNiYmJyrCckJITg4GBjKlRwcHCO\nSURebqTr16/PDz/8gMVi4c6dO0yaNClLGV26dDH6F+CPP/4w2nRvXW5ubvz0009A2pSk9Dc5ZYwl\n/ef0NQ45xd2+fXsWLFhgLPJO3x4bG4uLiwsA33///UM/tyIiIo8jjUjI39rvv/+Ol5cXSUlJpKam\nMn78eMqXL8+FCxcyLYr29/dn9OjR2NvbU7ZsWXr16oWDgwODBw/m0KFDvP7665QvX57PP/8803E5\n/XyvvLyRqGjRoowePZp3332XIkWKUKdOHcqVK0e3bt3w8fFh8+bN9OvXj02bNrF27VpeeOEFhg4d\nyty5cxkzZkym4/JrTcS9cWfXjoYNG9K4cWN69eoFQN++fbMcExAQkOlVrJUrV2b8+PEcPnw4S9lj\nxoxh3LhxLFq0iKJFixIQEEBCQkK2fd2wYUNGjRrFk08+SefOnbN8/sILL1C0aFFjfUT69t69ezNx\n4kQqV65Mv379GDduHD///HOez6eIiIjcn8misX0RkWxFREQwYeX5gg5DRKTQSIy9wIJRbTW16S/K\n2j7W1CYREREREbGaEgkREREREbGa1kiIiOQiMfZCQYcgIlJo3IzP+WUh8vejREJEJBcLRrUt6BAK\ntcjISNzd3Qs6jEJNffxoqJ/zztXVtaBDkHyiREJEJBcPsiBQ8i4hIUF9/JCpjx8N9bM8jrRGQkRE\nRERErKZEQkRERERErKZEQkRERERErKZEQkRERERErKZEQkRERERErKZEQkRERERErKZEQkRERERE\nrKZEQkRERERErKZEQkRERERErKZEQkRERERErKZEQkRERERErKZEQkRERERErGZb0AGIiPyVnThx\noqBDKNSioqIoWbJkQYdRqKmPH43HtZ9dXV2xsbEp6DCkgCiREBHJxcAp2wo6hMJvwx8FHUHhpz5+\nNB6zfr4ZH0PI5L7UqlWroEORAqJEQkQkFw6lKxV0CCIiIn9JWiMhIiIiIiJW04iEFApTp04lMjKS\nK1eucOvWLVxcXChVqhSzZs0q6ND+lHXr1rFy5Urs7OyYNm0alSplfjoeHh7O0KFDqVmzJhaLhTt3\n7tCiRQvef/99q+oJDw8nNDT0kfXX2rVrCQoKwsXFBYvFgtlsZty4cbi6uuZL+T/++CNbtmxh8uTJ\n+VKeiIiIZKVEQgoFHx8fIO0G9bfffuOjjz4q4Ijyx1dffUVYWBjbtm1j69at9O/fP8s+jRo1Iigo\nyPi9f//+RERE8Nxzz1lVl8lk+rPhWqVjx47Gedq3bx+TJk1i6dKljzQGEREReXBKJKRQu/dJe5Mm\nTdizZw+enp40a9aMvXv3EhcXx/z583F2dmbkyJFcvHiRBg0a8O2337J9+3ZOnjyJn58fZrOZEiVK\nMHnyZObMmUOdOnXo1q0bAO3bt+err75i7ty5HDlyhDt37tCrVy969uzJsWPHmDhxInZ2dpjNZoKC\ngnB0dGTSpEn873//o0aNGpw5c4bAwEAqVqyYKX5bW1uuX7/O+vXrGTBgQJ7a7O7uTlRUFA0aNMDH\nx4dLly5x69Yt3n//fVq2bMnu3bsJCgrCzs6OJ554gpkzZwIQHx+Pt7c30dHRtG3blvfeey/bfQ8c\nOEBoaCgmk4kzZ87Qrl07vL29/9R5euaZZ4iKigJg/fr1hIaGYmNjQ40aNZg4cSJr167lp59+4vLl\ny3z22WcsWbIkSz+fOHECHx8fSpUqRZUqVYyyV6xYwYYNG7CxsaFt27bZJmMiIiJiPa2RkEIv45P2\njD87OjqybNkyWrRowXfffceOHTtISkoiLCyMJk2acPnyZQAmTZqEn58fS5cupVmzZqxYsYJ27drx\nww8/AHD8+HEqV65M0aJFqVy5MitWrGDFihVG8nLt2jXGjRvH8uXLadiwIevXr+fEiRMcOHCAr7/+\nmrfffpujR49miTs5OZlixYrRv39/+vXrh5ub233beuPGDXbu3EmdOnWIj4+nefPmhISEEBgYaIxa\nxMfHExAQQEhICCVKlGDnzp1A2mtOZ8yYQVhYGF9//TXXr1/Pcd/IyEimT59OWFgYoaGhD3JaMvnh\nhx+oX78+ALdv32bx4sWsXLmS06dP89tvvwFw8eJFQkNDKVWqVLb9PHfuXIYMGcLSpUsxm9P+aTt/\n/jxbtmxh1apVhIaGsnnzZv744/F6q4qIiMjDohEJeWylT/0pX748cXFxnDp1imeffRaAli1bGu/F\nPnz4MGPHjsVisXD37l3q16/Ps88+i6+vL8nJyXz//fd06NABe3t74uLi6N27N3Z2dsTGxgLw5JNP\nMmPGDG7fvk1MTAxdunTh1KlTNGjQAIBatWplWfsAMGrUKGrXrk18fDwvvPACYWFhlC5dmldeeSXT\nfuHh4Xh5eZGSkkJUVBQjRozAzc2N5ORkjhw5wpdffonZbCY+Ph4AJycnfH19SUlJ4fz58zRt2pTi\nxYvj7u5O0aJFAahZsybnzp3Lcd86depgb2+Pvb39A0+J2rRpE5GRkVgsFsqWLYuvry+QluANHjwY\ngNOnTxMXFwdAvXr1AHLs54x92qhRI3bs2MHhw4eJiorCy8sLi8XCzZs3OX/+POXLl3+gmEVEROT/\nKJGQQu3em9zk5GTjZ1vb/7v8LRYLgPEkO+OxxYoVIzg4OEvZjRs3Jjw8nO3btzN//nz27dvH3r17\nWblyJWaz2UhK/P39GThwIC+++CJLlizh5s2bxgLjnCQkJHDq1ClmzJjBsmXLWLBgAREREcY0pIwy\nrpHo3bu38T7vDRs2EB8fz6pVq4iNjaVHjx4AjBkzhkWLFlGtWjX8/PyyrT+9P3LaN7cvH1q4cCE7\nduww+s9isVCiRAnmz5+fab+MayTS3b17l4kTJ7J+/XqcnJwYNGiQ8ZmdnR1Ajv2csU/T47e3t6dV\nq1Z88sknOcYrIiIPLjIykoSEhEdWX0RExCOrS+5PiYQUag4ODsYUpWPHjnHjxo0c93VxcWHz5s0A\n7Ny5k5SUFADc3Nz46aef+Mc//sGmTZtwcnKiSZMmvPzyy6xbt47ixYtTunRpYmNjKV++PGazme+/\n/57U1FTu3r1LXFwcVapUISkpie3bt9OgQQNcXFyM5OTUqVNER0dnisXOzo7ExETu3r2Ll5cX3bp1\n45lnnsHBwSHX9vr4+PDJJ58QFhZGbGwslStXBuC7777j7t27ACQmJlKhQgWuX7/O3r17jSlTv/zy\nC3fu3AHSRgKeeuqpHPfNKP2mPd2AAQPyvJ7jXjdu3MDW1hYnJycuXrxIZGQkSUlJmfbJqZ+rV6/O\nkSNHaN68OXv37gWgbt26xmhQkSJF+PTTTxk5ciT29vYPFJ+IiGTm7u7+yL6Q7kFeJCLWsTZR0xoJ\nKdTc3NwoVqwYffr04T//+Y9xY53ddJxWrVqRmJiIh4cHBw4coFSpUkDaU/kFCxbg6enJ2rVrqVOn\nDpC2cHvHjh20b98egGbNmvH777/j6enJ+fPnjSfhnp6evPfeewwbNgxPT0/WrVuHnZ0dVatW5Y03\n3iA4OJgaNWpkespftGhRBgwYQJ8+fejbty8vvPACFy9e5M033zQSnOw0bNiQKlWq8PXXXxvrON56\n6y1KlChB+fLlmTt3Lh4eHvTu3Zvx48fzz3/+kwULFnDlyhXq1q3L6NGj6dOnD3369MHBwSHHfTPK\nz7c9lSpVimbNmtGzZ0/mzp3LP//5T6ZMmZJpJCmnfh48eDDTp09n4MCBRqJQoUIFvLy8jHaUgV8u\njAAAH6dJREFUKVNGSYSIiEg+MVnufZwo8piKj49n7969tGvXjkuXLvHWW2+xadOmh1JXUlISmzZt\nonv37ty6dYuOHTvy/fff5zrdSR69iIgIJqw8X9BhiIj8JSXGXmDBqLYakShErO1jTW0S+f9KlCjB\nt99+y+LFi7FYLIwZM+ah1WVvb09kZCQhISHY2NgwdOhQJREiIiLyt6JEQuT/s7W1JTAw8JHVN3bs\n2EdWl4iIiEh+0yNQERERERGxmkYkRERykRh7oaBDEBH5S7oZH1PQIUgBUyIhIpKLBaPaFnQIhVpk\nZCTu7u4FHUahpj5+NB7XfnZ1dS3oEKQAKZEQEcnFo3obyeMqISFBffyQqY8fDfWzPI60RkJERERE\nRKymREJERERERKymREJERERERKymREJERERERKymREJERERERKymREJERERERKymREJERERERKym\nREJERERERKymREJERERERKymREJERERERKymREJERERERKymREJERERERKxmW9ABiIj8lZ04caKg\nQyjUoqKiKFmyZEGHUaipj/Ofq6srNjY2BR2GSIFTIiEikouBU7YVdAiF34Y/CjqCwk99nG9uxscQ\nMrkvtWrVKuhQRAqcEgkRkVw4lK5U0CGIiIj8JWmNhIiIiIiIWE2JhEgebdiwAXd3d+Li4h56XS+9\n9BKLFi3KtG3q1Km0adPG+H3y5MmZ/ptReHg4Q4YMyVNd1uz7Z2Wsq0mTJnk6Zu3atUydOjXL9uHD\nh5OUlGRV/QsXLuTQoUNWHSMiIiLZUyIhkkcbNmzAxcWFLVu2PPS6ypQpw/fff59p29GjRzGZTMbv\no0ePzvTfe2Xc936s2ffPSq/rz8YXEBCAvb29VXUPGDCAZ555xqpjREREJHtKJETyID4+nsjISHx8\nfNiwYYOxfd26dfTs2RMPDw/8/PwA2L17N71798bT0xNvb2+Sk5Otrs/Ozo7SpUtz6tQpACIiInB1\ndTU+P3bsGH379uXNN9/krbfe4vr161nKuHHjBiNHjqRLly58/vnneYot4yjBkCFD2LdvH3PmzGHF\nihUA/Pbbb3h6egLQrl07pk6dSu/evRkwYECW+k+cOEG/fv3w8vJi8ODBWWK0WCx57q9Lly4xZMgQ\nOnXqxL///W8A2rRpw61btxg9ejR+fn689dZbvPrqqxw7dowLFy7Qo0cPRowYQY8ePZg4cSKQlnRt\n3749t64XERGRPFIiIZIHmzdvpnXr1rRo0YKoqChiYmIAWLJkiXGj7e7uTlJSEtevXycgIICQkBBK\nlCjBzp07H6jO9u3bs379egA2bdpEu3btjM+uXbvGuHHjWL58OQ0bNjT2y+j06dP4+/vz5ZdfEhoa\nCqQlRLnFlpdRgvR9zp07x2uvvUZYWBjx8fEcO3Ys037+/v74+PgQHBxMo0aNWL58ebbl3C8mgAsX\nLjBr1izmzJlDSEhIllhTUlJYunQpQ4YMYc6cOQAcP36ckSNH8vXXX3PkyJEs8YmIiMifo0RCJA82\nbNhAx44dMZvNtGvXjk2bNgHQuXNn3nvvPZYvX84//vEP7O3tKV26NL6+vnh6ehIeHv5AaypMJhNt\n27Zl27ZtpKamEh4eTuPGjY2n+E8++SQBAQF4enqyceNGYmNjs5RRp04d7O3tKV68uLHNyckp19jS\ny88LBwcHatasCUC5cuVITEzM9PmpU6eoV68eAI0aNeLXX3/Ntq77xQQY05HKlStHQkJCllibNWsG\nQIMGDfj9998BqFq1KuXKlTOOP3PmTJ7bJiIiIven17+K3MelS5c4dOiQseD39u3bODo60r9/fwYM\nGEDXrl3ZvHkz/fv3JyQkhDFjxrBo0SKqVatmTHdKd+XKFT744APjabrFYsFkMtGvX79MIw6QdqNe\nqVIlli1bRoMGDTCbzcZx/v7+DBw4kBdffJElS5Zw8+bNLHFn92VJucV2r+ymGGXcdm/5uSUhd+/e\nxWzO/NwivS15iSljXen1ZByRSE1NNT5L356+Lf3ne+sXEXlQkZGRxkONjCIiIgogmseL+vivRYmE\nyH1s2LABDw8PfHx8jG3t27fn3LlzfP3117z//vv079+fU6dOER0dTWJiIhUqVOD69evs3bsXNzc3\n4zhnZ2djak5u0m+WO3TowJQpUwgICMj0eVxcHFWqVCEpKYnt27fToEGDPLUlt9gAzGYzd+7cITU1\n1RhBcHBwMKZyWfMPeK1atTh06BDPPPMM4eHhuLu7Z9vG+8WUk4yJy/79++nQoQMHDx401pKcPXuW\nK1eu4OTkxKFDh/Dw8ODHH3/Mc/wiIjlxd3fP8oV0ERERPPfccwUU0eNBffzwWZuoKZEQuY+NGzcy\nbdq0TNu6d+/Opk2bcHBwoFevXjg6OlKlShXq1KlD37596d27N9WqVeOf//wnc+bMoXXr1jg7O+e5\nzvSn6m3btiUgIICmTZtm+rxfv3689957uLi44OnpiZ+fHx07dqR27dq5lptdbB9++KHxee/evenZ\nsyc1atQwbvzbtWvHwIEDiYyM5Pnnn88S470/p/P19eWTTz7BbDbj6OjI5MmTOXr0aJZjHrS/MtaZ\nlJTEoEGD+OOPP5g+fToA1apV47PPPuPkyZM899xzmRari4iIyJ9nslgzKVpE5C9m9OjRdOjQgZYt\nWxrbLly4wJAhQ1izZs2fKjsiIoIJK8//2RBFpBBJjL3AglFtNSJRANTHD5+1faxJwyJSKD3K78YQ\nERF5HGlqk4j8rWX3zd6VKlXi66+/LoBoREREHh9KJEREcpEYe6GgQxCRv5Cb8TEFHYLIX4YSCRGR\nXCwY1bagQyjUIiMjs7zRS/KX+jj/6eUNImmUSIiI5OLeBZWSvxISEtTHD5n6WEQeFi22FhERERER\nqymREBERERERqymREBERERERqymREBERERERqymREBERERERqymREBERERERqymREBERERERqymR\nEBERERERqymREBERERERqymREBERERERqymREBERERERqymREBERERERqymREBERERERq9kWdAAi\nIn9lJ06cKOgQCrWoqChKlixZ0GEUan/HPnZ1dcXGxqagwxCR+1AiISKSi4FTthV0CIXfhj8KOoLC\n72/UxzfjYwiZ3JdatWoVdCgich9KJEREcuFQulJBhyAiIvKXpDUSIiIiIiJitYeaSERFRTFw4EDe\neOMNXn/9dSZNmsTdu3cBaNKkSZ7LyW7fOXPmsGLFCqviGTRoEHv27DF+HzBgAKtWrTJ+nzx5MqtX\nr8722PDwcIYMGQLAokWL6NKlC2fPns1z3efOneOVV14hOTnZ2LZo0SKmT59uVRvyW2JiIj///DPw\nYH0KsHbtWlq1aoWXlxf9+vVjyJAhnD9/Pr9DZe3atWzblvM0k+PHjxMVFQXA8OHDSUpKeuC69u/f\nz7Vr1wBo06YNt27dsvq4dBmvnZzcr21/B9m1PSfh4eG0b9+eLVu2ZNr+0ksvsWjRokzbpk6dSps2\nbfIlxvR/S3KqX0RERPLuoSUSqampvP/++7z77rusXr2aNWvWAPD5558DYDKZ8lyWNfvmpkmTJuzf\nvx8Ai8XCxYsXjd8h7UaocePG941j586dTJ8+HRcXlzzXXaVKFVq3bk1oaCgAsbGxrFmzhvfee+9B\nmpJvjh49ys6dO/90OR07diQ4OJjQ0FB69erFP//5zz91I5+dV199lbZt2+b4+datWzlz5gwAAQEB\n2NvbP3Bda9asMW6Krbn+1qxZw9WrV7Nsv18Z92vb30FObc/Ovn378PDwoH379pm2lylThu+//z7T\ntqNHj+bbvwHp5eRUv4iIiOTdQ1sj8fPPP+Pq6srzzz9vbPvoo4+M/5FbLBZmz57Nzp07KV26NPPm\nzePzzz+ndOnSeHh48NtvvzFx4kRCQkKwWCx8+umnHD58mDJlyhAYGAjAkSNHeOedd4iJieGjjz7i\n/PnzxMTEMHToUADefvttRo0aZSzYaty4MZMnTwbSnl43bNiQX3/9FYAbN24QGxvLU089xfHjx/Hz\n88NsNlOiRAmmTp1qtOGbb77hl19+4eOPP2b69OlUrVo1z30yePBgY3Rm3rx5vPXWW5QoUYLk5GQ+\n/vhjzp8/z927dxkyZAjNmjWjTZs2bNy4kWLFijF16lRq165N9+7djfKOHTvGxIkTsbOzw2w2ExQU\nREJCAiNGjMDBwYF+/fpRokQJAgMDsbOzo0KFCvj5+WFr+3+n3c/Pjxs3blC9enUg7Q01gwYNIioq\nCl9fX5o3b87WrVtZsmQJtra2uLu74+Pjk2s7X3zxRRo1asS2bdvo0KGD0bbk5GSGDBlC48aNWbdu\nHStWrMDe3h43Nzc+/vhjfvnlFyZOnIjZbKZhw4aMHDkST09PatWqhclkolSpUpQuXZqaNWuyaNEi\n7O3tuXjxIu3ataNNmzaEhYXh5OSEk5MTw4YNY+PGjVy/fp0xY8aQlJSEjY0N/v7+AIwaNYrKlStz\n/Phx6tSpw6RJk4z4d+3axbZt2zh58iSzZs3CYrEQEhLCTz/9REpKCosXLyY1NZURI0Zw69Ytbt++\nzdixY0lISDCOmz17NuXLlzfKjI+Px9vbm+joaF5++WUGDx6cY9tCQ0MxmUycOXOGdu3a4e3tza5d\nu5g8eTJlypShatWqODk50b9/f4YOHcrdu3dJSkpi/PjxPP3000adc+bMITY2lqioKM6fP8/QoUNZ\ns2YN0dHRLFy4kMqVKxMYGMiBAwdISUnBw8ODTp06ZTqXe/fuNa6f8uXL4+/vz8aNGzlx4gQ+Pj7c\nvHmTzp074+/vn2Pbp0+fzoEDB0hNTaVv3748/fTTrFmzBjs7O8qUKcMrr7xi7GtnZ4eDgwOnTp3C\n1dWViIgIXF1duXDhAgkJCfTo0cMYRVi/fj2//PILzZs3Z+bMmRQtWhRnZ2dmzJjB1atX8fX1JTk5\nGbPZjL+/vxHTb7/9lmP9IiIikncPbUTi9OnTmW5qAOzt7bGzswPSbqw6dOjAl19+SVxcHMePH89S\nRnrSERcXR+fOnQkLC8NsNrNjxw4Arl27xuLFi/nss88IDAykU6dOxvSQxMRE4uLiMr31wc3NjbNn\nz5KcnMz+/ft59tlnqVy5MufOnePgwYNG0vPpp5/i4+NDcHAwjRo1Yvny5UYZ3bp1w83NjSlTpliV\nRACULFmSfv36MW7cOCIiInjjjTcA2LBhA0WLFiUkJIRZs2bxySef5Km8a9euMW7cOJYvX07Dhg1Z\nv349kJZgBAQE0LJlS/z9/Zk3bx7Lli3DycmJzZs3ZyrjnXfe4ZVXXqFnz55GX8+fPx9fX1/CwsK4\nefMm8+bNIzg4mJCQEC5evMjBgwfvG1vdunU5efIk69evp2zZsixfvpw5c+YYN/JLliwxplK5u7tz\n584d/P398fPzY+XKlVy9epXo6GgAatWqxdixYzOVf/ToUQICAggLC+Orr76iXLlytGjRguHDh1O/\nfn3j2gkKCqJHjx6EhITQp08fZs+ebRw/cuRI1qxZw/bt20lMTDTKbtasGU8//TRTpkyhQoUKANSu\nXZvQ0FAqVqzI7t27uXLlCj179mT58uV88MEHLFq0iGbNmhnXRsYbaUhL0GbMmEFYWBhff/018fHx\nObYtMjKS6dOnExYWZkw1mzFjBtOnT2fx4sX88ssvAOzevZsKFSoQHBxs3DzfKz4+ni+++IL27dvz\nzTffGD//8MMP7N+/n+joaEJCQli2bBnz5s3LMoo0YcIEgoKCCAkJ4YknnmDDhg1A5hEWk8lE06ZN\ns237/v37OXnyJKtWrWLZsmXMmTOHypUr89prr+Hl5ZXtTXz79u2Na3nTpk20a9cOSPv7cXNz43//\n+x8A33//PZ07dyY0NJRRo0YREhJCp06diI2NJSgoiLfffpulS5fi5eVljIQC1KxZM9f6RUREJG8e\n2oiEyWQiJSUlx89LlixJzZo1AShbtmymG7l7FSlShPr16wNQr149Y/pKo0aNgLQbg0uXLuHo6Ei1\natX49ddfOX36NB06dMgSU7169Th8+DD79+9n+PDhJCYmsm/fPs6ePWtMazp16hT16tUz6vj888+z\nTHmyWCzWdIehd+/eLF26lLFjxxo3Y5GRkUZbypYtS5EiRYwbzdw8+eSTzJgxg9u3bxMTE0OXLl0A\ncHFxwdHRkatXr/L777/j7e2NxWLh9u3bODk55Vrmc889B0C5cuVISEjg5MmTREdH884772CxWLhx\n4wbR0dE0bNgw13Ju3LiB2Wzm4MGDREREEBERgcViISkpieTkZDp37sx7771H165d6dKlC0WKFOHM\nmTPGNTFlyhSjrPRzn1H9+vUpWrQokHb+09dGZGSxWIiMjGTEiBFA2ojU3LlzAXjqqaeMvkhvq4OD\nQ6ZjM57j9H4pW7YsCQkJODs7M3fuXBYvXkxSUhLFixfPdOy93N3djXhr1KjBuXPncmxbnTp1sLe3\nzzQ1Kzo6Gjc3NwBatmxJSkoKDRs2JCgoiAkTJvDyyy/TvHnzbPspPW6zOe25gbOzM3FxcRw8eJDD\nhw/j5eVlxBwTE0PlypWBtCTEbDZTrlw5IO1vITw8nLp162apJ6e2R0ZG8sILLwBQrFgxatSoke25\nSmcymWjbti29e/dmyJAhhIeH4+vra5TbrVs3Nm7cSN26dblw4QJ169alQ4cOjB8/nq5du9KxY0ec\nnZ05ePAgv//+O3PnzsVisdz3uheRv5bIyEgSEhIKOgyrRUREFHQIhZ76+K/loSUS1atXN9YDpEtK\nSiIqKoqaNWtm+aKZe29AMi5Kvnd+dPoN0b1PRQG6du3Kt99+S3R0NB9++GGWuBo3bsyBAwc4e/Ys\nVapU4dlnn2XVqlX8/vvv9OjRI8v+d+/eNerLjaenZ6ZpWyaTifbt2+Ph4ZFpPxsbGypWrGjcrKXH\nnrH96XVmbF/G/kjn7+/PwIEDefHFF1myZAk3b94EMEZ90qejBAcH3zf+jPGls1gs2Nvb4+7uzhdf\nfJHnMiDtfwKdO3dmz549DB48mI4dO2b6fMCAAXTt2pXNmzfz5ptvEhISkmM/p7cno9TU1ExxZnes\nyWTK1LcZz+W97bxfYnjv9bps2TLKly/PtGnTiIyMZNq0abken/FcZow3u7bd70uY0stydnbmm2++\nYe/evaxatYr//e9//Otf/8qxrOzO7euvv86AAQNyrCdjP9+9excbG5v7Xpf3yti3SUlJ913v4ODg\nQKVKlVi2bBkNGjTIdG7/8Y9/EBQUxJ49e2jVqhWQlly0aNGCbdu2MXjwYIKCgrC3tycoKAhnZ+f7\nxicifz3u7u5/u++RiIiIMB46ycOhPn74rE3UHtrUphdffJGLFy/y448/Amk3fjNmzODbb78Fsn9q\n6+DgQExMDJC5Ibdv3zamcxw6dMiYz5++z7Fjx6hYsSKQ9rR23759JCQkGNsyatSoEd999x1PPfUU\nkDbd6bfffiMuLs64ua9VqxaHDh0C0t7u4u7uft/2hoSEEBwcbEwBCg4OzpJEpLu37fXq1WPv3r0A\nXLx4EbPZTMmSJSlZsiSXL18mJSXFiCejuLg4qlSpQlJSEtu3bzfeiJVevqOjI5A2wgIQGhqa5Vt6\nTSZTrjeDVatW5fTp08bC49mzZxvnKKc2bd++nTNnztCmTRvq169vTDe7evUqgYGBWCwWAgMDcXZ2\npn///jRo0IDo6Ghq1KjB4cOHAfD19TXizs4vv/zCnTt3uHPnDqdOnaJq1aqZ2pIeT/369Y03dWU8\nl/dLHMxmc479YrFYjL6HtEXe6X2f03FHjx414j19+rRVC/UhbRHymTNnSElJMd6ytXv3bn7++Wea\nNWvG2LFjOXr0qFVl1q9fnx9++AGLxcKdO3cyrROBtOvHbDbzxx9pX2SV3n8Z/04zvqwgu7bXq1eP\n8PBwIG2U6vz587lOCUw/Lx06dGDBggVZFkPb2trywgsvMGvWLLp27QrA3LlzsbW15Y033qBjx46c\nPn2a+vXrs3XrVqOfNm7cmKl8ERER+fMe6tSmxYsXM3bsWObMmYOdnR0vvvgi3t7exucZ9wVo164d\nAwcOJDIyMtMi7XLlyrF+/Xo+/fRTypQpQ4sWLTh8+DBPPvkkgwcP5vz58/j6+gJpT3hdXV1zvPmv\nVasW58+fp1u3bkDazY+Dg4ORnEDaTewnn3yC2WzG0dGRyZMnZ7pJ+7NvkLn3+E6dOhEeHo6XlxfJ\nyclMnDgRAA8PDwYOHEj16tWNKT8Z9evXj/feew8XFxc8PT3x8/OjY8eOmcr39/dn9OjR2NvbU7Zs\nWXr16pWpjLp16xIQEGCsBbhX0aJFGT16NO+++y5FihShTp06lC1bNst+mzdv5ujRoyQmJuLs7Mys\nWbMAeOWVV9i7dy+9e/fGYrHg7e2NyWSiRIkS9OrVC0dHR6pUqUKdOnUYM2YMEyZMwGQy0aBBA1xd\nXXPsa1dXV8aMGcOZM2fo06cPDg4OPP/88/j7+1O8eHHjuPfffx9fX19Wr16Nvb09/v7+3L17N9vr\nL6MXXniBoUOH8vnnn2fZ12Qy0b17dz766CM2b95Mv3792LRpE2vXrjWOmzt3Lq6urpn6+d54rbmO\nhg4dire3N1WqVMHV1RUbGxtcXFwYOXIkX3zxBWazmffffz/P5QE0bNiQxo0bG9dE3759s+zj5+fH\nhx9+iK2tLS4uLnTq1MlYN+Pl5UXLli2NEYPs2v7cc89Rt25d+vXrR3JyMiNGjDCmeGUnvU/atm1L\nQEAATZs2zbQd0q6pI0eOGIlcxYoV6d+/P0888QRPPPEEb7/9NvXr12f06NFs3LgRk8lkTJXLr7c/\niYiICJgshewR3Z07d+jXrx9Lly7NNOddCo/w8HBWrFhBUFBQQYfyyPz8889Uq1aNihUrMm7cOBo3\nbpzlDUuPi9mzZ1O5cmVeffXVh15XREQEE1bm/3eiiEjOEmMvsGBUW01tkizUxw+ftX380EYkCsKh\nQ4cYN24c7777rpIIKVQsFgv/+te/KFGiBM7Ozo/t9x8MHDiQokWLGiObIiIiUnAKVSLxzDPP8M03\n3xR0GPKQNWrUyHjL1eOiefPm2b6V6XGzYMGCgg5BRERE/r9ClUiIiOS3xNgLBR2CyGPlZnzWF3qI\nyF+TEgkRkVwsGNW2oEMo1CIjI/P0Zjx5cH/HPs74sgoR+etSIiEikou/24LPv5uEhAT18UOmPhaR\nh+WhfY+EiIiIiIgUXkokRERERETEakokRERERETEakokRERERETEakokRERERETEakokRERERETE\naiaLxWIp6CBERP6KIiIiCjoEERGRR+q5557L875KJERERERExGqa2iQiIiIiIlZTIiEiIiIiIlZT\nIiEiIiIiIlZTIiEiIiIiIlZTIiEiIiIiIlazLegARET+aiZPnsyhQ4cwmUyMGTOGevXqFXRIhU54\neDhDhw6lZs2aWCwWateuzdixYws6rELhxIkT/Otf/6J///54eHjwxx9/MHLkSCwWC2XKlGHatGnY\n2dkVdJh/e/f28+jRo4mMjKR06dIAvPPOO7Rs2bKAo/x7mzZtGgcOHCAlJYUBAwZQr149Xcv57N4+\n/uGHH6y6jpVIiIhksG/fPqKioggLC+PUqVP4+voSFhZW0GEVSo0aNSIoKKigwyhUbt26xaRJk2ja\ntKmxLSgoCE9PT9q1a0dgYCBr1qyhd+/eBRjl3192/QwwYsQIJQ/5ZO/evZw6dYqwsDDi4uJ49dVX\nadKkCf369aN9+/a6lvNBTn1szXWsqU0iIhns3r2btm3bAuDq6sr169e5ceNGAUdVOOlrjPJfkSJF\n+OKLLyhbtqyxLTw8nNatWwPQunVrdu3aVVDhFRrZ9bPkr4wPGhwdHbl58yb79u2jTZs2gK7l/JBd\nH6emplr1b7MSCRGRDK5cuYKTk5Pxe+nSpbly5UoBRlR4nTp1ivfeew8PDw/dEOQTs9mMvb19pm23\nbt0ypn88+eSTXL58uSBCK1Sy62eA0NBQ3nzzTYYPH05cXFwBRFZ4mEwmihYtCsDXX39Nq1atdC3n\ns4x9/NVXX9GqVSvMZrNV17GmNomI5EJPzR+Op556Cm9vb1555RXOnTuHl5cXW7duxdZW/1t6mHQ9\nPzzdunWjVKlSuLm5sXDhQmbPns3HH39c0GH97W3bto01a9awePFi2rVrZ2zXtZx/tm3bxr///W8W\nL15MZGSkVdexRiRERDIoW7ZsphGImJgYypQpU4ARFU7lypXjlVdeAaBKlSo4Oztz6dKlAo6qcCpR\nogRJSUkAXLp0SdNxHpImTZrg5uYGwEsvvcSJEycKOKK/vx07drBw4UK++OILHBwcdC0/BPf2sbXX\nsRIJEZEMXnzxRbZs2QLA0aNHKVeuHMWLFy/gqAqf9evXs2TJEgAuX77M1atXKVeuXAFHVTg1bdrU\nuKa3bNlCixYtCjiiwmnIkCGcO3cOSFvEWqtWrQKO6O8tMTGR6dOnM3/+fEqWLAnoWs5v2fWxtdex\nyaKxIRGRTD777DPCw8OxsbFh3Lhx1K5du6BDKnRu3LjB8OHDSUhIIDk5GW9vb90U5IOjR48yZcoU\noqOjsbW1pVy5csyYMYNRo0aRlJRExYoVmTx5MjY2NgUd6t9adv3s6enJggULKFasGCVKlODTTz/N\ntN5KrLN69WrmzJlD1apVsVgsmEwmpk6diq+vr67lfJJdH7/22muEhobm+TpWIiEiIiIiIlbT1CYR\nEREREbGaEgkREREREbGaEgkREREREbGaEgkREREREbGaEgkREREREbGaEgkREREREbGaEgkRERER\nEbGaEgkREREREbHa/wNNrBMyzXfTFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2af6cc34e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sum(axis=1).sort_values().plot(kind = 'barh', title = 'Yhteenlasketut pisteet suurimmasta pienimpään')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oliko raati arvosteluissaan yksimielinen?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seuraavaksi selvitän, kuinka yksimielisesti raati äänesti. Aloitan piirtämällä laatikkodiagrammin, joka paljastaa kappaleiden yksittäiset arvosanat, vaihteluvälin, mediaanin sekä ylä- ja alaneljänneksen.\n",
    "\n",
    "Voin nähdä kuvioista, että kappaleiden pisteytyksessä on suurta vaihtelua. Tämä viittaa siihen, että raati ei ole arvostelussaan yksimielinen. Lisäksi huomaan, että tiettyjen kappaleiden laatikot ovat muita pidempiä, eli nämä kappaleet saavat ristiriitaisia arvosteluja."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2af4946748>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAFgCAYAAAArRJ8VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2af4917ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(data=df.T, orient='h')\n",
    "ax = sns.swarmplot(data=df.T, orient='h', color='black')\n",
    "ax.set_title('Kappaleiden arvosanajakauma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pystyn jo kuvaa katsomalla arvailla, mikä kappaleista on saanut ristiriitaisimman vastaanoton, mutta haluan todeta asian laskennallisesti. Käytän tarkoitukseeni standardipoikkeamaa eli keskimääräistä poikkeamaa odotusarvosta.\n",
    "$$D(X)=\\sigma_x = \\sqrt{\\sigma^{2}_x}$$\n",
    "\n",
    "Kappaleen *Lone Digger* arvosanat poikkeavat 1,8 pistettä odotusarvosta. Levyraadin ääniharava on myös kierroksen ristiriitaisin kappale. Pienin keskihajonta on Asan kappaleella *Mä haluun olla hippi*, eli tuomaristo on yksimielisesti pitänyt sita raadin heikoimpiin kuuluvana esityksenä."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Keskihajonta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kappale</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Caravan Palace – Lone Digger</th>\n",
       "      <td>1.790351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tungevaag &amp; Raaban – Parade</th>\n",
       "      <td>1.463850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chubby Wolf – You are the Description that brings me out of Myself</th>\n",
       "      <td>1.286375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Halestorm – Here's to US</th>\n",
       "      <td>1.281740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supperheads – Easy</th>\n",
       "      <td>1.264205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avril Lavigne – When You're Gone</th>\n",
       "      <td>1.253566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dream Theater – The Silent Man</th>\n",
       "      <td>1.136515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elina Born – Miss Calculation</th>\n",
       "      <td>1.135205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tony Bennett &amp; Lady Gaga – It Don't Mean a Thing</th>\n",
       "      <td>1.086990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asa – Mä haluun olla hippi</th>\n",
       "      <td>1.019162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Keskihajonta\n",
       "Kappale                                                         \n",
       "Caravan Palace – Lone Digger                            1.790351\n",
       "Tungevaag & Raaban – Parade                             1.463850\n",
       "Chubby Wolf – You are the Description that brin...      1.286375\n",
       "Halestorm – Here's to US                                1.281740\n",
       "Supperheads – Easy                                      1.264205\n",
       "Avril Lavigne – When You're Gone                        1.253566\n",
       "Dream Theater – The Silent Man                          1.136515\n",
       "Elina Born – Miss Calculation                           1.135205\n",
       "Tony Bennett & Lady Gaga – It Don't Mean a Thing        1.086990\n",
       "Asa – Mä haluun olla hippi                              1.019162"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.std(axis=1).sort_values(ascending=False), columns=['Keskihajonta'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysoi arvostelijat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kuka on avokätisin raatilainen?\n",
    "Edellisessä tarkastelussa huomasin, että sama kappale saa vaihtelevia pisteitä eri arvostelijoilta. Seuraavaksi haluan selvittää, miten raadit jäsenet eroavat toisistaan. Aloitan laskemalla, mitä raatilaiset antavat keskimäärin arvosanaksi kappaleille."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2af482b710>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAFSCAYAAAAAZv1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwBgkP8BQr+xGzGxaa4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2af49a1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.mean().sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keskiarvoista näkee, että Kirmo on antaa keskimäärin parhaimmat pisteet ja Joe huonoimmat. Ero voi johtua siitä, että Kirmo piti tämän kierroksen kappaleista enemmän kuin Joe tai sitten he käyttävät arvosteluasteikkoa eri tavalla."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalisoi arvosanat\n",
    "\n",
    "Arvosana 3 voi olla yhdelle jäsenelle hyvä ja toiselle huono. Tuomaristo käyttää arvosteluasteikkoa eri tavoin, joten pisteet eivät välttämättä ole vertailukelpoisia. Haluan, että lukuja on helppo vertailla keskenään, joten normalisoin luvut. Normalisoinnin jälkeen jälkeen arvosanat ovat nollan ja yhden välillä.\n",
    "\n",
    "$$X' = \\frac{X - X_{min}}{X_{max} - X_{min}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Määritellään kaavan mukainen normalisointifunktio\n",
    "def normalize_values(x):\n",
    "    return( (x - x.min()) / (x.max() - x.min()) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sami</th>\n",
       "      <th>Joe</th>\n",
       "      <th>Mikko</th>\n",
       "      <th>Taneli</th>\n",
       "      <th>Arttu</th>\n",
       "      <th>Ville</th>\n",
       "      <th>Kirmo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kappale</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tungevaag &amp; Raaban – Parade</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asa – Mä haluun olla hippi</th>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chubby Wolf – You are the Description that brings me out of Myself</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supperheads – Easy</th>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dream Theater – The Silent Man</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tony Bennett &amp; Lady Gaga – It Don't Mean a Thing</th>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elina Born – Miss Calculation</th>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caravan Palace – Lone Digger</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avril Lavigne – When You're Gone</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Halestorm – Here's to US</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Sami       Joe  Mikko  \\\n",
       "Kappale                                                                       \n",
       "Tungevaag & Raaban – Parade                         1.0000  0.000000   0.50   \n",
       "Asa – Mä haluun olla hippi                          0.1250  0.500000   0.75   \n",
       "Chubby Wolf – You are the Description that brin...  0.0000  0.142857   0.00   \n",
       "Supperheads – Easy                                  0.3750  0.642857   1.00   \n",
       "Dream Theater – The Silent Man                      0.4375  0.500000   0.00   \n",
       "Tony Bennett & Lady Gaga – It Don't Mean a Thing    0.6250  0.214286   0.50   \n",
       "Elina Born – Miss Calculation                       0.3750  0.571429   0.75   \n",
       "Caravan Palace – Lone Digger                        0.0000  0.571429   1.00   \n",
       "Avril Lavigne – When You're Gone                    1.0000  0.642857   0.25   \n",
       "Halestorm – Here's to US                            0.7500  1.000000   0.75   \n",
       "\n",
       "                                                    Taneli     Arttu   Ville  \\\n",
       "Kappale                                                                        \n",
       "Tungevaag & Raaban – Parade                           0.25  0.428571  0.2500   \n",
       "Asa – Mä haluun olla hippi                            0.00  0.428571  0.2500   \n",
       "Chubby Wolf – You are the Description that brin...    0.50  1.000000  0.0000   \n",
       "Supperheads – Easy                                    0.50  0.428571  1.0000   \n",
       "Dream Theater – The Silent Man                        0.50  0.571429  0.8750   \n",
       "Tony Bennett & Lady Gaga – It Don't Mean a Thing      0.50  1.000000  0.1250   \n",
       "Elina Born – Miss Calculation                         0.75  0.142857  0.5000   \n",
       "Caravan Palace – Lone Digger                          1.00  1.000000  0.1875   \n",
       "Avril Lavigne – When You're Gone                      0.50  0.142857  0.6875   \n",
       "Halestorm – Here's to US                              0.75  0.000000  0.6250   \n",
       "\n",
       "                                                       Kirmo  \n",
       "Kappale                                                       \n",
       "Tungevaag & Raaban – Parade                         0.909091  \n",
       "Asa – Mä haluun olla hippi                          0.181818  \n",
       "Chubby Wolf – You are the Description that brin...  0.000000  \n",
       "Supperheads – Easy                                  0.545455  \n",
       "Dream Theater – The Silent Man                      0.636364  \n",
       "Tony Bennett & Lady Gaga – It Don't Mean a Thing    0.727273  \n",
       "Elina Born – Miss Calculation                       0.636364  \n",
       "Caravan Palace – Lone Digger                        1.000000  \n",
       "Avril Lavigne – When You're Gone                    0.818182  \n",
       "Halestorm – Here's to US                            0.727273  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm = df.apply(normalize_values)\n",
    "df_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalisoinnin jälkeen laskemme voittajan uudelleen. Jos kaikki raatilaiset olisivat käyttäneet arvosteluasteikkoa yhtä laajasti, niin kilpailun olisi voittanut kappale *Lone Digger*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2af47aff28>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAFSCAYAAABi9jmzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERq/0/bVTRt7VhbggAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2af47772b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_norm.T.sum().sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ketkä arvostelijat ovat samankaltaisia?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Levyraadin pistetaulukko kuvastaa raatilaisten mieltymyksiä, joten voin selvittää kenen musiikkimaku on lähimpänä omaani. Aion verrata yksitellen jokaisen raatilaisen antamia pisteitä omiini ja katsoa, kuinka paljon pisteissä on eroa. Raatilainen, jonka pisteissä on vähiten eroa omiini, on mahdollisesti musiikkimaultaan samantyylinen kuin minä.\n",
    "\n",
    "Kahden tasolla olevan pisteen etäisyyden voi laskea Pythagoraan lauseella. Samaa menettelyä voi soveltaa myös moniulotteisemmissa avaruuksissa. Menetelmän nimi on euklidinen mittaus:\n",
    "\n",
    "$$ \\mathrm{d}(\\mathbf{p},\\mathbf{q}) = \\sqrt{\\sum_{i=1}^n (q_i-p_i)^2} $$\n",
    "\n",
    "Raatimme arvosteli 10 kappaletta, joten arvioin yhden raatilaisen etäisyyden toiseen raatilaiseen 10 kappaleen perusteella. Sovellamme siis euklidista mittausta 10-ulotteisessa avaruudessa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <style  type=\"text/css\" >\n",
       "        \n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col0 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col1 {\n",
       "            \n",
       "                background-color:  #4897c4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col2 {\n",
       "            \n",
       "                background-color:  #02395a;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col3 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col4 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col5 {\n",
       "            \n",
       "                background-color:  #a5bddb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col6 {\n",
       "            \n",
       "                background-color:  #d9d8ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col0 {\n",
       "            \n",
       "                background-color:  #b0c2de;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col1 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col2 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col3 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col4 {\n",
       "            \n",
       "                background-color:  #167bb6;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col5 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col6 {\n",
       "            \n",
       "                background-color:  #8fb4d6;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col0 {\n",
       "            \n",
       "                background-color:  #187cb6;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col1 {\n",
       "            \n",
       "                background-color:  #c8cde4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col2 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col3 {\n",
       "            \n",
       "                background-color:  #7bacd1;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col4 {\n",
       "            \n",
       "                background-color:  #4697c4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col5 {\n",
       "            \n",
       "                background-color:  #509ac6;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col6 {\n",
       "            \n",
       "                background-color:  #9ab8d8;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col0 {\n",
       "            \n",
       "                background-color:  #84b0d3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col1 {\n",
       "            \n",
       "                background-color:  #f4edf6;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col2 {\n",
       "            \n",
       "                background-color:  #e7e3f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col3 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col4 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col5 {\n",
       "            \n",
       "                background-color:  #9fbad9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col6 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col0 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col1 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col2 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col3 {\n",
       "            \n",
       "                background-color:  #1278b4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col4 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col5 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col6 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col0 {\n",
       "            \n",
       "                background-color:  #e0dded;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col1 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col2 {\n",
       "            \n",
       "                background-color:  #4897c4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col3 {\n",
       "            \n",
       "                background-color:  #3f93c2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col4 {\n",
       "            \n",
       "                background-color:  #034e7b;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col5 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col6 {\n",
       "            \n",
       "                background-color:  #509ac6;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col0 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col1 {\n",
       "            \n",
       "                background-color:  #96b6d7;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col2 {\n",
       "            \n",
       "                background-color:  #dfddec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col3 {\n",
       "            \n",
       "                background-color:  #f7f0f7;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col4 {\n",
       "            \n",
       "                background-color:  #8eb3d5;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col5 {\n",
       "            \n",
       "                background-color:  #89b1d4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col6 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Sami\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Joe\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">Mikko\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">Taneli\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">Arttu\n",
       "                \n",
       "                <th class=\"col_heading level0 col5\">Ville\n",
       "                \n",
       "                <th class=\"col_heading level0 col6\">Kirmo\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row0\">\n",
       "                    Sami\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col0\" class=\"data row0 col0\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col1\" class=\"data row0 col1\">\n",
       "                    1.40019\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col2\" class=\"data row0 col2\">\n",
       "                    1.71505\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col3\" class=\"data row0 col3\">\n",
       "                    1.5013\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col4\" class=\"data row0 col4\">\n",
       "                    1.98273\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col5\" class=\"data row0 col5\">\n",
       "                    1.25468\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow0_col6\" class=\"data row0 col6\">\n",
       "                    1.09199\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row1\">\n",
       "                    Joe\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col0\" class=\"data row1 col0\">\n",
       "                    1.40019\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col1\" class=\"data row1 col1\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col2\" class=\"data row1 col2\">\n",
       "                    1.10657\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col3\" class=\"data row1 col3\">\n",
       "                    0.916821\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col4\" class=\"data row1 col4\">\n",
       "                    1.79142\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col5\" class=\"data row1 col5\">\n",
       "                    0.846568\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow1_col6\" class=\"data row1 col6\">\n",
       "                    1.23788\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row2\">\n",
       "                    Mikko\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col0\" class=\"data row2 col0\">\n",
       "                    1.71505\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col1\" class=\"data row2 col1\">\n",
       "                    1.10657\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col2\" class=\"data row2 col2\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col3\" class=\"data row2 col3\">\n",
       "                    1.19896\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col4\" class=\"data row2 col4\">\n",
       "                    1.71874\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col5\" class=\"data row2 col5\">\n",
       "                    1.46575\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow2_col6\" class=\"data row2 col6\">\n",
       "                    1.22052\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row3\">\n",
       "                    Taneli\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col0\" class=\"data row3 col0\">\n",
       "                    1.5013\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col1\" class=\"data row3 col1\">\n",
       "                    0.916821\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col2\" class=\"data row3 col2\">\n",
       "                    1.19896\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col3\" class=\"data row3 col3\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col4\" class=\"data row3 col4\">\n",
       "                    1.33583\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col5\" class=\"data row3 col5\">\n",
       "                    1.27169\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow3_col6\" class=\"data row3 col6\">\n",
       "                    0.951022\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row4\">\n",
       "                    Arttu\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col0\" class=\"data row4 col0\">\n",
       "                    1.98273\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col1\" class=\"data row4 col1\">\n",
       "                    1.79142\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col2\" class=\"data row4 col2\">\n",
       "                    1.71874\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col3\" class=\"data row4 col3\">\n",
       "                    1.33583\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col4\" class=\"data row4 col4\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col5\" class=\"data row4 col5\">\n",
       "                    1.92952\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow4_col6\" class=\"data row4 col6\">\n",
       "                    1.61635\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row5\">\n",
       "                    Ville\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col0\" class=\"data row5 col0\">\n",
       "                    1.25468\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col1\" class=\"data row5 col1\">\n",
       "                    0.846568\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col2\" class=\"data row5 col2\">\n",
       "                    1.46575\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col3\" class=\"data row5 col3\">\n",
       "                    1.27169\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col4\" class=\"data row5 col4\">\n",
       "                    1.92952\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col5\" class=\"data row5 col5\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow5_col6\" class=\"data row5 col6\">\n",
       "                    1.33103\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row6\">\n",
       "                    Kirmo\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col0\" class=\"data row6 col0\">\n",
       "                    1.09199\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col1\" class=\"data row6 col1\">\n",
       "                    1.23788\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col2\" class=\"data row6 col2\">\n",
       "                    1.22052\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col3\" class=\"data row6 col3\">\n",
       "                    0.951022\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col4\" class=\"data row6 col4\">\n",
       "                    1.61635\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col5\" class=\"data row6 col5\">\n",
       "                    1.33103\n",
       "                \n",
       "                <td id=\"T_91eba5f4_4470_11e6_aa9a_f0def1ce0ecbrow6_col6\" class=\"data row6 col6\">\n",
       "                    nan\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.formats.style.Styler at 0x7f2af4733fd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy-moodulin funktio linalg.norm, laskee euklidisen etäisyyden kahden vektorin välillä.\n",
    "# Haluan verrata datataulun jokaista saraketta jokaiseen sarakkeeseen, joten luon silmukka-\n",
    "# rakenteen kahdella sisäkkäisellä lamda-funktiolla.\n",
    "\n",
    "D = df_norm.apply(lambda c1: df_norm.apply(lambda c2: np.linalg.norm(c1 - c2)))\n",
    "\n",
    "# Ei verrata raatilaisen musiikkimakua häneen itseensä <-- arvoksi NaN\n",
    "np.fill_diagonal(D.values, np.nan)\n",
    "D.style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kahden nimen risteyskohta kertoo raatilaisten etäisyyden toisistaan. Suuri luku tarkoittaa suurta eroa annetuissa arvosanoissa. Huomaan, että nimeni sarakkeella pienin arvo on Joen kohdalla (1,10657), eli hänen musiikkimakunsa eroaa vähiten omastani. Vähiten samanlainen musiikkimaku minulla on Artun kanssa (1,71874).\n",
    "\n",
    "Yritän tehdä taulukosta helppolukuisemman korvaamalla euklidisen etäisyyden sijoitusnumerolla. Toisin sanoen haluan nähdä helposti, kenen musiikkimaku on minua ensimmäiseksi, toiseksi ja kolmanneksi lähimpänä."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <style  type=\"text/css\" >\n",
       "        \n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col0 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col1 {\n",
       "            \n",
       "                background-color:  #0567a2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col2 {\n",
       "            \n",
       "                background-color:  #0567a2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col3 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col4 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col5 {\n",
       "            \n",
       "                background-color:  #dbdaeb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col6 {\n",
       "            \n",
       "                background-color:  #dbdaeb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col0 {\n",
       "            \n",
       "                background-color:  #9cb9d9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col1 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col2 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col3 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col4 {\n",
       "            \n",
       "                background-color:  #4295c3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col5 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col6 {\n",
       "            \n",
       "                background-color:  #4295c3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col0 {\n",
       "            \n",
       "                background-color:  #0567a2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col1 {\n",
       "            \n",
       "                background-color:  #9cb9d9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col2 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col3 {\n",
       "            \n",
       "                background-color:  #9cb9d9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col4 {\n",
       "            \n",
       "                background-color:  #9cb9d9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col5 {\n",
       "            \n",
       "                background-color:  #0567a2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col6 {\n",
       "            \n",
       "                background-color:  #9cb9d9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col0 {\n",
       "            \n",
       "                background-color:  #4295c3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col1 {\n",
       "            \n",
       "                background-color:  #dbdaeb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col2 {\n",
       "            \n",
       "                background-color:  #dbdaeb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col3 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col4 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col5 {\n",
       "            \n",
       "                background-color:  #9cb9d9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col6 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col0 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col1 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col2 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col3 {\n",
       "            \n",
       "                background-color:  #0567a2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col4 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col5 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col6 {\n",
       "            \n",
       "                background-color:  #023858;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col0 {\n",
       "            \n",
       "                background-color:  #dbdaeb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col1 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col2 {\n",
       "            \n",
       "                background-color:  #4295c3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col3 {\n",
       "            \n",
       "                background-color:  #4295c3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col4 {\n",
       "            \n",
       "                background-color:  #0567a2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col5 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col6 {\n",
       "            \n",
       "                background-color:  #0567a2;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col0 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col1 {\n",
       "            \n",
       "                background-color:  #4295c3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col2 {\n",
       "            \n",
       "                background-color:  #9cb9d9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col3 {\n",
       "            \n",
       "                background-color:  #dbdaeb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col4 {\n",
       "            \n",
       "                background-color:  #dbdaeb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col5 {\n",
       "            \n",
       "                background-color:  #4295c3;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col6 {\n",
       "            \n",
       "                background-color:  #fff7fb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Sami\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Joe\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">Mikko\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">Taneli\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">Arttu\n",
       "                \n",
       "                <th class=\"col_heading level0 col5\">Ville\n",
       "                \n",
       "                <th class=\"col_heading level0 col6\">Kirmo\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row0\">\n",
       "                    Sami\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col0\" class=\"data row0 col0\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col1\" class=\"data row0 col1\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col2\" class=\"data row0 col2\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col3\" class=\"data row0 col3\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col4\" class=\"data row0 col4\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col5\" class=\"data row0 col5\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow0_col6\" class=\"data row0 col6\">\n",
       "                    2\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row1\">\n",
       "                    Joe\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col0\" class=\"data row1 col0\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col1\" class=\"data row1 col1\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col2\" class=\"data row1 col2\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col3\" class=\"data row1 col3\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col4\" class=\"data row1 col4\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col5\" class=\"data row1 col5\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow1_col6\" class=\"data row1 col6\">\n",
       "                    4\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row2\">\n",
       "                    Mikko\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col0\" class=\"data row2 col0\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col1\" class=\"data row2 col1\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col2\" class=\"data row2 col2\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col3\" class=\"data row2 col3\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col4\" class=\"data row2 col4\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col5\" class=\"data row2 col5\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow2_col6\" class=\"data row2 col6\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row3\">\n",
       "                    Taneli\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col0\" class=\"data row3 col0\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col1\" class=\"data row3 col1\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col2\" class=\"data row3 col2\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col3\" class=\"data row3 col3\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col4\" class=\"data row3 col4\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col5\" class=\"data row3 col5\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow3_col6\" class=\"data row3 col6\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row4\">\n",
       "                    Arttu\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col0\" class=\"data row4 col0\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col1\" class=\"data row4 col1\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col2\" class=\"data row4 col2\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col3\" class=\"data row4 col3\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col4\" class=\"data row4 col4\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col5\" class=\"data row4 col5\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow4_col6\" class=\"data row4 col6\">\n",
       "                    6\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row5\">\n",
       "                    Ville\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col0\" class=\"data row5 col0\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col1\" class=\"data row5 col1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col2\" class=\"data row5 col2\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col3\" class=\"data row5 col3\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col4\" class=\"data row5 col4\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col5\" class=\"data row5 col5\">\n",
       "                    nan\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow5_col6\" class=\"data row5 col6\">\n",
       "                    5\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecb\" class=\"row_heading level6 row6\">\n",
       "                    Kirmo\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col0\" class=\"data row6 col0\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col1\" class=\"data row6 col1\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col2\" class=\"data row6 col2\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col3\" class=\"data row6 col3\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col4\" class=\"data row6 col4\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col5\" class=\"data row6 col5\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_91f44c68_4470_11e6_aa9a_f0def1ce0ecbrow6_col6\" class=\"data row6 col6\">\n",
       "                    nan\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.formats.style.Styler at 0x7f2af470c668>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.rank().style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nyt voin etsiä nimeni pystysarakkeesta ja näen riveistä, kenen musiikkimaku on lähimpänä minua. Rivitasolla näen, kuinka mones olen muiden raatilaisten sijoituslistalla. Parhaimmillaan musiikkimakuni on kolmanneksi lähimpänä toisia raatilaisia.\n",
    "\n",
    "Taulukosta nähdään, että Joen musiikkimaku on lähimpänä useimpia raatilaisia, sillä hänellä on eniten 1-sijoituksia. Poikkeuksellisin musiikkimaku on Artulla, joka on kauimpana useimpia raatilaisia.\n",
    "\n",
    "Tasapaksuja arvosanoja jakava Joe on lähempänä muita raatilaisia kuin ääripään arvosanoja jakava Arttu. Tämä johtuu euklidisen mittaustavasta, jossa erotusten summa korotetaan neliöön: yksi iso ero voi rangaista enemmän kuin monta pientä."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Samankaltaisuuden arviointi kuvaajasta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kokeilen vielä euklidisen mittauksen lisäksi toista tapaa etsiä musiikkimaultaan lähin raatilainen. Sijoitan raatilaiset pisteinä kolmiulotteiseen kuvaajaan, josta etäisyyden voi arvioida silmämääräisesti. Tällä hetkellä yhdellä raatilaisella on 10 kappaletta tai ominaisuutta, jotka joudun typistämään kolmeksi. Tarkoitukseni on niputtaa kymmenen tasoa kolmeksi tasoksi siten, että mahdollisimman vähän tietoa menisi hukkaan.\n",
    "\n",
    "### Pääkomponenttianalyysi\n",
    "\n",
    "Vähennän dimensioita pääkomponenttianalyysillä, joka etsii tasoja, joiden varianssi on mahdollisimman suuri. Toisin sanoen pääkomponenttianalyysi pyrkii keksimään ne tasot, jotka ovat kaikkein ilmaisuvoimaisimpia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAUDBAcHCAgICAcGCAgGBwgIBwcHBwgICAgICAcICAgI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\nAAAAAAAAAAAAAAAAAAAB/9k=\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/_UVHneBUBW0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f2af4735390>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"_UVHneBUBW0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Käytän SciKit Learnin funktiota PCA, joka osaa redusoida dimensioita\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "X = pca.fit_transform(df_norm.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAI8CAYAAADWeOIKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhRBCCCGEzZJkVQghhBBC2Kz/B80yY//vmbbPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2af0fc7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Piirrän kolmiulotteisen kuvaajan Matplotlib-kirjastolla\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure(figsize=(12,10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Lisään kuvaajan redusoidut dimensiot\n",
    "ax.scatter(X[:,0], X[:,1], X[:,2])\n",
    "\n",
    "# Nimeän pisteet raatilaisten mukaan\n",
    "for i, name in enumerate(df_norm.columns):\n",
    "    ax.text(X[i,0], X[i,1], X[i,2], name)\n",
    "\n",
    "# Hyvä kuvakulma\n",
    "ax.azim = 135"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}