NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Entropie textu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Každý cyklus\n",
    "Spočítat $P(i,j)$ - pravděpodobnost výskytu dvojice $i,j$ v textu  \n",
    "Spočítat $P(j|i)$ - pravděpodobnost výskytu $j$, když se vyskytlo $i$  \n",
    "Spočítat $H(J|I) = - \\sum_{i \\in I, j \\in J}P(i,j)log_2 P(j|i)$  \n",
    "Spočítat $PX(P(J|I)) = 2^{H(J|I)}$\n",
    "\n",
    "### Poškozování textu: znaky - opakovat 10×  \n",
    "Vytvoř množinu znaků (tečku beru také jako znak znak!)  \n",
    "10%, 5%, 1%, 0.1%, 0.01%, 0.001%  \n",
    "Vypočti min, max, střední hodnotu entropie  \n",
    "\n",
    "### Poškozování textu: slova - opakovat 10×  \n",
    "Vytvoř množinu slov  \n",
    "10%, 5%, 1%, 0.1%, 0.01%, 0.001%  \n",
    "Vypočti min, max, střední hodnotu entropie  \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from enum import IntEnum\n",
    "import sys\n",
    "import math\n",
    "from tqdm import tqdm, tnrange, tqdm_notebook\n",
    "import time\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Konstanty\n",
    "Nastavení hlavních parametrů"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FILES = [\"TEXTEN1.txt\", \"TEXTCZ1.txt\"]\n",
    "MESSUPPROB = [0,0.00001,0.0001,0.001,0.01,0.05,0.1]\n",
    "REPEAT = 10\n",
    "MESSUPTYPE = IntEnum('MESSUPTYPE', 'WORD CHAR')\n",
    "DEBUG = False\n",
    "SEED = 84840014\n",
    "INFO_INDEX = [\"Počet slov\",\"Počet znaků\",\"Velikost slovníku\",\"Velikost abecedy\",\"Průměrná délka slova v textu\",\"Průměrná délka slova ve slovníku\",\"Průměrná četnost slov\",\"Maximální četnost slov\",\"Druhá maximální četnost slov\",\"Třetí maximální četnost slov\",\"Nejnižší četnost slov\",\"Počet slov s nejnižší četností\",\"Počet slov délky 1 v textu\",\"Počet slov délky 1 ve slovníku\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Načtení dat ze souboru\n",
    "Načtení dat do listu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data(file):\n",
    "    with open(file, \"r\") as f:\n",
    "        data = np.array([x.replace(\"\\n\", \"\") for x in f.readlines()])\n",
    "        \n",
    "    if(DEBUG):\n",
    "        print(\"Metoda: get_data\")\n",
    "        print(\"Soubor: \",file)\n",
    "        print(\"Výsledek: \",data)\n",
    "        print()\n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poškození dat\n",
    "V následujících metodách jsou poškozována data.\n",
    "Nejprve se určí množina náhradníků (slov / znaků) a poté jsou data postupně procházena a s nastavenou pravděpodobností dojde ke změně."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def char_mess_up(seed, mess_prob, file):\n",
    "    original_data = get_data(file)\n",
    "    original_chars = np.unique(np.concatenate([list(x) for x in original_data]))\n",
    "    rnd = random.Random()\n",
    "    rnd.seed(seed)\n",
    "    \n",
    "    mess_up_data = original_data.copy()\n",
    "    for i in range(mess_up_data.size):\n",
    "        l = list(mess_up_data[i])\n",
    "        for j in range(len(l)):\n",
    "            if rnd.random() <= mess_prob:\n",
    "                l[j] = original_chars[rnd.randrange(original_chars.size)]\n",
    "        mess_up_data[i] = ''.join(l)\n",
    "    \n",
    "    \n",
    "    if(DEBUG):\n",
    "        print(\"Metoda: char_mess_up\")\n",
    "        print(\"Seed: \",seed)\n",
    "        print(\"Pravděpodobnost změny: \",mess_prob)\n",
    "        print(\"Soubor: \", file)\n",
    "        print(\"Původní data: \", original_data)\n",
    "        print(\"Znaky: \", original_chars)\n",
    "        print(\"Výsledek: \", mess_up_data)\n",
    "        print()\n",
    "        \n",
    "    \n",
    "    return mess_up_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def word_mess_up(seed, mess_prob, file):\n",
    "    original_data = get_data(file)\n",
    "    original_words = np.unique(original_data)\n",
    "    rnd = random.Random()\n",
    "    rnd.seed(seed)\n",
    "    \n",
    "    mess_up_data = original_data.copy()\n",
    "    for i in range(mess_up_data.size):\n",
    "        if rnd.random() <= mess_prob:\n",
    "            mess_up_data[i] = original_words[rnd.randrange(original_words.size)]\n",
    "            \n",
    "    if(DEBUG):\n",
    "        print(\"Metoda: word_mess_up\")\n",
    "        print(\"Seed: \",seed)\n",
    "        print(\"Pravděpodobnost změny: \",mess_prob)\n",
    "        print(\"Soubor: \", file)\n",
    "        print(\"Původní data: \", original_data)\n",
    "        print(\"Slova: \", original_words)\n",
    "        print(\"Výsledek: \", mess_up_data)\n",
    "        print()\n",
    "    \n",
    "    return mess_up_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Výpočet entropie\n",
    "Pro data (list slov) spočítáme entropii.\n",
    "Nejprve spočítáme četnosti slov $c(i)$ (odečteme 1 od četnosti posledního slova, protože netvoří dvojici), které budeme ukládat do slovníku **i:c(i)**.\n",
    "Poté spočítáme četnosti dvojic $c(i,j)$, které uložíme také do slovníku **(i,j):c(i,j)**.  \n",
    "$P(i,j) = \\dfrac{c(i,j)}{N-1}$, kde $N$ je velikost dat.  \n",
    "$P(j|i) = \\dfrac{c(i,j)}{\\sum_j c(i,j)} = \\dfrac{c(i,j)}{c(i)}$  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def entropy(data):\n",
    "    \n",
    "    data = np.concatenate(([\"<s>\"],data))\n",
    "    # Odečíst 1 od četnosti posledního slova, protože to už nemá dvojici a já chci z toho počítat psti\n",
    "    words, counts = np.unique(data, return_counts=True)\n",
    "    counts_words = dict(zip(words,counts))\n",
    "    \n",
    "    counts_words[data[-1]] -= 1   \n",
    "    \n",
    "    counts_pairs = dict()\n",
    "    for i in range(data.size - 1):\n",
    "        if (data[i],data[i+1]) in counts_pairs:\n",
    "            counts_pairs[(data[i],data[i+1])] += 1\n",
    "        else:\n",
    "            counts_pairs[(data[i],data[i+1])] = 1\n",
    "    \n",
    "    H = 0\n",
    "    N = data.size\n",
    "    for pair in counts_pairs:\n",
    "        H -= counts_pairs[pair]/(N-1)*math.log2(counts_pairs[pair]/counts_words[pair[0]])\n",
    "    \n",
    "    PX = math.pow(2,H)\n",
    "    \n",
    "    return (H,PX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zobrazení výsledků\n",
    "Zobrazení výsledků do tabulek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def show_result(results, title):\n",
    "    for i in range(len(FILES)):\n",
    "        for j in range(len(MESSUPTYPE)):\n",
    "            df1 = pd.DataFrame(results[i,j,:,:],index=MESSUPPROB, )\n",
    "            mean = df1.mean(axis=1)\n",
    "            df1['Min'] = pd.Series(df1.min(axis=1), index=df1.index)\n",
    "            df1['Max'] = pd.Series(df1.max(axis=1), index=df1.index)\n",
    "            df1['Avg'] = pd.Series(mean, index=df1.index)\n",
    "            display(HTML(\"<h2>\" + FILES[i] + \" - \" + str(MESSUPTYPE(j+1)).split(\".\")[1] + \"</h2>\"))\n",
    "            display(df1)\n",
    "            plt.plot(mean.values, label = str(FILES[i] + \" - \" + str(MESSUPTYPE(j+1)).split(\".\")[1]))\n",
    "            plt.plot(mean.values, 'k|')\n",
    "            \n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    plt.xlabel('Mess up')\n",
    "    plt.ylabel(title)\n",
    "    plt.title('Graf '+title.lower())\n",
    "    plt.xticks(list(range(mean.values.size)),[str(x) for x in MESSUPPROB])\n",
    "    plt.show()\n",
    "    \n",
    "    plt.figure()\n",
    "    for i in range(len(FILES)):\n",
    "        for j in range(len(MESSUPTYPE)):\n",
    "            df1 = pd.DataFrame(results[i,j,:,:],index=MESSUPPROB, )\n",
    "            mean = df1.mean(axis=1)\n",
    "            plt.plot(mean, label = str(FILES[i] + \" - \" + str(MESSUPTYPE(j+1)).split(\".\")[1]))\n",
    "            plt.plot(mean, 'k|')\n",
    "            \n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    plt.xlabel('Mess up')\n",
    "    plt.ylabel(title)\n",
    "    plt.title('Graf '+title.lower())\n",
    "    plt.show()\n",
    "    \n",
    "    plt.figure()\n",
    "    for i in range(len(FILES)):\n",
    "        for j in range(len(MESSUPTYPE)):\n",
    "            df1 = pd.DataFrame(results[i,j,:,:],index=MESSUPPROB, )\n",
    "            mean = df1.mean(axis=1)\n",
    "            plt.semilogx(mean, label = str(FILES[i] + \" - \" + str(MESSUPTYPE(j+1)).split(\".\")[1]))\n",
    "            plt.semilogx(mean, 'k|')\n",
    "            \n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    plt.xlabel('Mess up')\n",
    "    plt.ylabel(title)\n",
    "    plt.title('Graf '+title.lower())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Infromace o textech\n",
    "Zjištění informací o textu, jako velikost textu, velikost slovníku, abeceda,....\n",
    "Obsahuje i metodu pro zobrazení v tabulce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_info(data):\n",
    "#     results = np.zeros(shape=(len(INFO_INDEX),len(FILES)), dtype=np.dtype('U16'))\n",
    "    original_data = data\n",
    "    results = np.array([original_data.size], dtype=np.dtype('U16'))\n",
    "    \n",
    "    results = np.append(results, np.concatenate([list(x) for x in original_data]).size)\n",
    "    \n",
    "    \n",
    "    original_words, counts_words = np.unique(original_data, return_counts=True)\n",
    "    results = np.append(results, original_words.size)\n",
    "    \n",
    "    originalChars = np.unique(np.concatenate([list(x) for x in original_data]))\n",
    "    results = np.append(results, originalChars.size)\n",
    "    \n",
    "    results = np.append(results, \"{0:.2f}\".format(np.concatenate([list(x) for x in original_data]).size/original_data.size))\n",
    "    \n",
    "    results = np.append(results, \"{0:.2f}\".format(np.concatenate([list(x) for x in original_words]).size/original_words.size))\n",
    "    \n",
    "    results = np.append(results, \"{0:.2f}\".format(np.sum(counts_words)/original_words.size))\n",
    "    \n",
    "    c = counts_words.copy()\n",
    "    w = original_words.copy()\n",
    "    w = np.delete(w,np.argmax(counts_words))\n",
    "    c = np.delete(c,np.argmax(counts_words))\n",
    "    c2 = c.copy()\n",
    "    w2 = w.copy()\n",
    "    w2 = np.delete(w,np.argmax(c))\n",
    "    c2 = np.delete(c,np.argmax(c))\n",
    "    results = np.append(results, str(np.max(counts_words)) + \" [\" + original_words[np.argmax(counts_words)] + \"]\")\n",
    "    results = np.append(results, str(np.max(c)) + \" [\" + w[np.argmax(c)] + \"]\")\n",
    "    results = np.append(results, str(np.max(c2)) + \" [\" + w2[np.argmax(c2)] + \"]\")\n",
    "    \n",
    "    results = np.append(results, np.min(counts_words))\n",
    "    \n",
    "    results = np.append(results, sum([1 for x in counts_words if x == np.min(counts_words)]))\n",
    "    \n",
    "    results = np.append(results, sum([1 for x in original_data if len(x)==1]))\n",
    "    \n",
    "    results = np.append(results, sum([1 for x in original_words if len(x)==1]))\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_info(info):\n",
    "    for i in range(len(FILES)):\n",
    "        for j in range(len(MESSUPTYPE)):\n",
    "            df1 = pd.DataFrame(info[i,j,:,0,:].transpose(), index=INFO_INDEX, columns= MESSUPPROB)\n",
    "            df1.style.set_properties(**{'text-align': 'right'})\n",
    "            display(HTML(\"<h2>\" + FILES[i] + \" - \" + str(MESSUPTYPE(j+1)).split(\".\")[1] + \"</h2>\"))\n",
    "            display(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main\n",
    "### Hlavní cyklus.\n",
    "Nejprve se nastaví generátor náhodných čísel, který bude generovat semínka pro ostatní generátory (kvůli opakovatelnosti výsledků).\n",
    "Poté se projdou všechny soubory, všechny typy poškození dat se všemi pravděpodobnostmi.\n",
    "Nakonec je zavolána funkce pro zobrazení výsledků."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time:  31 m  50 s\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<h1>Entropie</h1>"
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      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<h2>TEXTEN1.txt - WORD</h2>"
      ],
      "text/plain": [
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       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>8</th>\n",
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       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Avg</th>\n",
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       "      <td>5.380644</td>\n",
       "      <td>5.381721</td>\n",
       "      <td>5.381419</td>\n",
       "      <td>5.382026</td>\n",
       "      <td>5.379853</td>\n",
       "      <td>5.378787</td>\n",
       "      <td>5.378512</td>\n",
       "      <td>5.378512</td>\n",
       "      <td>5.383784</td>\n",
       "      <td>5.380597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>5.454999</td>\n",
       "      <td>5.455194</td>\n",
       "      <td>5.460025</td>\n",
       "      <td>5.461863</td>\n",
       "      <td>5.457107</td>\n",
       "      <td>5.453780</td>\n",
       "      <td>5.457496</td>\n",
       "      <td>5.457479</td>\n",
       "      <td>5.453946</td>\n",
       "      <td>5.459724</td>\n",
       "      <td>5.453780</td>\n",
       "      <td>5.461863</td>\n",
       "      <td>5.457161</td>\n",
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       "  </tbody>\n",
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       "                0         1         2         3         4         5         6  \\\n",
       "0.00000  5.287422  5.287422  5.287422  5.287422  5.287422  5.287422  5.287422   \n",
       "0.00001  5.287422  5.287421  5.287459  5.287444  5.287457  5.287445  5.287422   \n",
       "0.00010  5.287632  5.287580  5.287668  5.287582  5.287764  5.287730  5.287773   \n",
       "0.00100  5.289558  5.288946  5.288968  5.289306  5.289287  5.289065  5.289621   \n",
       "0.01000  5.307556  5.306819  5.305664  5.305451  5.308209  5.306682  5.307719   \n",
       "0.05000  5.379469  5.383784  5.379752  5.380644  5.381721  5.381419  5.382026   \n",
       "0.10000  5.454999  5.455194  5.460025  5.461863  5.457107  5.453780  5.457496   \n",
       "\n",
       "                7         8         9       Min       Max       Avg  \n",
       "0.00000  5.287422  5.287422  5.287422  5.287422  5.287422  5.287422  \n",
       "0.00001  5.287402  5.287466  5.287422  5.287402  5.287466  5.287436  \n",
       "0.00010  5.287572  5.287868  5.287889  5.287572  5.287889  5.287706  \n",
       "0.00100  5.288975  5.289274  5.288640  5.288640  5.289621  5.289164  \n",
       "0.01000  5.307791  5.306078  5.307579  5.305451  5.308209  5.306955  \n",
       "0.05000  5.379853  5.378787  5.378512  5.378512  5.383784  5.380597  \n",
       "0.10000  5.457479  5.453946  5.459724  5.453780  5.461863  5.457161  "
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       "      <td>5.287422</td>\n",
       "      <td>5.287422</td>\n",
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       "      <td>5.287422</td>\n",
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       "    <tr>\n",
       "      <th>0.00001</th>\n",
       "      <td>5.287418</td>\n",
       "      <td>5.287372</td>\n",
       "      <td>5.287365</td>\n",
       "      <td>5.287425</td>\n",
       "      <td>5.287391</td>\n",
       "      <td>5.287331</td>\n",
       "      <td>5.287459</td>\n",
       "      <td>5.287376</td>\n",
       "      <td>5.287527</td>\n",
       "      <td>5.287317</td>\n",
       "      <td>5.287317</td>\n",
       "      <td>5.287527</td>\n",
       "      <td>5.287398</td>\n",
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       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>5.287520</td>\n",
       "      <td>5.286820</td>\n",
       "      <td>5.287389</td>\n",
       "      <td>5.287166</td>\n",
       "      <td>5.287108</td>\n",
       "      <td>5.287280</td>\n",
       "      <td>5.286887</td>\n",
       "      <td>5.287058</td>\n",
       "      <td>5.287078</td>\n",
       "      <td>5.287291</td>\n",
       "      <td>5.286820</td>\n",
       "      <td>5.287520</td>\n",
       "      <td>5.287160</td>\n",
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       "    <tr>\n",
       "      <th>0.00100</th>\n",
       "      <td>5.283008</td>\n",
       "      <td>5.284173</td>\n",
       "      <td>5.282471</td>\n",
       "      <td>5.283999</td>\n",
       "      <td>5.282727</td>\n",
       "      <td>5.284209</td>\n",
       "      <td>5.283638</td>\n",
       "      <td>5.284170</td>\n",
       "      <td>5.282833</td>\n",
       "      <td>5.283369</td>\n",
       "      <td>5.282471</td>\n",
       "      <td>5.284209</td>\n",
       "      <td>5.283460</td>\n",
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       "    <tr>\n",
       "      <th>0.01000</th>\n",
       "      <td>5.253860</td>\n",
       "      <td>5.249285</td>\n",
       "      <td>5.247749</td>\n",
       "      <td>5.249666</td>\n",
       "      <td>5.251462</td>\n",
       "      <td>5.249262</td>\n",
       "      <td>5.250257</td>\n",
       "      <td>5.250705</td>\n",
       "      <td>5.248097</td>\n",
       "      <td>5.249827</td>\n",
       "      <td>5.247749</td>\n",
       "      <td>5.253860</td>\n",
       "      <td>5.250017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05000</th>\n",
       "      <td>5.055594</td>\n",
       "      <td>5.055777</td>\n",
       "      <td>5.058352</td>\n",
       "      <td>5.056404</td>\n",
       "      <td>5.050547</td>\n",
       "      <td>5.052118</td>\n",
       "      <td>5.056965</td>\n",
       "      <td>5.058161</td>\n",
       "      <td>5.054394</td>\n",
       "      <td>5.059821</td>\n",
       "      <td>5.050547</td>\n",
       "      <td>5.059821</td>\n",
       "      <td>5.055813</td>\n",
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       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>4.730929</td>\n",
       "      <td>4.728755</td>\n",
       "      <td>4.729791</td>\n",
       "      <td>4.731405</td>\n",
       "      <td>4.730908</td>\n",
       "      <td>4.727018</td>\n",
       "      <td>4.734554</td>\n",
       "      <td>4.728783</td>\n",
       "      <td>4.726608</td>\n",
       "      <td>4.739884</td>\n",
       "      <td>4.726608</td>\n",
       "      <td>4.739884</td>\n",
       "      <td>4.730863</td>\n",
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       "                0         1         2         3         4         5         6  \\\n",
       "0.00000  5.287422  5.287422  5.287422  5.287422  5.287422  5.287422  5.287422   \n",
       "0.00001  5.287418  5.287372  5.287365  5.287425  5.287391  5.287331  5.287459   \n",
       "0.00010  5.287520  5.286820  5.287389  5.287166  5.287108  5.287280  5.286887   \n",
       "0.00100  5.283008  5.284173  5.282471  5.283999  5.282727  5.284209  5.283638   \n",
       "0.01000  5.253860  5.249285  5.247749  5.249666  5.251462  5.249262  5.250257   \n",
       "0.05000  5.055594  5.055777  5.058352  5.056404  5.050547  5.052118  5.056965   \n",
       "0.10000  4.730929  4.728755  4.729791  4.731405  4.730908  4.727018  4.734554   \n",
       "\n",
       "                7         8         9       Min       Max       Avg  \n",
       "0.00000  5.287422  5.287422  5.287422  5.287422  5.287422  5.287422  \n",
       "0.00001  5.287376  5.287527  5.287317  5.287317  5.287527  5.287398  \n",
       "0.00010  5.287058  5.287078  5.287291  5.286820  5.287520  5.287160  \n",
       "0.00100  5.284170  5.282833  5.283369  5.282471  5.284209  5.283460  \n",
       "0.01000  5.250705  5.248097  5.249827  5.247749  5.253860  5.250017  \n",
       "0.05000  5.058161  5.054394  5.059821  5.050547  5.059821  5.055813  \n",
       "0.10000  4.728783  4.726608  4.739884  4.726608  4.739884  4.730863  "
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       "      <td>4.747822</td>\n",
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       "      <td>4.747822</td>\n",
       "      <td>4.747822</td>\n",
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       "      <td>4.747805</td>\n",
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       "      <td>4.747770</td>\n",
       "      <td>4.747787</td>\n",
       "      <td>4.747802</td>\n",
       "      <td>4.747816</td>\n",
       "      <td>4.747822</td>\n",
       "      <td>4.747822</td>\n",
       "      <td>4.747851</td>\n",
       "      <td>4.747770</td>\n",
       "      <td>4.747851</td>\n",
       "      <td>4.747810</td>\n",
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       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>4.747704</td>\n",
       "      <td>4.747651</td>\n",
       "      <td>4.747690</td>\n",
       "      <td>4.747697</td>\n",
       "      <td>4.747893</td>\n",
       "      <td>4.747646</td>\n",
       "      <td>4.747734</td>\n",
       "      <td>4.747838</td>\n",
       "      <td>4.747679</td>\n",
       "      <td>4.747719</td>\n",
       "      <td>4.747646</td>\n",
       "      <td>4.747893</td>\n",
       "      <td>4.747725</td>\n",
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       "      <th>0.00100</th>\n",
       "      <td>4.746536</td>\n",
       "      <td>4.746806</td>\n",
       "      <td>4.747159</td>\n",
       "      <td>4.747070</td>\n",
       "      <td>4.746955</td>\n",
       "      <td>4.747206</td>\n",
       "      <td>4.747153</td>\n",
       "      <td>4.746776</td>\n",
       "      <td>4.746837</td>\n",
       "      <td>4.746534</td>\n",
       "      <td>4.746534</td>\n",
       "      <td>4.747206</td>\n",
       "      <td>4.746903</td>\n",
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       "    <tr>\n",
       "      <th>0.01000</th>\n",
       "      <td>4.739969</td>\n",
       "      <td>4.737798</td>\n",
       "      <td>4.738953</td>\n",
       "      <td>4.738956</td>\n",
       "      <td>4.737954</td>\n",
       "      <td>4.738655</td>\n",
       "      <td>4.740109</td>\n",
       "      <td>4.738209</td>\n",
       "      <td>4.739869</td>\n",
       "      <td>4.738138</td>\n",
       "      <td>4.737798</td>\n",
       "      <td>4.740109</td>\n",
       "      <td>4.738861</td>\n",
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       "    <tr>\n",
       "      <th>0.05000</th>\n",
       "      <td>4.695905</td>\n",
       "      <td>4.696378</td>\n",
       "      <td>4.699754</td>\n",
       "      <td>4.697576</td>\n",
       "      <td>4.695527</td>\n",
       "      <td>4.701062</td>\n",
       "      <td>4.699564</td>\n",
       "      <td>4.698084</td>\n",
       "      <td>4.695968</td>\n",
       "      <td>4.699761</td>\n",
       "      <td>4.695527</td>\n",
       "      <td>4.701062</td>\n",
       "      <td>4.697958</td>\n",
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       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>4.635429</td>\n",
       "      <td>4.630678</td>\n",
       "      <td>4.636439</td>\n",
       "      <td>4.639361</td>\n",
       "      <td>4.641940</td>\n",
       "      <td>4.638335</td>\n",
       "      <td>4.632059</td>\n",
       "      <td>4.637591</td>\n",
       "      <td>4.634345</td>\n",
       "      <td>4.640284</td>\n",
       "      <td>4.630678</td>\n",
       "      <td>4.641940</td>\n",
       "      <td>4.636646</td>\n",
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       "0.00000  4.747822  4.747822  4.747822  4.747822  4.747822  4.747822  4.747822   \n",
       "0.00001  4.747834  4.747805  4.747795  4.747770  4.747787  4.747802  4.747816   \n",
       "0.00010  4.747704  4.747651  4.747690  4.747697  4.747893  4.747646  4.747734   \n",
       "0.00100  4.746536  4.746806  4.747159  4.747070  4.746955  4.747206  4.747153   \n",
       "0.01000  4.739969  4.737798  4.738953  4.738956  4.737954  4.738655  4.740109   \n",
       "0.05000  4.695905  4.696378  4.699754  4.697576  4.695527  4.701062  4.699564   \n",
       "0.10000  4.635429  4.630678  4.636439  4.639361  4.641940  4.638335  4.632059   \n",
       "\n",
       "                7         8         9       Min       Max       Avg  \n",
       "0.00000  4.747822  4.747822  4.747822  4.747822  4.747822  4.747822  \n",
       "0.00001  4.747822  4.747822  4.747851  4.747770  4.747851  4.747810  \n",
       "0.00010  4.747838  4.747679  4.747719  4.747646  4.747893  4.747725  \n",
       "0.00100  4.746776  4.746837  4.746534  4.746534  4.747206  4.746903  \n",
       "0.01000  4.738209  4.739869  4.738138  4.737798  4.740109  4.738861  \n",
       "0.05000  4.698084  4.695968  4.699761  4.695527  4.701062  4.697958  \n",
       "0.10000  4.637591  4.634345  4.640284  4.630678  4.641940  4.636646  "
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       "      <td>4.747719</td>\n",
       "      <td>4.747754</td>\n",
       "      <td>4.747619</td>\n",
       "      <td>4.747602</td>\n",
       "      <td>4.747812</td>\n",
       "      <td>4.747755</td>\n",
       "      <td>4.747642</td>\n",
       "      <td>4.747560</td>\n",
       "      <td>4.747560</td>\n",
       "      <td>4.747824</td>\n",
       "      <td>4.747703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>4.746541</td>\n",
       "      <td>4.746859</td>\n",
       "      <td>4.746767</td>\n",
       "      <td>4.746841</td>\n",
       "      <td>4.746906</td>\n",
       "      <td>4.746984</td>\n",
       "      <td>4.746922</td>\n",
       "      <td>4.746957</td>\n",
       "      <td>4.747032</td>\n",
       "      <td>4.746926</td>\n",
       "      <td>4.746541</td>\n",
       "      <td>4.747032</td>\n",
       "      <td>4.746874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00100</th>\n",
       "      <td>4.738824</td>\n",
       "      <td>4.739129</td>\n",
       "      <td>4.738154</td>\n",
       "      <td>4.738725</td>\n",
       "      <td>4.739303</td>\n",
       "      <td>4.738997</td>\n",
       "      <td>4.738805</td>\n",
       "      <td>4.738208</td>\n",
       "      <td>4.738271</td>\n",
       "      <td>4.739018</td>\n",
       "      <td>4.738154</td>\n",
       "      <td>4.739303</td>\n",
       "      <td>4.738744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01000</th>\n",
       "      <td>4.658265</td>\n",
       "      <td>4.660046</td>\n",
       "      <td>4.658367</td>\n",
       "      <td>4.658615</td>\n",
       "      <td>4.658528</td>\n",
       "      <td>4.659160</td>\n",
       "      <td>4.659805</td>\n",
       "      <td>4.658697</td>\n",
       "      <td>4.658641</td>\n",
       "      <td>4.657391</td>\n",
       "      <td>4.657391</td>\n",
       "      <td>4.660046</td>\n",
       "      <td>4.658752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05000</th>\n",
       "      <td>4.338760</td>\n",
       "      <td>4.338600</td>\n",
       "      <td>4.336433</td>\n",
       "      <td>4.335633</td>\n",
       "      <td>4.337416</td>\n",
       "      <td>4.334084</td>\n",
       "      <td>4.336756</td>\n",
       "      <td>4.335966</td>\n",
       "      <td>4.337332</td>\n",
       "      <td>4.337688</td>\n",
       "      <td>4.334084</td>\n",
       "      <td>4.338760</td>\n",
       "      <td>4.336867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>3.999596</td>\n",
       "      <td>4.009205</td>\n",
       "      <td>4.011115</td>\n",
       "      <td>4.007893</td>\n",
       "      <td>4.015659</td>\n",
       "      <td>4.001416</td>\n",
       "      <td>4.007722</td>\n",
       "      <td>4.008888</td>\n",
       "      <td>4.008004</td>\n",
       "      <td>4.010868</td>\n",
       "      <td>3.999596</td>\n",
       "      <td>4.015659</td>\n",
       "      <td>4.008037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0         1         2         3         4         5         6  \\\n",
       "0.00000  4.747822  4.747822  4.747822  4.747822  4.747822  4.747822  4.747822   \n",
       "0.00001  4.747824  4.747747  4.747719  4.747754  4.747619  4.747602  4.747812   \n",
       "0.00010  4.746541  4.746859  4.746767  4.746841  4.746906  4.746984  4.746922   \n",
       "0.00100  4.738824  4.739129  4.738154  4.738725  4.739303  4.738997  4.738805   \n",
       "0.01000  4.658265  4.660046  4.658367  4.658615  4.658528  4.659160  4.659805   \n",
       "0.05000  4.338760  4.338600  4.336433  4.335633  4.337416  4.334084  4.336756   \n",
       "0.10000  3.999596  4.009205  4.011115  4.007893  4.015659  4.001416  4.007722   \n",
       "\n",
       "                7         8         9       Min       Max       Avg  \n",
       "0.00000  4.747822  4.747822  4.747822  4.747822  4.747822  4.747822  \n",
       "0.00001  4.747755  4.747642  4.747560  4.747560  4.747824  4.747703  \n",
       "0.00010  4.746957  4.747032  4.746926  4.746541  4.747032  4.746874  \n",
       "0.00100  4.738208  4.738271  4.739018  4.738154  4.739303  4.738744  \n",
       "0.01000  4.658697  4.658641  4.657391  4.657391  4.660046  4.658752  \n",
       "0.05000  4.335966  4.337332  4.337688  4.334084  4.338760  4.336867  \n",
       "0.10000  4.008888  4.008004  4.010868  3.999596  4.015659  4.008037  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HAFdXV7p06cJrr71G06ZN6dmzZ6H2z8fHh8qVK1sEF9u3b6d169YAtGrVitjYWIYOHWrO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\nIiIiImHx/2AkoX8sHJNAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15398c3a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AgQOpXr06b7/9NgAODg706NGDV199lWbNmtG3b99i7Z+Pjw9Vq1Y1Cy62b99O27ZtAWjT\npg2xsbGMGDHCdM3Hx4cqVaoAcOTIEQCaNWuWZxt16tTB2dk514XXCQkJAFy/fp1169axdu1amjZt\nSsOGDa26D39/f77//nvOnDmDv7+/VWVzI8GFEEIIIe4Zx45BUFDJthEXB4GBJdtGbl555RV69uxp\nkd64cWOLtAoVKuDi4oJSik6dOllMqSnp9sE4ejFv3jzCwsJYt27dHbdvLS8vLxYtWkSfPn1o164d\nBw4cYNOmTXmOApSEtm3bsmnTJtMi+V27dpmCm7Zt25p22kpNTWXfvn0MGDDAVDYpKQkwTjPLj5OT\nk8VuWTdu3MDT09MsrV27dixdutTqe8h6Xln9uVMSXAghhBDinuHra3zzX9JtlAUfHx86depUqLzz\n5s1j7969NG3alAULFjBkyBDq169fau0DODs7m0Yv9u/fj6ur6x21XxS9e/cmKiqK//73vwwdOpSO\nHTsWua6MjAzi4+PN0tzd3bGzs8uzTHBwMOvXr2ffvn3Y2tqSmJhoNnJx4cIFzp07x6lTp0hLSzNb\nb5EVVBT0pj4pKQkvLy+ztEqVKhETE4PWmt9++4233nqLy5cvU6lSJavuGTBNhyooyCksCS6EEEII\ncc9wcCibUYW7yfnz55k6dSrdu3dn0aJFNGrUiOHDh/PNN9+Uel9Gjx7N3LlzCQsLY+7cuaXe/tWr\nV9m9ezdKKdM0o6I6f/489erVQymF1hqlFJs3b853C9vs6y7s7Oxwd3enUaNGAAQEBFCpUiViY2M5\ndeqUxWJuPz8/AA4cOJBn/efOnSMxMdFi9MjGxoZHHnnE9P3jjz+Or68vQ4cOZf369Vbd98GDB7Gx\nsaFevXpWlcuLLOgWQgghhLiHjBgxAqUUCxYswMvLi4iICDZu3Mjq1atLvS9Zoxdffvkle/fuLfb6\nC5ruFRISwo0bN5gxYwY//PAD8+bNs6p8dtWqVWPTpk189913pq/NmzfPt0xgYCAODg7Exsayfft2\nHn74YdM1GxsbWrVqZbpWtWpVHnjgAdN1Hx8fGjZsyPr160lOTs61/qVLl6KUomvXrgX2fezYsXz1\n1Vf89NNPhb7nc+fOsW3bNtq0aUPlypULXS4/ElwIIYQQQtwj1q1bx1dffcW0adOoXr06YHyDHRQU\nxLhx44ptxx9rjBkzBhcXF8LDw+9o7UdCQgLHjx8nNTXVlJb1hvfatWsW+desWcPq1auZNWsWr7/+\nOi+88AKTJk3ixIkTpjwOmSvzcyufU8WKFenUqZPZy8XFJd8yNjY2PPTQQ8TGxrJjxw7atGljdr1N\nmzZs27aNXbt2maZLZTd58mSuXr3Kq6++SkZGhtm1uLg43nrrLZo2bVqoHa5GjhxJpUqVmDlzZoF5\nwTjq06dPHzIyMvj3v/9dqDKFIdOihBBCCCHuAnFxcURHR1ukN2jQgNatW5OUlMSoUaMICgpi5MiR\nputKKRYvXsxDDz3ExIkTWbBgQYm0nxdnZ2dGjx5tOgk7p6ioKM6ePWv6dH7r1q2mcxUGDBhArVq1\nAIiMjCQ8PJwtW7aYpiIFBQWhtWbixIm88MIL2NnZ8cwzz5CUlMSwYcN49NFHTTtlLVy4kM2bNzNw\n4EDTFrD29vY0btyYVatW4ePjg7u7O/7+/jRp0qRIzyg3wcHBbN68GaWURQDRpk0bZsyYYTElKkvf\nvn35+eefWbBgAYcPH+af//wnbm5uxMXF8cknn+Dp6cmaNWuwsbEpsB/u7u4MHjyY9957j+PHj5um\nZwH88ssvREdHo7UmMTGR/fv38/nnn5OcnMzcuXN57LHH7vxBZCnolL375UU5OQFUCCGEKG5yQved\nGzFihLaxscn12pkzZ7TBYMjzNXjwYK211mPGjNG2trZ5PquRI0dqW1tbvWfPnhJpX2vjCd3NmjWz\nqOPatWvazc1NGwwGixO6O3bsmGfdW7duNeWbOnWqRZrWWkdEROhatWppW1tb02ndPXr00K6urvr8\n+fNmeTds2KANBoOePXu2KW3Xrl26VatW2t7eXhsMhmI/rft///ufNhgMumLFijo1NdXs2tWrV7XB\nYNA2Njb6559/zrOODRs26CeeeEJ7eHjoSpUq6YYNG+rXX39dJyQkWOQdNGiQdnZ2zrWeU6dOaTs7\nO7OfWfbnbWtrq93d3XVQUJAeN26cPnr0aKHu0ZrfAUpnbp11v1NKBQJxcXFxBJb3lWBCCCGEFfbs\n2UOQcf/XIK31nuKsW/4+C3H3s+Z3gKy5EEIIIYQQQhQLCS6EEEKUKytWrCjrLgghxH1LggshhBDl\nigQXQghRciS4EEIIIYQQQhQL2YpWCCHEfef2bUhIgCtXLL+ePl3WvRNCiPuXBBdCCCHuarduWQYI\nuQUN2f+dmGhZj8EA7u5wB2d8CSGEKIAEF0IIIUrNX38VPkDI+pqUZFmPjQ14eBhfVaoYvzZv/ve/\ns3/N+rerqzHAeOaZ0r9vIYQoLyS4EEIIUSQ3bxY+QMj6940blvXY2FgGAy1a5B4gZH11cTEGCkII\nIe4uElwIIYQgNdW6aUcJCZCcbFmPra1lUFCnTu4BQlaas7NMVRJCiPuFBBdCCHGfSUmxbtrRlSvG\n4CInOzvLoKB+/fwDBSenuz9Q6NOnT1l3QQgh7lsSXAghxF1Ka+PogLWLmW/etKyrQgXLoMDHJ+/1\nCVWqgKPj3R8oFIUEF0IIUXLKPLhQSk0BpuRIPqa1blyIsm2BLcBBrXVgCXRPCCGKhdbG9QbWLmb+\n6y/LuipWtAwEGjXKP1CoXPn+DBSEEELcXco8uMh0CHgUyPrTl1ZQAaWUC7AU2AR4lVzXhBDCnNbG\nHYysXcx865ZlXfb2fwcCVaqApyf4+eW9kLlKFXBwkEBBiLudoRA7Diil2Lx5M3Xq1KFevXp55ps5\ncyavv/468fHx+Pr60qJFCzZt2mSWJy0tjRYtWpCcnMyhQ4dwdHQs9vYBOnbsyLZt2+jatStffvml\nWb6zZ89Sr1495syZw7hx40zpERER/PTTT/z4449cvnyZqVOnMnny5AL7B7Bz507+97//MXbsWJyd\nnQtVJrvU1FTeeustHnnkEdq3b291+cJISkrinXfeYd26dZw8eZL09HQaNGjAU089xejRo/H29gZg\n0KBBfPHFFyTltgUexv9mRowYwYIFCyyuHTt2jMaNG2Nvb8+lS5dyfRZZP5ss9vb2+Pj48OKLLzJq\n1ChUKf3huFuCizStdbyVZRYD0UAG0K34uySEKA+0Np6JYO1i5tu3LetycDAPCry8oEmT/LdHdXAo\n/XsWQpS8qKgos++XLl3Kpk2biIqKQmttSvfz8yMlJQWAvn370qVLF4u6WrRoAYCnpyezZs1i6NCh\nfPbZZ/Tv39+UZ86cORw5coSYmBgcHBxKpH0wBiRKKWJiYti7d6/ZtbyEhobi7e1NYGAgGzduLDB/\ndjt27CA8PJzBgwcXKbhISUkhLCwMpVSJBBenTp2ic+fO/Pbbb/Ts2ZOhQ4dSoUIFDhw4wMcff8z6\n9es5duwY8PezK4qoqCi8vb35888/WbNmDS+++KJFHqUUtWrVYubMmWituXLlCsuXL2fs2LFcuXKF\nadOm3dG9FtbdElz4KKV+B24CO4E3tdbn88qslBoM1AP+CYSWTheFEHc7reH6deumHSUkQFouY6WV\nK5sHA9WrQ7Nm+QcKlSqV/j0LIe5Offv2Nft+586dbNq0Kdc1P2fPngUgMDDQolxOL7/8MsuWLWP8\n+PE8/fTTuLm5cfr0aaZNm0aPHj34xz/+UaLtA9SuXZukpCTCwsJYv359gfnPnDlD7dq1SUhIwNPT\ns8D82WUPhIriTsvnJz09ne7duxMfH8/WrVt5+OGHza5HREQwa9asYmlr+fLl9O3bl9OnTxMdHZ1r\ncAHg4uJi9jMeOnQovr6+REZGEh4eXiqjF3dDcLELGAQcB7yBqcA2pZS/1tpio0OllA8wHQjWWmeU\n1hCPEKJ0ZWTAtWvWTTtKSID0dMu6HB3Ng4FatSAgIP9zFOztS/+ehRCiMBYvXkyLFi0YP348H330\nESEhIdjZ2TF//vxSad/JyYmXX36Z0NBQ9u3bR0BAQL75a9euXaR2wsLCTKMOdevWBYyfzp8+fZrv\nv/+eF198kY8//phBgwaZykyfPp1Jkybx9ddf4+fnR7169VBKMXXqVKZOnQpg1bSs/KxZs4YDBw4w\nY8YMi8ACwNHRsVhGC2JjYzl79iwvvPACp06dom/fvly4cIHq1asXWLZixYq0atWKL774gsuXL+Pl\nVfIrCco8uNBaZx8fO6SU+gk4C/QCPsmeVyllwDgVaorW+mRWsjXtjR07FhcXF7O0Pn36yO4holxY\nsWJFmfy3npEBf/5p3WhCQoKxXE5OTubBQJ06EBiY9/oEDw/jAmghROGsWLGCFStWmKVdv369jHpT\nvqSkpJCQkGCR7urqio2Njen7xo0bM378eGbOnImjoyMbN24kMjLSNLe/pNsHGD16NO+88w5Tp04t\n1OhFUfTo0YNffvmFlStXMn/+fDw8PADj9LBBgwaxdu1axo0bx2OPPUaNGjU4ePAg4eHhDBkyhCef\nfJKUlBQWL17Mq6++Svfu3enevTsAzZo1K5b+bdiwAaUU/fr1s6pcbs84vxGW6OhoGjRoQFBQEE2a\nNKFSpUqsWLGC1157rVDtnT59GqUUrq6uVvWzqMo8uMhJa31dKfUL8EAul52AlkCAUmpRZpoBUEqp\nW8DjWust+dU/d+5cAgNlYylRPhVHcJGebhkoFBQ0/Pln7oGCs7N5UFCvHrRsaRkoZP3bw8O4paoQ\nouTk9oHbnj17CAoKKqMelR9Tpkyx+ERdKcXOnTt58MEHzdJDQ0NZtWoVkZGRtGrVipCQkFJt39HR\nkTFjxjB16tRCjV4Uhb+/P4GBgaxcuZJu3bpZjIB8+OGHNGnShJdeeomvvvqKgQMHUr16dd5++20A\nHBwc6NGjB6+++irNmjUr1JQvaxw7dgwXFxdq1KhR6DI3btzIc2pYbrNx0tLSWLNmjenna29vzzPP\nPEN0dHSuwUV6eropeElISGDJkiXExcXRtWtXKpbSJ213XXChlHLEGFgsy+VyIuCfI2048AjQAzhT\nop0T4j6TlvZ3oFDYUYU//zSubcjJ1dV8tOCBB6B167zXJ7i7S6AghLBeyu0Ujl05VqJt+FbxxcGu\n9HdbeOWVV+jZs6dFeuPGlrvzV6hQARcXF5RSdOrUqVjm0lvTPhhHL+bNm0dYWBjr1q274/at5eXl\nxaJFi+jTpw/t2rXjwIEDbNq0qVA7ZRWHxMREnJycrCpTqVIlYmJich2p6Ny5s0Xa119/zdWrV80C\n/j59+vDMM89w9OhR/Pz8zPIfPXrUInjp1q0bH330kVX9vBNlHlwopWYDX2GcClUDCANuAysyr08H\namitB2rjT+JIjvKXgZta66Ol2nEh7kJZOx9dugR//GH59aef4OGHzQOF3Li5mQcFDRvmvz7B3d14\nmrMQQpS0Y1eOEfRByY6ixL0SR6B36c9y8PHxoVOnToXKO2/ePPbu3UvTpk1ZsGABQ4YMoX79+qXW\nPoCzs7Np9GL//v2lNu0mu969exMVFcV///tfhg4dSseOHYtcV0ZGBvHx5puXuru7Y5fHHzhnZ2dO\nnz5tVRs2NjY88sgjhc4fFRVFvXr1sLOz4+RJ44qA+vXrU6lSJaKjo/nPf/5jlr9evXosWbKE9PR0\nTp48SUREBPHx8diX4kLCMg8ugJrAcsADiAdigdZa66wJad5ArTLqmxB3hRs3/g4S8gocsq7lPHSt\nQgWoVs24LSqAr2/BgYLt3fCbQQghcuFbxZe4V+JKvI272fnz55k6dSrdu3dn0aJFNGrUiOHDh/PN\nN9+Uel9Gjx7N3LlzCQsLY+7cuaXe/tWrV9m9ezdKKY4cOVJwgXycP3/etABca206BySvLWx9fX3Z\nt28fv//+u1VToworKSmJmJgY/vrrL3x8fMyuKaVYvny5RXBRuXJlU/DSuXNn2rRpQ2BgIBMnTmTe\nvHnF3sfclPlbCK11vhPAtdaDC7gehnG0Q4h7Smpq/kFC9rTkHPum2doagwUvL2Pg0LgxdOr09/dZ\nwUS1auAIGfUBAAAgAElEQVTi8veBa888A598YtkXIYS4VzjYOZTJqMLdZMSIESilWLBgAV5eXkRE\nRDBy5EhWr15Nr169SrUvWaMXYWFhDBgwoNjrL2i6V0hICDdu3GDGjBm88cYbzJs3jzFjxhS6fHbV\nqlWzOJywefPmeebv2rUrK1asICoqigkTJhS6ncL64osv+Ouvv1i8eLFpMXuW48ePM2nSJHbs2EGb\nNm3yrKNp06b069eP999/n/Hjx1OzZs1i72dOZR5cCHE/uXUr74AhZ+CQmGhe1mAwns6cFRw88AAE\nB/8dJGT/6u5uzC+EEKJ8WbduHV999RXz5s0zbUUaEhLCp59+yrhx4+jSpUuprTnIMmbMGObNm3fH\n5ygkJCRw5coVateuTaXMg4MqV64MwLVr1ywWdK9Zs4bVq1ezcOFCQkJC2LdvH5MmTeLpp5/mgQeM\n+wI5ZJ5Ueu3atQLbr1ixolXTwp5//nlmzJhBREQEHTp0oHXr1mbXk5KSmDVrlsXoQmFFR0dTv359\nhgwZYnHt1q1bzJgxg+jo6HyDC4DXX3+dZcuW8c477/DOO+8UqS/WkOBCiAKkpcHlywWPLly6lPsa\nhipV/g4KateGVq0sRxe8vIz5cuz0J4QQohyJi4sjOjraIr1Bgwa0bt2apKQkRo0aRVBQECNHjjRd\nV0qxePFiHnroISZOnMiCBQtKpP28ODs7M3r0aNOZFDlFRUVx9uxZkjOH4bdu3UpERAQAAwYMoFYt\n4+z3rIPetmzZYpqKFBQUhNaaiRMn8sILL2BnZ8czzzxDUlISw4YN49FHHzXtpLRw4UI2b97MwIED\n2b59O2DcXalx48asWrUKHx8f3N3d8ff3p0mTJkV6RtnZ2tqydu1aHnvsMdq3b0+vXr1o27YtdnZ2\nHD58mOXLl+Pu7l6k4OLChQts3rzZbBQmuwoVKvDEE0/w+eefs2DBAoutgrPz8/OjS5cuLFmyhNDQ\nUNzc3KzujzUkuBDlUnq6cUFzYUYZEhIsd0dyczMPDpo3twwWqlUzjkTcTQud5TwXIYQoO/l9qq+U\nYuXKlaxcudLi2sCBA2ndujWTJ0/m0qVLfPnllxZ1BQUFERISwnvvvcfgwYNp0aJFsbefXz1jxoxh\n/vz5uZ6J8tFHH7Ft2zZT2S1btrBlyxYA2rVrZwoulFIWdbds2ZL//Oc/LF68mI0bN5KRkcHp06cZ\nN24caWlpfJJtrq+7uzsffPABzz77LHPmzGH8+PGm9keOHMm4ceO4desWU6ZMKZbgAoyB1759+5g7\ndy7r1q3jyy+/JD09nfr16/Piiy9aBAcF/Qyyrq9atQqtNU8//XSe+bt27cratWv55ptvTPnyqv9f\n//oXX3/9NZGRkcVygGB+VEkei343UUoFAnFxcXFyzsV9KuugtoJGF/74wzgSkfPcBWfn3Ncs5Pxa\ntaocyiaEKF+ynXMRpLXeU5x1y99nIe5+1vwOkJELcVfTGq5fL9wahj/+ME5hys7BwTxYaNMm7wAi\nc3qnEEIIIYQoIgkuRKnTuvBbq/7xh+XWqhUrmgcHLVvmPcpQymvahBBCCCHKNQkuRLFJSSn81qop\nKeZl7ezMt1Zt2hQ6d849YHB2/ntrVSGEEEIIcfeQ4ELk66+//p5yVNAoQ1KSeVkbG+P6hKygoGFD\naN8+92lJbm4SMAghhBBC3OskuLgLrFixolR38bl9u/Bbq+bcFlqpv7dWrVYN6taF1q0tRxeqVTOe\n9ixnMQghhBBClB8SXNwFiiO4yNpataDRhaytVXNyd/87OKheHQIDc1/H4OlpPB1aCCGEEEKInORt\n4l0sIwOuXi3cGob4eMutVV1czIODxo0tRxe8vIxTlypUKJt7FEIIIYQQ9w8JLspAcjJcvPj369Qp\nePNNy8Dh8mXLrVUdHc2DgwceyPtMBnv7srk/IYQQQghRPpXr4KI41zpobVzQnD1ouHAh938nJpqX\nNRiMAUdWUPDgg3mfxVC5crF0VwghhBBCiGJXroOLOXPmFBhcaG1c1JxbkJDz++Rk87KVKxvXL3h7\nG18tWvz97+zpAwbAhg0leKNCCCGEEEKUgnIbXGgN58//zsGD+Y8yXLwIN2+al3V2/js4qFULHnoo\n96DByals7k0IIYQQQoiyUO6Ci67DfyA9vgZXz3lx+zY0a/b3NTe3v4ODBg2gXbu/A4XsgYODQ9n1\nXwghhBBCiLtVuTuF4EKzMfzRvxqOobWxrfMng5ZO5r0tazl84SRXEjI4dAi++w6WLYOZM2H0aOjV\nyxhoNGhQMoFFaZ5xIYQQQoiSZzAYCnzZ2Niwbds2zp49m2++t956C4D4+Hg8PDzo3LmzRXtpaWk0\nbdqU+vXrk5KSYlX7WRITE5kyZQrNmzfHyckJBwcHmjVrxsSJE7l06RIA//d//1eounfs2GGqNyIi\ngm7dulGtWjUMBgPTp08v9HPcvn07YWFh3Lhxo0g/h+TkZMLCwoiNjS1S+bxcvHgRg8HAv/71L4tr\nw4cPx2AwEBERYXGtb9++2Nvbc+vWLbP0DRs28MQTT+Dh4UGlSpXw9fVlwoQJ/PnnnxZ19OvXz+xZ\n29vb4+vrS1hYmEW96enpZnnt7Ozw8PCgVatWjBs3juPHj9/hk7BU7kYuNvTZwE3Pm+y+sJt5n87j\n68vv8+npy7AFnCo40cyrGQHVAgioFkBzr+b4V/Wnkl2lEu2TBBdCCCHE/SUqKsrs+6VLl7Jp0yai\noqLQWpvS/fz8SElJAYxvPLt06WJRV4sWLQDw9PRk1qxZDB06lM8++4z+/fub8syZM4cjR44QExOD\ng4ODVe0DnDhxgs6dO3Px4kV69erFsGHDsLW15cCBA3z44Yds2LCBQ4cO4e/vb1F3lsTERMaMGYOz\nszM+Pj6A8c1taGgo1atXJzAwkI0bNxb6GQLExsYSHh7OkCFDcHR0tKoswI0bNwgLC8POzo7g4GCr\ny+fF29ubevXq5Rq07NixAzs7O7Zv357rtVatWlEh2xkAY8aMYcGCBQQGBjJx4kTc3NzYvXs38+fP\nZ+XKlXz//fc0aNDAlF8pReXKlfnggw/QWnPt2jXWr19PWFgYZ86c4ZNPPrFo98knn6Rfv36m/Pv3\n7+eTTz5h4cKFzJkzh1GjRhXTkwG01uXiBQQCOi4uTmfx8vLSWmt9Memi/vbXb/XMH2bqF9a8oP0W\n+mlDmEEzFW0IM2i/hX66z5o+euYPM/W3v36rLyZd1EIIIUR5ERcXpwENBOpS+Pt8PxoxYoQ2GAy5\nXjtz5oxWSum33367UHW1a9dOV61aVV+9elVrrfWpU6e0g4OD7tmzZ5Hav337tvb399fOzs76xx9/\ntLiemJioQ0NDC+xX7969tcFg0DExMWbp586d01prfenSJa2U0hEREQXWlWXGjBnaYDDo33//vdBl\nsrt48aLVbRbWgAEDdMWKFXVqaqopLTExUdvY2Oh+/fppV1dXs/y//fabVkrpCRMmmNKWLVumlVK6\nf//+OiMjwyz/rl27tIODg27RooXZtX79+mk3NzeL/jz44IPaxsZGJyQkmNLS0tK0UkqPHTvWIn9C\nQoJu3bq1trGx0d99912+92rN74ByNy0qN9Ucq/HEA08wIXgCK3qs4MjwIyS9mcRPL//E4qcW06le\nJ85dP0fEDxE8Gf0k3m97U21ONZ6MepIJ301gxcEVHIk/QlpGWsGN5WLFihXFfEdCCCGEuF8tXryY\na9euMX78eABCQkKws7Nj/vz5Rapv1apVHD58mMmTJ/Pggw9aXHdyciI8PDzfOj744ANWr17NqFGj\neOqpp8yu1apVq0j9Cg0NZeLEiQDUrFnTNJXrwoULLFmyBIPBYDGKEh4ejsFgYNOmTZw8eZLq1auj\nlGLSpEmmqUHWTMvKT3BwMLdv3+ann34ype3cuROA1157jevXr3Pw4EHTtdjYWJRSZiMo4eHhVKlS\nhffeew+llFn9Dz30EOPHj2f//v2sW7euwP60bdsWrTWnT58uVP/d3d1ZsWIFSqlcp3AVVbmbFpVd\njRo18rzmYOdAqxqtaFWjlSktQ2dw+s/T7Lu0j/1/7GffpX2sPLySt3YY50La29rjX9WfAK8Amldr\nTkC1AJp5NcO5onO+/SjO8zaEEEIIcW9KSUkhISHBIt3V1RUbGxvT940bN2b8+PHMnDkTR0dHNm7c\nSGRkJN7e3kVqd8OGDSil6NevX5HKHzp0iLFjxxIUFGRaH1IcevXqxYkTJ1i9ejULFy7E1dUVML4p\nfvnll1m7di2jR4/m0Ucfxdvbm3379hEREcGwYcPo3LkzycnJLFq0iOHDh9OzZ0+6desGQEBAQLH0\nLzg4GK01sbGxtG/fHjBOe/Lz8yMgIABvb2+2b99O06ZNgb+Di7Zt2wJw7NgxTp48ySuvvELlPA4y\nGzBgANOmTSMmJobu3bvn25+soMLNza3Q91C3bl2Cg4PZsWMHqampVKp050sBynVwkRXxF5ZBGWjg\n3oAG7g3o0biHKf1q6lUO/HHAFHTsvribpfuXcjvjNgD13eqb1nBkfa3tUtsiQhVCCCFE+TVlyhQm\nT55slqaUYufOnRYjCqGhoaxatYrIyEhatWpFSEhIkds9duwYHh4eeHl5WV02NTWVXr16YWtry8qV\nK7GzsytyP3Jq2rQpAQEBrF69mmeffZbq1aubXV+yZAn+/v4MGTKEdevWMXDgQGrXrs3s2bMBqFy5\nMt27d2f48OE0b96cvn37FlvfwLhexcPDw2zdxfbt203Bw8MPP0xsbCyvvvqq6Zqvr6/pzf+RI0cA\naJZ969IcGjRogIODA0ePHjVL11qbAtHr16+zZs0aNmzYQIsWLahfv75V9+Hv72/aWMDX19eqsrkp\n18FFcY0WuFdyp2PdjnSs29GUdiv9FseuHDMGHJf2s++PfSz4cQEJqcb/EFztXU3Bxvnr5zl59ST1\n3epLwCGEEELkJyUFjh0r2TZ8fctk3/lXXnmFnj17WqQ3btzYIq1ChQq4uLiglKJTp0539P4hMTER\npyIezjV8+HCOHz/OsmXLzBYdl4bq1asTGRlJ//79adeuHYcPH2bz5s04lOLPrk2bNqYdtzIyMvjx\nxx8ZMGAAYJymtGDBAsC4sPzgwYO89NJLprJJSUkopQp89k5OTiQmJpqlXb9+HU9PT7O0Dh06sHTp\nUqvvIWuhfFJSktVlc1Oug4uSVMGmAs28mtHMqxk0N6Zprfk96XdjsJE5yvH1r1/z66VfeSDyAWo4\n1aB9nfaml18VPwk2hBBCiOyOHYOgoJJtIy4OAgNLto1c+Pj40KlTp0LlnTdvHnv37qVp06YsWLCA\nIUOGWP2JdRZnZ2cuXrxodbno6Gg+/fRTBg4cyD//+c8itX2n/vnPfxIVFcXGjRsZPnw47dq1K3Jd\nGRkZxMfHm6V5eHhga5v32+Xg4GBiYmI4ePAgaWlpJCcn06ZNG8AYeIwfP54LFy5w+PBh0tPTzdZb\nODk5obUu8E19UlISderUMUtzdHTkyy+/JCMjg99++41Zs2Zx+fLlIk1rytrmt6gBZk4SXJQipRQ1\nnWtS07kmTzX8e7HTPzb+g+F9hrPt7Da2nd3G6sOrSdfpVHGoQrva7ehQpwPt67SnmVczbAw2+bQg\nhBBC3Od8fY1v/ku6jbvY+fPnmTp1Kt27d2fRokU0atSI4cOH88033xSpPl9fXw4dOsQff/xR6KlR\nv/76K8OGDcPX15dFixYVqd3icOXKFfbs2YNSisOHD99RXadPn8bHxwelFFprlFL88MMPpmAhN9nX\nXaSnp+Pp6WkawQkMDKRixYrExsZy5MgRi8XcWdsAHzhwIM/6T506RUpKisXola2tLY888ojp+86d\nO+Pn58ewYcNYs2aNVfd96NAhbG1tqVu3rlXl8iLBxV3AzmDH0w2f5umGTwNw49YNdv22yxRsTNg0\ngb/S/8K5ojPBtYNpX9s4shFUPYgKNhUKqF0IIYS4jzg4lMmowt1kxIgRKKVYsGABXl5eREREMHLk\nSFavXk2vXr2srq9r1658/vnnREVF8dprrxWY/9atW/Tq1Yu0tDRWrlxZotOQCprBMWzYMFJTU5k+\nfTpvvvkmCxcuZMSIEYUun12NGjXYtGmTWVrWYuy8tGzZEnt7e2JjY8nIyDALROzs7AgKCiI2Npaj\nR49SvXp1szfwfn5+1K9fn3Xr1vHOO+/k+hyXLl2KUoquXbsW2PfRo0czffp09uzZQ2Ah/x85ffo0\nsbGxdOjQAXt7+0KVKYhsRXsXcqzgSOf6nQl/JJwtg7Zw7Y1rbBu0jdfbvE5aRhrTtk2jzcdtcJvl\nRudlnQnfGs6WM1tIvZ1a1l0XQgghRAlat24dX331FdOmTTMtcA4JCSEoKIhx48YV6STr3r1707hx\nY6ZNm8bPP/9scT0xMZHQ0FDT9+PGjePAgQPMnj0738XI1kpISOD48ePcvHnTlJa1i9K1a9cs8q9c\nuZIvvviCOXPmMGHCBJ5//nnefPNNTp06VajyOdnb29OpUyezV0FThezs7GjVqhWxsbHs2LHDYpSj\nTZs2bN26lR9//NG00Du7yZMnEx8fT0hIiNnhhgA//fQTb7/9NgEBATz77LMF9n/UqFFUrFiRmTNn\nFpgXjKM+ffr0QWvNv//970KVKYwyH7lQSk0BpuRIPqa1tly9ZMz/HDAMCAAqAoeBqVrr/5VoR0tQ\nQQvL7W3taVenHe3qtOPf/Jvb6bfZd2kfW89uZdvZbczdNZcpW6ZgZ7DjwRoPmtZstK3VFqeKxTN/\nTgghhBAlKy4ujujoaIv0Bg0a0Lp1a5KSkhg1ahRBQUGMHDnSdF0pxeLFi3nooYeYOHGiaRFxYdnZ\n2bFu3Toef/xx2rZtS+/evWnbti22trYcOnSI5cuX4+XlZdoS9d1336VWrVq4urrm2l8wLmbO+pT+\ns88+49y5c6a1BVu2bDG9kR40aJDpaIC5c+cyffp0YmNjTW/Sg4KC0Frz5ptv0rNnT+zs7Hj22Wf5\n888/GTFiBE888QSvvPIKAO+++y5NmjRh0KBBpkXWlStXpmHDhqxYsYL69evj5uZGs2bNTFOSikNw\ncDA//PCD2TazWdq0acPs2bMtpkRlGTBgALt372bRokUcOnSIvn374urqyu7du/nkk0+oVq0an3/+\neaFGYDw9PRk4cCBLlizhxIkTPPDAA6Zrx44dIzo6Gq01iYmJ7Nu3j88//5yUlBQiIyPNpljdsYJO\n2SvpF8bA4gDgCVTNfLnnk38uMB4IAhoAEcBfQPMC2rlvTwBNz0jX+y/t15E/Ruqeq3vqqrOrmk4X\nb/lBSz3u23F6/dH1+krylbLuqhBCiHuQnNB950aMGKFtbGxyvXbmzBltMBjyfA0ePFhrrfWYMWO0\nra1tns9q5MiR2tbWVu/Zs8eq9rNcu3ZNT548WTdr1kw7OjpqBwcH7e/vrydOnKgvX76stdZ60qRJ\n+fY16xUdHW2qNzg4OM9827dvN+XLqjt7mtZah4eH65o1a2pbW1vTad3dunXTbm5uFid3r127VhsM\nBj137lxT2vbt23XLli21vb29NhgMxX5a99dff60NBoOuVKmSvnXrltm1y5cva6WUtrGx0Xv37s2z\njvXr1+vHHntMu7u7a3t7e92wYUM9YcIE0yns2fXr10+7u7vnWs+vv/6qbW1t9ZAhQ7TWxhO6sz9v\nW1tb7e7urlu2bKlfe+01ffz48ULdozW/A5TOMQRT2jJHLrpprYs8gVIpdQhYqbX+Tz55AoG4uLi4\nQs9Du1dprfkl4Rfjmo1z29h6ZivnE88D4F/Vn/a129Ohbgfa1W6Ht1PRDtwRQghRfuzZs4cg4w5N\nQVrrPcVZd3n6+yzEvcqa3wFlPi0qk49S6nfgJrATeFNrfb4wBZVxnMgJuFqC/bunKKVoVKURjao0\nYkjQEADOXjvLtrPb2Hp2K9+d+o53d78LgI+7j9n2t3Vd65Zhz4UQQgghxL3sbggudgGDgOOANzAV\n2KaU8tdaJxei/L+AysDqkurg/aCOax36u/anf/P+AFxMusgP534w7Uj10d6PAKjtUtsYaGTuSNXQ\no6GctSGEEEIIIQqlzIMLrfXGbN8eUkr9BJwFegGf5FdWKdUXCAWe0VpfKble3n+8nbzp1aQXvZoY\nt6xLSElg+/ntbD2zlW3ntrH84HIydAZVK1c1BRsd6nbAv6o/BiWbjAkhhBBCCEtlHlzkpLW+rpT6\nBXggv3xKqReAD4DntdabC1v/2LFjcXFxMUvr06dPgTs23e88HDx4ptEzPNPoGQCS/kpix/kdpnUb\n478bz630W7jau9KudjvTNKoW1VpgZ2NXxr0XQghRXFasWMGKFSvM0q5fv15GvRFC3GvKfEF3Tkop\nR+AcMFlrvTCPPH2AJUBvrXVMIeuVBWN3IPV2Kj/+/qNpGtWO8ztITUulsl1l2tZua5pG1apGK+xt\ni+cQFiGEEHcHWdAtRPl2Ty3oVkrNBr7COBWqBhAG3AZWZF6fDtTQWg/M/L4v8CkwCvhZKZV1Tn2q\n1jqxdHtfflSyq0THuh3pWLcjALfSb7Hn4h7TIvG3drzFpM2TqGhTkYdqPmQKNtrUakPlCpXLtvNC\nCCGEEKJUlHlwAdQElgMeQDwQC7TWWidkXvcGamXLPwSwARZlvrIsBV4s8d4KACrYVKB1zda0rtma\n19u+TnpGOgf+OGCaRrU4bjH/+eE/2BpsCfIOMjvYz62SW1l3XwghhBBClIAyDy601vkudtBaD87x\nfTEeISiKi43BhhbeLWjh3YLRrUejtebYlWOmU8SjD0Yze8dsFIpmXs1oX6c9Hep0oF2ddlStXLWs\nuy+EEEIIIYpBmQcX4v6klMLP0w8/Tz9ebfkqWmtOXzttWrPx9a9fE/lTJAC+VXxN06ja12lPLZda\nBdQuhBBCCCHuRhJciFKhlKK+W33qu9VnUMAgAH5L/I0fzv5gmkr1wZ4PAKjrWpcOdTqYgo0Gbg3k\nrA0hhBBCiHuABBeizNR0rkmfpn3o09Q4My4+OZ7Yc7GmReLL9i9Do/F29DY7RbyxZ2M5a0MIIYQQ\n4i4kwYW4a3hW9uQ5v+d4zu85AK7fvM7289tNU6lGfzuatIw0PCp50K5OO9NUqubVmmNrkP+UhRBC\nCCHKmrwjE3ctF3sXuvh0oYtPFwCSbyWz67ddpmlUE7+fyM20mzhVcKJt7bamqVQtq7ekgk2FMu69\nEEIIIUT5I3NLxD2jcoXKPFr/UcIeCWPzwM1cm3CN2MGxvBn8JgARP0TQ9uO2uMx0odPSTkzdMpXv\nT39Pyu2UMu65EEKI8sZgMBT4srGxYdu2bZw9ezbffG+99RYA8fHxeHh40LlzZ4v20tLSaNq0KfXr\n1yclJcWq9rMkJiYyZcoUmjdvjpOTEw4ODjRr1oyJEydy6dIlAP7v//6vUHXv2LHDVG9ERATdunWj\nWrVqGAwGpk+fXujnuH37dsLCwrhx40aRfg7JycmEhYURGxtbpPKFUZjnBtCvXz/c3HLfjj89PR2D\nwcC4ceNyvX7w4EEMBgOVK1fO81kEBweb/QwcHBwICAggMjKS0jw0W0YuxD2rom1F2tZuS9vabXmT\nN0nLSGPfpX2maVSRP0UStjUMO4MdrWq0MjvYz8Xepay7L4QQ4j4WFRVl9v3SpUvZtGkTUVFRZm/0\n/Pz8SEkxfgjWt29funTpYlFXixYtAPD09GTWrFkMHTqUzz77jP79+5vyzJkzhyNHjhATE4ODg4NV\n7QOcOHGCzp07c/HiRXr16sWwYcOwtbXlwIEDfPjhh2zYsIFDhw7h7+9vUXeWxMRExowZg7OzMz4+\nPoDxTXNoaCjVq1cnMDCQjRs3FvoZAsTGxhIeHs6QIUNwdHS0qizAjRs3CAsLw87OjuDgYKvLF6Sw\nzw2Mm9sUdYOa6OhoatSowZUrV1i7di0DBgywyKOUok6dOkyfPh2tNVeuXCEqKorRo0dz9epVpkyZ\nckf3Wmha63LxAgIBHRcXp0X5kJ6Rrg/+cVAv+mmR7vV5L11tTjXNVLQhzKAD3w/UY74Zo9cdXafj\nk+PLuqtCCHFXi4uL04AGArX8fS6SESNGaIPBkOu1M2fOaKWUfvvttwtVV7t27XTVqlX11atXtdZa\nnzp1Sjs4OOiePXsWqf3bt29rf39/7ezsrH/88UeL64mJiTo0NLTAfvXu3VsbDAYdExNjln7u3Dmt\ntdaXLl3SSikdERFRYF1ZZsyYoQ0Gg/79998LXSa7ixcvWt1mYVn73Pr166fd3NxyrSstLU0rpfTY\nsWNzvV67dm39xhtv6G7duunHH3881zzBwcG6RYsWZmmpqam6Vq1a2t3dvbC3lStrfgfItChx3zIo\nA/5V/QlpFcKq51dxYdwFfhnxCx88/QH+Vf1Zf3w9z616Ds/Znvi/60/If0NYeWglF5IulHXXhRBC\niDwtXryYa9euMX78eABCQkKws7Nj/vz5Rapv1apVHD58mMmTJ/Pggw9aXHdyciI8PDzfOj744ANW\nr17NqFGjeOqpp8yu1apVtPOrQkNDmThxIgA1a9Y0TeW6cOECS5YswWAwWIyihIeHYzAY2LRpEydP\nnqR69eoopZg0aZJpupA107LyUxzPrTC2bNnCb7/9xgsvvEDv3r35/vvv+eOPPwpV1t7enpYtW3Lt\n2jUSEhLuuC+FIdOiRLmhlMLHwwcfDx9eCnwJgHPXz5mmUX1/+nve2/0eAA3cGpidtVHXta6ctSGE\nEKJEpaSk5PoG0NXVFRsbG9P3jRs3Zvz48cycORNHR0c2btxIZGQk3t7eRWp3w4YNKKXo169fkcof\nOnSIsWPHEhQUZFofUhx69erFiRMnWL16NQsXLsTV1RUAd3d3Xn75ZdauXcvo0aN59NFH8fb2Zt++\nfURERDBs2DA6d+5McnIyixYtYvjw4fTs2ZNu3boBEBAQUCz9K8pz01rn+jNOS0vLs0x0dDSNGjWi\neU/SlAcAACAASURBVPPm+Pj4ULFiRVauXMno0aML1ebp06cxGAy4uJTOlHAJLkS5VtulNv2a9aNf\nM+Mvhj9u/MEP534wBRyf7PsEjaamc01joJG5bsO3iq8EG0IIUQZS0tM5llKyG3X4OjjgkO3NfGmZ\nMmUKkydPNktTSrFz506LT8ZDQ0NZtWoVkZGRtGrVipCQkCK3e+zYMTw8PPDy8rK6bGpqKr169cLW\n1paVK1diZ2dX5H7k1LRpUwICAli9ejXPPvss1atXN7u+ZMkS/P39GTJkCOvWrWPgwIHUrl2b2bNn\nA1C5cmW6d+/O8OHDad68OX379i22vkHRntv169fx9PTM9Vpu7ytu3brFF198wdixYwFwcHDg6aef\nJjo6OtfgIj093RS8JCQk8P7777N//36ee+45bG1L522/BBdCZOPl6MXzjZ/n+cbPA3A19Srbz203\nbX+76tAq0nU6ng6eZgf7Na3aFBtD6f8hEkKI8uZYSgpBcXEl2kZcUBCBTk4l2kZuXnnlFXr27GmR\n3rhxY4u0ChUq4OLiglKKTp063dEHXomJiTgV8X6HDx/O8ePHWbZsGQ0aNChyH4qievXqREZG0r9/\nf9q1a8fhw4fZvHkzDg4OpdJ+UZ6bo6MjX375pcXuTRkZGTz++OMW+WNiYrh+/Tp9+vQxpfXp04fu\n3bvz66+/mhbOZzl48KBF8PLcc8/x0UcfWdXPOyHBhRD5cK/kTtdGXenaqCsAN27dYOf5nWw9u5Vt\nZ7fxr+/+xa30W7hUdCG4djDt67SnQ50OBHoHYmdTfJ/eCCGEMPJ1cCAuKKjE2/h/9u47PMoyX+P4\n95lM6qT3QqgJEJBiAIGgWEDBAigJKrqu6Nr2WHZtu65lbStrL6i42HdFWdcEj2WPdS2oFBVsSCiG\nlpAEhhZqCCTP+WOGLAQCSUiYSXJ/rmsuZp73nXd+mUuTuedpvpCZmckpp5zSoHMff/xxvvvuO/r0\n6cOUKVO4/PLL6dq1a5NeNzIykrKyskY/79VXX+Xll1/m4osv5sILL2zSax+pCy+8kOnTp/PBBx9w\n9dVXc8IJJzT5WjU1Nbjd7v3a4uLi6v3Gvynvm9Pp5OSTTz6gvbq6+qDnT58+nYyMDIwxFBUVAZCR\nkUFwcDCvvvoqd911137nZ2RkMG3aNKqrq/nll1+47777cLvdhISENKrOI6FwIdII4UHhnNrtVE7t\ndioAlXsq+XrN17XDqO7+/G527N5BWGAYOek5tcOoBncYTIjz6P2PLSLSVoUFBPikV8GfFBcXc9dd\ndzF+/HiefvppevTowdVXX817773XpOv17NmThQsXsnbt2gYP8Vm2bBm//e1v6dmzJ08//XSTXrc5\nrF+/ngULFmCM4eeffz6ia61YsYLMzEyMMVhrMcbwxRdfkJOTc9Dzm/K+NUZFRQXvvfceVVVVB/RQ\nGGN47bXXDggX4eHhteFl5MiRDB48mEGDBnH77bfz8MMPN3uNB6PVokSOQIgzhOGdhnP78Nv58KIP\n2fzHzcz9zVzuOvEuQpwhPDLnEU76+0lE3R/F8JeGc/snt/Nh0Yds3bXV16WLiEgrdc0112CMYcqU\nKSQlJXHffffxwQcf8K9//atJ1xszZgzW2nr3r6irqqqKc889lz179vDPf/6zRYchHW64129/+1t2\n7tzJ5MmT+eyzz3jqqaca9fx9paWl8fHHH/PRRx/V/tunT596z2/s+9ZYb7zxBlVVVTz33HPk5+fv\nd7vnnnsoKiri66+/PuQ1jj32WCZOnMjUqVOb1DvVJIdbq7at3Ggn62iLf9lTvcd+X/a9nTJ3is19\nPdcmPJhguQsbcHeAHfTsIHvjBzfatxa/ZTfu2OjrUkVE6qV9Lo5cc+1zMXPmTGuMsU888URtW01N\njR04cKBNS0uzW7dubfTrV1VV2d69e9uoqCj79ddfH3C8oqLC3n777bWPr776autwOOxTTz112Hr3\ndbh9LtavX28XL15sd+7cWdv21FNPWYfDYX/++ecDzp8xY4Y1xthp06ZZa62dMGGCDQ8Pt0VFRbXn\nbNu2zRpj7M0339yoWhuise9bY/e5OOmkk2zPnj0Pen5lZaV1uVz2uuuuq2072D4X1lr7448/HvF7\n0JjfARoWJdKCAhwB9EvuR7/kflw7+FqstSzZsKR2GNXrP7/OI3MewWDok9SndhjVCZ1OIDk82dfl\ni4jIUTR//nxeffXVA9q7devGkCFD2Lp1K9dddx0DBgzg2muvrT1ujOFvf/sbgwcP5tZbb2XKlCmN\net3AwEDefPNNTjvtNIYNG8Z5553HsGHDcDqdLFy4kNdee42kpCTuvfde3n33XaZOnUp6ejrR0dEH\nrRdg2LBhdO7cGYBXXnmF1atXs3Wrp9f+s88+q53QPGnSJNLS0gB47LHHmDx5Ml9++WXtUKQBAwZg\nreVPf/oTEyZMIDAwkLPPPptNmzZxzTXXMGrUKK644goApk6dSu/evZk0aRKzZs0CPCtGde/enRkz\nZtC1a1diYmLo27dv7c7kR6Ix71tjFRcXM2vWLG6++eaDHg8ODubUU0/l9ddf57HHHsPhqH8wUp8+\nfRg1ahTPPvsst912W8svSXu49NFWbrSTb0akdampqbErNq2wL3/3sr30fy+1GVMyLHdhuQvb/cnu\n9rK3LrOv/PCKXbV5la9LFZF2TD0XR+6aa66xAQEBBz22cuVK63A46r1dcskl1lprf//731un01nv\ne3Xttddap9NpFyxY0KjX32vz5s32z3/+s+3bt68NDw+3YWFh9phjjrG33nqrXbdunbXW2ttvv/2Q\nte69vfrqq7XXPf744+s976uvvqo9b++1922z1tp77rnHdujQwTqdztrduseNG2djYmIO2Ll75syZ\n1uFw2Mcee6y27auvvrIDBw60ISEh1uFwNPtu3Q1536z19FzUt1P2nj17rMPhsDfccIO11toHH3zQ\nOhwO+8UXX9T7ui+88IJ1OBz2vffes9Z63ufs7OyDnvuf//zniH72xvwOMNba+nJHm2KMyQbmz58/\nn+zsbF+XI1Kv0q2lfLHqi9rlbxeuWwhAp6hO+y1/mxmbqb02ROSoWLBgAQM8KzQNsNYuaM5r6++z\niP9rzO8ADYsS8TOpEamcd8x5nHfMeQCs37GeL1d/WTuU6tWfXqXG1pAcnrzfxn69E3vjMIdeo2HG\njBn7rZUtIiIi0pwULkT8XHxYPGf3PJuze54NwJZdW5hdPJtZq2bx+arPuf6D69lds5uYkBhO6HQC\nwzsO58TOJ9I/uT9Ox/7/iytciIiISEtSuBBpZSKDIxmdMZrRGaMB2LF7B/NK5tUOo7rj0zvYuWcn\n4UHhDEsfVjuMalDqIB9XLiIiIm2dwoVIKxcWGMbJXU7m5C6eTXOqqquYXzq/dhfx+7+8n9s+uY0Q\nZwiuYhdT5k1hfNZ4OkR28HHlIiIi0tZoEz2RNiYoIIih6UO55fhb+L8L/49Nf9zE/Cvm89cRfyXA\nBHDThzeR/lg6Q54fwkNfPcTyTct9XbKIiIi0EQoXIm1cgCOA7JRsfj/k9wzuMJh1N6/jlXNeISUi\nhT9/9me6TelG9rRs7pt1H4vXL/Z1uSIiItKKKVyItDPRIdH8qu+vePO8N3Hf7OZfef8iMy6Tv375\nV7KezqL31N7c+emd/Lj2R9rLUtUiIiLSPDTnQqQdCw8KZ0LvCUzoPYGdu3fyYdGHFBQW8MS8J7hn\n1j1kxGaQm5VLblYuA1MHal8NEREROSSFC5F25FDL0IYGhjKu5zjG9RxHVXUVn6z4hPxF+Ty/4Hke\n+OoBOkV1YnzWeHKzchmaPvSwe2qIiDRGYWGhr0sQkXo05v9Pn+/QbYy5E7izTvNia22vQzznJOAR\noDewGrjPWvv3w7yOdgAVaYI9NXuYtWoWBYsKmLl4JuXbykkJT+GcnueQ2yuX4Z2GH7Cfhoi0LS28\nQ3dHh8OxpKamJqQ5rysizcvhcFTW1NT0sNauPtR5/vKJYCEwAtg75mJPfScaYzoD7wJTgQuAkcDz\nxphSa+1HLVumSPvjdDg5pcspnNLlFJ4840nmFM8hf1E+BYUFTP12qmeTvx5nk9srl1O6nEJQQJCv\nSxaRVsRau9oY0wOI93UtIlK/mpqa9YcLFuA/4WKPtdbdwHN/Cyy31v7B+3iJMeZ44HpA4UKkBTmM\ng2EdhzGs4zAeHfUo35Z+S0FhgWf41HfPEx0SzZjuY8jNyuW0bqcRGhjq65JFpBXwfmA57IcWEfF/\n/hIuMo0xa4BKYA7wJ2ttcT3nDgE+rtP2AfBYC9YnInUYYxiUNohBaYP464i/8uPaHykoLKCgsIBX\nfnyF8KBwzsw8k9ysXM7IPANXkMvXJYuIiEgL84dwMReYBCwBUoC7gFnGmGOstdsPcn4ysLZO21og\n0hgTbK3d1YK1ishBGGPol9yPfsn9uOfke1i8fjEFiwrIL8zn3PxzCXGGcHrG6eRm5XJW97OICony\ndckiIiLSAnweLqy1H+zzcKEx5mtgFXAu8JJvqhKRI9Ezvie3Db+N24bfxvJNyylY5OnR+NWbvyIo\nIIiRXUeSm5XLuB7jiAuL83W5IiIi0kx8vlrUwXgDxkfW2tsOcuxzYL619oZ92iYBj1lrYw5xzWxg\n/vDhw4mK2v9b04kTJx5yiU4RaR7FFcW8ufhN8hfl8+XqL3EYByd3OZncrFzO7nk2yeHJvi5RpN2b\nMWMGM2bM2K+toqKCWbNmQQusFiUibYvfhQtjTDieSV1/ttY+dZDj9wOnW2v77dP2GhBtrT3jENfV\nUrQifqR8Wzn/u/h/KSgs4NMVn1Jjazi+4/HkZuUyPms86VHpvi5RRLxacilaEWlbfL4LljHmIWPM\ncGNMJ2NMDvAmsBuY4T0+2Riz7x4WfwO6GmMeMMb0MMb8D5AHPHrUixeRJksOT+aqgVfx0UUfsfam\ntTw/9nkigiO4+aOb6fh4R4Y8P4SHvnqI5ZuW+7pUERERaSCfhwugA/AasBj4J+AGhlhrN3iPpwC1\nX2Faa1cCZ+LZ3+J7PEvQ/sZaW3cFKRFpJeLC4rj02Ev59wX/xn2zm+nnTCc1IpU/f/Znuk3pxrHT\njuW+WfexeP1iX5cqIiIih+B3w6JaioZFibQ+26q28d6y9ygoLODfy/7Ntqpt9EroRW5WLrlZufRN\n6osx5vAXEpEjomFRItJQPl8tSkSkPuFB4UzoPYEJvSdQuaeSD4s+pKCwgCnzpnDvrHvJiM2oDRoD\nUwcqaIiIiPiYwoWItAohzhDG9hjL2B5jqaqu4pMVn1CwqIAXvnuBB756gI5RHRnfczy5vXLJSc/B\nYfxh1KeIiEj7onAhIq1OUEAQozNGMzpjNM/UPMMXq74gf1E+r//8Oo/Pe5zk8OTaoDG803CcDv2q\nExERORr0F1dEWjWnw8nJXU7m5C4n8+QZTzKneA4FhZ5N+6Z+O5X4sHjG9RhHblYuI7qOICggyNcl\ni4iItFkKFyLSZjiMg2EdhzGs4zAeOe0Rvi39tjZovPDdC0QFRzG2x1hys3I5rdtphAaG+rpkERGR\nNkXhQkTaJGMMg9IGMShtEH8d8Vd+WvcTBYs8QeOVH1/BFejizO5nkpeVx+mZpxMeFO7rkkVERFo9\nhQsRafOMMfRN6kvfpL7cffLdLF6/uDZonJt/LiHOEEZnjCY3K5cx3ccQFRLl65JFRERaJe1zISLt\n2vJNy5lZOJOCwgLmlswl0BHIyK4jyeuVx7ge44gLi/N1iSI+p30uRKShmrxWozHmBGPMdGPMHGNM\nmrftImPM8c1XnohIy+oa05Wbcm5izm/mUHx9MQ+f9jDbd2/nsrcvI+nhJEb+YyTPfPMM5dvKfV2q\niIiI32tSuDDG5AIfADuBY4Fg76Eo4NbmKU1E5OjqENmB6wZfx+eTPqf0xlKeOuMpjDFc+961pD6S\nygkvncATc5+guKLY16WKiIj4pab2XNwOXGWtvRzYvU/7V4DGHIlIq5ccnsxVA6/io4s+Yu1Na3lh\nrGe1qT98/Ac6Pt6Rwc8P5sGvHqRoY5GvSxUREfEbTQ0XPYBZB2mvAKKbXo6IiP+JC4vjkmMv4d0L\n3mXdTeuYfs500iLSuOuzu8h4MoNjpx3LX2b9hUJ3oa9LFRER8ammhotyIOMg7ccDy5tejoiIf4sK\nieLCvhcy87yZuG9288aEN+gZ35MHvnqAXlN70evpXtzxyR38UP4D7WXBDBERkb2auhTtc8ATxphL\nAQukGmOGAg8D9zZXcSIi/swV5CKvVx55vfKo3FPJh0UfUlBYwFPfPMVfvvgL3WK6kZuVS26vXAal\nDsIY4+uSRUREWlRTw8X9eHo9/gOE4RkitQt42Fr7ZDPVJiLSaoQ4QxjbYyxje4ylqrqKT1d8SkFh\nAS9+/yIPzn6Q9Mh0xmeNJ69XHjnpOThMkxfrExER8VtHtM+FMSYIz/CocGCRtXZbcxXW3LTPhYj4\nwp6aPXyx6gsKCguYWTiTsm1lJIcnc07Pc8jNyuXEzifidGg/U/Fv2udCRBpKm+iJiBwlNbaGuSVz\nyV+UT0FhAasrVhMXGsfZPc8mNyuXEV1HEBQQ5OsyRQ6gcCEiDdXgr8uMMTOBSdbaLd779bLWjj/i\nykRE2hiHcZCTnkNOeg6PnPYI88vmU7CogILCAl74zrPU7ZgeY8jNymVUt1GEBob6umQREZFGaUxf\nfAWeydt774uISBMZYxiYOpCBqQOZPGIyC9ctrO3RmP7jdFyBLs7sfia5WbmckXkG4UHhvi5ZRETk\nsDQsSkTEzyxZv4SCQk+PxoKyBYQ4QxjVbRS5WbmM6TGG6BBtJyRHl4ZFiUhDHemE7kQ8G+oBLLHW\nrmuWqlqAwoWItEYrNq2oDRpzS+YS6AhkZNeR5GblMq7nOOLD4n1dorQDChci0lBNWgvRGBNpjHkF\nWAN87r2tMcZMN8ZENWeBIiLtWZeYLtyUcxNzfjOH4uuLeeS0R9i+ezuXv3M5yQ8nM+IfI5j6zVTK\ntpb5ulQREZEm79D9HDAYOAuI9t7OAgYC05qnNBER2VeHyA5cO/haPp/0OWU3lvH0GU8TYAK47r3r\nSHs0jRNeOoHH5z7O6orVvi5VRETaqSYNizLGbAdGWWu/rNN+AvC+tdbVTPU1Gw2LEpG2auPOjby9\n5G3yF+Xz0fKPqKqu4ri04zy7g2fl0i22m69LlFZOw6JEpKGa2nOxgYOvGFUBbGp6OSIi0lixobFM\n6j+Jdy94F/fNbl4d/yodIjtw12d3kfFkBv3/1p97P7+XRe5Fvi5VRETauKaGi78Ajxpjkvc2eO8/\nBNzbHIWJiEjjRQZHckGfCyg4twD3zW7yJ+STlZDFg7MfpPfU3vR6uhd3fHIH35d/T3tZLVBERI6e\npg6L+g7IAIKBvYN7OwK7gGX7nmut9YsxSBoWJSLtWeWeSj4q+oiCwgLeWvIWmys30zWma+3QqePS\njsMY4+syxU9pWJSINFRjNtHb1/82axUiItKiQpwhjOkxhjE9xlBVXcWnKz6loLCAl79/mYdmP0R6\nZDrjs8aTm5VLTnoOAY4AX5csIiKtkN9tomeMuQWYDDxurb3hEOddCNwMZOKZ6/EecLO1dmM956vn\nQkSkjj01e/hy9ZcULPLspVG2rYwkV1Jt0Dix84k4HU39HkraCvVciEhDHdFfDGPMACDL+/Bna+13\nR3i9QcAVwA+HOW8Y8Hfgd8C7QBqeJXCfBfKOpAYRkfbE6XByUueTOKnzSTxx+hPMLZlbGzSe+fYZ\n4kLjGNdjHLm9chnZdSRBAUG+LllERPxYk8KFd2fufwInAZu9zdHGmE+B86217iZcMxyYDlwG3HGY\n04cAK6y1T3sfrzLGTAP+0NjXFRERD4dxkJOeQ056Dg+f9jALyhZQUFhA/qJ8Xvz+RSKDIxnbYyy5\nWbmM6jaK0MBQX5csIiJ+pqmrRT0JRAC9rbWx1tpY4BggEpjSxGs+Dbxjrf2kAefOAdKNMacDGGOS\ngAnAv5v42iIisg9jDANSBzB5xGSWXLOEH6/6keuHXM/35d9zzuvnkPBQAue+cS6vL3ydbVXbfF2u\niIj4iaYOixoNjLTWFu5tsNYuMsZcDXzY2IsZY84H+uPZ4fuwrLWzjTG/Al43xoTg+TneBq5p7GuL\niMihGWPok9SHPkl9uOuku1i6YSkFiwrIL8zn/ILzCQ4IZnTGaHKzchnTYwzRIdG+LllERHykqUvR\nbgVOsNZ+X6f9WOBza21kI67VAfgWT1hZ6G37FPiuvgndxphewEfAI3jCTArwMPCNtfayep6TDcwf\nPnw4UVFR+x2bOHEiEydObGjJIiLitWLTCmYWzqSgsIA5JXMIdAQyousIcrNyObvn2cSHxfu6RGmk\nGTNmMGPGjP3aKioqmDVrFmhCt4gcRlPDxVtANDDRWlvqbUsDXgU2WWvPacS1xgEzgWpg7yLrAYD1\ntgXbOkUaY/4BhFhrz92nbRjwBZBirV17kNfRalEiIi2oZEsJbxa+SUFhAV+s/gKD4cTOJ5Kblcs5\nPc8hJSLF1yVKE2m1KBFpqKbOubgGz/yKlcaYImNMEbDC23ZtI6/1MdAHz7Coft7bt3gmd/erGyy8\nwoA9ddpq8AQS7QIlIuIDHSI7cO3ga/ls0meU3VjG1DOn4nQ4+d37vyPt0TSOf/F4Hp/7OKsrVh/+\nYiIi0io1eZ8L49nKdSTQ09tUaK39uFmKqjMsyhgzGUiz1l7sfXwxnmVnfwd8AKQCjwF7rLU59VxT\nPRciIj6wcedG3l7yNgWFBXxY9CFV1VUMSh3k2R28Vy4ZsRm+LlEOQz0XItJQjZ7QbYwJBN4HrrLW\nfoRn7kNzq5t4UoD02oPW/t27dO3VeOZabAb+A9zSArWIiMgRiA2NZVL/SUzqP4ktu7bw76X/pqCw\ngLs/v5tb/nMLfZP6kpeVR26vXHol9PJ1uSIicgSaOufCDeRYa5c1f0ktQz0XIiL+ZXvVdt7/5X0K\nCgt4d+m7bK3aSs/4np4ejaxc+if3x9NJLr6mngsRaaimzrmYDvymOQsREZH2xRXkIrdXLq/lvsa6\nm9fxzsR3GJw2mKnfTCX72WwynszgDx/9gXkl82jqEF4RETm6mrrPhRO41BgzEpgPbN/3YH1LyPqb\nGTNmaAlaERE/EOIM4azuZ3FW97PYXb2bT1d+SsGiAl7+/mUemv0QHSI7ML7nePJ65ZGTnkOAI8DX\nJYuIyEE0dVjUp4c6bq09uckVtZCDDYsaMGAA8+fP921hKOSIiNSnuqaaL1Z/QcGiAmYunknp1lKS\nXEmc0/MccnvlclLnk3A6mvo9mTSUhkWJSEM1ebWo1qY2XHzzDdkDPRuBJycnU15e7tvCgLFjx/L2\n22/7ugwREb9WY2uYVzKPgsICCgoLWLl5JbGhsYzrMY68XnmM6DKCYGewr8tskxQuRKShmvR1jzHm\nReB31tqtddpdwJPW2kubo7iWMOvSS+m/fDkOlws2bIC+fcHlgvDw/f89WNuhjrlcEBjo6x9PRKTN\nchgHQ9OHMjR9KA+d+hALyhbUBo2Xvn+JyOBIxnQfQ25WLqMzRhMaGHrQ66i3WESk5TR1WFQ1np2w\n19VpjwfKrbV+10e9t+eCadPo0bEjNxQXc8cNN7D2kktg+3bYtu3Q/1ZVHf5FgoKaFE7GPvMMb999\nN0REQGSk59+9t9BQ0GopIiL1stbys/tnChZ5gsZP634iLDCMMzPPJDcrlzMyzyAiOKL2fPUWN556\nLkSkoRoVAowxkXh2wDZAhDGmcp/DAcAZwLqDPddfvNijB+/ExXFVSAj06sWd11/P/6SlkRQUdOgn\n7t7tCRmHCiCHOrZhA6xefeCxHTs81x8z5uCv63DsHzb2vdUNIg25qXdFRNoYYwzHJB7DMYnHcOdJ\nd7J0w9LaoHF+wfkEBwQzKmMUuVm5jOlez+9aERFpFo3quTDG1HDgBnf7ssCd1tr7jrSw5lZ3Qvcv\nO3bQ97jjMFOnssdaLkxK4voOHegTHn50C6upYexZZ/H2Cy/A1q3/vW3Zsv/jQ932PXfXrkO/XkhI\nw0JIQ4KLy6VeFRHxays2rWBm4UwKCguYUzIHp8NJwlsJlH5b6uvSWhX1XIhIQzV2+NLJeHotPgFy\ngY37HKsCVllrW8Vv7IywMCLXr6dw6FCeLSvjyZISXiov59SYGK7v0IFRsbE4jsYHZ4cDnE5ISfHc\njtTu3Q0PIvveNmyAlSsPbK+pqf+1jPEM72pMD8qhzj1c75GISCN1ienCjTk3cmPOjazZsoY3F7/J\nU+8/5euyRETarEaFC2vt5wDGmC5AsbX2EJ88/V9aWhoxgYH8sWNHbujQgTfcbh4tLuaMn34iKyyM\n6zt04FdJSYQGtKL11AMDITbWcztS1nqGbTW292TrVli16sBzdu489OsFBTV92Ffdc10uT3ATEfFK\ni0zjmuOu4cO4D31diohIm9WkidfW2lXGmGhjzHFAInV2+rbW/qM5imtpN910U+39QIeDC5KSmJiY\nyJcVFTxaUsKVS5dy64oV/E9qasPmZTSR365aYsx/J6AnJx/59fbs8cwzaeyQr02bPPNV6h6rrj70\n64WHH/lclb3nBreN5S21So6IiIi0pKYuRTsGeBUIB7aw/zwMC7SKcHGwD1nGGE6IjuaE6Gh+2bGD\nJ9as4eHiYu5fvbrF5mW0mw97TidER3tuR8paqKxs2jyVkpIDz987sb4+gYHNN1clPBx81BumcCEi\nIiItqalLxj4CvAjcaq09zKey1isjLIwnMzO5p3NnnisrY4qv5mXIgYzxLNMbGgqJiUd+vepqT69K\nY+eqbN0KpaUHnrtnz6FfLyys+eaqhIRoYr1IIyhgi4i0nKaGizRgSlsOFvuKCQzkDx07cn1bmZch\nBwoIgKgoz+1IWetZtaspc1XKymDp0v3btm07fO0NDSIrVsD06f/tQYqO9vzM0dGeHhWFFGkHz98o\nogAAIABJREFUFC5ERFpOU8PFB8BAYHkz1uL3fDkvQ1oRYzy9CSEhkJBw5NerqTl0r8qhQkt5+f7t\nGzfCRRcd/HUcjv8GjX1DR9379R2LjPQMfRMREZF2q6mfBP4NPGSM6QX8BOze96C1tk1vfeqLeRnS\njjkcng/ukZFHfq2xY+H112HzZqio8Pxb937dx0uX7t++fXv91w8PP3wIOdT9NjJxXkREpL1qarh4\nzvvvnw9yzOLZrbtd0LwMaXX2zlVp6r4qu3d7QsahAsm+90tKYOHC/z7essUzdOxgQkKOrPdEGzuK\niIj4VFOXotUGAnVoXoa0G4GBEB/vuTVFTY1niNahAsm+9zdsgOXL9w8y9U2Y3zt3pjGBpO7QLv1/\nKiIi0mSNChfGmP8DJlprK7yPbwH+Zq3d7H0cB3xhre3V7JW2Ek2Zl6HlQeVo8Yv/zvbO7Wjq5Hlr\nPRsyHm441773y8v3P+9QGzpGRjZ+ONe+jzXvSkRE2jFj6xuecLCTjakGUqy167yPtwD9rbXLvY+T\ngFJrrd999WeMyQbmz58/n+zs7KP62r/s2MGUNWt4sayM3dYeMC9j7NixvP12m56mIuJfqqr+Gz4a\n0ntS9/GWLfVfOzT0yHpPwsI0tEv8zoIFCxgwYADAAGvtAl/XIyL+q7HDour+xdNfwAbICAtjSmYm\nd9czL6Ph8U5EmkVQkGclr6au5lVdffChXfUFFLcbfvll//Pq22F+72aTTe09iYz09A6JiIj4gNaN\nPIr2nZeR73bziHdeRvjmzTxRUsKvk5KICQz0dZkicjgBAUe227y1nl3hD9djsu/9NWv2f1xZefBr\nG3PooV2HCytRURraJSIiTdbYcGG9t7pt0giBDgcTk5I43zsv47yAAG4qKuKW5cs5NyGBK1NTGRoZ\nidHQCJG2yRjPylYuF6SlNe0au3YdfGhXfQFlxYr927durf/aYWGNH8617+PQUL8e2qV5biIiLacp\nw6JeNsbs8j4OAf5mjNm78L0WqW+EvftlDIyI4NkhQ3ipvJznysr4x9q19HG5uCIlhYuSk4nSxmQi\nUldwMCQmem5NUV3tmTvS0N6TtWthyZL9j9XUHPzagYFNmwy/935ERIsO7VK4EBFpOY391Pr3Oo+n\nH+ScfzSxlnYtOTiYP3XqxB87duSjTZuYVlrK73/5hT8sX875iYlcmZrKcRER6s0QkeYREAAxMZ5b\nU1jr2VCxMZPhi4v3P7Zr18Gvbcx/h2g1ZVPGqChPwBERkaOuUeHCWntJSxUiHg5jGBUby6jYWEp3\n7eLFsjKeKyvjpfJy+oeHc0VKChcmJRGp3gwR8SVjPDuyh4dDhw5Nu0ZlZcPmm+y9X1S0/+Nt2+q/\ntstVfwgpKmpavSIiclj6hOoH6uueTw0O5vbOnflTp058sHEj00pLuWbZMm4uKmJiUhJXpqQwMDLy\nKFcrItJMQkI8t6Skpj1/z56GDe3a+7isDAoLPRsziohIi1C48AOHG/sbYAxnxMVxRlwcJZWVvFBe\nzvNlZTxfVkZ2eDhXpqZyQWIi4erNEJH2xOmE2FjPrTHGjm2ZekREBL9bDN0Yc4sxpsYY8+hhzgsy\nxtxnjFlpjKk0xiw3xkw6SmX6TIeQEO7s3JkVgwfz9jHHkBIUxFVLl5IyZw5XLVnCd4daAUZERERE\npAX51VfdxphBwBXADw04/Q0gAbgEKAJS8MOw1FKcDgdj4uMZEx/P6spKni8r44WyMqaVlXFcRARX\npKZyfmIirgC/2yxdRERERNoov/kwbowJx7P61GXA5sOcOxo4ATjDWvuptXa1tXaetXbOUSjV73QM\nCeGeLl1YNWQIb/buTWxgIJcvWULq7NlcvXQpPx5q0qOISDujZWhFRFqO34QL4GngHWvtJw04dwzw\nLfBHY0yJMWaJMeYhY0xIy5bo35wOB2cnJPBe374sHzyYa9LSmLl+Pf2+/ZahCxbwclkZO6qrfV2m\niIhPKVyIiLQcvwgXxpjzgf7Anxr4lK54ei56A2cDvwPy8AQUATqHhnJf166sHjKE/N69iQgI4JIl\nS0ibM4frli3j5+3bD38REREREZFGMNZa3xZgTAc8vRAjrbULvW2fAt9Za2+o5zkfAMcDSdbabd62\nc/DMw3BZaw/YmckYkw3MHz58OFFRUfsdmzhxYrv4Jqto506eKy3lpfJy1u3ezbDISK5MTSUvIYFQ\nzc0QERE8O5jPmDFjv7aKigpmzZoFMMBau8AnhYlIq+AP4WIcMBOoBvZuPx0AWG9bsK1TpDHmZSDH\nWtt9n7aewM9Ad2vtATsk7Q0X8+fPJzs7uyV+lFajqqaG/12/nmmlpXyyeTMxTicXJydzZUoKPV0u\nX5cnIiJ+ZsGCBQwYMAAULkTkMPxhtaiPgT512l4GCoH76wYLr6+APGNMmLV2h7etB1ADlLRUoW1F\nkMPBuYmJnJuYyLIdO3i2rIyXy8t5vKSE4VFRXJmaSm5CAsEOvxg1JyIiIiKthM8/PVprt1trF+17\nA7YDG6y1hQDGmMnGmL/v87TXgA3AS8aYLGPMcOBB4IWDDYmS+mWGhfFQt26UDB3Ka1lZGODCwkLS\nZs/mpl9+YemOHYe9hoiIiIgI+EG4qEfd3ooUIL32oLXbgVOBaOAb4BXgLTwTu6UJgh0OJiYl8dmx\nx1I4aBC/Tk7mpfJyenz9Nad8/z2vr1tHVU2Nr8sUERERET/mD8OiDmCtPaXO40sOcs5SYNRRK6od\n6ely8WhGBpO7dCHf7WZaWRnnL1pEQmAglyQnc0VqKt1CQ31dpoiIiIj4GX/tuRA/EBIQwK+Sk/ni\n2GNZOGgQExMTebasjIx58zj1hx8ocLvZrd4MEREREfFSuJAG6e1y8URmJmuGDuXlnj3ZXl1N3s8/\nkz5nDrcuX86KnTt9XaKIiIiI+JjChTRKWEAAFycnMzs7mx8HDiQvIYGn16yh27x5jP7hB950u9mj\n3gwRERGRdknhQpqsT3g4T3XvTmlODs/36MGmPXsY//PPdJw7lztWrGB1ZaWvSxQRERGRo0jhQo6Y\nKyCAS1NSmDdgAN8NGMC4+HieKCmh89y5nPXjj7yzfr16M0RERETaAYULaVb9IyJ4pnt3SocOZVr3\n7pRXVTF24UK6zJvHXStWUKLeDBEREZE2S+FCWkS408nlqal8O3Ag3w4YwOmxsTxcXEynuXMZ+9NP\n/HvDBqoPuvm6iIiIiLRWChfS4gZERPBsjx6U5uTwdGYmxbt2cdZPP9F17lzuXbmS0l3aVF1ERESk\nLVC4kKMm0unkqrQ0FgwYwLzsbEbGxHD/6tV0nDOH8QsX8sHGjdSoN0NERESk1VK4kKPOGMNxkZG8\n0LMnpTk5PJGZyS87dzL6xx/pNm8ek1etoly9GSIiIiKtjsKF+FSU08nVaWn8MHAgs489lhOjorh3\n1SrS584lb+FCPlJvhoiIiEiroXAhfsEYw9CoKF7OyqJ06FAe6daNwh07OO3HH+k+bx4PrF7Nuqoq\nX5cpIiIiIoegcCF+JyYwkOs6dGDhoEF80b8/Q6OiuHPFCjrMmcN5P//MJ5s2YdWbISIiIuJ3FC7E\nbxljOD46mleysliTk8MDXbvyw7ZtjPjhB3p8/TUPr17NevVmiIiIiPgNhQtpFeICA7k+PZ3C447j\ns/79GRgRwW0rVpA2Zw4XLFrE55s3qzdDRERExMecvi5ApDGMMZwYHc2J0dGsr6ri5fJyni0rY8b3\n39MzLIwrU1L4dXIysYGBvi5VREREpN1Rz4W0WvFBQdzUsSNLjjuO//TrR1+Xiz8sX07q7Nn8urCQ\nL9WbISIiInJUqedCWj1jDKfExHBKTAzrqqp4qbycZ0tLeWXtWnqHhXFFaioXJSURo94MERERkRal\nngtpUxKDgvhjx44sGzyYD/v2pWdYGDcWFZE2Zw6TCguZU1Gh3gwRERGRFqKeC2mTHMZwamwsp8bG\nUr5rFy+Wl/NcWRl/X7uWvi4XV6amcmFSElFO/S8gIiIi0lzUcyFtXnJwMLd26kTR4MG816cPXUND\nuW7ZMlJnz+ayxYv5essW9WaIiIiINAN9bSvthsMYRsfFMToujtJdu3ihrIznysp4obyc/uHhXJmS\nwoVJSUSoN0NERESkSdRzIe1SanAwd3TuzIohQ3i3Tx/Sg4O5etkyUmbP5oolS5i/dauvSxQRERFp\ndfQVrbRrAcZwZlwcZ8bFUVxZyQtlZTzv7dEYGBHBlSkpnJ+YSLh6M0REREQOSz0XIl7pISHc1aUL\nK4cM4a1jjiExMJArli4ldc4c/mfpUr5Xb4aIiIjIIenrWJE6nA4HY+PjGRsfz6rKSp4vK+OFsjKe\nKS3luIgIrkxN5bzERFwBAb4uVURERMSvqOdC5BA6hYRwb5curBoyhJm9exPjdHLZkiWkzZ7NtcuW\nsXDbNl+XKCIiIuI3/C5cGGNuMcbUGGMebeD5w4wxu40xC1q6Nmm/Ah0OzklI4P1+/SgaPJj/SUvj\njXXr6PPttwxbsIB/lJezs7ra12WKiIiI+JRfhQtjzCDgCuCHBp4fBfwd+Lgl6xLZV5fQUCZ37Urx\n0KG80asXYQEBXLx4MWlz5vD7ZctYtH27r0sUERER8Qm/CRfGmHBgOnAZsLmBT/sb8Cowt6XqEqlP\noMNBXmIiH/Xrx7LjjuPylBReW7eO3t98wwnffcf08nIq1ZshIiIi7YjfhAvgaeAda+0nDTnZGHMJ\n0AW4u0WrEmmAjLAwHujWjZKhQ/lnr14EGsNF3t6MG3/5hSU7dvi6RBEREZEW5xerRRljzgf6AwMb\neH4mMBk43lpbY4xpyfJEGizI4eC8xETOS0xk6Y4dPFtaysvl5TxaUsJJ0dFckZLC+IQEgh3+lOtF\nREREmofPP+EYYzoAjwMXWmt3N+B8B56hUHdaa4v2NrdgiSJN0j0sjIczMigZOpRXs7KosZYLCgvp\nMGcONxcVsUy9GSIiItLGGGutbwswZhwwE6jmvyEhALDetmC7T5HeSdybgD37nO/w3t8DnGat/ewg\nr5MNzB8+fDhRUVH7HZs4cSITJ05sxp9K5OAKt2/n2bIy/l5ezqY9ezglOporU1M5Oz6eIPVmiIgf\nmDFjBjNmzNivraKiglmzZgEMsNZqdUYRqZc/hAsX0KlO88tAIXC/tbawzvkGyKpz/tXAyUAusNJa\nu/Mgr5MNzJ8/fz7Z2dnNVL1I0+ysribf7WZaaSlfbdlCYmAgl6akcHlKCl1DQ31dnojIfhYsWMCA\nAQNA4UJEDsPncy6stduBRfu2GWO2Axv2BgtjzGQgzVp7sbcXo+7564DKukFExF+FBgRwUXIyFyUn\n8/P27UwrLeWZNWu4f/VqTouJ4YrUVMbGxRGo3gwRERFpRfz1k0vd7pQUIN0XhYi0tN4uF1MyMynN\nyeGlHj3YWl1N3s8/03HuXG5bvpyVOw/oiBMRERHxSz4fFnW0aFiUtCY/btvGtNJSpq9dy9bqakbF\nxnJVaipnxsbiVG+GiBxlGhYlIg2lTykifqhveDhPd+9OaU4Oz/XowYbduzl74UI6z53LnStWUFxZ\n6esSRURERA6gcCHix1wBAfwmJYWvBwxgwYABnBUXx6MlJXSeO5cxP/3Eu+vXU91Oeh9FRETE/ylc\niLQSx0ZE8LcePSgdOpRnunendNcuxnh7M+5euZI1u3b5ukQRERFp5xQuRFqZCKeTK1JTmT9wIN9k\nZzM6NpaHVq+m45w5nP3TT7y3YUO9vRl1164XERERaU4KFyKt2MDISJ7r0YPSnByeysxkZWUlZ/z0\nE93mzuW+Vasoq9OboXAhIiIiLUnhQqQNiHQ6+W1aGt8NHMjc7GxGxMRw36pVdJw7l9yFC/lw40Zq\nNDdDREREWpjPN9ETkeZjjGFwZCSDIyN5NCOD6WvXMq20lFE//kiXkBCCd+5k+c6d2gVcREREWoR6\nLkTaqCink6vT0vhh4EBmH3ssw6OiWLpjB93mzSP722/566pVLNuxw9dlioiISBuinguRNs4Yw9Co\nKIZGReGOjWVSr17ku938ZdUqbl2xgr4uF3kJCeQlJJDlcvm6XBEREWnFFC5E2pEAY5iQmMiExER2\nVFfzwcaN5LvdPFRczJ9XrqRXWBgTvEGjt8uFMcbXJYuIiEgronAh0k6FBQRwTkIC5yQkUFldzYeb\nNpHvdvNYSQl3r1pFj9DQ2h6NfuHhChoiIiJyWAoXIu3IxIkTD9oeEhDA2Ph4xsbHs6umhv94g8bU\n0lLuW72ajH2CRraChoiIiNRD4UKkHakvXOwr2OHgjLg4zoiLY1pNDZ9s3ky+281zpaXcv3o1nUNC\nyEtIYEJCAoMiIhQ0REREpJbChYjUK9DhYFRsLKNiY3kmM5PPKyrId7v5R3k5DxcXkx4cXNujMSQy\nEoeChoiISLumcCEiDeJ0OBgRE8OImBieyszkC2+Pxox163ispIS0oCByvUEjJyqKAAUNERGRdkfh\nQkQaLcAYToqJ4aSYGJ7IzGSOt0cj3+1mypo1JAcFMT4+nryEBE6IisLp0JY6IiIi7YHChYgckQBj\nOD46muOjo3k0I4N5W7bUBo2ppaUkBAbWBo2ToqMVNERERNowhQsRaTaOfTbse7hbN77dupU3vEFj\nWlkZcU4nZ3uDxoiYGAIVNERERNoUhQsRaRHGGAZFRjIoMpIHunblu23byHe7ecPt5oXycmKcTsZ5\ng8bImBiCFTRERERaPYULEWlxxhiyIyLIjojgvi5d+HH7dk/QWLeOl8vLifTus5GXkMComBhCAgJ8\nXbKIiIg0gcKFiBxVxhj6hYfTLzycezp3ZtGOHbVBY/ratYQHBDAmLo68hARGx8YSpqAhIiLSaihc\niIjPGGPo7XLR2+Xizs6dKdy+nQLvHI3cdesIczg40xs0zoyLw6WgISIi4tcULkTEb2S5XNzucnF7\n584s27GDAu8cjfMWLSLU4eD02FjyEhI4Ky6OCKd+fYmIiPgb/XUWEb+UGRbGLZ06cUunTizfubO2\nR+OCwkKCjWFUbCwTEhIYEx9PlIKGiIiIX9BfZBHxe11DQ7m5Y0du7tiRVZWVtUHjosWLCTSG02Ji\nyEtIYGx8PLGBgb4uV0REpN1SuBCRVqVTSAg3pKdzQ3o6JZWVzFy/nny3m0uXLCFg6VJGREczITGR\ncXFxxAcF+bpcERGRdkXhQkRarQ4hIVzXoQPXdehA6a5dvOkNGlcsWcKVwMneHo1z4uNJVNAQERFp\ncdq1SkTahNTgYK5OS+PT/v0pzcnh6e7dsdZy9dKlpMyezSnff8/UNWso27XL16WKiIi0WX4XLowx\ntxhjaowxjx7inHOMMR8aY9YZYyqMMbONMacdzTpFxH8lBQVxZWoqH/fvT1lODtO6dyfIGH73yy+k\nzZnD8O++Y0pJCSWVlb4uVUREpE3xq3BhjBkEXAH8cJhThwMfAqcD2cCnwDvGmH4tW6GItDYJQUFc\nlprK+/36sTYnhxd79CAiIICbiopInzuXYQsW8FhxMasVNERERI6Y38y5MMaEA9OBy4A7DnWutfb6\nOk23GWPGAWM4fDARkXYqNjCQSSkpTEpJYfPu3byzYQP5bjd/Wr6cG4qKOC4igryEBPISEugSGurr\nckVERFodf+q5eBp4x1r7SWOfaIwxQASwsdmrEpE2KTowkIuSk3mrTx/WDRvGa1lZpAUH8+eVK+k6\nbx4Dv/2W+1et4pcdO3xdqoiISKvhFz0Xxpjzgf7AwCZe4mbABfyr2YoSkXYj0ulkYlISE5OS2LZn\nD/+3cSP5bjf3rlrFn1asoJ/LxYTERPISEugRFubrckVERPyWz8OFMaYD8Dgw0lq7uwnPvwDPMKqx\n1tr1zV2fiLQv4U4n5yYmcm5iIjuqq3nPGzTuX72a21es4BiXq3boVG+Xy9flioiI+BVjrfVtAZ65\nEjOBasB4mwMA620LtvUU6e3xeB7Is9a+f5jXyQbmDx8+nKioqP2OTZw4kYkTJx7RzyEibdvO6mo+\n3LSJfLebt9evZ0t1NVlhYbVBo4/LhWeEpkjrNmPGDGbMmLFfW0VFBbNmzQIYYK1d4JPCRKRV8Idw\n4QI61Wl+GSgE7rfWFtbzvIl4gsV51tp3G/A62cD8+fPnk52dfWRFi0i7tqumho+8PRpvbdjA5j17\nyAwNrQ0ax4aHK2hIm7JgwQIGDBgAChcichg+HxZlrd0OLNq3zRizHdiwN1gYYyYDadbai72PL8AT\nQK4DvjHGJHmfutNau+Vo1S4i7VOww8FZ8fGcFR9PVU0Nn3h7NKaVlvLX1avpGhJSGzQGRkQoaIiI\nSLvh83BRj7rdKSlA+j6PL8czdOpp722vvwOXtmxpIiL/FeRwMDoujtFxcTxTU8NnmzeT73bzYnk5\nDxYX0zE4uDZoDI6MxKGgISIibZhfhgtr7Sl1Hl9S5/HJR7ciEZHDC3Q4ODU2llNjY3k6M5MvKirI\nd7t5de1aHi0poUNwMLnx8eQlJJATFaWgISIibY5fhgsRkdbO6XBwckwMJ8fEMCUzk6+8QeMNt5sn\n1qwhJSiI8fHxTEhM5PioKAIUNEREpA1QuBARaWEBxjA8Oprh0dE8npHB3C1beMPtJt/t5unSUhID\nAxnvHTp1YlQUToc/7W8qIiLScAoXIiJHkcMYcqKiyImK4pFu3fhm61byvUHjb6WlxDmdnJOQwISE\nBE6OjiZQQUNERFoRhQsRER9xGMPgyEgGR0byYNeuzPcGjTfcbp4vKyPG6eRs7xyNkTExBCloiIiI\nn1O4EBHxA8YYBkZGMjAykr927coP27bVBo2XysuJCghgbHw8ExISODUmhpCAAF+XLCIicgCFCxER\nP2OMoX9EBP0jIri3SxcWbt9eO3TqlbVriQgIYExcHHkJCYyOjSVUQUNERPyEwoWIiB8zxtAnPJw+\n4eHc3aULi7Zvp8Dbo/HaunW4HA7O8gaN0+PicCloiIiIDylciIi0Ir1cLnq5XNzRuTNLduygwNuj\nMWHRIkIdDs6IjSUvIYEz4+KIcOpXvIiIHF36yyMi0kr1CAvj1k6duLVTJ4p27qwdOjWxsJAQh4PR\n3qBxVlwcUQoaIiJyFOivjYhIG9AtNJQ/duzIHzt2ZOXOnRSsX0++282vCgsJMobTvEFjbFwcMYGB\nvi5XRETaKIULEZE2pnNoKDemp3NjejrFlZW1Q6cmLV5MoDGMjIkhLyGBcfHxxCloiIhIM9Ki6SIi\nbVh6SAi/T0/ny+xsSoYO5dFu3dhRXc1lS5aQ9NVXnPbDDzxXWoq7qsrXpYqISBugcCEi0k6kBQdz\nTYcOfHbssZQOHcqTmZlUW8tVS5eSPHs2I77/nmfWrKF81y5flyoiIq2UwoWISDuUHBzMb9PS+E//\n/pTn5PC37t0JMIZrly0jdc4cTvruO54qKaFUQUNERBpBcy5ERNq5hKAgLk9N5fLUVDbs3s1b3sng\nNxQVce0vvzAsMpK8hARyExJIDwnxdbkiIuLHFC5ERKRWXGAgl6akcGlKCpt27+adDRt4w+3mj8uX\nc31REUP2Bo34eDqHhvq6XBER8TMKFyIiclAxgYH8OjmZXycnU7FnD+9u2EC+281ty5dzU1ERAyMi\nyEtIIC8hgW4KGiIigsKFiIg0QJTTyYVJSVyYlMTWPXv4v40beWPdOu5euZJbli/n2PDw2qDRPSzM\n1+WKiIiPKFyIiEijRDidnJeYyHmJiWyvruY9b4/G5FWruG3FCvq4XOQlJDAhIYEsl8vX5YqIyFGk\ncCEiIk3mCgggLzGRvMREdlZX8/7GjeS73TxcXMydK1fSKyystkfjGJcLY4yvSxYRkRakcCEiIs0i\nNCCAcxISOCchgcrqaj7atIl8t5snSkq4Z9UquoeG1vZo9AsPV9AQEWmDFC5ERKTZhQQEMCY+njHx\n8VTV1PCxN2g8U1rK5NWr6RYSUtujMSAiQkFDRKSNULgQEZEWFeRwcEZcHGfExTGtpoZPN28m3+3m\nhfJyHigupvM+QeM4BQ0RkVZN4UJERI6aQIeD02JjOS02lqmZmXxeUUG+280/yst5uLiY9OBgcr1B\nY2hkJA4FDRGRVkXhQkREfMLpcDAiJoYRMTE8lZnJl96g8fq6dTxeUkJqUFBt0BgWFUWAgoaIiN9T\nuBAREZ8LMIYTo6M5MTqaJzIymO0NGvluN0+uWUNSYCDjvUFjeFQUTofD1yWLiMhBKFyIiIhfcRjD\n8dHRHB8dzaMZGXy9ZQtveIPGM6WlJAQGck58PHkJCZwUHU2ggoaIiN9QuBAREb/lMIYhUVEMiYri\n4W7d+HbrVvLdbt5wu3m2rIxYp5OzvUFjREwMQQoaIiI+5Xe/hY0xtxhjaowxjx7mvJOMMfONMZXG\nmKXGmIuPVo3tyYwZM3xdQquk963x9J41TXt634wxDIqM5IFu3SgaPJj5AwZwZWoqsyoqOOOnn0ia\nPZtJhYW8u349u2pq6r3OtddeexSrFhFpX/wqXBhjBgFXAD8c5rzOwLvAf4B+wBPA88aYU1u4xHan\nPX1waU563xpP71nTtNf3zRhDdkQEk7t2Zelxx/HDwIFcm5bGvK1bGbNwIYlffcWvFi3irfXr2Vld\nvd9z33jjDR9VLSLS9vnNsChjTDgwHbgMuOMwp/8WWG6t/YP38RJjzPHA9cBHLVeliIj4G2MMfcPD\n6Rsezj1duvDz9u21k8FfXbiQ8IAAzoqLIy8hgdNjY31drohIm+Y34QJ4GnjHWvuJMeZw4WII8HGd\ntg+Ax1qkMhERaTV6u1z0drm4s3NnFm/fTsH69byxbh1569YR5nBQs2ePr0sUEWmz/CJcGGPOB/oD\nAxv4lGRgbZ22tUCkMSbYWrurOesTEZHWqafLxW0uF7d16sSyHTsocLu5V/tliIi0GJ+HC2NMB+Bx\nYKS1dncLvlQIwGWXXUZERMR+B0aNGsXo0aNb8KVbr4qKChYsWODrMlodvW+Np/esafQ64iVQAAAK\nBklEQVS+Nc5pwEM1NXrPDuH999/ngw8+2K9t69ate++GHPWCRKRVMdZa3xZgzDhgJlAN7P06KQCw\n3rZgW6dIY8znwHxr7Q37tE0CHrPWxtTzOjnAV83+A4iIiLQfw6y1s31dhIj4L5/3XOCZO9GnTtvL\nQCFwf91g4TUHOL1O22ne9vp8DwxoYo0iIiICi31dgIj4N5/3XByMMeZT4Lu9PRPGmMlAmrX2Yu/j\nzsBPwFTgRWAEnqFVZ1hr6070FhER+f/27jVWrqqMw/jzBwoGjfpBY0moEqIWiVoNGLl4QUshFv0g\nKOCVgApePiimKeAN70qAEiqoREPVChqNeEODVEqCQFGDDRpuGmmlXAoigmIrVbr8sPcx4zjTnnPY\nu3tm+vySlc7s9c4+a72ZnnXefZmRJO0AI/U9Fz36K569gHn/7SxlPXAUcDjVGYlTgXdYWEiSJEnd\nGckzF5IkSZLGz6ieuZAkSZI0ZiwuJEmSJDVibIuLJO9Lsi7J5iQ3JHnJduIPS3Jjkn8m+X2SEwbE\nvDHJrfU+b0rS/4lUY6/pvCV5Z5JrkjxYt1Xb2+e4aeO91hN7fJKtSS5rfuTdaen/51OSXJjknjru\ntiQT9QU1LeXtA3WuNiW5M8myJHu0N4sdbyZ5SzI3ySVJbk/yWJJlQ+Imej1oOmc7w1ogaXrGsrhI\nchxwLnAm8GLgJuBnSZ42JH4f4HLgKmABcD7w1SSLemIOAS4FvkL1beE/BH6QZP/WJrKDtZE34JVU\neTsMOAjYAFyZZK9WJrGDtZSz3tizgWuaH3l3Wvr/OYfqY6ufCRwNPBd4F3B3W/PY0VrK25uBz9X7\n3A84CTgW+Exb89jRZpo3YA/gfuBTVB8IMmifE70etJEzJnwtkDQDpZSxa8ANwPk9zwPcBSwdEn8W\n8Nu+bd8Cftrz/NvAj/pi1gBf7Hq+o5y3Aa/ZBXgYeGvX8x3lnNV5uhY4EVgBXNb1XEc5Z8C7gT8A\nu3Y9vzHL2xeAVX0x5wDXdD3frvLW99qrgWUDtk/0etBGzgbETdRaYLPZpt/G7sxFfQTzAKqjdQCU\nUgrVUc2Dh7zsoLq/18/64g+eRszYajFv/Z4IzAEenPVgR0TLOTsTuK+UsqKZ0Y6GFnP2Ouo/7pJs\nTPK7JGckGbvfYYO0mLfrgQOmLk9Jsi+wGPhJMyPv1izzNh0Tux60mLN+E7MWSJqZUfiG7pl6GrAr\ncF/f9vuA+UNeM3dI/JOT7FFKeXQbMXMf33BHRlt563cW1aUqk/CdI63kLMnLqM5YLGhysCOirffZ\nvsCrgW8CrwGeDXyJ6nfYp5oZeqdayVsp5Vv1pS7XJkn9M75cSjmrwbF3aTZ5m45JXg/aylm/SVoL\nJM3AOBYXGlFJTqe6nvuVpZQtXY9nFCV5EvAN4F2llL92PZ4xsgvVHz8n10dZ1ybZG1jCZBQXrUhy\nGPAhqsvKfkVVlC1Pcm8p5dNdjk2Ty7VA2rmNY3HxAPAY8Iy+7c8ANg55zcYh8X/rOfo+LGbYPsdN\nW3kDIMkSYCmwsJRy8+Mf7khoPGdJ9gOeBfy4PpIM9QcrJNkCzC+lrGti8B1p6312L7ClLiym3ArM\nTbJbKeXfj2/YnWsrb58EVvZcfndzXeBeBExCcTGbvE3HJK8HbeUMmNi1QNIMjN31yqWUfwE3Agun\nttV/pC2kur54kDW98bUj6u3bilnUFzO2WswbSZYCHwaOLKWsbWrMXWspZ7cBL6D6BJoFdfsRsLp+\nvKGh4XeixffZdVRH3XvNB+6dgMKizbztCfTnZ2vP/sfaLPM2HRO7HrSYs4ldCyTNUNd3lM+mUZ1u\n3QS8nerjFS8C/gI8ve7/HPD1nvh9gL9TXQM6H3gvsAU4vCfmYOBR4IN1zMeBfwL7dz3fEc/baXWe\nXk915GuqPbHr+Y5qzgb8jEn7tKg23md7Aw8By4HnAEdRHWU9vev5jnjezqzzdlwdv4jqU7cu7Xq+\nXeWt3raAqsD/NbCyfv68nv6JXg9aytlErwU2m236rfMBzHrg1UK6HthMdTTpwJ6+FcDqvvhXUB2t\n2Vwvrm8bsM9jqI4sbwZ+S3X0pfO5jnLegHVUp9j728e6nuuo5mzA/iequGgrZ8BLqY6sbqpjTgPS\n9VxHOW9UZ6c/Cvwe+Ee97+XAk7uea8d52zrgd9YdfTETvR40nbOdYS2w2WzTayml9xJmSZIkSZqd\nsbvnQpIkSdJosriQJEmS1AiLC0mSJEmNsLiQJEmS1AiLC0mSJEmNsLiQJEmS1AiLC0mSJEmNsLiQ\nJEmS1AiLC0mSJEmNsLiQNFSSryXZmuSLA/ourPsu7mJskiRp9FhcSNqWAtwJHJ9kj6mN9eM3AX/q\namCSJGn0WFxI2p61wAbg6J5tR1MVFmt7A1M5I8kdSTYlWZvkmJ7+pya5JMn9df/tSU6o++YkuSDJ\nPUk2J1mX5LRhg0pydZJlfdu+33smpd7HR5JcmuSRJHclee/jyoYkSRrK4kLS9hTgYuCknm0nASuA\n9MV+CHgrcDKwP3AesDLJy+v+TwP7AUfW/74HeKDuez/wWuANwHOBtwDrGxj/Eqoi6EXA54Hzkyxs\nYL+SJKnPbl0PQNJYuAT4fJJ5VAclDgGOA141FZBkd+AMYGEp5Zf15vV1YXEK8AtgHrC2lDJ1xuPO\nnp8xD/hDKeX6+vmGhsZ+XSnl7PrxBUkOBU4Frmpo/5IkqWZxIWm7SikPJLkcOJHqbMVPSikPJv9z\n4uLZwJ7AqvxvxxzgN/XjLwHfS3IAcCXwg1LKmrrva/VrbweuAC4vpaxqYPhrBjx/fwP7lSRJfSwu\nJE3XCuACqsukBt238KT638XAPX19jwKUUq5I8sw6ZhHw8yQXllKWllLWJtkHeA1wOPCdJKtKKccO\nGc9W/v+yrDkzm5IkSWqS91xImq4rgN2pDkpcOaD/Fqoi4lmllDv62t1TQaWUv5RSVpZS3k51edLJ\nPX2PlFK+W0o5heqyq2OSPHXIeP4M7DX1JMkuwPMHxB004Pmt25usJEmaOc9cSJqWUsrWJPvVj8uA\n/keSnAOcl2RX4FrgKcChwMOllJVJPgHcCNwMPIHqBu5bAJKcCtxLdfN1AY4FNpZSHhoypNXAuUkW\nA38EPggMKkQOTbIE+CFwBNUN44tnkQJJkrQdFheSpq2U8sh2+j+a5H7gdGBf4CGq+y0+W4dsqR/v\nA2ymusn7TXXf34GlVPduPAb8mm0XARcDLwS+Dvyb6pOpVg+IOxc4EPg48DBwainl59uahyRJmp0M\nOAApSRMhyTrgvFLK8q7HIknSzsB7LiRJkiQ1wuJC0iTz1KwkSTuQl0VJkiRJaoRnLiRJkiQ1wuJC\nkiRJUiMsLiRJkiQ1wuJCkiRJUiMsLiRJkiQ1wuJCkiRJUiMsLiRJkiQ1wuJCkiRJUiMsLiRJkiQ1\n4j+ZCwKH6g+30AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15398e2d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MANzd3enSpQtvvPEGjRo1smrKly2OHj2Kt7c3Dz74oNXbXLt2Lc+pYblNWUpL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       "<h1>Perplexity</h1>"
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      "text/html": [
       "<h2>TEXTEN1.txt - WORD</h2>"
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00000</th>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00001</th>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054612</td>\n",
       "      <td>39.055632</td>\n",
       "      <td>39.055242</td>\n",
       "      <td>39.055596</td>\n",
       "      <td>39.055255</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054084</td>\n",
       "      <td>39.055820</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054084</td>\n",
       "      <td>39.055820</td>\n",
       "      <td>39.055014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>39.060311</td>\n",
       "      <td>39.058927</td>\n",
       "      <td>39.061295</td>\n",
       "      <td>39.058971</td>\n",
       "      <td>39.063911</td>\n",
       "      <td>39.062967</td>\n",
       "      <td>39.064141</td>\n",
       "      <td>39.058695</td>\n",
       "      <td>39.066723</td>\n",
       "      <td>39.067290</td>\n",
       "      <td>39.058695</td>\n",
       "      <td>39.067290</td>\n",
       "      <td>39.062323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00100</th>\n",
       "      <td>39.112510</td>\n",
       "      <td>39.095924</td>\n",
       "      <td>39.096524</td>\n",
       "      <td>39.105662</td>\n",
       "      <td>39.105158</td>\n",
       "      <td>39.099147</td>\n",
       "      <td>39.114215</td>\n",
       "      <td>39.096713</td>\n",
       "      <td>39.104808</td>\n",
       "      <td>39.087629</td>\n",
       "      <td>39.087629</td>\n",
       "      <td>39.114215</td>\n",
       "      <td>39.101829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01000</th>\n",
       "      <td>39.603490</td>\n",
       "      <td>39.583280</td>\n",
       "      <td>39.551589</td>\n",
       "      <td>39.545768</td>\n",
       "      <td>39.621419</td>\n",
       "      <td>39.579523</td>\n",
       "      <td>39.607979</td>\n",
       "      <td>39.609939</td>\n",
       "      <td>39.562951</td>\n",
       "      <td>39.604131</td>\n",
       "      <td>39.545768</td>\n",
       "      <td>39.621419</td>\n",
       "      <td>39.587007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05000</th>\n",
       "      <td>41.627603</td>\n",
       "      <td>41.752301</td>\n",
       "      <td>41.635784</td>\n",
       "      <td>41.661537</td>\n",
       "      <td>41.692647</td>\n",
       "      <td>41.683924</td>\n",
       "      <td>41.701473</td>\n",
       "      <td>41.638703</td>\n",
       "      <td>41.607946</td>\n",
       "      <td>41.600010</td>\n",
       "      <td>41.600010</td>\n",
       "      <td>41.752301</td>\n",
       "      <td>41.660193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>43.865032</td>\n",
       "      <td>43.870957</td>\n",
       "      <td>44.018098</td>\n",
       "      <td>44.074220</td>\n",
       "      <td>43.929151</td>\n",
       "      <td>43.827965</td>\n",
       "      <td>43.941020</td>\n",
       "      <td>43.940487</td>\n",
       "      <td>43.833024</td>\n",
       "      <td>44.008930</td>\n",
       "      <td>43.827965</td>\n",
       "      <td>44.074220</td>\n",
       "      <td>43.930888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1          2          3          4          5  \\\n",
       "0.00000  39.054632  39.054632  39.054632  39.054632  39.054632  39.054632   \n",
       "0.00001  39.054632  39.054612  39.055632  39.055242  39.055596  39.055255   \n",
       "0.00010  39.060311  39.058927  39.061295  39.058971  39.063911  39.062967   \n",
       "0.00100  39.112510  39.095924  39.096524  39.105662  39.105158  39.099147   \n",
       "0.01000  39.603490  39.583280  39.551589  39.545768  39.621419  39.579523   \n",
       "0.05000  41.627603  41.752301  41.635784  41.661537  41.692647  41.683924   \n",
       "0.10000  43.865032  43.870957  44.018098  44.074220  43.929151  43.827965   \n",
       "\n",
       "                 6          7          8          9        Min        Max  \\\n",
       "0.00000  39.054632  39.054632  39.054632  39.054632  39.054632  39.054632   \n",
       "0.00001  39.054632  39.054084  39.055820  39.054632  39.054084  39.055820   \n",
       "0.00010  39.064141  39.058695  39.066723  39.067290  39.058695  39.067290   \n",
       "0.00100  39.114215  39.096713  39.104808  39.087629  39.087629  39.114215   \n",
       "0.01000  39.607979  39.609939  39.562951  39.604131  39.545768  39.621419   \n",
       "0.05000  41.701473  41.638703  41.607946  41.600010  41.600010  41.752301   \n",
       "0.10000  43.941020  43.940487  43.833024  44.008930  43.827965  44.074220   \n",
       "\n",
       "               Avg  \n",
       "0.00000  39.054632  \n",
       "0.00001  39.055014  \n",
       "0.00010  39.062323  \n",
       "0.00100  39.101829  \n",
       "0.01000  39.587007  \n",
       "0.05000  41.660193  \n",
       "0.10000  43.930888  "
      ]
     },
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       "<h2>TEXTEN1.txt - CHAR</h2>"
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Avg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00000</th>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
       "      <td>39.054632</td>\n",
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       "    <tr>\n",
       "      <th>0.00001</th>\n",
       "      <td>39.054518</td>\n",
       "      <td>39.053293</td>\n",
       "      <td>39.053102</td>\n",
       "      <td>39.054728</td>\n",
       "      <td>39.053810</td>\n",
       "      <td>39.052163</td>\n",
       "      <td>39.055629</td>\n",
       "      <td>39.053394</td>\n",
       "      <td>39.057486</td>\n",
       "      <td>39.051809</td>\n",
       "      <td>39.051809</td>\n",
       "      <td>39.057486</td>\n",
       "      <td>39.053993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>39.057282</td>\n",
       "      <td>39.038353</td>\n",
       "      <td>39.053738</td>\n",
       "      <td>39.047710</td>\n",
       "      <td>39.046144</td>\n",
       "      <td>39.050795</td>\n",
       "      <td>39.040154</td>\n",
       "      <td>39.044776</td>\n",
       "      <td>39.045333</td>\n",
       "      <td>39.051105</td>\n",
       "      <td>39.038353</td>\n",
       "      <td>39.057282</td>\n",
       "      <td>39.047539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00100</th>\n",
       "      <td>38.935345</td>\n",
       "      <td>38.966795</td>\n",
       "      <td>38.920848</td>\n",
       "      <td>38.962091</td>\n",
       "      <td>38.927746</td>\n",
       "      <td>38.967764</td>\n",
       "      <td>38.952350</td>\n",
       "      <td>38.966705</td>\n",
       "      <td>38.930599</td>\n",
       "      <td>38.945069</td>\n",
       "      <td>38.920848</td>\n",
       "      <td>38.967764</td>\n",
       "      <td>38.947531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01000</th>\n",
       "      <td>38.156571</td>\n",
       "      <td>38.035771</td>\n",
       "      <td>37.995311</td>\n",
       "      <td>38.045820</td>\n",
       "      <td>38.093212</td>\n",
       "      <td>38.035166</td>\n",
       "      <td>38.061395</td>\n",
       "      <td>38.073222</td>\n",
       "      <td>38.004476</td>\n",
       "      <td>38.050054</td>\n",
       "      <td>37.995311</td>\n",
       "      <td>38.156571</td>\n",
       "      <td>38.055100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05000</th>\n",
       "      <td>33.257171</td>\n",
       "      <td>33.261393</td>\n",
       "      <td>33.320822</td>\n",
       "      <td>33.275848</td>\n",
       "      <td>33.141031</td>\n",
       "      <td>33.177156</td>\n",
       "      <td>33.288791</td>\n",
       "      <td>33.316405</td>\n",
       "      <td>33.229535</td>\n",
       "      <td>33.354763</td>\n",
       "      <td>33.141031</td>\n",
       "      <td>33.354763</td>\n",
       "      <td>33.262291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>26.555325</td>\n",
       "      <td>26.515341</td>\n",
       "      <td>26.534375</td>\n",
       "      <td>26.564074</td>\n",
       "      <td>26.554925</td>\n",
       "      <td>26.483427</td>\n",
       "      <td>26.622121</td>\n",
       "      <td>26.515844</td>\n",
       "      <td>26.475904</td>\n",
       "      <td>26.720674</td>\n",
       "      <td>26.475904</td>\n",
       "      <td>26.720674</td>\n",
       "      <td>26.554201</td>\n",
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       "                 0          1          2          3          4          5  \\\n",
       "0.00000  39.054632  39.054632  39.054632  39.054632  39.054632  39.054632   \n",
       "0.00001  39.054518  39.053293  39.053102  39.054728  39.053810  39.052163   \n",
       "0.00010  39.057282  39.038353  39.053738  39.047710  39.046144  39.050795   \n",
       "0.00100  38.935345  38.966795  38.920848  38.962091  38.927746  38.967764   \n",
       "0.01000  38.156571  38.035771  37.995311  38.045820  38.093212  38.035166   \n",
       "0.05000  33.257171  33.261393  33.320822  33.275848  33.141031  33.177156   \n",
       "0.10000  26.555325  26.515341  26.534375  26.564074  26.554925  26.483427   \n",
       "\n",
       "                 6          7          8          9        Min        Max  \\\n",
       "0.00000  39.054632  39.054632  39.054632  39.054632  39.054632  39.054632   \n",
       "0.00001  39.055629  39.053394  39.057486  39.051809  39.051809  39.057486   \n",
       "0.00010  39.040154  39.044776  39.045333  39.051105  39.038353  39.057282   \n",
       "0.00100  38.952350  38.966705  38.930599  38.945069  38.920848  38.967764   \n",
       "0.01000  38.061395  38.073222  38.004476  38.050054  37.995311  38.156571   \n",
       "0.05000  33.288791  33.316405  33.229535  33.354763  33.141031  33.354763   \n",
       "0.10000  26.622121  26.515844  26.475904  26.720674  26.475904  26.720674   \n",
       "\n",
       "               Avg  \n",
       "0.00000  39.054632  \n",
       "0.00001  39.053993  \n",
       "0.00010  39.047539  \n",
       "0.00100  38.947531  \n",
       "0.01000  38.055100  \n",
       "0.05000  33.262291  \n",
       "0.10000  26.554201  "
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Avg</th>\n",
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       "    <tr>\n",
       "      <th>0.00000</th>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
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       "    <tr>\n",
       "      <th>0.00001</th>\n",
       "      <td>26.868322</td>\n",
       "      <td>26.867780</td>\n",
       "      <td>26.867593</td>\n",
       "      <td>26.867121</td>\n",
       "      <td>26.867432</td>\n",
       "      <td>26.867719</td>\n",
       "      <td>26.867983</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868635</td>\n",
       "      <td>26.867121</td>\n",
       "      <td>26.868635</td>\n",
       "      <td>26.867877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>26.865904</td>\n",
       "      <td>26.864904</td>\n",
       "      <td>26.865636</td>\n",
       "      <td>26.865762</td>\n",
       "      <td>26.869420</td>\n",
       "      <td>26.864818</td>\n",
       "      <td>26.866458</td>\n",
       "      <td>26.868385</td>\n",
       "      <td>26.865428</td>\n",
       "      <td>26.866170</td>\n",
       "      <td>26.864818</td>\n",
       "      <td>26.869420</td>\n",
       "      <td>26.866289</td>\n",
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       "    <tr>\n",
       "      <th>0.00100</th>\n",
       "      <td>26.844150</td>\n",
       "      <td>26.849176</td>\n",
       "      <td>26.855748</td>\n",
       "      <td>26.854097</td>\n",
       "      <td>26.851959</td>\n",
       "      <td>26.856623</td>\n",
       "      <td>26.855641</td>\n",
       "      <td>26.848626</td>\n",
       "      <td>26.849751</td>\n",
       "      <td>26.844117</td>\n",
       "      <td>26.844117</td>\n",
       "      <td>26.856623</td>\n",
       "      <td>26.850989</td>\n",
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       "    <tr>\n",
       "      <th>0.01000</th>\n",
       "      <td>26.722246</td>\n",
       "      <td>26.682053</td>\n",
       "      <td>26.703435</td>\n",
       "      <td>26.703488</td>\n",
       "      <td>26.684938</td>\n",
       "      <td>26.697912</td>\n",
       "      <td>26.724827</td>\n",
       "      <td>26.689659</td>\n",
       "      <td>26.720394</td>\n",
       "      <td>26.688339</td>\n",
       "      <td>26.682053</td>\n",
       "      <td>26.724827</td>\n",
       "      <td>26.701729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05000</th>\n",
       "      <td>25.918405</td>\n",
       "      <td>25.926899</td>\n",
       "      <td>25.987650</td>\n",
       "      <td>25.948442</td>\n",
       "      <td>25.911619</td>\n",
       "      <td>26.011224</td>\n",
       "      <td>25.984218</td>\n",
       "      <td>25.957580</td>\n",
       "      <td>25.919532</td>\n",
       "      <td>25.987765</td>\n",
       "      <td>25.911619</td>\n",
       "      <td>26.011224</td>\n",
       "      <td>25.955333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>24.854395</td>\n",
       "      <td>24.772682</td>\n",
       "      <td>24.871798</td>\n",
       "      <td>24.922224</td>\n",
       "      <td>24.966817</td>\n",
       "      <td>24.904515</td>\n",
       "      <td>24.796410</td>\n",
       "      <td>24.891675</td>\n",
       "      <td>24.835719</td>\n",
       "      <td>24.938167</td>\n",
       "      <td>24.772682</td>\n",
       "      <td>24.966817</td>\n",
       "      <td>24.875440</td>\n",
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       "                 0          1          2          3          4          5  \\\n",
       "0.00000  26.868092  26.868092  26.868092  26.868092  26.868092  26.868092   \n",
       "0.00001  26.868322  26.867780  26.867593  26.867121  26.867432  26.867719   \n",
       "0.00010  26.865904  26.864904  26.865636  26.865762  26.869420  26.864818   \n",
       "0.00100  26.844150  26.849176  26.855748  26.854097  26.851959  26.856623   \n",
       "0.01000  26.722246  26.682053  26.703435  26.703488  26.684938  26.697912   \n",
       "0.05000  25.918405  25.926899  25.987650  25.948442  25.911619  26.011224   \n",
       "0.10000  24.854395  24.772682  24.871798  24.922224  24.966817  24.904515   \n",
       "\n",
       "                 6          7          8          9        Min        Max  \\\n",
       "0.00000  26.868092  26.868092  26.868092  26.868092  26.868092  26.868092   \n",
       "0.00001  26.867983  26.868092  26.868092  26.868635  26.867121  26.868635   \n",
       "0.00010  26.866458  26.868385  26.865428  26.866170  26.864818  26.869420   \n",
       "0.00100  26.855641  26.848626  26.849751  26.844117  26.844117  26.856623   \n",
       "0.01000  26.724827  26.689659  26.720394  26.688339  26.682053  26.724827   \n",
       "0.05000  25.984218  25.957580  25.919532  25.987765  25.911619  26.011224   \n",
       "0.10000  24.796410  24.891675  24.835719  24.938167  24.772682  24.966817   \n",
       "\n",
       "               Avg  \n",
       "0.00000  26.868092  \n",
       "0.00001  26.867877  \n",
       "0.00010  26.866289  \n",
       "0.00100  26.850989  \n",
       "0.01000  26.701729  \n",
       "0.05000  25.955333  \n",
       "0.10000  24.875440  "
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
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       "  <tbody>\n",
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       "      <th>0.00000</th>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
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       "      <td>26.868092</td>\n",
       "      <td>26.868092</td>\n",
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       "    <tr>\n",
       "      <th>0.00001</th>\n",
       "      <td>26.868122</td>\n",
       "      <td>26.866700</td>\n",
       "      <td>26.866172</td>\n",
       "      <td>26.866819</td>\n",
       "      <td>26.864305</td>\n",
       "      <td>26.863992</td>\n",
       "      <td>26.867902</td>\n",
       "      <td>26.866838</td>\n",
       "      <td>26.864741</td>\n",
       "      <td>26.863222</td>\n",
       "      <td>26.863222</td>\n",
       "      <td>26.868122</td>\n",
       "      <td>26.865881</td>\n",
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       "    <tr>\n",
       "      <th>0.00010</th>\n",
       "      <td>26.844243</td>\n",
       "      <td>26.850173</td>\n",
       "      <td>26.848443</td>\n",
       "      <td>26.849837</td>\n",
       "      <td>26.851043</td>\n",
       "      <td>26.852492</td>\n",
       "      <td>26.851335</td>\n",
       "      <td>26.851991</td>\n",
       "      <td>26.853383</td>\n",
       "      <td>26.851410</td>\n",
       "      <td>26.844243</td>\n",
       "      <td>26.853383</td>\n",
       "      <td>26.850435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.00100</th>\n",
       "      <td>26.701039</td>\n",
       "      <td>26.706681</td>\n",
       "      <td>26.688637</td>\n",
       "      <td>26.699214</td>\n",
       "      <td>26.709915</td>\n",
       "      <td>26.704246</td>\n",
       "      <td>26.700694</td>\n",
       "      <td>26.689650</td>\n",
       "      <td>26.690815</td>\n",
       "      <td>26.704638</td>\n",
       "      <td>26.688637</td>\n",
       "      <td>26.709915</td>\n",
       "      <td>26.699553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01000</th>\n",
       "      <td>25.250943</td>\n",
       "      <td>25.282133</td>\n",
       "      <td>25.252728</td>\n",
       "      <td>25.257071</td>\n",
       "      <td>25.255536</td>\n",
       "      <td>25.266603</td>\n",
       "      <td>25.277913</td>\n",
       "      <td>25.258507</td>\n",
       "      <td>25.257519</td>\n",
       "      <td>25.235639</td>\n",
       "      <td>25.235639</td>\n",
       "      <td>25.282133</td>\n",
       "      <td>25.259459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05000</th>\n",
       "      <td>20.234712</td>\n",
       "      <td>20.232459</td>\n",
       "      <td>20.202092</td>\n",
       "      <td>20.190900</td>\n",
       "      <td>20.215869</td>\n",
       "      <td>20.169231</td>\n",
       "      <td>20.206616</td>\n",
       "      <td>20.195551</td>\n",
       "      <td>20.214682</td>\n",
       "      <td>20.219682</td>\n",
       "      <td>20.169231</td>\n",
       "      <td>20.234712</td>\n",
       "      <td>20.208179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10000</th>\n",
       "      <td>15.995515</td>\n",
       "      <td>16.102417</td>\n",
       "      <td>16.123740</td>\n",
       "      <td>16.087779</td>\n",
       "      <td>16.174607</td>\n",
       "      <td>16.015717</td>\n",
       "      <td>16.085864</td>\n",
       "      <td>16.098874</td>\n",
       "      <td>16.089013</td>\n",
       "      <td>16.120987</td>\n",
       "      <td>15.995515</td>\n",
       "      <td>16.174607</td>\n",
       "      <td>16.089451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1          2          3          4          5  \\\n",
       "0.00000  26.868092  26.868092  26.868092  26.868092  26.868092  26.868092   \n",
       "0.00001  26.868122  26.866700  26.866172  26.866819  26.864305  26.863992   \n",
       "0.00010  26.844243  26.850173  26.848443  26.849837  26.851043  26.852492   \n",
       "0.00100  26.701039  26.706681  26.688637  26.699214  26.709915  26.704246   \n",
       "0.01000  25.250943  25.282133  25.252728  25.257071  25.255536  25.266603   \n",
       "0.05000  20.234712  20.232459  20.202092  20.190900  20.215869  20.169231   \n",
       "0.10000  15.995515  16.102417  16.123740  16.087779  16.174607  16.015717   \n",
       "\n",
       "                 6          7          8          9        Min        Max  \\\n",
       "0.00000  26.868092  26.868092  26.868092  26.868092  26.868092  26.868092   \n",
       "0.00001  26.867902  26.866838  26.864741  26.863222  26.863222  26.868122   \n",
       "0.00010  26.851335  26.851991  26.853383  26.851410  26.844243  26.853383   \n",
       "0.00100  26.700694  26.689650  26.690815  26.704638  26.688637  26.709915   \n",
       "0.01000  25.277913  25.258507  25.257519  25.235639  25.235639  25.282133   \n",
       "0.05000  20.206616  20.195551  20.214682  20.219682  20.169231  20.234712   \n",
       "0.10000  16.085864  16.098874  16.089013  16.120987  15.995515  16.174607   \n",
       "\n",
       "               Avg  \n",
       "0.00000  26.868092  \n",
       "0.00001  26.865881  \n",
       "0.00010  26.850435  \n",
       "0.00100  26.699553  \n",
       "0.01000  25.259459  \n",
       "0.05000  20.208179  \n",
       "0.10000  16.089451  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7SjdoIBOlRdHo0aOHxeudO3cSGRlpdcWwmJgYAHx9fTOdl9Gbb77JF198wejR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\nRAIAANhEAgAA2EQCAADYRAIAANgCVeZKJnSdrwkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15399164748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rGDRokKXMG2+8waFDh4iMjMTZ2blY2gdQSqGUIjIykp9++snqXE6mTZtGjRo1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\nOnSoJQG6QoUKNGnShLVr19KwYUPc3d1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Yw5tvvnlMkAgLCysLKoMHD6Zv376cfvrpPPDA\nA0ybNq3aazyaVrlKndPZ3Zn7zr6P7278jk1TNvH3wX+noLCA6xdeT/y0eM577TxmfDODrXlbfV2q\niIhIjZs8eTLGGJ555hni4+N59NFH+fDDD5kzZ45X9xs2bBjW2hP2hzhaYWEhY8aMoaioiLfeeqtG\npxKdasrWxIkT2b9/P1OnTuWzzz7j2WefrdTXl5eYmMgnn3zCxx9/XPbf7t27n/D6yv7cKmvu3LkU\nFhby4osv8vbbbx/xePjhh9mwYQPffvvtSe/Ru3dvUlJSmDlzplejTpXl8yBhjLnJGPOjMSbP81hi\njBlS7vwrxpiSox4f+LJmqT0tI1sypd8U/jPhP2TdkcXzlzxPkDOIOz66gxZPtaDvS3157MvHWLdz\nna9LFRERqXYLFizgX//6F4888kjZ4uPf//73JCUlcfvtt3vVAXrs2LF06dKFRx55hP/+97/HnM/P\nz+ePf/xj2evbb7+dFStW8Pjjj9OjRw/vv5mj7Ny5k7Vr13LgwIGyY6GhoQD8+uuvx1z/1ltvMW/e\nPKZNm8bdd9/N5Zdfzr333svGjRsr9PVHCwoKYtCgQUc8TrYGorI/t8pKTU2lQ4cOXHfddSQnJx/x\nuPPOOwkODiY1NfWU97nrrrs4cOAA06dP97qWiqoLU5u2AncD6wEDTADeNcb0stau9lyzyHP8cMw8\nWMs1Sh0QHxbPDUk3cEPSDeQdyOP99e8zf/V8Hv78Ye759B66uruWbSvbM75nlRaiiYiI1LRly5Yd\n94Nh27Zt6devHwUFBdxyyy0kJSVx8803l503xvD888/Tt29f7rvvPp555plKva+/vz8LFizgwgsv\nZMCAAYwdO5YBAwbgdDr56aefePPNN4mPj+eRRx7hvffeY+bMmTRv3pzIyMgTfpAdMGAArVq1AuCN\nN95gy5YtFBQUAKV9EQ4vNp4wYQKJiYkATJ8+nalTp/Lll1+WTSdKSkrCWsu9997L6NGj8ff3Z+TI\nkezevZvJkydz0UUXceONNwIwc+ZMunbtyoQJE/j888+B0iDRoUMH0tLSaNOmDVFRUfTo0aOso3dV\nVObnVllbt27l888/5w9/+MNxzwcGBnLBBRcwe/Zspk+fjsNx4rGA7t27c9FFF/HPf/6T+++/v2a3\ngT1ZtzpfPYCdwLWe568A8yv59Y2ia6aU2lu41y5YvcBeNf8qG/m3SMufsK2fam1v//ft9svNX9ri\nkmJflygiUm+os3X1mDx5svXz8zvuuU2bNlmHw3HCx7XXXmuttfbWW2+1TqfzhD+rm2++2TqdTrt8\n+fJKvf9hv/76q33wwQdtjx49bFhYmA0JCbHdunWz9913n83JybHWWvvAAw+ctNbDj9TU1LL7nnXW\nWSe87quvviq77vC9yx+z1tqHH37YNmvWzDqdzrIu1yNGjLBRUVHHdLyeP3++dTgcdvr06WXHvvrq\nK3vaaafZoKAg63A4qr3LdUV+btaWdrY+UYfpoqIi63A47O23326ttfbvf/+7dTgc9osvvjjh+778\n8svW4XDYRYsWWWtLf859+vQ57rWffvpplb73iv49YGwd6jJsjHEAYygND72stWuNMa8AI4BDwG5g\nMfCAtXbXSe7TB1i2bNky+vTpUwuVS11xqPgQn236jPmr57NgzQK2791OQlgCIzuOZFTnUZzX6jz8\n/apvz2sRkYZm+fLlJJXulJRkrV1e3ffXv9EidV9F/x6oC1ObMMZ0A5YCQUABMMpau9ZzehEwD0gH\n2gJ/BT4wxvS3dSkFSZ3g7+fPBW0v4IK2F/Ds0Gf5etvXZTtAPb/seSKDIhnWYRjJnZO5sO2FhPj7\nZss/ERERkfquTgQJYA3QE4gALgdeN8acY61dY60tvyXBKmPMSmADcC7wf7VeqdQbfg4/BrQYwIAW\nA3j8gsf5cfuPZSMVb6x4gxD/EIa0G0Jyp2Qu6XAJkUGRvi5ZREREpN6oE0HCWlsEHF5y/70x5gxg\nCjDxONemG2N2AO04RZC47bbbjllgkpKSckS3QGkcjDH0SuhFr4RePHzew6zbua6sq/b4BePxd/hz\nfpvzSe6UzIhOI4gLjfN1ySIiNS4tLY20tLQjjuXl5fmoGhGpb+rUGonDjDGfAputtdcd51wzYDMw\nwlr73gm+XvMvpcK25m3lnTXvsGDNAv6z+T9YazmrxVkkd05mVKdRtIxs6esSRURqjdZIiEhF/x6o\nC30kphpjzjbGtDTGdDPG/BUYCMwyxoQaY/5ujOnrOX8+8A6wDvjQp4VLg9E8ojk3972ZxdcsJvuO\nbF4a/hKuQBd3f3I3rZ5uxWn/PI2pX0xlde7qU99MREREpJGoC1Ob4oDXgCZAHrACuNBau9gYEwT0\nAK4GIoFMSgPEg9baQz6qVxowd6ib63pfx3W9ryP/YD6L1i9i/pr5TP1iKvcvvp9OsZ1I7lTaq6JP\nkz7qVSEiIiKNls+DhLX2+pOcOwAMOdF5kZrkCnQxtttYxnYby/5D+/lk4yfMX1O6+9PUL6fSIqIF\nozqNIrlzMgOaD8DP4efrkkVERERqjc+DhEh9EOwfzLCOwxjWcRhFJUV8vvlz5q+ez9yf5/L0N0/j\nDnEzstNIRnUaxaDWgwh0Bvq6ZBEREZEapSAhUklOh5NBrQcxqPUgnrn4Gb7N+JYFqxcwb/U8Xlz+\nIq5AF5d2uJTkTskMaTeE0IBQX5csIlLnrF6tdWcidVVF/3zWyV2bqko7QogvWGv5Kecn5q+ez/w1\n81mxfQVBziCGtBvCqE6jGNZhGFHBUb4uU0TkpGph16YWDodjbUlJSVB131tEqo/D4ThQUlLS0Vq7\n5UTXaERCpJoYY+ge353u8d156NyH2LBrQ1lX7WveuQanw8l5rc4juXMyIzqOoEl4E1+XLCJS66y1\nW4wxHYFYX9ciIidWUlKy42QhAjQiIVIrMgsyeWfNO8xfPZ/PNn1GiS2hf/P+JHdKZlTnUbSJauPr\nEkVEgJofkRCRhkNBQqSW7dy3k/fWvcf8NfP58JcPOVh8kF4Jvcp2gOrq7qptZUXEZxQkRKSiFCRE\nfGhP4R4WrV/EgjULeG/dexQUFtA+uj3JnUt7VZzW9DQcxud9I0WkEVGQEJGK0hoJER8KCwhjdNfR\njO46moNFB/k0/VPmr57Py9+/zGNfPUYzVzNGdhxJcudkzm55Nk6H/siKiIhI3aBPJSJ1RKAzkKHt\nhzK0/VCeL3mer7Z8VbYD1LP/fZaY4BhGdBzBqM6jGNxmMEFObXgiIiIivqM5EyJ1kNPhZGCrgTx9\n8dNsuXUL317/LTcm3ciXW79kWNow3I+7ueLtK5izag4FBwtOeJ+0tLRarFpEREQaEwUJkTrOGMPp\niacz9fyprJm0hlW/X8XdA+5m3c51jH17LO7H3QxLG8Yr37/Czn07j/haBQkRERGpKZraJFKPGGPo\n4u5CF3cXHjjnAdJ3p5duK7tmPr9b+DscxsHAVgNJ7pTMyE4jfV2uiIiINGAakRCpx1pHtea2/rfx\nxbVfkHlHJs8NfQ5/hz+3fngrzaY348stXzLzvzPZsW+Hr0sVERGRBkZBQqSBSAhL4H9O+x/+Pf7f\n5NyZw+sjX8ffz59bFt1CkyeaMDxtOLN/ms2+Q/t8XaqIiIg0AAoSIg1QVHAUV/W8ir6Jfcm8I5Pp\nF00nZ28OV8y7goRpCUx4ZwKfbPyE4pJiX5cqIiIi9ZRXQcIY06a6CxGRmhEXGsfkMybz9fVfs27y\nOu7ofwdfbf2KC964gObTm3PHh3fwfdb3NMTmlCIiIlJzvB2R+MUY83/GmPHGGG1mL1JPtI9pz0Pn\nPsS6yev45vpvuLzL5byx4g36/LMPXWd2ZeoXU9n06yZflykiIiL1gLdBog+wAngSyDbGvGCMOaP6\nyhKR6pCSknLc48YYzkg8g2cufoaM2zP44MoP6N2kN49+8Sitn27N2a+czQvfvcCu/btquWIRERGp\nL0xVpjMYY5zAcGACMARYB/wv8Ia1Nrc6CvSyrj7AsmXLltGnTx9flSFS7+wp3MM7a94hdWUqH234\nCD/jx9D2QxnfYzyXdrhU3bRFGoHly5eTlJQEkGStXe7rekSk7qrSYmtrbZG1dj4wGrgbaAdMA7Ya\nY143xjSphhpFpJaEBYQxvsd4Fo1bRMbtGTx+weNkFmQyeu5o4qfF87t3f8fi9MVapC0iIiJVCxLG\nmNOMMTOBLOB2SkNEW+ACoCnwbpUrFBGfSAhLYEq/KXx7w7esnbyWW/veymebP+P818+n5VMt+cNH\nf+DH7B+1SFtERKSR8mpqkzHmduBaoCPwAfAS8IG1tqTcNc2ATdbaWu+eralNIjXDWss3Gd8wa8Us\n3vrpLXbu30m3uG6M6z6OK7tfSYuIFr4uUUSqSFObRKSivB2RmAi8CbS01o601r5XPkR45AC/q1J1\nIlKnGGPo16wfzw59lqw7sngv5T26xXXj4f88TMunWjLw1YG8uOxFdu/f7etSRUREpIZ5GyQuAB6z\n1maVP2hKtQCw1hZaa1+raoEiUjf5+/lzSYdLSLssje13bue1ka8R5AzipvdvIuGJBJJnJzN/9XwO\nFB3wdakiIiJSA7wNEhuA2OMcjwbSvS9HROqj8MBwru55NR+O/5Btt23jb+f/jS15W7hszmUkTEvg\nhoU38Nmmzyg5ZuBSRERE6itvg4Q5wfEwQL9+FGnEmoQ34bb+t/Hdjd+xetJqbj7jZj5J/4TzXjuP\nVk+14p5P7mHl9pW+LlNERESqqFKLrY0xT3qeTgFeBPaVO+0H9AWKrbUDqq1CL2ixtUjdYq1lydYl\npK5MZfaq2ezav4se8T3KFmk3czXzdYki4qHF1iJSUZUdkejteRige7nXvYFOwI+UNqcTESljjGFA\niwHMvGQmWXdksfCKhXSK7cRDnz1Ei+ktGPTaIF5e/jK/HvjV16WKiIhIBXm7/esrwBRrbX71l1R1\nGpEQqR/yD+Yzf/V8Zq2YxeL0xQT4BXBph0sZ32M8F7e7mEBnoK9LFGl0NCIhIhXlVY8Ha+211V2I\niDQ+rkAXE3pNYEKvCWTkZ/DWT2+RujKVUbNHERUUxeguoxnfYzwDWgzAYarUP1NERESqWYVHJIwx\n84EJ1tp8z/MTstYmV0dx3tKIhEj9tipnFakrU3lz5ZtszttMi4gWjOs+jnHdx9E1rquvyxNp0DQi\nISIVVZlf8eUBttzzkz1ERLzWNa4rU8+fysYpG/l8wudc3O5inv/uebr9oxu9X+jNtCXTyMjP8HWZ\nIiIijZpXayTqOo1IiDQ8B4sOsuiXRaSuTOVfa/9FYXEhg1oPYlz3cVzW5TJcgS5flyjSIGhEQkQq\nyqtJx8aYTic5d5H35YiIHF+gM5CRnUYyd/Rcsu/M5qXhL1FiS/jdwt8RPy2esW+PZeHahRQWF/q6\nVBERkUbB29WLy40xk8ofMMYEGmOeBd6telkiIicWGRTJdb2vY/E1i9ly2xYePvdh1uxYw4i3RtDk\niSZMfG8iX235ioY44ioiIlJXeBskJgAPG2M+MMbEG2N6Ad8Dg4Gzq6s4EZFTaeZqxh8G/IEfb/qR\nFTet4IY+N/D++vc565WzaPtMWx5Y/ACrc1f7ukwREZEGx6sgYa2dA/QE/IFVwFLgP0Afa+1/q688\nEZGK6x7fnb8N/hubbt3EZ9d8xvmtz+fZb5+ly8wuJP0zielLp5NVkOXrMkVERBqEqm7MHgD4eR5Z\nwIEqVyQiUkUO42Bgq4G8OPxFsu/MZt6YebSKbMU9n95Ds+nNuPCNC3n9x9cpOFjg61JFRETqLW8X\nW18BrKR0q9cOwCXAjcAXxpg21VeeiEjVBDmDSO6czLwx88i+I5vnL3mewuJCrnnnGuKnxZMyL4X3\n1r3HoeJDvi5VRESkXvF2ROJl4D5r7XBrba619mOgO5AB/FBt1YmIVKOo4ChuSLqBzyZ8xuZbN/PQ\nwIdYuX0lw9KG0fTJpkz+YDJLty7VIm0REZEK8KqPhDGmo7V27QnOXWWtfaPKlVWB+kiISEVZa1mx\nfUVZJ+2MggzaRLUp66TdMbajr0sUqVXqIyEiFeXtYuu1AMaYAGNMR2OMs9y5SoUIY8xNxpgfjTF5\nnscSY8yQo6552BiTaYzZZ4z52BjTzpu6RUSOZoyhZ0JP/n7B39l862YWX72Yc1uey9PfPE2n5zpx\nxotn8PTXT7N9z3ZflyoiIlKneLtGIsQY8zKwj9Jdm1p4js8wxtxTydttBe4G+gBJwGLgXWNMZ889\n7wYmU7oG4wxgL/ChMSbAm9pFRE7Ez+HHea3P4+URL5N9RzZzR8+laXhT/vDxH0h8MpEhs4Ywa8Us\n9hTu8XWpIiIiPuftGom/Urr967kcuVPTJ8DYytzIWvu+tfbf1toN1tpfrLUPAHuAfp5LpgCPWGvf\ns9b+BFwNNAVGelm7iMgpBfsHc3mXy3nninfIvjOb54Y+x75D+7hqwVXET4tn3PxxLFq/iKKSIl+X\nKiIi4hPeBomRwGRr7ZdA+UUWq4C23hZjjHF4doQKAZYYY1oDCcCnh6+x1uYD3wD9vX0fEZHKiA6O\n5n9O+x8+v/Zz0qekc//Z9/N91vcMfXMoTZ9oyi2LbuGbbd9okbaIiDQq3gYJN5BznOOhHBksKsQY\n080YUwAcBGYCozzrMBI89zt6cvJ2zzkRkVrVKrIV9519H6t+v4rlNy7n6p5X8/bPb9Pv5X50eLYD\nf/7sz/yy6xdflykiIlLjvA0S31HaO+Kww+Hhekq7XFfWGkqnSp0B/AN43RjTycvaRERqnDGG3k16\nM+3CaWy9bSufXPUJZ7U4iyeWPkH7Ge3p91I/Znwzg5y9x/udi4iISP3n7favZwGLgFnABOAFoAtw\nJjDQWrusSkUZ8zHwC/B3YAPQy1q7otz5z4DvrbW3neDr+wDLzjnnHCIiIo44l5KSQkpKSlXKExE5\nof2H9vOvdf9i1opZLPplEdZaLmp3EeO6j2NExxGEBoT6ukSRMmlpaaSlpR1xLC8vj88//xy0/auI\nnIJXQQLAGNMWuIfSkYQwYDnwmLV2ZZWLMuZTYLO19jpjTCbwuLV2uueci9KpTVdba+ee4OvVR0JE\nfG7Hvh3MXTWXWStnsWTrEkL9QxnVeRTju4/n/Dbn43Q4T30TkVqmPhIiUlFe/ytmrd0A3FDVAowx\nUykd3dgChAPjgIHAhZ5LngIeMMb8AmwCHgG2Ae9W9b1FRGpSbEgsE0+fyMTTJ7Jx90beXPkms1bM\nYtaKWcSHxnNFtysY32M8SU2SMMb4ulwREZFKqXCQ8IwEVIhnZ6WKigNeA5oAecAK4EJr7WLPvf5u\njAmhdPpUJPAFcLG1trAS7yEi4lNtotrwwDkPcP/Z97M8azmzVswi7ac0nv7maTrEdGB89/GM6zGO\nNlFtfF2qiIhIhVR4apMxpoRT78hkAGut9atqYVWhqU0iUh8UlRSxOH0xqStTmb96PnsK99C/WX/G\ndR/HmK5jcIe6fV2iNEKa2iQiFVWZIDGwoje11v7H64qqgYKEiNQ3+w7tY+HahcxaMYt///JvjDEM\naTeEcd3HMbzjcEL8Q3xdojQSChIiUlEVntrk63AgItKQhfiHcEW3K7ii2xXk7s1lzqo5zFo5i5R5\nKYQFhJHcOZnx3cczqPUg/Bw+HfQVEREBqrDY2hgTBfwO6Ow59DPwirV2V3UUJiLSWLlD3Uw6YxKT\nzpjEL7t+4c2Vb5K6MpXXf3ydJmFNyhZp907orUXaIiLiM141pDPGnEPpDkq3AFGexy1AuueciIhU\ng3bR7Xhw4IOsmbSGb6//ltFdRpO6MpWkfybRZWYXHv38UdJ3p/u6TBERaYS8bUi3ktIO1hOttcWe\nY37ATOBMa233aq2y8vVpjYSINFhFJUV8svGTskXa+w7tY0DzAYzvMZ7RXUYTExLj6xKlHtMaCRGp\nKK9GJIB2wBOHQwSA5/mTnnMiIlJDnA4nQ9oN4Y1Rb7D9zu3MGjULV6CLyR9MpskTTRjx1gjmrprL\n/kP7fV2qiIg0YN4GieX8tjaivM7Aj96XIyIilREWEMa4HuP4YNwHZNyewbQLp5G9J5sxb48hflo8\n1717HZ9u/JTikuJT30xERKQSvF1s/QzwtDGmHfC151g/YBJwjzGmx+ELrbUrqlaiiIhURHxYPLf0\nvYVb+t7C+p3rSV2ZyqwVs3jlh1doGt6UK7tdybge4+gZ31OLtEVEpMq8XSNRcopLLD5sTqc1EiIi\npay1fJPxDakrUnlr1Vvs2LeDru6ujOs+jiu7X0nLyJa+LlHqGK2REJGK8jZIVPhfHmvt5kq/QRUp\nSIiIHOtQ8SE+3vgxs1bM4p0177C/aD9ntzi7bJF2VHCUr0uUOkBBQkQqqtJrJIwx/sBDgMNau/lU\nj+ovWUREvOHv58/Q9kN587I32X7ndl4f+Toh/iFMfH8iCU8kMGr2KOb9PI8DRQd8XaqIiNQDlQ4S\n1tpDwGU1UIuIiNSS8MBwrup5Ff8e/28ybs/gscGPsS1/G5fPvZyEaQlcv/B6Ptv0GSX2VDNZaTQX\n5AAAIABJREFURUSksfJ216Z3gJHVWYiIiPhGQlgCt/a7lf/e8F/WTFrDLX1vYXH6Ys577TxaPtWS\nuz++mxXbtW+GiIgcyds1Eg8AdwCfAsuAveXPW2ufqZbqvKQ1EiIiVWOtZem2paSuSGX2qtns3L+T\n7nHdGd9jPCndUmge0dzXJUoN0RoJEakob4NE+klOW2ttG+9LqjoFCRGR6lNYXMhHGz5i1opZvLv2\nXQ4WHWRgq4GM6z6Oy7tcTmRQpK9LlGqkICEiFeVVkKjrFCRERGpG/sF8FqxewKyVs1icvhinw8ml\nHS5lfPfxDG0/lEBnoK9LlCpSkBCRivJ2jQQAxpgAY0xHY4y3je1ERKQecQW6uKbXNXx81cdsvW0r\nfz3/r6TvTid5TjIJTyRw479u5PPNn2uRtohII+BVkDDGhBhjXgb2AauAFp7jM4wx91RjfSIiUkc1\nDW/K7f1vZ/n/LGfV71fx+9N+z0cbPmLgqwNp/XRr7v3kXlblrPJ1mSIiUkO8HZH4K9ATOBcov+H4\nJ8DYKtYkIiL1TBd3Fx49/1E2TtnIF9d+wdB2Q3lh2Qt0+0c3ej3fi2lLppGRn+HrMkVEpBp5GyRG\nApOttV8C5RdZrALaVrkqERGplxzGwVktzuIfl/6D7DuzefeKd+kQ04EHFj9A8+nNOf/18/nf7/+X\nvAN5vi5VRESqyNsg4QZyjnM8lCODhYiINFIBfgEM7zicOaPnsP3O7bw8/GUArl94PfHT4hkzdwzv\nrnmXwuJCH1cqIiLe8DZIfAdcUu714fBwPbC0ShWJiEiDExEUwbW9r+XTqz9l621b+cugv7Bu5zpG\nzh5JkyeacNN7N/Hlli+1SFtEpB7xdrel+4BFxpgunntM8Tw/ExhYXcWJiEjDk+hK5M4z7+TOM+/k\np5yfSF2Ryps/vckLy16gVWQrrux2JeN7jKezu7OvSxURkZPwakTCszaiJ6UhYiVwIaVTnfpba5dV\nX3lV89hjj5U9T0tL82ElIiJyPN3iuvHXwX8lfUo6/5nwHy5ocwEzv5tJl5ld6PNCH55c+iSZBZm+\nLlNERI6jUg3pjDEO4E5gBBAALAb+ZK3dXzPleedwQ7ro6Gh27twJwPDhw1m4cKFvCxMRkVM6WHSQ\nD9Z/QOrKVP617l8UlRQxqPUgxncfz6jOo3AFunxdYoOmhnQiUlGVHZG4H5gKFAAZwC3Ac9VdVE3I\nyKg72w5qdERE5MQCnYGM6jyKt8e8zfY7t/PCpS9QVFLEhHcnED8tnivevoJ/rf2XFmmLiPhYZUck\n1gOPW2v/6Xk9GHgfCLa27qyQOzwiERYVxe7TTsMZGkrCv/9N9tVXQ1gYhIaWPso/P/p1+ecBAdVa\nn0ZHREQqb2veVtJ+SmPWilmszFlJTHAMY7uOZVyPcfRv1h9jzHG/Li0tjZSUlFqutv7SiISIVFRl\nF1u3ABYdfmGt/cQYY4GmwLbqLKw67GnenPaTJzPlu++wixbBsmWwd2/pY8+e0v8WVuA3Wv7+FQsc\nJztX/vnBg6XvHRICJ/iHT0REjtQ8ojl3DbiLuwbcxYrtK8oWac/8biZtotpwZbcrGddjHJ1iOx3x\ndQoSIiI1o7JBwsmRnawBDgH+1VNO9XLl5DCgXTv+EBFB8UcfMeWNN5icmEj7kJDfLjp06LdwUT5g\nVPR5Xh5kZh7/mpON9oSFlYaIsDAID//tUZnXR58LCQGHtzv6iojUHz3ie9Djgh78dfBf+Xzz58xa\nMYsZ387gL1/8hdOansa47uO4otsVJIQl+LpUEZEGq7JTm0ooHZE4WO7wMEoXXe89fMBam1xdBXrj\n6MXW2w4coGOfPoS8+CI7Dh3i4uhobk5M5KLoaBw1NSJgLRw4cNyAMfyee1h4661QUPDbY8+eE78+\n/HzPnlN946UjHt6EkuO9Dg1VMBGReuNA0QHeX/c+s1bO4v1171NsixncZjAFrxaw5JMlvi6v3tDU\nJhGpqMqOSLx2nGOzqqOQmtQsKIjwXbvY1K8fb+XkMCMjg6ErV9I+OJhJiYlMSEggwultS40TMAaC\ng0sfsbFHnktIgCuvrPw9S0p+CyYVDSGHX2dlHT+gnCpIniiYeBNSwsLAz6/y37eISAUEOYO4rMtl\nXNblMnbt38XbP79N6spUDhYfPPUXi4hIpVXq07O19tqaKqQmDB48uOx5YmIiQX5+TGjShGsSElia\nn88z27Zx54YNPJCezjXx8UxOTKRTaKgPKz4Fh+O3D+ZNmlT9fiUlsH9/5UNJQQHk5MDGjceeKznF\nmvuQkMpP2TrR67AwqO4AKCINQnRwNDcm3ciNSTcyfP5wX5cjItIgNehPYXfffXfZ88TExLLnxhjO\njIjgzIgIMg8e5PnMTF7IzOS5zEwuiIrilsRELo6Jwa+hL4R2OH5bAJ5QDfOIrT0ymFRm5GTnTti0\n6dhzxcUnf8/g4KpP4Sr/uoEFEy0yFRERkZrSsD41ncSJPkw1DQzk4datub9lS+bm5PBMRgbDfvqJ\nNkFBTEpM5LqEBCL9q3cteYP9YGdM6YhDSAjEx1f9fofXmVR0DUn517t3w5Ytx54vKjr5ewYGVl8o\nCQ8v3fHLhxQkREREpKY0+iBxWKDDwfiEBMYnJPBNfj4ztm3jno0b+WN6OlcnJDA5MZGu1TTtSR/s\nKqj8OpO4uKrfz9rSrXcrG0oKCkp359q27djzhw6d/D0DAqo+hav868DAqv8cRBoZ/Z0rIlIzGk2Q\nqIy+Lhd9u3Rh2sGDvJCVxfOZmTyfmcmgyEhuTkxkWGxsw5/21BAZA0FBpY+jF8B7q7Cw8qHk8OPw\nAvjy5w+eYlGov3/lgsfWrbBgAbhcEBHx28PlKv05iDQCChIiIjVDQeIkEgIDeahVK+5t0YJ5ubnM\nyMhg1KpVtAwMZFJiIr9r0oRoH09dER8LCICYmNJHdTh0yLtQsmcPbN9+7PkDByD5BLsxBwQcGSyO\nDhoVeX24H4qIiIg0OgoSFRDgcJASH09KfDzf5eczIyODB9LTeWjTJsbFx3NzYiI9wsJ8XaY0BP7+\nEB1d+qgOl14Kr74K+fml07MOP072ev36I18XFJx4m2CHozRUeBtEDj9vYIvcRUREGgP9611Jp7lc\nvOZy8XjbtryYlcU/MjJ4KSuLgRER3NysGSNiYnCqiZvUFQ5H6TSuqkzlKikpHeE4UfA4XijJyoI1\na448f7L1JCEhVR8dCQrS6IiIiEgtUpDwUlxAAPe3bMldzZuzYMcOZmRkcPmqVTQPDGRi06bc0KQJ\nsQEBx/1a7aQj9Ur5UYfmzb27x+EduE41GnL06y1bjny9b9+J38Pfv2pB5PBULf0iQEREpEIUJKrI\n3+FgTFwcY+Li+L6ggGczMvjzpk38edMmrvRMe+odHn7E1yhISG2pM/8/K78DV1V6lhxeQ1LRIJKX\nB+npx54/0VQtY0oXqld1dERrp0REpBHweZAwxtwLjAI6AfuBJcDd1tp15a55BbjmqC/9t7V2aK0V\nWgG9w8N5uVMnHmvThpeyspiZmckr2dkMcLm4uVkzkmNj8ddvO6UW1ZkgUV2qYw2Jtb9N1aro6Mj2\n7bBu3ZHnCwtP/B7BwVUfHQkO1lQtERGp03weJICzgRnAd5TW81fgI2NMZ2vt/nLXLQImAIf/ZT3F\nPpm+ExsQwD0tW3Jn8+Ys3LmTGRkZXPHzzzQNCGBi06YcLCnxdYkijdfhUYejRgor7cCByk3TysuD\njIwjX+/de+L7O51VX8TucmmqloiI1BifB4mjRxWMMROAHCAJ+LLcqYPW2txaLK3KnA4HyW43yW43\nK/bs4dmMDKZu2cL+3bsZ9MMPjPGcizvBWgoRqcMO9ySpSrPE4uJTL1w/+vXmzceeO9kvJw5P1apK\nKKnnf0dpOqmISM3weZA4jkjAAruOOn6uMWY7sBtYDDxgrT36mjqrR1gY/+zYkcfatOG80FD8jWHy\n+vVMWr+e8yIjGRsXx6jY2BMu0BaRBsjPD6KiSh/esrZ0ZKMyoyO5ubBhw5HnT9YMMSioakEkIqJ0\nZy4fTdVSkBARqRl1KkgYYwzwFPCltfbncqcWAfOAdKAtpdOfPjDG9Lf2RKsm66Yof39aBAWxsGdP\ndhQWMn/HDubk5HDTunVMXLeO86OiGOMJFWp2JyKnZEzpblNhYdC0qff3OXiw8qMj2dlHvi4oOPH9\n/fyOnXpVmWlah//r5+f99ygiItWqTgUJYCbQBRhQ/qC1dk65l6uMMSuBDcC5wP+d6Ga33XYbERER\nRxxLSUmpM7+Zig0I4MamTbmxaVNyCguZn5vL7Nxcbli7lpvWrWNwVBRj3W5GxMYSpVAhIjUpMBDc\n7tKHt4qLf9tV62RBpPzzrVth1aojry0uPvF7hIVVfjTkwAHvv6cGLi0tjbS0tCOO5eXl+agaEalv\nTF35hb4x5llgGHC2tXZLBa7PAe631r54nHN9gGXLli2jT58+1V9sFZ1qmD374EHmeUYqvsjLw2kM\nF3pGKkbExhKhLsAi0lBZC/v3V24R+/Fe7/9tr47hXbuy8KeffPhN1S/Lly8nKSkJIMlau9zX9YhI\n3VUnPpF6QsQIYGAFQ0QzIAbIqunaasKpRkQSAgOZlJjIpMREMg8e5O3cXObk5HDNmjUEGMOQ6GjG\nxMUxLCYGl0KFiDQkxpSupwgJgSZNvL9PYeFvAWPSpOqrT0REyvj8U6gxZiaQAgwH9hpj4j2n8qy1\nB4wxocBDlK6RyAbaAY8B64APfVByrWoaGMgtzZpxS7NmbDtwoDRU5OYyfvVqAo3h4pgYxrjdDIuJ\nIUyhQkSkVEAAxMaWPrSJhYhIjagLnzxvonSXps+OOn4t8DpQDPQArqZ0R6dMSgPEg9baQ7VXpu81\nCwri1ubNubV5czYfDhU5OVy5ejVBDgeXeEYqLomJIVQLEkVERESkBvk8SFhrT9otyVp7ABhSS+XU\nGy2DgrijeXPuaN6c9P37mesJFWN//pkQh4NLY2IYExfHxdHRhChUiEgjVlc22BARaWh8HiSk6loH\nB3NXixbc1aIFG/bvZ25ODnNyc7l81SpCHQ6GxcYy1u1mSHQ0QQoVItLIKEiIiNQMBYkGpm1wMPe0\nbMk9LVuyft8+5nhGKkbl5BDu58dwz0jFRdHRBDpOOhgkIiIiInJCChINWPuQEO5v2ZL7W7Zkzd69\npdOfcnNJ/eknXH5+jIiNZWxcHBdERRGgUCEiIiIilaAg0Uh0Cg3lj6Gh/LFVK1bt3cvcnBxm5+by\nxvbtRDqdjIyNZYzbzeCoKPwVKkRERETkFBQkGqGuoaF0bd2ahzyhYnZuLrNzcng1O5sop5Pk2FjG\nxMVxXmSkQoWIiIiIHJeCRCNmjKFbWBjdwsJ4uFUrVuzdy5ycHGbn5PBydjYxTifJbjdj3G7OjYzE\nqVAhIiIiIh4KEgKUhoqeYWH0DAvjL61b88OePcz27P70YlYWbn9/LvOEinMiI/Ezxtcli4iIiIgP\nKUjIMYwx9A4Pp3d4OH9t04ZlBQVluz89n5lJvL8/l7vdjImLY0BEhEKFiIiISCOkICEnZYzhNJeL\n01wuHmvThm8LCpjjGal4LjOTJgEBpaHC7ebMiAgcChUiIiIijYKChFSYMYa+Lhd9XS4eb9uWb/Lz\nmZ2Tw9zcXGZkZJAYEMDouDjGuN30dbkUKkREREQaMAUJ8YrDGPpHRNA/IoIn27VjSV4ec3JzeSsn\nh6e2baN5YCCj3W7GxsVxeng4RqFCREREpEFRkJAqcxjDWZGRnBUZyfR27fgyL485OTnM2r6dJ7dt\no2VgIGM8IxVJChUiIiIiDYKChFQrP2MYGBnJwMhInmnfns9//ZXZOTm8kp3N41u30iYoqCxU9AoL\nU6gQERERqacUJKTG+BnDeVFRnBcVxbPt2/PZr78yJzeXf2Zm8rctW2gXHMwYz/Sn7qGhChUiIiIi\n9YiChNQKp8PB4OhoBkdH81z79iz+9Vfm5OTwj8xMpm7ZQsfg4LKRim5hYb4uV0REREROQUFCap2/\nw8FF0dFcFB3NP0pK+HT3bubk5vLMtm08snkzXUJCykJF59BQX5crIiIiIsehICE+FeBwcHFMDBfH\nxPB8hw58vGsXc3JzeWLrVv60aRPdQkMZ42l+1zEkxNflioiIiIiHgoTUGYEOB5fGxnJpbCwHiov5\naPduZufk8PetW3lw0yZ6hoaWjVS0U6gQERER8SkFCamTgvz8GB4by/DYWPYXF/Nvz0jF1M2buT89\nnd5hYWUjFW2Cg31droiIiEijoyAhdV6wnx+j3G5Gud3sKy5m0a5dzM7J4eHNm7k3PZ3TwsMZ43Yz\n2u2mlUKFiIiISK1QkJB6JcTPj8vcbi5zu9lbXMz7O3cyJyeHBzdt4q6NGzkjPJyxcXFc7nbTIijI\n1+WKiIiINFgKElJvhfr5la6ZiIujoKiI93buZE5uLvdt3MgdGzbQ3+VijNvN5W43zRQqRERERKqV\ngoQ0COFOJynx8aTEx5NfVMTCHTuYk5vL3Rs3ctuGDZwVEcEYz0hG08BAX5crIiIiUu8pSEiD43I6\nGZ+QwPiEBH49dIiFnulPd2zYwJRffuHsiAjGxsVxmdtNfECAr8sVERERqZcUJKRBi/T35+qEBK5O\nSGD3oUO84xmpmPLLL9y8fj0DIyMZ43aT7HYTp1AhIiIiUmEOXxcgUlui/P25tkkTFvXoQfaZZ/LP\njh1xGsPk9etpsmQJg3/4gRczM9lRWOjrUkVERETqPAUJaZRi/P35XZMmfNSzJ1lnnsk/OnQA4KZ1\n60hYsoSLfvyRl7Oy2HXokI8rFREREambFCSk0XMHBHBj06Z80qsXWWeeyYz27Sm0lhvWriV+yRKG\nrljBq1lZ7FaoEBERESmjNRIi5cQFBDAxMZGJiYlkHzzIvB07mJOTw3Vr1+Jct44Lo6IYGxfH8NhY\nIpz64yMiIiKNlz4JiZxAQmAgkxITmZSYSMbBg8zLzWVOTg5Xr1lDgDEMiY5mTFwcw2JicClUiIiI\nSCOjTz8iFZAYGMgtzZpxS7NmbDtwgLdzc5mdm8v41asJNIahMTGMcbu5NCaGMIUKERERaQT0iUek\nkpoFBXFr8+bc2rw5mz2hYk5ODimrVxPkcHBJdDRj4+IYGhNDqJ+fr8sVERERqREKEiJV0DIoiDua\nN+eO5s1J37+fuZ5QMebnnwlxOLg0JoYxcXFcHB1NiEKFiIiINCAKEiLVpHVwMHe1aMFdLVqwYf9+\n5ubkMCc3l8tXrSLU4WB4bCxj3G6GREcTpFAhIiIi9ZyChEgNaBsczD0tW3JPy5as27evbKRiVE4O\n4X5+DPeMVFwUHU2gQ7swi4iISP2jICFSwzqEhHB/y5bc37Ila/buZY4nVKTm5ODy82NkbCxj4uK4\nICqKAIUKERERqScUJERqUafQUB4MDeXBVq1YtXcvc3NymJ2by+vbtxPpdDIyNpaxbjfnR0Xhr1Ah\nIiIidZiChIiPdA0NpWvr1jzkCRWzc3OZnZPDq9nZRDudjPKMVAyKjMSpUCEiIiJ1jIKEiI8ZY+gW\nFka3sDAebtWKFXv3Micnh9k5ObycnU2M08llbjdj4uIYGBGhUCEiIiJ1goKESB1ijKFnWBg9w8L4\nS+vWfL9nD3M8uz/9MysLt79/aahwuzknMhI/Y3xdsoiIiDRSPv/VpjHmXmPMt8aYfGPMdmPMAmNM\nh+Nc97AxJtMYs88Y87Expp0v6hWpLcYY+oSH87e2bdnQty/f9unDNQkJfLBzJ4N+/JHEJUuYvG4d\nn//6K8XWHvceaWlptVy1iIiINBY+DxLA2cAMoC8wGPAHPjLGBB++wBhzNzAZuBE4A9gLfGiMCaj9\nckVqnzGG010uHm/blk39+vF1nz6Mi4/n3Z07GfjDDzRfupQp69fzVV4eJeVChYKEiIiI1BSfT22y\n1g4t/9oYMwHIAZKALz2HpwCPWGvf81xzNbAdGAnMqbViReoAYwx9XS76eoLF1/n5zMnJYW5uLs9k\nZJAYEMDouDjGuN0cf5xCREREpOp8HiSOIxKwwC4AY0xrIAH49PAF1tp8Y8w3QH8UJKQRcxjDmRER\nnBkRwZPt2rEkL485ubm8lZPDU9u24b9rF5euWEE/l4v+ERGcHh6Oy1kX/9iLiIhIfVOnPlEYYwzw\nFPCltfZnz+EESoPF9qMu3+45JyKUhoqzIiM5KzKS6e3a8WVeHjcGBVFkLdO2biVv0yYMpdvO9nO5\n6O9y0c/lolNICA4t2hYREZFKqlNBApgJdAEG+LoQkfrMzxgGRkbSMSSEhT17UmIta/ft4+v8fL7O\nz2dpfj4vZ2VhAZefH33LBYu+LhfR/v6+/hZERESkjqszQcIY8ywwFDjbWptV7lQ2YIB4jhyViAe+\nP9k9b7vtNiIiIo44lpKSQkpKSrXULFJfOIyhc2gonUNDubZJEwAKior4b0FBWbCYmZnJw5s3A9Ah\nOLgsWPRzuegWGqr+FSINUFpa2jGbMuTl5fmoGhGpb4w9wbaRtVpEaYgYAQy01m48zvlM4HFr7XTP\naxeloeJqa+3c41zfB1i2bNky+vTpU7PFi9Rhw4cPZ+HChRW61lrLxgMHSoNFXh5f5+fz4969FFlL\nqMPB6eWCRT+Xi/gAbZom0hAtX76cpKQkgCRr7XJf1yMidZfPRySMMTOBFGA4sNcYE+85lWetPeB5\n/hTwgDHmF2AT8AiwDXi3lssVqVcqM/pmjKFtcDBtg4MZF1/6x3BfcTHLCwpY6pkS9Vp2Nn/bsgWA\nVkFBR4xa9AoLI0CjFiIiIo2Gz4MEcBOli6k/O+r4tcDrANbavxtjQoAXKN3V6QvgYmttYS3WKVLv\nVHUaX4ifX9kCbigdtdh28GDZdKiv8/OZl5tLobUEGkNSePgRoxbNg4Kq49sQERGROsjnQcJaW6Ff\nYVpr/wT8qUaLEZGTMsbQPCiI5kFBjI6LA+BgSQk/7tlTFizm79jBk9u2AZAYEHBEsEgKDyfYz8+X\n34KIiIhUE58HCRGp3wIdDs5wuTjD5WKK51i2Z9Ti8OOhTZvYV1KC0xh6hYWVBYv+Lhetg4Iw2n5W\nRESk3lGQEJFqlxAYyEi3m5FuNwBFJSWs3Lu3LFh8tGsXz2ZkAOD29z8iWJwWHk64muaJiIjUefrX\nWkRqnNPhoHd4OL3Dw5mYmAjAzkOH+KbcqMVjW7aQX1yMA+jmaZp3OFx0UNM8ERGROkdBQkR8Isbf\nn6ExMQyNiQGgxFpWl2uatyQ/nxc9TfMinU76ehZy94+I4IzwcKLUNE9ERMSnFCREpE5wGEPX0FC6\nhobyO0/TvPyiIr4tN2rxbEYGf/Y0zesUElI2YtHP5aJraCh+GrUQERGpNQoSIlJnuZxOBkdHMzg6\nGijdfvaX/fvLgsXS/HzeyM6mGAjz8+P08PCyYNHX5SJOTfNERERqjIKEiNQbxhjah4TQPiSEqxIS\nANhbXMyygoKycPG/2dlM9TTNaxMUdMSoRc+wMPzVNE9ERKRaKEiISL0W6ufHOZGRnFOuad6WctvP\nLs3LY25uLoesJcjhICksjP4REWWLuRMDA338HYiIiNRPChIi0qAYY2gZFETLoCDGeprmHSgu5oc9\ne8qmQ83JyWHa1q0ANAsMLBux6Ody0ScsjCA1zRMRETklBQkRafCC/PzoFxFBv4gIbvUcyzx4sGz7\n2aX5+TyQns7+khL8jaF3uaZ5/VwuWqlpnoiIyDEUJESkUWoaGMgot5tRnqZ5h8o1zVuan88Hu3bx\njKdpXny5pnn9XC5Od7kI1aiFiIg0cgoSIiKAv8NBn/Bw+oSH83tP07wdhYV8U1DA0rw8vs7PZ+qW\nLRR4mub1OGrUokNwsEYtRESkUVGQEBE5gdiAAC6JieEST9O8YmtZvXcvSz1Toj7/9Veez8wEIMrp\nPCJY9HW5iHDqr1gREWm49K+ciEgF+RlDt7AwuoWFcUPTpgD8eugQ35bbfvapbdvYXVSEATp7muYd\n3oK2s5rmiYhIA6IgISJSBZH+/lwYHc2F5ZrmrSvXNO/r/Hxezc6mBAj38+OM8PDSYBERQd/wcGLV\nNE9EROopBQkRkWpkjKFjSAgdQ0K4xtM0b09REd+VG7V4MSuLRz1N89oFBx8xatE9NFRN80REpF5Q\nkBARqWFhTifnRkVxblQUUDpqsenAgSNGLWbn5HDIWoIdDk47PGrhCRhN1DRPRETqIAUJEZFaZoyh\ndXAwrYODSYmPB2B/cTHfe5rmfZ2fT1pODo97mua1CAw8Ilj0Dg8nUKMWIiLiYwoSIiJ1QLCfH2dG\nRHBmRETZsW0HDvBNuSlR96anc6CkhABP07z+ERFl06JaBAZq+1kREalVChIiInVUs6AgmgUFcZmn\naV5hSQkryo1aLNyxg6e2bQMgISCgbMSin8tFUni4muaJiEiNUpAQEaknAhwOTnO5OM3lYrLnWE5h\nId94gsXS/Hwe3rSJvSUl+FHaNK98uGinpnkiIlKNFCREROqxuIAAhsXGMiw2Fihtmrdq796yYLH4\n11+Z6WmaF3NU07wzXC5caponIiJe0r8gIiINiJ8x9AgLo0dYGDd6mubt9jTNW5qXx9f5+TyxbRu/\neprmdQ0NPSJcdA4JwaFRCxERqQAFCRGRBi7K35+LoqO5yNM0r8Ra1u3bx9Jy28/+b1YWJYDLz4++\n5YJFX5eLGH9/334DIiJSJylIiIg0Mg5j6BQaSqfQUK5t0gSAAk/TvMPh4vnMTB7ZvBmADuWa5vXz\nNM1zavtZEZFGT0FCREQIdzo5LyqK88o1zUs/cOCIUYs3c3IospYQh4PTPU3zDj8S1DRPRKTRUZAQ\nEZFjGGNoExxMm+BgxpVrmresXF+LN7Zv5zFP07xWQUFloaK/y0WvsDACNGohItKgKUiIiEiFBPv5\ncVZkJGdFRpYd23rgQFmw+Do/nwW5uRy0lkBj6OMZtTi8BW0zNc0TEWlQFCRERMRrzYN0p1F6AAAM\nnElEQVSCaB4UxOi4OKC0ad4P5ZrmvbNjB9M9TfOaBgQcMWqRFB5OsJrmiYjUWwoSIiJSbQIcDs7w\n9Ki4xXMs++BBvik3JepPmzaxr6QEpzH09Gw/2z8ign4uF22CgjRqISJSTyhIiIhIjUoIDGREYCAj\nPE3zikpK+MnTNO/r/Hw+3r2b5zxN82L9/Y+YDnV6eDjhaponIlIn6W9nERGpVU6Hg17h4fQKD+em\nxEQAdh46xLeeYLE0P5/Htmwhv7gYB6VN8/qX2yGqYyWb5qWlpZGSklJD342ISOOlICEiIj4X4+/P\nxTExXBwTA5Q2zVuzb1/ZqMWS/HxezMrCAhGepnn9yzXNizpJ0zwFCRGRmqEgISIidY7DGLqEhtIl\nNJTrPE3z8ouK+K9nrcXSvDyezcjgz56meR2Dg8vWWfRzuegaEqKmeSIiNUxBQkRE6gWX08n5UVGc\nX65p3ob9+8umQ32dn88b2dkUw/+3d/+xdtf1HcefL2qlaQ02042agDaMCSObuOAykDG6VXQwZxxM\nUGEaycSN/eEgpIBOcdMNiQKBAc5oqKyDLi4qMsRCERKG4mZo/REsaKQMhBZERidroUI/++P7vcnh\neE7vPZfz7fd7Tp+P5JPe8/18zve+P+/c3s99f38dltQ3fR+5335se/bZdgOXpCllISFJmkhJOHjx\nYg5evJjTli0DYHv9oXkzhcXqrVv5FQsJSWqEhYQkaWosXrCAY5Yu5Zj6Q/NKKbxl0aKWo5Kk6eQF\npJKkqZUEP5VCkpphISFJkiRpZBYSkqSp5qNfJakZFhKSpKlmISFJzehEIZHkmCQ3JHk4ya4kb+nr\nX11v7203tRXvNFu7dm3bIUwcczY/5m105mx+zJskNaMThQSwBPg2cCZQhoz5KrA/sKxuHmJqgAvu\n6MzZ/Ji30Zmz+TFvktSMTjz+tZSyDlgHkGTYAzaeKaX8ZM9FJUmSJGmYrpyRmIsVSR5Ncm+Sq5L8\nUtsBSZIkSXurTpyRmIOvAl8ANgO/ClwI3JTkqFLKsEuhJEmSJDVkIgqJUsrne17ek+R7wI+AFcDt\nA96yCGDTpk3NBzdltm3bxoYNG9oOY6KYs/kxb6MzZ/Nj3kbTs3b6keCSditdO6CfZBfw1lLKDbOM\newz4YCnlMwP63glc21CIkiTtDU4tpVzXdhCSumsizkj0S3IA8DJgy5AhNwOnAg8AT++hsCRJmgaL\ngOVUa6kkDdWJMxJJlgAHAwE2AGdTXbL0RN0uoLpHYms97iKqR8a+ppTy8zZiliRJkvZmXSkkjqUq\nHPqDuYbqsyWuB14LLAUeoTpK8mEfBytJkiS1oxOFhCRJkqTJMkmfIyFJkiSpIywkJEmSJI1sYgqJ\nJH+VZHOSHUm+meS3Zxm/IsndSZ5O8oMk7x4w5m1JNtX7/E6S45ubwZ437pwl+fMkdyR5om7rZ9vn\nJGriZ61n7NuT7EryxfFH3p6G/n++NMmVSR6px92b5A+bm8We11De/rrO1fYkDya5JMm+zc1izxol\nZ0mWJbk2yX1JnktyyZBxU70WwPjztresB5J2byIKiSSnABdTPb3pt4DvADcnefmQ8cuBG4GvAYcD\nlwGfTXJcz5jXA9cBn6G6kfvLwPVJDmtsIntQEzkDjqXK2QrgSOAh4JYkr2hkEi1oKG+9Yz8B3DH+\nyNvT0P/PhcCtwCuBE4FXA+8FHm5qHntaQ3l7J3Bhvc9DgdOBk4G/b2oee9KoOQP2BR4DPgp8e8g+\np3otgGbyxl6wHkiag1JK5xvwTeCyntcBfgysGjL+IuC7fdvWAjf1vP5X4Ia+MXcBV7U9367mbMB7\n9gG2Aae1Pd+u563O1Z3Ae4DVwBfbnmuXcwb8BfBDYEHb85uwvP0jsL5vzCeBO9qebxs563vv7cAl\nA7ZP9VrQVN4GjJu69cBms83eOn9Goj4yeQTVUTgASimF6mjlUUPedmTd3+vmvvFHzWHMRGowZ/2W\nAAupPutj4jWctwuAR0spq8cTbTc0mLM/pv5jLsnWJN9Lcn6Szv/OmosG8/YN4IiZS0ySHAScAHxl\nPJG3Z545m4upXQug0bz1m6r1QNLcTMInW78cWAA82rf9UeCQIe9ZNmT8fkn2LaU8s5sxy15YuJ3Q\nVM76XUR1qUn/IjypGslbkt+lOhNx+DiD7YimftYOAv4A+BfgeKoPovwU1e+sj44n9FY1krdSytr6\ncpU7k6T+Hv9USrlojLG3ZT45m4tpXgugubz1m7b1QNIcTEIhoQ5Kch7VtdfHllJ2th1PVyV5CfDP\nwHtLKf/TdjwTZB+qP3TOqI+ebkxyAHAO01FINCLJCuADVJeG/RdVAXZ5ki2llI+1GZuml+uBtPea\nhELiceA5YP++7fsDW4e8Z+uQ8f/bc2R92Jhh+5wkTeUMgCTnAKuAlaWUe154uJ0x9rwlORR4FfDv\n9RFiqB9ykGQncEgpZfM4gm9JUz9rW4CddRExYxOwLMmLSinPvrCwW9dU3v4OWNNzCd09dTH7aWDS\nC4n55GwupnktgObyBkz1eiBpDjp/vXEp5efA3cDKmW31H2Qrqa4HHuSu3vG1N9bbdzfmuL4xE6nB\nnJFkFfBB4E2llI3jirkLGsrbvcBvUj0N5vC63QDcVn/90JjCb0WDP2tfpzqa3usQYMsUFBFN5m0x\n0J+fXT37n1jzzNlcTO1aAI3mbarXA0lz1Pbd3nNpVKdMtwPvonqk4aeBnwK/XPdfCFzTM3458DOq\nazYPAc4EdgJv6BlzFPAMcHY95iPA08Bhbc+3wzk7t87Rn1AdzZppS9qeb5fzNuB7TNtTm5r4WTsA\neBK4HPg14I+ojp6e1/Z8O563C+q8nVKPP47q6VfXtT3fNnJWbzucqpD/FrCmfv3rPf1TvRY0mLep\nXw9sNtvsrfUA5hxotWg+AOygOlL0up6+1cBtfeN/j+oozI56If2zAfs8ieqI8Q7gu1RHVVqfa1dz\nBmymOkXe3z7c9ly7nLcB+5+qQqKpnAG/Q3XEdHs95lwgbc+1y3mjOsv8IeAHwP/V+74c2K/tubaY\ns10Dfmfd3zdmqteCJvK2t6wHNptt9y2l9F6CLEmSJEmz6/w9EpIkSZK6x0JCkiRJ0sgsJCRJkiSN\nzEJCkiRJ0sgsJCRJkiSNzEJCkiRJ0sgsJCRJkiSNzEJCkiRJ0sgsJCRJkiSNzEJCEgBJPpdkV5Kr\nBvRdWfdd3UZskiSpeywkJM0owIPA25PsO7Ox/vodwH+3FZgkSeoeCwlJvTYCDwEn9mw7kaqI2Ng7\nMJXzk9yfZHuSjUlO6ulfmuTaJI/V/fcleXfdtzDJFUkeSbIjyeYk5w4LKsntSS7p2/al3jMk9T7+\nJsl1SZ5K8uMkZ76gbEiSpKEsJCT1KsDVwOk9204HVgPpG/sB4DTgDOAw4FJgTZJj6v6PAYcCb6r/\n/Uvg8brv/cCbgT8FXg2cCjwwhvjPoSp4Xgt8HLgsycox7FeSJPV5UdsBSOqca4GPJzmQ6mDD64FT\ngN+fGZDkxcD5wMpSyn/Wmx+oi4j3Af8BHAhsLKXMnMl4sOd7HAj8sJTyjfr1Q2OK/eullE/UX1+R\n5GjgLOBrY9q/JEmqWUhIep5SyuNJbgTeQ3UW4iullCeS552QOBhYDKzP8zsWAhvqrz8FfCHJEcAt\nwPWllLvqvs/V770PWAfcWEpZP4bw7xrw+v1j2K8kSepjISFpkNXAFVSXOg26z+Al9b8nAI/09T0D\nUEpZl+SV9ZjjgFuTXFlKWVVK2ZhkOXA88Abg80nWl1JOHhLPLn7x0qqFo01JkiSNk/dISBpkHfBi\nqoMNtwzo/z5VwfCqUsr9fe3hmUGllJ+WUtaUUt5FdYnRGT19T5VS/q2U8j6qS6dOSrJ0SDw/AV4x\n8yLJPsBvDBh35IDXm2abrCRJGp1nJCT9glLKriSH1l+XAf1PJfkkcGmSBcCdwEuBo4FtpZQ1Sf4W\nuBu4B1hEdXP19wGSnAVsoboxugAnA1tLKU8OCek24OIkJwA/As4GBhUdRyc5B/gy8Eaqm7lPmEcK\nJEnSLCwkJA1USnlqlv4PJXkMOA84CHiS6v6If6iH7Ky/Xg7soLoB+x1138+AVVT3WjwHfIvd/8F/\nNfAa4BrgWaonRN02YNzFwOuAjwDbgLNKKbfubh6SJGl+MuBgoyRNnCSbgUtLKZe3HYskSXsD75GQ\nJEmSNDILCUnTwtOrkiTtQV7aJEmSJGlknpGQJEmSNDILCUmSJEkjs5CQJEmSNDILCUmSJEkjs5CQ\nJEmSNDILCUmSJEkjs5CQJEmSNDILCUmSJEkjs5CQJEmSNLL/B/snTdD08ZB0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15398a38240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nzy6WtlatWkVISAinT58mOjraZiAB4OHhYfEZDx06lMaNG7No0SLCw8MLHcjY\nS6Y2CSGEEMXg7Fljq9Znn4WaNcHfHyZNgpQUeOUV+O47Yy3Exo3GvSEaN5a7Ugv7LF26lKtXrxIa\nGgrA8OHDcXJyYuHChaXSfuXKlRk3bhybN2/mwIED+eavW7duodoJCwtj4sSJANSvX988Hevs2bMs\nX74ck8nE8uXLLcrMnDkTk8nEF198QXx8PNWqVUMpxfTp081TfgoytSov69ev5+DBg0yZMsUqiABw\nc3MrllGA2NhY4uPj6dOnD71792bHjh389ttvdpWtWLEirVu3JikpiYsXLxa5L/mREQkhhBCiEC5f\nNu44/eWXxsjDyZNGYBAYCAMHGiMO7dqBi4t99dn65Vjc3lJSUrh8+bLVcU9PTxwcHMyvmzRpQmho\nKLNmzcLNzY1t27axaNEiatasWSrtA4wZM4Z58+Yxffp0u0YlCqNHjx789NNPrFmzhoULF1KlShXA\nmOI1cOBANmzYwPjx4+natSu1a9fm0KFDhIeHM3jwYB577DFSUlJYunQpL730Et27d6d79+4ANGvW\nrFj6t3nzZpRS9OvXr0DlbJ3jvEZOoqOjufvuuwkMDOS+++7DxcWF1atXM8HOuY+nT59GKYWnp2eB\n+lkYMiIhhBBC2CElBf73P3j5ZSNY8PGBnj2NG8V17Qoff2yMOPzwA8yaZQQS9gYRIIHEnWjatGn4\n+PhYPKpVq0ZcXJxV3qlTp9KgQQMWLVpE69atGT58eKm27+bmxtixY9myZYtdoxKF0bRpUwICAgAI\nDg4mJCSEkJAQXDL/I7377ruYTCZeeOEFbty4wYABA6hVqxZz584FwNXVlR49egBG8JBVvmnTpsXS\nv+PHj+Ph4UHt2rXtLnP9+nWrc5x1nm1NO0pLS2P9+vXmvwfOzs48/fTTREdH26w/PT2dy5cvc/ny\nZX766ScmTpxIXFwcTzzxBBUrVizcGy0AGZEQQgghbEhLg717b4447NoF//wDNWoYQcKoUdC5M9Sp\nU9Y9vX2l3Ejh+KXjJd5O46qNcXVyLfF2choyZAg9e/a0Ot6kSROrYxUqVMDDwwOlFJ06dSqWue8F\naR+MUYkFCxYQFhbGxo0bi9x+QVWvXp0lS5bQt29f2rdvz8GDB4mJicHNza1U2k9MTMxzFyZbXFxc\n2Lp1q80RCFu7cX322WdcuXLF4oeFvn378vTTT3Ps2DH8/Pws8h87dsxqHUpwcDDvv/9+gfpZWBJI\nCCGEEBi04tmUAAAgAElEQVQLpI8duxk4fP01JCZC5crGAuk5c4wAws9P1jaUluOXjhP4TmCJtxM3\nJI6AmgEl3k5Ovr6+dOrUya68CxYsYP/+/fj7+xMZGcngwYNp2LBhqbUP4O7uztixY5k+fTo//vhj\nqUydyal3795ERUXx6aefMnToUDp27FjoujIyMkhISLA45u3tjZOTk8387u7unD59ukBtODg48PDD\nD9udPyoqigYNGuDk5MQvv/wCQMOGDXFxcSE6OprXXnvNIn+DBg147733SE9P55dffiEiIoKEhASc\nnZ0L1M/CkkBCCCHEHevcOSNwyHpcuAAVKkDbtvDvfxuBQ6tW4CjflmWicdXGxA2xnmZTEu2UZ+fO\nnWP69Ol0796dJUuW0KhRI0aMGMHnn39e6n0ZM2YM8+fPJywsjPnz55d6+1euXGHv3r0opTh69GiR\n6jp37hwNGjRAKYXWGqUU27dvz3Xb2MaNG3PgwAHOnz9foOlN9kpKSmLr1q38/fff+Pr6WqQppVi1\napVVIFGpUiVzoNKlSxfatm1LQEAAkydPZsGCBcXex5zkT6MQQog7xp9/Wi6Q/uknY3ShZUvo188I\nHIKCwLX0Z7kIG1ydXMtkpKC8GTlyJEopIiMjqV69OhEREYwaNYq1a9fSq1evUu1L1qhEWFgY/fv3\nL/b685uyNXz4cK5fv87rr7/OpEmTWLBgAWPHjrW7fHY1atQgJibG4ljz5s1zzf/UU0+xevVqoqKi\nePnll+1ux14ff/wxf//9N0uXLjUvNM9y4sQJpkyZwq5du2jbtm2udfj7+9OvXz/efvttQkNDueuu\nu4q9n9nJYmshhBC3rdRUI2B45RVo3RqqVoUePYxF0w8/DOvWQUICxMXBG2/AI49IECHKl40bN7Jl\nyxZmzJhBrVq1AONiOjAwkPHjx3P9+vVS79PYsWPx8PAo8n0KLl++zIkTJ0hNTTUfq1SpEgBXr161\nyr9+/XrWrl3L7NmzmThxIn369GHKlCmcPHnSnMc18z+wrfI5VaxYkU6dOlk8PDw8cs3/r3/9C39/\nfyIiItizZ49VelJSElOmTMm33dxER0fTsGFDBg8ebN51KusxYcIEKlWqlOui6+wmTpzIP//8Y3XH\n8ZIgIxJCCCFuG+npRlCQNeKwcyf8/TdUq2YsjB42zPi3Xr2y7qkQEBcXZ/PC8O677+aBBx4gKSmJ\n0aNHExgYyKhRo8zpSimWLl1KmzZtmDx5MpGRkSXSfm7c3d0ZM2aM+Q7SOUVFRREfH09ycjIA33zz\nDREREQD079+fOpk7FGTdNO3rr782TycKDAxEa83kyZPp06cPTk5OPP300yQlJTFs2DA6d+5s3rFq\n8eLFbN++nQEDBrBz507A2OWoSZMmfPTRR/j6+uLt7U3Tpk257777CnWOsnN0dGTDhg107dqVDh06\n0KtXL9q1a4eTkxNHjhxh1apVeHt7W00/ssdvv/3G9u3bLUZXsqtQoQKPPvoo69atIzIy0mp73uz8\n/Pzo1q0b7733HlOnTsXLy6vA/bGb1rpcPYBJQAYwL8fxcOA3IAX4f8A9edQRAOi4uDgthBDi9pWR\nofWxY1ovXqz1M89o7empNWjt5qb1E09oPX++1gcPGvmEfeLi4jSggQBdMt/zd8R39MiRI7WDg4PN\ntDNnzmiTyZTrY9CgQVprrceOHasdHR1zPVejRo3Sjo6Oet++fSXSvtZad+zYUTdr1syqjqtXr2ov\nLy9tMpn03LlzLdI6duyYa93ffPONOd/06dOtjmmtdUREhK5Tp452dHTUJpNJx8fH6x49emhPT099\n7tw5i7ybN2/WJpNJz5kzx3xsz549unXr1trZ2VmbTCYdFhZm8zwU1rVr1/T06dN18+bNtZubm3Zx\ncdH33XefnjRpkv7999/N+QYOHKjd3d1zrcdkMunRo0drrbWeN2+eNplMevv27bnmX7FihTaZTHrL\nli1a69w/G621/uabb4r03u39O6B0Cd5KvKCUUq2Bj4BrwHat9fjM4y8DLwP9gTPAa4A/4Ke1/sdG\nPQFAXFxcnHk/YiGEELeH3367OeLw5Zdw/jw4OcEDDxhrHDp3hvvvN46Jgtu3bx+BgYEAgVrrfcVd\nv3xHC1H+2ft3oNxMbVJKuQFRwIvA1BzJY4AZWuutmXn7A38AzwBrS7OfQgghSte1a8ZWrFmBw7Fj\nxvHmzaFPHyNwaN8eSmkreSGEEJnKTSABLAG2aK2/UkqZAwmlVAOgBvBl1jGtdaJS6jvgQSSQEEKI\n28pff8Hu3TcDhx9+gIwMaNjQCBqmTzcWSue4B5MQQohSVi4CCaVUH6AF0MpGcg2MOVp/5Dj+R2aa\nEEKIW1h6Ouzff/NeDt9+awQTPj7QqRO8+KIRQDRoUNY9FUIIkV2ZBxJKqbuABUAXrfWNsu6PEEKI\nkqU1/PzzzXUO27cb93eoVAk6dICICCNw8PcHk2xSLoQQ5VaZBxJAIOAD7FM39xBzADoopUYCjQEF\nVMdyVKI6sD+viseNG2e1H3Dfvn3p27dvMXVdCCEEwOrVq/P82/r775YLpM+dM+4W3aYNjB5tBA5t\n2hh3lRalZ/Xq1axevdri2LVr18qoN0KIW015CCRiMHZgym45cAyYpbU+pZT6HegMHARQSrkDbTDW\nVeRq/vz5siOEEEKUgpyBRGIifPPNzcDhyBHjuL8//OtfRuDQoQNUrlxGHRaA7R/Xsu3WIoQQeSrz\nQEJrnQwczX5MKZUMXNZaZ+7NwQJgilLqJMb2rzOAX4FPSrGrQgghcpGRYRk4fP+9sfahXj1jS9b/\n/MdY71C9eln3VAghRHEp80AiFxY3t9Bav6GUcgXeBjyBb4HHbd1DQgghRP60Nu74nJxc+Mf16zef\nHzwIn34K3t7GaMOAAUYA0bAh2LjxrRBCiNtAuQwktNadbBybDkwv9c4IIUQZ0RpSU4vvYj/nIyMj\n/z44ORmLoG093NyMEYZKlYygJDrauLeDLJAWQog7Q7kMJISwZdSoUSxatKisuyGKSX6Lc28VGRmQ\nklK0i/3cLvhTUoxgIj8VK+Z+se/pCbVr2w4CciuT/WHv3aGffhpatizauRRCCHFrkUBC3DLWrVsn\ngcRt5M033yy1QCI9vegX+rld8Kem2tcHF5fcL9arVDHWEthzYZ/z4epq7H4khBBClDb5+hFClInz\n589bvL5xo/gu9nNe8P/9d/79Ucq4KM/t1/oaNQp3oZ91sS/TfYQQQtxubutAYsEnX1H9QPzNA+Yp\nAnmt/MuWZjWlQNk+DCiU7SkIugDtWVRuX5mcbaqsPtqcDpFLnbn0MasKlSMte90ql75YHLfqRS7n\nCkDb7qMGktL+YfzCWJQ2gTZhUg6gTShtQmE8z3pkvTZhAu0AmMzl0A7Znme+xrKc8TD6mfXIeo/5\nPezJJ3XBpUtQp87NC/4bdtyO0sEh7wt2L6/CX+y7uMii4KK4HaapCVEUJjt+LVBKsX37durVq0eD\nPG7VPmvWLCZOnEhCQgKNGzemZcuWxMTEWORJS0ujZcuWJCcnc/jwYdzc3Oxuv0OHDgAkJiYyd+5c\nNm3axKlTp0hPT+eee+7hySefZPTo0dSoUYMvv/ySrl275lt3bGwsbdu2BSAiIoLvv/+e7777josX\nL/Laa68xefLkfOsA2LlzJzExMUyYMMGu95RTcnIyb775Jp07dyYoKKjA5XNz4cIFateuzYQJE5gz\nZ45F2ogRI/jvf//LjBkz+M9//mORFhISwoYNG0hMTKRCthvlbN68mSVLlrB3715SUlKoV68ewcHB\nTJo0CS8vL4s6+vXrx6pVq8yvK1SoQP369enbty+vvPKKRb3p6ek4ZZuT6uDggLu7Ow0bNqR9+/YM\nHTqURo0aFcs5gds8kFj5+79BfgW8fVSB+Vfbl3w7iptxVcbNwEJpB1BZzy0DEHNwgnWaIme6ZZr5\ndebzrDoURmBjLpctr1U5TJhwuPncRpq5nMXznGlGuinrucr2HCN4U9qEUjfLOahs9SgjeFPK6J9J\n3SyXddyUWfbE6TR6P59AVVdvKrs55HmRnzVCUKGCXOyXVxJIiDtdVFSUxesVK1YQExNDVFQUOtuv\nZ35+fqSkpADGRWa3bt2s6mqZueDIx8eH2bNnM3ToUFauXMlzzz1nzvPmm29y9OhRtm7diqura4Ha\nBzh58iRdunThwoUL9OrVi2HDhuHo6MjBgwd599132bx5M4cPH6Zp06ZWdWdJTExk7NixuLu74+vr\nCxgXslOnTqVWrVoEBASwbds2u88hGAFJeHg4gwcPLlQgcf36dcLCwnBycirWQKJmzZo0aNCA2NhY\nq7Rdu3bh5OTEzp07baa1bt3a4mJ/7NixREZGEhAQwOTJk/Hy8mLv3r0sXLiQNWvW8NVXX3H33Xeb\n8yulqFSpEu+88w5aa65evcqmTZsICwvjzJkzLFu2zKrdxx57jH79+pnz//jjjyxbtozFixfz5ptv\nMnr06GI5L7d1ILG5x5c0bdbc4phG53EhksvP5CqPtMw6LbJnH9TI9p83Z7tZ5ayOF6JMVrlc35uy\n3X+Fdf+z15+9LznbzV4ut/ds1ce8zmMe5TJ0Bg++/SCxI2LJ0BmkZ6SToTOM5/rm8+JKy5leEmkF\nq+dGsbSfVoRzVexqwFxTNdRfCm/lTVVdFR/tQ9WMqlRNq0rVG1XxueFD1X+qUvXvqvik+lDVtSpV\nXaviVsENJRGFEKIcCQkJsXi9e/duYmJibAbZ8fHGbImAgACrcjm9+OKLfPjhh4SGhvLkk0/i5eXF\n6dOnmTFjBj169ODxxx8vcPtpaWn83//9H3/++Sfffvst999/v0V6RESE+Vf36tWr59rHPn36cOPG\nDZYtW4aPjw9g/AIeHx9PnTp1+OOPP6hZs2ae7y+nvK4FSqN8XoKCgvjoo4/466+/cHZ2BiApKYlD\nhw7Rt29ftm7dapH//PnznD17lj59+piPrVy5ksjISPr168eKFSvM32XPP/88AwYMoFOnTvTs2ZO4\nuDiL7zknJyeLz3L48OG0adOGlStXMnfuXLy9vS3abty4sdXnNmvWLJ544gnGjx9PkyZN6NKlS5HP\nyW0dSNSu6kmDGlXKuhuimDhmONK4auOy7sYdS2tduGDJRrCSnpHOQ+8+xPu93udSyiUupVwiISXB\n/PzAHwfMz6//c92qLxUdKpqDCp9KmQGGS47XrlXxcTWeV3GtQgWHCjbelRBClH9Lly6lZcuWhIaG\n8v777zN8+HCcnJxYuHBhoer76KOPOHLkCHPmzLEKIgAqV65MeHh4nnW88847rF27ljFjxvDEE09Y\npNWpU6dQ/Zo6dSoREREopbjrrrsA49f4c+fO8dlnnzFkyBA+/PBD+vXrZy4THh7O9OnT+d///keD\nBg3w9fVFKcWUKVOYMmUKQIGmVuUlKCiIqKgovv/+e/P0sN27dwMwYcIEoqOjOXToEP7+/oAxuqKU\nshgZCQ8Pp2rVqvz3v/+1+kGsTZs2hIaG8tprr7Fx40a6d++eZ3/atWvH3r17OX36tFUgYYu3tzer\nV6/G19eXiIgICSSEEKVHKYWDcsABB5ywc0/QPFRIq8D/+f1fvvlSb6RyOfXyzYAjOcEq+Pjj+h8c\nvnjYfDwtI82qHo+KHuYAwxxw5BF8eDh7YFIyN1IIUXJSUlK4fPmy1XFPT08cHBzMr5s0aUJoaCiz\nZs3Czc2Nbdu2sWjRogL/2p9l8+bNKKUsLsgL4vDhw4wbN47AwEDeeOONQtVhS69evTh58iRr165l\n8eLFeHp6AsYF8IsvvsiGDRsYM2YMnTt3pmbNmhw4cICIiAiGDRtGly5dSE5OZsmSJYwYMYKePXsS\nHBwMQIsWLYqlf0FBQWitiY2NNQcSu3btws/PjxYtWlCzZk127txpFUi0a9cOgOPHj/PLL78wZMgQ\nKlWqZLON/v37M2PGDLZu3ZpvIHH69GkAqzUVealfvz5BQUHs2rWL1NRUXFxc7C5riwQS4pbRs2fP\nsu6CKEa1a9e2K5+Lkwt3Od3FXe532ZVfa03i34kWIxy2go8Tl06wM2UnCSkJXP3rqlU9DsrBIvDI\nHmTkFny4OBXtD7IQ4s4ybdo0Xn31VYtjSil2795tNVIwdepUPvroIxYtWkTr1q0ZPnx4ods9fvw4\nVapUoXr16gUum5qaSq9evXB0dGTNmjUWC3uLyt/fnxYtWrB27VqeeeYZatWqZZH+3nvv0bRpUwYP\nHszGjRsZMGAAdevWNU/DqlSpEt27d2fEiBE0b94832ljBeXn50eVKlUs1kns3LnTHCg8+OCDxMbG\n8tJLL5nTGjdubL7QP3r0KADNmjXLtY27774bV1dXjh07ZnFca20OOq9du8b69evZvHkzLVu2pGHD\nhgV6H02bNmXHjh3Ex8fTuHHRZnpIICFuGXIPidtLaGhoidSrlMLD2QMPZw/u8b7HrjI30m9wJfVK\nrsHHpVTj9ak/T5mDkb/S/rKqx9XJ1TrgyCP48HbxxsHkYKNHQgjAuCvj8eMl307jxsY+zaVsyJAh\nNn8ka9KkidWxChUq4OHhgVKKTp06FWmdWGJiIpUrVy5U2REjRnDixAk+/PBDiwXBpaFWrVosWrSI\n5557jvbt23PkyBG2b9+Oayl+dm3btmXHjh0AZGRk8N1339G/f3/AmGoUGRkJGIu+Dx06xAsvvGAu\nm5SUhFIq33NfuXJlEhMTLY5du3bNvA4ly0MPPcSKFSsK/B6yFrEnJSUVuGxOEkgIIcpEedrlx8nB\niepu1anuZt+vc1prUm6kWK3tsAhAUi9x9tpZ9l3Yx6WUS1xOvUyGzrCoR6HwcvEqUPAhC83FHeX4\ncQgMLPl24uIgIKDk28nB19eXTp062ZV3wYIF7N+/H39/fyIjIxk8eHCBf4nO4u7uzoULFwpcLjo6\nmuXLlzNgwACeffbZQrVdVM8++yxRUVFs27aNESNG0L594XdzzMjIICEhweJYlSpVcMzjLp9BQUFs\n3bqVQ4cOkZaWRnJysnnb27Zt2xIaGspvv/3GkSNHSE9Pt1gfUblyZbTW+V7AJyUlUa9ePYtjbm5u\nfPLJJ2RkZPDrr78ye/ZsLl68WKipSdevXzf3p6gkkBBCiAJSSlGpQiUqVahEPc96+RcA0jPSufrX\nVZuLyxOSE7iUajw/dPGQORhJ+sf6y6aCQwXbAUcuwUdV16qlstB89erV5So4FLeJxo2Ni/zSaKcc\nO3fuHNOnT6d79+4sWbKERo0aMWLECD7//PNC1de4cWMOHz7MH3/8Yff0pp9//plhw4bRuHFjlixZ\nUqh2i8OlS5fYt28fSimOHDlSpLpOnz5tXpxt7Hyp+Pbbb82BgS3Z10mkp6fj4+NjHpkJCAigYsWK\nxMbGcvToUauF1llb7x48eDDX+k+dOkVKSorVqJSjoyMPP/yw+XWXLl3w8/Nj2LBhrF+/vkDv+/Dh\nwzg6OlK/fv0ClbNFAgkhhCgFDiYHqrhWoYprFRph382A/k7723K0w0bwkZCSwLFLx8zBx40M6zv7\nuVd0L1Dw4ensWeCF5hJIiBLh6lomIwXlzciRI1FKERkZSfXq1YmIiGDUqFGsXbuWXr16Fbi+p556\ninXr1hEVFcWECRPyzf/PP//Qq1cv0tLSWLNmTYlOJcpvxHXYsGGkpqYyc+ZMXnnlFRYvXszIkSPt\nLp9d7dq1rW70l7VQOjetWrXC2dmZ2NhYMjIyLIIOJycnAgMDiY2N5dixY9SqVcviYt3Pz4+GDRuy\nceNG5s2bZ/M8Zm0J+9RTT+Xb9zFjxjBz5kz27dtHgJ3/T06fPk1sbCwPPfSQeQvbopBAQgghyqmK\njhWp7V6b2u72LUzXWpP0T5Ll+o6cAUhKAj9f+Zndv+4mITmBP//606oeB2UEPQUJPoQQJWPjxo1s\n2bKFBQsWmBcfDx8+nOXLlzN+/Hi6detW4Bu39e7dm1mzZjFjxgw6dOhA69atLdITExOZM2cOM2bM\nAGD8+PEcPHiQyMjIPBcKF9Tly5e5dOkS9erVM1/UZu1mdPXqVavF1mvWrOHjjz9m6dKlDBkyhLi4\nOF555RW6detmnuaVvXx+nJ2d7Z5alsXJyYnWrVubF1yPGjXKIr1t27Z88cUXnD592ubNBl999VUG\nDhzI8OHDWbZsmUXg8/333zN37lxatGjBM888k29fRo8ezdy5c5k1axZr167NN/+lS5fo27cvWmur\nO3AXlgQSQghxm1BK4V7RHfeK7tztbd8iyLSMNK6kXsl1d6us56evnjbnSU1LtarHM96T789/z/21\nrfekF0LYFhcXR3R0tNXxu+++mwceeICkpCRGjx5NYGCgxQWrUoqlS5fSpk0bJk+ebF7gay8nJyc2\nbtzII488Qrt27ejduzft2rXD0dGRw4cPs2rVKqpXr27ehvStt96iTp06eHp62uwvGAuNs359X7ly\nJWfPnjWvBfj666/NN4obOHCgede++fPnM3PmTGJjY82/7AcGBqK15pVXXqFnz544OTnxzDPP8Oef\nfzJy5EgeffRRhgwZAsBbb73Ffffdx8CBA80LoCtVqsS9997L6tWradiwIV5eXjRr1sw8rag4BAUF\n8e2331ps7Zqlbdu2zJkzx2paU5b+/fuzd+9elixZwuHDhwkJCcHT05O9e/eybNkyatSowbp16+wa\nWfHx8WHAgAG89957nDx5knvuubnByPHjx4mOjjZ2MkxM5MCBA6xbt46UlBQWLVpkMU2qSLTWt90D\nCAB0XFycFkIIUbyS/0nW8VfjddxvcfqLn7/Qy/cv1+7N3LWarvRLW17SV1KulHUXRRHExcVpQAMB\nWr6jC23kyJHawcHBZtqZM2e0yWTK9TFo0CCttdZjx47Vjo6OuZ6rUaNGaUdHR71v374CtZ/l6tWr\n+tVXX9XNmjXTbm5u2tXVVTdt2lRPnjxZX7x4UWut9ZQpU/Lsa9YjOjraXG9QUFCu+Xbu3GnOl1V3\n9mNaax0eHq7vuusu7ejoqE0mkz5//rwODg7WXl5e+vz58xZ5N2zYoE0mk54/f7752M6dO3WrVq20\ns7OzNplMOiIiIs/zUFCfffaZNplM2sXFRf/zzz8WaRcvXtRKKe3g4KD379+fax2bNm3SXbt21d7e\n3trZ2Vnfe++9+uWXX9ZXrlj//ezXr5/29va2Wc/PP/+sHR0d9eDBg7XWWqelpVmcb0dHR+3t7a1b\ntWqlJ0yYoE+cOGHXe7T374DSJXgr8bKilAoA4uLi4uyeMyaEEKLwnnrqKR6Z8ghTtk+hokNF5nSd\nQ//m/WWHqVvQvn37CDR2SgrUWu8r7vrlO1qI8s/evwNy21YhhBBFppRiVJtRHB9xnK53d2XgJwN5\naPlDHL54uKy7JoQQooRIICGEEKLY1Kxck+ju0XzZ/0suJl+kxdIW/Pt//+b6P9fLumtCCCGKmQQS\nQgghiizn1q+dGnTix5d+ZMbDM1jywxL8lvjx8dGPuR2n0wohxJ1KAgkhhBBFZuseEhUdK/JK+1c4\nOuIoLWu05F/r/kW3Vd04eeVkGfRQCCFEcZNAQgghRImq71mfzX0380mfTziWcIymbzUl7Osw/kr7\nq6y7JoQQoggkkBBCCFEqnm70NEeGH2H8g+OJ+DaCpm81ZdvJbWXdLSGEEIUkgYQQQohSU6lCJWZ2\nnsmPL/1IXY+6PBb9GD3X9eTXxF/LumtCCCEKSAIJIYQQpc7Px48v+39JdPdovo3/Fr8lfszbPY8b\n6TfKumtCCCHsJIGEEEKIMqGUIsQ/hBMjTzCoxSD+/f/+TeA7gcSejS3rrgkhhLCDBBJCCCHKlIez\nB5GPR/LD4B9wcXKh/bL2PP/J8yQkJ5R114QQQuRBAgkhhBDlQkDNAHa/sJu3n3ybTcc30WhxI96J\ne4cMnVHWXRNCCGGDBBJCCCHKDZMyMSRwCCdGnuCZxs8wdOtQHnz/QfZd2FfWXRNCCJGDBBJCCCHK\nHZ9KPnwQ/AHfDvqWlBsptH63NaM/H821v66VddeEEEJkkkBCCCFEuRVUN4h9Q/Yxp+sclh1YRqPF\njVh1aBVa67LumhAWTCZTvg8HBwd27NhBfHx8nvneeOMNABISEqhSpQpdunSxai8tLQ1/f38aNmxI\nSkpKgdrPkpiYyLRp02jevDmVK1fG1dWVZs2aMXnyZH7//XcAvvzyS7vq3rVrl7neiIgIgoODqVGj\nBiaTiZkzZ9p9Hnfu3ElYWBjXr18v1OeQnJxMWFgYsbElt2mDPecNoF+/fnh5edmsIz09HZPJxPjx\n422mHzp0CJPJRKVKlXI9F0FBQRafgaurKy1atGDRokWl9jfSsVRaEUIIIQrJycGJ8Q+Op9d9vRi/\nbTzPbniW9/a9x5JuS/Dz8Svr7gkBQFRUlMXrFStWEBMTQ1RUlMVFnZ+fHykpKQCEhITQrVs3q7pa\ntmwJgI+PD7Nnz2bo0KGsXLmS5557zpznzTff5OjRo2zduhVXV9cCtQ9w8uRJunTpwoULF+jVqxfD\nhg3D0dGRgwcP8u6777J582YOHz5M06ZNrerOkpiYyNixY3F3d8fX1xcwLpCnTp1KrVq1CAgIYNu2\ngt10MjY2lvDwcAYPHoybm1uBygJcv36dsLAwnJycCAoKKnD5/Nh73sDYmU4pVah2oqOjqV27Npcu\nXWLDhg3079/fKo9Sinr16jFz5ky01ly6dImoqCjGjBnDlStXmDZtWpHeq1201rfdAwgAdFxcnBZC\nCHF7+eLnL/Q9kfdop3An/UrMKzr5n+Sy7tJtJS4uTgMaCNDyHV1oI0eO1CaTyWbamTNntFJKz507\n16662rdvr6tVq6avXLmitdb61KlT2tXVVffs2bNQ7d+4cUM3bdpUu7u76++++84qPTExUU+dOjXf\nfvXu3VubTCa9detWi+Nnz57VWmv9+++/a6WUjoiIyLeuLK+//ro2mUz6/PnzdpfJ7sKFCwVu014F\nPQqAMkUAACAASURBVG/9+vXTXl5eNutKS0vTSik9btw4m+l169bVkyZN0sHBwfqRRx6xmScoKEi3\nbNnS4lhqaqquU6eO9vb2tvdt2WTv3wGZ2iSEEOKW8ug9j3Jo2CGmdJjCvN3zaLKkCZtPbC7rbglR\nYpYuXcrVq1cJDQ0FYPjw4Tg5ObFw4cJC1ffRRx9x5MgRXn31Ve6//36r9MqVKxMeHp5nHe+88w5r\n165l9OjRPPHEExZpderUKVS/pk6dyuTJkwG46667zNOxfvvtN9577z1MJpPV6Eh4eDgmk4mYmBh+\n+eUXatWqhVKKKVOmmKf8FGRqVV6K47zZ4+uvv+bXX3+lT58+9O7dm6+++oo//vjDrrLOzs60atWK\nq1evcvny5SL3JT8ytUkIIcQtx9nRmVcfepVn/Z9l5OcjCV4TzFP3PkXk45HU96xf1t0Twi4pKSk2\nL/Y8PT1xcHAwv27SpAmhoaHMmjULNzc3tm3bxqJFi6hZs2ah2t28eTNKKfr161eo8ocPH2bcuHEE\nBgaa13MUh169enHy5EnWrl3L4sWL8fT0BMDb25sXX3yRDRs2MGbMGDp37kzNmjU5cOAAERERDBs2\njC5dupCcnMySJUsYMWIEPXv2JDg4GIAWLVoUS/8Kc9601jY/47S0tFzLREdH06hRI5o3b46vry8V\nK1ZkzZo1jBkzxq42T58+jclkwsPDw+5+FpaMSAghhLhl3e19N5+FfMbHvT5m/+/7abKkCTO/ncnf\naX+XddeEyNe0adPw8fGxeFSrVo24uDirvFOnTqVBgwYsWrSI1q1bM3z48EK3e/z4capUqUL16tUL\nXDY1NZVevXrh6OjImjVrcHJyKnQ/cvL39zdf9D/zzDOEhIQQEhKCs7MzAO+99x5aawYPHsyNGzcY\nMGAAdevWZc6cOQBUqlSJ7t27A9C8eXNz+SZNmhRL/wpz3q5du2b1Gfv4+PD/27vz8Kiq+4/j7+9k\nJewgooCy7wQhYVERQVTUFlFRwLhrLXWtVVsVpSzi0h9qsSqote4iViuopeKOa61Awi4CsiiygxDW\nsCTn98dMcAJJyEwmuTOTz+t57pPJvWfufJJLQr5z7jnn2GOPLXb8xL59+3jzzTfJysoCIC0tjQED\nBjBp0qRiz5+fn8+WLVvYsmULS5cu5fbbb2fevHkMHDiQxMSK7y9Qj4SIiMQ0M2NQ+0H0b9mfez+7\nl1GfjuKleS8x4VcTOL3F6V7Hk3LYnZ/Pd4GByRWpXVoaaUE9AJVl2LBhDB48+LD9xf3hm5ycTO3a\ntTEz+vXrF/YgXvAPkq5Zs2ZYz73xxhtZsmQJL730Ei1btgw7QzgaNWrE448/zuWXX07v3r1ZtGgR\nM2bMIC0trVJeP5zvW40aNXj77bcPm0WpoKCA/v37H9Z+2rRp5ObmHiwkALKyshg0aBDLli07OKi9\n0IIFC2jQoEGRfRdccAHPPvtsSDnDpUJCRETiQo3kGow7cxxXnHAF1//nes54+QyyOmXxSP9HOLZm\neLeAiLe+272bzGLenY+07MxMMsL8w7o8WrduTb9+/crU9tFHH2XOnDmkp6fz2GOP8dvf/pYWLVqE\n9bq1atVi3bp1IT9v0qRJvPDCC1x55ZVceumlYb12eV166aW88sorvP/++9x444307t077HMVFBSw\nadOmIvvq169f4jv54XzfEhMTOe200w7bn5+fX2z7V155hVatWmFmLF++HIBWrVqRkpLCpEmTGD16\ndJH2rVq14umnnyY/P5/vv/+e+++/n02bNh3sxaloKiRERCSudDq6E59f9TkvzXuJP334J9pNaMfY\n08ZyQ/cbSPTpv71Y0i4tjezMzEp5nWi2evVqRo8ezaBBg5gwYQJt27blxhtvZPr06WGdr127dixc\nuJANGzaU+TadZcuWcf3119OuXTsmTJgQ1utGwubNm8nJycHMWLRoUbnOtXLlSlq3bo2Z4ZzDzPji\niy84+eSTi20fzvctFLm5uUyfPp19+/Yd1vNgZrz66quHFRI1atQ4WKicccYZ9OzZk+7duzNixAge\nfvjhiGc8lH6jiohI3DEzruxyJee2PZd7Pr6HP7z3B56f+zxP/vpJTmxyotfxpIzSEhI86SmINjfd\ndBNmxmOPPUbDhg25//77ufnmm3n99dcZMmRIyOc799xzeeONN3jllVe4/fbbj9h+3759DBkyhAMH\nDvDaa69V6K1ER7pl6/rrr2fPnj088MADDB8+nCeeeIKbbrqpzM8P1rhxYz766KMi+9LT00tsH+r3\nLVRvvPEG+/bt45lnnjk40LzQ4sWLGTlyJDNnzix2xqhCXbt2JSsri4kTJ3L77beHPSC/rDwfbG1m\n15nZPDPLDWz/NbOzg44/b2YFh2zveplZRERiQ71q9XhywJP879r/4TMfJz17EsP+PYwtuyt+WkSR\nSJg6dSr//ve/GTt2LI0aNQL8079mZmZy2223hbUC9NChQ+nQoQNjx45l1qxZhx3fvn07f/7znw9+\nfttttzF//nweeughOnfuHP4Xc4gtW7awZMkS8vLyDu6rXr06ANu2bTus/Wuvvcabb77Jww8/zJ13\n3slFF13E8OHDWbFiRZmef6jU1FT69etXZCttDESo37dQTZo0iTZt2nDNNdcwaNCgItsf//hHqlWr\nVuKg62B33HEHeXl5jB8/PuwsZRUNPRKrgTuBZYABVwFvm1kX59ziQJvpgf2FZaam4xARkTLr0bgH\nM6+dyVOzn+KeT+5h6ndTGXfGOK7sciU+8/w9NamisrOzi/3DsGXLlpx44ons2LGD3//+92RmZnLz\nzTcfPG5mPPXUU/Ts2ZO7776bxx57LKTXTUpKYurUqfTv359evXoxdOhQevXqRWJiIgsXLuTVV1+l\nYcOGjB07lmnTpjFx4kSOO+446tSpU+Ifsr169aJZs2YAvPzyy/z444/s2LED8K+LUDjY+KqrrqJx\n48YAjB8/ngceeIAvv/zy4O1EmZmZOOcYPnw4gwcPJikpifPPP5+tW7dy0003cdZZZzFs2DAAJk6c\nSMeOHbnqqqv4/PPPAX8h0aZNGyZPnkyLFi2oW7cunTt3Priid3mE8n0L1erVq/n888/505/+VOzx\nlJQUzjzzTP75z38yfvx4fL6Sf2+lp6dz1lln8fe//5177rmnYqeBLW21Oq82YAtwdeDx88CUEJ9f\nJVbNFBGR0K3bsc5dNuUyx2hcr2d7uXnr53kdKapoZevIuOmmm1xCQkKxx1atWuV8Pl+J29VXX+2c\nc+4Pf/iDS0xMLPF7dfPNN7vExESXk5MT0usX2rZtmxs5cqTr3Lmzq1GjhktLS3OdOnVyd999t9u4\ncaNzzrkRI0aUmrVwmzRp0sHznnLKKSW2++qrrw62Kzx38D7nnLv33ntdkyZNXGJi4sFVrs877zxX\nt27dw1a8njJlivP5fG78+PEH93311VeuW7duLjU11fl8voivcl2W75tz/pWtS1ph+sCBA87n87nb\nbrvNOefcuHHjnM/nc1988UWJr/vss886n8/npk+f7pzzf58zMjKKbfvxxx+X62sv6+8Bc4dMR+Ul\nM/MBQ/AXD12cc0vM7HngPGA/sBX4BBjhnPu5lPNkANnZ2dlkZGRUQnIREYk1n676lBv+cwNLtyzl\nlp63MLrvaGqm6H78nJwcMv0DnDOdczmRPr/+jxaJfmX9PRAV/blm1snMduC/ZWkicIFzbkng8HTg\nCqAfcAfQB3jXyjOBsoiIVHl9m/Vl7nVzub/f/TyV/RTtJrTjjUVvHDbfu4iIFC8qCgngO+AEoAfw\nJPCSmbUDcM697pyb5pxb5Jx7BxgQaNfXq7AiIhIfkhOSufOUO/n2hm/p0bgHQ/41hLMnnc2yLcu8\njiYiEvWiYbA1zrkDQOGQ+zlm1gO4Bbi+mLYrzWwz0AqYUdp5b7311sMGmGRlZRVZLVBERKRpnaZM\nHTqVaUuncfP0m+n0ZCfu6nUXd51yF9WSqnkdr8JMnjyZyZMnF9mXm5vrURoRiTVRUUgUwwekFHfA\nzJoA9YEjLi04fvx43X8pIiJlNqDNAPo178eDXzzIg18+yCsLXuGJc57gnNbneB2tQhT35lrQvdEi\nIqXy/NYmM3vAzHqbWdPAWIkH8Y+DeMXMqpvZODPrGTh+OvAWsBR439PgIiISl9KS0hjbbywLrl9A\n8zrN+dWrv+LC1y9kde5qr6OJiEQVzwsJ4GjgRfzjJD4CMoH+zrlPgHygM/A2sAR4BpgFnOqc2+9N\nXBERqQraHtWWDy//kNcufI2vV39N+wnteeirh9ifr/9+REQgCm5tcs5dW8qxPODsko6LiIhUJDNj\naKehnNP6HEbNGMVdH9/Fi/NeZOKvJ3Jq01O9jici4qlo6JEQERGJarVSajH+7PFkD8umVkot+rzQ\nhyvfupKNuzZ6HU1ExDOe90iIiIjEii7HdOHLa77k+TnPc8dHd/DOknd4oN8DDMscRoIvwet4MWXx\n4sVeRxCREpT151OFhIiISAh85uM3Gb/hvHbncddHd3HDuzfw3NznePLXT9KtUTev48WCzT6fL++y\nyy5L9TqIiJTM5/PlFRQUbC6tjQoJERGRMByVdhT/GPgPrul6Ddf/53p6PNOD67tdz3397qNutbpe\nx4tazrkfzawtcJTXWUSkZAUFBZudcz+W1kaFhIiISDmcfNzJZA/L5omZT/DnGX/mX4v/xcNnPsxl\nnS/DzLyOF5UCf5yU+geKiEQ/DbYWEREpp0RfIn848Q98d+N3nNbsNK546wr6vtiXRRsXeR1NRKTC\nqJAQERGJkMa1GvPaRa/xwWUfsG7HOro83YU7P7yTnft2eh1NRCTiVEiIiIhE2Jktz2TB9QsY1WcU\nj818jA4TOjB18VScc15HExGJGBUSIiIiFSAlMYURp45g0Q2LSG+YzqDXBzFg8gBWbF3hdTQRkYhQ\nISEiIlKBWtRtwbSsaUwdOpUFGxbQcWJHxn42lr0H9hZpN3nyZI8SioiER4WEiIhIBTMzzm93Potv\nXMwtPW/h3s/vJf3JdD5c/uHBNiokRCTWqJAQERGpJNWTq/OXM/7CvOvm0ahmI/q/0p+h/xrKmu1r\nvI4mIhIyFRIiIiKVrEODDsy4cgYvX/Ayn676lHYT2rFq2yqvY4mIhESFhIiIiAfMjMs6X8aSm5Yw\nuMNgFmxYwPwN872OJSJSZiokREREPFQntQ5PD3iatOQ0xnw2xus4IiJlFlYhYWYtIh1ERESkqkpK\nSKJNvTZMWTyFuevneh1HRKRMwu2R+N7MZpjZZWaWGtFEIiIiVVCTWk1oWbeleiVEJGaEW0hkAPOB\nvwLrzexpM+sRuVgiIiJVyyWXXMLIPiN567u3yFmX43UcEZEjCquQcM7Ndc7dAjQCrgGOBb40s4Vm\ndpuZNYhkSBERkXiXlZXFJemX0Lpea0Z/OtrrOCIiR1SuwdbOuQPOuSnAYOBOoBXwMLDazF4ys2Mj\nkFFERKRKSPQlMrLPSP699N/MXjvb6zgiIqUqVyFhZt3MbCKwDrgNfxHREjgTf2/F2+VOKCIiUoVk\ndcqibf226pUQkagX7qxNt5nZAuC/+AuGK4CmzrkRzrmVzrkvgKvwj6UQERGRMkrwJTCqzyj+s+w/\nfPPTN17HEREpUbg9EtcDr+IvHs53zk1zzhUc0mYj8JtypRMREamChnQcQvuj2jP6s9FeRxERKVG4\nhcSZwP8559YF7zS/4wGcc/uccy+WN6CIiEhVU9gr8d737/H16q+9jiMiUqxwC4nlwFHF7K8HrAw/\njoiIiAAM7jiYjg06qldCRKJWuIWElbC/BpAX5jlFREQkwGc+RvcdzQfLP+CrH7/yOo6IyGESQ2ls\nZn8NPHTAvWa2O+hwAtATmBuhbCIiIlXaoPaD6NywM6M+HcVHV3zkdRwRkSJC7ZHoGtgMSA/6vCvQ\nDpiHf7YmERERKSef+RjVZxQfr/yYL374wus4IiJFhNQj4Zw7DcDMngducc5tr5BUIiIiAsD57c7n\nhIYnMOrTUXxy5SdexxEROSisMRLOuatVRIiIiFQ8n/kY03cMM1bN4NNVn3odR0TkoDL3SJjZFOAq\n59z2wOMSOecGlTuZiIiIADCw7UAyjs1g1Kej+PTKTzErac4TEZHKE0qPRC7+QdaFj0vbREREJELM\njNF9RvP5D58zY9UMr+OIiAAh9Eg4564u7rGIiIhUvAFtBtCtUTdGfTqK05qdpl4JEfFcWGMkzKxd\nKcfOCj+OiIiIFMfMGNN3DF/++CUfr/zY6zgiImEvSJdjZjcG7zCzFDN7Ani7/LFERETkUOe0Ooee\njXsycsZInHNHfoKISAUKt5C4Cv+CdO+aWUMz6wLMAc4AekcqnIiIiPzCzBjddzRf//Q1Hyz/wOs4\nIlLFhTv96+vACUASsAj4GvgMyHDOzYpcPBEREQl2VsuzOLHJiYz6dJR6JUTEU+H2SBRKBhIC2zog\nr9yJREREpESFYyW+WfMN733/ntdxRKQKC3ew9cXAAvxTvbYBfg0MA74wsxaRiyciIiKHOrPFmfQ6\nrhcjP9VYCRHxTrg9Es8CdzvnBjrnNjnnPgTSgTXA3IilExERkcMU9krMXjub/yz7j9dxRKSKCreQ\nyHDOPRm8wzm31Tk3BLixhOeIiIhIhPRr3o/ex/dm9Kej1SshIp4Id7D1EgAzSzaztmaWGHTs5VDO\nZWbXmdk8M8sNbP81s7MPaXOvma01s91m9qGZtQont4iISLwo7JXIXpfNv5f+2+s4IlIFhTtGIs3M\nngV245+16fjA/sfN7K4QT7cauBPIADKBT4C3zax94Jx3AjfhH4PRA9gFvG9myeFkFxERiRenNT+N\nvs36agYnEfFEuLc2PYh/+te+FJ2p6SNgaCgncs79xzn3nnNuuXPue+fcCGAncGKgyS3AWOfcNOfc\nQuAKoBFwfpjZRURE4saYvmOYu34ub333ltdRRKSKCbeQOB+4yTn3JRD8FsgioGW4YczMF5gRKg34\nr5k1B44BPi5s45zbDnwDnBTu64iIiMSLU5ueSr/m/Rj92WgKXIHXcUSkCgm3kGgAbCxmf3WKFhZl\nYmadzGwHsBeYCFwQGIdxTOB8Gw55yobAMRERkSpvTN8xzN8wn6mLp3odRUSqkHALidn4144oVFg8\nXIt/letQfYf/VqkewJPAS2bWLsxsIiIiVcopx5/CmS3OVK+EiFSqxCM3KdbdwHQz6xA4xy2BxycD\nfUI9mXPuALAi8OkcM+uBf2zEOMCAhhTtlWgIzDnSeW+99VZq165dZF9WVhZZWVmhRhQREYlqY/qO\n4eTnTuZf3/6LIR2HlOk5kydPZvLkyUX25ebmVkQ8EYlDFu4sD2bWErgLf09CDSAH+D/n3IJyhzL7\nGPjBOXeNma0FHnLOjQ8cq4W/qLjCOfdGCc/PALKzs7PJyMgobxwREZGYcPYrZ7N6+2rmXzefBF9C\nWOfIyckhMzMTINM5lxPRgCISV8LtkcA5txz4bXkDmNkDwHTgR6AmcCn+Xo3+gSaPAiPM7HtgFTAW\n+Al4u7yvLSIiEk/G9B3Dic+eyBvfvsHFnS72Oo6IxLkyFxKBnoAyCcysVFZHAy8CxwK5wHygv3Pu\nk8C5xplZGvA0UAf4AjjHObcvhNcQERGJez2b9OScVucw5rMxDO4wOOxeCRGRsgilR2IbR56RyQJt\nyvybyzl3bRnajAZGl/WcIiIiVdWYvmPo8Y8evLbwNS7tfKnXcUQkjoVSSJxWYSlEREQkIro37s6A\nNgO49/N7GdppKIm+sO9iFhEpVZl/uzjnPqvIICIiIhIZo/uMptsz3Zi8YDKXn3C513FEJE6Fu44E\nZlbXzP5oZs8GttvNrF4kw4mIiEjoMhtlMrDtQO79/F4OFBzwOo6IxKmwCgkzOxX/DEq/B+oGtt8D\nKwPHRERExEOj+4zm+5+/55X5r3gdRUTiVLg9EhOAfwLNnXODnHODgBbAa4FjIiIi4qGux3blgnYX\nMPbzsezP3+91HBGJQ+EWEq2AR5xz+YU7Ao//GjgmIiIiHhvddzQrtq7g5fkvex1FROJQuIVEDtC+\nmP3tgXnhxxEREZFI6dywMxe2v5Cxn49lX76WXxKRyAq3kHgM+FtgsPUpge2PwHhgvJl1LtwiF1VE\nRERCNbrvaH7Y9gMvzn3R6ygiEmfCnVx6cuDjuBKOOcJYnE5EREQiq9PRnRjccTD3fXEfV3a5kuSE\nZK8jiUicCLdHovkRthZBH0VERMRDo/qMYnXuap6f87zXUUQkjoRcSJhZEjAK8DnnfjjSFvnIIiIi\nEooODTowtNNQ7vviPvYe2Ot1HBGJEyEXEs65/cCFFZBFREREKsjIU0eyZvsanp3zrNdRRCROhHtr\n01vA+ZEMIiIiIhWnfYP2XJJ+CQ988QB5B/K8jiMicSDcQmIZMNLM/mVmw83s98FbJAOKiIhIZIzs\nM5J1O9fxj5x/eB1FROJAuLM2/QbYBmQGtmAO//SwIiIiEkXa1G/DpemX8uCXD/Kbrr+hWlI1ryOJ\nSAwLq0fCOde8lE0zNYmIiESpP5/6Zzbs3MDfs//udRQRiXHh3toEgJklm1lbMwu3Z0NEREQqUev6\nrbn8hMv5y1d/Yc/+PV7HEZEYFlYhYWZpZvYssBtYBBwf2P+4md0VwXwiIiISYSN6j2DTrk08Nfsp\nr6OISAwLt0fiQeAEoC8QPPXDR8DQcmYSERGRCtSyXkuuPOFK/vLVX9i1b5fXcUQkRoVbSJwP3OSc\n+xL/4OpCi4CW5U4lIiIiFWrEqSP4ec/PPDn7Sa+jiEiMCreQaABsLGZ/dYoWFiIiIhKFmtdtztVd\nrmbcV+PUKyEiYQm3kJgN/Dro88Li4Vrg63IlEhERkUpxT+972Ja3jQmzJngdRURiULiFxN3AA2b2\nJP61KG4xsw+Aq4F7IhVOREREKk7TOk25pus1jPtqHDv27vA6jojEmHDXkfgS/2DrRGAB0B//rU4n\nOeeyIxdP5Bc333yz1xEkgiZPnux1BBEB7u59N9v3bueJmU94HUVEYkxIhYSZ+czsDjP7Cvgn8DPQ\nxznXwTl3mXNuQYWkFAHeeOMNryNIBKmQEIkOx9c+nt9m/JaHv36Y7Xu3ex1HRGJIqD0S9wAPADuA\nNcDvAd1YKSIhW7NmjdcRJIJUGMa24b2Hs3PfTh7/5nGvo4hIDAl1ReorgBucc38HMLMzgP+Y2bXO\nuYKIpyunf3/wAQuWLcOC9hV57Fzx+4t5fKTjB89nFvo5Ip0jhMdhZzKr9K9rPzD31VdJdI4EIAH/\nP+CDH50j0YyEwPHCx4mArzCvBZ3Ri8dV5TXL8PprVq2C7GxITISEBP9W+Li4fYce9/mKnls8NXny\nZLKysryOIWFqUqsJwzKG8cjXj3DKqad4HUdEYkSohcTxwPTCT5xzH5mZAxoBP0UyWCSMrlcPGjb0\nOoZESsuWdG3UKOynJ+Tn+7eCAhKL+1jasYICEvLzSz9WljbhHAvKF6ljpb2uz1XiDM7dupXv+T5f\n6cVHPO8L9TkquuQIhvcezjM5zzB5oXqXRKRsQi0kEim6kjX43yhOikycyPomI4OuGRkHPw/+86jI\n46A/nIprE0rbktqH0jbkHJWdybniMx3peSU8LkvbAuc4bfVq3u/ShXznOOAc+c6RX1DAAfDvKygg\nH/wfnSvy+AD42wb2B5+jpI/5UHRf4fmCnx9osz/o8yLnOqRtcfsOHPJahY8P4A2DX3p8zH7p8TE7\n2AMU/LhIm+KOFfYUFe4LbDuuvZZHn32WZkDzggKaFxRQq6AADhyA/PxfPgY/rsx9eXkV8xqVWagF\nM6vYAmfOHLjjDjjmGDj2WP/Hwq1OHRUyMaBRzUb8LvN3PPfuc15HEZEYEWohYcALZrY3aF8q8JSZ\nHVzNxjk3KBLhyivR5yPJF+4MtxJtkvbto1udOl7HqFQFRypuInDsiMVVBI8dcI68QOG1t3p17t6/\nnz0Fv9wVWS8xkWbVq9M8NZXm1ar5P6am0iywVUtI8OIyRFZBwS+FRTQUTJHYt28f7NoFU6bAunWw\ne3fRrzkl5ZeiIrjIOPRxw4aQnOzNdREA7jrlLp6a9pTXMUQkRoRaSLxYzL5XIhFERA7nM8NnFp1d\nfuV0zOrVrOvdmw379rEqL4+VwduePUzdtIkf9u7lQNA7+MckJx8sLAqLjMKC47iUlNh448Dn829J\ncXZVBw6Ed97xP96xA9av92/r1v3yuPDz//3P/3jjRn9hFaxevcN7NIorPurWVS9HBTi25rFc1P4i\nXuVVr6OISAwIqZBwzl1dUUFEjmTw4MFeR5AIaty4MWbGMSkpHJOSwom1ax/WJt851uzde7C4CC44\nvsjNZc3evQdvgfMBTVJSDhYXRYqN1FSOTUkhQX94Vo6aNf1b69alt8vPh02bSi44fvwRZs70P965\ns+hzk5NLLjaCP2/YEFJTK+5rjUNXd71ahYSIlEmoPRIinnn8cU1LGE8aN258xDYJZhyfmsrxqan0\nKea2tn0FBfwYVFwUFhqLd+3i3S1b2Lh//8G2SWY0PaS4aBbUo9EgKQlToVG5EhJ++cO/S5fS2+7c\nCRs2FF9wrF8Ps2b5H2/YcHgvR926Zbu1ql499XIA9arV8zqCiMQIFRIi4olITBWa7PPRKi2NVmlp\nxR7flZ/PD0G3SxUWG7N27OD1TZvYduCXIe1pPt8vvRiHjM9onppKnXi7FSnCKnzq1xo1/FvLlqW3\ny8+HLVtKLjjWrIHZs/2Ptx+y+FpSkr8HoyxjOapVi/iXqCl0RSTWWPCMOfHCzDKA7OzsbDKCZm0S\nEQm2bf/+Ij0Zh95CtTvone06iYkljs9omppK9XgYCF7V7N5dtNg49BarwscbNvgHlQerXbtsYznq\n1/ePiSmDgQMH8k7hOBMP5eTkkJmZCZDpnMvxOo+IRC/1SIhIlVUnKYmuSUl0rVnzsGPOOTYFlYN1\n9AAAHGlJREFUCo1Dx2e8s2ULP+TlsT/ojZijk5JKHJ9xfGoqybEwELyqSUuDFi38W2kKCvy9HKUV\nHHPn+h9v21b0uYmJv/RylDaW45hjKu7rFBGpICokRESKYWYcnZzM0cnJ9KxV67Dj+c6xrnAgePAY\njT17+G9uLj/t3Uthf4YBjQsHghczPqOxBoJHN58PGjTwb+nppbfds8ffg1FSwTFvHrz/vv/zoDE8\ngH+a3GHDYNAg6NdPU+GKSNRTISEiEoYEM5qkptIkNZXexRzfX1DA6qAZpwqLjWV79vDB1q2s37fv\nYNtEM44PmnHq0PEZDZOTNRA8VlSrBs2a+bfSFBTA1q1Fi4zRo+GTT+CZZ6BWLRgwAC64AM4+2z8+\nREQkyqiQEBGpAEk+Hy2qVaNFtWr+WYMOsSc/n1XFjM/I2bGDKZs28XPQPfnVAgPBixuf0Sw1lbqJ\niSo0Yo3P5x8/Ub8+dOzo3/f66/D227BwoX9xvylT4NVX/dPXnnWWv6g491z/7FIiIlFAhYSIiAeq\nJSTQvnp12levXuzx3AMHDt4qFVxsfJ6by4vr17MraCB4rYSEEsdnNEtNpUaiftXHDDP/7VPp6TBq\nFCxfDlOn+rerrvJPmdu3r//2p/PPh0aNvE4sIlWY/ncREYlCtRMTOaFGDU4o5pYW5xxbCgeCHzI+\n490tW1iVl8e+oIHgRxUOBC9mfEbT1FRSIjAQXFOXVpCWLeGPf/Rva9f6eyymToVbboEbb4QTT/QX\nFRdcAK1aeZ1WRKoYzwsJMxsOXAC0A/YA/wXudM4tDWrzPHDlIU99zzn3q0oLKiISJcyMo5KTOSo5\nme7FDAQvcI51+/YdLC6Ci42ZO3awOi+P/KD2jZKTSxyf0SQlhcQyFBoqJMrviN+/Ro3g+uv929at\nMG2a//ankSPhjjv8vRiDBvm39HQtriciFc7zQgLoDTwOzMaf50HgAzNr75zbE9RuOnAV/glQAPZW\nZkgRkVjhM6NxSgqNU1LoVbv2Ycf3FxTwU2Ag+Kqg8Rkr9uzhk61bWRs0EDwBOL6U8RnHJCfj0x+s\nERFSIVa3Llx+uX/btcs/E9SUKfDoozBmjH9K28KiomfPMq9lISISCs8LiUN7FczsKmAjkAl8GXRo\nr3NuUyVGExGJS0k+n78YKGF15rz8fH7Yu7fIauAr8/KYv2sXb2/ezJaggeApZjRLTWXfrl1sP3CA\nWhqPUfmqV/+laNi3D2bM8BcVL70EDz/sX6vi/PP9tz/17etfwVtEJAKi8Td+HcABPx+yv6+ZbQC2\nAp8AI5xzh7YREZFySk1IoG1aGm3T0oo9vqNwIHjhlLa7d/NUXh4dZ83iydatGXDUUZWcWA5KTvbP\n8HTWWTBxInz9tb+omDoVnnzS35Nx7rn+oqJ/f/+ifCIiYYqqQsL88xc+CnzpnPs26NB04E1gJdAS\n/+1P75rZSc4FjSgUEZEKVzMxkfQaNUgPGgi+pG5dEqtX59yFC7n46KP5W6tWHK0F1byVkACnnOLf\nHnnEv/r21Km/9FakpfnXqBg0CH79a6hTx+vEIhJjou2myYlAB+Di4J3Oudedc9Occ4ucc+8AA4Ae\nQN/KjygiIoeq5vPxbno6r7Rvz4c//0z7mTN5cf169F5PlDCDrl3h3nv961QsWeIfpP3TT3DZZXD0\n0f6i4u9/hy1bvE4rIjEianokzOwJ4FdAb+fcutLaOudWmtlmoBUwo6R2t956K7UPGWiYlZWlmUVE\nRCqAmXFpw4b0r1uXW7//nqu++45JGzbwdJs2JY7HEI+0aQN33snk449n8nPP+VfXnj0b3n+fXK+z\niUjMsGh4tyhQRJwH9HHOrShD+ybAD8B5zrlpxRzPALKzs7PJyMiIeF4RESmquOlfp2/ZwnVLl7J5\n/37ua96c3zdpQoJmeIpumzeT88QTZI4ZA5DpnMvxOpKIRC/Pb20ys4nApcAlwC4zaxjYUgPHq5vZ\nODPraWZNzex04C1gKfC+d8lFRKRQcT2959Svz8Lu3bn22GO5fflyTsrJYf7OnR6kkzI76igYONDr\nFCISIzwvJIDrgFrAp8DaoG1I4Hg+0Bl4G1gCPAPMAk51zu2v7LAiIlJ2NRMT+Vvr1nzVtSu78vPJ\nzM5mxIoV5OXnH/nJIiIS1TwfI+GcK7WYcc7lAWdXUhwREakAJ9WuzZxu3Xjwxx+5/4cf+NemTTzT\nti29NVOQiEjMioYeCRERqQKSfT5GNWvG3G7dqJeUxKlz53L90qVsD1rgTkREYocKCRERqVQdqlfn\ny65debxVK17ZsIEOM2fyzubNXscSEZEQqZAQEZFK5zPjpiZNWNS9OyfUqMF5CxcydNEiNuzb53U0\nEREpIxUSIiLimeNTU5mWns6r7dvzybZttJ85kxfWrdNCdiIiMUCFhIiIeMrMyGrYkMXduzOgfn2u\nXrKE/vPns2LPHq+jiYhIKVRIiIhIVDgqOZmX2rdneno6y3bvptOsWTyyejUHCgq8jiYiIsVQISEi\nIlHl7MBCdr9r1Ig/LV/OSXPmME8L2YmIRB0VEiIiEnVqJCYyvlUrvs7IIK+ggG7Z2dyjhexERKKK\nCgkREYlaPWvVIjszk5FNm/Lw6tWcMHs2n2/b5nUsERFBhYSIiES5ZJ+PPwcWsmuQlESfuXO5bskS\ncrWQnYiIp1RIiIhITGhfvTqfd+3KhNatmbRxIx1mzuStTZu8jiUiUmWpkBARkZjhM+OGxo35tnt3\nMmrW5IJFixi8aBHr9+71OpqISJWjQkJERGLOcampvNOpE6916MBn27bRftYsntNCdiIilUqFhIiI\nxCQzY+jRR7O4Rw/Oq1+f3yxZwhnz5rFcC9mJiFQKFRIiIhLT6icl8UL79rzfuTMr8vJInzWLh378\nUQvZiYhUMBUSIiISF/rXq8fC7t25vlEj7lqxgp45OczdscPrWCIicUuFhIiIxI3qCQk8EljIbr9z\ndMvOZviKFezRQnYiIhGnQkJEROJOj1q1mJ2ZyZjmzfnr6tV0nj2bT7du9TqWiEhcUSEhIiJxKdnn\n456mTZnXrRvHJCdz2rx5DFuyhG3793sdTUQkLqiQEBGRuNauenU+69KFJ1u35rWNG+kwaxZTtZCd\niEi5qZAQEZG45zPjusBCdt1q1mTQokVcuHAh67SQnYhI2FRIiIhIldEkNZW3O3Xinx068GVuLu1n\nzuQfa9dqITsRkTCokBARkSrFzBgSWMjuggYN+O3SpZw+bx7f797tdTQRkZiiQkJERKqkeklJPN+u\nHR927syqvDzSZ8/m/7SQnYhImamQEBGRKu2MevVY0L07NzZqxN0rVtAjJ4ccLWQnInJEKiRERKTK\nq56QwMOtWvFNRgb5ztEjO5s7ly9ntxayExEpkQoJERGRgG6BhezGNm/O3376ic6zZjFDC9mJiBRL\nhYSIiEiQJJ+P4U2bMr97dxqnpNBv3jyu/e47tmohOxGRIlRIiIiIFKNNWhozunTh6TZteGPTJjrM\nmsWbWshOROQgFRIiIiIl8JkxrFEjvu3Rg541a3LRokUMWriQtVrITkREhYSIiMiRNE5JYWqnTrzR\noQP/zc2lw8yZPLN2LQVayE5EqjAVEiIiImVgZlwUWMjuwgYNGLZ0Kf3mzmWZFrITkSpKhYSIiEgI\n6iYl8Wy7dnx0wgms3ruX9Fmz+MsPP7BfC9mJSBWjQkJERCQMp9ety4Lu3fl9kybcs3IlPXJyyNZC\ndiJShaiQEBERCVNaQgLjWrZkZmYmAD2ys/mTFrITkSpChYSIiEg5ZdasycyMDB5o0YIn1qwhfdYs\nPtZCdiIS51RIiIiIRECSz8edxx/P/G7dOC4lhTPmzeMaLWQnInFMhYSIiEgEtU5L45MuXfh7mzZM\n2bSJ9jNn8sbGjThNFSsicUaFhIiISIT5zPhtYCG7k2vXZsi333LBwoWs0UJ2IhJHVEiIiIhUkEYp\nKUzp1Ik3O3bkmx076DBzJk9rITsRiROeFxJmNtzMZprZdjPbYGZTzaxNMe3uNbO1ZrbbzD40s1Ze\n5BUREQnVoAYN+LZ7d4YcfTTXLV3KaXPnsuSQhewmT57sUToRkfB4XkgAvYHHgZ7AGUAS8IGZVSts\nYGZ3AjcBw4AewC7gfTNLrvy4IiIioaublMQzbdvyyQknsHbfPk6YNYsHghayUyEhIrHG80LCOfcr\n59zLzrnFzrkFwFXA8UBmULNbgLHOuWnOuYXAFUAj4PxKDywiIlIOp9Wty/xu3fhDkyaMXLmSbtnZ\nzN6+3etYIiIh87yQKEYdwAE/A5hZc+AY4OPCBs657cA3wEleBBQRESmPagkJ/CWwkF2CGT1zcli8\ne7dmdhKRmBJVhYSZGfAo8KVz7tvA7mPwFxYbDmm+IXBMREQkJmUEFrK7r3lzvt+zh3GrV3sdSUSk\nzKKqkAAmAh2Ai70OIiIiUhkSfT6GN21K62rVGL5iBf/evNnrSCIiZZLodYBCZvYE8Cugt3NuXdCh\n9YABDSnaK9EQmFPaOW+99VZq165dZF9WVhZZWVkRySwiIhIpbdPS6HjUUVyyeDH/y8igY/XqFf6a\nkydPPmyQd25uboW/rojEB4uG+zEDRcR5QB/n3Ipijq8FHnLOjQ98Xgt/UXGFc+6NYtpnANnZ2dlk\nZGRUbHgREZEIGDhwIK9OmcLJc+awKz+fmZmZ1E9KqvQcOTk5ZGZmAmQ653IqPYCIxAzPb20ys4nA\npcAlwC4zaxjYUoOaPQqMMLNzzSwdeAn4CXi78hOLiIhEXlZWFjUSE3mnUye25+czeNGig1PDiohE\nI88LCeA6oBbwKbA2aBtS2MA5Nw7/WhNP45+tqRpwjnNuX2WHFRERqQiFt902q1aNf3XsyBe5udz6\n/fcepxIRKZnnYyScc2UqZpxzo4HRFRpGREQkCvSpU4cJrVvzu6VLSa9Rg981auR1JBGRw3heSIiI\niMjhhjVqxPydO7lp2TLapaXRp04dryOJiBQRDbc2iYiISDHGt2rFqbVrc+HChazcs8frOCIiRaiQ\nEBERiVJJPh+vd+xIncREzlu4kB0HDngdSUTkIBUSIiIiUax+UhJvp6ezKi+PK777joIomLZdRARU\nSIiIiES9jtWr82r79ry9eTOjVq3yOo6ICKBCQkREJCYMOOooHmzRgvt++IF/btzodRwREc3aJCIi\nEivuOO44FuzcydXffUfratXIqFnT60giUoWpR0JERCRGmBnPtG1Lp+rVOW/hQtbv3et1JBGpwlRI\niIiIxJBqCQlM7dSJfOcYtGgRewsKvI4kIlWUCgkREZEY0zglhbc6dSJnxw6uW7oUp5mcRMQDKiRE\nRERiUI9atXi2XTteWL+e8T/95HUcEamCNNhaREQkRl3asCELdu7kT8uX0yEtjbPr1/c6kohUIeqR\nEBERiWH3t2jBOfXqcfG337Jk926v44hIFaJCQkREJIYlmPFqhw40Sklh4IIFbN2/3+tIIlJFqJAQ\nERGJcbUSE3mnUyc27d/Pxd9+ywHN5CQilUCFhIiISBxolZbGGx078vHWrdyxYoXXcUSkClAhISIi\nEidOr1uXR1u1YvxPP/HcunVexxGROKdZm0REROLIjY0bs2DXLq5bupS2aWn0ql3b60giEqfUIyEi\nIhJHzIzHW7fmpFq1GLRwIT/m5XkdSUTilAoJERGROJPs8/Gvjh2p5vNx3sKF7MrP9zqSiMQhFRIi\nIiJxqEFyMu+kp7Ns926u/u47nHNeRxKROKNCQkREJE51rlGDl9u3541Nm7jvhx+8jiMicUaFhIiI\nSBy7oEED7m3WjJGrVjFl0yav44hIHNGsTSIiInFuRNOmLNi1i8sXL6ZltWqcUKOG15FEJA6oR0JE\nRCTOmRnPt2tH27Q0zluwgI379nkdSUTigAoJERGRKqB6QgJvderEnoICLlq0iH0FBV5HEpEYp0JC\nRESkijg+NZWpnTrxv+3buWnZMs3kJCLlokJCRESkCjm5dm2ebtOGZ9atY8KaNV7HEZEYpsHWIiIi\nVczVxx7Lgl27+MP339O+enVOr1vX60giEoPUIyEiIlIFjWvRgjPq1mXwokV8v3u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      "text/plain": [
       "<matplotlib.figure.Figure at 0x153a129e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h1>Info o textu</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2>TEXTEN1.txt - WORD</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>1e-05</th>\n",
       "      <th>0.0001</th>\n",
       "      <th>0.001</th>\n",
       "      <th>0.01</th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Počet slov</th>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet znaků</th>\n",
       "      <td>972917</td>\n",
       "      <td>972917</td>\n",
       "      <td>972958</td>\n",
       "      <td>973612</td>\n",
       "      <td>979596</td>\n",
       "      <td>1006862</td>\n",
       "      <td>1040612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Velikost slovníku</th>\n",
       "      <td>9607</td>\n",
       "      <td>9607</td>\n",
       "      <td>9606</td>\n",
       "      <td>9601</td>\n",
       "      <td>9587</td>\n",
       "      <td>9557</td>\n",
       "      <td>9568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Velikost abecedy</th>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Průměrná délka slova v textu</th>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.43</td>\n",
       "      <td>4.55</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Průměrná délka slova ve slovníku</th>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Průměrná četnost slov</th>\n",
       "      <td>23.01</td>\n",
       "      <td>23.01</td>\n",
       "      <td>23.02</td>\n",
       "      <td>23.03</td>\n",
       "      <td>23.06</td>\n",
       "      <td>23.13</td>\n",
       "      <td>23.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximální četnost slov</th>\n",
       "      <td>14721 [,]</td>\n",
       "      <td>14721 [,]</td>\n",
       "      <td>14720 [,]</td>\n",
       "      <td>14708 [,]</td>\n",
       "      <td>14591 [,]</td>\n",
       "      <td>13982 [,]</td>\n",
       "      <td>13233 [,]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Druhá maximální četnost slov</th>\n",
       "      <td>13299 [the]</td>\n",
       "      <td>13299 [the]</td>\n",
       "      <td>13297 [the]</td>\n",
       "      <td>13288 [the]</td>\n",
       "      <td>13158 [the]</td>\n",
       "      <td>12644 [the]</td>\n",
       "      <td>11985 [the]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Třetí maximální četnost slov</th>\n",
       "      <td>9368 [of]</td>\n",
       "      <td>9368 [of]</td>\n",
       "      <td>9367 [of]</td>\n",
       "      <td>9362 [of]</td>\n",
       "      <td>9279 [of]</td>\n",
       "      <td>8909 [of]</td>\n",
       "      <td>8398 [of]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nejnižší četnost slov</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov s nejnižší četností</th>\n",
       "      <td>3811</td>\n",
       "      <td>3811</td>\n",
       "      <td>3801</td>\n",
       "      <td>3739</td>\n",
       "      <td>3069</td>\n",
       "      <td>1243</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 v textu</th>\n",
       "      <td>28195</td>\n",
       "      <td>28195</td>\n",
       "      <td>28194</td>\n",
       "      <td>28173</td>\n",
       "      <td>27934</td>\n",
       "      <td>26857</td>\n",
       "      <td>25467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 ve slovníku</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "                                      0.00000      0.00001      0.00010  \\\n",
       "Počet slov                             221098       221098       221098   \n",
       "Počet znaků                            972917       972917       972958   \n",
       "Velikost slovníku                        9607         9607         9606   \n",
       "Velikost abecedy                           74           74           74   \n",
       "Průměrná délka slova v textu             4.40         4.40         4.40   \n",
       "Průměrná délka slova ve slovníku         7.43         7.43         7.43   \n",
       "Průměrná četnost slov                   23.01        23.01        23.02   \n",
       "Maximální četnost slov              14721 [,]    14721 [,]    14720 [,]   \n",
       "Druhá maximální četnost slov      13299 [the]  13299 [the]  13297 [the]   \n",
       "Třetí maximální četnost slov        9368 [of]    9368 [of]    9367 [of]   \n",
       "Nejnižší četnost slov                       1            1            1   \n",
       "Počet slov s nejnižší četností           3811         3811         3801   \n",
       "Počet slov délky 1 v textu              28195        28195        28194   \n",
       "Počet slov délky 1 ve slovníku             43           43           43   \n",
       "\n",
       "                                      0.00100      0.01000      0.05000  \\\n",
       "Počet slov                             221098       221098       221098   \n",
       "Počet znaků                            973612       979596      1006862   \n",
       "Velikost slovníku                        9601         9587         9557   \n",
       "Velikost abecedy                           74           74           74   \n",
       "Průměrná délka slova v textu             4.40         4.43         4.55   \n",
       "Průměrná délka slova ve slovníku         7.43         7.43         7.43   \n",
       "Průměrná četnost slov                   23.03        23.06        23.13   \n",
       "Maximální četnost slov              14708 [,]    14591 [,]    13982 [,]   \n",
       "Druhá maximální četnost slov      13288 [the]  13158 [the]  12644 [the]   \n",
       "Třetí maximální četnost slov        9362 [of]    9279 [of]    8909 [of]   \n",
       "Nejnižší četnost slov                       1            1            1   \n",
       "Počet slov s nejnižší četností           3739         3069         1243   \n",
       "Počet slov délky 1 v textu              28173        27934        26857   \n",
       "Počet slov délky 1 ve slovníku             43           43           43   \n",
       "\n",
       "                                      0.10000  \n",
       "Počet slov                             221098  \n",
       "Počet znaků                           1040612  \n",
       "Velikost slovníku                        9568  \n",
       "Velikost abecedy                           74  \n",
       "Průměrná délka slova v textu             4.71  \n",
       "Průměrná délka slova ve slovníku         7.43  \n",
       "Průměrná četnost slov                   23.11  \n",
       "Maximální četnost slov              13233 [,]  \n",
       "Druhá maximální četnost slov      11985 [the]  \n",
       "Třetí maximální četnost slov        8398 [of]  \n",
       "Nejnižší četnost slov                       1  \n",
       "Počet slov s nejnižší četností            465  \n",
       "Počet slov délky 1 v textu              25467  \n",
       "Počet slov délky 1 ve slovníku             43  "
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       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>1e-05</th>\n",
       "      <th>0.0001</th>\n",
       "      <th>0.001</th>\n",
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       "      <th>0.1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Počet slov</th>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
       "      <td>221098</td>\n",
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       "      <td>221098</td>\n",
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       "      <th>Počet znaků</th>\n",
       "      <td>972917</td>\n",
       "      <td>972917</td>\n",
       "      <td>972917</td>\n",
       "      <td>972917</td>\n",
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       "      <td>972917</td>\n",
       "      <td>972917</td>\n",
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       "      <th>Velikost slovníku</th>\n",
       "      <td>9607</td>\n",
       "      <td>9616</td>\n",
       "      <td>9705</td>\n",
       "      <td>10508</td>\n",
       "      <td>17728</td>\n",
       "      <td>42082</td>\n",
       "      <td>64282</td>\n",
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       "      <th>Velikost abecedy</th>\n",
       "      <td>74</td>\n",
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       "      <td>74</td>\n",
       "      <td>74</td>\n",
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       "      <th>Průměrná délka slova v textu</th>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.40</td>\n",
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       "      <th>Průměrná délka slova ve slovníku</th>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.42</td>\n",
       "      <td>7.33</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.70</td>\n",
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       "    <tr>\n",
       "      <th>Průměrná četnost slov</th>\n",
       "      <td>23.01</td>\n",
       "      <td>22.99</td>\n",
       "      <td>22.78</td>\n",
       "      <td>21.04</td>\n",
       "      <td>12.47</td>\n",
       "      <td>5.25</td>\n",
       "      <td>3.44</td>\n",
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       "    <tr>\n",
       "      <th>Maximální četnost slov</th>\n",
       "      <td>14721 [,]</td>\n",
       "      <td>14721 [,]</td>\n",
       "      <td>14720 [,]</td>\n",
       "      <td>14706 [,]</td>\n",
       "      <td>14581 [,]</td>\n",
       "      <td>14006 [,]</td>\n",
       "      <td>13326 [,]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Druhá maximální četnost slov</th>\n",
       "      <td>13299 [the]</td>\n",
       "      <td>13299 [the]</td>\n",
       "      <td>13292 [the]</td>\n",
       "      <td>13262 [the]</td>\n",
       "      <td>12907 [the]</td>\n",
       "      <td>11450 [the]</td>\n",
       "      <td>9695 [the]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Třetí maximální četnost slov</th>\n",
       "      <td>9368 [of]</td>\n",
       "      <td>9368 [of]</td>\n",
       "      <td>9365 [of]</td>\n",
       "      <td>9353 [of]</td>\n",
       "      <td>9167 [of]</td>\n",
       "      <td>8430 [of]</td>\n",
       "      <td>7656 [of]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nejnižší četnost slov</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov s nejnižší četností</th>\n",
       "      <td>3811</td>\n",
       "      <td>3820</td>\n",
       "      <td>3910</td>\n",
       "      <td>4712</td>\n",
       "      <td>11638</td>\n",
       "      <td>34338</td>\n",
       "      <td>55066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 v textu</th>\n",
       "      <td>28195</td>\n",
       "      <td>28195</td>\n",
       "      <td>28195</td>\n",
       "      <td>28195</td>\n",
       "      <td>28195</td>\n",
       "      <td>28195</td>\n",
       "      <td>28195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 ve slovníku</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>57</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
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       "                                      0.00000      0.00001      0.00010  \\\n",
       "Počet slov                             221098       221098       221098   \n",
       "Počet znaků                            972917       972917       972917   \n",
       "Velikost slovníku                        9607         9616         9705   \n",
       "Velikost abecedy                           74           74           74   \n",
       "Průměrná délka slova v textu             4.40         4.40         4.40   \n",
       "Průměrná délka slova ve slovníku         7.43         7.43         7.42   \n",
       "Průměrná četnost slov                   23.01        22.99        22.78   \n",
       "Maximální četnost slov              14721 [,]    14721 [,]    14720 [,]   \n",
       "Druhá maximální četnost slov      13299 [the]  13299 [the]  13292 [the]   \n",
       "Třetí maximální četnost slov        9368 [of]    9368 [of]    9365 [of]   \n",
       "Nejnižší četnost slov                       1            1            1   \n",
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       "Počet slov délky 1 v textu              28195        28195        28195   \n",
       "Počet slov délky 1 ve slovníku             43           43           44   \n",
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       "Počet slov                             221098       221098       221098   \n",
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       "Velikost slovníku                       10508        17728        42082   \n",
       "Velikost abecedy                           74           74           74   \n",
       "Průměrná délka slova v textu             4.40         4.40         4.40   \n",
       "Průměrná délka slova ve slovníku         7.33         7.00         6.81   \n",
       "Průměrná četnost slov                   21.04        12.47         5.25   \n",
       "Maximální četnost slov              14706 [,]    14581 [,]    14006 [,]   \n",
       "Druhá maximální četnost slov      13262 [the]  12907 [the]  11450 [the]   \n",
       "Třetí maximální četnost slov        9353 [of]    9167 [of]    8430 [of]   \n",
       "Nejnižší četnost slov                       1            1            1   \n",
       "Počet slov s nejnižší četností           4712        11638        34338   \n",
       "Počet slov délky 1 v textu              28195        28195        28195   \n",
       "Počet slov délky 1 ve slovníku             57           72           74   \n",
       "\n",
       "                                     0.10000  \n",
       "Počet slov                            221098  \n",
       "Počet znaků                           972917  \n",
       "Velikost slovníku                      64282  \n",
       "Velikost abecedy                          74  \n",
       "Průměrná délka slova v textu            4.40  \n",
       "Průměrná délka slova ve slovníku        6.70  \n",
       "Průměrná četnost slov                   3.44  \n",
       "Maximální četnost slov             13326 [,]  \n",
       "Druhá maximální četnost slov      9695 [the]  \n",
       "Třetí maximální četnost slov       7656 [of]  \n",
       "Nejnižší četnost slov                      1  \n",
       "Počet slov s nejnižší četností         55066  \n",
       "Počet slov délky 1 v textu             28195  \n",
       "Počet slov délky 1 ve slovníku            74  "
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       "      <th>Počet slov</th>\n",
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       "      <td>222412</td>\n",
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       "      <td>222412</td>\n",
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       "      <th>Počet znaků</th>\n",
       "      <td>1030631</td>\n",
       "      <td>1030628</td>\n",
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       "      <td>1031250</td>\n",
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       "      <th>Velikost slovníku</th>\n",
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       "      <td>42825</td>\n",
       "      <td>42821</td>\n",
       "      <td>42802</td>\n",
       "      <td>42580</td>\n",
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       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
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       "      <th>Průměrná délka slova v textu</th>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.67</td>\n",
       "      <td>4.79</td>\n",
       "      <td>4.95</td>\n",
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       "      <th>Průměrná délka slova ve slovníku</th>\n",
       "      <td>7.72</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Průměrná četnost slov</th>\n",
       "      <td>5.19</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.20</td>\n",
       "      <td>5.22</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.39</td>\n",
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       "    <tr>\n",
       "      <th>Maximální četnost slov</th>\n",
       "      <td>13788 [,]</td>\n",
       "      <td>13788 [,]</td>\n",
       "      <td>13787 [,]</td>\n",
       "      <td>13776 [,]</td>\n",
       "      <td>13635 [,]</td>\n",
       "      <td>13069 [,]</td>\n",
       "      <td>12425 [,]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Druhá maximální četnost slov</th>\n",
       "      <td>12931 [.]</td>\n",
       "      <td>12931 [.]</td>\n",
       "      <td>12929 [.]</td>\n",
       "      <td>12920 [.]</td>\n",
       "      <td>12805 [.]</td>\n",
       "      <td>12259 [.]</td>\n",
       "      <td>11706 [.]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Třetí maximální četnost slov</th>\n",
       "      <td>4486 [a]</td>\n",
       "      <td>4486 [a]</td>\n",
       "      <td>4486 [a]</td>\n",
       "      <td>4480 [a]</td>\n",
       "      <td>4445 [a]</td>\n",
       "      <td>4246 [a]</td>\n",
       "      <td>4018 [a]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nejnižší četnost slov</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov s nejnižší četností</th>\n",
       "      <td>26315</td>\n",
       "      <td>26314</td>\n",
       "      <td>26299</td>\n",
       "      <td>26180</td>\n",
       "      <td>24963</td>\n",
       "      <td>20016</td>\n",
       "      <td>15672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 v textu</th>\n",
       "      <td>60432</td>\n",
       "      <td>60432</td>\n",
       "      <td>60426</td>\n",
       "      <td>60378</td>\n",
       "      <td>59801</td>\n",
       "      <td>57380</td>\n",
       "      <td>54468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 ve slovníku</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0.00000    0.00001    0.00010    0.00100  \\\n",
       "Počet slov                           222412     222412     222412     222412   \n",
       "Počet znaků                         1030631    1030628    1030679    1031250   \n",
       "Velikost slovníku                     42826      42825      42821      42802   \n",
       "Velikost abecedy                        117        117        117        117   \n",
       "Průměrná délka slova v textu           4.63       4.63       4.63       4.64   \n",
       "Průměrná délka slova ve slovníku       7.72       7.72       7.72       7.72   \n",
       "Průměrná četnost slov                  5.19       5.19       5.19       5.20   \n",
       "Maximální četnost slov            13788 [,]  13788 [,]  13787 [,]  13776 [,]   \n",
       "Druhá maximální četnost slov      12931 [.]  12931 [.]  12929 [.]  12920 [.]   \n",
       "Třetí maximální četnost slov       4486 [a]   4486 [a]   4486 [a]   4480 [a]   \n",
       "Nejnižší četnost slov                     1          1          1          1   \n",
       "Počet slov s nejnižší četností        26315      26314      26299      26180   \n",
       "Počet slov délky 1 v textu            60432      60432      60426      60378   \n",
       "Počet slov délky 1 ve slovníku           73         73         73         73   \n",
       "\n",
       "                                    0.01000    0.05000    0.10000  \n",
       "Počet slov                           222412     222412     222412  \n",
       "Počet znaků                         1037779    1065661    1099956  \n",
       "Velikost slovníku                     42580      41786      41227  \n",
       "Velikost abecedy                        117        117        117  \n",
       "Průměrná délka slova v textu           4.67       4.79       4.95  \n",
       "Průměrná délka slova ve slovníku       7.72       7.72       7.71  \n",
       "Průměrná četnost slov                  5.22       5.32       5.39  \n",
       "Maximální četnost slov            13635 [,]  13069 [,]  12425 [,]  \n",
       "Druhá maximální četnost slov      12805 [.]  12259 [.]  11706 [.]  \n",
       "Třetí maximální četnost slov       4445 [a]   4246 [a]   4018 [a]  \n",
       "Nejnižší četnost slov                     1          1          1  \n",
       "Počet slov s nejnižší četností        24963      20016      15672  \n",
       "Počet slov délky 1 v textu            59801      57380      54468  \n",
       "Počet slov délky 1 ve slovníku           73         73         73  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2>TEXTCZ1.txt - CHAR</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>1e-05</th>\n",
       "      <th>0.0001</th>\n",
       "      <th>0.001</th>\n",
       "      <th>0.01</th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Počet slov</th>\n",
       "      <td>222412</td>\n",
       "      <td>222412</td>\n",
       "      <td>222412</td>\n",
       "      <td>222412</td>\n",
       "      <td>222412</td>\n",
       "      <td>222412</td>\n",
       "      <td>222412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet znaků</th>\n",
       "      <td>1030631</td>\n",
       "      <td>1030631</td>\n",
       "      <td>1030631</td>\n",
       "      <td>1030631</td>\n",
       "      <td>1030631</td>\n",
       "      <td>1030631</td>\n",
       "      <td>1030631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Velikost slovníku</th>\n",
       "      <td>42826</td>\n",
       "      <td>42829</td>\n",
       "      <td>42917</td>\n",
       "      <td>43620</td>\n",
       "      <td>50076</td>\n",
       "      <td>73469</td>\n",
       "      <td>94520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Velikost abecedy</th>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Průměrná délka slova v textu</th>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Průměrná délka slova ve slovníku</th>\n",
       "      <td>7.72</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.33</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Průměrná četnost slov</th>\n",
       "      <td>5.19</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.18</td>\n",
       "      <td>5.10</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximální četnost slov</th>\n",
       "      <td>13788 [,]</td>\n",
       "      <td>13788 [,]</td>\n",
       "      <td>13784 [,]</td>\n",
       "      <td>13779 [,]</td>\n",
       "      <td>13642 [,]</td>\n",
       "      <td>13124 [,]</td>\n",
       "      <td>12483 [,]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Druhá maximální četnost slov</th>\n",
       "      <td>12931 [.]</td>\n",
       "      <td>12931 [.]</td>\n",
       "      <td>12930 [.]</td>\n",
       "      <td>12921 [.]</td>\n",
       "      <td>12801 [.]</td>\n",
       "      <td>12296 [.]</td>\n",
       "      <td>11670 [.]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Třetí maximální četnost slov</th>\n",
       "      <td>4486 [a]</td>\n",
       "      <td>4486 [a]</td>\n",
       "      <td>4486 [a]</td>\n",
       "      <td>4481 [a]</td>\n",
       "      <td>4445 [a]</td>\n",
       "      <td>4283 [a]</td>\n",
       "      <td>4093 [a]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nejnižší četnost slov</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov s nejnižší četností</th>\n",
       "      <td>26315</td>\n",
       "      <td>26319</td>\n",
       "      <td>26417</td>\n",
       "      <td>27212</td>\n",
       "      <td>34396</td>\n",
       "      <td>60628</td>\n",
       "      <td>84021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 v textu</th>\n",
       "      <td>60432</td>\n",
       "      <td>60432</td>\n",
       "      <td>60432</td>\n",
       "      <td>60432</td>\n",
       "      <td>60432</td>\n",
       "      <td>60432</td>\n",
       "      <td>60432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Počet slov délky 1 ve slovníku</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>94</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0.00000    0.00001    0.00010    0.00100  \\\n",
       "Počet slov                           222412     222412     222412     222412   \n",
       "Počet znaků                         1030631    1030631    1030631    1030631   \n",
       "Velikost slovníku                     42826      42829      42917      43620   \n",
       "Velikost abecedy                        117        117        117        117   \n",
       "Průměrná délka slova v textu           4.63       4.63       4.63       4.63   \n",
       "Průměrná délka slova ve slovníku       7.72       7.72       7.72       7.70   \n",
       "Průměrná četnost slov                  5.19       5.19       5.18       5.10   \n",
       "Maximální četnost slov            13788 [,]  13788 [,]  13784 [,]  13779 [,]   \n",
       "Druhá maximální četnost slov      12931 [.]  12931 [.]  12930 [.]  12921 [.]   \n",
       "Třetí maximální četnost slov       4486 [a]   4486 [a]   4486 [a]   4481 [a]   \n",
       "Nejnižší četnost slov                     1          1          1          1   \n",
       "Počet slov s nejnižší četností        26315      26319      26417      27212   \n",
       "Počet slov délky 1 v textu            60432      60432      60432      60432   \n",
       "Počet slov délky 1 ve slovníku           73         73         76         94   \n",
       "\n",
       "                                    0.01000    0.05000    0.10000  \n",
       "Počet slov                           222412     222412     222412  \n",
       "Počet znaků                         1030631    1030631    1030631  \n",
       "Velikost slovníku                     50076      73469      94520  \n",
       "Velikost abecedy                        117        117        117  \n",
       "Průměrná délka slova v textu           4.63       4.63       4.63  \n",
       "Průměrná délka slova ve slovníku       7.60       7.33       7.13  \n",
       "Průměrná četnost slov                  4.44       3.03       2.35  \n",
       "Maximální četnost slov            13642 [,]  13124 [,]  12483 [,]  \n",
       "Druhá maximální četnost slov      12801 [.]  12296 [.]  11670 [.]  \n",
       "Třetí maximální četnost slov       4445 [a]   4283 [a]   4093 [a]  \n",
       "Nejnižší četnost slov                     1          1          1  \n",
       "Počet slov s nejnižší četností        34396      60628      84021  \n",
       "Počet slov délky 1 v textu            60432      60432      60432  \n",
       "Počet slov délky 1 ve slovníku          117        117        117  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "results_H = np.zeros(shape=(len(FILES), len(MESSUPTYPE), len(MESSUPPROB), REPEAT ))\n",
    "results_P = np.zeros(shape=(len(FILES), len(MESSUPTYPE), len(MESSUPPROB), REPEAT ))\n",
    "info = np.empty(shape=(len(FILES), len(MESSUPTYPE), len(MESSUPPROB), REPEAT, len(INFO_INDEX) ), dtype=\"U16\")\n",
    "rnd = random.Random()\n",
    "rnd.seed(SEED)\n",
    "for file in tqdm_notebook(FILES, desc='File', leave = False):\n",
    "    for mess_up_type in tqdm_notebook(MESSUPTYPE, desc='MessUp', leave = False):\n",
    "        for mess_up_prob in tqdm_notebook(MESSUPPROB, desc='Prob', leave = False):\n",
    "            for r in tqdm_notebook(range(REPEAT), desc='Repeat', leave = False):\n",
    "                if(mess_up_type == MESSUPTYPE.WORD):\n",
    "                    data = word_mess_up(rnd.randrange(sys.maxsize),mess_up_prob, file)\n",
    "                if(mess_up_type == MESSUPTYPE.CHAR):\n",
    "                    data = char_mess_up(rnd.randrange(sys.maxsize),mess_up_prob, file)\n",
    "                \n",
    "                (h,p) = entropy(data)\n",
    "                results_H[FILES.index(file), int(mess_up_type)-1, MESSUPPROB.index(mess_up_prob), r] = h\n",
    "                results_P[FILES.index(file), int(mess_up_type)-1, MESSUPPROB.index(mess_up_prob), r] = p\n",
    "                info[FILES.index(file), int(mess_up_type)-1, MESSUPPROB.index(mess_up_prob), r, :] = get_info(data)\n",
    "end = time.time()\n",
    "print(\"Time: \",int((end-start)//60) ,\"m \",int((end-start)%60) ,\"s\")\n",
    "print()\n",
    "\n",
    "display(HTML(\"<h1>Entropie</h1>\"))\n",
    "show_result(results_H, \"Entropie\")\n",
    "display(HTML(\"<h1>Perplexity</h1>\"))\n",
    "show_result(results_P, \"Perplexity\")\n",
    "display(HTML(\"<h1>Info o textu</h1>\"))\n",
    "show_info(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shrnutí výsledků"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entropie a perplexity původních textů\n",
    "\n",
    "|             |Anglický text|Český text|\n",
    "|-------------|------------:|---------:|\n",
    "|Entropie     |5.287422     |4.747822  |\n",
    "|Perplexity   |39.054632    |26.868092 |\n",
    "\n",
    "Vidíme, že anglický text má vyšší entropii než český. To je způsobeno hlavně tím, že anglický text oproti českému má daleko vyšší průměrnou četnost slov (23:5), což je způsobeno tím, že v českém textu se vyskytuje mnohem více slov, které jsou použity pouze jednou."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entropie po náhodných změnách znaků\n",
    "Při náhodné změně znaků dochází k vytváření nových slov. To znamená, že přibývá slov, které se vyskytnou pouze 1. Tedy se entropie snižuje.  \n",
    "Při změně slova $i$ na $i'$, pokud vznikne nové slovo\n",
    "$$-\\dfrac{c(i,j)}{N-1}log_2 \\dfrac{c(i,j)}{c(i)} \\geq -\\dfrac{c(i,j)-1}{N-1}log_2 \\dfrac{c(i,j)-1}{c(i)-1}-0$$\n",
    "Nerovnost platí díky tomu, že $1 \\leq c(i,j) \\leq c(i)$  \n",
    "Při změně slova $i$ na $i'$, pokud nevznikne nové slovo\n",
    "$$-\\dfrac{c(i,j)}{N-1}log_2 \\dfrac{c(i,j)}{c(i)}-\\dfrac{c(i',j)}{N-1}log_2 \\dfrac{c(i',j)}{c(i')} \\stackrel{?}{\\lesseqgtr} -\\dfrac{c(i,j)-1}{N-1}log_2 \\dfrac{c(i,j)-1}{c(i)-1}-\\dfrac{c(i',j)+1}{N-1}log_2 \\dfrac{c(i',j)+1}{c(i')+1}$$\n",
    "Zde už není odpověď zcela jasná. Mohou nastat všechny případy.\n",
    "\n",
    "|c(i,j)   |c(i)     |c(i',j)  |c(i')    |Nerovnost|\n",
    "|--------:|--------:|--------:|--------:|:-------:|\n",
    "|        2|     1000|        2|     1000|    <    |\n",
    "|        3|     1000|        2|     1000|    >    |\n",
    "|        2|       10|        2|     1000|    >    |\n",
    "|        2|     1000|        2|       10|    <    |\n",
    "\n",
    "Z tabulky si přibližně dokážeme představit, co se při změnách děje.\n",
    "Protože český text obsahuje 2× více jednopísmenných slov, a právě u těchto slov nevzniká nové slovo, častěji se bude entropie zvyšovat.\n",
    "Z grafu také vidíme, že entropie českého textu klesá pomaleji."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entropie po náhodných změnách slov\n",
    "Při této změně nedochází k vzniku nových slov, pouze se upravují jejich četnosti, tomu odpovídá druhý vzorec uvedený výše.\n",
    "Při náhodné změně slov se v podstatě vyrovnávají četnosti.\n",
    "\n",
    "Myslím si, že důvodem snížení entropie českého textu je vlastnost logaritmu a vysoký počet slov s četností 1.\n",
    "Přišli o 11 000 slov s četností 1, buď ztrátou slova (maximálně 1 500 slov) , a nebo zvýšením četnosti (cca 9 500 slov).\n",
    "U prvního případu entropie roste a u druhého klesá díky vlastnosti logaritmu. \n",
    "\n",
    "U anglického textu je to obdobné, ale protože počet slov s malou četností je mnohem méně, mohlo dojít k nárůstu entropie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Psací úloha\n",
    "Co víme:\n",
    "$$H_1 = H_2 = E$$\n",
    "$$E = H_1 = -\\sum \\dfrac{c(i,j)}{N}log_2\\dfrac{c(i,j)}{c(i)}$$\n",
    "$$N*E = -\\sum c(i,j)log_2\\dfrac{c(i,j)}{c(i)}$$\n",
    "$$E = H_2 = -\\sum \\dfrac{c(u,v)}{M}log_2\\dfrac{c(u,v)}{c(u)}$$\n",
    "$$M*E = -\\sum c(u,v)log_2\\dfrac{c(u,v)}{c(u)}$$\n",
    "$$E \\stackrel{?}{\\lesseqgtr} -\\sum \\dfrac{c(x,y)}{M+N+1}log_2\\dfrac{c(x,y)}{c(x)}$$\n",
    "$$(M+N+1)E \\stackrel{?}{\\lesseqgtr} -\\sum c(x,y)log_2\\dfrac{c(x,y)}{c(x)}$$\n",
    "Nyní rozepíšeme sumu nového textu na entropie $T_1$, $T_2$ a spojovací dvojice:\n",
    "$$ -\\sum c(x,y)log_2\\dfrac{c(x,y)}{c(x)} = -\\sum c(i,j)log_2\\dfrac{c(i,j)}{c(i)} -\\sum c(u,v)log_2\\dfrac{c(u,v)}{c(u)} - 1log_2 \\dfrac{1}{c(i')} = N*E + M*E - log_2 \\dfrac{1}{c(i')}$$\n",
    "kde $i'$ je poslední slovo v textu $T_1$.\n",
    "Jelikož o hodnotě $-log_2 \\dfrac{1}{c(i')}$ víme pouze to, že je větší rovna $0$, nemůžeme porovnat obě strany nerovnice,protože neznáme porovnání k hodnote $E$. Tedy mohou nastat všechny nerovnost (i rovnost).\n",
    "\n",
    "Entropie spojeného textu, ač jí neumíme určit přesně, bude přibližně E, protože jak jsme si ukázali, závisí pouze na spojovacím členu. "
   ]
  }
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