Report-Freq-Old.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do not spend too much time trying to get very tiny metrics improvement. Once you have a model with a correct predictive power, you should better spend time explaining your data cleaning & preparation pipeline as well as explanations & visualizations of the results.\n",
    "\n",
    "The goal is to see your fit with our company culture & engineering needs, spending 50h on an over-complicated approach will not give you bonus points compared to a simple, yet effective, to-the-point solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset you will be working with is called Emo-DB and can be found [here](http://emodb.bilderbar.info/index-1280.html).\n",
    "\n",
    "It is a database containing samples of emotional speech in German. It contains samples labeled with one of 7 different emotions: Anger, Boredom, Disgust, Fear, Happiness, Sadness and Neutral. \n",
    "\n",
    "Please download the full database and refer to the documentation to understand how the samples are labeled (see \"Additional information\")\n",
    "   \n",
    "The goal of this project is to develop a model which is able to **classify samples of emotional speech**. Feel free to use any available library you would need, but beware of re-using someone else's code without mentionning it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The end-goal is to deliver us:\n",
    "* This report filled with your approach, in the form of an **iPython Notebook**.\n",
    "* A **5-10 slides PDF file**, containing a very brief presentation covering the following points:\n",
    "    * Introduction to the problem (what are we trying to achieve and why) - max 1 slide\n",
    "    * Libraries used - max 1 slide\n",
    "    * Data Processing Pipeline - max 2 slides\n",
    "    * Feature Engineering (if relevant) - max 1 slide\n",
    "    * Modeling - max 1 slide\n",
    "    * Results & Visualization - max 2 slides\n",
    "* The goal of the presentation is to make it **understandable by a business person**, apart from how modeling techniques which do not have to be explained how they work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import platform\n",
    "import glob\n",
    "import ntpath\n",
    "import os\n",
    "from scipy.io import wavfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "#from torch import Tensor\n",
    "from torch import nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shernand/dl_epfl/lib/python3.6/site-packages/pandas_profiling/plot.py:15: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"/home/shernand/pythonroots/python_3.6.5_built/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n",
      "    \"__main__\", mod_spec)\n",
      "  File \"/home/shernand/pythonroots/python_3.6.5_built/lib/python3.6/runpy.py\", line 85, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 486, in start\n",
      "    self.io_loop.start()\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 127, in start\n",
      "    self.asyncio_loop.run_forever()\n",
      "  File \"/home/shernand/pythonroots/python_3.6.5_built/lib/python3.6/asyncio/base_events.py\", line 422, in run_forever\n",
      "    self._run_once()\n",
      "  File \"/home/shernand/pythonroots/python_3.6.5_built/lib/python3.6/asyncio/base_events.py\", line 1432, in _run_once\n",
      "    handle._run()\n",
      "  File \"/home/shernand/pythonroots/python_3.6.5_built/lib/python3.6/asyncio/events.py\", line 145, in _run\n",
      "    self._callback(*self._args)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 117, in _handle_events\n",
      "    handler_func(fileobj, events)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2666, in run_cell\n",
      "    self.events.trigger('post_run_cell', result)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/IPython/core/events.py\", line 88, in trigger\n",
      "    func(*args, **kwargs)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\", line 160, in configure_once\n",
      "    activate_matplotlib(backend)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 311, in activate_matplotlib\n",
      "    matplotlib.pyplot.switch_backend(backend)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 231, in switch_backend\n",
      "    matplotlib.use(newbackend, warn=False, force=True)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/matplotlib/__init__.py\", line 1410, in use\n",
      "    reload(sys.modules['matplotlib.backends'])\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
      "    _bootstrap._exec(spec, module)\n",
      "  File \"/home/shernand/dl_epfl/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 16, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use(BACKEND)\n"
     ]
    }
   ],
   "source": [
    "import pandas_profiling\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import speechpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation & Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_main_folder = 'data'\n",
    "wav_folder = 'wav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "platform_system = platform.system()\n",
    "file_separator=''\n",
    "if(platform_system=='Windows'):\n",
    "    file_separator = '\\\\'\n",
    "else:#(platform_system=='Linux'):\n",
    "    file_separator = '/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav_path = data_main_folder + file_separator + wav_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav_files = glob.glob(wav_path+file_separator+'*.wav')\n",
    "wav_files.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/wav/03a01Fa.wav']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wav_files[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Additional Information</h2>\n",
    "\n",
    "\n",
    "Every utterance is named according to the same scheme:\n",
    "<ul><li>Positions 1-2: number of speaker\n",
    "    <li>Positions 3-5: code for text\n",
    "    <li>Position 6: emotion (sorry, letter stands for german emotion word)\n",
    "    <li>Position 7: if there are more than two versions these are numbered a, b, c ....\n",
    "</ul>\n",
    "\n",
    "Example: 03a01Fa.wav is the audio file from Speaker 03 speaking text a01 with the emotion \"Freude\" (Happiness).\n",
    "\n",
    "<h3>Information about the speakers</h3>\n",
    "\n",
    "<ul><li>03 - male, 31 years old\n",
    "    <li>08 - female, 34 years\n",
    "    <li>09 - female, 21 years\n",
    "    <li>10 - male, 32 years\n",
    "    <li>11 - male, 26 years\n",
    "    <li>12 - male, 30 years\n",
    "    <li>13 - female, 32 years\n",
    "    <li>14 - female, 35 years\n",
    "    <li>15 - male, 25 years\n",
    "    <li>16 - female, 31 years\n",
    "</ul>\n",
    "\n",
    "<table class=\"aussen\" border=\"0\" align=\"left\" width=\"100%\">\n",
    "<tr><td>\n",
    "\n",
    "<h3>Code of texts</h3>\n",
    "\n",
    "<table align=\"left\" width=\"800\" bgcolor=\"#313131\" border=\"1\" frame=\"solid\" rules=\"rows\" cellpadding=\"3\" cellspacing=\"1\">\n",
    "<tr><th>code</th><th>text (german)</th><th>try of an english translation</th></tr>\n",
    "<tr><td class=\"mittig\">a01</td><td>Der Lappen liegt auf dem Eisschrank.</td><td>The tablecloth is lying on the frigde.</td></tr>\n",
    "<tr><td class=\"mittig\">a02</td><td>Das will sie am Mittwoch abgeben.</td><td>She will hand it in on Wednesday.</td></tr>\n",
    "<tr><td class=\"mittig\">a04</td><td>Heute abend könnte ich es ihm sagen.</td><td>Tonight I could tell him.</td></tr>\n",
    "<tr><td class=\"mittig\">a05</td><td>Das schwarze Stück Papier befindet sich da oben neben dem Holzstück.</td><td>The black sheet of paper is located up there besides the piece of timber.</td></tr>\n",
    "<tr><td class=\"mittig\">a07</td><td>In sieben Stunden wird es soweit sein.</td><td>In seven hours it will be.</td></tr>\n",
    "<tr><td class=\"mittig\">b01</td><td>Was sind denn das für Tüten, die da unter dem Tisch stehen?</td><td>What about the bags standing there under the table?</td></tr>\n",
    "<tr><td class=\"mittig\">b02</td><td>Sie haben es gerade hochgetragen und jetzt gehen sie wieder runter.</td><td>They just carried it upstairs and now they are going down again.</td></tr>\n",
    "<tr><td class=\"mittig\">b03</td><td>An den Wochenenden bin ich jetzt immer nach Hause gefahren und habe Agnes besucht.</td><td>Currently at the weekends I always went home and saw Agnes.</td></tr>\n",
    "<tr><td class=\"mittig\">b09</td><td>Ich will das eben wegbringen und dann mit Karl was trinken gehen.</td><td>I will just discard this and then go for a drink with Karl.</td></tr>\n",
    "<tr><td class=\"mittig\">b10</td><td>Die wird auf dem Platz sein, wo wir sie immer hinlegen.</td><td>It will be in the place where we always store it.</td></tr>\n",
    "</table>\n",
    "<p>&nbsp;<br><p>\n",
    "\n",
    "</td></tr>\n",
    "\n",
    "<tr><td>\n",
    "\n",
    "<h3><br>Code of emotions:</h3>\n",
    "\n",
    "<table align=\"left\" width=\"600\" bgcolor=\"#313131\" border=\"1\" frame=\"solid\" rules=\"rows\" cellpadding=\"3\" cellspacing=\"1\">\n",
    "<tr><th>letter</th><th>emotion (english)</th><th>letter</th><th>emotion (german)</th></tr>\n",
    "<tr><td class=\"mittig\">A</td><td>anger</td><td class=\"mittig\">W</td><td>Ärger (Wut)</td></tr>\n",
    "<tr><td class=\"mittig\">B</td><td>boredom</td><td class=\"mittig\">L</td><td>Langeweile</td></tr>\n",
    "<tr><td class=\"mittig\">D</td><td>disgust</td><td class=\"mittig\">E</td><td>Ekel</td></tr>\n",
    "<tr><td class=\"mittig\">F</td><td>anxiety/fear</td><td class=\"mittig\">A</td><td>Angst</td></tr>\n",
    "<tr><td class=\"mittig\">H</td><td>happiness</td><td class=\"mittig\">F</td><td>Freude</td></tr>\n",
    "<tr><td class=\"mittig\">S</td><td>sadness</td><td class=\"mittig\">T</td><td>Trauer</td></tr>\n",
    "<tr><td colspan=\"4\">N = neutral version</td></tr>\n",
    "\n",
    "</td></tr>\n",
    "</table>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constructing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_german = ['W','L','E','A','F','T','N']\n",
    "emotion_english = ['Anger','Boredom','Disgust','Fear','Happiness','Sadness','Neutral']\n",
    "emotion_numbers = np.arange(0,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loading_wav_file(file_wav, emotion_german,emotion_numbers):\n",
    "    \"\"\"Loads a wav file, and returns its respective y (label) and x (sound signal).\"\"\"\n",
    "    path, filename = os.path.split(file_wav)\n",
    "    filename, file_extension = os.path.splitext(filename)\n",
    "    individual_number = int(filename[0:2])\n",
    "    code_text = filename[2:5]\n",
    "    emotion_label = filename[5]\n",
    "    index_emotion = np.argwhere(np.char.strip(emotion_german)==emotion_label)[0][0]\n",
    "    y = emotion_numbers[index_emotion]\n",
    "    fs, x = wavfile.read(file_wav) \n",
    "    return y, x, fs, code_text, individual_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shernand/dl_epfl/lib/python3.6/site-packages/scipy/io/wavfile.py:273: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  WavFileWarning)\n"
     ]
    }
   ],
   "source": [
    "y=[]\n",
    "x=[]\n",
    "fs_array=[]\n",
    "code_text_array = []\n",
    "ind_numb_array=[]\n",
    "length_array = []\n",
    "for file_wav in wav_files:\n",
    "    y_file, x_file, fs, code_text, individual_number = loading_wav_file(file_wav, emotion_german,emotion_numbers)\n",
    "    y.append(y_file)\n",
    "    x.append(x_file)\n",
    "    length_array.append(len(x_file))\n",
    "    fs_array.append(fs)\n",
    "    code_text_array.append(code_text)\n",
    "    ind_numb_array.append(individual_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_complete = pd.DataFrame(columns=['y','x','length','fs','ind_numb','text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_complete['y'] = y\n",
    "data_complete['x'] = x\n",
    "data_complete['length'] = length_array\n",
    "data_complete['fs'] = fs_array\n",
    "data_complete['ind_numb'] = ind_numb_array\n",
    "data_complete['text'] = code_text_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Profiling of the data-set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "            .stats tr {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .stats td{\n",
       "                color: #555;\n",
       "                padding: 1px;\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Sample table */\n",
       "            table.sample {\n",
       "                border: 0;\n",
       "                margin-bottom: 2em;\n",
       "                margin-left:1em;\n",
       "            }\n",
       "            .sample tr {\n",
       "                border:0;\n",
       "            }\n",
       "            .sample td, .sample th{\n",
       "                padding: 0.5em;\n",
       "                white-space: nowrap;\n",
       "                border: none;\n",
       "\n",
       "            }\n",
       "\n",
       "            .sample thead {\n",
       "                border-top: 0;\n",
       "                border-bottom: 2px solid #ddd;\n",
       "            }\n",
       "\n",
       "            .sample td {\n",
       "                width:100%;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* There is no good solution available to make the divs equal height and then center ... */\n",
       "            .histogram {\n",
       "                margin-top: 3em;\n",
       "            }\n",
       "        /* Freq table */\n",
       "\n",
       "            table.freq {\n",
       "                margin-bottom: 2em;\n",
       "                border: 0;\n",
       "            }\n",
       "            table.freq th, table.freq tr, table.freq td {\n",
       "                border: 0;\n",
       "                padding: 0;\n",
       "            }\n",
       "\n",
       "            .freq thead {\n",
       "                font-weight: 600;\n",
       "                white-space: nowrap;\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "\n",
       "            }\n",
       "\n",
       "            td.fillremaining{\n",
       "                width:auto;\n",
       "                max-width: none;\n",
       "            }\n",
       "\n",
       "            td.number, th.number {\n",
       "                text-align:right ;\n",
       "            }\n",
       "\n",
       "        /* Freq mini */\n",
       "            .freq.mini td{\n",
       "                width: 50%;\n",
       "                padding: 1px;\n",
       "                font-size: 12px;\n",
       "\n",
       "            }\n",
       "            table.freq.mini {\n",
       "                 width:100%;\n",
       "            }\n",
       "            .freq.mini th {\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "                white-space: nowrap;\n",
       "                max-width: 5em;\n",
       "                font-weight: 400;\n",
       "                text-align:right;\n",
       "                padding-right: 0.5em;\n",
       "            }\n",
       "\n",
       "            .missing {\n",
       "                color: #a94442;\n",
       "            }\n",
       "            .alert, .alert > th, .alert > td {\n",
       "                color: #a94442;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Bars in tables */\n",
       "            .freq .bar{\n",
       "                float: left;\n",
       "                width: 0;\n",
       "                height: 100%;\n",
       "                line-height: 20px;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                background-color: #337ab7;\n",
       "                border-radius: 3px;\n",
       "                margin-right: 4px;\n",
       "            }\n",
       "            .other .bar {\n",
       "                background-color: #999;\n",
       "            }\n",
       "            .missing .bar{\n",
       "                background-color: #a94442;\n",
       "            }\n",
       "            .tooltip-inner {\n",
       "                width: 100%;\n",
       "                white-space: nowrap;\n",
       "                text-align:left;\n",
       "            }\n",
       "\n",
       "            .extrapadding{\n",
       "                padding: 2em;\n",
       "            }\n",
       "\n",
       "            .pp-anchor{\n",
       "\n",
       "            }\n",
       "\n",
       "</style>\n",
       "\n",
       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>6 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>535 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>0.0% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>25.2 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>48.1 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>3 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_fs\"><code>fs</code></a> has constant value 16000 <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_x\"><code>x</code></a> is an unsupported type, check if it needs cleaning or further analysis <span class=\"label label-warning\">Warning</span></li><li><a href=\"#pp_var_y\"><code>y</code></a> has 127 / 23.7% zeros <span class=\"label label-info\">Zeros</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_fs\"><s>fs</s><br/>\n",
       "            <small>Constant</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is constant and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Constant value</th>\n",
       "            <td>16000</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_ind_numb\">ind_numb<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>10</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>1.9%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>11.473</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>3</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>16</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-3697809938689095488\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAS1JREFUeJzt3MFpw0AARUHbpCQXkZ58dk8pIj2tGwgPS2C0Wc3cBXt5fIEWXccY4wL86Xb0AWBmX0cfgHXdHz%2Bbn/l9fn/gJPtZEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAhu8y5g663Z2W7MzsyCQBAIBIFAEAgEgUAQCASBQPAdhLfs%2BUPJCiwIBAtyQmddgz0sCAQLsoE7T%2BdjQSAIBMJ0r1gr/PCYdVgQCNMtyEqs4f9nQSBYkMn4iDcXCwJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBALhOsYYRx8CZmVBIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAILwAwTkb0sR3IEUAAAAASUVORK5CYII%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-3697809938689095488,#minihistogram-3697809938689095488\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-3697809938689095488\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-3697809938689095488\"\n",
       "                                                  aria-controls=\"quantiles-3697809938689095488\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-3697809938689095488\" aria-controls=\"histogram-3697809938689095488\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-3697809938689095488\" aria-controls=\"common-3697809938689095488\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-3697809938689095488\" aria-controls=\"extreme-3697809938689095488\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-3697809938689095488\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>9</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>12</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>14</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>16</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>16</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>13</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>5</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>3.7023</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.3227</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>0.051534</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>11.473</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>3.009</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.8383</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>6138</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>13.707</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>4.3 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-3697809938689095488\">\n",
       "            <img 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OXPmaPr06YqKimp1m5SUlJYPBb0Y0dHReuqpp/Too49q3Lhx6tatm0aPHq0VK1bosssu06ZNm7Rq1SqtXLlS8fHxWrduna699tr23C0AABBAfK5gbdu2TaNHjz7v7T744IN2HWfIkCF69tlnv3dbYmKiduzY0a79AwCAwOVzr8EaOHCgsrKytHfv3pa1p59%2BWvfcc0%2BrF58DAAD4Ip8rWGvWrNHJkyfVv3//lrXx48fL5XJp7dq1FiYDAABoG5%2B7RHjw4EHt3LlT0dHRLWt9%2BvRRXl6e/uVf/sXCZAAAAG3jc2ewGhsbFRoa2mo9ODhYp0%2BftiARAADAhfG5gpWYmKi1a9fqq6%2B%2Baln78ssvtXLlSq%2BPagAAAPBVPneJcMmSJfr5z3%2BuMWPGKDw8XC6XSw0NDerdu/cPvusPAADAl/hcwerdu7d27dqlN954Q3/5y18UHBysvn37auzYsQoJCbE6HgAAwHn5XMGSpG7dumnixIlWxwAAALgoPlewvvjiC61fv16ffPKJGhsbW21/7bXXLEgFAADQdj5XsJYsWaLa2lqNHTtWYWFhVscBAAC4YD5XsCoqKvTaa68pJibG6igAAAAXxec%2BpuGyyy7jzBUAAOjSfK5gzZkzRwUFBXK73VZHAQAAuCg%2Bd4nwjTfe0J/%2B9Ce99NJL%2BvGPf6zgYO8O%2BOKLL1qUDAAAoG18rmCFh4dr3LhxVscAAAC4aD5XsNasWWN1BAAAgHbxuddgSdL//M//KD8/X4sXL25Ze%2B%2B99yxMBAAA0HY%2BV7DKysqUlpam0tJSlZSUSPrmw0dnz57Nh4wCAIAuwecK1oYNG/SLX/xCO3fuVFBQkKRvfj/h2rVrtXHjRovTAQAAnJ/PvQbrz3/%2Bs5577jlJailYkjR58mQtWbLEqlgAAOiWx96yOgK6CJ87g9WzZ8/v/R2EtbW16tatmwWJAAAALozPFayRI0dq9erVOnXqVMvaZ599poULF2rMmDEWJgMAAGgbn7tEuHjxYv3sZz9TUlKSmpubNXLkSJ0%2BfVrXXHON1q5da3U8AACA8/K5gtWrVy%2BVlJTowIED%2Buyzz3TppZeqb9%2B%2Buv76671ekwUAAOCrfK5gSdIll1yiiRMnWh0DAADgovhcwUpNTT3nmSo%2BCwsAAPg6nytYU6ZM8SpYzc3N%2Buyzz1ReXq6f/exnFiYDAABoG58rWLm5ud%2B7/uqrr%2Brdd9/t5DQAAAAXzuc%2BpuGHTJw4Ubt27bI6BgAAwHl1mYL10Ucfye12Wx0DAADgvHzuEuGdd97Zau306dM6evSobr75ZgsSAQAAXBifK1h9%2BvRp9S7C0NBQpaen6/bbb7coFQAAQNv5XMHi09oBAEBX53MF6%2BWXX27zbadPn96BSQAAAC6OzxWspUuXyuVytXpBe1BQkNdaUFAQBQsAAPgknytYTz75pLZs2aKsrCwNHDhQbrdbR44c0RNPPKGf/vSnSkpKsjoiAADAOflcwVq7dq02b96suLi4lrVRo0apd%2B/euuuuu1RSUmJhOgAAgPPzuc/B%2BvzzzxUZGdlqPSIiQtXV1UaPVVhYqLFjx2r48OHKzMzUsWPHJEllZWVKT0/XyJEjNXXqVBUXFxs9LgAA8G8%2BV7Di4%2BO1du1a1dXVtazV19dr/fr1uvLKK40d5/nnn1dxcbG2bdumgwcPqn///nr66adVW1ur%2B%2B%2B/X3feeafKysq0dOlSPfLIIyovLzd2bAAA4N987hLhkiVLtGDBAtntdvXo0UPBwcE6deqULr30Um3cuNHYcbZs2aKFCxeqX79%2BkqRly5ZJkp566in16dNH6enpkqTk5GSlpqaqqKhIQ4cONXZ8AIB0y2NvWR0B6BA%2BV7DGjh2r/fv368CBA6qpqZHb7VZcXJxuuOEG9ezZ08gxvvzySx07dkxfffWVpkyZouPHjyspKUkrVqxQZWWlEhISvG6fkJCgPXv2tHn/tbW1cjgcXms2W5hiY2ON5PclISHBXl8DHfPwYBYezAJoG396jvhcwZKk7t27a8KECaqpqVHv3r2N77%2BmpkaS9Morr2jr1q1yu93KycnRsmXL1NjY6PUCe0mKioryumR5Pna7XQUFBV5r2dnZysnJaX94HxUR0d3qCD6FeXgwCw9mAZybPz1HfK5gNTY2avny5dq1a5ckqaKiQvX19XrooYf0m9/8RhEREe0%2Bxrefp3X33Xe3lKm5c%2BfqnnvuUXJycrv3n5GRodTUVK81my1MdXUN7d63rwkJCVZERHfV159Wc7PL6jiWYx4ezMKDWQBt0xHPkejoHkb311Y%2BV7DWrVunqqoq5eXl6eGHH25Zb25uVl5enn71q1%2B1%2BxiXX365JHmVtfj4eLndbp09e1ZOp9Pr9nV1dYqJiWnz/mNjY1tdDnQ4TqqpyX9/sDY3u/z6/l0o5uHBLDyYBXBu/vQc8bmLna%2B%2B%2Bqr%2B67/%2BS5MnT275pc8RERFas2aNSktLjRyjV69eCg8PV1VVVctadXW1LrnkEqWkpKiiosLr9hUVFRo2bJiRYwMAAP/ncwWroaFBffr0abUeExOjv//970aOYbPZlJ6erscff1z/%2B7//q%2BPHj2vjxo2aNm2abrvtNlVXV6uoqEhff/21Dhw4oAMHDuiOO%2B4wcmwAAOD/fO4S4ZVXXql3331XSUlJXr978JVXXtE//dM/GTvOggULdObMGd1%2B%2B%2B06e/asJk2apGXLlqlHjx7atGmTVq1apZUrVyo%2BPl7r1q3Ttddea%2BzYQEfoSm933/Pg9VZHAIAO5XMF61//9V81d%2B5czZw5Uy6XS1u3blVFRYVeffVVLV261NhxunXrpuXLl2v58uWttiUmJmrHjh3GjgUAAAKLzxWsjIwM2Ww2PffccwoJCdHjjz%2Buvn37Ki8vT5MnT7Y6HgAAwHn5XME6ceKEZs6cqZkzZ1odBQAA4KL43IvcJ0yY4PXaKwAAgK7G5wpWUlLSBf1aGgAAAF/jc5cIf/SjH%2BnXv/61Nm/erCuvvFKXXHKJ1/b169dblAwAAKBtfK5gffrpp%2BrXr58kXdDv/wMAAPAVPlOw5s%2Bfrw0bNujZZ59tWdu4caOys7MtTAUAAHDhfOY1WPv27Wu1tnnzZguSAAAAtI/PFKzve%2Bcg7yYEAABdkc8UrG9/sfP51gAAAHydzxQsAAAAf0HBAgAAMMxn3kV49uxZLViw4LxrfA4WAADwdT5TsK677jrV1taedw0AAMDX%2BUzB%2BsfPvwIAAOjKeA0WAACAYRQsAAAAwyhYAAAAhlGwAAAADPOZF7nj4tzy2FtWR2izPQ9eb3UEAAA6BWewAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhNqsDAAg8tzz2ltURLsieB6%2B3OgKALoYzWAAAAIZRsAAAAAwL%2BIK1evVqDRw4sOX7srIypaena%2BTIkZo6daqKi4stTAcAALqigH4NVlVVlXbs2NHyfW1tre6//34tXbpU06ZN0%2BHDh3Xfffepb9%2B%2BGjp0qIVJAQBAVxKwZ7BcLpeWL1%2BuzMzMlrWdO3eqT58%2BSk9PV2hoqJKTk5WamqqioiLrggIAgC4nYM9gvfjiiwoNDdW0adP02GOPSZIqKyuVkJDgdbuEhATt2bPngvZdW1srh8PhtWazhSk2NrZ9obs4m83/%2B3xISLDXV/iH9j52eVwAbeNPz5GALFh/%2B9vflJ%2Bfr2effdZr3el0Ki4uzmstKipKdXV1F7R/u92ugoICr7Xs7Gzl5ORcXGA/ER3dw%2BoInSYiorvVEWCQqccujwvg3PzpORKQBWvNmjWaMWOG%2Bvfvr2PHjhnff0ZGhlJTU73WbLYw1dU1GD9WVxII9z8kJFgREd1VX39azc0uq%2BPAkPY%2BdnlcAG3TEc8Rq/5xH3AFq6ysTO%2B9955KSkpabYuOjpbT6fRaq6urU0xMzAUdIzY2ttXlQIfjpJqaAvsHayDd/%2BZmV0DdX39n6v8ljwvg3PzpORJwBau4uFjHjx/XjTfeKElyu92SpKSkJP385z9vVbwqKio0bNiwTs8JAAC6roArWIsWLdK8efNavq%2BpqVFGRoZ27Nghl8ulTZs2qaioSGlpaXrnnXd04MAB2e12CxMDAICuJuAKVmRkpCIjI1u%2Bb2pqkiT16tVLkrRp0yatWrVKK1euVHx8vNatW6drr73WkqwAfENX%2B92JAKwXcAXru3784x/ryJEjLd8nJiZ6ffgoAADAhfKfD5wAAADwERQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABgWsAWrurpa2dnZSkpKUnJyshYtWqT6%2BnpJUlVVlX7605/quuuu080336wtW7ZYnBYAAHQlAVuwsrKyFBERoX379umll17SJ598okcffVSNjY2aM2eO/vmf/1lvvvmmNmzYoE2bNqm0tNTqyAAAoIsIyIJVX1%2BvIUOGaMGCBerRo4d69eql2267TYcOHdL%2B/ft19uxZ3XfffQoLC9PgwYN1%2B%2B23y263Wx0bAAB0ETarA1ghIiJCa9as8Vr761//qtjYWFVWVmrgwIEKCQlp2ZaQkKCioqI277%2B2tlYOh8NrzWYLU2xsbPuCd3E2m//3%2BZCQYK%2BvAIC286efnQFZsL6rvLxczz33nAoLC7Vnzx5FRER4bY%2BKipLT6ZTL5VJw8Pn/59vtdhUUFHitZWdnKycnx2juriY6uofVETpNRER3qyMAQJfjTz87A75gHT58WPfdd58WLFig5ORk7dmz53tvFxQU1OZ9ZmRkKDU11WvNZgtTXV1Du7J2dYFw/0NCghUR0V319afV3OyyOg4AdCkd8bPTqn/cB3TB2rdvn37xi1/okUce0fTp0yVJMTEx%2Bvzzz71u53Q6FRUV1aazV5IUGxvb6nKgw3FSTU2B/RduIN3/5mZXQN1fADDBn352%2Bs/Fzgv0pz/9SQsXLtRvf/vblnIlSUOGDNGRI0fU1NTUslZeXq5hw4ZZERMAAHRBAVmwmpqatGzZMuXm5mrs2LFe21JSUhQeHq7CwkKdPn1aH3zwgbZv365Zs2ZZlBYAAHQ1AVmw3n//fR09elSrVq3S0KFDvf5zOBx6/PHH9fbbb2v06NF68MEHNX/%2BfI0fP97q2AAAoIsIyNdgjRo1SkeOHDnnbV544YVOSgMAAPxNQJ7BAgAA6EgULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsH6HtXV1br33nuVlJSkG2%2B8UevWrZPL5bI6FgAA6CJsVgfwRXPnztXgwYO1d%2B9eHT9%2BXHPmzNHll1%2Buf//3f7c6GgAA6AI4g/Ud5eXl%2Bvjjj5Wbm6uePXuqT58%2ByszMlN1utzoaAADoIjiD9R2VlZWKj49XZGRky9rgwYP12Wef6dSpUwoPDz/vPmpra%2BVwOLzWbLYwxcbGGs/bldhs/t/nQ0KCvb4CANrOn352UrC%2Bw%2Bl0KiIiwmvt27JVV1fXpoJlt9tVUFDgtfbAAw9o7ty55oL%2Bv0O/nmx8nxeitrZWdrtdGRkZAV8gpW/m8cwzT1oyD6sfC9/FY8ODWXgwCw9m4VFbW6v8/Hy/moX/VEWD3G53u/58RkaGXnrpJa//MjIyDKXzLQ6HQwUFBa3O2AUq5uHBLDyYhQez8GAWHv44C85gfUdMTIycTqfXmtPpVFBQkGJiYtq0j9jYWL9p4AAA4MJxBus7hgwZor/%2B9a86ceJEy1p5ebn69%2B%2BvHj16WJguyGcEAAAHrklEQVQMAAB0FRSs70hISNDQoUO1fv16nTp1SkePHtXWrVs1a9Ysq6MBAIAuImTFihUrrA7ha2644QaVlJToP/7jP7Rr1y6lp6frrrvuUlBQkNXRfFKPHj00evRozvD9P%2BbhwSw8mIUHs/BgFh7%2BNosgd3tf0Q0AAAAvXCIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBSNWr16tgQMHWh3DUoWFhRo7dqyGDx%2BuzMxMHTt2zOpIlvjoo480e/ZsjRo1Stdff71yc3O9fnm6v3vzzTeVnJys%2BfPnt9q2e/duTZs2TSNGjNCMGTN08OBBCxJ2nnPNorS0VGlpaRoxYoQmTZqk3/3udxYk7DznmsW3GhoaNH78eC1atKgTk3W%2Bc83iyy%2B/1H333afhw4crOTlZ69evl8vlsiBl%2B1Gw0G5VVVXasWOH1TEs9fzzz6u4uFjbtm3TwYMH1b9/fz399NNWx%2Bp0TU1NuvfeezV8%2BHC9/fbbKikp0YkTJxQov/L0iSee0KpVq3TVVVe12lZVVaWFCxcqNzdX77zzjjIzM/XAAw%2BopqbGgqQd71yz%2BPDDD5Wbm6ucnBz98Y9/1JIlS/SrX/1Khw4dsiBpxzvXLP5Rfn6%2BTp061UmprHGuWbjdbj3wwAOKj4/XwYMH9eyzz6qsrEzvvvuuBUnbj4KFdnG5XFq%2BfLkyMzOtjmKpLVu2aP78%2BerXr5/Cw8O1bNkyLVu2zOpYnc7hcMjhcOjWW29Vt27dFB0drZtuuklVVVVWR%2BsUoaGh2r59%2B/f%2B5VFUVKSUlBSlpKQoNDRUaWlpGjBggIqLiy1I2vHONQun06k5c%2BZo4sSJstlsSklJ0YABA/y2YJ1rFt/6%2BOOPVVJSottuu60Tk3W%2Bc83ij3/8o7744gs9/PDDCg8P19VXX63t27drzJgxFiRtPwoW2uXFF19UaGiopk2bZnUUy3z55Zc6duyYvvrqK02ZMkVJSUnKyckJqMti34qLi9OgQYNkt9vV0NCg48ePq7S0VOPHj7c6WqeYPXu2evbs%2Bb3bKisrlZCQ4LWWkJCg8vLyzojW6c41i3Hjxik7O7vl%2B6amJjkcDsXFxXVWvE51rllI35y5WbFihebPn6%2BIiIhOTNb5zjWLw4cPa8CAAdqwYYOSkpI0YcIEbdmypZMTmkPBwkX729/%2Bpvz8fC1fvtzqKJb69hLPK6%2B8oq1bt2rHjh2qqakJyDNYwcHBys/P12uvvaaRI0cqOTlZTU1NWrBggdXRLOd0OhUZGem1FhkZqbq6OosS%2BY68vDyFhYVpypQpVkexhN1uV1BQkGbMmGF1FEvV1NTo/fff12WXXab9%2B/frl7/8pTZs2KC9e/daHe2iULBw0dasWaMZM2aof//%2BVkexlNvtliTdfffdiouLU69evTR37lzt27dPX3/9tcXpOteZM2eUlZWlyZMn69ChQ3rjjTfUs2dP5ebmWh3NJ3z7WME33G631q1bp5KSEhUWFio0NNTqSJ3u%2BPHj%2Bu1vf6sVK1YoKCjI6jiWcrvdiomJ0d13363u3bsrJSVFN910k/bs2WN1tItCwcJFKSsr03vvved1mj9QXX755ZLkdWo/Pj5ebrdbx48ftyqWJcrKynTs2DE99NBD6tmzp%2BLi4pSTk6P//u//ltPptDqepaKjo1vNwOl0KiYmxqJE1nK5XFq0aJH27dunF154Qf369bM6kiXWrl2r6dOnB/y7sCXpiiuuaHX5MD4%2BXg6Hw6JE7WOzOgC6puLiYh0/flw33nijJM%2B/zJOSkvTLX/5SU6dOtTJep%2BrVq5fCw8NVVVWlwYMHS5Kqq6t1ySWXKDY21uJ0nau5uVkul8vrTM2ZM2csTOQ7hgwZooqKCq%2B18vLygHqu/KPVq1frk08%2B0QsvvKCoqCir41imuLhYEREReumllyRJjY2Ncrlcev3117vsu%2Bcu1tVXX60vvvhCDQ0N6tGjh6RvfpbGx8dbnOziULBwURYtWqR58%2Ba1fF9TU6OMjAzt2LGj1etM/J3NZlN6eroef/xxJSYmKjw8XBs3btS0adNkswXWU2zEiBEKCwtTfn6%2BsrKy1NjYqMLCQiUmJgb0X6KSdMcddyg9PV379%2B/XmDFjtHPnTn3%2B%2BedKS0uzOlqnO3z4sIqLi7V79%2B6Af1wcOHDA6/utW7eqpqZGixcvtiiRdVJTUxUREaH//M//1MKFC/XBBx9o7969euqpp6yOdlGC3LwoAAYcO3ZMEyZM0JEjR6yOYokzZ85ozZo12rVrl86ePatJkybpkUceaflXWCCpqKjQo48%2Bqo8//ljdunXT6NGjtWjRIr99h9g/Gjp0qKRv3hUnqaVgf/tOwdLSUq1fv17V1dXq37%2B/li5dqsTERGvCdrBzzWLJkiX6wx/%2B0OofIImJiV36XWM/5HyPi3%2BUn5%2Bv6upqrV27tvMCdqLzzeLPf/6zli9frsrKSsXExGjevHld9qMrKFgAAACG8SJ3AAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDs/wA39bs%2BUUpLuwAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-3697809938689095488\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">16</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14</td>\n",
       "        <td class=\"number\">69</td>\n",
       "        <td class=\"number\">12.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:97%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">13</td>\n",
       "        <td class=\"number\">61</td>\n",
       "        <td class=\"number\">11.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:86%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">58</td>\n",
       "        <td class=\"number\">10.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:81%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">15</td>\n",
       "        <td class=\"number\">56</td>\n",
       "        <td class=\"number\">10.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:79%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11</td>\n",
       "        <td class=\"number\">55</td>\n",
       "        <td class=\"number\">10.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:77%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">9.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:69%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9</td>\n",
       "        <td class=\"number\">43</td>\n",
       "        <td class=\"number\">8.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:60%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10</td>\n",
       "        <td class=\"number\">38</td>\n",
       "        <td class=\"number\">7.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:53%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">12</td>\n",
       "        <td class=\"number\">35</td>\n",
       "        <td class=\"number\">6.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:49%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-3697809938689095488\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">9.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:84%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">58</td>\n",
       "        <td class=\"number\">10.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9</td>\n",
       "        <td class=\"number\">43</td>\n",
       "        <td class=\"number\">8.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:74%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10</td>\n",
       "        <td class=\"number\">38</td>\n",
       "        <td class=\"number\">7.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:65%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11</td>\n",
       "        <td class=\"number\">55</td>\n",
       "        <td class=\"number\">10.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:94%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">12</td>\n",
       "        <td class=\"number\">35</td>\n",
       "        <td class=\"number\">6.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:49%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">13</td>\n",
       "        <td class=\"number\">61</td>\n",
       "        <td class=\"number\">11.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:86%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14</td>\n",
       "        <td class=\"number\">69</td>\n",
       "        <td class=\"number\">12.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:97%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">15</td>\n",
       "        <td class=\"number\">56</td>\n",
       "        <td class=\"number\">10.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:79%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">16</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_length\">length<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>530</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>99.1%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>44474</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>19608</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>143652</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-6242690979570606700\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAABGVJREFUeJzt3cFLVGsYx/Hf6GXIJCRzRkcCN2FEEERTi2QQN3LHXLSLiFaVbaqVhs7FqRBr6cY2/QGCqyIS7i4oSswLQUIFujBKxmawJpXoONhzdwNi97lNXMep%2B/0s33POnHcxX97D8M5MyMxMAL6parsnAFSy37Z7Av%2BF%2BB9/lnzNX8O/b8FM8KthBQEcBAI4CARwEAjgIBDAQSCAg0AAB4EADgIBHAQCOAgEcBAI4CAQwPFL7Ob9EewAxveouEB%2B5I0LbBUesQAHgQAOAgEcBAI4CARwEAjgIBDAQSCAg0AAB4EADgIBHAQCOAgEcBAI4Ki47e6VrNSt%2BHx/5OfHCgI4CARwEAjgIBDAQSCAg0AAB4EADgIBHAQCOAgEcBAI4CAQwMFmxS3ED2T//AikwhBVZSGQXwDb8LcOgfwPVfJfTFRavCEzs%2B2eBODJZrMaHx/XqVOnFI1Gy3pvPsVCxcvlchodHVUulyv7vQkEcBAI4CAQwEEgqHiRSESXLl1SJBIp%2B735FAtwsIIADgIBHAQCOAgEcBAI4CAQwEEggIPt7iiL5eVlpdNpTU9P68mTJ5Kk/fv369ixY8VzTp8%2Bra6uLs3MzGh4eFjV1dWqrq7WzZs3tXfvXr19%2B1apVErr6%2Bv6%2BvWrBgcHdfDgQX38%2BFH9/f1aWVlRoVDQlStXlEgkFASBUqmUFhYWVCgUdPbsWZ08ebK0iRtQBufOnbOxsTE7fvx4cay1tfWb5yaTSZuenjYzs7t379rFixfNzOz8%2BfN27949MzN79uyZdXd3m5nZtWvX7Pbt22ZmNj8/b4lEwoIgsDt37lgqlTIzs6WlJWtvb7dsNlvSvHnEQlmMjIwokUj863nv3r3T6uqq4vG4JKmrq0uTk5MqFAqamppSMpmUJB09elT5fF6ZTEaPHj3SiRMnJEktLS1qbm7WixcvNozX19fryJEjxdXre/GIhbLYtWuXPn36tGl8YGBAb968USwWU39/v7LZrBoaGorHw%2BGwduzYoQ8fPqimpkbhcLh4LBKJaHFxcdM10Wj0H8ffv39f0rxZQbBt0um0%2Bvr6NDY2ppaWFg0NDZX8GqFQaNOYmZU07iEQbJszZ86ovr5ekpRMJvXq1SvFYjFls9niOZ8/f1YQBNq9e7e%2BfPmiIAiKxxYXFxWLxdTU1LThmkwm446XgkCwLebm5tTT06NCoSBJevr0qQ4cOKBYLKY9e/ZoampKknT//n21t7crHA6rra1NExMTkqTHjx%2BrublZjY2N6ujo0IMHDyRJs7OzWlpa0qFDhzaM53I5PX/%2BXG1tbSXNk%2B3u2HL5fF6XL19WEAR6%2BfKlDh8%2BrNbWVtXV1enhw4eqra3Vzp07NTQ0pMbGRr1%2B/Vo3btxQKBRSTU2Nbt26pWg0qkwmo4GBAa2tramqqkrXr1/Xvn37tLKyoqtXryqfz8vM1Nvbq3g8rrW1NaXTac3Pz2t9fV0XLlxQZ2dnSXMnEMDBIxbgIBDAQSCA429Unz2M9PTtkgAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-6242690979570606700,#minihistogram-6242690979570606700\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-6242690979570606700\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-6242690979570606700\"\n",
       "                                                  aria-controls=\"quantiles-6242690979570606700\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-6242690979570606700\" aria-controls=\"histogram-6242690979570606700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-6242690979570606700\" aria-controls=\"common-6242690979570606700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-6242690979570606700\" aria-controls=\"extreme-6242690979570606700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-6242690979570606700\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>19608</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>24963</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>32426</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>41443</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>52934</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>75740</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>143652</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>124044</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>20509</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>16451</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.3699</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>4.105</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>44474</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>12459</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.5343</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>23793475</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>270640000</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>4.3 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6242690979570606700\">\n",
       "            <img 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jt2LHD2%2BUBAACUy2cDVlFRkT788EM98sgjSkpKUmRkpGbOnKm2bdtq7Nixev/9971dIgAAgFs2bxdwof3792vVqlX6xz/%2BoYKCAt1%2B%2B%2B1avny5unTp4jwmNjZWs2fP1l133eXFSgEAANzzuYB15513qnnz5powYYKGDBmiRo0alTmmd%2B/eOnHihBeqAwAAqJjPBawVK1aoW7duFR73/fffX4ZqAAAAqs7nXoPVunVrTZw4URs3bnSO/e1vf9Mjjzwih8PhxcoAAAAqx%2BcCVnJysvLy8tSiRQvnWJ8%2BfVRaWqqUlBQvVgYAAFA5PneJ8PPPP9f777%2Bvxo0bO8euu%2B46paam6o9//KMXKwMAAKgcnzuDVVhYqODg4DLjgYGBOnXqlBcqAgAAqBqfC1ixsbFKSUnRyZMnnWO//vqr5syZ4/JRDQAAAL7K5y4Rzpo1Sw899JBuueUWhYSEqLS0VAUFBYqOjtbrr7/u7fIAAAAq5HMBKzo6Wh988IE%2B%2B%2BwzHTp0SIGBgWrevLl69uypoKAgb5cHAABQIZ8LWJJUt25dDRgwwNtlAAAAXBKfC1iHDx/WwoUL9dNPP6mwsLDM/KZNm7xQFQAAQOX5XMCaNWuWsrOz1bNnT9WvX9/b5QAAAFSZzwWsnTt3atOmTQoPD/d2KQAAAJfE5z6moUmTJpy5AgAANZrPBawJEyYoPT1dxhhvlwIAAHBJfO4S4WeffaZvv/1W7733nq699loFBrpmwLfffrtS62zdulVJSUmKi4tTWlqay9yHH36oJUuW6MiRI2revLkef/xx9ezZU5JUWlqqRYsWae3atcrNzVXHjh01e/ZsRUdHW3MHAQBAredzASskJES9evWq1hrLli3TqlWr1KxZszJze/bsUVJSktLT03XzzTdr/fr1evTRR/XRRx%2BpadOmeuONN/T%2B%2B%2B9r2bJlioyMVFpamhISErR69WoFBARUqy4AAOAffC5gJScnV3uN4OBgrVq1Ss8%2B%2B6xOnz7tMvfuu%2B%2Bqd%2B/e6t27tyTp7rvv1sqVK7VmzRqNHz9eGRkZGjt2rG644QZJ0tSpUxUXF6fvv/9enTp1qnZtAACg9vO512BJ0oEDB7R48WLNnDnTObZ9%2B/ZK337MmDEKDQ11O7dr1y61a9fOZaxdu3bKzMxUYWGh9u3b5zIfEhKiZs2aKTMzs4r3AgAA%2BCufO4P15Zdf6pFHHlHz5s31888/Kzk5WYcPH9aYMWP03HPPqX///tVa3%2BFwqGHDhi5jDRs21L59%2B3Ty5EkZY9zO5%2BTkVPpnZGdny263u4zZbPUVERFx6YUDFbDZKvd8KSgo0OUrzqIv5aM37tEX9%2BjLWT4XsNLS0vTEE0/ogQceUMeOHSWd/f2EKSkpeuGFF6odsCRV%2BA7F6r6DMSMjQ%2Bnp6S5jCQkJSkxMrNa6wMU0btygSseHhdXzUCU1G30pH71xj7645%2B998bmA9eOPP2rlypWS5PKi8kGDBmnWrFnVXr9x48ZyOBwuYw6HQ%2BHh4WrUqJECAwPdzjdp0qTSPyM%2BPl79%2BvVzGbPZ6isnp%2BDSCwcqUNn9FRQUqLCwesrNPaWSklIPV1Vz0Jfy0Rv36It7vtaXqj75tIrPBazQ0FAVFhaqbt26LuPZ2dllxi5FTEyMdu7c6TKWmZmpO%2B%2B8U8HBwWrZsqV27dqlbt26SZJyc3N16NAh59m0yoiIiChzOdBuz1Nxsfc3Gmqvqu6vkpJS9qQb9KV89MY9%2BuKev/fF5y6Q3nTTTZo3b57y8/OdYwcPHlRSUpJuueWWaq9/77336p///Kc%2B/fRTnT59WqtWrdLPP/%2Bsu%2B%2B%2BW5I0evRorVixQvv371d%2Bfr5SU1PVtm1bdejQodo/GwAA%2BAefO4M1c%2BZMPfDAA4qLi1NJSYluuukmnTp1Si1btlRKSkql1jgXhoqLiyVJGzdulHT2TFWrVq2Umpqq5ORkHT16VC1atNDLL7%2Bsq666SpI0atQo2e123X///SooKFBcXFyZ11MBAABcTIDxwd9JU1RUpC1btujgwYO64oor1Lx5c/Xo0aNGf9Cn3Z7nkXXveO4Lj6yLmmfdlB6VOs5mC1Tjxg2Uk1Pg16fvL0Rfykdv3KMv7vlaX666yv3HNnmaz53BkqQ6depowIAB3i4DAADgkvhcwOrXr99Fz1Rt2rTpMlYDAABQdT4XsAYPHuwSsEpKSnTw4EFlZmbqgQce8GJlAAAAleNzAWv69Olux9evX69//etfl7kaAACAqvO5j2koz4ABA/TBBx94uwwAAIAK1ZiAtXv37mr/ChsAAIDLwecuEY4aNarM2KlTp7R//34NHDjQCxUBAABUjc8FrOuuu67MuwiDg4M1YsQIjRw50ktVAQAAVJ7PBazKflo7AACAr/K5gPWPf/yj0scOGTLEg5UAAABcGp8LWE8%2B%2BaRKS0vLvKA9ICDAZSwgIICABQAAfJLPBaxXXnlFr776qiZOnKjWrVvLGKO9e/dq2bJluu%2B%2B%2BxQXF%2BftEgEAAC7K5wJWSkqKli5dqsjISOdY165dFR0drXHjxmnt2rVerA4AAKBiPvc5WD///LMaNmxYZjwsLExHjx71QkUAAABV43MBKyoqSikpKcrJyXGO5ebmauHChfrDH/7gxcoAAAAqx%2BcuEc6aNUvTpk1TRkaGGjRooMDAQOXn5%2BuKK67QCy%2B84O3yAAAAKuRzAatnz5769NNPtWXLFh07dkzGGEVGRurWW29VaGiot8sDAACokM8FLEmqV6%2Be%2Bvfvr2PHjik6Otrb5QAAAFSJz70Gq7CwUElJSercubPuuOMOSWdfg/Xwww8rNzfXy9UBAABUzOcC1oIFC7Rnzx6lpqYqMPD/yispKVFqaqoXKwMAAKgcnwtY69ev1/PPP69BgwY5f%2BlzWFiYkpOTtWHDBi9XBwAAUDGfC1gFBQW67rrryoyHh4fr999/v/wFAQAAVJHPBaw//OEP%2Bte//iVJLr978KOPPtI111zjrbIAAAAqzefeRfinP/1JkydP1vDhw1VaWqrXXntNO3fu1Pr16/Xkk096uzwAAIAK%2BVzAio%2BPl81m08qVKxUUFKSXXnpJzZs3V2pqqgYNGuTt8gAAACrkcwHrxIkTGj58uIYPH%2B7tUgAAAC6Jz70Gq3///i6vvQIAAKhpfC5gxcXFad26dd4uAwAA4JL53CXCq6%2B%2BWs8%2B%2B6yWLl2qP/zhD6pTp47L/MKFC71UGQAAQOX4XMDat2%2Bfrr/%2BeklSTk6Ol6sBAACoOp8JWFOnTlVaWppef/1159gLL7yghIQEL1YFAABQdT4TsDZv3lxmbOnSpR4JWF9//bUeeughlzFjjIqKirRixQqNGTNGdevWdZn/r//6L%2BcvnwYAALgYnwlY7t456Kl3E8bGxiozM9Nl7KWXXtIPP/wgSYqKinIb%2BAAAACrDZwLWuV/sXNGYJ/zyyy967bXX9D//8z86fPjwZfmZAACg9vKZgOVNixYt0vDhw3XNNdfo8OHDKigoUEJCgrZt26a6devqoYce0tixYysd%2BLKzs2W3213GbLb6ioiI8ET5gCTJZqvcp64EBQW6fMVZ9KV89MY9%2BuIefTnL7wPWkSNHtGHDBm3YsEGSFBISolatWumBBx5QWlqavvrqKz322GMKDQ3ViBEjKrVmRkaG0tPTXcYSEhKUmJhoef3AOY0bN6jS8WFh9TxUSc1GX8pHb9yjL%2B75e18CjI98bHq7du3KvIh83bp1Zcas/hys%2BfPn68SJE5o/f365xyxYsEDbt2/Xm2%2B%2BWak1L%2BcZrNtSt1q%2BJmqmj6ffWqnjgoICFRZWT7m5p1RSUurhqmoO%2BlI%2BeuMefXHP1/pS1SefVvGZM1hdunRRdnZ2hWNWW79%2BvZKSki56TFRUlNavX1/pNSMiIsqEKbs9T8XF3t9oqL2qur9KSkrZk27Ql/LRG/foi3v%2B3hefCVjnf/7V5bJnzx4dPXpUPXr0cI6tW7dOOTk5%2BtOf/uQcO3DggKKjoy97fQAAoGby61eg7d69W40aNVJISIhzrE6dOpo/f74%2B//xzFRUV6YsvvtDf//53jR492ouVAgCAmsRnzmB5w2%2B//aarrrrKZWzAgAGaNWuW5s6dq6ysLF155ZWaNWuWBg4c6KUqAQBATePXAWvChAmaMGFCmfH4%2BHjFx8d7oSIAAFAb%2BPUlQgAAAE8gYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWMyvf1UOUJvc8dwX3i6h0tZN6eHtEgDAoziDBQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYzObtAryhdevWqlOnjgICApxj9957r55%2B%2Bml9%2BeWXWrhwoQ4cOKCrr75aEyZM0N133%2B3FagEAQE3jlwFLkj766CNde%2B21LmPZ2dn693//dz355JO666679M0332jSpElq3ry5OnTo4KVKAQBATcMlwvO8//77uu666zRixAgFBwere/fu6tevn959911vlwYAAGoQvz2DtXDhQm3fvl35%2Bfm64447NGPGDO3atUvt2rVzOa5du3Zat25dldbOzs6W3W53GbPZ6isiIqLadQO1gc3me8/tgoICXb7i/9Ab9%2BiLe/TlLL8MWJ06dVL37t01f/58HT58WFOmTNGcOXPkcDgUGRnpcmyjRo2Uk5NTpfUzMjKUnp7uMpaQkKDExMRq1w7UBo0bN/B2CeUKC6vn7RJ8Fr1xj7645%2B998cuAlZGR4fzvG264QdOnT9ekSZPUpUsXS9aPj49Xv379XMZstvrKySmwZH2gpvPFfwtBQYEKC6un3NxTKikp9XY5PoXeuEdf3PO1vnjrCZ1fBqwLXXvttSopKVFgYKAcDofLXE5OjsLDw6u0XkRERJnLgXZ7noqLvb/RAF/gy/8WSkpKfbo%2Bb6I37tEX9/y9L353gXT37t1KSUlxGdu/f7/q1q2r3r17a%2BfOnS5zO3fu1I033ng5SwQAADWc3wWsJk2aKCMjQ0uXLtWZM2d08OBBLVq0SPHx8brnnnt09OhRvfvuuzp9%2BrS2bNmiLVu26N577/V22QAAoAbxu4AVGRmppUuXavPmzYqLi9OoUaN066236oknnlCTJk308ssva%2BXKlerSpYvmzZunBQsWqE2bNt4uGwAA1CB%2B%2BRqs2NhYvf322%2BXOrV69%2BjJXBAAAahO/O4MFAADgaQQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxm83YB3nL06FHNmzdP27ZtU1BQkHr16qVZs2YpNzdX/fv3V926dV2OnzJlisaNG%2BelagEAQE3itwFr4sSJiomJ0ebNm5WXl6eEhATNnz9fkyZNkiRlZmZ6uUIAAFBT%2BeUlwtzcXMXExGjatGlq0KCBmjZtqqFDh2rbtm3eLg0AANQCfnkGKywsTMnJyS5jWVlZioiIcH7/5z//Wf/85z9VXFyskSNHKjExUXXq1KnU%2BtnZ2bLb7S5jNlt9l/UBf2az%2Bd5zu6CgQJev%2BD/0xj364h59OcsvA9aFMjMztXLlSi1ZskR169ZV586dddttt%2BnZZ5/Vnj17NHnyZNlsNj322GOVWi8jI0Pp6ekuYwkJCUpMTPRE%2BUCN07hxA2%2BXUK6wsHreLsFn0Rv36It7/t6XAGOM8XYR3vTNN99o0qRJevTRRzVmzBi3x7z55pt6%2BeWXtWXLlkqteTnPYN2WutXyNQFP%2B3j6rd4uoYygoECFhdVTbu4plZSUerscn0Jv3KMv7vlaX7z1hM6vz2Bt3rxZTzzxhJ5%2B%2BmkNGTKk3OOioqL022%2B/yRijgICACteNiIgoE6bs9jwVF3t/owG%2BwJf/LZSUlPp0fd5Eb9yjL%2B75e1/89gLpt99%2Bq6SkJC1atMglXH355ZdasmSJy7EHDhxQVFRUpcIVAACAXwas4uJiPfXUU5o%2Bfbp69uzpMhcaGqoXXnhBq1evVlFRkTIzM/Xf//3fGj16tJcoCNlOAAAPc0lEQVSqBQAANY1fXiL87rvvtH//fj3zzDN65plnXOY%2B%2BugjpaWlKT09XX/5y18UGhqq%2B%2B%2B/Xw888ICXqgUAADWNXwasrl27au/eveXOR0VF6bbbbruMFQEAgNrELy8RAgAAeBIBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsJjN2wUA8D93PPeFt0uoknVTeni7BAA1DGewAAAALMYZLACoAGfcAFQVZ7AAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBi/C5CAKhlatLvTuT3JqK24gwWAACAxQhYAAAAFiNgAQAAWIyA5cbRo0c1fvx4xcXFqW/fvlqwYIFKS0u9XRYAAKgheJG7G5MnT1b79u21ceNGHT9%2BXBMmTNCVV16pBx980NulAUCtUpNekC/xonxUHgHrApmZmfrhhx/02muvKTQ0VKGhoRo7dqyWL19OwAIAP1fTAmFNUtvCKwHrArt27VJUVJQaNmzoHGvfvr0OHjyo/Px8hYSEVLhGdna27Ha7y5jNVl8RERGW1wsAQG1gs9WuVy0RsC7gcDgUFhbmMnYubOXk5FQqYGVkZCg9Pd1l7NFHH9XkyZOtK/T/%2B/Cxm5SRkaH4%2BHgC3AWys7PpjRv0xT36Uj564x59cY%2B%2BnFW74qJFjDHVun18fLzee%2B89lz/x8fEWVefKbrcrPT29zBkz0Jvy0Bf36Ev56I179MU9%2BnIWZ7AuEB4eLofD4TLmcDgUEBCg8PDwSq0RERHh16kdAAB/xxmsC8TExCgrK0snTpxwjmVmZqpFixZq0KCBFysDAAA1BQHrAu3atVOHDh20cOFC5efna//%2B/Xrttdc0evRob5cGAABqiKDZs2fP9nYRvubWW2/V2rVrNXfuXH3wwQcaMWKExo0bp4CAAG%2BX5laDBg3UrVs3zrC5QW/coy/u0Zfy0Rv36It79EUKMNV9RTcAAABccIkQAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwvO3r0qBISEhQXF6fu3btrxowZys3NlSTt2bNH9913n7p06aKBAwfq1Vdfdbnthx9%2BqLvuukudO3fWsGHD9PnnnzvnSktLlZaWpv79%2Bys2Nlbjxo3T4cOHnfMOh0NTpkxR9%2B7d1bNnTz355JMqLCy8PHe6iubNm6fWrVs7v//yyy81YsQI3XTTTbrzzju1Zs0al%2BNXrFih22%2B/XTfddJNGjx6tnTt3OudOnz6tv/zlL%2BrVq5fi4uKUmJionJwc5/zRo0c1fvx4xcXFqW/fvlqwYIFKS0s9fyerYMmSJerZs6c6deqksWPH6siRI5Loy%2B7duzVmzBh17dpVPXr00PTp052/tN2ferN161Z1795dU6dOLTPnyceM6jxeXS4X682GDRt09913q3Pnzrr99tv1zjvvuMx7co9UtD897WJ9OaegoEB9%2BvTRjBkznGP%2BsGeqxcCr/vjHP5oZM2aY/Px8k5WVZYYNG2ZmzZplTp06ZW699VazePFiU1BQYHbu3Gm6detm1q9fb4wxZvfu3SYmJsZ8%2BumnprCw0KxevdrceOONJisryxhjzIoVK0zfvn3Nvn37TF5envnP//xPc9ddd5nS0lJjjDGPPvqoGT9%2BvDl%2B/Lg5duyYiY%2BPN3PnzvVaH8qze/du061bN9OqVStjjDG//vqr6dSpk3n33XdNYWGh%2BeKLL0zHjh3Njh07jDHGbNq0yXTt2tV899135tSpU%2Bbll182PXr0MAUFBcYYY5KTk82wYcPML7/8YnJycsyjjz5qJkyY4Px5Q4cONU899ZTJzc01Bw8eNAMHDjSvvvrq5b/j5Vi5cqUZNGiQ2b9/v8nLyzNz5841c%2BfO9fu%2BFBUVmR49epiFCxea06dPmxMnTpgHH3zQTJ482a96s3TpUjNw4EAzatQoM2XKFJc5Tz5mVPfxytu9%2Bf77702HDh3Mxx9/bIqKisynn35q2rdvb77%2B%2BmtjjGf3SEX705t9OV9ycrLp0qWLSUpKco7V9j1TXQQsLzp58qSZMWOGsdvtzrHXX3/dDBw40Kxbt87cfPPNpri42Dm3YMEC89BDDxljjJkzZ45JSEhwWW/kyJHm5ZdfNsYYc%2Bedd5rly5c75/Ly8ky7du3M9u3bjd1uN23atDF79uxxzm/ZssV06tTJnDlzxiP39VKUlJSYkSNHmhdffNEZsF555RUzZMgQl%2BOmTJlinn76aWOMMePHjzfz5s1zWaNHjx5m7dq1pqioyHTp0sVs3LjROb9v3z7TunVrc%2BzYMbNjxw7Ttm1b43A4nPNvvvmmuf322z15N6ukX79%2Bzgeg8/l7X3755RfTqlUrs2/fPufYm2%2B%2BaQYMGOBXvVm%2BfLnJzc01SUlJZf5n6cnHjOo%2BXl0OF%2BvNli1bTHp6usvY0KFDzZIlS4wxnt0jFe1PT7tYX87Zs2eP6dGjh3nmmWdcAlZt3zPVxSVCLwoLC1NycrKuvPJK51hWVpYiIiK0a9cutW7dWkFBQc65du3aOU9L79q1S%2B3atXNZr127dsrMzFRhYaH27dvnMh8SEqJmzZopMzNTe/bsUVBQkMtlt/bt2%2Bv333/XgQMHPHV3q%2Bztt99WcHCw7rrrLudYefe7vL4EBgaqbdu2yszM1KFDh5SXl6f27ds752%2B44QZdccUV2rVrl3bt2qWoqCg1bNjQOd%2B%2BfXsdPHhQ%2Bfn5nrqblfbrr7/qyJEjOnnypAYPHuy8FHHixAm/7oskRUZGqm3btsrIyFBBQYGOHz%2BuDRs2qE%2BfPn7VmzFjxig0NNTtnCcfM6rzeHW5XKw3vXr1UkJCgvP74uJi2e12RUZGSvLsHqlof3raxfoiScYYzZ49W1OnTlVYWJhz3B/2THURsHxIZmamVq5cqUmTJsnhcLhsZklq1KiRHA6HSktL5XA4XP7BSlLDhg2Vk5OjkydPyhhT7rzD4VBISIgCAgJc5iS5vG7Am3777TctXrxY//Ef/%2BEyXl5fztV9sb44HA5JKnP7sLAw5/yFc77Ul2PHjkmSPvroI7322mtavXq1jh07pqeeesqv%2ByKd/R/e4sWLtWnTJt10003q3r27iouLNW3aNL/vzTmefMyozuOVL0pNTVX9%2BvU1ePBgSZ7dIxXtT2/LyMhQQECAhg0b5jLOnqkYActHfPPNNxo3bpymTZum7t27l3vc%2BZvVGHPRNS82X9FtvS05OVnDhg1TixYtqnzb2tqXc7U9/PDDioyMVNOmTTV58mRt3ry5Sre/lHlf7osknTlzRhMnTtSgQYO0bds2ffbZZwoNDdX06dMrdfva3JvzXe77WZXHK19gjNGCBQu0du1aLVmyRMHBwS5zFd32UuZ82fHjx7Vo0SLNnj3b5e/yfP6%2BZy6GgOUDNm/erPHjx2vWrFkaM2aMJCk8PLxMUnc4HGrUqJECAwPVuHFj5zOn8%2BfDw8Odx7ibb9KkicLDw5Wfn6%2BSkhKXOUlq0qSJJ%2B5ilXz55Zfavn27yyn7c9zd75ycHIWHh5c7f64v5465cP7kyZPOvri7bUBAgPO23nTuUvL5z/qioqJkjFFRUZHf9kU6u2eOHDmixx9/XKGhoYqMjFRiYqI%2B/vhjt/8W/Kk353jyMaM6j1e%2BorS0VDNmzNDmzZv11ltv6frrr3fOeXKPVPSY5k0pKSkaMmSIy2W%2Bc9gzFSNgedm3336rpKQkLVq0SEOGDHGOx8TEaO/evSouLnaOZWZm6sYbb3TOX3iN/tx8cHCwWrZsqV27djnncnNzdejQIXXs2FFt27aVMUY//PCDy23DwsLUvHlzT93VSluzZo2OHz%2Buvn37Ki4uznlqOi4uTq1atSpzv3fu3OnSl/Pvd0lJiXbv3q0bb7xR0dHRatiwocv8jz/%2BqDNnzigmJkYxMTHKyspyvrVfOtuXFi1aqEGDBp68y5XStGlThYSEaM%2BePc6xo0ePqk6dOurdu7ff9kU6e39KS0tdnvGeOXNGktS9e3e/7s05nnzMqM7jla%2BYN2%2BefvrpJ7311luKjo52mfPkHunQocNF96c3rVmzRqtWrVJcXJzi4uL0yiuv6IMPPlBcXBx7pjI8/jJ6lKuoqMjccccd5u233y4zd/r0adO3b1/z/PPPm99//9189913pmvXruaTTz4xxhizd%2B9e06FDB/PJJ5%2BYwsJC8%2B6775rOnTub7OxsY8zZd6n06dPH%2BfbZp59%2B2gwfPty5/pQpU8zDDz9sjh8/brKysszw4cNNSkrKZbnfFXE4HCYrK8v5Z/v27aZVq1YmKyvLHD161HTu3Nm88847prCw0Hz66aemY8eOzneqbNmyxXTp0sVs377d/P7772bx4sWmd%2B/e5tSpU8aYs%2B9SGTp0qPnll1/MiRMnzIQJE8zkyZOdP3vkyJFm1qxZJi8vz%2Bzbt8/069fPrFy50it9cGfevHmmf//%2B5ueffza//fabiY%2BPNzNmzDC//fabX/flxIkTplu3buavf/2r%2Bf33382JEyfMxIkTzb/927/5ZW/cvSPMk48Z1X28upzc9Wbbtm0mNjbW5R3d5/PkHqlof14u7vpy/uNwVlaWmTdvnklMTHR%2BVIK/7JlLRcDyoq%2B//tq0atXKxMTElPlz5MgRs3fvXjNq1CgTExNj%2BvTpY9544w2X269fv94MHDjQtG/f3txzzz3mq6%2B%2Bcs6VlpaaRYsWmVtuucV07NjRPPLIIy6fH5Kbm2umTp1qOnXqZGJjY82cOXPM6dOnL9t9r4rDhw87P6bBGGO%2B%2Buorc/fdd5v27dubgQMHlvnYgjfeeMP07t3bxMTEmNGjR5u9e/c6506fPm1mz55tYmNjTefOnc3jjz9ucnNznfNZWVnm4YcfNh07djTdu3c3zz//vPMzXXzB%2BfV36tTJJCUlmfz8fGOMf/fFGGMyMzPNfffdZ7p27Wq6d%2B9upkyZYo4dO2aM8Z/enHv8aNOmjWnTpo3z%2B3M8%2BZhRncery%2BFivZk5c6bL2Lk/Dz74oPP2ntwjFe1PT6poz5zv%2Beefd/mYhtq%2BZ6orwJga/ioyAAAAH8NrsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACz2/wBgR%2BvQhUrPnAAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6242690979570606700\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">33195</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">33401</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23680</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">28915</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">47396</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">38242</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">62823</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">26981</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">32100</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">42339</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (520)</td>\n",
       "        <td class=\"number\">520</td>\n",
       "        <td class=\"number\">97.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6242690979570606700\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">19608</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">21913</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">22893</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23037</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23414</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">99447</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">108023</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">108608</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">122871</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">143652</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_text\">text<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>10</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>1.9%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.0%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable160976601350252452\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>a05</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:18%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 11.6%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        62\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>a02</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:17%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 11.2%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        60\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>a07</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:16%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 10.3%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        55\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (7)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 66.9%\">\n",
       "            358\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable160976601350252452, #minifreqtable160976601350252452\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable160976601350252452\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">a05</td>\n",
       "        <td class=\"number\">62</td>\n",
       "        <td class=\"number\">11.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a02</td>\n",
       "        <td class=\"number\">60</td>\n",
       "        <td class=\"number\">11.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:96%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a07</td>\n",
       "        <td class=\"number\">55</td>\n",
       "        <td class=\"number\">10.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:88%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b10</td>\n",
       "        <td class=\"number\">52</td>\n",
       "        <td class=\"number\">9.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:84%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b03</td>\n",
       "        <td class=\"number\">52</td>\n",
       "        <td class=\"number\">9.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:84%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b09</td>\n",
       "        <td class=\"number\">52</td>\n",
       "        <td class=\"number\">9.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:84%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a04</td>\n",
       "        <td class=\"number\">51</td>\n",
       "        <td class=\"number\">9.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:82%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b01</td>\n",
       "        <td class=\"number\">51</td>\n",
       "        <td class=\"number\">9.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:82%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b02</td>\n",
       "        <td class=\"number\">51</td>\n",
       "        <td class=\"number\">9.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:82%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a01</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">9.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:79%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_x\"><s>x</s><br/>\n",
       "            <small>Unsupported</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is an unsupported type, check if it needs cleaning or further analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Unsupported value</th>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_y\">y<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>1.3%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>2.7065</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>6</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>23.7%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-1477918978671312741\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAASNJREFUeJzt28EJwkAQQFEVS7IIe/JsTxZhT%2Btd5GOEkCW%2Bdw/s5WeGhD2OMcYB%2BOi09QFgZuetD/DucnssfuZ5v65wEjBBIAkEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCATCdBemfuGSFWvZRSDMaQ8vLisWBIFAsGLtwNJVZrY1ZmYCmcwvezvrsWJBMEH4yr9ONhMEgkAgCASCQCAIBIKvWAv4Ifd/TBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgXAcY4ytDwGzMkEgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgvACLkxppDzMr7QAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-1477918978671312741,#minihistogram-1477918978671312741\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-1477918978671312741\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-1477918978671312741\"\n",
       "                                                  aria-controls=\"quantiles-1477918978671312741\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-1477918978671312741\" aria-controls=\"histogram-1477918978671312741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-1477918978671312741\" aria-controls=\"common-1477918978671312741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-1477918978671312741\" aria-controls=\"extreme-1477918978671312741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-1477918978671312741\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>4</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>2.1624</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.79895</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.3952</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>2.7065</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>1.9232</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.14663</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>1448</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>4.6759</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>4.3 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-1477918978671312741\">\n",
       "            <img 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o/fvw5HePqq69WdHS01qxZo1mzZunkyZNat26dhg4dqttuu00lJSUqLS3VhAkT9M4772jPnj1yOp2mniIAAAhytitY48aNU9%2B%2BfTVz5kxNnDhRvXr1avOYkSNHtn4o6LlISEjQb3/7Wz322GMaMWKEIiMjNWzYMC1dulQXXHCB1q9fr%2BXLl2vZsmVKSUnRypUrdeWVV3bmaQEAgBBiu4K1efNmDRs2zO/jPvzww04dJz09Xc8999z3bhs6dKjKyso6tX8AABC6bHcPVv/%2B/TVr1izt2rWrde2ZZ57RPffc0%2BbmcwAAADuyXcEqLi7WsWPH1K9fv9a1UaNGyev1asWKFRYmAwAA6BjbXSJ88803tXXrViUkJLSupaamatWqVfrHf/xHC5MBAAB0jO3OYJ08eVJRUVFt1sPDw9XY2GhBIgAAgLNju4I1dOhQrVixQl9%2B%2BWXr2hdffKFly5b5fFQDAACAXdnuEuGCBQt055136tprr1VsbKy8Xq9OnDihPn36/OBP/QEAANiJ7QpWnz59tG3bNv3hD3/QX/7yF4WHh6tv374aPny4IiIirI4HAADgl%2B0KliRFRkbqxhtvtDoGAADAObFdwfrrX/%2Bq1atX68CBAzp58mSb7b///e8tSAUAANBxtitYCxYsUG1trYYPH67o6Gir4wAAAJw12xWsqqoq/f73v1diYqLVUQAAAM6J7T6m4YILLuDMFQAACGi2K1gzZ85USUmJWlparI4CAABwTmx3ifAPf/iD3nvvPb300ku6%2BOKLFR7u2wFffPFFi5IBAAB0jO0KVmxsrEaMGGF1DAAAgHNmu4JVXFxsdQQACGi3PvGW1RE6bPuc66yOAHQJ292DJUl/%2BtOftGbNGj3yyCOta%2B%2B//76FiQAAADrOdgWrsrJSEyZM0M6dO1VRUSHpmw8fnTFjBh8yCgAAAoLtCtbjjz%2Bun//859q6davCwsIkffP7CVesWKG1a9danA4AAMA/292D9X//9396/vnnJam1YEnSLbfcogULFlgVC0AIG7Jwh9URAAQY253B6tmz5/f%2BDsLa2lpFRkZakAgAAODs2K5gDR48WI8%2B%2BqiOHz/eunbo0CHNmzdP1157rYXJAAAAOsZ2lwgfeeQR3XHHHcrMzFRzc7MGDx6sxsZGXX755VqxYoXV8QAAAPyyXcHq3bu3KioqtGfPHh06dEjnnXee%2Bvbtq%2Buuu87nniwAAAC7sl3BkqQePXroxhtvtDoGAADAObFdwcrOzm73TBWfhQUAAOzOdgVr7NixPgWrublZhw4dksvl0h133GFhMgAAgI6xXcEqKir63vVXX31Vf/zjH7s5DQAAwNmz3cc0/JAbb7xR27ZtszoGAACAXwFTsD7%2B%2BGO1tLRYHQMAAMAv210inDZtWpu1xsZGHTx4UDfffLMFiQAAAM6O7QpWampqm58ijIqKUk5OjqZOnWpRKgAAgI6zXcHi09oBAECgs13Bevnllzv82IkTJ3ZhEgAAgHNju4K1cOFCeb3eNje0h4WF%2BayFhYVRsAAAgC3ZrmD95je/0caNGzVr1iz1799fLS0t2r9/v55%2B%2Bmn97Gc/U2ZmptURAQAA2mW7grVixQpt2LBBycnJrWtDhgxRnz59dNddd6miosLCdACAUHbrE29ZHQEBwnafg/XZZ58pPj6%2BzXpcXJxqamqMHmvdunUaPny4Bg0apLy8PB0%2BfFiSVFlZqZycHA0ePFjjxo1TeXm50eMCAIDgZruClZKSohUrVqi%2Bvr51raGhQatXr9Yll1xi7DgvvPCCysvLtXnzZr355pvq16%2BfnnnmGdXW1ur%2B%2B%2B/XtGnTVFlZqYULF2rx4sVyuVzGjg0AAIKb7S4RLliwQHPnzpXT6VRMTIzCw8N1/PhxnXfeeVq7dq2x42zcuFHz5s3TZZddJklatGiRJOm3v/2tUlNTlZOTI0nKyspSdna2SktLlZGRYez4AAAgeNmuYA0fPlyvv/669uzZoyNHjqilpUXJycm6/vrr1bNnTyPH%2BOKLL3T48GF9%2BeWXGjt2rOrq6pSZmamlS5equrpaaWlpPo9PS0vT9u3bO7z/2tpaud1unzWHI1pJSUlG8p8REWG7E5BBxeEI/vmeeQ3xWvphzKZrhcL7DB0XTO832xUsSTr//PN1ww036MiRI%2BrTp4/x/R85ckSStGPHDm3atEktLS0qKCjQokWLdPLkSZ8b7CWpV69ePpcs/XE6nSopKfFZy8/PV0FBQefDo9skJMRYHaHbxMWdb3UEhKhQep/Bv2D6u8h2BevkyZNasmSJtm3bJkmqqqpSQ0ODHnroIf36179WXFxcp49x5vO07r777tYyNXv2bN1zzz3Kysrq9P5zc3OVnZ3ts%2BZwRKu%2B/kSn9/1twdT07cj098uOIiLCFRd3vhoaGtXc7LU6ji3xPutaofA%2BQ8d1xd9FVpV42xWslStXat%2B%2BfVq1apUefvjh1vXm5matWrVKv/jFLzp9jAsvvFCSfMpaSkqKWlpadPr0aXk8Hp/H19fXKzExscP7T0pKanM50O0%2BpqYm/gcWSELp%2B9Xc7A2p5wv74HWHbwumv4ts90%2BzV199Vf/2b/%2BmW265pfWXPsfFxam4uFg7d%2B40cozevXsrNjZW%2B/bta12rqalRjx49NHLkSFVVVfk8vqqqSgMHDjRybAAAEPxsV7BOnDih1NTUNuuJiYn66quvjBzD4XAoJydHTz31lP785z%2Brrq5Oa9eu1fjx4zVp0iTV1NSotLRUX3/9tfbs2aM9e/bo9ttvN3JsAAAQ/Gx3ifCSSy7RH//4R2VmZvr87sEdO3boH/7hH4wdZ%2B7cuTp16pSmTp2q06dPa8yYMVq0aJFiYmK0fv16LV%2B%2BXMuWLVNKSopWrlypK6%2B80tixAQBAcLNdwfqnf/onzZ49W1OmTJHX69WmTZtUVVWlV199VQsXLjR2nMjISC1ZskRLlixps23o0KEqKyszdiwAABBabFewcnNz5XA49PzzzysiIkJPPfWU%2Bvbtq1WrVumWW26xOh4AAIBftitYR48e1ZQpUzRlyhSrowAAAJwT293kfsMNN/jcewUAABBobFewMjMzz%2BrX0gAAANiN7S4R/uhHP9KvfvUrbdiwQZdccol69Ojhs3316tUWJQMAAOgY2xWsTz/9VJdddpkkndXv/wMAALAL2xSswsJCPf7443ruueda19auXav8/HwLUwEAAJw929yDtXv37jZrGzZssCAJAABA59imYH3fTw7y04QAACAQ2aZgnfnFzv7WAAAA7M42BQsAACBYULAAAAAMs81PEZ4%2BfVpz5871u8bnYAEAALuzTcG65pprVFtb63cNAADA7mxTsL79%2BVcAAACBjHuwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCYbX7ZM4DOufWJt6yO0GHb51xndQQA6FKcwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwLOQL1qOPPqr%2B/fu3fl1ZWamcnBwNHjxY48aNU3l5uYXpAABAIArpX5Wzb98%2BlZWVtX5dW1ur%2B%2B%2B/XwsXLtT48eP17rvv6r777lPfvn2VkZFhYVIAABBIQvYMltfr1ZIlS5SXl9e6tnXrVqWmpionJ0dRUVHKyspSdna2SktLrQsKAAACTsiewXrxxRcVFRWl8ePH64knnpAkVVdXKy0tzedxaWlp2r59%2B1ntu7a2Vm6322fN4YhWUlJS50J/R0REyPbjbuFwMN%2BuEkiz5X3WtQLptYCuF0zvt5AsWH/729%2B0Zs0aPffccz7rHo9HycnJPmu9evVSfX39We3f6XSqpKTEZy0/P18FBQXnFhiWSEiIsTpC0GK2OIPXAr4tLu58qyMYE5IFq7i4WJMnT1a/fv10%2BPBh4/vPzc1Vdna2z5rDEa36%2BhNGjxNMTd%2BOTH%2B/8HeBNFveZ10rkF4L6HoNDY1qbvYa3adVJT7kClZlZaXef/99VVRUtNmWkJAgj8fjs1ZfX6/ExMSzOkZSUlKby4Fu9zE1NZl90aBr8f3qOswWZ/BawLc1N3uD5jURcgWrvLxcdXV1Gj16tCSppaVFkpSZmak777yzTfGqqqrSwIEDuz0nAAAIXCFXsObPn68HH3yw9esjR44oNzdXZWVl8nq9Wr9%2BvUpLSzVhwgS988472rNnj5xOp4WJAQBAoAm5ghUfH6/4%2BPjWr5uamiRJvXv3liStX79ey5cv17Jly5SSkqKVK1fqyiuvtCQrAAAITCFXsL7r4osv1v79%2B1u/Hjp0qM%2BHjwIAAJwtfjwGAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAwL2YJVU1Oj/Px8ZWZmKisrS/Pnz1dDQ4Mkad%2B%2BffrZz36ma665RjfffLM2btxocVoAABBIQrZgzZo1S3Fxcdq9e7deeuklHThwQI899phOnjypmTNn6qc//aneeOMNPf7441q/fr127txpdWQAABAgQrJgNTQ0KD09XXPnzlVMTIx69%2B6tSZMmae/evXr99dd1%2BvRp3XfffYqOjtaAAQM0depUOZ1Oq2MDAIAA4bA6gBXi4uJUXFzss/b5558rKSlJ1dXV6t%2B/vyIiIlq3paWlqbS0tMP7r62tldvt9llzOKKVlJTUueDfERERkv242zgczLerBNJseZ91rUB6LaDrBdP7LSQL1ne5XC49//zzWrdunbZv3664uDif7b169ZLH45HX61V4uP9vvtPpVElJic9afn6%2BCgoKjOZG10pIiLE6QtBitjiD1wK%2BLS7ufKsjGBPyBevdd9/Vfffdp7lz5yorK0vbt2//3seFhYV1eJ%2B5ubnKzs72WXM4olVff6JTWb8rmJq%2BHZn%2BfuHvAmm2vM%2B6ViC9FtD1Ghoa1dzsNbpPq0p8SBes3bt36%2Bc//7kWL16siRMnSpISExP12Wef%2BTzO4/GoV69eHTp7JUlJSUltLge63cfU1GT2RYOuxfer6zBbnMFrAd/W3OwNmtdEyP7T7L333tO8efP05JNPtpYrSUpPT9f%2B/fvV1NTUuuZyuTRw4EArYgIAgAAUkgWrqalJixYtUlFRkYYPH%2B6zbeTIkYqNjdW6devU2NioDz/8UFu2bNH06dMtSgsAAAJNSBasDz74QAcPHtTy5cuVkZHh88ftduupp57S22%2B/rWHDhmnOnDkqLCzUqFGjrI4NAAACREjegzVkyBDt37%2B/3cf87ne/66Y0AAAg2ITkGSwAAICuRMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEUrO9RU1Oje%2B%2B9V5mZmRo9erRWrlwpr9drdSwAABAgHFYHsKPZs2drwIAB2rVrl%2Brq6jRz5kxdeOGF%2Btd//VerowEAgADAGazvcLlc%2BuSTT1RUVKSePXsqNTVVeXl5cjqdVkcDAAABgjNY31FdXa2UlBTFx8e3rg0YMECHDh3S8ePHFRsb63cftbW1crvdPmsOR7SSkpKMZo2IoB93JYeD%2BXaVQJot77OuFUivBXS9YHq/UbC%2Bw%2BPxKC4uzmftTNmqr6/vUMFyOp0qKSnxWXvggQc0e/Zsc0H1TZG7o/cB5ebmGi9vwaK2tlZOpzMkZrT3V7ec9X8TSvM5V7zP/Aul1xHvs65RW1urNWvWBNWMgqcqGtTS0tKp/z43N1cvvfSSz5/c3FxD6f7O7XarpKSkzdky/B0zah9YPH/jAAAG9UlEQVTz8Y8Z%2BceM2sd8/AvGGXEG6zsSExPl8Xh81jwej8LCwpSYmNihfSQlJQVNAwcAAGePM1jfkZ6ers8//1xHjx5tXXO5XOrXr59iYmIsTAYAAAIFBes70tLSlJGRodWrV%2Bv48eM6ePCgNm3apOnTp1sdDQAABIiIpUuXLrU6hN1cf/31qqio0C9/%2BUtt27ZNOTk5uuuuuxQWFmZ1tDZiYmI0bNgwzq61gxm1j/n4x4z8Y0btYz7%2BBduMwlo6e0c3AAAAfHCJEAAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwClYAqqmp0b333qvMzEyNHj1aK1eulNfrtTqW7bzxxhvKyspSYWGh1VFsqaamRvn5%2BcrMzFRWVpbmz5%2BvhoYGq2PZyieffKI77rhD11xzjbKysjRnzhy53W6rY9nSo48%2Bqv79%2B1sdw3b69%2B%2Bv9PR0ZWRktP755S9/aXUs21m3bp2GDx%2BuQYMGKS8vT4cPH7Y6UqdRsALQ7NmzlZycrF27dmnTpk3atWuXnn32Watj2crTTz%2Bt5cuX69JLL7U6im3NmjVLcXFx2r17t1566SUdOHBAjz32mNWxbOPUqVO68847NWzYMFVWVqqiokJ1dXXi17e2tW/fPpWVlVkdw7Z27Nghl8vV%2Bmfx4sVWR7KVF154QeXl5dq8ebPefPNN9evXT88884zVsTqNghVgXC6XPvnkExUVFalnz55KTU1VXl6enE6n1dFsJSoqSlu2bKFg/YCGhgalp6dr7ty5iomJUe/evTVp0iTt3bvX6mi20djYqMLCQs2cOVORkZFKTEzUTTfdpAMHDlgdzVa8Xq%2BWLFmivLw8q6MgQG3cuFGFhYW67LLLFBsbq0WLFmnRokVWx%2Bo0ClaAqa6uVkpKiuLj41vXBgwYoEOHDun48eMWJrOXGTNmqGfPnlbHsK24uDgVFxfrwgsvbF37/PPPlZSUZGEqe4mPj9fUqVPlcDgkSX/605/0X//1X7r11lstTmYvL774oqKiojR%2B/Hiro9jW6tWrNWrUKA0ZMkSLFy/WiRMnrI5kG1988YUOHz6sL7/8UmPHjlVmZqYKCgp09OhRq6N1GgUrwHg8HsXFxfmsnSlb9fX1VkRCEHC5XHr%2B%2Bed13333WR3FdmpqapSenq6xY8cqIyNDBQUFVkeyjb/97W9as2aNlixZYnUU2xo0aJCysrK0c%2BdOOZ1OffDBB1q2bJnVsWzjyJEjkr65jLpp0yaVlZXpyJEjnMGCNVpaWqyOgCDy7rvv6q677tLcuXOVlZVldRzbSUlJkcvl0o4dO/TZZ5/p4YcftjqSbRQXF2vy5Mnq16%2Bf1VFsy%2Bl0aurUqYqMjNSPf/xjFRUVqaKiQqdOnbI6mi2c%2Bf/Z3XffreTkZPXu3VuzZ8/W7t279fXXX1ucrnMoWAEmMTFRHo/HZ83j8SgsLEyJiYkWpUKg2r17t%2B69914tWLBAM2bMsDqObYWFhSk1NVWFhYWqqKgIissXnVVZWan3339f%2Bfn5VkcJKBdffLGam5tVV1dndRRbOHObwrevzKSkpKilpSXgZ0TBCjDp6en6/PPPff6Cd7lc6tevn2JiYixMhkDz3nvvad68eXryySc1ceJEq%2BPYTmVlpcaMGePzESjh4d/8ldmjRw%2BrYtlGeXm56urqNHr0aGVmZmry5MmSpMzMTG3bts3idPbw8ccfa8WKFT5rBw8eVGRkJPc7/n%2B9e/dWbGys9u3b17pWU1OjHj16BPyMKFgBJi0tTRkZGVq9erWOHz%2BugwcPatOmTZo%2BfbrV0RBAmpqatGjRIhUVFWn48OFWx7Gl9PR0HT9%2BXCtXrlRjY6OOHj2qNWvWaMiQIfwAhaT58%2Bfr1VdfVVlZmcrKyrRhwwZJUllZmbKzsy1OZw8XXHCBnE6nNmzYoFOnTunQoUN68sknlZubq4iICKvj2YLD4VBOTo6eeuop/fnPf1ZdXZ3Wrl2r8ePHt/6ASaAKa%2BGGnoBz5MgRLV68WP/zP/%2Bj2NhYTZs2TQ888IDCwsKsjmYbGRkZkr4pEpJa36gul8uyTHayd%2B9e/fM//7MiIyPbbNuxY4dSUlIsSGU/%2B/fv1/Lly/XRRx8pOjpaP/3pTzV//nwlJydbHc12Dh8%2BrBtuuEH79%2B%2B3Ooqt/O///q9Wr16t/fv3KzIyUpMmTVJhYaGioqKsjmYbp06dUnFxsbZt26bTp09rzJgxWrx4ccBflaFgAQAAGMYlQgAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAw7P8BA2jt9WoyegQAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-1477918978671312741\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">127</td>\n",
       "        <td class=\"number\">23.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">81</td>\n",
       "        <td class=\"number\">15.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:64%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">79</td>\n",
       "        <td class=\"number\">14.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:62%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:56%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">69</td>\n",
       "        <td class=\"number\">12.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:54%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">62</td>\n",
       "        <td class=\"number\">11.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:49%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">46</td>\n",
       "        <td class=\"number\">8.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:36%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-1477918978671312741\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">127</td>\n",
       "        <td class=\"number\">23.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">81</td>\n",
       "        <td class=\"number\">15.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:64%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">46</td>\n",
       "        <td class=\"number\">8.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:36%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">69</td>\n",
       "        <td class=\"number\">12.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:54%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:56%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">46</td>\n",
       "        <td class=\"number\">8.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">69</td>\n",
       "        <td class=\"number\">12.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:87%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:89%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">62</td>\n",
       "        <td class=\"number\">11.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:78%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">79</td>\n",
       "        <td class=\"number\">14.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAm0AAAIiCAYAAACaBf8HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3X98zfX///H7zsawXza2JS1EmG28KRGS%2BdGkH5OSH9%2BK9BvDIlEppLzfNfTxo6L3h/jUu/f6pNDyfsdU%2BqX3J%2B/ELAuz8iPZtB2zYT/P9w85b4fJeJ2Xs9fZ7Xq57MKerx/ncV4vcx7uz9frxcfhcDgEAACAGs3m6QIAAABwfjRtAAAAFkDTBgAAYAE0bQAAABZA0wYAAGABNG0AAAAWQNMGAABgATRtAAAAFkDTBgAAYAE0bQAAABbg5%2BkCAFxabdq0OeeyOnXqqGHDhoqNjdXAgQOVkJAgHx%2BfS1gdAOBcfPi/R4Ha5VTT1r59e4WHh7ssKykp0Z49e/TLL79IkuLj4zV//nzVrVv3ktcJAHBF0wbUMqeatkWLFqlv375VrpOenq4nnnhCx44d06hRo/Tkk09eyhIBAFXgmjYAZ%2Bnbt68mTpwoSXrnnXdUUlLi4YoAAFzTBqBKN910k55//nkdP35cP/zwgzp27OiyfNOmTXr77bf1/fffy263KzAwUG3bttWdd96p2267rcp92u12LVu2TBs3btTPP/%2Bs0tJShYSEqH379rr//vvVpUuXs7bp3bu3Dhw4oDfeeEMlJSWaN2%2Be9u3bpzfffFPXXHONJGnnzp1aunSpNm/erEOHDsnPz08RERG67rrrdM8991R5Hd/Ro0e1fPlybdiwQT///LPKysrUuHFjderUSSNHjlRcXJzL%2Bvv371efPn0kSdu2bVN2drZee%2B01bdmyRXa7XREREerTp4%2BSk5PVoEGDizrmAPBHSNoAVKlhw4bO3xcWFrosmzdvnkaOHKn169crMDBQXbp0UVhYmDZt2qRJkyZpwoQJqqiocNkmLy9PgwYN0uuvv649e/aoXbt26tq1q%2BrWratPP/1UI0aM0MqVK89Zz549e5ScnKw6deqoa9euqlevniTp//7v/3TnnXfqgw8%2BUElJia655hp17NhRRUVFevfdd3X33Xdr06ZNLvs6cOCA7rjjDi1YsEC7d%2B9W27Zt1bVrVzkcDqWlpenuu%2B/We%2B%2B9d85avvvuOw0fPlyZmZlq27atWrRooQMHDmjFihVKSkqq9jEGgAviAFCrtG7d2tG6dWvH%2BvXr/3C97Oxs57rbtm1zjq9bt87RunVrR%2BfOnR3/%2Bte/XLb5v//7P0e3bt0crVu3dixbtsxl2axZsxytW7d2JCQkOA4fPuwcLy8vd7zwwguO1q1bO6655hpHUVGRy3bx8fGO1q1bO3r37u147bXXzqpz8ODBjtatWzv%2B/Oc/OyoqKlz2O2/ePEfr1q0dt956q8s2w4cPd7Ru3doxaNAgx6FDh5zjFRUVjgULFjhat27tiI2NdezZs8e5bN%2B%2Bfc7j0atXL8eSJUsclZWVzuUffPCBc/mOHTv%2B6NACwEUhaQNQpXXr1kmSQkJCFB0d7Rx/7bXXJElPPvmkrrvuOpdtOnfurMmTJ0uSli9f7rIsIiJCt9xyi8aOHatGjRo5x319fZWcnCybzaajR4/q%2B%2B%2B/r7KeyspKPfzww2eN79ixQ5I0aNAg2Wz/%2BSvN19dX48aN07hx43TvvfeqtLRUkrR161Zt3rxZkvTSSy8pIiLCuY3NZtPYsWMVExOj0tJS/f3vf6%2BylquvvloPPfSQy%2BNQEhMTFRYW5nwNAHA3rmkDcJaNGzdq8eLFkqQHH3xQfn4n/6o4dOiQMjMzJUn9%2BvWrctu%2BffvKx8dHv/zyi3JyctSiRQtJ0kMPPXTO16tfv74aNWqkvLw85eXlVblO165dXZqyU4KDg3X48GGtXbtW48ePd1lms9k0ZswYl7EvvvhC0sm7aFu2bHnO95CZmXnWtOopVV2z5%2BPjo6ioKOXn56ugoKDK7QDACJo2oJZavHix3n//fZex0tJS/fTTT9q3b5%2Bkk%2BnVgw8%2B6Fy%2Bc%2BdO5%2B%2BnTJlyzn37%2BfmprKxMP/30k7NpO7X/jRs3avv27crNzVVhYaEcvz916OjRo5JOJmpViYyMrHJ8xIgRmjNnjl599VV98cUXuvXWW3X99def8yHCu3fvlnQyLTuXq666StLJ6%2BiqcuWVV1Y57u/vL0kqKys7574B4GLRtAG11LZt284a8/X1VWhoqPr06aMhQ4boxhtvdFl%2B5MgR5%2B83bNhw3tc41YhJUlZWlsaMGaP9%2B/dfVL0hISFVjp%2BaMl28eLEyMjKUkZEhSQoPD1f//v01YsQIRUVFnVVTcHDwOV8rKChI0snm68SJE86bHk7hYcMAPIGmDail/ujhuudy6hquOnXqKCMjo9r/xdWJEyf02GOP6ZdfflHz5s01evRoXX/99QoNDVWdOnUk/efRHud77ao8/PDDGjp0qNLT07Vx40Z9/fXXysvL0//8z/8oNTVVL730km6%2B%2BeZqv0/Hac8cr2pKFgA8gaYNQLWdegxIWVmZCgoKnBfen8/nn3%2BuX375RT4%2BPlqyZImaNWt21jonTpwwVFtwcLAGDRqkQYMGqaKiQl9//bUWLVqkLVu26Omnn1bXrl0VGhrqTOzOfIzJ6U4tq1evHqkagBqDf0ICqLbWrVs7f79r165qb/fTTz9JOnktWFUN2969e/Xbb78Zru8UX19f3XDDDVqxYoUiIyNVXFzsnA4%2BdS3b6dfnnenUsj%2B67g0ALjWaNgDVFh4erpiYGEnSu%2B%2B%2BW%2BU6OTk5SkxMdD4aRPrPNWLn%2Bu%2BwFi1a5Pz9mQ/l/SNbt27VU089paVLl1a5vG7dumrcuLGk/yR5p67T27lzZ5WNW2VlpfNxJzfccEO1awEAs9G0Abggjz76qCQpLS1Nb775psv1Xz///LPGjRunrKwsHTx40Dnetm1bSdKvv/6q9PR05/jx48c1a9YsZWRkOP%2BbrHPdsVkVX19frVy5UvPnz9dnn3121vL169drx44dqlOnjjp16iRJateunXr06CFJeuqpp5Sfn%2B9cv7y8XC%2B//LKys7MVFBSkoUOHVrsWADAb17QBuCA33XSTHn74YS1ZskSzZ8/WihUrdNVVV6mgoEA7duxQRUWF2rVrp0mTJjm36dixo3r06KEvv/xSSUlJ6tChg/z9/bV9%2B3b5%2Bflp2bJl%2Buijj7RlyxatWLFCP/74ox577DF17tz5D2uJjY3VqFGjtHTpUj3yyCNq2rSpmjVrJpvNpv379zunZZ9%2B%2BmmFh4c7t3vxxRc1YsQIZWRkqHfv3mrdurX8/f21a9cuFRQUqF69epozZ845HzMCAJ5A0wbggk2cOFHdunXTW2%2B9pe%2B//16bNm2Sv7%2B/YmNjNWDAAA0fPvysC/hfeeUVpaSkaMOGDdq%2BfbsiIiKUkJCgRx99VFdeeaUuu%2Bwy/fDDD/ruu%2B%2B0a9euat%2BZ%2BuSTT6pz585atWqVtm/fri1btqi8vFzh4eG69dZbdc8995z1n91HRkbqvffe05tvvqn169dr165dKi8vV2RkpG666SY98MADVV57BwCe5OM4fW4DAAAANRLXtAEAAFgATRsAAIAF0LQBAABYAE0bAACotb744gt169ZNycnJf7heZWWl5s2bpz59%2Bqhz58564IEHtG/fPudyu92uCRMmqFu3burRo4eefvppw//Ty5lo2gAAQK30xhtvaNasWdW6W/ztt9/Whx9%2BqCVLlujTTz9V8%2BbNNWbMGOezKqdNm6bjx48rLS1NK1euVHZ2tlJSUtxaL00bAAColfz9/fXee%2B9Vq2lLTU3VyJEj1bJlSwUGBio5OVnZ2dnaunWrDh8%2BrPT0dCUnJyssLEyRkZEaPXq0Vq5cqbKyMrfVy3PaAACAZeXm5iovL89lLDw8XBEREefd9r777qvWa5w4cUK7d%2B9Wu3btnGOBgYFq1qyZMjIydPToUfn6%2BqpNmzbO5TExMTp27Jj27NnjMm4ETZtZqvlgUMto0ULatUu6%2BmopJ8fT1biP3e7pCtzLx0cKCpKOHpW86BGMeaXeNSlgs/koLCxA%2BfnFqqz0nvNUt26Qp0twGy/9UZIkhYR46IVN%2BlxMnT9fCxcudBkbO3askpKS3PYaR44ckcPhUMgZBy8kJEQFBQVq2LChAgMDXR4KfmrdgoICt9VB04bqadhQ8vU9%2BStqLh%2Bf/3x52yeNF7HZfOTj4yObzcermjZvwo%2BSdQwZMkS9e/d2GTv9v61zpz/6/wguxf9VQNMGAADMZzMnMY%2BIiKjWVKgRDRs2lM1mk/2M2Rm73a5GjRopLCxMRUVFqqiokK%2Bvr3OZJDVq1MhtdXjXnAMAAKiZbDZzvi4Bf39/XX311crMzHSOFRYWau/evWrfvr2io6PlcDiUlZXlXJ6RkaHg4GC1aNHCbXXQtAEAAJzh0KFD6t%2B/v/NZbMOGDdOKFSuUnZ2toqIipaSkKDo6WnFxcQoLC1NCQoJeeeUV5efn69dff9WiRYt01113yc/PfZOaTI8CAADzXaJU7ELExcVJksrLyyVJ6enpkk6mZGVlZcrJyVFpaakkaejQocrLy9O9996r4uJidenSxeUGiJkzZ%2Bq5555Tnz59VKdOHd16663nfWDvhfJxXIor52ojb7t7tGNH6bvvpE6dpC1bPF2N%2B3jb3aM2239ueaus9HQ1buNtd4/6%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%2B%2B63LWHl5ucaMGaOxY8fq3nvv1XfffeeyXYsWLbRmzRq31UrTBgAAzFdDk7akpCTFxMQoPT1dv/32mx555BE1btxY999/v8t6S5cudfm%2BsLBQAwYMUL9%2B/Zxjzz//vAYNGmRarTXzCF5CI0aM0J///GeXsUWLFmno0KEeqggAAC9ks5nzZUBGRoaysrI0adIkBQUFqXnz5ho5cqRSU1PPu%2B0rr7yifv36qU2bNoZquBC1PmkbOHCg5s6dq8mTJzsjzXXr1unuu%2B%2Bu9j5yc3OVl5fnMhbeooUiGjZ0a60e1bat66/eoob%2By%2B%2BinXo/Xva%2B/Py86/34%2BtpcfvUW3vTHzkt/lFRZ6ekK3K/Kz%2BDwcEVERJx328zMTDVt2lQhISHOsZiYGOXk5KioqEiBgYFVbvfzzz9r1apVSk9Pdxlfu3at/vrXv%2BrgwYPq0KGDZs6cqSuvvPIi3lXVan3TdtNNN2nmzJn617/%2Bpeuvv1779u1Tdna2br755mrvIzU1VQsXLnQZGzt%2BvJLGj3d3uZ73t795ugJUR0CApytwq1BPF2CS4OD6ni4B5%2BFlP0o6csSDL25SB1zlZ/DYsUpKSjrvtna7XcHBwS5jpxq4goKCczZtS5Ys0Z133qmwsDDnWMuWLVW/fn2lpKSosrJSs2bN0oMPPqi0tDTVrVv3Qt9WlWp90xYQEKC%2BfftqzZo1uv7667Vu3Tp1797d5UScz5AhQ9S7d2%2BXsfDbbpOWL3d3uZ7Ttu3Jhm34cCkry9PVuM/GjZ6uwL1stpOfMsXFXvVP6oJy74o7fH1tCg6ur8LC46qo8J7z5OfnPR2Ol/4oeaUqP4PDw6u9vcPhuKDXs9vtWr16tf7xj3%2B4jE%2BfPt3l%2B5kzZ6pLly7697//reuvv/6CXuNcan3TJp2cIh03bpxmzJih9evX65577rmg7SMiIs6OYXNy3FhhDZKVJW3Z4ukq3Mdb/zaurPSq91Ze7ukKzFFRUanycu85T942lSh53Y%2BSZ5n0B6TKz%2BBqCgsLk91udxmz2%2B3y8fE5Z3izYcMGtWjRQlFRUX%2B478DAQIWEhOjQoUMXVVtVvPBH7MJdf/31CggI0P/%2B7/9q165d6tOnj6dLAgDAu9TAGxFiY2N18OBB5efnO8cyMjLUqlUrBZxjbnzDhg3q3r27y1hRUZGmT5/u0qDl5%2BcrPz//vM3dhaBpk2Sz2XTbbbdp7ty56tOnj%2BrX5zoTAAC8Xbt27RQXF6c5c%2BaoqKhI2dnZWrZsmYYNGyZJ6t%2B/vzZv3uyyzY4dO3TFFVe4jAUGBmrr1q2aNWuW7Ha7jhw5ohkzZqhNmzbq2LGj2%2BqlafvdwIEDVVRUpNtuu83TpQAA4H1qYNImSfPnz1dubq66d%2B%2Bu%2B%2B67TwMHDtTw4cMlSTk5OTp27JjL%2Bnl5eWrcuPFZ%2B1m0aJEcDocSEhLUq1cvlZWVacmSJWc9pNcIrmn73W%2B//aamTZueFXkCAADvddlll%2BmNN96octmPP/541tj27durXPfyyy8/6y5Wd6Np08lnvLz44ot64IEH3NoRAwCA3/H5alitP4KLFy/WzTffrM6dOzvnsAEAgJvV0OlRK6n1SdsjjzyiRx55xNNlAAAA/KFa37QBAIBLoJalYmbgCAIAAFgASRsAADAfSZthNG0AAMB8NG2GcQQBAAAsgKQNAACYj6TNMI4gAACABZC0AQAA85G0GUbTBgAAzEfTZhhHEAAAwAJI2gAAgPlI2gzjCAIAAFgASRsAADAfSZthNG0AAMB8NG2GcQQBAAAsgKQNAACYj6TNMI4gAACABZC0AQAA85G0GUbTBgAAzEfTZhhHEAAAwAJI2gAAgPlI2gzjCAIAAFgASRsAADAfSZthNG0AAMB8NG2GcQQBAAAsgKQNAACYj6TNMJo2AABgPpo2wziCAAAAFkDSBgAAzEfSZhhHEAAAwAJI2gAAgPlI2gyjaQMAAOajaTOMIwgAAGABJG0AAMB8JG2GcQQBAAAsgKQNAACYj6TNMJo2AABgPpo2wziCAAAAFkDSBgAAzEfSZhhHEAAA1FoHDhzQww8/rC5duig%2BPl4vv/yyKisrz1pvwYIFio6OVlxcnMvX4cOHJUklJSV69tln1bNnT3Xp0kXjxo1TQUGBW2ulaQMAAOaz2cz5MigpKUmRkZFKT0/XsmXLlJ6eruXLl1e5bmJiojIyMly%2BGjduLEmaN2%2BeMjMzlZqaqo8//lgOh0NTp041XN/paNoAAID5amDTlpGRoaysLE2aNElBQUFq3ry5Ro4cqdTU1AvaT3l5ud577z2NHj1aTZo0UcOGDTVhwgR99tlnOnTokKEaT8c1bWax2z1dgXud%2BsHYuFGqIja2rIYNPV2Be3XsKH33nXTjjdKWLZ6uxn1yCz1dAaohpF6Jp0twHx8fSXUV5F8qORyersbN/D1dgFvl5uYqLy/PZSw8PFwRERHn3TYzM1NNmzZVSEiIcywmJkY5OTkqKipSYGCgy/o//vijhg4dqp07d6pJkyaaOnWqevToob179%2Bro0aOKiYlxrtuyZUvVq1dPmZmZioyMNPguT6JpAwAA5jPpRoTU1FQtXLjQZWzs2LFKSko677Z2u13BwcEuY6cauIKCApem7bLLLlNUVJQmTpyoiIgIpaam6tFHH9WaNWtk/z2oOXNfwcHBbr2ujaYNAABY1pAhQ9S7d2%2BXsfDw8Gpv76hmkjp48GANHjzY%2Bf3IkSP10Ucfac2aNerZs%2BcF7eti0bQBAADzmZS0RUREVGsqtCphYWHOlOwUu90uHx8fhYWFnXf7pk2bKjc317mu3W5XQECAc/mRI0fUqFGji6qtKtyIAAAAzFcDb0SIjY3VwYMHlZ%2Bf7xzLyMhQq1atXJovSXr11Ve1adMml7Hs7GxFRUUpKipKISEhyszMdC7buXOnSktLFRsba6jG09G0AQCAWqldu3aKi4vTnDlzVFRUpOzsbC1btkzDhg2TJPXv31%2BbN2%2BWdDJFmzFjhvbs2aOSkhItXbpUe/fu1R133CFfX1/dfffdev3113Xw4EEVFBRo7ty56tevn/ORIO7A9CgAADBfDf0fEebPn69p06ape/fuCgwM1NChQzV8%2BHBJUk5Ojo4dOyZJmjhxoqST17LZ7Xa1atVKb775pi677DJJ0rhx41RcXKzExESVl5crPj5e06dPd2utPg6zr5qrrY4c8XQF7mWzSUFB0tGjPPKjJjv1yI9OnbzqkR95XvbIDz8/m0JDA1RQUKzycu/5eQoPruvpEtzHx0eqW1cq9cJHfvh76JEfs2ebs183P8C2JiNpAwAA5quhSZuV0LQBAADz0bQZxhEEAACwAJI2AABgPpI2wziCAAAAFkDSBgAAzEfSZhhNGwAAMB9Nm2EcQQAAAAsgaQMAAOYjaTOMIwgAAGABJG0AAMB8JG2G0bQBAADz0bQZxhEEAACwAJI2AABgPpI2wziCAAAAFkDSBgAAzEfSZhhNGwAAMB9Nm2EcQQAAAAsgaQMAAOYjaTOMIwgAAGABJG0AAMB8JG2G0bQBAADz0bQZxhEEAACwAJI2AABgPpI2w2jaAACA%2BWjaDOMIAgAAWABJGwAAMB9Jm2EcQQAAAAsgaQMAAOYjaTOMpg0AAJiPps0wjiAAAIAFkLQBAADzkbQZxhEEAACwAJI2AABgPpI2w2jaAACA%2BWjaDOMIAgAAWABJGwAAMB9Jm2EcQQAAAAsgaQMAAOYjaTOMpg0AAJiPps0wjiAAAIAF1Jimbf/%2B/WrTpo2ys7Mv6evee%2B%2B9SklJuaSvCQBArWOzmfNVi9Sudytp3759%2Buc//%2BnpMgAAQA1w4MABPfzww%2BrSpYvi4%2BP18ssvq7Kyssp133nnHSUkJKhjx45KTExUenq6c9mUKVPUrl07xcXFOb%2BuvfZat9Za665pW7dunbZv367%2B/ft7uhQAAGqPGpqKJSUlKSYmRunp6frtt9/0yCOPqHHjxrr//vtd1vv44481Z84cLV68WO3bt9eqVas0YcIE/eMf/1BUVJQk6bHHHlNSUpJptdbIps1ut2vWrFn65ptvVFxcrK5du2r69OmKjIzU/v371adPHy1dulQvv/yyfvrpJ7Vt21YpKSm64oorJEmvvvqqli1bJj8/Pz3yyCPauHGjOnXqpAYNGjinQtPT0/X9999LkioqKvTss88qLS1N/v7%2BmjZtmgYMGFDtenNzc5WXl%2BcyFl6/viLCw910RGqAUz9sNfSH7qJ17OjpCtyrbVvXX72En593/bnz9bW5/Oo1fHw8XYH7nHov3vSeJMnh8Nxrm/T5UeVncHi4IiIizrttRkaGsrKytGzZMgUFBSkoKEgjR47U8uXLz2raTpw4occff1zXXHONJGnw4MFKSUnR999/72zazFYjm7YpU6bIz89PH330kXx9ffXcc89p6tSpWrp0qXOdFStWaPHixfL399d9992nv/71r5o%2BfbrWr1%2Bv119/XUuXLlW7du00e/ZsZWZmqlOnTnrggQe0a9culZSUaN68ec59paWl6cUXX9QzzzyjhQsXavr06brpppvk51e9w5OamqqFCxe6jI0dM0ZJ48a554DUJAEBnq7Avb77ztMVmONvf/N0BW4V6ukCTBIcXN/TJeB86tTxdAXuVVLi6QrcrsrP4LFjq5V4ZWZmqmnTpgoJCXGOxcTEKCcnR0VFRQoMDHSOJyYmumxbWFio4uJiRUZGOse%2B%2BeYbbdiwQT///LNatmyp6dOnKzY29mLf2llqXNOWn5%2BvTz/9VGvXrnUexEmTJqlXr14unfSwYcOcB6pHjx7KyMiQJG3cuFE9evRwziNPnjxZq1ev/sPX7NSpk2644QZJUv/%2B/bV48WLl5%2BdXq0uXpCFDhqh3794uY%2BH160tHj1Zre0uw2U42bMXF0jnm%2Bi3pxhs9XYF7tW17smEbPlzKyvJ0NW5TsOELT5fgVr6%2BNgUH11dh4XFVVHjPz1NogBc1OD4%2BJxu2sjLPplPexKSkrcrP4GrOdNntdgUHB7uMneo9CgoKXJq20zkcDj3zzDPq0KGDrrvuOklSVFSUbDabxo8fr4CAAC1cuFCjRo3Sxx9/rNBQ9/zTs8Y1bXV%2B/1fNwIEDXcZ9fX118OBBhYWFSZJzKlSS6tevr5Lf//WQl5enK6%2B80rksKChIzZs3/8PXPH1f/v7%2BkqTS0tJq1xwREXF2g3fkiHc1N6dUVnrX%2B9qyxdMVmCMry6veW3m5F/2ZO01FRaV3vTdvbG4cDu98X16kys/gC%2BC4wPNbVlamKVOmaPfu3VqxYoVzfMyYMS7rPfHEE0pLS1N6eroGDx580fWdrsY1bfXq1ZMkff7551V2pvv375ck%2BZzjOoPKysqzpjVt5%2Bnuz7UvAADgJjXwmuiwsDDZ7XaXMbvdLh8fH2dIdLoTJ05o9OjROn78uN5%2B%2B%2B0/TNB8fX3VpEkT5ebmuq3emncEdbLJ%2BvHHH53fl5WV6dChQ9XatlGjRvrll1%2Bc3xcVFSknJ8ftNQIAgAtQA5/TFhsbq4MHDyo/P985lpGRoVatWingjGu4HQ6HkpOT5efnpzfffNOlYXM4HJo9e7ayTrsspbS0VHv37nXrTQo1rmmrU6eOBgwYoJSUFP366686ceKE5s6dq1GjRlUrwuzatas%2B//xzbdu2TSdOnNBLL73kTO%2Bkk9OfBw8eVGFhocrLy818KwAAoAY79Vy1OXPmqKioSNnZ2Vq2bJmGDRsm6eR17ps3b5Ykffjhh9q9e7f%2B67/%2By3kp1Sk%2BPj7av3%2B/ZsyYoUOHDqm4uFgpKSmqU6eO%2Bvbt67Z6a1zTJknTpk1Ts2ZC9a8iAAAgAElEQVTNdMstt%2BiGG27Q7t279eqrr1ZrGvP222/XnXfeqfvuu08JCQnq0KGDrrzySue2t912m3JychQfH%2B/WyBIAAPyBGpi0SdL8%2BfOVm5ur7t2767777tPAgQM1fPhwSVJOTo6OHTsmSVq5cqUOHDig6667zuUBus8884wk6YUXXlDz5s01aNAgdevWTTt27NDy5cvVoEEDwzWe4uO40CvwLKC0tFR169Z1fh8fH6/Ro0e77ULAajly5NK91qVgs0lBQSfviPWmGxEaNvR0Be7VsePJx5h06uRVNyLk5RZ6ugS38vOzKTQ0QAUFxV51I0J4cN3zr2QVPj5S3bpSaan33YhwRkp0yWzaZM5%2Br7/enP3WQDUyaTPi22%2B/VefOnbVt2zZVVFTo/fffV15enq6vRScVAIAap4YmbVZS4%2B4eNapz585KTk7WhAkTlJ%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%2BPl4vv/yyKisrq1x3xYoVSkhIUKdOnTRs2DBt377duaykpETPPvusevbsqS5dumjcuHEqKChwa600bQAAwHw2mzlfBiUlJSkyMlLp6elatmyZ0tPTtXz58rPW%2B%2BSTT7RgwQK99NJL%2BvrrrxUfH69HH31Ux44dkyTNmzdPmZmZSk1N1ccffyyHw6GpU6caru90NG0AAMB8JjVtubm5yszMdPnKzc2tVkkZGRnKysrSpEmTFBQUpObNm2vkyJFKTU09a93U1FQNGjRIHTp0UL169fTggw9Kkj799FOVl5frvffe0%2BjRo9WkSRM1bNhQEyZM0GeffaZDhw657RByTZtJ8kq9qx/287MpVFJBuU3l5Z6uxo1yCz1dgVs5z9OGL1ReXnW8D5glr7DU0yW4jZ%2BfTaF166qguMyrfpbCw4M8XYLbpaamauHChS5jY8eOVVJS0nm3zczMVNOmTRUSEuIci4mJUU5OjoqKihQYGOiy7oABA5zf22w2RUdHKyMjQ9HR0Tp69KhiYmKcy1u2bKl69eopMzNTkZGRRt6iE00bAAAwnUM%2Bpux3yJAh6t27t8tYeHh4tba12%2B0KDg52GTvVwBUUFLg0bXa73aW5O7VuQUGB7Ha7JJ21r%2BDgYLde10bTBgAALCsiIkIREREXvb3D4XDbuheyr4vhXXN4AACgRqqsNOfLiLCwMGdKdordbpePj4/CwsJcxkNDQ6tcNywszLnumcuPHDmiRo0aGSvyNDRtAADAdDWxaYuNjdXBgweVn5/vHMvIyFCrVq0UEBBw1rqZmZnO7ysqKvTDDz%2BoQ4cOioqKUkhIiMvynTt3qrS0VLGxscaKPA1NGwAAqJXatWunuLg4zZkzR0VFRcrOztayZcs0bNgwSVL//v21efNmSdKwYcO0atUqff/99zp%2B/Lhee%2B011a1bV7169ZKvr6/uvvtuvf766zp48KAKCgo0d%2B5c9evXT40bN3ZbvVzTBgAATGc0FTPL/PnzNW3aNHXv3l2BgYEaOnSohg8fLknKyclxPoetZ8%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%2B1Sa1t2pYvX64//elPSktL83QpAACgBrLb7ZowYYK6deumHj166Omnn9aJEyfOuf66det0%2B%2B23q2PHjkpISNC7777rXLZgwQJFR0crLi7O5evw4cPVrqfWXtNWVFSkDh06yGartX0rAACXjBVTsWnTpqm0tFRpaWkqKyvT%2BPHjlZKSomeeeeasdbdt26ZJkyZp7ty56tWrl7766iuNGTNGV111la699lpJUmJiov785z9fdD21smm75557tHnzZv373//WunXr1Lt3b6WlpamoqEhXXnmlnnjiCd1www3V3l9ubq7y8vJcxvz8GigiIsLdpXuMr6/N5VfUTJwna%2BA81XycI/czq2mr6jM4PDzc8Gfw4cOHlZ6erg8%2B%2BEBhYWGSpNGjR2v8%2BPF68sknVadOHZf17Xa7HnnkEfXt21eSdOONN6p169bavHmzs2kzqlY2bW%2B99ZbuvfdedejQQW3atNEbb7yhNWvWKCQkRKtWrdKTTz6pjRs3nnVCziU1NVULFy50GRszZozGjRtnRvkeFRxc39MloBo4T9bAear5OEc1X1WfwWPHjlVSUpKh/e7YsUO%2Bvr5q06aNcywmJkbHjh3Tnj17XMYlqWfPnurZs6fz%2B/LycuXl5SkyMtI59uOPP2ro0KHauXOnmjRpoqlTp6pHjx7VrqlWNm2nKywslJ%2Bfn%2BrXry9fX1/deeeduuOOOy5o2nTIkCHq3bu3y5ifXwMVFBS7u1yP8fW1KTi4vgoLj6uiwoIZdy3BebIGzlPN563nKDQ0wGOvbVbSVtVncHh4uOH92u12BQYGysfHxzkWEhIiSSooKDjv9ikpKWrQoIHzhsfLLrtMUVFRmjhxoiIiIpSamqpHH31Ua9as0VVXXVWtmmp903bLLbdo9erV6tmzp7p3765evXrplltuuaCmLSIi4qwYNi/vqMrLvecH/ZSKikqvfF/ehvNkDZynmo9zVPNV9RlcXatXr9bkyZOrXJacnCyHw3HB%2B3Q4HEpJSVFaWppWrFghf39/SdLgwYM1ePBg53ojR47URx99pDVr1mjChAnV2netb9oaNmyod999V999950%2B/fRTzZ8/X%2B%2B8847efvtt%2BfnV%2BsMDAIBb1MQbERITE5WYmFjlsq%2B%2B%2BkpFRUWqqKiQr6%2BvpJPpmyQ1atSoym0qKys1depUbdu2Te%2B8846ioqL%2B8PWbNm2q3Nzcatdb66%2BwLCkp0fHjx9WpUydNnDhRaWlp2rlzp7KysjxdGgAAXsNqz2mLjo6Ww%2BFw6QcyMjIUHBysFi1aVLnNiy%2B%2BqF27dlXZsL366qvatGmTy1h2dvZ5G7vT1fqm7YUXXtCTTz6p/Px8ORwOZWZmqrKyUpdffrmnSwMAAB4SFhamhIQEvfLKK8rPz9evv/6qRYsW6a677nLOxI0YMUJr166VJP373//WmjVrtGTJEjVs2PCs/dntds2YMUN79uxRSUmJli5dqr179%2BqOO%2B6odk21fv5v4sSJeu6555SQkKDy8nI1a9ZMc%2BbMcd7eCwAAjKuJ06PnM3PmTD333HPq06eP6tSpo1tvvVXJycnO5fv27dORI0ckSStXrtTRo0cVHx/vso/OnTtr6dKlmjhxoqST17LZ7Xa1atVKb775pi677LJq1%2BPjuJir7HBeeXlHPV2CW/n52RQaGqCCgmIuyq3BOE/WwHmq%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%2BffsuqB6aNgAAYDorNm3Tpk3T8ePHlZaWppUrVyo7O1spKSnnXL9z587KyMhw%2BWrfvr0k6e2339aHH36oJUuW6NNPP1Xz5s01ZswYORyOatdD0wYAAHCGw4cPKz09XcnJyQoLC1NkZKRGjx6tlStXqqys7IL3l5qaqpEjR6ply5YKDAxUcnKysrOztXXr1mrvgxsRAACA6cxKxXJzc5WXl%2BcyFh4eroiICEP73bFjh3x9fdWmTRvnWExMjI4dO6Y9e/a4jJ9y8OBB3X///dq%2BfbuCg4M1btw4JSYm6sSJE9q9e7fatWvnXDcwMFDNmjVTRkaG/vSnP1WrJpo2k9StG%2BTpEtzK9nsm6%2BcX4Py9NwipV%2BLpEtzLx0eSFBpQR7qAyL2myyss9XQJqIbwiGBPl%2BA%2BHTtK332n0D43SFu2eLoa9/KivxukkwnWwoULXcbGjh2rpKQkQ/u12%2B0KDAyUz%2B9/r0pSSEiIJKmgoOCs9cPCwtS8eXM9/vjjatWqldavX6/JkycrIiJCV111lRwOh3P70/dX1b7OhaYNAACYzqykbciQIerdu7fLWHh4eLW2Xb16tSZPnlzlsuTk5Au63qxXr17q1auX8/tbbrlF69ev1/vvv69JkyZJ0gXtryo0bQAAwHRmNW0REREXPRWamJioxMTEKpd99dVXKioqUkVFhXx9fSWdTN8kqVGjRtXaf9OmTbV9%2B3Y1bNhQNpvNuf0pdru92vuSuBEBAADgLNHR0XI4HMrKynKOZWRkKDg4WC1atDhr/XfeeUdr1651GcvOzlZUVJT8/f119dVXKzMz07mssLBQe/fudd5dWh00bQAAwHRWe%2BRHWFiYEhIS9Morryg/P1%2B//vqrFi1apLvuukt%2BficnKkeMGOFs1EpLS/X8888rIyNDZWVlSktL0%2Beff66hQ4dKkoYNG6YVK1YoOztbRUVFSklJUXR0tOLi4qpdE9OjAAAAVZg5c6aee%2B459enTR3Xq1NGtt96q5ORk5/J9%2B/bpyJEjkqT77rtPxcXFGj9%2BvPLy8nTFFVdo0aJFio2NlSQNHTpUeXl5uvfee1VcXKwuXbqcdQPF%2Bfg4jF4Vhyr9fg69hs0mBQVJR4961/8f55V3j9atK5WWetUdYt5296ifn02hoQEqKChWebn3/EB5492j6tSJu0fdZOpUc/Y7e7Y5%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%2BXXXnvtBdVzQdOjTZs2VUZGxgW9AAAAgBWnR6dNm6bS0lKlpaWprKxM48ePV0pKip555pmz1p01a5ZmzZrl/L68vFwDBw5U//79nWOPPfaYkpKSLroepkcBAADOcPjwYaWnpys5OVlhYWGKjIzU6NGjtXLlSpWVlZ13%2B%2BXLl%2Bvyyy/XjTfe6LaaLihp279/v/r06aO1a9fqoYce0mOPPab169fr22%2B/VaNGjTR9%2BnT16NFDkvTJJ5/oL3/5i3Jzc3XjjTeqcePG1X6dKVOmKCAgQH5%2Bfvrggw9ks9n0wAMP6KGHHpIktWnTRm%2B88YZ69uwpSXrnnXf0xhtv6JNPPnHWuHjxYv3lL3/RL7/8ogEDBuixxx7T5MmTlZWVpbi4OC1cuFAhISGSJIfDoVmzZmnVqlUKDAzUAw88cEFTubm5ucrLy3MZq18/XOHhEdXeR01ns7n%2B6jV8fDxdgXudej9e9r78/LzrD56vr83lV6/RsaOnK3Cftm1df/UWW7Z47KXNStqq%2BgwODw9XRISxz%2BAdO3bI19dXbdq0cY7FxMTo2LFj2rNnj8v4mQoLC/X666/rb3/7m8v4N998ow0bNujnn39Wy5YtNX36dMXGxla7JkN3j/73f/%2B3XnrpJbVt21bTp0/Xiy%2B%2BqLVr16qwsFDJycmaNGmShgwZok2bNmnixImKjo6u9r7T0tI0ZcoUffXVV1qzZo2mTZumxMTEap%2BEVatW6d1331VWVpbuuece/fzzz3r55ZdVr1493XHHHVq5cqVGjRolSfr888/1xBNP6Ouvv9Znn32mpKQkdejQQe3bt6/Wa6WmpmrhwoUuY2PGjNW4cRcfgdZUAQGersDd6nq6AHPUqePpCtwqtK53nqfg4PqeLsG9vvvO0xW43xkfupbnZf%2Bgk6r%2BDB47dqyhaUjp5PVsgYGB8jntmJ0KewoKCv5w27feekudO3fW1Vdf7RyLioqSzWbT%2BPHjFRAQoIULF2rUqFH6%2BOOPFRoaWq2aDDVt8fHxzsYmISFBq1atUmVlpb788ks1aNBA/%2B///T/ZbDbdeOONuvbaa1VcXFztfV9xxRW64447JEkDBgzQU089pZ9%2B%2BqnaTdudd96poKAgde7cWUFBQerevbuioqIkSe3bt9dPP/3kXDciIkLDhg2TJN10002Kjo7W559/Xu2mbciQIerdu7fLWP364Tp6tFqbW4LNdrJhKy625nUJ5xLkX%2BrpEtzLx%2Bdkw1ZWJjkcnq7GbQqKzz8VYSW%2BvjYFB9dXYeFxVVR4zw9UaJ8bPF2C%2B7Rte7JhGz5cysrydDVewazPjqo%2Bg8PDw6u17erVqzV58uQqlyUnJ8txEX%2BPVlRU6O2339acOXNcxseMGePy/RNPPKG0tDSlp6dr8ODB1dq3oabtiiuucP6%2BXr16qqioUFlZmX799Vc1adJEttPm0po3b67MzMyL2nf9%2Bif/NXquOzaq0qRJE%2Bfv/f39FRkZ6fJ9ael/PqxbtWrlsu2VV16pQ4cOVfu1IiIizmomjxzxrubmlEtxt84l5UWNjQuHw6veW3m5N/2h%2B4%2BKikrvem8enHozTVaWd74vDzDrs6Oqz%2BDqSkxMVGJiYpXLvvrqKxUVFamiokK%2Bvr6STqZvktSoUaNz7vPbb79VaWnpee8M9fX1VZMmTZSbm1vteg1dUGE7xwVOpaWlqqiocBmrvMCzda59V6WqffucEQH/0f7OXOZwOOTv71/t1wcAAN4lOjpaDodDWaclrRkZGQoODlaLFi3Oud2GDRvUtWtX%2Bfn9JxdzOByaPXu2y75KS0u1d%2B9e5yxgdZhyFWxERIQOHTrkEitmZ2e7bf9169Z1Sd327t1raH9nPndu7969hi9gBAAA/2G157SFhYUpISFBr7zyivLz8/Xrr79q0aJFuuuuu5wN2YgRI7R27VqX7Xbs2OEyWyidDJL279%2BvGTNm6NChQyouLlZKSorq1Kmjvn37VrsmU5q2bt26qaioSH//%2B99VWlqq9PR0bd261W37b968udLT01VeXq6MjAx99tlnhva3f/9%2BffDBByorK9OGDRv0448/ql%2B/fu4pFgAAWNLMmTMVFBSkPn366Pbbb1f79u1dHsi/b98%2BHTlyxGWbvLy8Kp%2BY8cILL6h58%2BYaNGiQunXrph07dmj58uVq0KBBtesx5f8eveyyyzRnzhylpKToL3/5i3r27Knhw4dri5uuC3jqqaf07LPP6tprr9W1116rUaNGafHixRe9v4SEBG3dulWzZs1SUFCQZs6c%2BYfRJwAAuDBWvB46KChIc%2BfOPefyTz755Kyxjz/%2BuMp1GzZsqNmzZxuqx8dxMbdG4LzOaLwtz2aTgoKko0et%2BYN3LiH1Sjxdgnv5%2BEh160qlpV51I0JeoXfd5evnZ1NoaIAKCoq96kaE8IhgT5fgPh07nnyESadO3ncjgof%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%2BzJaRkaF%2B/frp7rvvPu%2B6K1asUEJCgjp16qRhw4Zp%2B/btzmUlJSV69tln1bNnT3Xp0kXjxo1TQUHBBdVC0wYAAExnxaZtzZo1SkpKUrNmzc677ieffKIFCxbopZde0tdff634%2BHg9%2BuijOnbsmCRp3rx5yszMVGpqqj7%2B%2BGM5HA5NnTr1guqhaQMAAKhCSUmJUlNT1aFDh/Oum5qaqkGDBqlDhw6qV6%2BeHnzwQUnSp59%2BqvLycr333nsaPXq0mjRpooYNG2rChAn67LPPdOjQoWrXw40IAADAdGalYrm5ucrLy3MZCw8PV0REhOF9Dx48uNrrZmZmasCAAc7vbTaboqOjlZGRoejoaB09elQxMTHO5S1btlS9evWUmZmpyMjIar0GTZtJQkI8XYF75ebm6s03UzVkyBC3/CDUHP6eLsCtcnNzlZrqfecpPNz7ztOCBUu97jzJ4fB0BW6Tm5ur1AULNOSf//Suc%2BRBZv3xWLAgVQsXLnQZGzt2rJKSksx5wXOw2%2B0KOePDPyQkRAUFBbLb7ZKk4OBgl%2BXBwcEXdF0bTRuqJS8vTwsXLlTv3r35C6wG4zxZA%2Bep5uMcWceQIUPUu3dvl7Hw8PBqbbt69WpNnjy5ymWzZ8/WoEGDLqgWx3k60/MtPx%2BaNgAAYFkREREX3VgnJiYqMTHRLXWEhoY6E7VT7Ha7rr76aoWFhTm/DwgIcC4/cuSIGjVqVO3X4EYEAAAAg2JjY5WZmen8vqKiQj/88IM6dOigqKgohYSEuCzfuXOnSktLFRsbW%2B3XoGkDAAC4CP3799fmzZslScOGDdOqVav0/fff6/jx43rttddUt25d9erVS76%2Bvrr77rv1%2Buuv6%2BDBgyooKNDcuXPVr18/NW7cuNqvx/QoqiU8PFxjx46t9nUC8AzOkzVwnmo%2BzhEkKSEhQb/88osqKipUWVmpuLg4SdI///lPNW3aVDk5Oc7nsPXs2VOPP/64JkyYoN9%2B%2B01xcXFasmSJ6tWrJ0kaN26ciouLlZiYqPLycsXHx2v69OkXVI%2BPw%2BhVcQAAADAd06MAAAAWQNMGAABgATRtAAAAFkDTBgAAYAE0bQAAABZA0wYAAGABNG0AAAAWQNMGAABgATRtAAAAFkDThioNGzZMf//732W32z1dCgAAEP%2BNFc5h0aJFWr9%2BvbKzs9W9e3fdfvvt6tOnj/z9/T1dGqpQWVmp/Px8lZaWnrXs8ssv90BFOFNFRYV8fX0lnTxfWVlZatKkiUJDQz1cGQCroGnDH9q3b5/Wr1%2Bv9evXa/fu3erbt69uu%2B02devWzdOl4Xdr1y314FkAAAqWSURBVK7VjBkzVFhY6DLucDjk4%2BOjHTt2eKgynPLll19qypQp%2BvLLL1VeXq577rlHP/74oyRp7ty5io%2BP93CFON327du1ceNGHTp0SP7%2B/oqMjFS/fv3UrFkzT5eGWo6mDdVSWlqq9957T3PnzlVRUZGaNm2qhx56SEOHDvV0abVe9%2B7dddddd6l///5VJqFXXXWVB6rC6QYOHKj77rtPgwYN0urVq7VgwQJ9%2BOGH2r59u2bPnq3333/f0yXid2%2B99ZZmzZql6OhoRUVFSZL27t2rnTt3aubMmbrrrrs8XCFqMz9PF4Ca7ZtvvtGHH36odevWKSAgQEOHDtXAgQN1%2BPBhzZ49W9nZ/7%2B9%2B42psgrgOP57ruzmlaYNF6hcRTY3WWvKHwMJ6hob6IxCNlPnRn%2BoXLPxQvw3aNXWTFet0OmsFlOjN7WW6UZbLBC0cBPcmIwitgwswZG4lOv0Cru3XoDk9V4UN7mHJ76fV3DO8%2BL3RvfjnOec56zeeOMN0zEnNZ/Pp5KSEkVF8c95ojp37pwKCwslSQ0NDXr66aflcrn02GOPqaury2w4BDlw4ID27t2r3NzcoPG6ujq9%2B%2B67lDYYxUEEhPXee%2B/J4/Fo48aNGhwc1J49e1RfX68tW7ZowYIFWrp0qT777DMdOXLEdNRJLz8/X01NTaZj4A6io6PV398vn8%2BnxsbGke3Qv//%2Bm7I9wXi9Xi1btixk3OPxhLyCAEQa/1sgrPb2dm3atEl5eXmaNm1a2GdiY2P16quvRjgZpKH3oG6aNm2aysrKlJqaKrfbLcuygp4tLS2NdDzc5plnntELL7ygKVOmKDExUcnJyfL5fHr77beVlZVlOh5uUVBQoG%2B//VZr1qwJGq%2Burtazzz5rKBUwhNKGsA4dOjSm5zZs2DC%2BQRBWS0tL0O/z5s1TX1%2Bf%2Bvr6gsZvL3CInPz8fFVXV0uSjh07ppKSEvX39ys/P1%2BS5HA49NBDD2nr1q0mY0LS5s2bR34OBAJ6//33dejQIS1YsECWZamzs1Pd3d3Ky8szmBLgIAJgezdPid7O7/fr4sWLmjVrloFU8Hg8SklJ0bx583TgwAEVFxeP%2BiyroWaVlZWN%2Bdldu3aNYxLgzihtgM0tXrxYZ86cCRn3er3KyclRc3OzgVQ4deqUPv/8c129elXNzc1asmRJ2Ocsy1JVVVWE0wGwI7ZHAZuqqalRTU2NBgcHg7Z3burp6Rm5zBWRl5GRoYyMDElSUVGRvvjiC8OJMBZer1fffPONurq6dOPGjaA5y7K0c%2BdOQ8kAShtgW4888ojOnz%2Bv77//Xk6nM2R%2B4cKFYcscIo/CZh%2Bvv/66Ojs7tWjRIr4AgwmH7VHA5u72vhSAsUtJSVFtba1mzpxpOgoQgpU2wOauXbumffv2hZ1zOByKi4tTZmYm3yAFxmDhwoUh26LARMFKG2BzxcXF%2Bvnnn%2BXz%2BTR//nw5HA51dnbK5XJp7ty5I1eB7Nmzh29cAnfx559/qqysTFlZWYqLi5PDEXwH/apVqwwlA1hpA2zP4/HI7XZr27ZtevDBByVJV69e1QcffKDk5GQVFhbq8OHDqqiooLQBd/Hxxx/r9OnT6ujoCPtOG6UNJrHSBthcVlaW6urqNHXq1KDxGzduaMWKFaqvr1cgEFBaWlrIpbwAgiUnJ6uysnLUK1oAk/j2KGBzg4OD%2BuWXX0LGOzo6dPnyZUlSW1ubYmJiIh0NsJ05c%2BYoKSnJdAwgLLZHAZt78cUX9dJLL%2BmJJ55QfHy8oqKi1NPTo%2BPHj2v16tUaGBjQ888/r02bNpmOCkx427dv11tvvaXVq1eHfactMTHRUDKA7VHgf%2BGHH37QiRMndPHiRQUCAc2cOVMZGRkqKCiQZVk6ffo02z3AGIRbZbMsa%2BRzce3t7QZSAUMobQAADOvu7r7jfHx8fISSAKEobYDN9fT0qLKyUmfPng17v9SXX35pIBUA4H7jnTbA5kpLS3X9%2BnVlZ2fL5XKZjgPYWk5OjizLGnW%2Brq4ugmmAYJQ2wOY6Ojp0/PhxTZ8%2B3XQUwPY2bNgQ9Lvf79cff/yhhoYGvfLKK4ZSAUMobYDNzZ8/XwMDA6ZjAP8L69atCzuen5%2BvvXv36rnnnotwIuA/vNMG2NzJkydVVVWl9evXKz4%2BnisKgHHg9/u1ZMkSLqiGUZQ2wOa4ogC4f3766aeQMZ/Pp9raWrW0tKimpsZAKmAIpQ2wOa4oAO6fcH8EOZ1OJSQkqLy8XJmZmQZSAUMobcD/xKVLl3ThwgU9%2BuijpqMAAMYBBxEAm%2Bvt7VV5ebkaGxsVFRWltrY2/fXXX3r55Ze1f/9%2BzZ0713REwFb6%2BvrU1dUln88XMpednW0gETCE0gbY3DvvvKOYmBjV19dr%2BfLlkqSYmBhlZ2drx44d%2BvTTTw0nBOyjsrJSFRUV8vv9IXO8IwrT2B4FbC4tLU0nTpxQdHS0Fi9erDNnzkgaenna4/Ho1KlThhMC9pGZmamtW7dq5cqVmjp1quk4QBBW2gCbc7lcCve315UrV8KuFgAYXSAQUEFBgaZMmWI6ChDCcfdHAExkS5cuVXl5uTo7OyVJ/f39ampqUklJiZYtW2Y2HGAzhYWFqq6uNh0DCIvtUcDmrly5ou3bt6uhoWFkzOFwaOXKlXrzzTc1Y8YMc%2BEAm9mxY4e%2B%2B%2B47ud1uud3ukMuqP/zwQ0PJAEobYEs3V9VudenSJTmdTjmdTknSAw88IIkvIgD3oqys7I7zu3btilASIBSlDbChpKSkka8eSEOn2m7HFxGA8fPJJ5/otddeMx0DkwylDbChu30F4VZ8EQG4/249qQ1ECqdHARuiiAFmsd4BEzg9CgDAPQr3SgIw3ihtAAAANkBpAwAAsAFKGwAAgA1Q2gAAuEccRIAJlDYAAO7RmjVrTEfAJMQ9bQCASa2oqGjMp0GrqqrGOQ0wOu5pAwBMasnJySM/X79%2BXUePHlVaWpoSExMVCAT022%2B/qbW1VevXrzeYEmClDQCAEaWlpVq1apWefPLJoPHa2lpVV1dr9%2B7dhpIBlDYAAEakpqaqqalJUVHBG1GDg4NKT09XS0uLoWQABxEAABgRGxurr776KmT88OHDevjhhw0kAv7DShsAAMOOHTum0tJSRUdHa/bs2fL7/ert7ZXX69VHH32k3Nxc0xExiVHaAAC4hdfr1Y8//qje3l4NDAwoNjZWjz/%2BuOLi4kxHwyRHaQMAIIyBgYGQMafTaSAJMIQrPwAAGHby5Ent3LlTXV1d8vv9IfPt7e0GUgFDWGkDAGBYbm6u0tPTlZeXJ5fLFTKfnp5uIBUwhNIGAMCwlJQUNTc3h1z5AUwEXPkBAMCw9PR0/frrr6ZjAGGx0gYAwLCvv/5aBw8e1FNPPSW32y2HI3htY%2B3atYaSAZQ2AABG5OTkjDpnWZbq6uoimAYIRmkDAExq586dU0JCgiTp999/l2VZoz6bmJgYqVhACEobAGBSW7RokVpbWyVJSUlJYUvbP//8I8uyuPIDRlHaAACTWk9Pj%2BbMmSNJ6u7uvuOz8fHxkYgEhEVpAwAAsAGu/AAAALABShsAAIANUNoAAABsgNIGAABgA5Q2AAAAG6C0AQAA2AClDQAAwAb%2BBeskGktg/vVQAAAAAElFTkSuQmCC\" class=\"center-img\">\n",
       "    <img 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Is677bBhw6r0HidOnNCPP/6otm3bOtsCAwN1xRVXKCsrS8eOHZOvr69at27tXB4dHa3jx49r165dLu1GULSZxYMPBDXFlVdKO3ZIV18tedGv7HFUcHWAFRw8eMzTXXArm81HYWEBOny4SBVe9HfQ3z/I011wGx8fKSBAKiqSvO0ioiBPnSaTPhfT581TWlqaS1tSUpKSk5Pd9h5HjhyRw%2BFQSEiIS3tISIgKCgpUv359BQYGujwM/PS6BQUFbusHRRuqpn59ydf31FdUaz4%2B3vch421sNh/5%2BPjIZvPxqqLNm/j4/O/Fv6fqbdCgQYqNjXVpCw8PN%2BW9fu82gEtxiwBFGwAAMJ/NnMvoIyIiqjQVakT9%2BvVls9lkt9td2u12uxo0aKCwsDAVFhaqvLxcvr6%2BzmWS1KBBA7f1gxsRAACA%2BWw2c16XQJ06dXT11VcrOzvb2Xb06FHt3r1b11xzjaKiouRwOJSTk%2BNcnpWVpeDg4Ep/b/PFomgDAAD4jQMHDqhfv37OZ7ElJCRo2bJl2rlzpwoLC5WamqqoqCjFxMQoLCxMcXFxmjt3rg4fPqxff/1VCxYs0D333CM/P/dNajI9CgAAzHeJUrELERMTI0kqKyuTJGVmZko6lZKVlpYqNzdXJSUlkqTBgwcrPz9fQ4cOVVFRkTp37uxyA8T06dP1zDPPqE%2BfPqpVq5Zuu%2B228z6w90LxcF2zeNvdox06SN98I3XsKG3e7OneuI033j3qjRdOe9vdo35%2BNoWGBqigoEhlZRWe7o7beNPdozbb/%2B4erfCeUyTJg3eP1qljzn5PnjRnv9UQSRsAADBfNUzarIaiDQAAmI%2BizTBGEAAAwAJI2gAAgPlI2gxjBAEAACyApA0AAJiPpM0wijYAAGA%2BijbDGEEAAAALIGkDAADmI2kzjBEEAACwAJI2AABgPpI2wyjaAACA%2BSjaDGMEAQAALICkDQAAmI%2BkzTBGEAAAwAJI2gAAgPlI2gyjaAMAAOajaDOMEQQAALAAkjYAAGA%2BkjbDGEEAAAALIGkDAADmI2kzjKINAACYj6LNMEYQAADAAkjaAACA%2BUjaDGMEAQAALICkDQAAmI%2BkzTCKNgAAYD6KNsMYQQAAAAsgaQMAAOYjaTOMEQQAALAAkjYAAGA%2BkjbDKNoAAID5KNoMYwQBAAAsgKQNAACYj6TNMEYQAADAAkjaAACA%2BUjaDKNoAwAA5qNoM4wRBAAAsACSNgAAYD6SNsMo2gAAgPko2gxjBAEAACyApA0AAJiPpM0wijYAAFBj/fLLL5o2bZq2bNmievXqqX///ho3bpxsvykyH3jgAX311VcubWVlZRo1apSSkpI0dOhQffPNNy7bXXnllVq9erXb%2BkrRBgAAzFdNk7bk5GRFR0crMzNThw4d0qOPPqqGDRvq/vvvd1lv8eLFLt8fPXpU/fv3V9%2B%2BfZ1tzz77rO666y7T%2Blo9R/ASGj58uP70pz%2B5tC1YsECDBw/2UI8AAPBCNps5LwOysrKUk5Oj8ePHKygoSC1atFBiYqLS09PPu%2B3cuXPVt29ftW7d2lAfLkSNT9oGDBig2bNna8KECc5I84MPPtC9995b5X3k5eUpPz/fpS38yisVUb%2B%2BW/vqUW3auH4FLiE/P%2B/6/6Wvr83lq7eopkHKRTl9LN50TJJUUeHpHrhfpZ/B4eGKiIg477bZ2dlq2rSpQkJCnG3R0dHKzc1VYWGhAgMDK93u559/1sqVK5WZmenSvmbNGv35z3/W/v371b59e02fPl3Nmze/iKOqXI0v2m655RZNnz5dX375pbp27ao9e/Zo586d%2BsMf/lDlfaSnpystLc2lLWnMGCWPGePu7nre3/7m6R64lY%2BnO2ASHy87sNDQAE93wRTBwXU93QWcR10vO0XHjnnwzU2qgCv9DE5KUnJy8nm3tdvtCg4Odmk7XcAVFBScs2hbuHCh7r77boWFhTnbWrZsqbp16yo1NVUVFRWaMWOGHnroIWVkZKh27doXeliVqvFFW0BAgG6%2B%2BWatXr1aXbt21QcffKDu3bu7nIjzGTRokGJjY13awm%2B/XVq61N3d9Zw2bU4VbEOGSDk5nu6N2zi%2B/sbTXXA7Hx/J4fB0L9zLbi/ydBfcytfXpuDgujp6tFjl5d4TfdSu7T3Ftc12qmArLvbOdMqbVPoZHB5e5e0dF/gD0263a9WqVfrnP//p0j516lSX76dPn67OnTvr66%2B/VteuXS/oPc6lxhdt0qkp0tGjR2vatGlat26d7rvvvgvaPiIi4uwYNjfXjT2sRnJypM2bPd0L1DBlZd75qVleXuFVx%2BbnhZ8oFRUUbW5jUtJW6WdwFYWFhclut7u02e12%2Bfj4nDO8Wb9%2Bva688ko1a9bsd/cdGBiokJAQHThw4KL6Vhkvm62/OF27dlVAQID%2B8Y9/aMeOHerTp4%2BnuwQAgHephjcitGvXTvv379fhw4edbVlZWWrVqpUCAipPjtevX6/u3bu7tBUWFmrq1KkuBdrhw4d1%2BPDh8xZ3F4KiTZLNZtPtt9%2Bu2bNnq0%2BfPqrrbRcxAACAs7Rt21YxMTGaNWuWCgsLtXPnTi1ZskQJCQmSpH79%2BmnTpk0u22zbtk2XX365S1tgYKC2bNmiGTNmyG6368iRI5o2bZpat26tDh06uK2/FG3/NWDAABUWFur222/3dFcAAPA%2B1TBpk6R58%2BYpLy9P3bt317BhwzRgwAANGTJEkpSbm6vjx4%2B7rJ%2Bfn6%2BGDRuetZ8FCxbI4XAoLi5OvXr1UmlpqRYuXHjWQ3qN8MIrEC7OoUOH1LRp07MiTwAA4L0aN26sRYsWVbrshx9%2BOKtt69atla572WWXnXUXq7tRtOnUM16ef/55Pfjgg26tiAEAwH/x%2BWpYjR/B1157TX/4wx/UqVMn5xw2AABws2o6PWolNT5pe/TRR/Xoo496uhsAAAC/q8YXbQAA4BKoYamYGRhBAAAACyBpAwAA5iNpM4yiDQAAmI%2BizTBGEAAAwAJI2gAAgPlI2gxjBAEAACyApA0AAJiPpM0wijYAAGA%2BijbDGEEAAAALIGkDAADmI2kzjBEEAACwAJI2AABgPpI2wyjaAACA%2BSjaDGMEAQAALICkDQAAmI%2BkzTBGEAAAwAJI2gAAgPlI2gyjaAMAAOajaDOMEQQAALAAkjYAAGA%2BkjbDGEEAAAALIGkDAADmI2kzjKINAACYj6LNMEYQAADAAkjaAACA%2BUjaDKNoAwAA5qNoM4wRBAAAsACSNgAAYD6SNsMYQQAAAAsgaQMAAOYjaTOMog0AAJiPos0wRhAAAMACSNoAAID5SNoMYwQBAAAsgKQNAACYj6TNMIo2AABgPoo2wxhBAAAACyBpAwAA5iNpM4wRBAAANdYvv/yiRx55RJ07d1bv3r310ksvqaKi4qz15s%2Bfr6ioKMXExLi8Dh48KEk6efKknn76afXs2VOdO3fW6NGjVVBQ4Na%2BUrQBAADz2WzmvAxKTk5Wo0aNlJmZqSVLligzM1NLly6tdN34%2BHhlZWW5vBo2bChJmjNnjrKzs5Wenq61a9fK4XBo0qRJhvt3Joo2AABgvmpYtGVlZSknJ0fjx49XUFCQWrRoocTERKWnp1/QfsrKyvT2229r5MiRatKkierXr6%2BxY8fq448/1oEDBwz18Uxc02YSR4XD011wOx9Jjq%2B/8XQ33MrH5uPpLrhXhw7SN9/I57qO0ubNnu6N%2B%2BQd9XQPUAVBtiJPd8F9bDZJdRVgK5Z09lSZtQV4ugNulZeXp/z8fJe28PBwRUREnHfb7OxsNW3aVCEhIc626Oho5ebmqrCwUIGBgS7r//DDDxo8eLC2b9%2BuJk2aaNKkSerRo4d2796tY8eOKTo62rluy5Yt5e/vr%2BzsbDVq1MjgUZ5C0QYAAMxn0o0I6enpSktLc2lLSkpScnLyebe12%2B0KDg52aTtdwBUUFLgUbY0bN1azZs00btw4RUREKD09XSNGjNDq1atlt9sl6ax9BQcHu/W6Noo2AABgWYMGDVJsbKxLW3h4eJW3dziqNjM2cOBADRw40Pl9YmKi3n//fa1evVo9e/a8oH1dLIo2AABgPpOStoiIiCpNhVYmLCzMmZKdZrfb5ePjo7CwsPNu37RpU%2BXl5TnXtdvtCgj43/TzkSNH1KBBg4vqW2W4EQEAAJivGt6I0K5dO%2B3fv1%2BHDx92tmVlZalVq1YuxZckvfzyy9q4caNL286dO9WsWTM1a9ZMISEhys7Odi7bvn27SkpK1K5dO0N9PBNFGwAAqJHatm2rmJgYzZo1S4WFhdq5c6eWLFmihIQESVK/fv20adMmSadStGnTpmnXrl06efKkFi9erN27d%2BvOO%2B%2BUr6%2Bv7r33Xr366qvav3%2B/CgoKNHv2bPXt29f5SBB3YHoUAACYr5r%2BRoR58%2BZpypQp6t69uwIDAzV48GANGTJEkpSbm6vjx49LksaNGyfp1LVsdrtdrVq10uuvv67GjRtLkkaPHq2ioiLFx8errKxMvXv31tSpU93aVx%2BH2VfN1VDeOKo%2BPt53XN76yA919K5HfuR72SM//PxsCg0NUEFBkcrKvOdxEuH1queH8kWx2aS6daXiYqmSp%2BNbWoCHHvkxc6Y5%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%2BpenuwEAAKqBX375RY888og6d%2B6s3r1766WXXlJFRUWl6y5fvlxxcXHq0KGD4uPjlZmZ6Vw2ceJEtW3bVjExMc7X9ddf79a%2B1rhr2j744ANt3bpV/fr183RXAACoOappKpacnKzo6GhlZmbq0KFDevTRR9WwYUPdf//9LuutXbtWs2bN0muvvaZrrrlGK1eu1NixY/XPf/5TzZo1kyQ99thjSk5ONq2v1bJos9vtmjFjhr744gsVFRWpS5cumjp1qho1aqS9e/eqT58%2BWrx4sV566SX99NNPatOmjVJTU3X55ZdLkl5%2B%2BWUtWbJEfn5%2BevTRR/XJJ5%2BoY8eOqlevnnMqNDMzU99%2B%2B60kqby8XE8//bQyMjJUp04dTZkyRf37969yf/Py8pSfn%2B/S1rBhuCIiItw0IjBNhw6e7oF7tWnj%2BtVL%2BPlVzx/2F8vX1%2Bby1WtU0w/li%2BLj87%2Bv3nRc50iQLgmTxrGyz%2BDw8Kp9BmdlZSknJ0dLlixRUFCQgoKClJiYqKVLl55VtJ04cUKPP/64rrvuOknSwIEDlZqaqm%2B//dZZtJmtWhZtEydOlJ%2Bfn95//335%2BvrqmWee0aRJk7R48WLnOsuWLdNrr72mOnXqaNiwYfrzn/%2BsqVOnat26dXr11Ve1ePFitW3bVjNnzlR2drY6duyoBx98UDt27NDJkyc1Z84c574yMjL0/PPPa/LkyUpLS9PUqVN1yy23yM%2BvasOTnp6utLQ0l7ZRo5I0erR51bannP455jW%2B%2BcbTPTDH3/7m6R64VainO2CS4OC6nu4Czsff39M9cK%2BiIk/3wO0q%2BwxOSkqqUuKVnZ2tpk2bKiQkxNkWHR2t3NxcFRYWKjAw0NkeHx/vsu3Ro0dVVFSkRo0aOdu%2B%2BOILrV%2B/Xj///LNatmypqVOnql27dhd7aGepdkXb4cOH9dFHH2nNmjXOQRw/frx69erlUkknJCQ4B6pHjx7KysqSJH3yySfq0aOHcx55woQJWrVq1e%2B%2BZ8eOHXXjjTdKkvr166fXXntNhw8frnJSNmjQIMXGxrq0NWwYLoejSptbho%2BPvO%2BYruvo6S64V5s2pwq2IUOknBxP98ZtCtb/29NdcCtfX5uCg%2Bvq6NFilZd7MPlws1B/L0qkfHxOFWwnTnjfDz5PMSlpq%2BwzODw8vErb2u12BQcHu7Sdrj0KCgpcirYzORwOTZ48We1CfuqZAAAgAElEQVTbt9cNN9wgSWrWrJlsNpvGjBmjgIAApaWl6YEHHtDatWsVGuqe/3pWu6KtVq1akqQBAwa4tPv6%2Bmr//v0KCwuTJOdUqCTVrVtXJ0%2BelCTl5%2BerefPmzmVBQUFq0aLF777nmfuqU6eOJKmkpKTKfY6IiDirwOPfuEVs3uzpHpgjJ8erjq2szHsKmzOVl1d417F50aE4CwyHw7NTijivyj6DL4TjAj%2BwS0tLNXHiRP34449atmyZs33UqFEu6z3xxBPKyMhQZmamBg4ceNH9O1O1K9r8/xtFb9iwodLKdO/evZIkn3PM01VUVJw1rWk7T3V/rn0BAAA3qYbXBoaFhclut7u02e12%2Bfj4OEOiM504cUIjR45UcXGx3nzzzd9N0Hx9fdWkSRPl5eW5rb/VbwR1qsj64YcfnN%2BXlpbqwIEDVdq2QYMG2rdvn/P7wsJC5ebmur2PAADgAlTD57S1a9dO%2B/fv1%2BHDh51tWVlZatWqlQICAlzWdTgcSklJkZ%2Bfn15//XWXgs3hcGjmzJnKOeOylJKSEu3evdutNylUu6KtVq1a6t%2B/v1JTU/Xrr7/qxIkTmj17th544IEqRZhdunTRhg0b9N133%2BnEiRN68cUXnemddGr6c//%2B/Tp69KjKysrMPBQAAFCNnX6u2qxZs1RYWKidO3dqyZIlSkhIkHTqOvdNmzZJkt577z39%2BOOP%2Br//%2Bz/npVSn%2Bfj4aO/evZo2bZoOHDigoqIipaamqlatWrr55pvd1t9qV7RJ0pQpU3TFFVfo1ltv1Y033qgff/xRL7/8cpWmMe%2B44w7dfffdGjZsmOLi4tS%2BfXs1b97cue3tt9%2Bu3Nxc9e7d262RJQAA%2BB3VMGmTpHnz5ikvL0/du3fXsGHDNGDAAA0ZMkSSlJubq%2BPHj0uSVqxYoV9%2B%2BUU33HCDywN0J0%2BeLEl67rnn1KJFC911113q1q2btm3bpqVLl6pevXqG%2B3iaj%2BNCr8CzgJKSEtWuXdv5fe/evTVy5Ei3XQhYFd43ql5696jNy65n7NDh1GNMOnb0qhsR8vOOeroLbuXnZ1NoaIAKCoq86kaE8HrVMge4ODabVLeuVFzsfTci/Gba75LZuNGc/Xbtas5%2BqyEv%2Bhd2yldffaVOnTrpu%2B%2B%2BU3l5ud555x3l5%2Beraw06qQAAVDvVNGmzkmp396hRnTp1UkpKisaOHavDhw%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%2BfP14osv6vPPP1fv3r01YsQIHT9%2BXJI0Z84cZWdnKz09XWvXrpXD4dCkSZMM9%2B9MFG0AAMB8JhVteXl5ys7Odnnl5eVVqUtZWVnKycnR%2BPHjFRQUpBYtWigxMVHp6elnrZuenq677rpL7du3l7%2B/vx566CFJ0kcffaSysjK9/fbbGjlypJo0aaL69etr7Nix%2Bvjjj3XgwAG3DSHXtJnk4MFjnu6CW/n52RQaGiC7vUhlZZXHxpaUd9TTPXArPz%2BbQiUVrP%2B3d50nWEL%2Bce/5O%2BfnJ4XWlQpOVHjVv6Xw8CBPd8Ht0tPTlZaW5tKWlJSk5OTk826bnZ2tpk2bKiQkxNkWHR2t3NxcFRYWKjAw0GXd/v37O7%2B32WyKiopSVlaWoqKidOzYMUVHRzuXt2zZUv7%2B/srOzlajRo2MHKITRRsAADCdQz6m7HfQoEGKjY11aQsPD6/Stna7XcHBwS5tpwu4goICl6LNbre7FHen1y0oKJDdbpeks/YVHBzs1uvaKNoAAIBlRUREKCIi4qK3dzgcblv3QvZ1MbimDQAAmK6iwpyXEWFhYc6U7DS73S4fHx%2BFhYW5tIeGhla6blhYmHPd3y4/cuSIGjRoYKyTZ6BoAwAApquORVu7du20f/9%2BHT582NmWlZWlVq1aKSAg4Kx1s7Oznd%2BXl5fr%2B%2B%2B/V/v27dWsWTOFhIS4LN%2B%2BfbtKSkrUrl07Y508A0UbAACokdq2bauYmBjNmjVLhYWF2rlzp5YsWaKEhARJUr9%2B/bRp0yZJUkJCglauXKlvv/1WxcXFeuWVV1S7dm316tVLvr6%2Buvfee/Xqq69q//79Kigo0OzZs9W3b181bNjQbf3lmjYAAGA6o6mYWebNm6cpU6aoe/fuCgwM1ODBgzVkyBBJUm5urvM5bD179tTjjz%2BusWPH6tChQ4qJidHChQvl7%2B8vSRo9erSKiooUHx%2BvsrIy9e7dW1OnTnVrX30cZl81V0Pl53vnIz8KCrzskR9ehvNkDZyn6s9bz5EnH/lx8qQ5%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%2BPyOnjwYJX7U2OvaSssLFT79u1ls9XYuhUAgEvGiqnYlClTVFJSooyMDJWWlmrMmDFKTU3V5MmTz1r3u%2B%2B%2B0/jx4zV79mz16tVLn332mUaNGqWrrrpK119/vSQpPj5ef/rTny66PzWyaLvvvvu0adMmff311/rggw8UGxurjIwMFRYWqnnz5nriiSd04403Vnl/eXl5ys/Pd2nz86uniIgId3fdY3x9bS5fUT1xnqyB81T9cY7cz6yirbLP4PDwcMOfwQcPHlRmZqbeffddhYWFSZJGjhypMWPG6Mknn1StWrVc1rfb7Xr00Ud18803S5JuuukmRUZGatOmTc6izagaWbS98cYbGjp0qNq3b6/WrVtr0aJFWr16tUJCQrRy5Uo9%2BeST%2BuSTT846IeeSnp6utLQ0l7ZRo0Zp9OjRZnTfo4KD63q6C6gCzpM1cJ6qP85R9VfZZ3BSUpKSk5MN7Xfbtm3y9fVV69atnW3R0dE6fvy4du3a5dIuST179lTPnj2d35eVlSk/P1%2BNGjVytv3www8aPHiwtm/friZNmmjSpEnq0aNHlftUI4u2Mx09elR%2Bfn6qW7eufH19dffdd%2BvOO%2B%2B8oGnTQYMGKTY21qXNz6%2BeCgqK3N1dj/H1tSk4uK6OHi1WebkFM%2B4agvNkDZyn6s9bz1FoaIDH3tuspK2yz%2BDw8HDD%2B7Xb7QoMDJSPj4%2BzLSQkRJJUUFBw3u1TU1NVr1495w2PjRs3VrNmzTRu3DhFREQoPT1dI0aM0OrVq3XVVVdVqU81vmi79dZbtWrVKvXs2VPdu3dXr169dOutt15Q0RYREXFWDJuff0xlZd7zD/208vIKrzwub8N5sgbOU/XHOar%2BKvsMrqpVq1ZpwoQJlS5LSUmRw%2BG44H06HA6lpqYqIyNDy5YtU506dSRJAwcO1MCBA53rJSYm6v3339fq1as1duzYKu27xhdt9evX11tvvaVvvvlGH330kebNm6fly5frzTfflJ9fjR8eAADcojreiBAfH6/4%2BPhKl3322WcqLCxUeXm5fH19JZ1K3ySpQYMGlW5TUVGhSZMm6bvvvtPy5cvVrFmz333/pk2bKi8vr8r9rfFXWJ48eVLFxcXq2LGjxo0bp4yMDG3fvl05OTme7hoAAF7Das9pi4qKksPhcKkHsrKyFBwcrCuvvLLSbZ5//nnt2LGj0oLt5Zdf1saNG13adu7ced7C7kw1vmh77rnn9OSTT%2Brw4cNyOBzKzs5WRUWFLrvsMk93DQAAeEhYWJji4uI0d%2B5cHT58WL/%2B%2BqsWLFige%2B65xzkTN3z4cK1Zs0aS9PXXX2v16tVauHCh6tevf9b%2B7Ha7pk2bpl27dunkyZNavHixdu/erTvvvLPKfarx83/jxo3TM888o7i4OJWVlemKK67QrFmznLf3AgAA46rj9Oj5TJ8%2BXc8884z69OmjWrVq6bbbblNKSopz%2BZ49e3TkyBFJ0ooVK3Ts2DH17t3bZR%2BdOnXS4sWLNW7cOEmnrmWz2%2B1q1aqVXn/9dTVu3LjK/fFxXMxVdjiv/Pxjnu6CW/n52RQaGqCCgiIuyq3GOE/WwHmq/rz1HIWHB3nsvf/5T3P2%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%2B%2BKD27NlzQf2haAMAAKazYtE2ZcoUFRcXKyMjQytWrNDOnTuVmpp6zvU7deqkrKwsl9c111wjSXrzzTf13nvvaeHChfroo4/UokULjRo1Sg6Ho8r9oWgDAAD4jYMHDyozM1MpKSkKCwtTo0aNNHLkSK1YsUKlpaUXvL/09HQlJiaqZcuWCgwMVEpKinbu3KktW7ZUeR/ciAAAAExnViqWl5en/Px8l7bw8HBFREQY2u%2B2bdvk6%2Bur1q1bO9uio6N1/Phx7dq1y6X9tP379%2Bv%2B%2B%2B/X1q1bFRwcrNGjRys%2BPl4nTpzQjz/%2BqLZt2zrXDQwM1BVXXKGsrCxde%2B21VeoTRZtJ/P2DPN0Ft7L9N5OtXTtAfl70tybIVuTpLrjXf09UqL9N8qI7tfKPe9HBeLHwiGBPd8F9OnSQvvlGoX1ulDZv9nRv3OsCpuOsID09XWlpaS5tSUlJSk5ONrRfu92uwMBA%2Bfj4ONtCQkIkSQUFBWetHxYWphYtWujxxx9Xq1attG7dOk2YMEERERG66qqr5HA4nNufub/K9nUuXvTxCwAAqiuzkrZBgwYpNjbWpS08PLxK265atUoTJkyodFlKSsoFXW/Wq1cv9erVy/n9rbfeqnXr1umdd97R%2BPHjJemC9lcZijYAAGA6s4q2iIiIi54KjY%2BPV3x8fKXLPvvsMxUWFqq8vFy%2Bvr6STqVvktSgQYMq7b9p06baunWr6tevL5vN5tz%2BNLvdXuV9SdyIAAAAcJaoqCg5HA7l5OQ427KyshQcHKwrr7zyrPWXL1%2BuNWvWuLTt3LlTzZo1U506dXT11VcrOzvbuezo0aPavXu38%2B7SqqBoAwAAprPaIz/CwsIUFxenuXPn6vDhw/r111%2B1YMEC3XPPPfL778Xdw4cPdxZqJSUlevbZZ5WVlaXS0lJlZGRow4YNGjx4sCQpISFBy5Yt086dO1VYWKjU1FRFRUUpJiamyn1iehQAAKAS06dP1zPPPKM%2BffqoVq1auu2225SSkuJcvmfPHh05ckSSNGzYMBUVFWnMmDHKz8/X5ZdfrgULFqhdu3aSpMGDBys/P19Dhw5VUVGROnfufNYNFOfj4zB6VRwqdeyYp3vgXjabFBAgFRV51%2B%2BP88q7R%2BvWlYqLvepEedvdo35%2BNoWGBqigoEhlZd5zbN5496g6duTuUTeZNMmc/c6cac5%2BqyOSNgAAYDov%2Bn%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%2BrKxMAwYMUL9%2B/Zxtjz32mJKTky%2B6P0yPAgAA/MbBgweVmZmplJQUhYWFqVGjRho5cqRWrFih0tLS826/dOlSXXbZZbrpppvc1qcLStr27t2rPn36aM2aNXr44Yf12GOPad26dfrqq6/UoEEDTZ06VT169JAkffjhh3rhhReUl5enm266SQ0bNqzy%2B0ycOFEBAQHy8/PTu%2B%2B%2BK5vNpgcffFAPP/ywJKl169ZatGiRevbsKUlavny5Fi1apA8//NDZx9dee00vvPCC9u3bp/79%2B%2Buxxx7ThAkTlJOTo5iYGKWlpSkkJESS5HA4NGPGDK1cuVKBgYF68MEHL2gqNy8vT/n5%2BS5t9eqFKyIiosr7qO5sNtevXsPbDsjH539fvejY/LzslilfX5vLV6/RoYOne%2BA%2Bbdq4fvUWmzd77K3NStoq%2BwwODzf%2BGbxt2zb5%2BvqqdevWzrbo6GgdP35cu3btcmn/raNHj%2BrVV1/V3/72N5f2L774QuvXr9fPP/%2Bsli1baurUqWrXrl2V%2B2ToR%2BFf/vIXvfjii2rTpo2mTp2q559/XmvWrNHRo0eVkpKi8ePHa9CgQdq4caPGjRunqKioKu87IyNDEydO1GeffabVq1drypQpio%2BPr/JJWLlypd566y3l5OTovvvu088//6yXXnpJ/v7%2BuvPOO7VixQo98MADkqQNGzboiSee0Oeff66PP/5YycnJat%2B%2Bva655poqvVd6errS0tJc2kaNStLo0RcfgVZXdet6ugfu5nUHdIq/v6d74FahXnqagoO97MC%2B%2BcbTPXC/33zoWt7p/9h5kco%2Bg5OSkgxNQ0qnrmcLDAyUzxljdjrsKSgo%2BN1t33jjDXXq1ElXX321s61Zs2ay2WwaM2aMAgIClJaWpgceeEBr165VaGholfpkqGjr3bu3s7CJi4vTypUrVVFRoU8//VT16tXT//t//082m0033XSTrr/%2BehUVFVV535dffrnuvPNOSVL//v31xz/%2BUT/99FOVi7a7775bQUFB6tSpk4KCgtS9e3c1a9ZMknTNNdfop59%2Bcq4bERGhhIQESdItt9yiqKgobdiwocpF26BBgxQbG%2BvSVq9euC7gcKs9m%2B1UwVZcbM3rEs4lwFbs6S64l4/PqYLtxAnJ4fB0b9ym4IQX/aXTqYQtOLiujh4tVnm59xxbaJ8bPd0F92nT5lTBNmSIlJPj6d54BbM%2BOyr7DA4PD6/StqtWrdKECRMqXZaSkiLHRfwcLS8v15tvvqlZs2a5tI8aNcrl%2ByeeeEIZGRnKzMzUwIEDq7RvQ0Xb5Zdf7vyzv7%2B/ysvLVVpaql9//VVNmjSR7YzpmRYtWig7O/ui9l33v/HOue7YqEyTJk2cf65Tp44aNWrk8n1JSYnz%2B1atWrls27x5cx04cKDK7xUREXFWMXnsmHcVN6ddirt1Li2vOpj/TYk6HF51osrKvOdYzlReXuFdx%2BbBqTfT5OR453F5gFk/kir7DK6q%2BPh4xcfHV7rss88%2BU2FhocrLy%2BXr6yvpVPomSQ0aNDjnPr/66iuVlJSc985QX19fNWnSRHl5eVXur6ELKmznuGampKRE5eXlLm0VF3i2zrXvylS2b5/fRMC/t7/fLnM4HKpTp06V3x8AAHiXqKgoORwO5ZyRtGZlZSk4OFhXXnnlObdbv369unTpIr8zLsZ1OByaOXOmy75KSkq0e/du5yxgVZhyFWxERIQOHDjgEivu3LnTbfuvXbu2S%2Bq2e/duQ/v77XPndu/e7VU3EQAA4GlWe05bWFiY4uLiNHfuXB0%2BfFi//vqrFixYoHvuucdZkA0fPlxr1qxx2W7btm0us4XSqSBp7969mjZtmg4cOKCioiKlpqaqVq1auvnmm6vcJ1OKtm7duqmwsFB///vfVVJSoszMTG3ZssVt%2B2/RooUyMzNVVlamrKwsffzxx4b2t3fvXr377rsqLS3V%2BvXr9cMPP6hv377u6SwAALCk6dOnKygoSH369NEdd9yha665xuWB/Hv27NGRI0dctsnPz6/0iRnPPfecWrRoobvuukvdunXTtm3btHTpUtWrV6/K/THlRvrGjRtr1qxZSk1N1QsvvKCePXtqyJAh2uym6wL%2B%2BMc/6umnn9b111%2Bv66%2B/Xg888IBee%2B21i95fXFyctmzZohkzZigoKEjTp0//3egTAABcGCteZhsUFKTZs2efc/mHH354VtvatWsrXbd%2B/fqaOXOmof74OC7m1gic17Fjnu6Be9lsUkCAVFRkzX945xJk86JbfCWvvc03/7j3HIsk%2BfnZFBoaoIKCIq%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%2BrVq5WcnKwrrrjivOt%2B%2BOGHmj9/vl588UV9/vnn6t27t0aMGKHjx49LkubMmaPs7Gylp6dr7dq1cjgcmjRp0gX1h6INAACgEidPnlR6errat29/3nXT09N11113qX379vL399dDDz0kSfroo49UVlamt99%2BWyNHjlSTJk1Uv359jR07Vh9//LEOHDhQ5f5wIwIAADCdWalYXl6e8vPzXdrCw8MVERFheN8DBw6s8rrZ2dnq37%2B/83ubzaaoqChlZWUpKipKx44dU3R0tHN5y5Yt5e/vr%2BzsbDVq1KhK70HRZpKgIE/3wL3y8vK0eHG6Bg0a5JZ/CNVHgKc74FZ5eXlK//Ofve48hXvXaVJeXp7mz1/sdedJDoene%2BA2eXl5Sp8/X4P%2B9S/vOkceZNZfj/nz05WWlubSlpSUpOTkZHPe8BzsdrtCQkJc2kJCQlRQUCC73S5JCg4OdlkeHBx8Qde1UbShSvLz85WWlqbY2Fh%2BgFVjnCdr4Nn4MAMAAAy4SURBVDxVf5wj6xg0aJBiY2Nd2sLDw6u07apVqzRhwoRKl82cOVN33XXXBfXFcZ7K9HzLz4eiDQAAWFZERMRFF9bx8fGKj493Sz9CQ0OdidppdrtdV199tcLCwpzfBwT8b%2BrgyJEjatCgQZXfgxsRAAAADGrXrp2ys7Od35eXl%2Bv7779X%2B/bt1axZM4WEhLgs3759u0pKStSuXbsqvwdFGwAAwEXo16%2BfNm3aJElKSEjQypUr9e2336q4uFivvPKKateurV69esnX11f33nuvXn31Ve3fv18FBQWaPXu2%2Bvbtq4YNG1b5/ZgeRZWEh4crKSmpytcJwDM4T9bAear%2BOEeQpLi4OO3bt0/l5eWqqKhQTEyMJOlf//qXmjZtqtzcXOdz2Hr27KnHH39cY8eO1aFDhxQT8//bu9uYrKsGjuO/CxgP0nrABcmDSHOTWreCGIhQCAU5Q4FGRi6tLF2r0SZlBq0nR7JqYY6eY2bUC1tFOalFgIChS3BjOgppEqhBI7EUKBG88H4Bcnt5XT7tVo7/%2BH5e4Tn/F7/N6X6c8z/n/x998MEH8vb2liQ9%2BeST%2Bvvvv5WWlqYTJ04oMTFRL7300kXlsZ38f9%2BKAwAAwGXH9igAAIAFUNoAAAAsgNIGAABgAZQ2AAAAC6C0AQAAWAClDQAAwAIobQAAABZAaQMAALAAShsAAIAFUNrg0v33369NmzbpyJEjpqMAAADxGSucxdtvv62Kigq1trYqLi5OCxcu1B133CEvLy/T0eDC0NCQ/vzzTw0MDDjNBQYGGkiEM9ntdrm7u0sa/vvau3evJk2apOuuu85wMgBWQWnDOR08eFAVFRWqqKjQvn37dOedd2rBggWaM2eO6WgY8e233%2Brll19WT0%2BPw/jJkydls9nU3NxsKBlOqaur07PPPqu6ujqdOHFCDzzwgFpaWiRJhYWFSkxMNJwQp2tqalJtba26urrk5eWlgIAAJScnKzQ01HQ0jHOUNlyQgYEBffHFFyosLFRfX5%2BCgoK0fPlyZWVlmY427sXFxSkzM1Pz5s1zuRJ64403GkiF06Wnp2vp0qW65557tHnzZhUVFWnLli1qampSQUGBSktLTUfEiE8//VT5%2Bfm66aabFBISIkk6cOCAfvnlF61Zs0aZmZmGE2I88zAdAFe2H3/8UVu2bNH3338vX19fZWVlKT09Xd3d3SooKFBra6uee%2B450zHHtf7%2BfmVnZ8vDg3/OV6r9%2B/crIyNDklRTU6O7775bPj4%2BuvXWW9Xe3m42HBxs2LBBRUVFSk5OdhivqqrSK6%2B8QmmDURxEgEuvvvqqEhIS9Pjjj2twcFDr169XdXW1nn76aU2dOlWzZ8/Whx9%2BqK%2B//tp01HEvNTVV9fX1pmPgHHx9fdXT06P%2B/n5t3759dDv0r7/%2BomxfYXp7ezV37lyn8YSEBKdXEICxxv8WcKm5uVkrV65USkqKJkyY4PIZf39/LV%2B%2BfIyTQRp%2BD%2BqUCRMmKDc3VzNnzlRwcLBsNpvDszk5OWMdD2dYsGCBHnzwQbm7uyssLEwRERHq7%2B/Xiy%2B%2BqLi4ONPxcJq0tDR99dVXWrRokcN4WVmZFi5caCgVMIzSBpc2btx4Qc%2BtWLHi8gaBS42NjQ5/njx5srq7u9Xd3e0wfmaBw9hJTU1VWVmZJGnr1q3Kzs5WT0%2BPUlNTJUlubm669tprtWrVKpMxIempp54a/XloaEivvfaaNm7cqKlTp8pms6mtrU0dHR1KSUkxmBLgIAJgeadOiZ7Jbrfr0KFDuuGGGwykQkJCgiIjIzV58mRt2LBBy5YtO%2BuzrIaalZube8HPFhQUXMYkwLlR2gCLmzFjhnbv3u003tvbq6SkJDU0NBhIhZ07d%2Brjjz9WX1%2BfGhoaNGvWLJfP2Ww2lZSUjHE6AFbE9ihgUeXl5SovL9fg4KDD9s4pnZ2do5e5YuzFxMQoJiZGkrRkyRJ98sknhhPhQvT29urLL79Ue3u7jh8/7jBns9m0du1aQ8kAShtgWTfffLN%2B%2B%2B03fffdd/L09HSanzZtmssyh7FHYbOOJ554Qm1tbZo%2BfTpfgMEVh%2B1RwOLO974UgAsXGRmpyspKTZw40XQUwAkrbYDF/fPPP3rrrbdczrm5uSkgIECxsbF8gxS4ANOmTXPaFgWuFKy0ARa3bNky/fTTT%2Brv79eUKVPk5uamtrY2%2Bfj4KCQkZPQqkPXr1/ONS%2BA8Dh48qNzcXMXFxSkgIEBubo530KenpxtKBrDSBlheQkKCgoOD9cwzz%2Biqq66SJPX19en1119XRESEMjIyVFpaqnXr1lHagPN49913tWvXLrW0tLh8p43SBpNYaQMsLi4uTlVVVfL29nYYP378uObNm6fq6moNDQ0pKirK6VJeAI4iIiJUXFx81itaAJP49ihgcYODg/r555%2BdxltaWnTkyBFJUlNTk/z8/MY6GmA5gYGBCg8PNx0DcIntUcDiHnroIT388MO67bbbFBQUJA8PD3V2dqq2tlaZmZkaGBjQ0qVLtXLlStNRgSve6tWr9cILLygzM9PlO21hYWGGkgFsjwL/ChUVFdq2bZsOHTqkoaEhTZw4UTExMUpLS5PNZtOuXbvY7gEugKtVNpvNNvq5uObmZgOpgGGUNgAARnR0dJxzPigoaIySAM4obYDFdXZ2qri4WK2trS7vl9q0aZOBVACAS4132gCLy8nJ0bFjxxQfHy8fHx/TcQBLS0pKks1mO%2Bt8VVXVGKYBHFHaAItraWlRbW2trr76atNRAMtbsWKFw5/tdrsOHDigmpoaPfroo4ZSAcMobYDFTZkyRQMDA6ZjAP8KWVlZLsdTU1NVVFSke%2B%2B9d4wTAf/DO22Axe3YsUMlJSVavHixgoKCuKIAuAzsdrtmzZrFBdUwitIGWBxXFACXTl1dndNYf3%2B/Kisr1djYqPLycgOpgGGUNsDiuKIAuHRc/RLk6emp0NBQ5eXlKTY21kAqYBilDfiXOHz4sH7//XfdcsstpqMAAC4DDiIAFtfV1aW8vDxt375dHh4eampq0h9//KFHHnlE77zzjkJCQkxHBCylu7tb7e3t6u/vd5qLj483kAgYRmkDLG7NmjXy8/NTdXW17rrrLkmSn5%2Bf4uPjlZ%2Bfr/fff99wQsA6iouLtW7dOtntdqc53hGFaWyPAhYXFRWlbdu2ydfXVzNmzNDu3bslDb88nZCQoJ07dxpOCFhHbGysVq1apfnz58vb29t0HMABK22Axfn4%2BMjV715Hjx51uVoA4OyGhoaUlpYmd3d301EAJ27nfwTAlWz27NnKy8tTW1ubJKmnp0f19fXKzs7W3LlzzYYDLCYjI0NlZWWmYwAusT0KWNzRo0e1evVq1dTUjI65ublp/vz5ev7553XNNdeYCwdYTH5%2Bvr755hsFBwcrODjY6bLqN954w1AygNIGWNKpVbXTHT58WJ6envL09JQkeXl5SeKLCMDFyM3NPed8QUHBGCUBnFHaAAsKDw8f/eqBNHyq7Ux8EQG4fN577z099thjpmNgnKG0ARZ0vq8gnI4vIgCX3ukntYGxwulRwIIoYoBZrHfABE6PAgBwkVy9kgBcbpQ2AAAAC6C0AQAAWAClDQAAwAIobQAAXCQOIsAEShsAABdp0aJFpiNgHOKeNgDAuLZkyZILPg1aUlJymdMAZ8c9bQCAcS0iImL052PHjmnz5s2KiopSWFiYhoaGtG/fPu3Zs0eLFy82mBJgpQ0AgFE5OTlKT0/X7bff7jBeWVmpsrIyvfnmm4aSAZQ2AABGzZw5U/X19fLwcNyIGhwcVHR0tBobGw0lAziIAADAKH9/f3322WdO46Wlpbr%2B%2BusNJAL%2Bh5U2AABGbN26VTk5OfL19dWkSZNkt9vV1dWl3t5eFRYWKjk52XREjGOUNgAATtPb26sffvhBXV1dGhgYkL%2B/v%2BbMmaOAgADT0TDOUdoAAHBhYGDAaczT09NAEmAYV34AADBix44dWrt2rdrb22W3253mm5ubDaQChrHSBgDAiOTkZEVHRyslJUU%2BPj5O89HR0QZSAcMobQAAjIiMjFRDQ4PTlR/AlYArPwAAGBEdHa29e/eajgG4xEobAAAjPv/8c3300UdKTExUcHCw3Nwc1zbuu%2B8%2BQ8kAShsAAKOSkpLOOmez2VRVVTWGaQBHlDYAwLi2f/9%2BhYaGSpJ%2B/fVX2Wy2sz4bFhY2VrEAJ5Q2AMC4Nn36dO3Zs0eSFB4e7rK0nTx5UjabjSs/YBSlDQAwrnV2diowMFCS1NHRcc5ng4KCxiIS4BKlDQAAwAK48gMAAMACKG0AAAAWQGkDAACwAEobAACABVDaAAAALIDSBgAAYAGUNgAAAAv4L8Fz20yN3DDPAAAAAElFTkSuQmCC\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>length</th>\n",
       "      <th>fs</th>\n",
       "      <th>ind_numb</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[22, 101, 83, 9, 27, 83, 63, 43, 38, 61, 45, 3...</td>\n",
       "      <td>30372</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>[9, 3, 13, 13, 11, 15, 17, 15, 17, 24, 22, 22,...</td>\n",
       "      <td>25780</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[0, 1, -5, -1, -1, -1, -3, -1, -5, 0, -1, -9, ...</td>\n",
       "      <td>30045</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[23, 82, 88, -12, -114, -206, -112, -10, 59, 8...</td>\n",
       "      <td>32100</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>[2, -10, -5, 0, 7, -5, 2, -10, -12, 0, -15, -1...</td>\n",
       "      <td>23037</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x7fb5e117c710>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_file = pandas_profiling.ProfileReport(data_complete)\n",
    "profile_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    127\n",
       "1     81\n",
       "6     79\n",
       "4     71\n",
       "3     69\n",
       "5     62\n",
       "2     46\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_complete.y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       535.000000\n",
       "mean      44473.785047\n",
       "std       16451.022909\n",
       "min       19608.000000\n",
       "25%       32425.500000\n",
       "50%       41443.000000\n",
       "75%       52934.500000\n",
       "max      143652.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_complete.length.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Comments\n",
    "1. **Sampling rate** is constant 16 kHz. This is nice so we do not have to handle variable sampling rate signals.\n",
    "2. The **length** of each audio-sample **varies**. So we need to pad or cut signals. \n",
    "3. Label 0 (Anger) has the double of samples than the rest (**unbalanced data-set**). We may have to do data-augmentation to balance the data set.\n",
    "4. Signals are quite **long** and are **audio** signals. It is a good idea to grab first some frequency-related features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First\n",
    "Let us first remove the signals which have the longest (1%) length and the shortest length (1%) so we are sure we do not hold possible outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_complete_pr = data_complete[(data_complete.length > data_complete.length.quantile(.01))&(data_complete.length < data_complete.length.quantile(.99))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "        .variablerow {\n",
       "            border: 1px solid #e1e1e8;\n",
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       "            border: 1px solid #e1e1e8;\n",
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       "            margin-left: 0;\n",
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       "\n",
       "        .ignore {\n",
       "            opacity: 0.4;\n",
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       "\n",
       "        .container.pandas-profiling {\n",
       "            max-width:975px;\n",
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       "\n",
       "        .col-md-12 {\n",
       "            padding-left: 2em;\n",
       "        }\n",
       "\n",
       "        .indent {\n",
       "            margin-left: 1em;\n",
       "        }\n",
       "\n",
       "        .center-img {\n",
       "            margin-left: auto !important;\n",
       "            margin-right: auto !important;\n",
       "            display: block;\n",
       "        }\n",
       "\n",
       "        /* Table example_values */\n",
       "            table.example_values {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .example_values th {\n",
       "                border: 0;\n",
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       "                color: #555;\n",
       "                font-weight: 600;\n",
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       "\n",
       "            .example_values tr, .example_values td{\n",
       "                border: 0;\n",
       "                padding: 0;\n",
       "                color: #555;\n",
       "            }\n",
       "\n",
       "        /* STATS */\n",
       "            table.stats {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .stats th {\n",
       "                border: 0;\n",
       "                padding: 0 2em 0 0;\n",
       "                color: #555;\n",
       "                font-weight: 600;\n",
       "            }\n",
       "\n",
       "            .stats tr {\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "            .stats td{\n",
       "                color: #555;\n",
       "                padding: 1px;\n",
       "                border: 0;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Sample table */\n",
       "            table.sample {\n",
       "                border: 0;\n",
       "                margin-bottom: 2em;\n",
       "                margin-left:1em;\n",
       "            }\n",
       "            .sample tr {\n",
       "                border:0;\n",
       "            }\n",
       "            .sample td, .sample th{\n",
       "                padding: 0.5em;\n",
       "                white-space: nowrap;\n",
       "                border: none;\n",
       "\n",
       "            }\n",
       "\n",
       "            .sample thead {\n",
       "                border-top: 0;\n",
       "                border-bottom: 2px solid #ddd;\n",
       "            }\n",
       "\n",
       "            .sample td {\n",
       "                width:100%;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* There is no good solution available to make the divs equal height and then center ... */\n",
       "            .histogram {\n",
       "                margin-top: 3em;\n",
       "            }\n",
       "        /* Freq table */\n",
       "\n",
       "            table.freq {\n",
       "                margin-bottom: 2em;\n",
       "                border: 0;\n",
       "            }\n",
       "            table.freq th, table.freq tr, table.freq td {\n",
       "                border: 0;\n",
       "                padding: 0;\n",
       "            }\n",
       "\n",
       "            .freq thead {\n",
       "                font-weight: 600;\n",
       "                white-space: nowrap;\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "\n",
       "            }\n",
       "\n",
       "            td.fillremaining{\n",
       "                width:auto;\n",
       "                max-width: none;\n",
       "            }\n",
       "\n",
       "            td.number, th.number {\n",
       "                text-align:right ;\n",
       "            }\n",
       "\n",
       "        /* Freq mini */\n",
       "            .freq.mini td{\n",
       "                width: 50%;\n",
       "                padding: 1px;\n",
       "                font-size: 12px;\n",
       "\n",
       "            }\n",
       "            table.freq.mini {\n",
       "                 width:100%;\n",
       "            }\n",
       "            .freq.mini th {\n",
       "                overflow: hidden;\n",
       "                text-overflow: ellipsis;\n",
       "                white-space: nowrap;\n",
       "                max-width: 5em;\n",
       "                font-weight: 400;\n",
       "                text-align:right;\n",
       "                padding-right: 0.5em;\n",
       "            }\n",
       "\n",
       "            .missing {\n",
       "                color: #a94442;\n",
       "            }\n",
       "            .alert, .alert > th, .alert > td {\n",
       "                color: #a94442;\n",
       "            }\n",
       "\n",
       "\n",
       "        /* Bars in tables */\n",
       "            .freq .bar{\n",
       "                float: left;\n",
       "                width: 0;\n",
       "                height: 100%;\n",
       "                line-height: 20px;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                background-color: #337ab7;\n",
       "                border-radius: 3px;\n",
       "                margin-right: 4px;\n",
       "            }\n",
       "            .other .bar {\n",
       "                background-color: #999;\n",
       "            }\n",
       "            .missing .bar{\n",
       "                background-color: #a94442;\n",
       "            }\n",
       "            .tooltip-inner {\n",
       "                width: 100%;\n",
       "                white-space: nowrap;\n",
       "                text-align:left;\n",
       "            }\n",
       "\n",
       "            .extrapadding{\n",
       "                padding: 2em;\n",
       "            }\n",
       "\n",
       "            .pp-anchor{\n",
       "\n",
       "            }\n",
       "\n",
       "</style>\n",
       "\n",
       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>7 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>523 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>0.0% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>28.7 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>56.2 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>3 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>2 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>1 </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_fs\"><code>fs</code></a> has constant value 16000 <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_ind_numb\"><code>ind_numb</code></a> is highly correlated with <a href=\"#pp_var_index\"><code>index</code></a> (ρ = 0.95108) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_x\"><code>x</code></a> is an unsupported type, check if it needs cleaning or further analysis <span class=\"label label-warning\">Warning</span></li><li><a href=\"#pp_var_y\"><code>y</code></a> has 126 / 24.1% zeros <span class=\"label label-info\">Zeros</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_fs\"><s>fs</s><br/>\n",
       "            <small>Constant</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is constant and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Constant value</th>\n",
       "            <td>16000</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_ind_numb\"><s>ind_numb</s><br/>\n",
       "            <small>Highly correlated</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is highly correlated with <a href=\"#pp_var_index\"><code>index</code></a> and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Correlation</th>\n",
       "            <td>0.95108</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_index\">index<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>523</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>267.57</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>534</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.2%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-7595120582340782254\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAASJJREFUeJzt2NFpAzEUAEHbpKQUkZ7y7Z5ShHtSGggLZzjfc27mX/AQWgl0XWutC/Cn29EDwGQfRw9wlM/vn81rHvevHSZhsnGBTD64W2ebGtQze/wq0/ZsXCBnN/mCOCOB7OhVN/XkF%2BHd/YtAHBD24hcLgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIFzXWuvoIWAqLwgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAiEXyClG8yJhh3IAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-7595120582340782254,#minihistogram-7595120582340782254\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-7595120582340782254\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-7595120582340782254\"\n",
       "                                                  aria-controls=\"quantiles-7595120582340782254\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-7595120582340782254\" aria-controls=\"histogram-7595120582340782254\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-7595120582340782254\" aria-controls=\"common-7595120582340782254\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-7595120582340782254\" aria-controls=\"extreme-7595120582340782254\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-7595120582340782254\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>27.1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>134.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>267</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>402.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>507.9</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>534</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>534</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>268</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>154.88</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.57885</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.2073</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>267.57</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>134.24</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.00049504</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>139940</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>23989</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>4.2 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-7595120582340782254\">\n",
       "            <img 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7hggUL9Mwzz5z2eQoLCzVkyBDV19cH1o4/YT4pKUnFxcVBty8uLlb37t1P%2B/wxMTHq1q1b0Ed09C9VV1dv9ePo0fpTDwMAgAvYvo88/uEUVwXWf//7X40fP16SAoElSddff71KS0tP%2BzwJCQk6ePCgFi5cqJqaGu3fv19LlixRnz59NH78eJWVlSk/P1%2BHDx9WQUGBCgoKNHbsWOs/DwAAaJpcFVgtW7Y84d8grKioUPPmzc/oPM8//7yKi4t19dVXa9iwYWrZsqX%2B%2BMc/qm3btlq6dKlWrFih3r17a/78%2BVq4cKG6du1q80cBAABNmKueg9WrVy/Nnz9fDz30UGBt9%2B7devjhh9W/f/8zOleXLl20fPnyEx7r27evVq9e3ahZAQBA6HJVYD344IOaMGGCkpOTdfToUfXq1Us1NTW68sortWDBAqfHAwAAkOSywGrfvr3WrVungoIC7d69WxdeeKE6duyoa665Jug5WQAAAE5yVWBJ0gUXXKDBgwc7PQYAAMBJuSqwUlNTf/JK1ebNm8/hNAAAACfmqsC64YYbggLr6NGj2r17t4qKijRhwgQHJwMAAPieqwJrxowZJ1zftGmT3nnnnXM8DQAAwIm56n2wTmbw4MF69dVXnR4DAABAUhMJrO3bt8sY4/QYAAAAklz2EOG4ceMarNXU1Ki0tFRpaWkOTAQAANCQqwIrLi6uwasIIyIilJGRoTFjxjg0FQAAQDBXBRbv1g4AANzAVYH1yiuvnPZtR4wYcRYnAQAAODlXBdacOXNUX1/f4AntYWFhQWthYWEEFgAAcIyrAuu5557T888/r7vvvltdunSRMUY7d%2B7Us88%2Bq1tuuUXJyclOjwgAAOCuwFqwYIGWLVumdu3aBdb69OmjSy%2B9VLfffrvWrVvn4HQAAADHuOp9sD7//HO1bt26wXqrVq1UVlbmwEQAAAANuSqwYmNjtWDBAvl8vsBaVVWVFi9erMsuu8zByQAAAL7nqocIZ8%2BerenTpysvL0%2BRkZEKDw/XwYMHdeGFF%2BqZZ55xejwAAABJLgusa6%2B9Vlu3blVBQYH27t0rY4zatWun3/zmN2rZsqXT4wEAAEhyWWBJ0i9%2B8QsNGjRIe/fu1aWXXur0OAAAAA246jlYtbW1mjlzpnr27KmhQ4dKOvYcrDvuuENVVVUOTwcAAHCMqwJr4cKF2rFjhxYtWqTw8O9HP3r0qBYtWuTgZAAAAN9zVWBt2rRJTz/9tK6//vrAH31u1aqVcnJy9Nprrzk8HQAAwDGuCqzq6mrFxcU1WI%2BOjtahQ4fO/UAAAAAn4KrAuuyyy/TOO%2B9IUtDfHty4caN%2B9atfOTUWAABAEFe9ivDmm2/Wvffeq9GjR6u%2Bvl5/%2BctfVFxcrE2bNmnOnDlOjwcAACDJZYGVmZkpj8ejFStWqFmzZvrzn/%2Bsjh07atGiRbr%2B%2BuudHg8AAECSywJr//79Gj16tEaPHu30KAAAACflqudgDRo0KOi5VwAAAOcjVwVWcnKyNmzY4PQYAAAAP8lVDxFecskl%2BsMf/qBly5bpsssu0wUXXBB0fPHixQ5NBgAA8D1XBdauXbv061//WpLk8/kcngYAAODEXBFY2dnZeuKJJ7R8%2BfLA2jPPPKOsrCwHpwIAADgxVzwHa8uWLQ3Wli1b5sAkAAAAp%2BaKwDrRKwd5NSEAADhfuSKwjv9h51OtAQAAnA9cEVgAAABuQmABAABY5opXER45ckTTp08/5RrvgwUAAM4Hrgis3r17q6Ki4pRrAAAA5wNXBNYP3/8KAADgfMdzsAAAACwjsAAAACwjsAAAACwjsAAAACwjsAAAACwL2cAqKytTVlaWkpOT5fV6NWvWLFVVVUmSCgsLlZGRoV69emnYsGFas2aNw9MCAAA3CdnAuvvuu9WqVStt2bJFL730kj799FM9/vjjqqio0JQpUzRu3DgVFhZqzpw5mjt3roqKipweGQAAuERIBlZVVZUSEhI0ffp0RUZGqn379ho5cqTef/99rV27VnFxccrIyFBERIS8Xq9SU1OVn5/v9NgAAMAlXPFGo7a1atVKOTk5QWvl5eWKiYlRSUmJ4uPjg47Fx8drw4YNp33%2BiooKVVZWBq15PC0UExPz84c%2BgWbNQrKPAQBNkMfTtO7TQjKwfqyoqEgrVqzQn/70Jz333HNq165d0PE2bdrI5/Od9vny8vKUm5sbtJaVlaWpU6damRcAgKYmKirS6RGsCvnA%2BuCDDzR58mRNnz5dXq9Xzz33XKPPmZmZqdTU1KA1j6eFfL7qRp/7h7iCBQBoKmzfRx7nVLiFdGBt2bJF999/v%2BbOnasRI0ZIkqKiouT3%2B4Nu5/P5FB0dfdrnjYmJafBwYGXlAdXV1Td%2BaAAAmqCmdh8ZspdAPvzwQ82cOVNPPfVUIK4kKTExUcXFxUG3LS4uVvfu3c/1iAAAwKVCMrDq6ur00EMPacaMGbr22muDjqWnp6usrEz5%2Bfk6fPiwCgoKVFBQoLFjxzo0LQAAcJswY4xxeohz7f3339dvf/tbNW/evMGxjRs36quvvtJjjz2m0tJSxcbGavr06UpLS2vU96ysPNCorz8Rjydc1y16y/p5AQA41zZMu%2BasnPfii1uelfOeSkg%2BB6tPnz7auXPnSY/HxsZq9erV53AiAADQlITkQ4QAAABnE4EFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgWcgG1ltvvSWv16vs7OwGx9avX6/09HT17NlTo0aN0ttvv%2B3AhAAAwK08Tg/ghGeffVarVq3S5Zdf3uDYjh07NHPmTOXm5urqq6/Wpk2bdM8992jjxo1q3769A9MCAAC3CckrWBEREScNrPz8fKWkpCglJUUREREaPny4OnfurDVr1jgwKQAAcKOQDKxbb71VLVu2POGxkpISxcfHB63Fx8erqKjoXIwGAACagJB8iPCn%2BP1%2BtW7dOmitdevW2rVr12mfo6KiQpWVlUFrHk8LxcTEWJnxuGbNQrKPAQBNkMfTtO7TCKwTMMY06uvz8vKUm5sbtJaVlaWpU6c26rwAADRVUVGRTo9gFYH1I1FRUfL7/UFrfr9f0dHRp32OzMxMpaamBq15PC3k81VbmfE4rmABAJoK2/eRxzkVbgTWjyQkJKi4uDhoraioSMOGDTvtc8TExDR4OLCy8oDq6uqtzAgAQFPT1O4juQTyI2PHjtW//vUvbd26VYcPH9aqVav0%2Beefa/jw4U6PBgAAXCIkr2AlJiZKkurq6iRJr7/%2BuqRjV6o6d%2B6sRYsWKScnR2VlZerUqZOWLl2qiy%2B%2B2LF5AQCAu4RkYJ3qLRfS0tKUlpZ2jqYBAABNDQ8RAgAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWEZgAQAAWOZxeoCmqKKiQpWVlUFrHk8LxcTEWP0%2BzZrRxwCApsHjaVr3aQTWWZCXl6fc3NygtXvuuUf33nuv1e9TUVGhCe0/VWZmpvV4CyUVFRXKy8tjHxuBPbSDfbSDfWw89rDxmlYunicyMzP10ksvBX1kZmZa/z6VlZXKzc1tcLUMZ4Z9bDz20A720Q72sfHYw8bjCtZZEBMTQ/EDABDCuIIFAABgGYEFAABgWbN58%2BbNc3oI/HyRkZHq16%2BfIiMjnR7F1djHxmMP7WAf7WAfG489bJwwY4xxeggAAICmhIcIAQAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwAAAALCOwXKisrEx33nmnkpOTNXDgQC1cuFD19fVOj3Veeuutt%2BT1epWdnd3g2Pr165Wenq6ePXtq1KhRevvttwPH6uvr9cQTT2jQoEHq27evbr/9du3Zs%2Bdcjn7eKCsrU1ZWlpKTk%2BX1ejVr1ixVVVVJknbs2KFbbrlFvXv3Vlpamp5//vmgr/2pPQ41n3zyiSZMmKDevXvL6/Vq2rRpqqyslCQVFhYqIyNDvXr10rBhw7RmzZqgr33hhRc0ZMgQ9erVS%2BPHj1dxcbETP8J5Zf78%2BerSpUvgc/bw9HXp0kUJCQlKTEwMfDz66KOS2EerDFxn5MiR5qGHHjJVVVVm9%2B7dJi0tzTz//PNOj3XeWbZsmUlLSzPjxo0z06ZNCzq2fft2k5CQYLZu3Wpqa2vN6tWrTffu3U15ebkxxpgXXnjBDBw40OzatcscOHDA/P73vzfp6emmvr7eiR/FUTfeeKOZNWuWOXjwoCkvLzejRo0ys2fPNjU1NeY3v/mNWbJkiamurjbFxcWmX79%2BZtOmTcaYU%2B9xKDl8%2BLDp37%2B/yc3NNYcPHzb79u0zt9xyi5kyZYr5%2BuuvTY8ePUx%2Bfr6pra01//znP01SUpL5z3/%2BY4wxZvPmzaZPnz7mo48%2BMjU1NWbp0qXmmmuuMdXV1Q7/VM7Zvn276devn%2BncubMxxrCHZ6hz585mz549DdbZR7u4guUyRUVF%2BuSTTzRjxgy1bNlScXFxmjhxovLy8pwe7bwTERGhVatW6fLLL29wLD8/XykpKUpJSVFERISGDx%2Buzp07B/5vLS8vTxMnTtQVV1yhiy66SNnZ2SotLdXHH398rn8MR1VVVSkhIUHTp09XZGSk2rdvr5EjR%2Br999/X1q1bdeTIEU2ePFktWrRQt27dNGbMmMDv4qn2OJTU1NQoOztbd911l5o3b67o6Ghdd911%2BvTTT7V27VrFxcUpIyNDERER8nq9Sk1NVX5%2BvqRjv4ujRo1S9%2B7ddeGFF%2BqOO%2B6QJL3xxhtO/kiOqa%2Bv18MPP6yJEycG1thDO9hHuwgslykpKVFsbKxat24dWOvWrZt2796tgwcPOjjZ%2BefWW29Vy5YtT3ispKRE8fHxQWvx8fEqKipSbW2tdu3aFXT8oosu0uWXX66ioqKzOvP5plWrVsrJydEvf/nLwFp5ebliYmJUUlKiLl26qFmzZoFj8fHxgYcMfmqPQ03r1q01ZswYeTweSdJnn32ml19%2BWUOHDj3pPp1sH8PDw3XVVVeF5D5K0sqVKxUREaH09PTAGnt45hYvXqwBAwaoT58%2Bmjt3rqqrq9lHywgsl/H7/WrVqlXQ2vHY8vl8TozkSn6/PyhSpWP76PP59O2338oYc9LjoayoqEgrVqzQ5MmTT/i72KZNG/n9ftXX1//kHoeqsrIyJSQk6IYbblBiYqKmTp160n08vk/s4/e%2B%2BeYbLVmyRA8//HDQOnt4Znr06CGv16vXXntNeXl5%2Buijj/TII4%2Bwj5YRWC5kjHF6hCbhVPvIPgf74IMPdPvtt2v69Onyer0nvV1YWFjgv9nDYLGxsSoqKtLGjRv1%2Beef64EHHjitr2Mfj8nJydGoUaPUqVOnM/5a9vB7eXl5GjNmjJo3b64rrrhCM2bM0Lp163TkyJFTfi37ePoILJeJjo6W3%2B8PWvP7/QoLC1N0dLRDU7lPVFTUCfcxOjpabdq0UXh4%2BAmPt23b9lyOed7YsmWL7rzzTs2ePVu33nqrpGO/iz/%2BP1e/3x/Yv5/a41AWFhamuLg4ZWdna926dfJ4PA32yefzBfaJfTymsLBQ27ZtU1ZWVoNjJ9oj9vD0dejQQUePHj3hv3vs489HYLlMQkKCysvLtX///sBEhrlUAAADLUlEQVRaUVGROnXqpMjISAcnc5eEhIQGLy8uKipS9%2B7dFRERoSuvvFIlJSWBY1VVVfriiy%2BUlJR0rkd13IcffqiZM2fqqaee0ogRIwLrCQkJ2rlzp%2Brq6gJrx/fw%2BPGT7XGoKSws1JAhQ4LeTiU8/Ng/v0lJSQ32qbi4OGgff/i7ePToUW3fvj3k9nHNmjXat2%2BfBg4cqOTkZI0aNUqSlJycrM6dO7OHp2n79u1asGBB0FppaamaN2%2BulJQU9tEm517AiJ9rzJgxZvbs2ebAgQNm165dJjU11axYscLpsc5bM2fObPA2DTt37jSJiYnmjTfeMLW1tSY/P9/07NnTVFRUGGOM%2Bfvf/24GDBgQeJuGuXPnmtGjRzsxvqOOHDlihg4dalauXNng2OHDh83AgQPN008/bQ4dOmQ%2B%2Bugj06dPH/PGG28YY069x6GkqqrKeL1es2DBAnPo0CGzb98%2Bc/vtt5ubb77ZfPPNN6Znz57mH//4h6mtrTVbt241SUlJZseOHcYYYwoKCkzv3r3Ntm3bzKFDh8ySJUtMSkqKqampcfinOrf8fr8pLy8PfGzbts107tzZlJeXm7KyMvbwNO3du9f06NHDLF261Bw%2BfNh89tln5oYbbjCPPvoov4uWEVguVF5ebu644w6TlJRkvF6vefrpp0Py/ZlOJSEhwSQkJJiuXbuarl27Bj4/btOmTSYtLc1069bN3HTTTebdd98NHKuvrzdPPfWU6d%2B/v0lKSjKTJk0Kyfdveu%2B990znzp0De/fDjy%2B//NLs3LnTjBs3ziQkJJgBAwaYF198Mejrf2qPQ80nn3xibrnlFpOUlGSuvvpqM23aNLN3715jjDHvvvuuGT58uOnWrZtJS0sLvJfYcS%2B%2B%2BKJJSUkxCQkJZvz48Wbnzp1O/AjnlT179gTeB8sY9vBMvPvuuyYzM9P06NHD9OvXz%2BTk5Jja2trAMfbRjjBjeMYaAACATTwHCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwLL/B2WKxEJITxQeAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-7595120582340782254\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">534</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">167</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">169</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">170</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">171</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">172</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">173</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">174</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">175</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">176</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (513)</td>\n",
       "        <td class=\"number\">513</td>\n",
       "        <td class=\"number\">98.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-7595120582340782254\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">530</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">531</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">532</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">533</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">534</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_length\">length<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>518</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>99.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>43940</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>23632</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>95610</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-6242690979570606700\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAThJREFUeJzt3MFtwkAUQMEQpSSKoCfO9JQi0tNy5YCewMLxYmbullaynr5Xa/swxhhfwF3fWy8AZvaz9QJe4Xj%2Bffqav8tphZWwNyYIhOkmyJJpAGsxQSBMN0H%2Bi30LjzBBIAgEgkAgCASCQCAIBIJAIHzsOcgSz56dODd5fyYIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUDwqsmKfNb7/kwQCAKBIBAI9iCTsW%2BZiwkCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQvO6%2BA/46vx6B8JBP/U7lMMYYWy/i1pIbwX7MFpU9CASBQJjuEQtmYoJAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAuAIAQSX/edmykgAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-6242690979570606700,#minihistogram-6242690979570606700\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-6242690979570606700\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-6242690979570606700\"\n",
       "                                                  aria-controls=\"quantiles-6242690979570606700\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-6242690979570606700\" aria-controls=\"histogram-6242690979570606700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-6242690979570606700\" aria-controls=\"common-6242690979570606700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-6242690979570606700\" aria-controls=\"extreme-6242690979570606700\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-6242690979570606700\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>23632</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>25486</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>32534</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>41443</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>52723</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>71104</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>95610</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>71978</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>20188</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>14623</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.33281</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>0.93631</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>43940</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>11628</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.98282</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>22980615</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>213850000</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>4.2 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6242690979570606700\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XtU1HX%2Bx/EXl1JTuSXgWduTbabJTbxBDZVKm13MTMPQXde1LO%2BYpq2e0t/m2k/btZtpa7RbbaUV0amjuVluN7cLnS5qAaKtllsQBMGMKApy%2Bfz%2B6OfUqCXWB6aZ7/NxjsfD5zvz5fNOYJ7zHaAQY4wRAAAArAn19wYAAACCDYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgGYEFAABgWbi/N%2BAUxhjV1NSppcX4eyttLjQ0RDExnR0zr8TMTpjZafNKzOyEmZ0wb2xsV7%2B8X65gtZOQkBCFhob4exvtIjQ0xFHzSszsBE6bV2JmJ3DavO2JwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALCMwAIAALAs3N8bgHNcft/b/t7CSdk0J8PfWwAABCiuYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFhGYAEAAFgW7u8N4Ke5/L63/b0FAABwFK5gAQAAWEZgAQAAWEZgAQAAWBa0gfXmm2/K5XJp7ty5xxx78cUXNXLkSPXv319jxozRW2%2B95T3W0tKie%2B%2B9VxdffLEGDx6syZMn64svvmjPrQMAgAAXlIH1t7/9TXfccYfOPPPMY46VlJRowYIFmj9/vt59911NmjRJs2bNUkVFhSRp3bp1euGFF/TQQw/p9ddfV8%2BePTVz5kwZY9p7DAAAEKCCMrA6dOigZ5999riBlZ%2BfryFDhmjIkCHq0KGDrrrqKvXu3VsbNmyQJOXl5WnSpEk6%2B%2Byz1aVLF82dO1d79uzRRx991N5jAACAABWUgTVx4kR17dr1uMeKi4uVkJDgs5aQkKDCwkLV19dr9%2B7dPse7dOmiM888U4WFhW26ZwAAEDwc93uwPB6PIiMjfdYiIyO1e/du7du3T8aY4x53u92tfh%2BVlZWqqqryWYuNjVXHjsePPvw8hYe3/vlHWFioz99O4LSZnTavxMxO4LR525PjAkvSCb%2Bf6qd%2Bv1VeXp5Wr17tszZr1izl5OT8pPOifUVHdz7p%2B0REdGqDnfy8OW1mp80rMbMTOG3e9uC4wIqOjpbH4/FZ83g8iomJUVRUlEJDQ497/PTTT2/1%2B8jOzlZmZqbPWmxsrGprD6m5ueXHbx7tyu2ua/Vtw8JCFRHRyVH/xk6b2WnzSszshJmdMO%2BPebJsg%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%2BrZs6eysrLUoUMHuVwuZWZmKj8/39/bBgAAASLc3xvwh/j4ePXt21d5eXm66aabVF9fr82bN2vo0KEqLi5WQkKCz%2B0TEhK0adOmVp%2B/srJSVVVVPmuxsbHq2LGrlf0DxxMe3r7Pl8LCQn3%2BDnZOm1diZidw2rztyZGBFRoaqlWrVmnSpEl67LHHJElpaWmaN2%2BeZsyYofj4eJ/bR0VFye12t/r8eXl5Wr16tc/arFmzlJOT89M3D3yPS%2B56099baLUP/vcyf2/hR4uI6OTvLbQ7Zg5%2BTpu3PTgysA4fPqxp06bpsssu07Rp03Tw4EEtWbJE8%2BfPt3L%2B7OxsZWZm%2BqzFxsaqtvaQmptbrLwPIJC53XX%2B3sJJCwsLVUREJ0d9HjNz8M/shHmjozv75f06MrAKCgpUWlqqm2%2B%2BWWFhYeratatmz56tUaNG6cILL5TH4/G5vdvtVkxMTKvPHxcXp7i4uGPW3e46NTUF5wcwcDIC%2BfOgubkloPf/YzBz8HPavO3BkS%2B6Njc3q6WlRcYY79rhw4clSS6XS0VFRT63LyoqUr9%2B/dp1jwAAIHA5MrD69%2B%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%2B/srJSVVVVPmuxsbHq2LHrT988EATCwwPvuV1YWKjP307AzMHPafO2J0cGljFGknTDDTd4YyonJ0c33nijXC7XTz5/Xl6eVq9e7bM2a9Ys5eTk/ORzA8EgOrqzv7fwo0VEdPL3FtodMwc/p83bHhwZWN26dZMkRUREeNd69OghY4waGxvl8Xh8bu92uxUTE9Pq82dnZyszM9NnLTY2VrW1h9Tc3PITdg4EB7e7zt9bOGlhYaGKiOjkqM9jZg7%2BmZ0wr7%2Be0DkysLp3764uXbqopKREiYmJkqSysjKdcsopGjJkiNavX%2B9z%2B6KiIvXr16/V54%2BLi1NcXNwx6253nZqagvMDGDgZgfx50NzcEtD7/zGYOfg5bd724MgXXcPDw5WVlaUHH3xQ//3vf1VdXa0HHnhAI0eO1OjRo1VWVqb8/Hw1NDRoy5Yt2rJli6699lp/bxsAAAQIR17BkqR58%2Bbp8OHDGjt2rBobG3XppZdq0aJF6ty5s3Jzc3XHHXdoyZIl6tGjh1asWKFzzz3X31sGAAABIsQc%2BY5vtLm2eInw8vvetno%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%2BvckpKSrR%2B/Xrv25WVlZoxY4Zuu%2B02jRw5Uh9%2B%2BKGmT5%2Bus846S8nJyX7cKQAACCSOvYLV0tKiP/7xj5o0aZJ37YUXXlDPnj2VlZWlDh06yOVyKTMzU/n5%2Bf7bKAAACDiOvYL19NNPq0OHDho5cqTuu%2B8%2BSVJxcbESEhJ8bpeQkKBNmzad1LkrKytVVVXlsxYbG6uOHbv%2BtE0DQCuEh9t57hwWFurztxM4bWanzdueHBlYX3/9tVatWqUnnnjCZ93j8Sg%2BPt5nLSoqSm63%2B6TOn5eXp9WrV/uszZo1Szk5OT9uwwBwEqKjO1s9X0REJ6vnCwROm9lp87YHRwbW8uXLNWbMGPXq1UulpaXWz5%2Bdna3MzEyftdjYWNXWHlJzc4v19wcA3%2BV211k5T1hYqCIiOjnqa5fTZnbCvLafcLSW4wKroKBA27Zt08aNG485Fh0dLY/H47PmdrsVExNzUu8jLi5OcXFxx6y73XVqagrOD2AAPx%2B2v840N7c47muX02Z22rztwXGBtWHDBlVXV2vYsGGSJGOMJCk9PV3XX3/9MeFVVFSkfv36tfs%2BAQBA4HJcYC1cuFA33XST9%2B2KigplZ2dr/fr1amlpUW5urvLz83XVVVfp3Xff1ZYtW5SXl%2BfHHQMAgEDjuMCKjIxUZGSk9%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%2B%2B%2Babuvfde5ebmavPmzf7eMgAACBCODKza2lolJSVp3rx56ty5s7p3767Ro0frgw8%2B0BtvvKHGxkZNnz5dp512mhITEzV27Fjl5eX5e9sAACBAODKwIiIitHz5cnXr1s27Vl5erri4OBUXF6tPnz4KCwvzHktISFBRUZE/tgoAAAJQuL838HNQWFiotWvXas2aNdq0aZMiIiJ8jkdFRcnj8ailpUWhoSdu0srKSlVVVfmsxcbGqmPHrlb3DQDHEx5u57lzWFioz99O4LSZnTZve3J8YH344YeaPn265s2bJ5fLpU2bNh33diEhIa0%2BZ15enlavXu2zNmvWLOXk5PykvQJAa0RHd7Z6voiITlbPFwicNrPT5m0Pjg6s1157TbfccosWL16sq6%2B%2BWpIUExOjvXv3%2BtzO4/EoKiqqVVevJCk7O1uZmZk%2Ba7GxsaqtPaTm5hYreweA7%2BN211k5T1hYqCIiOjnqa5fTZnbCvLafcLSWYwNr69atWrBggVauXKkLLrjAu56UlKSnnnpKTU1NCg//5j9PYWGh%2BvXr1%2Bpzx8XFKS4u7ph1t7tOTU3B%2BQEM4OfD9teZ5uYWx33tctrMTpu3PTjyRdempiYtWrRI8%2BfP94krSRoyZIi6dOmiNWvW6NChQ/roo4/07LPPavz48X7aLQAACDSODKzt27drz549uuOOO5ScnOzzp6qqSg8%2B%2BKDeeecdpaWlac6cOZo7d66GDh3q720DAIAA4ciXCAcNGqRdu3b94G2eeuqpdtoNAAAINo68ggUAANCWCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLCCwAAADLwv29AQCAXZff97a/t9Bqm%2BZk%2BHsLQJvgChYAAIBlBBYAAIBlvEQIAEArBdLLrxIvwfoTV7AAAAAsI7AAAAAsI7AAAAAsI7AAAAAs45vcAQB%2BE2jfNA60FlewAAAALCOwAAAALOMlQgAAglQgvQQbbL%2BziytYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYAAAAlhFYx1FWVqYpU6YoPT1dw4YN04oVK9TS0uLvbQEAgAAR7u8N/Bzl5OQoMTFRr7zyiqqrqzV16lR169ZN1113nb%2B3BgAAAgBXsI5SWFionTt3av78%2Beratat69uypSZMmKS8vz99bAwAAAYIrWEcpLi5Wjx49FBkZ6V1LTEzUZ599pgMHDqhLly4nPEdlZaWqqqp81mJjY9WxY1fr%2BwUAIBiEhwfXNR8C6ygej0cRERE%2Ba0diy%2B12tyqw8vLytHr1ap%2B1wYMH65577lFcXJy9zUr64H8vs3o%2BGyorK5WXl6fs7Gzr8/5cMXPwz%2By0eSVmdsLMTpu3PQVXLlpijPlJ98/OztZzzz3n/bNixQq9//77x1zVClZVVVVavXq1Y%2BaVmNkJnDavxMxO4LR52xNXsI4SExMjj8fjs%2BbxeBQSEqKYmJhWnSMuLo5nAgAAOBhXsI6SlJSk8vJy1dTUeNcKCwvVq1cvde7c2Y87AwAAgYLAOkpCQoKSk5N1991368CBA9qzZ48effRRjR8/3t9bAwAAASLs9ttvv93fm/i5ufDCC7Vx40YtXbpU//znP5WVlaXJkycrJCTkR5%2Bzc%2BfOSktLc8xVMKfNKzGzEzhtXomZncBp87aXEPNTv6MbAAAAPniJEAAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDICCwAAwDIC6wTKyso0c%2BZMpaeny%2BVyaeHChaqtrZUklZSUaMKECRo4cKCGDx%2BuRx55xOe%2BL774okaOHKn%2B/ftrzJgxeuutt7zHWlpadO%2B99%2Briiy/W4MGDNXnyZH3xxRfe4x6PR3PmzJHL5dIFF1yg2267TfX19e0y886dO/X73/9eAwcOlMvl0pw5c1RVVSVJKigoUFZWlgYMGKARI0Zow4YNPvd9/PHHdemll2rAgAEaP368ioqKvMcaGhr0P//zP7rooouUnp6u2bNny%2B12e4%2BXlZVpypQpSk9P17Bhw7RixQq1tLS0y8xHLFu2TH369PG%2BHazz9unTR0lJSUpOTvb%2BWbp0aVDPLElr1qzRBRdcoNTUVE2aNEmlpaWSgm/m999/3%2BffNjk5WUlJSd6P7WCb94gdO3Zo4sSJGjRokDIyMjR//nzV1NQE7cxFRUWaOHGiBg4cqAsvvFAPP/yw91hbPv6c6LEP/8/gB1155ZVm4cKF5sCBA6a8vNyMGTPG3HrrrebQoUPmwgsvNKtWrTJ1dXWmqKjIpKWlmZdfftkYY8yOHTtMUlKSeeONN0x9fb1Zv3696devnykvLzfGGPP444%2BbYcOGmd27d5v9%2B/ebP/3pT2bkyJGmpaXFGGPMrFmzzJQpU0x1dbWpqKgw2dnZZunSpW0%2Bb0NDgzn//PPN6tWrTUNDg6murjYTJkwwM2bMMF999ZVJTU01%2Bfn5pr6%2B3rz99tsmJSXFfPzxx8YYY1599VUzaNAgs337dnPo0CGTm5trMjIyTF1dnTHGmOXLl5sxY8aYL7/80rjdbjNr1iwzdepU7/sePXq0WbRokamtrTWfffaZGT58uHnkkUfafOYjduzYYdLS0kzv3r2NMSao5%2B3du7f54osvjlkP5pnXrl1rLrvsMrNnzx6zf/9%2Bs3TpUrN06dKgnvm71qxZY2666aagnbexsdFkZGSYu%2B%2B%2B2zQ0NJiamhpz3XXXmZycnKCc2e12m/T0dHPXXXeZgwcPmk8%2B%2BcQMGzbMvPjii236%2BHOixz58i8D6Afv27TMLFy40VVVV3rUnnnjCDB8%2B3GzatMmcd955pqmpyXtsxYoV5vrrrzfGGLNkyRIzc%2BZMn/ONHTvW5ObmGmOMGTFihHnssce8x/bv328SEhLMtm3bTFVVlTn33HNNSUmJ9/iWLVtMamqqOXz4cJvMeoTH4zHPPPOMaWxs9K499thj5pJLLjF///vfzdVXX%2B1z%2Bzlz5pjFixcbY4yZMmWKWbZsmfdYc3OzycjIMBs3bjSNjY1m4MCB5pVXXvEe3717t%2BnTp4%2BpqKgwH3/8senbt6/xeDze408%2B%2BaS59NJL22pUH83NzWbs2LHmr3/9qzewgnne7wusYJ45MzPzuA8CwTzzEWVlZSYtLc2UlZUF7bxffvml6d27t9m9e7fP%2B/71r38dlDO//vrrJikpyecxaO3ateb6669v08efEz324Vu8RPgDIiIitHz5cnXr1s27Vl5erri4OBUXF6tPnz4KCwvzHktISPBeVi4uLlZCQoLP%2BRISElRYWKj6%2Bnrt3r3b53iXLl105plnqrCwUCUlJQoLC/N5qSoxMVEHDx7Up59%2B2lbjSpIiIyM1duxYhYeHS5I%2B/fRTPf/887r88su/d6bvmzk0NFR9%2B/ZVYWGhPv/8c%2B3fv1%2BJiYne42effbY6duyo4uJiFRcXq0ePHoqMjPQeT0xM1GeffaYDBw605ciSpKefflodOnTQyJEjvWvBPK8k3X333Ro6dKgGDRqkxYsXq66uLmhn/uqrr1RaWqp9%2B/bpiiuu8L7MU1NTE7Qzf9fKlSt1zTXX6Be/%2BEXQzhsfH6%2B%2BffsqLy9PdXV1qq6u1ubNmzV06NCgnTkkJMTn7cjISJWUlLTp48%2BJHvvwLQLrJBQWFmrt2rWaPn26PB6PIiIifI5HRUXJ4/GopaVFHo/H5xNO%2BuaD3%2B12a9%2B%2BfTLGfO9xj8ejLl26%2BHzyHLntd1/3b0tlZWVKSkrSFVdcoeTkZM2ePft7Zz6ypx%2Ba2ePxSNIx94%2BIiPAeP/pYe8389ddfa9WqVfrjH//osx6s80pSamqqXC6XNm/erLy8PG3fvl1LliwJ2pkrKiokSS%2B99JIeffRRrV%2B/XhUVFVq0aFHQznxEaWmpNm/erOuuu05S8H5ch4aGatWqVXr11Vc1YMAAuVwuNTU1ad68eUE5c//%2B/dWpUyetXLlShw4d0ueff64nn3xS%2B/bta9PHnxM99uFbBFYrffjhh5o8ebLmzZsnl8v1vbf77gelMeYHz/lDx09037bWo0cPFRYW6qWXXtLevXv1hz/8oVX3C8SZly9frjFjxqhXr14nfd9AnFeS8vLyNHbsWJ166qk6%2B%2ByzNX/%2BfG3cuFGNjY0nvG8gznzk/d5www2Kj49X9%2B7dlZOTo9dee%2B2k7v9jjvv7c3ndunUaPny4YmNjW32fQJz38OHDmjZtmi677DJ98MEH%2Bve//62uXbtq/vz5rbp/oM0cGRmpBx54QAUFBcrIyNAtt9yiUaNGea8stfc8R19NA4HVKq%2B99pqmTJmiW2%2B9VRMnTpQkxcTEHPPsxOPxKCoqSqGhoYqOjvY%2B8/nu8ZiYGO9tjnf89NNPV0xMjA4cOKDm5mafY5J0%2Bumnt8WIxxUSEqKePXtq7ty52rhxo8LDw4/Zs9vtVkxMjCT94MxHbnP08X379nlnPt59Q0JCvPdtCwUFBdq2bZtmzpx5zLHjzRPo836fM844Q83Nzcf9uAze2AHGAAAE%2BUlEQVSGmY%2B8zP/dZ949evSQMUaNjY1BOfMRL7/8sjIzM71vB%2BvHdUFBgUpLS3XzzTera9euio%2BP1%2BzZs/Wvf/0raD%2BuBw0apPz8fG3dulV5eXmKiopSfHx8mz7%2BnOixD9/iv8YJbN26VQsWLNDKlSt19dVXe9eTkpK0a9cuNTU1edcKCwvVr18/7/GjX5M%2BcrxDhw4655xzVFxc7D1WW1urzz//XCkpKerbt6%2BMMdq5c6fPfSMiInTWWWe11aiSvvkidemll/pc6j3ySZOSknLMTEVFRT4zf3em5uZm7dixQ/369dMvf/lLRUZG%2Bhz/5JNPdPjwYSUlJSkpKUnl5eXeH6mWvpm5V69e6ty5c5vMKkkbNmxQdXW1hg0bpvT0dI0ZM0aSlJ6ert69ewfdvNI3P8p%2B5513%2Bqzt2bNHp556qoYMGRKUM3fv3l1dunRRSUmJd62srEynnHJK0M4sffPj9GVlZcrIyPCuJScnB%2BW8zc3Namlp8bn6cvjwYUmSy%2BUKupkbGhr0/PPP%2B3yf19tvv63%2B/fu36ePPiR778B3t8I30AauxsdFcfvnl5umnnz7mWENDgxk2bJi5//77zcGDB8327dvNoEGDzOuvv26MMWbXrl0mOTnZvP7666a%2Bvt7k5%2Beb/v37m8rKSmPMNz9lMnToUO%2BPyS5evNhcc8013vPPmTPH3HDDDaa6utqUl5eba665xtx5551tPnNtba1xuVzmzjvvNAcPHjTV1dVm8uTJ5je/%2BY35%2BuuvTf/%2B/c0zzzxj6uvrzRtvvGFSUlK8P22yZcsWM3DgQLNt2zZz8OBBs2rVKjNkyBBz6NAhY8w3P2kyevRo8%2BWXX5qamhozdepUk5OT433fY8eONbfeeqvZv3%2B/2b17t8nMzDRr165t03k9Ho8pLy/3/tm2bZvp3bu3KS8vN2VlZUE3rzHGVFRUmNTUVJObm2saGhrMp59%2Baq644gqzdOnSoPw3PmLZsmXm4osvNnv37jVff/21yc7ONgsXLgzqmZ999lmTlpbmsxas89bU1Ji0tDRzzz33mIMHD5qamhozbdo089vf/jYoZ25ubjaZmZnmL3/5i2lsbDRvvvmm6devnykqKmrTx58TPfbhWwTWD3j//fdN7969TVJS0jF/SktLza5du8y4ceNMUlKSGTp0qFm3bp3P/V9%2B%2BWUzfPhwk5iYaEaNGmXee%2B8977GWlhazcuVKc/7555uUlBRz4403en9HiTHfhM7cuXNNamqqGTx4sFmyZIlpaGhol7l37txpJkyYYFJSUsx5551n5syZYyoqKowxxrz33nvmqquuMomJiWb48OHH/Nj7unXrzJAhQ0xSUpIZP3682bVrl/dYQ0ODuf32283gwYNN//79zc0332xqa2u9x8vLy80NN9xgUlJSjMvlMvfff7/397K0ly%2B%2B%2BML7axqMCd5533vvPZOdnW1SU1NNWlqaWb58uamvrw/qmb%2B7t9TUVLNgwQJz4MCBoJ75wQcfNCNGjDhmPVjnLSwsNBMmTDCDBg0yLpcr6L92ffzxx2b06NEmJSXFDB8%2B3GzevNl7rC0ff0702IdvhBjj5%2B/ABAAACDJ8DxYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBlBBYAAIBl/wf0xFrUMSjBKwAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6242690979570606700\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">33401</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23680</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">28915</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">33195</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">47396</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">38242</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">62823</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">26981</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">32100</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">42339</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (508)</td>\n",
       "        <td class=\"number\">508</td>\n",
       "        <td class=\"number\">97.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6242690979570606700\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">23632</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23680</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23702</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23969</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">24068</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">89943</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">90773</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">94480</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">95421</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">95610</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_text\">text<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>10</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>1.9%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.0%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable160976601350252452\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>a05</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:18%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 11.7%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        61\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>a07</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:16%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 10.5%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        55\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>a02</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:16%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 10.3%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        54\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (7)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 67.5%\">\n",
       "            353\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable160976601350252452, #minifreqtable160976601350252452\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable160976601350252452\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">a05</td>\n",
       "        <td class=\"number\">61</td>\n",
       "        <td class=\"number\">11.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a07</td>\n",
       "        <td class=\"number\">55</td>\n",
       "        <td class=\"number\">10.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:90%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a02</td>\n",
       "        <td class=\"number\">54</td>\n",
       "        <td class=\"number\">10.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:88%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b10</td>\n",
       "        <td class=\"number\">52</td>\n",
       "        <td class=\"number\">9.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:85%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a04</td>\n",
       "        <td class=\"number\">51</td>\n",
       "        <td class=\"number\">9.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:83%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b01</td>\n",
       "        <td class=\"number\">51</td>\n",
       "        <td class=\"number\">9.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:83%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b09</td>\n",
       "        <td class=\"number\">51</td>\n",
       "        <td class=\"number\">9.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:83%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b03</td>\n",
       "        <td class=\"number\">50</td>\n",
       "        <td class=\"number\">9.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:82%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">b02</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">9.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:80%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">a01</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">9.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:80%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_x\"><s>x</s><br/>\n",
       "            <small>Unsupported</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is an unsupported type, check if it needs cleaning or further analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Unsupported value</th>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_y\">y<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>1.3%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>2.6711</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>6</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>24.1%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-1477918978671312741\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAASNJREFUeJzt28EJwkAQQFEVS7IIe/JsTxZhT%2Btd5GOEkCW%2Bdw/s5WdmCTmOMcYB%2BOi09QFgZuetD/DucnssfuZ5v65wEjBBIAkEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCATCdD9M/cJPVqxlF4Ewpz28uKxYEAQCwYq1A0tXmdnWmJkJZDK/7O2sx4oFQSAQBALBHYSv/OvdyASBIBAIAoHgDrKAD3L/xwSBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAjHMcbY%2BhAwKxMEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEwgu3%2BhppIc1QjAAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-1477918978671312741,#minihistogram-1477918978671312741\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-1477918978671312741\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-1477918978671312741\"\n",
       "                                                  aria-controls=\"quantiles-1477918978671312741\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-1477918978671312741\" aria-controls=\"histogram-1477918978671312741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-1477918978671312741\" aria-controls=\"common-1477918978671312741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-1477918978671312741\" aria-controls=\"extreme-1477918978671312741\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-1477918978671312741\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>4</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>2.16</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.80865</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.381</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>2.6711</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>1.9227</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.17622</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>1397</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>4.6656</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>4.2 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-1477918978671312741\">\n",
       "            <img 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9%2B9SsdOXJEI0aM0A9/%2BEN17txZ69atU48ePbRp0yZt375dgwcP1qpVq7RmzRrdcsstBu8dAAAIZLa7RFheXq6dO3fq9ddf1/nz5zV27Fi9%2BOKLGjx4cNNthg4dqqeeekoTJky46uMkJyfrpZde%2BtZtQ4cOVWFh4VXvGwAABDfbFaz77rtPffr00ezZszVp0iR179692W1GjhzZ9LU2AAAAdmO7grVt2zYNGzbM5%2B0%2B/PDDDkgDAID/8qdv0Dj0s3FWRzDKdq/B6tevn%2BbMmaP9%2B/c3rb3wwgt69NFHm318AgAAgB3ZrmDl5%2Bfr7Nmz6tu3b9PaqFGj1NDQoNWrV1uYDAAAoHVsd4nwnXfe0a5duxQbG9u0lpiYqLVr1%2BqHP/yhhckAAABax3ZnsC5cuKCIiIhm66GhoaqtrbUgEQAAwJWxXcEaOnSoVq9erS%2B%2B%2BKJp7fPPP9eKFSu8PqoBAADArmx3iXDx4sV6%2BOGHdfvttysqKkoNDQ06f/68evfu/Z2fWwUAAGAntitYvXv31u7du/X73/9ef/3rXxUaGqo%2Bffpo%2BPDhCgsLszoeAACAT7YrWJIUHh6uMWPGWB0DAADgqtiuYP3tb3/TunXrdOLECV24cKHZ9t/97ncWpAIAAGg92xWsxYsXq7KyUsOHD1dkZKTVcQAAAK6Y7QpWaWmpfve73ykuLs7qKAAAAFfFdh/T0KNHD85cAQAAv2a7gjV79mwVFBSosbHR6igAAABXxXaXCH//%2B9/ryJEjeu2113TdddcpNNS7A7766qsWJQMA/3DvM%2B9aHaHV9sy/w%2BoIQLuwXcGKiorSiBEjrI4BAABw1WxXsPLz862OAAAA0Ca2ew2WJP35z3/W%2BvXr9eSTTzatvf/%2B%2BxYmAgAAaD3bFaySkhJNnDhR%2B/btU3FxsaSvPnx0xowZfMgoAADwC7YrWE8//bR%2B8pOfaNeuXQoJCZH01fcTrl69Whs2bLA4HQAAgG%2B2K1j/93//p%2BnTp0tSU8GSpHHjxqm8vNyqWAAAAK1mu4LVrVu3b/0OwsrKSoWHh1uQCAAA4MrYrmANGjRIq1at0rlz55rWTp48qYULF%2Br222%2B3MBkAAEDr2O5jGp588kk99NBDSk1NVX19vQYNGqTa2lrddNNNWr16tdXxAAAAfLJdwerZs6eKi4t18OBBnTx5Up07d1afPn10xx13eL0mCwAAwK5sV7AkqVOnThozZozVMQAAAK6K7QpWenp6i2eq%2BCwsAB1tyJK9VkcA4GdsV7DGjx/vVbDq6%2Bt18uRJuVwuPfTQQxYmAwAAaB3bFay8vLxvXX/jjTf0xz/%2BsYPTAAAAXDnbfUzDdxkzZox2795tdQwAAACf/KZgffTRR2psbLQ6BgAAgE%2B2u0T44IMPNlurra1VeXm57rnnHgsSAQAAXBnbFazExMRm7yKMiIhQRkaGpk2bZlEqAACA1rNdweLT2gEAgL%2BzXcF6/fXXW33bSZMmtWMSAACAq2O7grVkyRI1NDQ0e0F7SEiI11pISAgFCwAA2JLt3kX4y1/%2BUsOHD9fLL7%2BsQ4cO6U9/%2BpO2b9%2BuESNG6Pnnn9fRo0d19OhRffjhh20%2B1saNGzV8%2BHANHDhQWVlZOnXqlCSppKREGRkZGjRokO677z4VFRW1%2BVgAACB42O4M1urVq7V582YlJCQ0rQ0ZMkS9e/fWzJkzVVxcbOQ4L7/8soqKirRt2zbFx8frmWee0QsvvKBZs2bpscce05IlSzRhwgQdPnxYc%2BfOVZ8%2BfZSSkmLk2AAAILDZrmB9%2BumniomJabYeHR2tiooKY8fZsmWLFi5cqBtvvFGStHTpUknSr371KyUmJiojI0OSlJaWpvT0dO3YsYOCBQAAWsV2lwh79eql1atXq7q6ummtpqZG69at0/XXX2/kGJ9//rlOnTqlL774QuPHj1dqaqpycnJ05swZlZWVKSkpyev2SUlJKi0tNXJsAAAQ%2BGx3Bmvx4sVasGCBnE6nunbtqtDQUJ07d06dO3fWhg0bjBzj9OnTkqS9e/dq69atamxsVE5OjpYuXaoLFy54XZ6UpO7du3sVPl8qKyvldru91hyOSMXHx7c9/NeEhdmuHwcUhyPw53v5McRj6bsxm/bF8wxfF0gzsl3BGj58uN566y0dPHhQp0%2BfVmNjoxISEnTnnXeqW7duRo5x%2Bd2IjzzySFOZmjdvnh599FGlpaW1ef9Op1MFBQVea9nZ2crJyWnzvtFxYmO7Wh2hw0RHd7E6AoIUzzN8XSDNyHYFS5K6dOmiu%2B66S6dPn1bv3r2N7/%2Baa66R9NXrui7r1auXGhsbdenSJXk8Hq/bV1dXKy4urtX7z8zMVHp6uteawxGp6urzbUjdXCA1fTsy/fuyo7CwUEVHd1FNTa3q6xusjmNLPM/aF88zfF17zMiqEm%2B7gnXhwgUtX75cu3fvliSVlpaqpqZGTzzxhH7xi194laKr1bNnT0VFRenYsWPq37%2B/JKmiokKdOnXSyJEjVVhY6HX70tJSDRgwoNX7j4%2BPb3Y50O0%2Bq7o6nlj%2BJJh%2BX/X1DUF1f2EfwfS443nmWyDNyHb/NFuzZo2OHTumtWvXKjT0n/Hq6%2Bu1du1aI8dwOBzKyMjQc889p7/85S%2BqqqrShg0bNGHCBE2ePFkVFRXasWOHvvzySx08eFAHDx7UAw88YOTYAAAg8NmuYL3xxhv67//%2Bb40bN67pS5%2Bjo6OVn5%2Bvffv2GTvOggULdOedd2ratGkaM2aMEhMTtXTpUvXo0UObNm3S9u3bNXjwYK1atUpr1qzRLbfcYuzYAAAgsNnuEuH58%2BeVmJjYbD0uLk7/%2BMc/jB0nPDxcy5cv1/Lly5ttGzp0aLPLhAAAAK1luzNY119/vf74xz9Kktd3D%2B7du1f/8i//YlUsAACAVrPdGawf/ehHmjdvnqZOnaqGhgZt3bpVpaWleuONN7RkyRKr4wEAAPhku4KVmZkph8Oh7du3KywsTM8995z69OmjtWvXaty4cVbHAwAA8Ml2BevMmTOaOnWqpk6danUUAACAq2K7gnXXXXfpyJEjTe8gBADALu595l2rI8BP2O5F7qmpqdqzZ4/VMQAAAK6a7c5gfe9739PPfvYzbd68Wddff706derktX3dunUWJQMAAGgd2xWsTz75RDfeeKOkr74DEAAAwN/YpmDl5ubq6aef1ksvvdS0tmHDBmVnZ1uYCgAA4MrZ5jVYBw4caLa2efNmC5IAAAC0jW0K1tc/tb2lNQAAALuzTcH6to9l4KMaAACAP7JNwQIAAAgUFCwAAADDbPMuwkuXLmnBggU%2B1/gcLAAAYHe2KViDBw9WZWWlzzUAAAC7s03B%2BvrnXwEAAPgzXoMFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGG2%2BS5CAG1z7zPvWh2h1fbMv8PqCADQrjiDBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGBY0BesVatWqV%2B/fk0/l5SUKCMjQ4MGDdJ9992noqIiC9MBAAB/FNRflXPs2DEVFhY2/VxZWanHHntMS5Ys0YQJE3T48GHNnTtXffr0UUpKioVJAQCAPwnaM1gNDQ1avny5srKymtZ27dqlxMREZWRkKCIiQmlpaUpPT9eOHTusCwoAAPxO0J7BevXVVxUREaEJEybomWeekSSVlZUpKSnJ63ZJSUnas2fPFe27srJSbrfba83hiFR8fHzbQn9DWFjQ9uMO4XAw3/biT7Pleda%2B/OmxgPYXSM%2B3oCxYf//737V%2B/Xq99NJLXusej0cJCQlea927d1d1dfUV7d/pdKqgoMBrLTs7Wzk5OVcXGJaIje1qdYSAxWxxGY8FfF10dBerIxgTlAUrPz9fU6ZMUd%2B%2BfXXq1Cnj%2B8/MzFR6errXmsMRqerq80aPE0hN345M/77wT/40W55n7cufHgtofzU1taqvbzC6T6tKfNAVrJKSEr3//vsqLi5uti02NlYej8drrbq6WnFxcVd0jPj4%2BGaXA93us6qrM/ugQfvi99V%2BmC0u47GAr6uvbwiYx0TQFayioiJVVVVp9OjRkqTGxkZJUmpqqh5%2B%2BOFmxau0tFQDBgzo8JwAAMB/BV3BWrRokf7zP/%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%2BnEiRP6%2Bc9/rgsXLmj27Nn6wQ9%2BoLfffltPP/20Nm3apH379lkdGQAA%2BImgLFg1NTVKTk7WggUL1LVrV/Xs2VOTJ0/WoUOH9NZbb%2BnSpUuaO3euIiMj1b9/f02bNk1Op9Pq2AAAwE8EZcGKjo5Wfn6%2Brrnmmqa1zz77TPHx8SorK1O/fv0UFhbWtC0pKUmlpaVWRAUAAH7IYXUAO3C5XNq%2Bfbs2btyoPXv2KDo62mt79%2B7d5fF41NDQoNBQ3520srJSbrfba83hiFR8fLzR3GFhQdmPO4zDwXzbiz/NludZ%2B/KnxwLaXyA934K%2BYB0%2BfFhz587VggULlJaWpj179nzr7UJCQlq9T6fTqYKCAq%2B17Oxs5eTktCkrOlZsbFerIwQsZovLeCzg66Kju1gdwZigLlgHDhzQT37yEy1btkyTJk2SJMXFxenTTz/1up3H41H37t1bdfZKkjIzM5Wenu615nBEqrr6vJHclwVS07cj078v/JM/zZbnWfvyp8cC2l9NTa3q6xuM7tOqEh%2B0BevIkSNauHChnn32WQ0fPrxpPTk5Wa%2B88orq6urkcHw1HpfLpQEDBrR63/Hx8c0uB7rdZ1VXZ/ZBg/bF76v9MFtcxmMBX1df3xAwj4mg/KdZXV2dli5dqry8PK9yJUkjR45UVFSUNm7cqNraWn344YfauXOnpk%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%2BpoAADAT3AG6xvKysrUq1cvxcTENK31799fJ0%2Be1Llz5xQVFeVzH5WVlXK73V5rDkek4uPjjWYNC6MftyeHg/m2F3%2BaLc%2Bz9uVPjwW0v0B6vlGwvsHj8Sg6Otpr7XLZqq6ublXBcjqdKigo8Fp7/PHHNW/ePHNB9VWRe6jnCWVmZhovb4GisrJSTqczKGZ06GfjrvjvBNN8rhbPM9%2BC6XHE86x9VFZWav369QE1o8CpigY1Nja26e9nZmbqtdde8/qTmZlpKN0/ud1uFRQUNDtbhn9iRi0nBCHFAAAG9ElEQVRjPr4xI9%2BYUcuYj2%2BBOCPOYH1DXFycPB6P15rH41FISIji4uJatY/4%2BPiAaeAAAODKcQbrG5KTk/XZZ5/pzJkzTWsul0t9%2B/ZV165dLUwGAAD8BQXrG5KSkpSSkqJ169bp3LlzKi8v19atWzV9%2BnSrowEAAD8R9tRTTz1ldQi7ufPOO1VcXKyf/vSn2r17tzIyMjRz5kyFhIRYHa2Zrl27atiwYZxdawEzahnz8Y0Z%2BcaMWsZ8fAu0GYU0tvUV3QAAAPDCJUIAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYfqiiokKzZs1SamqqRo8erTVr1qihocHqWLbz9ttvKy0tTbm5uVZHsaWKigplZ2crNTVVaWlpWrRokWpqaqyOZSsff/yxHnroIQ0ePFhpaWmaP3%2B%2B3G631bFsadWqVerXr5/VMWyvX79%2BSk5OVkpKStOfn/70p1bHstzGjRs1fPhwDRw4UFlZWTp16pTVkdqMguWH5s2bp4SEBO3fv19bt27V/v379eKLL1ody1aef/55rVy5UjfccIPVUWxrzpw5io6O1oEDB/Taa6/pxIkT%2BvnPf251LNu4ePGiHn74YQ0bNkwlJSUqLi5WVVWV%2BPrW5o4dO6bCwkKrY/iNvXv3yuVyNf1ZtmyZ1ZEs9fLLL6uoqEjbtm3TO%2B%2B8o759%2B%2BqFF16wOlabUbD8jMvl0scff6y8vDx169ZNiYmJysrKktPptDqarURERGjnzp0UrO9QU1Oj5ORkLViwQF27dlXPnj01efJkHTp0yOpotlFbW6vc3FzNnj1b4eHhiouL0913360TJ05YHc1WGhoatHz5cmVlZVkdBX5qy5Ytys3N1Y033qioqCgtXbpUS5cutTpWm1Gw/ExZWZl69eqlmJiYprX%2B/fvr5MmTOnfunIXJ7GXGjBnq1q2b1TFsKzo6Wvn5%2Bbrmmmua1j777DPFx8dbmMpeYmJiNG3aNDkcDknSn//8Z/3mN7/Rvffea3Eye3n11VcVERGhCRMmWB3Fb6xbt06jRo3SkCFDtGzZMp0/f97qSJb5/PPPderUKX3xxRcaP368UlNTlZOTozNnzlgdrc0oWH7G4/EoOjraa%2B1y2aqurrYiEgKAy%2BXS9u3bNXfuXKuj2E5FRYWSk5M1fvx4paSkKCcnx%2BpItvH3v/9d69ev1/Lly62O4jcGDhyotLQ07du3T06nUx988IFWrFhhdSzLnD59WtJXl023bt2qwsJCnT59mjNYsEZjY6PVERBADh8%2BrJkzZ2rBggVKS0uzOo7t9OrVSy6XS3v37tWnn36q//qv/7I6km3k5%2BdrypQp6tu3r9VR/IbT6dS0adMUHh6u73//%2B8rLy1NxcbEuXrxodTRLXP7/2SOPPKKEhAT17NlT8%2BbN04EDB/Tll19anK5tKFh%2BJi4uTh6Px2vN4/EoJCREcXFxFqWCvzpw4IBmzZqlxYsXa8aMGVbHsa2QkBAlJiYqNzdXxcXFAXH5oq1KSkr0/vvvKzs72%2Boofu26665TfX29qqqqrI5iicsvU/j6lZlevXqpsbHR72dCwfIzycnJ%2Buyzz7z%2BA%2B9yudS3b1917drVwmTwN0eOHNHChQv17LPPatKkSVbHsZ2SkhKNHTvW6yNQQkO/%2Bk9mp06drIplG0VFRaqqqtLo0aOVmpqqKVOmSJJSU1O1e/dui9PZ00cffaTVq1d7rZWXlys8PDxoX//Ys2dPRUVF6dixY01rFRUV6tSpk9/PhILlZ5KSkpSSkqJ169bp3LlzKi8v19atWzV9%2BnSro8GP1NXVaenSpcrLy9Pw4cOtjmNLycnJOnfunNasWaPa2lqdOXNG69ev15AhQ3gDhaRFixbpjTfeUGFhoQoLC7V582ZJUmFhodLT0y1OZ089evSQ0%2BnU5s2bdfHiRZ08eVLPPvusMjMzFRYWZnU8SzgcDmVkZOi5557TX/7yF1VVVWnDhg2aMGFC0xtM/FVIIy/o8TunT5/WsmXL9L//%2B7%2BKiorSgw8%2BqMcff1whISFWR7ONlJQUSV8VCUlNT1SXy2VZJjs5dOiQfvzjHys8PLzZtr1796pXr14WpLKf48ePa%2BXKlTp69KgiIyP1gx/8QIsWLVJCQoLV0Wzn1KlTuuuuu3T8%2BHGro9jan/70J61bt07Hjx9XeHi4Jk%2BerNzcXEVERFgdzTIXL15Ufn6%2Bdu/erUuXLmns2LFatmyZ31%2BVoWABAAAYxiVCAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDs/wGL/vA3xLR54wAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-1477918978671312741\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">126</td>\n",
       "        <td class=\"number\">24.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">81</td>\n",
       "        <td class=\"number\">15.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:64%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">77</td>\n",
       "        <td class=\"number\">14.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:61%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:56%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">66</td>\n",
       "        <td class=\"number\">12.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:52%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">56</td>\n",
       "        <td class=\"number\">10.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:45%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">46</td>\n",
       "        <td class=\"number\">8.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:37%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-1477918978671312741\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">126</td>\n",
       "        <td class=\"number\">24.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">81</td>\n",
       "        <td class=\"number\">15.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:64%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">46</td>\n",
       "        <td class=\"number\">8.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:37%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">66</td>\n",
       "        <td class=\"number\">12.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:52%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:56%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">46</td>\n",
       "        <td class=\"number\">8.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:60%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">66</td>\n",
       "        <td class=\"number\">12.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:85%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">71</td>\n",
       "        <td class=\"number\">13.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:92%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">56</td>\n",
       "        <td class=\"number\">10.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:73%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">77</td>\n",
       "        <td class=\"number\">14.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAm0AAAIiCAYAAACaBf8HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XtYlHX%2B//EXMxgqJ0EZNNM0Fc%2B6muYxU7Eoq6Us87CZpp08oJJmdnBTs9wK0y2ttF1Nd6ulzVKX3J%2BJnbay7%2BZqiqxkoeUhFQxGBEVO8/uDnHUSZfDmduaW5%2BO65gI%2B92HefJThzes%2BTIDL5XIJAAAAfs3m6wIAAABQOZo2AAAAC6BpAwAAsACaNgAAAAugaQMAALAAmjYAAAALoGkDAACwAJo2AAAAC6BpAwAAsACaNgAAAAsI9HUBAC6u1q1bn3NZrVq1VK9ePXXo0EG33Xab4uLiFBAQcBGrAwCcSwDvPQrULKebtk6dOikqKspj2alTp7Rnzx799NNPkqQBAwbopZde0mWXXXbR6wQAeKJpA2qY003bkiVLNGjQoArXSU1N1SOPPKITJ05o7NixevTRRy9miQCACnBOG4CzDBo0SNOmTZMkvf322zp16pSPKwIAcE4bgArdcMMNevrpp3Xy5En997//VZcuXTyWb968WW%2B%2B%2Baa%2B%2BeYbOZ1OhYSEqE2bNrrjjjt06623VrhPp9OpFStW6NNPP9WPP/6ooqIihYeHq1OnTrr33nvVo0ePs7YZOHCgDh48qNdff12nTp3SwoULtX//fr3xxhu6%2BuqrJUm7d%2B/W8uXLtWXLFh05ckSBgYFyOBy65pprdPfdd1d4Ht/x48e1cuVKbdq0ST/%2B%2BKOKi4vVoEEDde3aVWPGjFHHjh091j9w4IBiY2MlSTt27FBmZqZeffVVbdu2TU6nUw6HQ7GxsUpMTFTdunUvaM4B4HxI2gBUqF69eu7P8/LyPJYtXLhQY8aM0caNGxUSEqIePXooMjJSmzdv1vTp0zV16lSVlpZ6bJOdna0hQ4botdde0549e9SuXTv17NlTl112mT7%2B%2BGONHj1aq1evPmc9e/bsUWJiomrVqqWePXuqdu3akqR///vfuuOOO/T%2B%2B%2B/r1KlTuvrqq9WlSxfl5%2BfrnXfe0V133aXNmzd77OvgwYO6/fbb9fLLL%2Bv7779XmzZt1LNnT7lcLqWkpOiuu%2B7Su%2B%2B%2Be85atm7dqpEjRyo9PV1t2rRR8%2BbNdfDgQa1atUoJCQlezzEAVIkLQI0SExPjiomJcW3cuPG862VmZrrX3bFjh3v8ww8/dMXExLi6d%2B/u%2Br//%2Bz%2BPbf7973%2B7evfu7YqJiXGtWLHCY9m8efNcMTExrri4ONfRo0fd4yUlJa5nnnnGFRMT47r66qtd%2Bfn5HtsNGDDAFRMT4xo4cKDr1VdfPavOoUOHumJiYlx/%2BMMfXKWlpR77XbhwoSsmJsZ1yy23eGwzcuRIV0xMjGvIkCGuI0eOuMdLS0tdL7/8sismJsbVoUMH1549e9zL9u/f756P/v37u5YtW%2BYqKytzL3///ffdy3ft2nW%2BqQWAC0LSBqBCH374oSQpPDxcbdu2dY%2B/%2BuqrkqRHH31U11xzjcc23bt314wZMyRJK1eu9FjmcDh08803a9KkSapfv7573G63KzExUTabTcePH9c333xTYT1lZWV64IEHzhrftWuXJGnIkCGy2f73kma32zV58mRNnjxZo0aNUlFRkSRp%2B/bt2rJliyTp%2Beefl8PhcG9js9k0adIktW/fXkVFRfrb3/5WYS2tWrXS/fff73E7lPj4eEVGRrqfAwCqG%2Be0ATjLp59%2BqqVLl0qS7rvvPgUGlr9UHDlyROnp6ZKk66%2B/vsJtBw0apICAAP3000/au3evmjdvLkm6//77z/l8derUUf369ZWdna3s7OwK1%2BnZs6dHU3ZaWFiYjh49qvXr12vKlCkey2w2myZOnOgx9q9//UtS%2BVW0LVq0OOf3kJ6eftZh1dMqOmcvICBATZo0UU5OjnJzcyvcDgCMoGkDaqilS5fqvffe8xgrKirSDz/8oP3790sqT6/uu%2B8%2B9/Ldu3e7P585c%2BY59x0YGKji4mL98MMP7qbt9P4//fRT7dy5U1lZWcrLy5Prl7sOHT9%2BXFJ5olaR6OjoCsdHjx6tBQsW6JVXXtG//vUv3XLLLerVq9c5byL8/fffSypPy87lqquuklR%2BHl1FmjZtWuF4UFCQJKm4uPic%2BwaAC0XTBtRQO3bsOGvMbrcrIiJCsbGxGjZsmK677jqP5ceOHXN/vmnTpkqf43QjJkkZGRmaOHGiDhw4cEH1hoeHVzh%2B%2BpDp0qVLlZaWprS0NElSVFSUbrzxRo0ePVpNmjQ5q6awsLBzPldoaKik8uarsLDQfdHDadxsGIAv0LQBNdT5bq57LqfP4apVq5bS0tK8fourwsJCjR8/Xj/99JOaNWumCRMmqFevXoqIiFCtWrUk/e/WHpU9d0UeeOABDR8%2BXKmpqfr000/15ZdfKjs7W3/5y1%2BUnJys559/XjfddJPX36frjHuOV3RIFgB8gaYNgNdO3wakuLhYubm57hPvK/PZZ5/pp59%2BUkBAgJYtW6Yrr7zyrHUKCwsN1RYWFqYhQ4ZoyJAhKi0t1ZdffqklS5Zo27ZteuKJJ9SzZ09FRES4E7tf38bkTKeX1a5dm1QNgN/gT0gAXouJiXF//t1333m93Q8//CCp/Fywihq2ffv26eeffzZc32l2u13XXnutVq1apejoaBUUFLgPB58%2Bl%2B3M8/N%2B7fSy8533BgAXG00bAK9FRUWpffv2kqR33nmnwnX27t2r%2BPh4961BpP%2BdI3aut8NasmSJ%2B/Nf35T3fLZv367HH39cy5cvr3D5ZZddpgYNGkj6X5J3%2Bjy93bt3V9i4lZWVuW93cu2113pdCwCYjaYNQJU89NBDkqSUlBS98cYbHud//fjjj5o8ebIyMjJ06NAh93ibNm0kSYcPH1Zqaqp7/OTJk5o3b57S0tLcb5N1ris2K2K327V69Wq99NJL%2BuSTT85avnHjRu3atUu1atVS165dJUnt2rVT3759JUmPP/64cnJy3OuXlJTohRdeUGZmpkJDQzV8%2BHCvawEAs3FOG4AqueGGG/TAAw9o2bJlmj9/vlatWqWrrrpKubm52rVrl0pLS9WuXTtNnz7dvU2XLl3Ut29fff7550pISFDnzp0VFBSknTt3KjAwUCtWrNAHH3ygbdu2adWqVfr22281fvx4de/e/by1dOjQQWPHjtXy5cv14IMPqnHjxrryyitls9l04MAB92HZJ554QlFRUe7tnn32WY0ePVppaWkaOHCgYmJiFBQUpO%2B%2B%2B065ubmqXbu2FixYcM7bjACAL9C0AaiyadOmqXfv3vrrX/%2Bqb775Rps3b1ZQUJA6dOigwYMHa%2BTIkWedwL9o0SIlJSVp06ZN2rlzpxwOh%2BLi4vTQQw%2BpadOmatiwof773/9q69at%2Bu6777y%2BMvXRRx9V9%2B7dtWbNGu3cuVPbtm1TSUmJoqKidMstt%2Bjuu%2B8%2B683uo6Oj9e677%2BqNN97Qxo0b9d1336mkpETR0dG64YYbNG7cuArPvQMAXwpwnXlsAwAAAH6Jc9oAAAAsgKYNAADAAmjaAAAALICmDQAA1Fj/%2Bte/1Lt3byUmJp53vbKyMi1cuFCxsbHq3r27xo0bp/3797uXO51OTZ06Vb1791bfvn31xBNPGH6nl1%2BjaQMAADXS66%2B/rnnz5nl1tfibb76pf/zjH1q2bJk%2B/vhjNWvWTBMnTnTfq3LWrFk6efKkUlJStHr1amVmZiopKala66VpAwAANVJQUJDeffddr5q25ORkjRkzRi1atFBISIgSExOVmZmp7du36%2BjRo0pNTVViYqIiIyMVHR2tCRMmaPXq1SouLq62erlPGwAAsKysrCxlZ2d7jEVFRcnhcFS67T333OPVcxQWFur7779Xu3bt3GMhISG68sorlZaWpuPHj8tut6t169bu5e3bt9eJEye0Z88ej3EjaNrM4uWNQX2qeXPpu%2B%2BkVq2kvXt9XU2ljjn9/5aCAQFSaKh0/Ljk73dADC884usSvGO3S/XrSz//LFXhfUl9IdtW19clVMpmC1BkZLBycgpUVubn/0klRV11ua9LqFyzZtI330i/%2BY30y7tw%2BLXjx33zvCb9Xkx%2B6SUtXrzYY2zSpElKSEiotuc4duyYXC6XwsPDPcbDw8OVm5urevXqKSQkxOOm4KfXzc3NrbY6aNpqsnr1yn8h1qvn60ouGQEB/3v4e9NmGWdOKgyz2QIUEBAgmy3AEk2bJYSHl7%2BW/uoXOi6OYcOGaeDAgR5jZ75tXXU63/sRXIz3KqBpAwAA5rOZcxq9w%2BHw6lCoEfXq1ZPNZpPT6fQYdzqdql%2B/viIjI5Wfn6/S0lLZ7Xb3MkmqX79%2BtdXBhQgAAMB8Nps5j4sgKChIrVq1Unp6unssLy9P%2B/btU6dOndS2bVu5XC5lZGS4l6elpSksLEzNmzevtjpo2gAAAH7lyJEjuvHGG933YhsxYoRWrVqlzMxM5efnKykpSW3btlXHjh0VGRmpuLg4LVq0SDk5OTp8%2BLCWLFmiO%2B%2B8U4GB1XdQk8OjAADAfBcpFauKjh07SpJKSkokSampqZLKU7Li4mLt3btXRUVFkqThw4crOztbo0aNUkFBgXr06OFxAcTcuXP11FNPKTY2VrVq1dItt9xS6Q17qyrAdTHOnKuJrHDSdJcu0tatUteu0rZtvq6mUla4etRm%2B9/Vo2Vlvq7m/Cxz9Whg4P%2BuHv3lhdVfWeHq0cBAmyIigpWbW6CSEj//TyqLXD3aubP0%2BedS377S9u2%2BrqZyvrp6NCjInP2eOmXOfv0QSRsAADCfHyZtVkPTBgAAzEfTZhgzCAAAYAEkbQAAwHwkbYYxgwAAABZA0gYAAMxH0mYYTRsAADAfTZthzCAAAIAFkLQBAADzkbQZxgwCAABYAEkbAAAwH0mbYTRtAADAfDRthjGDAAAAFkDSBgAAzEfSZhgzCAAAYAEkbQAAwHwkbYbRtAEAAPPRtBnGDAIAAFgASRsAADAfSZthzCAAAIAFkLQBAADzkbQZRtMGAADMR9NmGDMIAABgASRtAADAfCRthjGDAAAAFkDSBgAAzEfSZhhNGwAAMB9Nm2HMIAAAgAWQtAEAAPORtBnGDAIAAFiAXzdtBw8eVMeOHbV3794qb5uUlKRRo0aZUBUAAKgym82cRw3i14dHGzdurLS0NF%2BXAQAAjKphDZYZmEEAAAAL8Oum7cCBA2rdurUyMzM1cOBA/f3vf9cDDzygLl26aNCgQfr888/d63700UeKi4tTly5dNHXqVBUWFnrsa/369YqPj9dvfvMbxcbGKjk5WZJ09OhRXXPNNfrkk0/c6z788MMaP378RfkeAQCoETg8aphfHx79tT//%2Bc96/vnn1aZNG82ePVvPPvus1q9fr7y8PCUmJmr69OkaNmyYNm/erGnTpqlt27aSpLS0ND3xxBN6%2BeWX1atXL23btk3333%2B/WrVqpa5du%2BqRRx7R/Pnz1bt3b%2B3YsUOfffaZPvjgA6/rysrKUnZ2tsdYVPPmctSrV63ff7Vr08bzo5%2Bzws/m6RqtUKsCLfLjb7d7fvRjgRb4h7fbbR4f/V7nzr6uoHIxMZ4f/dn27b6uAAZY5FW73IABA9SpUydJUlxcnNasWaOysjJ9/vnnqlu3rn73u9/JZrPpuuuuU7du3VRQUCBJeu%2B999S/f3/17dtXktStWzfddNNNWrt2rbp27ao777xT69at0/Lly7VhwwY98sgjio6O9rqu5ORkLV682GNs0pQpSpgypZq%2Bc5O99ZavK/BKqK8LqILgYF9X4I36vi6gavz9jyBJEb4uoArCwur4ugTvnHFExe8tX%2B7rCioX6sNXUgv8UePvLNW0XXHFFe7Pa9eurdLSUhUXF%2Bvw4cNq1KiRbGf8h2jWrJnS09MlSfv27dPmzZvVsWNH93KXy%2BVu4gICAjRv3jzdeuut6tChg%2B66664q1TVs2DANHDjQYyzq1lullSur/D1eVG3alDdsI0dKGRm%2BrqZSxz/d6usSKmWzlTdsBQVSWZmvqzm/0KKffV2Cd%2Bz28obN6ZRKS31dzXnl2mr7uoRK2e02hYXVUV7eSZWW%2Bvl/UkkRt8b5uoTKxcSUN2xjx0q7d/u6Gv9F02aYpZo22zn%2BwYuKilT6qxfzsjN%2BY9auXVsjRozQrFmzzrnvw4cPKzAwUIcPH9bJkydVt25dr%2BtyOBxyOByegxdwmxKfyciQtm3zdRWV8vcm6ExlZRaot6TE1xVUTWmp39dcYvP3f/T/KS0tU0mJBeq10uG83butVS8s55Joex0Oh44cOSKXy%2BUey8zMdH/etGlTffvttx7bHD582N3oFRUV6fe//73mzJmjVq1aadGiRRencAAAagouRDDskvhue/furfz8fP3tb39TUVGRUlNTtf2Mv3buvPNObd26VatXr1ZRUZF27dqloUOHasOGDZKkJUuWyOFw6NZbb9UTTzyhd955x2N7AABgEE2bYZfEd9uwYUMtWLBAy5cv1zXXXKN169Zp5MiR7uUtWrTQggUL9Kc//UndunVTQkKCxo0bp8GDB2v37t1auXKlnnrqKUnlqdzYsWP15JNPqri42FffEgAAgIcA15nHFFF9AgJ8XUHlunSRtm6Vuna1xDltx5z%2B/1/VZiu/OOv4cf8/py288IivS/BOYKBUv770889%2Bf05bts37c2F9JTDQpoiIYOXmFljinLaoqy73dQmV69y5/CrXvn2tcU7b8eO%2Bed4%2BfczZ7xdfmLNfP2SpCxEAAACq08GDBzVnzhxt375ddevW1eDBgzVt2rSzLn4cO3asvv76a4%2BxkpISTZw4UZMmTdKoUaO0detWj%2B2aN2%2BudevWVVutNG0AAMB8fnr%2BWUJCgtq3b6/U1FT9/PPPevDBB9WgQQPde%2B%2B9Hust/9V9%2BPLy8jR48GBdf/317rGnn35aQ4YMMa1W/5xBAABwafHDCxHS0tKUkZGh6dOnKzQ0VM2aNdOYMWPcb3V5PosWLdL111%2Bv1q1bG6qhKkjaAACAZVX4VpJRUWffP7UC6enpaty4scLDw91j7du31969e5Wfn6%2BQkJAKt/vxxx%2B1Zs0apaameoyvX79ef/rTn3To0CF17txZc%2BfOVdOmTS/gu6oYSRsAADCfSUlbcnKyhgwZ4vHwJimTJKfTqbCwMI%2Bx0w1cbm7uObdbtmyZ7rjjDkVGRrrHWrRooVatWumtt97Spk2bFBkZqfvuu09FRUUXMFkVI2kDAACWVeFbSUZFeb19VW%2Bi4XQ6tXbtWv3zn//0GJ89e7bH13PnzlWPHj30n//8R7169arSc5wLTRsAADCfSRciVPhWkl6KjIyU0%2Bn0GHM6nQoICPBI0c60adMmNW/eXE2aNDnvvkNCQhQeHq4jR6rv9kocHgUAAObzwwsROnTooEOHDiknJ8c9lpaWppYtWyo4OLjCbTZt2qQ%2Bv7rnXH5%2BvmbPnu3RoOXk5CgnJ6fS5q4qaNoAAECN1K5dO3Xs2FELFixQfn6%2BMjMztWLFCo0YMUKSdOONN2rLli0e2%2BzatUtXXHGFx1hISIi2b9%2BuefPmyel06tixY5ozZ45at26tLl26VFu9NG0AAMB8fpi0SdJLL72krKws9enTR/fcc49uu%2B0291th7t27VydOnPBYPzs7Ww0aNDhrP0uWLJHL5VJcXJz69%2B%2Bv4uJiLVu27Kyb9BrBOW0AAKDGatiwoV5//fUKl3377bdnje3cubPCdS%2B//HItXry4Wmv7NZo2AABgPj99RwQroWkDAADmo2kzjBkEAACwAJI2AABgPpI2w5hBAAAACyBpAwAA5iNpM4ymDQAAmI%2BmzTBmEAAAwAJI2gAAgPlI2gxjBgEAACyApA0AAJiPpM0wmjYAAGA%2BmjbDmEEAAAALIGkDAADmI2kzjBkEAACwAJI2AABgPpI2w2jaAACA%2BWjaDGMGAQAALICkDQAAmI%2BkzTBmEAAAwAJI2gAAgPlI2gyjaQMAAOajaTOMGQQAALAAkjYAAGA%2BkjbDaNpMcszp8nUJlbLZpFBJxz/dqrIyX1dTufB6Ab4uoXJdukhbtyr0uq7Stm2%2Brub8Skp8XUHV1Kvn6woql3PC1xVcco4dOO7rEirlfi395%2BfWeC31dQG4YDRtAADAfCRthtG0AQAA89G0GcYMAgAAWABJGwAAMB9Jm2E0bQAAwHw0bYYxgwAAABZA0gYAAMxH0mYYMwgAAGABJG0AAMB8JG2G0bQBAADz0bQZxgwCAABYAEkbAAAwH0mbYcwgAACABZC0AQAA85G0GUbTBgAAzEfTZhgzCAAAYAEkbQAAwHwkbYYxgwAAoMY6ePCgHnjgAfXo0UMDBgzQCy%2B8oLKysrPWe/nll9W2bVt17NjR43H06FFJ0qlTp/T73/9e/fr1U48ePTR58mTl5uZWa600bQAAwHw2mzkPgxISEhQdHa3U1FStWLFCqampWrlyZYXrxsfHKy0tzePRoEEDSdLChQuVnp6u5ORkbdiwQS6XS4899pjh%2Bs5E0wYAAMznh01bWlqaMjIyNH36dIWGhqpZs2YaM2aMkpOTq7SfkpISvfvuu5owYYIaNWqkevXqaerUqfrkk0905MgRQzWeiXPaAACAZWVlZSk7O9tjLCoqSg6Ho9Jt09PT1bhxY4WHh7vH2rdvr7179yo/P18hISEe63/77bcaPny4du/erUaNGumxxx5T3759tW/fPh0/flzt27d3r9uiRQvVrl1b6enpio6ONvhdlqNpAwAA5jPpQoTk5GQtXrzYY2zSpElKSEiodFun06mwsDCPsdMNXG5urkfT1rBhQzVp0kTTpk2Tw%2BFQcnKyHnroIa1bt05Op1OSztpXWFhYtZ7XRtMGAAAsa9iwYRo4cKDHWFRUlNfbu1wur9YbOnSohg4d6v56zJgx%2BuCDD7Ru3Tr169evSvu6UDRtAADAfCYlbQ6Hw6tDoRWJjIx0p2SnOZ1OBQQEKDIystLtGzdurKysLPe6TqdTwcHB7uXHjh1T/fr1L6i2inAhAgAAMJ8fXojQoUMHHTp0SDk5Oe6xtLQ0tWzZ0qP5kqRXXnlFmzdv9hjLzMxUkyZN1KRJE4WHhys9Pd29bPfu3SoqKlKHDh0M1XgmmjYAAFAjtWvXTh07dtSCBQuUn5%2BvzMxMrVixQiNGjJAk3XjjjdqyZYuk8hRtzpw52rNnj06dOqXly5dr3759uv3222W323XXXXfptdde06FDh5Sbm6sXX3xR119/vfuWINWBw6MAAMB8fvqOCC%2B99JJmzZqlPn36KCQkRMOHD9fIkSMlSXv37tWJEyckSdOmTZNUfi6b0%2BlUy5Yt9cYbb6hhw4aSpMmTJ6ugoEDx8fEqKSnRgAEDNHv27GqtNcBl9llzNdSxY76uoHI2mxQaKh0/LlVw82e/E14vwNclVK5LF2nrVqlrV2nbNl9Xc34lJb6uwHt2u1Ra6usqKpWdc8LXJVQqMNCmiIhg5eYWqKTE/3/wL7ss1NclVMpyr6Xhla9jivnzzdlvNd/A1p%2BRtAEAAPP5adJmJTRtAADAfDRthjGDAAAAFkDSBgAAzEfSZhgzCAAAYAEkbQAAwHwkbYbRtAEAAPPRtBnGDAIAAFgASRsAADAfSZthzCAAAIAFkLQBAADzkbQZRtMGAADMR9NmWI2fwdGjR%2BsPf/iDx9iSJUs0fPhwH1UEAABwthqftN1222168cUXNWPGDNl%2B%2BSvgww8/1F133eX1PrKyspSdne0xVqdOlKKiHNVaa3U7/UePZf746dLF1xVUrk0bz4%2BoUQID/f%2BHyW63eXz0d1Z4fbLSa2lZmQ%2Bf3AoT5OdqfNN2ww03aO7cufq///s/9erVS/v371dmZqZuuukmr/eRnJysxYsXe4xNnDhJkycnVHe5pggO9nUFXtq61dcVeO%2Btt3xdwaXHbvd1BZWKiLDKD5MUFlbH1yVccqzwWnrsmK8rgBE1vmkLDg7WoEGDtG7dOvXq1Usffvih%2BvTpo8jISK/3MWzYMA0cONBjrE6dKB0/Xt3VVi%2BbrfxFpqDAx399eSn0uq6%2BLqFybdqUN2wjR0oZGb6u5vy%2B/trXFXjPbpdKS31dRaVy8wp9XUKl7HabwsLqKC/vpEpL/f8HPzDQ/zshq72W%2BgxJm2E1vmmTyg%2BRTp48WXPmzNHGjRt19913V2l7h8Mhh8PzUOixY9b54S0rs0it27b5ugLvZWRYq15Ui5ISK/wglSstLbNEvVb6PW%2BZ11JfsdI/pp9iBiX16tVLwcHB%2Bvvf/67vvvtOsbGxvi4JAADAA0mbJJvNpltvvVUvvviiYmNjVacO53oAAFCtSNoMYwZ/cdtttyk/P1%2B33nqrr0sBAAA4C0nbL37%2B%2BWc1btxYffr08XUpAABcekjaDKNpU/l91p599lmNGzfOfa82AABQjfj9aliNn8GlS5fqpptuUvfu3TVixAhflwMAAFChGp%2B0Pfjgg3rwwQd9XQYAAJc2kjbDanzTBgAALgKaNsOYQQAAAAsgaQMAAOYjaTOMGQQAALAAkjYAAGA%2BkjbDaNoAAID5aNoMYwYBAAAsgKQNAACYj6TNMGYQAADAAkjaAACA%2BUjaDKNpAwAA5qNpM4wZBAAAsACSNgAAYD6SNsOYQQAAAAsgaQMAAOYjaTOMpg0AAJiPps0wZhAAAMACSNoAAID5SNoMYwYBAECNdfDgQT3wwAPq0aOHBgwYoBdeeEFlZWUVrvv2228rLi5OXbp0UXx8vFJTU93LZs6cqXbt2qljx47uR7du3aq1VpI2AABgPj9N2hISEtS%2BfXulpqbq559/1oMPPqgGDRro3nvv9Vhvw4YNWrBggZYuXapOnTppzZo1mjp1qv75z3%2BqSZMmkqTx48crISHBtFr9cwYBAMClxWYz5ZGVlaX09HSPR1ZWllclpaWlKSMjQ9OnT1doaKiaNWumMWPGKDk5%2Bax1CwsL9fDDD%2Bvqq69WrVq1NHToUAUHB%2Bubb76p7pk6J5I2AABgWcnJyVq8eLHH2KRJk7xKvNLT09W4cWOFh4e7x9q3b69FuaklAAAgAElEQVS9e/cqPz9fISEh7vH4%2BHiPbfPy8lRQUKDo6Gj32FdffaVNmzbpxx9/VIsWLTR79mx16NDhQr%2B1s9C0AQAA85l0eHTYsGEaOHCgx1hUVJRX2zqdToWFhXmMnW7gcnNzPZq2M7lcLj355JPq3LmzrrnmGklSkyZNZLPZNGXKFAUHB2vx4sUaO3asNmzYoIiIiKp%2BWxWiaQMAAJblcDjkcDgueHuXy1Wl9YuLizVz5kx9//33WrVqlXt84sSJHus98sgjSklJUWpqqoYOHXrB9Z2Jpg0AAJjPDy9EiIyMlNPp9BhzOp0KCAhQZGTkWesXFhZqwoQJOnnypN58883zJmh2u12NGjXy%2Bvw6b/jfDAIAgEuPSRciGNGhQwcdOnRIOTk57rG0tDS1bNlSwcHBHuu6XC4lJiYqMDBQb7zxhkfD5nK5NH/%2BfGVkZLjHioqKtG/fPveVpdWBpg0AANRIp%2B%2BrtmDBAuXn5yszM1MrVqzQiBEjJEk33nijtmzZIkn6xz/%2Boe%2B//15//OMfFRQU5LGfgIAAHThwQHPmzNGRI0dUUFCgpKQk1apVS4MGDaq2ejk8CgAAzOeHh0cl6aWXXtKsWbPUp08fhYSEaPjw4Ro5cqQkae/evTpx4oQkafXq1Tp48KD7woPT4uPjNW/ePD3zzDN67rnnNGTIEOXn56tTp05auXKl6tatW221BriqegYevHLsmK8rqJzNJoWGSsePS%2Be4%2BbNfCa8X4OsSKteli7R1q9S1q7Rtm6%2BrOb%2BSEl9X4D27XSot9XUVlcrOOeHrEioVGGhTRESwcnMLVFLi/z/4l10W6usSKmW519LwytcxxebN5uy3Vy9z9uuHSNoAAID5/DRpsxKaNgAAYD6aNsNo2kwSXnjE1yVULjBQUn2FFv1sjUNlVqjxtK%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%2BpwAANQ4Nps5jxqkZn23kvbv36//9//%2Bn6/LAAAAfuDgwYN64IEH1KNHDw0YMEAvvPCCysrKKlx31apViouLU9euXTVixAjt3LnTvezUqVP6/e9/r379%2BqlHjx6aPHmycnNzq7XWGte0ffjhh9qwYYOvywAAoGbx06QtISFB0dHRSk1N1YoVK5SamqqVK1eetd5HH32kl19%2BWc8//7y%2B/PJLDRgwQA899JBOnDghSVq4cKHS09OVnJysDRs2yOVy6bHHHjNc35n88kIEp9OpefPm6auvvlJBQYF69uyp2bNnKzo6WgcOHFBsbKyWL1%2BuF154QT/88IPatGmjpKQkXXHFFZKkV155RStWrFBgYKAefPBBffrpp%2Bratavq1q3rPhSampqqb775RpJUWlqq3//%2B90pJSVFQUJBmzZqlwYMHe11vVlaWsrOzPcai7HY5oqKqaUZMYrd7fkTN0qWLryvwTps2nh/9WGCg//8dbLfbPD76PSsc/goI%2BN9Hf6/3HAnSRWHS3FT4OzgqSg6Ho9Jt09LSlJGRoRUrVig0NFShoaEaM2aMVq5cqXvvvddj3eTkZA0ZMkSdO3eWJN13331atWqVPv74Y8XFxendd9/Vc889p0aNGkmSpk6dqptvvllHjhxRdHR0tXyvftm0zZw5U4GBgfrggw9kt9v11FNP6bHHHtPy5cvd66xatUpLly5VUFCQ7rnnHv3pT3/S7NmztXHjRr322mtavny52rVrp/nz5ys9PV1du3bVuHHj9N133%2BnUqVNauHChe18pKSl69tln9eSTT2rx4sWaPXu2brjhBgUGejc9ycnJWrx4scfYpIkTlTB5cvVMiNnq1fN1BZceKzTCW7f6uoKqeestX1dQqQhfF1AFYWF1fF3Cpad2bV9XULmCAl9XUO0q/B08aZISEhIq3TY9PV2NGzdWeHi4e6x9%2B/bau3ev8vPzFRIS4rHumYGOzWZT27ZtlZaWprZt2%2Br48eNq3769e3mLFi1Uu3ZtpaenX7pNW05Ojj7%2B%2BGOtX7/ePYnTp09X//79PTrpESNGuCehb9%2B%2BSktLkyR9%2Bumn6tu3r7p16yZJmjFjhtauXXve5%2BzatauuvfZaSdKNN96opUuXKicnx6suXZKGDRumgQMHeoxF2e3Szz97tb3P2O3lDZvTKZWW%2BrqaylmlubTbrTGf3bv7ugLvtGlT3rCNHCllZPi6mvPK3fQvX5dQKbvdprCwOsrLO6nSUh%2BmLl6KqO3nyZVUnrDVri0VFkoul6%2Br8VsuBZiy3wp/B3t5pMvpdCosLMxj7HTvkZub69G0OZ1Oj%2Bbu9Lq5ublyOp2SdNa%2BwsLCqvW8Nr9r2mrVqiVJuu222zzG7Xa7Dh06pMjISElyHwqVpDp16ujUqVOSpOzsbDVt2tS9LDQ0VM2aNTvvc565r6CgIElSUVGR1zU7HI6zG7wjR6SSEq/34VOlpdapFdVn2zZfV1A1GRl%2BX3NJif83QaeVlpZZo14LlOg%2B7Ody%2BfbwYw1V4e/gKnBVodGubN2q7OtC%2BF3TVvuXePmzzz5TRMTZBxsOHDggSQoIqLhjLysrO%2Buwpq2S4%2Bjn2hcAAKgeZvWzRs5GiYyMdKdkpzmdTgUEBLhDotMiIiIqXLdVq1budZ1Op4KDg93Ljx07pvr16194gb/il7mzzWbTt99%2B6/66uLhYR44c8Wrb%2BvXr66effnJ/nZ%2Bfr71791Z7jQAAwHtlZeY8jOjQoYMOHTqknJwc91haWppatmzp0XydXjc9Pd39dWlpqf773/%2Bqc%2BfOatKkicLDwz2W7969W0VFRerQoYOxIs/gd01brVq1NHjwYCUlJenw4cMqLCzUiy%2B%2BqLFjx3oVO/bs2VOfffaZduzYocLCQj3//PPu9E4qP/x56NAh5eXlqYRDggAA1Fjt2rVTx44dtWDBAuXn5yszM1MrVqzQiBEjJJWf575lyxZJ5efSr1mzRt98841OnjypV199VZdddpn69%2B8vu92uu%2B66S6%2B99poOHTqk3Nxcvfjii7r%2B%2BuvVoEGDaqvX75o2SZo1a5auvPJK3Xzzzbr22mv1/fff65VXXvHqMOZvf/tb3XHHHbrnnnsUFxenzp07q2nTpu5tb731Vu3du1cDBgxQVlaW2d8KAACQfyZtkvTSSy8pKytLffr00T333KPbbrtNI0eOlCTt3bvXfR%2B2fv366eGHH9bUqVN1zTXX6Msvv9SyZcvcwdDkyZPVuXNnxcfHKzY2VsHBwXrmmWeMF3iGAJfZZ835QFFRkS677DL31wMGDNCECRM0dOjQi1eEl4dzfSowUKpfv/wqVyukjtX414qprHL1qJe3tPG5Ll3Kb0/StavfX4iQnZXn6xIqFRhoU0REsHJzCyxxIUJUXb/MFjzZbFKdOtLJk9a4EOFXh/0ull%2BuF6x2v1w/WCNY4Kehar7%2B%2Bmt1795dO3bsUGlpqd577z1lZ2erV69evi4NAIAay1%2BTNiuxyJ/a3uvevbsSExM1depU5eTkqEmTJlq0aJHHbT0AAMDFVdMaLDNcck2bJI0ZM0ZjxozxdRkAAADV5pJs2gAAgH8haTOOpg0AAJiOps24S%2B5CBAAAgEsRSRsAADAdSZtxJG0AAAAWQNIGAABMR9JmHE0bAAAwHU2bcRweBQAAsACSNgAAYDqSNuNI2gAAACyApA0AAJiOpM04mjYAAGA6mjbjODwKAABgASRtAADAdCRtxpG0AQAAWABJGwAAMB1Jm3E0bQAAwHQ0bcZxeBQAAMACSNoAAIDpSNqMI2kDAACwAJI2AABgOpI242jaAACA6WjajOPwKAAAgAWQtAEAANORtBlH0gYAAGABJG0AAMB0JG3G0bQBAADT0bQZx%2BFRAAAACyBpAwAApiNpM46kDQAAwAJI2gAAgOlI2oyjaTNJtq2ur0uoVKDNpghJubbaKrFZ4Kcp54SvK6hUYKBNERHBys0rVEmJn89pVp6vK/BKYOAv/083/cv/5xTVLvuE//%2BbBwZKEXWk3MIyv/8/GhUV6rPnpmkzjsOjAAAAFkDSBgAATEfSZhxJGwAAgAWQtAEAANORtBlH0wYAAExH02Ych0cBAAAsgKQNAACYjqTNOJI2AACACjidTk2dOlW9e/dW37599cQTT6iwsPCc63/44Yf67W9/qy5duiguLk7vvPOOe9nLL7%2Bstm3bqmPHjh6Po0ePel0PSRsAADCdFZO2WbNmqaioSCkpKSouLtaUKVOUlJSkJ5988qx1d%2BzYoenTp%2BvFF19U//799cUXX2jixIm66qqr1K1bN0lSfHy8/vCHP1xwPTRtAADAdGY1bVlZWcrOzvYYi4qKksPhMLTfo0ePKjU1Ve%2B//74iIyMlSRMmTNCUKVP06KOPqlatWh7rO51OPfjggxo0aJAk6brrrlNMTIy2bNnibtqMomkDAACWlZycrMWLF3uMTZo0SQkJCYb2u2vXLtntdrVu3do91r59e504cUJ79uzxGJekfv36qV%2B/fu6vS0pKlJ2drejoaPfYt99%2Bq%2BHDh2v37t1q1KiRHnvsMfXt29frmmjaAACA6cxK2oYNG6aBAwd6jEVFRRner9PpVEhIiAICAtxj4eHhkqTc3NxKt09KSlLdunU1ePBgSVLDhg3VpEkTTZs2TQ6HQ8nJyXrooYe0bt06XXXVVV7VRNMGAAAsy%2BFwXPCh0LVr12rGjBkVLktMTJTL5aryPl0ul5KSkpSSkqJVq1YpKChIkjR06FANHTrUvd6YMWP0wQcfaN26dZo6dapX%2B6ZpAwAApvPHCxHi4%2BMVHx9f4bIvvvhC%2Bfn5Ki0tld1ul1SevklS/fr1K9ymrKxMjz32mHbs2KG3335bTZo0Oe/zN27cWFlZWV7Xyy0/AACA6crKzHmYpW3btnK5XMrIyHCPpaWlKSwsTM2bN69wm2effVbfffddhQ3bK6%2B8os2bN3uMZWZmVtrYnYmmDQAA4FciIyMVFxenRYsWKScnR4cPH9aSJUt05513KjCw/EDl6NGjtX79eknSf/7zH61bt07Lli1TvXr1ztqf0%2BnUnDlztGfPHp06dUrLly/Xvn37dPvtt3tdE4dHAQCA6fzx8Ghl5s6dq6eeekqxsbGqVauWbrnlFiUmJrqX79%2B/X8eOHZMkrV69WsePH9eAAQM89tG9e3ctX75c06ZNk1R%2BLpvT6VTLli31xhtvqGHDhl7XE%2BC6kLPsUKns7OO%2BLqFSgYE2RUQEKze3QCUlFvxp8kPMafVjTqsX81n9rDSnUVGhPnvuf/7TnP3edJM5%2B/VHHB4FAACwAA6PAgAA01nx8Ki/IWkDAACwAJI2AABgOpI242jaAACA6WjajOPwKAAAgAWQtAEAANORtBlH0gYAAGABJG0AAMB0JG3G0bQBAADT0bQZx%2BFRAAAACyBpAwAApiNpM46kDQAAwAJI2gAAgOlI2oyjaQMAAKajaTOOw6MAAAAWQNIGAABMR9JmHEkbAACABZC0AQAA05G0GVdjk7avvvpK/fr10%2BDBg31dCgAAl7yyMnMeNUmNbdpWrlyp3/zmN0pJSfF1KQAAAJWqsYdH8/Pz1blzZ9lsNbZvBQDgoqlpqZgZamTTdvfdd2vLli36z3/%2Bow8//FADBw5USkqK8vPz1bRpUz3yyCO69tprfV0mAACAW41s2v76179q1KhR6ty5s1q3bq3XX39d69atU3h4uNasWaNHH31Un376qWrVquXV/rKyspSdne0xFhhYVw6Hw4zyq43dbvP4COOY0%2BrHnFYv5rP6MafeIWkzrkY2bWfKy8tTYGCg6tSpI7vdrjvuuEO33357lQ6bJicna/HixR5jEydO1OTJk6u7XFOEhdXxdQmXHOa0%2BjGn1Yv5rH7M6fnRtBlX45u2m2%2B%2BWWvXrlW/fv3Up08f9e/fXzfffHOVmrZhw4Zp4MCBHmOBgXWVm1tQ3eVWK7vdprCwOsrLO6nSUn6aqgNzWv2Y0%2BrFfFY/K81pRESwr0uAATW%2BaatXr57eeecdbd26VR9//LFeeuklvf3223rzzTcVGOjd9DgcjrMOhWZnH1dJiX//8J5WWlpmmVqtgjmtfsxp9WI%2Bqx9zen4kbcbV%2BAPwp06d0smTJ9W1a1dNmzZNKSkp2r17tzIyMnxdGgAAgFuNT9qeeeYZOZ1OzZ49WxEREUpPT1dZWZkuv/xyX5cGAMAlg6TNuBrftE2bNk1PPfWU4uLiVFJSoiuvvFILFixQZGSkr0sDAOCSQdNmXI1t2v7yl7%2B4P1%2B0aJEPKwEAAKhcjW3aAADAxUPSZlyNvxABAADACkjaAACA6UjajKNpAwAApqNpM47DowAAABZA0gYAAExH0mYcSRsAAIAFkLQBAADTkbQZR9MGAABMR9NmHIdHAQAALICmDQAAmK6szJyHmZxOp6ZOnarevXurb9%2B%2BeuKJJ1RYWFjhuu%2B9957atGmjjh07ejx27Njxy/dfpoULFyo2Nlbdu3fXuHHjtH///irVQ9MGAABMZ8WmbdasWTp58qRSUlK0evVqZWZmKikp6Zzrd%2B/eXWlpaR6PTp06SZLefPNN/eMf/9CyZcv08ccfq1mzZpo4caJcLpfX9dC0AQAA/MrRo0eVmpqqxMRERUZGKjo6WhMmTNDq1atVXFxc5f0lJydrzJgxatGihUJCQpSYmKjMzExt377d631wIQIAADCdWalYVlaWsrOzPcaioqLkcDgM7XfXrl2y2%2B1q3bq1e6x9%2B/Y6ceKE9uzZ4zF%2B2qFDh3Tvvfdq586dCgsL0%2BTJkxUfH6/CwkJ9//33ateunXvdkJAQXXnllUpLS9NvfvMbr2qiaQMAAJaVnJysxYsXe4xNmjRJCQkJhvbrdDoVEhKigIAA91h4eLgkKTc396z1IyMj1axZMz388MNq2bKlNm7cqBkzZsjhcOiqq66Sy%2BVyb3/m/ira17nQtAEAANOZlbQNGzZMAwcO9BiLioryatu1a9dqxowZFS5LTEys0vlm/fv3V//%2B/d1f33zzzdq4caPee%2B89TZ8%2BXZKqtL%2BK0LQBAADTmdW0ORyOCz4UGh8fr/j4%2BAqXffHFF8rPz1dpaansdruk8vRNkurXr%2B/V/hs3bqydO3eqXr16stls7u1PczqdXu9L4kIEAACAs7Rt21Yul0sZGRnusbS0NIWFhal58%2BZnrf/2229r/fr1HmOZmZlq0qSJgoKC1KpVK6Wnp7uX5eXlad%2B%2Bfe6rS71B0wYAAExntVt%2BREZGKi4uTosWLVJOTo4OHz6sJUuW6M4771RgYPmBytGjR7sbtaKiIj399NNKS0tTcXGxUlJS9Nlnn2n48OGSpBEjRmjVqlXKzMxUfn6%2BkpKS1LZtW3Xs2NHrmjg8CgAAUIG5c%2BfqqaeeUmxsrGrVqqVbbrlFiYmJ7uX79%2B/XsWPHJEn33HOPCgoKNGXKFGVnZ%2BuKK67QkiVL1KFDB0nS8OHDlZ2drVGjRqmgoEA9evQ46wKKygS4jJ4VhwplZx/3dQmVCgy0KSIiWLm5BSop4U3hqgNzWv2Y0%2BrFfFY/K81pVFSoz577scfM2e/8%2Bebs1x%2BRtAEAANPxhvHGcU4bAACABZC0AQAA05G0GUfSBgAAYAEkbQAAwHQkbcbRtAEAANPRtBnH4VEAAAALIGkDAACmI2kzjqQNAADAAkjaAACA6UjajKNpAwAApqNpM47DowAAABZA0gYAAExH0mYcSRsAAIAFkLSZJOqqy31dQuU6d5Y%2B/1wRt8ZJ27f7uppKHTtw3NclVMr2y59BgYHB7s/9VXhgga9L8M4vExlR2yb5%2BV/q2Sf8vEALinKE%2BbqEynXpIm3dqojYa6Vt23xdTeVcLp88LUmbcTRtAADAdDRtxvl5FgAAAACJpA0AAFwEJG3GkbQBAABYAEkbAAAwHUmbcTRtAADAdDRtxnF4FAAAwAJI2gAAgOlI2owjaQMAALAAkjYAAGA6kjbjaNoAAIDpaNqM4/AoAACABZC0AQAA05G0GUfSBgAAYAEkbQAAwHQkbcbRtAEAANPRtBnH4VEAAAALIGkDAACmI2kzjqYNAACYjqbNOA6PAgAAWABJGwAAMB1Jm3EkbQAAABZA0gYAAExH0mYcTRsAADAdTZtxHB4FAACwAJI2AABgOpI240jaAAAALICkDQAAmM6KSZvT6dTs2bP173//WzabTdddd51mzZql2rVrn7Xuk08%2BqbVr13qMlZaWKj4%2BXvPnz9fMmTO1bt062e129/KgoCBt2bLF63pI2gAAgOnKysx5mGnWrFk6efKkUlJStHr1amVmZiopKanCdefNm6e0tDT3Y9u2bbrqqqt04403utcZP368xzpVadgkmjYAAICzHD16VKmpqUpMTFRkZKSio6M1YcIErV69WsXFxZVuv3LlSl1%2B%2BeW67rrrqq0mDo8CAADTmZWKZWVlKTs722MsKipKDofD0H537dolu92u1q1bu8fat2%2BvEydOaM%2BePR7jv5aXl6fXXntNb731lsf4V199pU2bNunHH39UixYtNHv2bHXo0MHrmmjaAACAZSUnJ2vx4sUeY5MmTVJCQoKh/TqdToWEhCggIMA9Fh4eLknKzc0977Z//etf1b17d7Vq1co91qRJE9lsNk2ZMkXBwcFavHixxo4dqw0bNigiIsKrmmjaAACA6cxK2oYNG6aBAwd6jEVFRXm17dq1azVjxowKlyUmJsrlclW5ntLSUr355ptasGCBx/jEiRM9vn7kkUeUkpKi1NRUDR061Kt907QBAADTmdW0ORyOCz4UGh8fr/j4%2BAqXffHFF8rPz1dpaan7ik%2Bn0ylJql%2B//jn3%2BfXXX6uoqEjdunU773Pb7XY1atRIWVlZXtfLhQgAAAC/0rZtW7lcLmVkZLjH0tLSFBYWpubNm59zu02bNqlnz54KDPxfLuZyuTR//nyPfRUVFWnfvn1q0qSJ1zXRtAEAANNZ7ZYfkZGRiouL06JFi5STk6PDhw9ryZIluvPOO90N2ejRo7V%2B/XqP7Xbt2qUrrrjCYywgIEAHDhzQnDlzdOTIERUUFCgpKUm1atXSoEGDvK6Jpg0AAKACc%2BfOVWhoqGJjY/Xb3/5WnTp1UmJionv5/v37dezYMY9tsrOz1aBBg7P29cwzz6hZs2YaMmSIevfurV27dmnlypWqW7eu1/VwThsAADCdFd8RITQ0VC%2B%2B%2BOI5l3/00UdnjW3YsKHCdevVq6f58%2BcbqqdKSdvBgwfVsWNH7d27t8pPlJSUpFGjRlV5O7PNnDnTo2sGAADVz2qHR/1RlZK2xo0bKy0tzaxaAAAAcA4cHgUAAKaraamYGarUtB04cECxsbFav3697r//fo0fP14bN27U119/rfr162v27Nnq27evpPLjvM8995yysrJ03XXXVXhS3rnMnDlTwcHBCgwM1Pvvvy%2BbzaZx48bp/vvvlyS1bt1ar7/%2Buvr16ydJevvtt/X666/ro48%2Bcte4dOlSPffcc/rpp580ePBgjR8/XjNmzFBGRoY6duyoxYsXu%2B9s7HK5NG/ePK1Zs0YhISEaN25clQ7lVvgWGldeKUe9el7vwydiYjw/%2BjmbBS6bOV2jFWq1RpGSTt%2BNPCDA72sOtMCfwXa7zeOj3%2BvSxdcVVK5NG8%2BP/mzbNl9XAAMMvcT8%2Bc9/1vPPP682bdpo9uzZevbZZ7V%2B/Xrl5eUpMTFR06dP17Bhw7R582ZNmzZNbdu29XrfKSkpmjlzpr744gutW7dOs2bNUnx8vNc30FuzZo3eeecdZWRk6O6779aPP/6oF154QbVr19btt9%2Bu1atXa%2BzYsZKkzz77TI888oi%2B/PJLffLJJ0pISFDnzp3VqVMnr56rwrfQmDBBCVOmeP39%2BtTy5b6uwCuhvi6gCoKDfV2BN%2Br4uoCqqV3b1xVUKsJCUxoWZpFit271dQXe%2B9X7TPqlM96S6WIjaTPOUNM2YMAAd2MTFxenNWvWqKysTJ9//rnq1q2r3/3ud7LZbLruuuvUrVs3FRQUeL3vK664QrfffrskafDgwXr88cf1ww8/eN203XHHHQoNDVX37t0VGhqqPn36uG9g16lTJ/3www/udR0Oh0aMGCFJuuGGG9S2bVt99tlnXjdtFb6FxrBh0t//7tX2PhMTU96wjR0r7d7t62oqdfyfn/u6hErZbOUNW0GB/79AhQae9HUJ3gkIKG/YCgulC3hLmYspt9DP/9FVnrCFhdVRXt5JlZb6f70Rsdf6uoTKtWlT3rCNHCmdcfNUePL310QrMNS0nXnzuNq1a6u0tFTFxcU6fPiwGjVqJNsZhzKaNWum9PT0C9p3nTrlfxEWFhZ6vX2jRo3cnwcFBSk6Otrj66KiIvfXLVu29Ni2adOmOnLkiNfPVeFbaPz4Y/nDCnbvlrZv93UVlbLSD7wlrmry%2BwJ/cfp1xOXy%2B5pLSvy7vjOVlpZZo14rHc7LyLBWvbAcQ02b7RznlxQVFam0tNRjrKyKL7bn2ndFKtp3wK8i4PPt79fLXC6XgoKCvH5%2BAABwfn7%2BN5clmHImqsPh0IaWEpcAABP1SURBVJEjR%2BQ641BGZmZmte3/sssu80jd9u3bZ2h/v77v3L59%2By74zWcBAADMYErT1rt3b%2BXn5%2Btvf/ubioqKlJqaqu3VePitWbNmSk1NVUlJidLS0vTJJ58Y2t%2BBAwf0/vvvq7i4WJs2bdK3336r66%2B/vnqKBQAA3Fy3GpjStDVs2FALFizQ8uXLdc0112jdunUaOXJkte3/8ccf17Zt29StWzf98Y9/dF8FeqHi4uK0fft29ezZU08//bTmzp2r5s2bV1O1AACAps24AJfLzy/HsqpQC9ygonNn6fPPpb59LXEhwrEDx31dQqVstvJ/%2BuPH/f/FJDzQ%2B6u5fcpmk%2BrUkU6e9PtJzT7h3/VJUmCgTRERwcrNLbDEhQhRjjBfl1C5Ll3Kb03Stas1LkTw0a/99u3N2W8VrnG0PAvcChIAAFidn//NZQkXvWl7%2Bumn9c4775xz%2Bfjx4zVhwoSLWBEAAID/u%2BhN26xZszRr1qyL/bQAAMCHSNqM4/AoAAAwHU2bcRZ5x2AAAICajaQNAACYjqTNOJI2AAAACyBpAwAApiNpM46mDQAAmI6mzTgOjwIAAFgASRsAADAdSZtxJG0AAAAWQNIGAABMR9JmHE0bAAAwHU2bcRweBQAAsACSNgAAYDqSNuNo2gAAgOlo2ozj8CgAAIAFkLQBAADTkbQZR9IGAABgASRtAADAdCRt/7%2B9Ow%2BKsv7jAP5eYJAjMQ9ABSRKR02Ny1lEDIVCzAFBByUsrTxmHK%2BRLTWwNBvDLlEHNa8yyT9EDUXJEQEVQcdrRDkETQV1xFgwkDWBhV1%2BfzRt7Q9U5PDrs8/7NbMzy/f76L7nEfXD93raj0UbERERdToWbe3H6VEiIiIiCeBIGxEREXU6jrS1H0faiIiIiCSAI21ERETU6TjS1n4s2oiIiKjTsWhrP06PEhEREUkAR9qIiIio03Gkrf040kZEREQkASzaiIiIqNPp9Z3z6mz5%2BfkICgrClClTnnptYmIigoOD4eXlhaioKBQUFBj66uvrsXz5cvj7%2B8PHxwcLFy5EVVXVM2Vh0UZERESdTopF28GDB7FgwQK4uro%2B9dpjx44hISEB3377LU6fPo2AgADMmTMHjx49AgCsXbsWhYWFSEpKQlpaGpqamhATE/NMeVi0EREREbWgvr4eSUlJcHd3f%2Bq1SUlJmDRpEtzd3WFlZYVZs2YBAI4fP47Gxkbs27cPc%2BfORZ8%2BffDyyy9j0aJFOHHiBMrLy1udhxsRiIiIqNN11qiYWq1GRUWFUZu9vT0cHBza/XtPnjy51dcWFhZi/Pjxhq/NzMwwePBg5OfnY/DgwdBoNBgyZIih/7XXXoOVlRUKCwvh6OjYqs9g0dZZNBrRCZ5KrVYjKSEBkcnJHfLN3dm6iQ7QCmq1Gj//nITIyEgJ3FNb0QFaRa1WI2n7dkncU3sJ3FK1Wo2EhJ8kcT8BAE1NohM8leHf0iNHpHFPBemsP8qEhCRs2LDBqG3%2B/PlYsGBB53zgY1RXV6NbN%2BP/qbp164aqqipUV1cDAOzs7Iz67ezsnmldG6dHZayiogIbNmxo9hMKtR3vacfjPe1YvJ8dj/dUrMjISCQnJxu9IiMjW/VrU1JSMHDgwBZfycnJz5yl6SmV6dP6n4YjbURERCRZDg4ObR7hDAsLQ1hYWIfk6N69u2FE7R/V1dUYMGAAevToYfja1vbfIfkHDx6gZ8%2Berf4MjrQRERERtdPQoUNRWFho%2BFqn0%2BHKlStwd3eHi4sLunXrZtR/7do1aLVaDB06tNWfwaKNiIiIqA3GjRuHCxcuAACioqJw4MABXLp0CbW1tfjhhx9gaWmJMWPGwNzcHFOmTMHmzZtx7949VFVVIT4%2BHkFBQejVq1erP4/TozJmb2%2BP%2BfPnw97eXnQUk8F72vF4TzsW72fH4z01XcHBwSgrK4NOp4Ner8ewYcMAAEeOHIGTkxNKSkoM57D5%2B/tDpVJh0aJFuH//PoYNG4atW7fCysoKALBw4UL89ddfCAsLQ2NjIwICAvDFF188Ux5FU3tXxRERERFRp%2BP0KBEREZEEsGgjIiIikgAWbUREREQSwKKNiIiISAJYtBERERFJAIs2IiIiIglg0UZEREQkASzaiIiIiCSARRsRERGRBLBoI2qHqKgo7N69G9XV1aKjEBGRieNjrMhAp9PB3NxcdAxJ2bhxI9LT03Hjxg34%2BflhwoQJeOutt9ClSxfR0SRNr9fjzz//hFarbdbXt29fAYmk779/v/V6PYqLi9GnTx90795dcDIiai0WbTIzb948rFy5Er169TJqz8vLw2effYaDBw8KSiZtd%2B7cQXp6OtLT03H9%2BnW8/fbbCA0NxciRI0VHk5zDhw9j5cqVqKmpMWpvamqCQqFAUVGRoGTSlZOTg08//RQ5OTlobGzE%2B%2B%2B/j6tXrwIA4uPjERAQIDihdBUUFCArKwvl5eXo0qULHB0dERQUBFdXV9HRyASxaJMZlUqFnJwcLF68GJMnT0ZdXR3Wrl2LPXv2YMaMGViwYIHoiJKm1Wqxb98%2BxMfH4%2BHDh3BycsLs2bPx7rvvio4mGX5%2BfoiIiMC4ceNaHLF89dVXBaSStvDwcEyfPh2TJk1CSkoKEhIScOjQIRQUFGD16tVITk4WHVGSdu3ahVWrVmHw4MFwcXEBANy%2BfRvXrl3Dl19%2BiYiICMEJydSwaJOhCxcuIC4uDjY2Nvjjjz/Qv39/LFu2zPCPDj27M2fO4NChQzh69ChsbW0REhKC8PBwVFZWYvXq1VAqlVi2bJnomJLg7e2Ns2fPwsLCQnQUk%2BHp6YmLFy9CoVAgOjoa/fr1Q3R0NADAy8sLFy9eFJxQmgIDAxETE4OgoCCj9szMTHz11Vc4duyYoGRkqrgRQYY8PDwQHByM/Px8VFVVITQ0lAVbG33zzTcYPXo05s6di4aGBqxfvx7Hjx/HJ598gv79%2B2PEiBHYtm0bDhw4IDqqZISEhODcuXOiY5gUW1tb1NTUoK6uDqdOnTJMh1ZVVbE4bgeNRoMxY8Y0ax89enSz6X2ijsC/rTKTlZWFuLg49O7dG4cOHcK9e/ewYsUK7NmzBytWrODU0zMqKipCdHQ0xo4dCxsbmxavcXBwwOzZs59zMmmJj483vLexsUFMTAy8vLzg7OwMhUJhdK1KpXre8SQvNDQUH3zwAczNzeHm5gYPDw/U1dVhxYoV8PPzEx1PssLCwrB//35MmTLFqD01NRUTJkwQlIpMGadHZcbHxweLFy82Wmuh1WqxceNGJCYmIjc3V2A6kqtp06a16jqFQoHExMROTmMaQkJCkJqaCgAIDg7GggULUFNTg5CQENjZ2UGr1WLVqlVYvHgxunbtKjitdHz88ceG93q9HtnZ2XBwcED//v2hUChQUlKCu3fvYuzYsVi9erXApGSKWLTJzP3799GzZ88W%2B37//XcMGDDgOSciMvbPLtH/p9PpUFFRgd69ewtIJT2jR4%2BGp6cn%2BvXrh59%2B%2BgkzZsx47LUcvWy9mJiYVl/Loo06Gos2Gbp06RJSUlJQXl6OTZs2Qa/X4%2BjRoxg3bpzoaERwd3fH5cuXm7VrNBoEBgbi/PnzAlJJz9mzZ7Fz5048fPgQ58%2Bfx/Dhw1u8jqOXRNLBNW0ys2fPHnz99dd45513kJ2dDQCoqKhAXFwc1Go1pk%2BfLjghyVVaWhrS0tLQ0NBgNAX1j7KyMh7%2B/Ax8fHzg4%2BMD4O/p519%2B%2BUVwItOj0Wjw66%2B/orS0FPX19UZ9CoUCcXFxgpKRqeLuUZnZtm0btm3bhq%2B%2B%2BsowBeXo6IgtW7Zg165dgtORnL3%2B%2BusYMmQIAMDS0rLZa%2BDAgdiwYYPglNLEgq1zzJs3Dz/%2B%2BCMqKipQX19v9KqrqxMdj0wQR9pkprKyEl5eXgBgtG6of//%2BUKvVomIRwcXFBTNnzoRCoXji%2BiuiF0V%2Bfj4yMjIeu06YqKOxaJMZV1dXnDlzBr6%2BvkbtqampfKYjvRAePXr02BE1MzMzODo6wtfXl9%2BvJNzAgQObTYsSdSZuRJCZw4cPY9myZQgMDMSRI0cQFRWFq1evIjc3F2vWrEFwcLDoiCRzM2bMQGFhIerq6vDKK6/AzMwMJSUlsLa2houLCyorK1FZWYn169fzmZkk1J07dxATEwM/Pz84OjrCzMx4xVF4eLigZGSqWLTJUH5%2BPvbv34/bt2/DysoKLi4umDx5Mg/WpRfCzp07cePGDSxZsgQvvfQSAODhw4f47rvv4OHhgYkTJyI5ORk///wzDh48KDgtyVlsbCySk5PRtWvXFp%2BTm5OTIyAVmTIWbUT0QvHz80NmZiasrKyM2uvr6zFu3DgcP34cer0e3t7ePAyahPLw8MD27dsfe5wKUUfjmjYZmDZtWouHlbaE5zWRaA0NDbhy5Yphw8w/rl69iurqagBAQUEBevToISIekUHfvn0xaNAg0TFIRli0yYCHh4fhfW1tLVJSUuDt7Q03Nzfo9Xpcv34deXl5mDp1qsCURH/78MMP8dFHH%2BHNN9%2BEk5MTLCwsUFZWhqysLERERECr1WL69OmIjo4WHZVkbunSpVi%2BfDkiIiJaXNPm5uYmKBmZKk6PyoxKpUJ4eDj8/f2N2jMyMpCamop169YJSkb0r/T0dJw8eRIVFRXQ6/Xo2bMnfHx8EBYWBoVCgQsXLnBKioRraZRNoVAYHsVWVFQkIBWZMhZtMuPl5YVz587BwsJ4kLWhoQFKpZJrhIiIWunu3btP7HdycnpOSUguOD0qMw4ODkhKSsJ7771n1J6cnAx7e3tBqYj%2BVVZWhu3bt%2BPGjRstnoG1e/duAamImmNRRs8bizaZWbJkCVQqFTZt2oQ%2BffpAp9OhvLwcGo0G8fHxouMRQaVSoba2FqNGjYK1tbXoOESPFRgY%2BMRNXpmZmc8xDckBp0dlSKPRIDs7G%2BXl5dBqtXBwcMDIkSPh6OgoOhoRPD09kZWVBTs7O9FRiJ7o/0d9dTodbt%2B%2BjRMnTmDWrFmYPHmyoGRkqli0yZhWq23WZmlpKSAJ0b8mTpyIbdu2oVevXqKjELVJfn4%2BEhISsHXrVtFRyMSwaJOZ06dPIy4uDqWlpdDpdM36uduJRDt9%2BjQSExMxdepUODk58RgFkhydTofhw4dzYxd1OBZtMhMUFASlUomxY8e2uF5IqVQKSEX0Lx6jQFLR0mOq6urqkJGRgdzcXKSlpQlIRaaMRZvMeHp64vz5882O/CB6UfAYBZKKln7AsLS0hKurK2JjY%2BHr6ysgFZky/s8tM0qlEsXFxRg6dKjoKEQt%2Bqcou3//Pu7du8fvVXphFRcXi45AMsORNpnZu3cvduzYgYCAADg7OzdbLxQZGSkoGdHfysvLERsbi1OnTsHCwgIFBQVQq9WYOXMmNm3aBBcXF9ERiQwqKytRWlqKurq6Zn2jRo0SkIhMGYs2mQkMDHxsn0Kh4LlCJNy8efNgY2MDlUqF4OBg5OXlobGxEWvWrMHNmzexZcsW0RGJAADbt2/H2rVrW9zUxfWX1BlYtMnArVu34OrqCgC4efPmEw%2BD5M48Es3b2xsnT56Era0t3N3dcfnyZQB/L/AePXo0zp49Kzgh0d98fX2xePFijB8/HlZWVqLjkAxwTZsMhIaGIi8vDwAwfvz4Fos27syjF4W1tTVa%2BlnywYMHLY5oEImi1%2BsRFhYGc3Nz0VFIJli0ycCRI0cM7zn9SS%2B6ESNGIDY2FtHR0QCAmpoaFBcX4/vvv8eYMWPEhiP6j4kTJyI1NRVhYWGio5BMcHqUiF4oDx48wNKlS3HixAlDm5mZGcaPH4/PP/8c3bp1ExeO6D9WrVqF3377Dc7Ozi1u7FqzZo2gZGSqWLQRkXAlJSXN2u7fvw9LS0vDo9W6dOkCgOsu6cURExPzxP7Vq1c/pyQkFyzaiEi4QYMGGZ56AIDrLsmkbN68GXPmzBEdg0wAizYiEu5pT0H4Lz4RgaTmv7ugidqDGxGISDgWYmTKODZCHcXs6ZcQERFRWz3pbEyiZ8GijYiIiEgCWLQRERERSQCLNiIiIiIJYNFGRETUibgRgToKizYiIqJONGXKFNERyETwnDYiIqJWmjZtWqt3gyYmJnZyGpIbntNGRETUSh4eHob3tbW1SElJgbe3N9zc3KDX63H9%2BnXk5eVh6tSpAlOSqeJIGxERURuoVCqEh4fD39/fqD0jIwOpqalYt26doGRkqli0ERERtYGXlxfOnTsHCwvjSauGhgYolUrk5uYKSkamihsRiIiI2sDBwQFJSUnN2pOTk2Fvby8gEZk6jrQRERG1wbFjx6BSqWBra4s%2BffpAp9OhvLwcGo0G8fHxCAoKEh2RTAyLNiIiojbSaDTIzs5GeXk5tFotHBwcMHLkSDg6OoqORiaIRRsREVE7abXaZm2WlpYCkpAp45EfREREbXD69GnExcWhtLQUOp2uWX9RUZGAVGTKONJGRETUBkFBQVAqlRg7diysra2b9SuVSgGpyJSxaCMiImoDT09PnD9/vtmRH0SdhUd%2BEBERtYFSqURxcbHoGCQjHGkjIiJqg71792LHjh0ICAiAs7MzzMyMx0EiIyMFJSNTxaKNiIioDQIDAx/bp1AokJmZ%2BRzTkBywaCMiImqlW7duwdXVFQBw8%2BZNKBSKx17r5ub2vGKRTLBoIyIiaqU33ngDeXl5AIBBgwa1WLQ1NTVBoVDwyA/qcCzaiIiIWqmsrAx9%2B/YFANy9e/eJ1zo5OT2PSCQjLNqIiIiIJIBHfhARERFJAIs2IiIiIglg0UZEREQkASzaiIiIiCSARRsRERGRBLBoIyIiIpIAFm1EREREEvA/5UYHMOIaJvoAAAAASUVORK5CYII%3D\" class=\"center-img\">\n",
       "    <img 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zzz2rdurVrLDg4WFdddZXS0tJ07Ngx%2Bfv7q0WLFq7tbdq00fHjx7Vr1y63cSNo2szixRuCeqxpU2nnTql5c8kCb8NzLM/3V/L9/KSgIKmgQPL1Fx6E5O3zdgme8feX6taVDh2SvPx%2BpOXJuSLU2yWUy2bzU0REkI4cKVBpqY9/k0qKtPv%2BnFrtudRrT04m/V5MeeklJScnu42NGTNGCQkJlfYYR48eldPpVFhYmNt4WFiYcnNzVatWLQUHB7vdDPzMvrm5uZVWB01bVVar1ulfiLVqebuSy4af338/fL1pswyb7fSE2mw%2B37RZgc3mJz8/P9lsfpZo2iyB51KvGjhwoGJjY93GIiMjTXmsC10GcCkuEaBpAwAA5rOZ8zJ6u93u0VKoEbVq1ZLNZpPD4XAbdzgcql27tiIiIpSfn6%2BSkhL5%2B/u7tklS7dq1K60OLkQAAADms9nM%2BbgEqlWrpubNmys9Pd01lpeXp927d6tdu3Zq1aqVnE6nMjIyXNvT0tIUGhpa5vs2XyyaNgAAgD84dOiQbr31Vte92AYPHqylS5cqMzNT%2Bfn5SkpKUqtWrRQdHa2IiAjFxcVp3rx5OnLkiA4ePKj58%2Berf//%2BCgiovEVNlkcBAID5LlEqVhHR0dGSpOLiYklSamqqpNMpWVFRkbKyslRYWChJGjRokHJycjR06FAVFBSoc%2BfObhdAzJw5U08//bT69OmjwMBA3XHHHeXesLeiuLmuWaxw9WhMjLR5s9Shg7Rli7erKZcVrh612f579WhpqberuTDLXD0aGCjZ7VJ2tlSJ9zsygxWuHg0IsCk8PEi5uQUqLvbxb1JZ5OpRiz2Xeu0qqWrVzDnvqVPmnNcHkbQBAADz%2BWDSZjU0bQAAwHw0bYYxgwAAABZA0gYAAMxH0mYYMwgAAGABJG0AAMB8JG2G0bQBAADz0bQZxgwCAABYAEkbAAAwH0mbYcwgAACABZC0AQAA85G0GUbTBgAAzEfTZhgzCAAAYAEkbQAAwHwkbYYxgwAAABZA0gYAAMxH0mYYTRsAADAfTZthzCAAAIAFkLQBAADzkbQZxgwCAABYAEkbAAAwH0mbYTRtAADAfDRthjGDAAAAFkDSBgAAzEfSZhgzCAAAYAEkbQAAwHwkbYbRtAEAAPPRtBnGDAIAAFgASRsAADAfSZthzCAAAIAF%2BHTTtm/fPkVHRysrK6vCxyYlJWno0KEmVAUAACrMZjPnowrx6eXRBg0aKC0tzdtlAAAAo6pYg2UGZhAAAMACfLpp27t3r1q0aKHMzEzFxsbqH//4hx5%2B%2BGHFxMTopptu0hdffOHa95NPPlFcXJxiYmI0fvx4nTx50u1ca9asUXx8vK699lr16dNHKSkpkqTDhw/r%2Buuv12effeba989//rMee%2ByxS/I1AgBQJbA8aphPL4/%2B0Ztvvqnnn39eLVu21PTp0/Xss89qzZo1ysvLU2JioiZOnKiBAwdq48aNmjBhglq1aiVJSktL05NPPqmXX35ZXbt21ZYtW/TQQw%2BpefPm6tChgyZNmqTZs2erW7du2rp1qzZs2KCPPvrI47qys7OVk5PjNhbZtKnstWpV6tdf6Vq2dP/s46zws3mmRivUqsBAb1fgmYAA988%2BLCDA9//H%2B/vb3D77vJgYb1dQPis9l27Z4u0KYIDvPwuepXfv3mrXrp0kKS4uTitWrFBpaam%2B%2BOIL1axZU//zP/8jm82mG2%2B8UR07dlRBQYEk6YMPPlCvXr3Uo0cPSVLHjh112223aeXKlerQoYP69%2B%2BvVatWadGiRVq7dq0mTZqkunXrelxXSkqKkpOT3cbGjBunhHHjKukrN9m773q7Ao8EebuACqhRw9sVeCDI7u0KKiYiwtsVlCvc2wVUQGioFb5JJW3e7O0KPGeF51I/P%2B89tiX%2BmvVtlmraGjZs6Prv6tWrq6SkREVFRTp48KDq168v21nfEE2aNFF6erokaffu3dq4caOio6Nd251Op6uJ8/Pz06xZs9SvXz%2B1bdtW9913X4XqGjhwoGJjY93GIvv1k5YsqfDXeEm1bHn6SWbIECkjw9vVlKvg377/5G2znW7YTpyQSku9Xc2FBRVke7sEzwQEnG7YjhyRiou9Xc0F5Qb6/p8W/v42hYbWUF7eCZWU%2BPg3qaTwPjd4u4TyWey51Gto2gyzVNNmO8//8MLCQpWUlLiNlZ71G7N69eoaPHiwpk2bdt5zHzx4UAEBATp48KBOnDihmjVrelyX3W6X3f6H1OIiblPiNRkZlojMfb0JOltpqQXqLSrydgUVU1zs8zUX%2B/n6//T/KikpVXGxBeq1wHOTi0WeS2Fdl0Xba7fbdejQITmdTtdYZmam678bN26sn376ye2YgwcPuhq9wsJCPfXUU5oxY4aaN2%2BuefPmXZrCAQCoKrgQwbDL4qvt1q2b8vPz9fe//12FhYVKTU3VDz/84Nrev39/bd68WcuXL1dhYaG2b9%2BuAQMGaO3atZKk%2BfPny263q1%2B/fnryySf13nvvuR0PAAAMomkz7LL4auvVq6c5c%2BZo0aJFuv7667Vq1SoNGTLEtb1Zs2aaM2eO3njjDXXs2FEJCQkaOXKk%2Bvbtqx07dmjJkiV6%2BumnJZ1O5R588EFNnTpVRT6%2BFAMAAKoOP%2BfZa4qoPN68QsdTMTGnr8zq0MESr8M4luf736o2mxQUJBUU%2BP5r2kLy9nm7BM8EBkp2u5Sd7fOvacu5ItTbJZQrIMCm8PAg5eYWWOI1bZF2359Tqz2Xylu/9rt3N%2Be8X35pznl9kKUuRAAAAKhM%2B/bt04wZM/TDDz%2BoZs2a6tu3ryZMmHDOxY8PPvigvv32W7ex4uJijR49WmPGjNHQoUO1efNmt%2BOaNm2qVatWVVqtNG0AAMB8Pvr6s4SEBLVp00apqan67bff9Mgjj6hOnTp64IEH3PZbtGiR27/z8vLUt29f3Xzzza6xv/zlL7rnnntMq9U3ZxAAAFxefPBChLS0NGVkZGjixIkKCQlRkyZNNGLECNdbXV7IvHnzdPPNN6tFixaGaqgIkjYAAGBZZb6VZGTkufdPLUN6eroaNGigsLAw11ibNm2UlZWl/Px8BQcHl3ncr7/%2BqhUrVig1NdVtfM2aNXrjjTd04MABtW/fXjNnzlTjxo0v4qsqG0kbAAAwn0lJW0pKiu655x63D0%2BSMklyOBwKDXW/2OVMA5ebm3ve4xYsWKB7771XEWe9vV6zZs3UvHlzvfvuu1q/fr0iIiL0pz/9SYWFhRcxWWUjaQMAAJZV5ltJRkZ6fHxFb6LhcDi0cuVK/etf/3Ibnz59utu/Z86cqc6dO%2Bu7775T165dK/QY50PTBgAAzGfShQhlvpWkhyIiIuRwONzGHA6H/Pz83FK0s61fv15NmzZVo0aNLnju4OBghYWF6dChQxdVW1lYHgUAAObzwQsR2rZtqwMHDujIkSOusbS0NF1zzTUKCgoq85j169er%2Bx/uOZefn6/p06e7NWhHjhzRkSNHym3uKoKmDQAAVEmtW7dWdHS05syZo/z8fGVmZmrx4sUaPHiwJOnWW2/Vpk2b3I7Zvn27GjZs6DYWHBysH374QbNmzZLD4dDRo0c1Y8YMtWjRQjExMZVWL00bAAAwnw8mbZL00ksvKTs7W927d9ewYcN01113ud4KMysrS8ePH3fbPycnR3Xq1DnnPPPnz5fT6VRcXJx69eqloqIiLViw4Jyb9BrBa9oAAECVVa9ePS1cuLDMbT/99NM5Y9u2bStz3yuvvFLJycmVWtsf0bQBAADz%2Beg7IlgJTRsAADAfTZthzCAAAIAFkLQBAADzkbQZxgwCAABYAEkbAAAwH0mbYTRtAADAfDRthjGDAAAAFkDSBgAAzEfSZhgzCAAAYAEkbQAAwHwkbYbRtAEAAPPRtBnGDAIAAFgASRsAADAfSZthzCAAAIAFkLQBAADzkbQZRtMGAADMR9NmGDMIAABgASRtAADAfCRthjGDAAAAFkDSBgAAzEfSZhhNGwAAMB9Nm2HMIAAAgAWQtAEAAPORtBlG02aSY3lOb5dQLptNCpJU8O/NKi31djXlCwn183YJ5YuJkTZvVtANHaQtW7xdzQUVFfr%2B9%2BgZgZKKwu3eLqN8jmPeruCyk3/M979PbTappqTjX1jjuTTY2wXgotG0AQAA85G0GUbTBgAAzEfTZhgzCAAAYAEkbQAAwHwkbYbRtAEAAPPRtBnGDAIAAFgASRsAADAfSZthzCAAAIAFkLQBAADzkbQZRtMGAADMR9NmGDMIAABgASRtAADAfCRthjGDAAAAFkDSBgAAzEfSZhhNGwAAMB9Nm2HMIAAAgAWQtAEAAPORtBnGDAIAgCpr3759evjhh9W5c2f17t1bL7zwgkpLS8/Z7%2BWXX1arVq0UHR3t9nH48GFJ0qlTp/TUU0%2BpZ8%2Be6ty5s8aOHavc3NxKrZWmDQAAmM9mM%2BfDoISEBNWtW1epqalavHixUlNTtWTJkjL3jY%2BPV1pamttHnTp1JElz585Venq6UlJStHbtWjmdTj3xxBOG6zsbTRsAADCfDzZtaWlpysjI0MSJExUSEqImTZpoxIgRSklJqdB5iouL9f7772vUqFGqX7%2B%2BatWqpfHjx%2Buzzz7ToUOHDNV4Nl7TBgAALCs7O1s5OTluY5GRkbLb7eUem56ergYNGigsLMw11qZNG2VlZSk/P1/BwcFu%2B//0008aNGiQduzYofr16%2BuJJ55Qjx49tHv3bh07dkxt2rRx7dusWTNVr15d6enpqlu3rsGv8jSaNgAAYD6TLkRISUlRcnKy29iYMWOUkJBQ7rEOh0OhoaFuY2cauNzcXLemrV69emrUqJEmTJggu92ulJQUPfroo1q1apUcDocknXOu0NDQSn1dG00bAACwrIEDByo2NtZtLDIy0uPjnU6nR/sNGDBAAwYMcP17xIgR%2Buijj7Rq1Sr17NmzQue6WDRtAADAfCYlbXa73aOl0LJERES4UrIzHA6H/Pz8FBERUe7xDRo0UHZ2tmtfh8OhoKAg1/ajR4%2Bqdu3aF1VbWbgQAQAAmM8HL0Ro27atDhw4oCNHjrjG0tLSdM0117g1X5L0yiuvaOPGjW5jmZmZatSokRo1aqSwsDClp6e7tu3YsUOFhYVq27atoRrPRtMGAACqpNatWys6Olpz5sxRfn6%2BMjMztXjxYg0ePFiSdOutt2rTpk2STqdoM2bM0K5du3Tq1CktWrRIu3fv1t133y1/f3/dd999eu2113TgwAHl5ubqxRdf1M033%2By6JUhlYHkUAACYz0ffEeGll17StGnT1L17dwUHB2vQoEEaMmSIJCkrK0vHjx%2BXJE2YMEHS6deyORwOXXPNNXrrrbdUr149SdLYsWNVUFCg%2BPh4FRcXq3fv3po%2BfXql1urnNPtVc1XUsWPerqB8NpsUFCQVFEhl3PzZ54SE%2Bnm7hPLFxEibN0sdOkhbtni7mgsqKrTOj35goFRU5O0qyudw%2BP4PfkCATeHhQcrNLVBxse//4NeoEeLtEspls0k1a0rHj1vjufQPd7G4dGbPNue8lXwDW19G0gYAAMzno0mbldC0AQAA89G0GcYMAgAAWABJGwAAMB9Jm2HMIAAAgAWQtAEAAPORtBlG0wYAAMxH02YYMwgAAGABJG0AAMB8JG2GMYMAAAAWQNIGAADMR9JmGE0bAAAwH02bYVV%2BBocPH66//vWvbmPz58/XoEGDvFQRAADAuap80nbXXXfpxRdf1OTJk2X7/a%2BAjz/%2BWPfdd5/H58jOzlZOTo7bWM2akbLb7ZVaa2U780ePZf74iYnxdgXla9nS/TOqlIAA3/9h8ve3uX32dVZ4fvLz%2B%2B9nX6%2B3tNSLD%2B7rk2MBVb5pu%2BWWWzRz5kx988036tq1q/bs2aPMzEzddtttHp8jJSVFycnJbmOjR4/R2LEJlV2uKWrU8HYFHtq82dsVeO7dd71dQbkCvV1ABQVaoODw8CBvl%2BCx0FCr/OBbhxWeS/PzvV0BjKjyTVtQUJBuuukmrVq1Sl27dtXHH3%2Bs7t27KyIiwuNzDBw4ULGxsW5jNWtGqqCgsqutXDbb6SeZEye8/NeXh4Ju6ODtEsrXsuXphm3IECkjw9vVXFDRN9ZpggMDpaIib1dRvvx8H/%2Bh1%2BmELTS0hvLyTqikxPd/8KtV8/1G2M/vv8%2BlTqe3q/FhJG2GVfmmTTq9RDp27FjNmDFD69at0/3331%2Bh4%2B12%2BzlLoceOWaMRkk7XaYlat2zxdgWey8iwVr2oFMXFVvhBOq2kpNQS9VohYT3TizidFnku9RaaNsOYQUldu3ZVUFA9C%2BT2AAAgAElEQVSQ/vGPf2jnzp3q06ePt0sCAABwQ9ImyWazqV%2B/fnrxxRfVp08f1bDCCxMAALASkjbDmMHf3XXXXcrPz1e/fv28XQoAAMA5SNp%2B99tvv6lBgwbq3r27t0sBAODyQ9JmGE2bTt9n7dlnn9XIkSNd92oDAACViN%2BvhlX5GXz99dd12223qVOnTho8eLC3ywEAAChTlU/aHnnkET3yyCPeLgMAgMsbSZthVb5pAwAAlwBNm2HMIAAAgAWQtAEAAPORtBnGDAIAAFgASRsAADAfSZthNG0AAMB8NG2GMYMAAAAWQNIGAADMR9JmGDMIAABgASRtAADAfCRthtG0AQAA89G0GcYMAgAAWABJGwAAMB9Jm2HMIAAAgAWQtAEAAPORtBlG0wYAAMxH02YYMwgAAGABJG0AAMB8JG2GMYMAAKDK2rdvnx5%2B%2BGF17txZvXv31gsvvKDS0tIy9122bJni4uIUExOj%2BPh4paamurZNmTJFrVu3VnR0tOujY8eOlVorSRsAADCfjyZtCQkJatOmjVJTU/Xbb7/pkUceUZ06dfTAAw%2B47bd27VrNmTNHr7/%2Butq1a6cVK1Zo/Pjx%2Bte//qVGjRpJkh577DElJCSYVqtvziAAALi82GymfGRnZys9Pd3tIzs726OS0tLSlJGRoYkTJyokJERNmjTRiBEjlJKScs6%2BJ0%2Be1J///Gddd911CgwM1IABAxQUFKTvv/%2B%2BsmfqvEjaAACAZaWkpCg5OdltbMyYMR4lXunp6WrQoIHCwsJcY23atFFWVpby8/MVHBzsGo%2BPj3c7Ni8vTwUFBapbt65r7Ouvv9b69ev166%2B/qlmzZpo%2Bfbratm17sV/aOWjaAACA%2BUxaHh04cKBiY2PdxiIjIz061uFwKDQ01G3sTAOXm5vr1rSdzel0aurUqWrfvr2uv/56SVKjRo1ks9k0btw4BQUFKTk5WQ8%2B%2BKDWrl2r8PDwin5ZZaJpAwAAlmW322W32y/6eKfTWaH9i4qKNGXKFP38889aunSpa3z06NFu%2B02aNEmrV69WamqqBgwYcNH1nY2mDQAAmM8HL0SIiIiQw%2BFwG3M4HPLz81NERMQ5%2B588eVKjRo3SiRMn9M4771wwQfP391f9%2BvU9fn2dJ3xvBgEAwOXHpAsRjGjbtq0OHDigI0eOuMbS0tJ0zTXXKCgoyG1fp9OpxMREBQQE6K233nJr2JxOp2bPnq2MjAzXWGFhoXbv3u26srQy0LQBAIAq6cx91ebMmaP8/HxlZmZq8eLFGjx4sCTp1ltv1aZNmyRJH374oX7%2B%2BWf97//%2Br6pVq%2BZ2Hj8/P%2B3du1czZszQoUOHVFBQoKSkJAUGBuqmm26qtHpZHgUAAObzweVRSXrppZc0bdo0de/eXcHBwRo0aJCGDBkiScrKytLx48clScuXL9e%2BfftcFx6cER8fr1mzZumZZ57Rc889p3vuuUf5%2Bflq166dlixZopo1a1ZarX7Oir4CDx45dszbFZTPZpOCgqSCAuk8N3/2KSGhft4uoXwxMdLmzVKHDtKWLd6u5oKKCq3zox8YKBUVebuK8jkcvv%2BDHxBgU3h4kHJzC1Rc7Ps/%2BDVqhHi7hHLZbFLNmtLx49Z4Lj3PBZHm27jRnPN27WrOeX0QSRsAADCfjyZtVkLTBgAAzEfTZhhNm0lC8vZ5u4TyBQZKQXYFFWRbYu3JKst5gZKKvtns7TLKFXiFBZabJdeSc2Bn319yVnaetyu47AQ7fX/JWU6bpCDVdBZITgusj8r3l5xRNpo2AABgPpI2w5hBAAAACyBpAwAA5iNpM4ymDQAAmI%2BmzTBmEAAAwAJI2gAAgPlI2gxjBgEAACyApA0AAJiPpM0wmjYAAGA%2BmjbDmEEAAAALIGkDAADmI2kzjKYNAACYj6bNMGYQAADAAkjaAACA%2BUjaDGMGAQAALICkDQAAmI%2BkzTCaNgAAYD6aNsOYQQAAAAsgaQMAAOYjaTOMGQQAALAAkjYAAGA%2BkjbDaNoAAID5aNoMYwYBAAAsgKQNAACYj6TNMGYQAADAAkjaAACA%2BUjaDKNpAwAA5qNpM4wZBAAAsACSNgAAYD6SNsOYQQAAAAsgaQMAAOYjaTOMpg0AAJiPps0wZhAAAMACSNoAAID5SNoMYwYBAAAsgKQNAACYj6TNMJo2AABgPpo2w5hBAAAAC/CZpm3v3r1q0aKFMjMzL%2BnjDh06VElJSZf0MQEAqHJsNnM%2BqpCq9dVK2rNnj/7v//7P22UAAAAfsG/fPj388MPq3LmzevfurRdeeEGlpaVl7rt06VLFxcWpQ4cOGjx4sLZt2%2BbadurUKT311FPq2bOnOnfurLFjxyo3N7dSa61yTdvHH3%2BstWvXersMAACqFh9N2hISElS3bl2lpqZq8eLFSk1N1ZIlS87Z75NPPtHLL7%2Bs559/Xl999ZV69%2B6tRx99VMePH5ckzZ07V%2Bnp6UpJSdHatWvldDr1xBNPGK7vbD55IYLD4dCsWbP09ddfq6CgQF26dNH06dNVt25d7d27V3369NGiRYv0wgsv6JdfflHLli2VlJSkhg0bSpJeeeUVLV68WAEBAXrkkUf0%2Beefq0OHDqpZs6ZrKTQ1NVXff/%2B9JKmkpERPPfWUVq9erWrVqmnatGnq27evx/VmZ2crJyfHbSxSkj0ysnImxCwBAe6fUbXExHi7As%2B0bOn%2B2YcFBPj%2B38H%2B/ja3zz7PCmWeaRyssFR3ngTpkjBpfsr8HRwZKbvdXu6xaWlpysjI0OLFixUSEqKQkBCNGDFCS5Ys0QMPPOC2b0pKiu655x61b99ekvSnP/1JS5cu1aeffqq4uDi9//77eu6551S/fn1J0vjx43X77bfr0KFDqlu3bqV8rT7523rKlCkKCAjQRx99JH9/fz399NN64okntGjRItc%2BS5cu1euvv65q1app2LBheuONNzR9%2BnStW7dOr732mhYtWqTWrVtr9uzZSk9PV4cOHTRy5Ejt3LlTp06d0ty5c13nWr16tZ599llNnTpVycnJmj59um655RYFeNjMpKSkKDk52W1szOjRShg7tnImxGwREd6uwCOB3i6gAgKtUOzmzd6uoGLefdfbFZQr3NsFVEBoaA1vl3D5qWGBOT12zNsVVLoyfwePGaOEhIRyj01PT1eDBg0UFhbmGmvTpo2ysrKUn5%2Bv4OBgt33PDnRsNptatWqltLQ0tWrVSseOHVObNm1c25s1a6bq1asrPT398m3ajhw5ok8//VRr1qxxTeLEiRPVq1cvt0568ODBrkno0aOH0tLSJEmff/65evTooY4dO0qSJk%2BerJUrV17wMTt06KAbbrhBknTrrbfq9ddf15EjRzzq0iVp4MCBio2NdRuLlKTsbI%2BO95qAgNMN25EjUnGxt6spV1G4Z/8/vC0wUCoq8nYV5Qvs3MHbJXimZcvTDduQIVJGhreruaDc9f/2dgnl8ve3KTS0hvLyTqikxIupi4fCr/B2BR6w2U43bCdOeDfJ8nFO%2BZly3jJ/B3u40uVwOBQaGuo2dqb3yM3NdWvaHA6HW3N3Zt/c3Fw5HA5JOudcoaGhlfq6Np9r2gJ/jyjuuusut3F/f38dOHBAEb%2BnQmeWQiWpRo0aOnXqlCQpJydHjRs3dm0LCQlRkyZNLviYZ5%2BrWrVqkqTCwkKPa7bb7ec2ePv2WeM3t3S6YbNKrag8W7Z4u4KKycjw%2BZqLi63zC7ukpNQa9frcb6kLKC2lafOCMn8HV4DT6ay0fStyrovhcz8O1atXlyRt2LBB4eHnLjbs3btXkuTnV3bHXlpaes6ypq2cdfTznQsAAFQOs/pZf/%2BLPzYiIsKVkp3hcDjk5%2BfnConOCA8PL3Pf5s2bu/Z1OBwKCgpybT969Khq16598QX%2BgU%2B%2BatJms%2Bmnn35y/buoqEiHDh3y6NjatWtr//79rn/n5%2BcrKyur0msEAACeOxNEVvaHEW3bttWBAwd05MgR11haWpquueYat%2BbrzL7p6emuf5eUlOjHH39U%2B/bt1ahRI4WFhblt37FjhwoLC9W2bVtjRZ7F55q2wMBA9e3bV0lJSTp48KBOnjypF198UQ8%2B%2BKBHsWOXLl20YcMGbd26VSdPntTzzz/vSu%2Bk08ufBw4cUF5enoot8DouAABgjtatWys6Olpz5sxRfn6%2BMjMztXjxYg0ePFjS6de5b9q0SdLp19KvWLFC33//vU6cOKFXX31VV1xxhXr16iV/f3/dd999eu2113TgwAHl5ubqxRdf1M0336w6depUWr0%2B17RJ0rRp03TVVVfp9ttv1w033KCff/5Zr7zyikfLmHfeeafuvfdeDRs2THFxcWrfvr0aN27sOrZfv37KyspS7969le3rFwoAAHCZ8MWkTZJeeuklZWdnq3v37ho2bJjuuusuDRkyRJKUlZXlug9bz5499ec//1njx4/X9ddfr6%2B%2B%2BkoLFixwBUNjx45V%2B/btFR8frz59%2BigoKEjPPPOM8QLP4uc0%2B1VzXlBYWKgrrvjvJUe9e/fWqFGjNGDAgEtXxL59l%2B6xLlZgoGS3n77K1QIXIhTZG3i7BI9Y5urRKyzyWs6YmNO3J%2BnQwecvRMjJzvN2CeUKCLApPDxIubkFlrgQIbJ6%2Bft4nc0mBQVJBQXWuBAhJMQrD/v79YKV7vfrB6sEn0zajPj222/VqVMnbd26VSUlJfrggw%2BUk5Ojrl27ers0AACqLF9N2qzE564eNapTp05KTEzU%2BPHjdeTIETVq1Ejz5s1zu60HAAC4tKpag2WGy65pk6QRI0ZoxIgR3i4DAACg0lyWTRsAAPAtJG3G0bQBAADT0bQZd9ldiAAAAHA5ImkDAACmI2kzjqQNAADAAkjaAACA6UjajKNpAwAApqNpM47lUQAAAAsgaQMAAKYjaTOOpA0AAMACSNoAAIDpSNqMo2kDAACmo2kzjuVRAAAACyBpAwAApiNpM46kDQAAwAJI2gAAgOlI2oyjaQMAAKajaTOO5VEAAAALIGkDAACmI2kzjqQNAADAAkjaAACA6UjajKNpAwAApqNpM47lUQAAAAsgaQMAAKYjaTOOpA0AAMACSNoAAIDpSNqMo2kDAACmo2kzjuVRAAAACyBpAwAApiNpM46kDQAAwAJI2gAAgOlI2oyjaTNJzhWh3i6hXAEBNoVLyg0MUrGfBX6aHMe8XUG5AgJsCg8PUn5%2BgYqLfXxOs/O8XYFHXN%2Bn6//t%2B3OKSpdz0tsVlC8gQAoPknILpeJib1dzYZGRIV57bJo241geBQAAsACSNgAAYDqSNuNI2gAAACyApA0AAJiOpM04mjYAAGA6mjbjWB4FAACwAJI2AABgOpI240jaAAAAyuBwODR%2B/Hh169ZNPXr00JNPPqmTJ89/88CPP/5Yd955p2JiYhQXF6f33nvPte3ll19Wq1atFB0d7fZx%2BPBhj%2BshaQMAAKazYtI2bdo0FRYWavXq1SoqKtK4ceOUlJSkqVOnnrPv1q1bNXHiRL344ovq1auXvvzyS40ePVpXX321OnbsKEmKj4/XX//614uuh6YNAACYzqymLTs7Wzk5OW5jkZGRstvths57%2BPBhpaam6p///KciIiIkSaNGjdK4ceP0%2BOOPKzAw0G1/h8OhRx55RDfddJMk6cYbb1RUVJQ2bdrkatqMomkDAACWlZKSouTkZLexMWPGKCEhwdB5t2/fLn9/f7Vo0cI11qZNGx0/fly7du1yG5eknj17qmfPnq5/FxcXKycnR3Xr1nWN/fTTTxo0aJB27Nih%2BvXr64knnlCPHj08rommDQAAmM6spG3gwIGKjY11G4uMjDR8XofDoeDgYPn5%2BbnGwsLCJEm5ubnlHp%2BUlKSaNWuqb9%2B%2BkqR69eqpUaNGmjBhgux2u1JSUvToo49q1apVuvrqqz2qiaYNAABYlt1uv%2Bil0JUrV2ry5MllbktMTJTT6azwOZ1Op5KSkrR69WotXbpU1apVkyQNGDBAAwYMcO03YsQIffTRR1q1apXGjx/v0blp2gAAgOl88UKE%2BPh4xcfHl7ntyy%2B/VH5%2BvkpKSuTv7y/pdPomSbVr1y7zmNLSUj3xxBPaunWrli1bpkaNGl3w8Rs0aKDs7GyP6%2BWWHwAAwHSlpeZ8mKVVq1ZyOp3KyMhwjaWlpSk0NFRNmzYt85hnn31WO3fuLLNhe%2BWVV7Rx40a3sczMzHIbu7PRtAEAAPxBRESE4uLiNG/ePB05ckQHDx7U/Pnz1b9/fwUEnF6oHD58uNasWSNJ%2Bu6777Rq1SotWLBAtWrVOud8DodDM2bM0K5du3Tq1CktWrRIu3fv1t133%2B1xTSyPAgAA0/ni8mh5Zs6cqaefflp9%2BvRRYGCg7rjjDiUmJrq279mzR0ePHpUkLV%2B%2BXMeOHVPv3r3dztGpUyctWrRIEyZMkHT6tWwOh0PXXHON3nrrLdWrV8/jevycF/MqO5QrJ%2BeYt0soV0CATeHhQcrNLVBxsQV/mnwQc1r5mNPKxXxWPivNaWRkiNce%2B1//Mue8t91mznl9EcujAAAAFsDyKAAAMJ0Vl0d9DUkbAACABZC0AQAA05G0GUfTBgAATEfTZhzLowAAABZA0gYAAExH0mYcSRsAAIAFkLQBAADTkbQZR9MGAABMR9NmHMujAAAAFkDSBgAATEfSZhxJGwAAgAWQtAEAANORtBlH0wYAAExH02Ycy6MAAAAWQNIGAABMR9JmHEkbAACABZC0AQAA05G0GVdlk7avv/5aPXv2VN%2B%2Bfb1dCgAAl73SUnM%2BqpIq27QtWbJE1157rVavXu3tUgAAAMpVZZdH8/Pz1b59e9lsVbZvBQDgkqlqqZgZqmTTdv/992vTpk367rvv9PHHHys2NlarV69Wfn6%2BGjdurEmTJumGG27wdpkAAAAuVbJpe/vttzV06FC1b99eLVq00MKFC7Vq1SqFhYVpxYoVevzxx/X5558rMDDQo/NlZ2crJyfHbSwgoKbsdrsZ5Vcaf3%2Bb22cYx5xWPua0cjGflY859QxJm3FVsmk7W15engICAlSjRg35%2B/vr3nvv1d13312hZdOUlBQlJye7jY0ePVpjx46t7HJNERpaw9slXHaY08rHnFYu5rPyMacXRtNmXJVv2m6//XatXLlSPXv2VPfu3dWrVy/dfvvtFWraBg4cqNjYWLexgICays0tqOxyK5W/v02hoTWUl3dCJSX8NFUG5rTyMaeVi/msfFaa0/DwIG%2BXAAOqfNNWq1Ytvffee9q8ebM%2B/fRTvfTSS1q2bJneeecdBQR4Nj12u/2cpdCcnGMqLvbtH94zSkpKLVOrVTCnlY85rVzMZ%2BVjTi%2BMpM24Kr8Af%2BrUKZ04cUIdOnTQhAkTtHr1au3YsUMZGRneLg0AAMClyidtzzzzjBwOh6ZPn67w8HClp6ertLRUV155pbdLAwDgskHSZlyVb9omTJigp59%2BWnFxcSouLtZVV12lOXPmKCIiwtulAQBw2aBpM67KNm1/%2B9vfXP89b948L1YCAABQvirbtAEAgEuHpM24Kn8hAgAAgBWQtAEAANORtBlH0wYAAExH02Ycy6MAAAAWQNIGAABMR9JmHEkbAACABZC0AQAA05G0GUfTBgAATEfTZhzLowAAABZA0wYAAExXWmrOh5kcDofGjx%2Bvbt26qUePHnryySd18uTJMvf94IMP1LJlS0VHR7t9bN269fevv1Rz585Vnz591KlTJ40cOVJ79uypUD00bQAAwHRWbNqmTZumEydOaPXq1Vq%2BfLkyMzOVlJR03v07deqktLQ0t4927dpJkt555x19%2BOGHWrBggT799FM1adJEo0ePltPp9LgemjYAAIA/OHz4sFJTU5WYmKiIiAjVrVtXo0aN0vLly1VUVFTh86WkpGjEiBFq1qyZgoODlZiYqMzMTP3www8en4MLEQAAgOnMSsWys7OVk5PjNhYZGSm73W7ovNu3b5e/v79atGjhGmvTpo2OHz%2BuXbt2uY2fceDAAT3wwAPatm2bQkNDNXbsWMXHx%2BvkyZP6%2Beef1bp1a9e%2BwcHBuuqqq5SWlqZrr73Wo5po2gAAgGWlpKQoOTnZbWzMmDFKSEgwdF6Hw6Hg4GD5%2Bfm5xsLCwiRJubm55%2BwfERGhJk2a6M9//rOuueYarVu3TpMnT5bdbtfVV18tp9PpOv7s85V1rvOhaQMAAKYzK2kbOHCgYmNj3cYiIyM9OnblypWaPHlymdsSExMr9HqzXr16qVevXq5/33777Vq3bp0%2B%2BOADTZw4UZIqdL6y0LQBAADTmdW02e32i14KjY%2BPV3x8fJnbvvzyS%2BXn56ukpET%2B/v6STqdvklS7dm2Pzt%2BgQQNt27ZNtWrVks1mcx1/hsPh8PhcEhciAAAAnKNVq1ZyOp3KyMhwjaWlpSk0NFRNmzY9Z/9ly5ZpzZo1bmOZmZlq1KiRqlWrpubNmys9Pd21LS8vT7t373ZdXeoJmjYAAGA6q93yIyIiQnFxcZo3b56OHDmigwcPav78%2Berfv78CAk4vVA4fPtzVqBUWFuovf/mL0tLSVFRUpNWrV2vDhg0aNGiQJGnw4MFaunSpMjMzlZ%2Bfr6SkJLVq1UrR0dEe18TyKAAAQBlmzpypp59%2BWn369FFgYKDuuOMOJSYmurbv2bNHR48elSQNGzZMBQUFGjdunHJyctSwYUPNnz9fbdu2lSQNGjRIOTk5Gjp0qAoKCtS5c%2BdzLqAoj5/T6KviUKacnGPeLqFcAQE2hYcHKTe3QMXFvClcZWBOKx9zWrmYz8pnpTmNjAzx2mM/8YQ5550925zz%2BiKSNgAAYDreMN44XtMGAABgASRtAADAdCRtxpG0AQAAWABJGwAAMB1Jm3E0bQAAwHQ0bcaxPAoAAGABJG0AAMB0JG3GkbQBAABYAEkbAAAwHUmbcTRtAADAdDRtxrE8CgAAYAEkbQAAwHQkbcaRtAEAAFgASZtJIu2h3i6hfDEx0ubNCu9zg7Rli7erKVf%2BMae3SyiX7fc/g6pVC1JgoHdrKU%2Bw85i3S/DM73MafoV8/hkr56S3K7j88FxqAqd3nktJ2ozz8adAAABwOaBpM47lUQAAAAsgaQMAAKYjaTOOpA0AAMACSNoAAIDpSNqMo2kDAACmo2kzjuVRAAAACyBpAwAApiNpM46kDQAAwAJI2gAAgOlI2oyjaQMAAKajaTOO5VEAAAALIGkDAACmI2kzjqQNAADAAkjaAACA6UjajKNpAwAApqNpM47lUQAAAAsgaQMAAKYjaTOOpg0AAJiOps04lkcBAAAsgKQNAACYjqTNOJI2AAAACyBpAwAApiNpM46mDQAAmI6mzTiWRwEAACyApA0AAJiOpM04kjYAAAALIGkDAACms2LS5nA4NH36dP3nP/%2BRzWbTjTfeqGnTpql69ern7Dt16lStXLnSbaykpETx8fGaPXu2pkyZolWrVsnf39%2B1vVq1atq0aZPH9ZC0AQAA05WWmvNhpmnTpunEiRNavXq1li9frszMTCUlJZW576xZs5SWlub62LJli66%2B%2Bmrdeuutrn0ee%2Bwxt30q0rBJNG0AAADnOHz4sFJTU5WYmKiIiAjVrVtXo0aN0vLly1VUVFTu8UuWLNGVV16pG2%2B8sdJqYnkUAACYzqxULDs7Wzk5OW5jkZGRstvths67fft2%2Bfv7q0WLFq6xNm3a6Pjx49q1a5fb%2BB/l5eXptdde07vvvus2/vXXX2v9%2BvX69ddf1axZM02fPl1t27b1uCaaNgAAYFkpKSlKTk52GxszZowSEhIMndfhcCg4OFh%2Bfn6usbCwMElSbm7uBY99%2B%2B231alTJzVv3tw11qhRI9lsNo0bN05BQUFKTk7Wgw8%2BqLVr1yo8PNyjmmjaAACA6cxK2gYOHKjY2Fi3scjISI%2BOXblypSZPnlzmtsTERDmdzgrXU1JSonfeeUdz5sxxGx89erTbvydNmqTVq1crNTVVAwYM8OjcNG0AAMB0ZjVtdrv9opdC4%2BPjFR8fX%2Ba2L7/8Uvn5%2BSopKXFd8elwOCRJtWvXPu85v/32WxUWFqpjx44XfGx/f3/Vr19f2dnZHtfLhQgAAAB/0KpVKzmdTmVkZLjG0tLSFBoaqqZNm573uPXr16tLly4KCPhvLuZ0OjV79my3cxUWFmr37t1q1KiRxzXRtAEAANNZ7ZYfERERiouL07x583TkyBEdPHhQ8%2BfPV//%2B/V0N2fDhw7VmzRq347Zv366GDRu6jfn5%2BWnv3r2aMWOGDh06pIKCAiUlJSkwMFA33XSTxzXRtAEAAJRh5syZCgkJUZ8%2BfXTnnXeqXbt2SkxMdG3fs2ePjh496nZMTk6O6tSpc865nnnmGTVp0kT33HOPunXrpu3bt2vJkiWqWbOmx/XwmjYAAGA6K74jQkhIiF588cXzbv/kk0/OGVu7dm2Z%2B9aqVUuzZ882VE%2BFkrZ9%2B/YpOjpaWVlZFX6gpKQkDR06tMLHmW3KlCluXTMAAKh8Vlse9UUVStoaNGigtLQ0s2oBAADAebA8CgAATFfVUjEzVKhp27t3r/r06aM1a9booYce0mOPPaZ169bp22%2B/Ve3atTV9%2BnT16NFD0ul13ueee07Z2dm68cYby3xR3vlMmTJFQUFBCggI0D//%2BU/ZbDaNHDlSDz30kCSpRYsWWrhwoXr27ClJWrZsmRYuXKhPPvnEVePrr7%2Bu5557Tvv371ffvn312GOPafLkycrIyFB0dLSSk5NddzZ2Op2aNWuWVqxYoeDgYI0cObJCS7llvoVG06ay16rl8Tm8omVL988%2BzmaBy2bO3Djbz88C9Tp9vcDfnZlIn59QKcACfwb7%2B9vcPvu8mBhvV1A%2BKz2Xbtni7QpggKGnmDfffFPPP/%2B8WrZsqenTp%2BvZZ5/VmjVrlJeXp8TERE2cOFEDBw7Uxo0bNWHCBLVq1crjc69evVpTpkzRl19%2BqVWrVmnatGmKj4/3%2BAZ6K1as0HvvvXjAHxsAABYrSURBVKeMjAzdf//9%2BvXXX/XCCy%2BoevXquvvuu7V8%2BXI9%2BOCDkqQNGzZo0qRJ%2Buqrr/TZZ58pISFB7du3V7t27Tx6rDLfQmPcOCWMG%2Bfx1%2BtVf3hvNF/l%2BfU13lejhrcr8ESQtwuoGAtMariFpjQ01PfnU5K0ebO3K/CcFZ5Lz3pLpkuNpM04Q01b7969XY1NXFycVqxYodLSUn3xxReqWbOm/ud//kc2m0033nijOnbsqIKCAo/P3bBhQ919992SpL59%2B%2Br//b//p19%2B%2BcXjpu3ee%2B9VSEiIOnXqpJCQEHXv3t11A7t27drpl19%2Bce1rt9s1ePBgSdItt9yiVq1aacOGDR43bWW%2BhUa/ftKSJR4d7zUtW55%2BkhkyRDrrhn%2B%2B6vgXvv/k7ed3urc4cUK6iHc/uaRqOj3/efQqm%2B2/k%2Brjz/q5hd6uoHz%2B/jaFhtZQXt4JlZT49nxKUnifG7xdQvks9lzqLT7%2B42sJhpq2s28eV716dZWUlKioqEgHDx5U/fr1ZTtrOaNJkyZKT0%2B/qHPX%2BP0v7JMnT3p8fP369V3/Xa1aNdWtW9ft34WF/312veaaa9yObdy4sQ4dOuTxY5X5FhoXcYWt12RkWCIyt8IP/JlveafTAvU6fb3AP7DApWLFxd6uwHMlJaUqLvbt%2BZRkiecmF4s8l8K6DDVttvO8xqSwsFAlJSVuY6UVfLI937nLUta5/f4QAV/ofH/c9v/bu/egqO4zjOPPIoMIUWsUUJEQWjNqNeXmLCqpCi1iHQ2YUam22tYkM6nGTKA1KVhjkxhML2IcjE2iMQ3NH2JSDEozGsFLNE68TIhcIqbeQiqWiwXFKCzs0j8yIaGgIrAezu73M8MMnHPcfeaI%2BPL%2BLqelpUV9%2B/bt9PsDAICb6%2BW/c5mCU2ai%2Bvv7q7KyUi3fGh86c%2BZMj72%2Bl5dXm65beXl5t17v//edKy8v7/LDZwEAAJzBKUXbpEmTdPXqVW3dulU2m035%2Bfk6ceJEj73%2Bvffeq/z8fDU3N6u4uFj79%2B/v1uv9%2B9//1vbt29XU1KSCggKdOnVKcXFxPRMWAACwuW4PcErRNnToUK1du1ZbtmyR1WrVjh07tGDBgh57/bS0NBUWFmr8%2BPFav3596yrQroqPj9eJEyc0YcIEPf/883ruuecUEhLSQ2kBAABFW/dZWlp6%2Bxo3kzJwWXWnhYd/tZw%2BIsIUk2ev1vf%2Bb1UPD8nHR7p2rff/MLmrpd7oCJ3j4SH5%2Bkpfftnrb2p159dKGcbT00ODBvmqtvZLUyxE8PMfYHSEWzPZz1KjlraPHeuc172NNY6mZ4KtIAEAgNn18t%2B5TOGOF23PP/%2B8tm3bdsPzv/71r7VkyZI7mAgAAKD3u%2BNF28qVK7Vy5co7/bYAAMBAdNq6j%2BFRAADgdBRt3WeSJwYDAAC4NzptAADA6ei0dR%2BdNgAAABOg0wYAAJyOTlv3UbQBAACno2jrPoZHAQAATIBOGwAAcDo6bd1Hpw0AAMAE6LQBAACno9PWfRRtAADA6Sjauo/hUQAAABOg0wYAAJyOTlv3UbQBAACno2jrPoZHAQAATIBOGwAAcDo6bd1Hpw0AAMAE6LQBAACno9PWfRRtAADA6Sjauo/hUQAAABOg0wYAAJyOTlv30WkDAAAwATptAADA6ei0dR9FGwAAcDqKtu5jeBQAAMAE6LQBAACno9PWfXTaAAAATICiDQAAOJ3D4ZwPZysuLlZcXJzmzZt3y2uzsrIUHx%2BviIgIzZ8/XyUlJa3nGhsb9cwzz2jy5MmKiorSE088odra2tvKQtEGAACczoxF244dO7Rs2TIFBwff8tq9e/cqMzNTf/rTn3T48GHFxMToscce07Vr1yRJ69atU2lpqbKzs7V79261tLQoNTX1tvJQtAEAAHSgsbFR2dnZCg0NveW12dnZeuihhxQaGipvb2898sgjkqR9%2B/apublZ77zzjpYsWaJhw4bpO9/5jp588knt379flZWVnc7DQgQAAOB0zuqKVVVVqbq6us0xPz8/%2Bfv7d/u1586d2%2BlrS0tLNWPGjNavPTw8NGbMGBUXF2vMmDGqr6/X2LFjW89/73vfk7e3t0pLSxUQENCp96Boc5aWFqMT3FJVVZWyMzOVtGtXj3xzO9tdRgfohKqqKr3%2BeraSkpJMcE/7Gx2gU6qqqpS9ZYsp7qmfCW5pVVWVMjPNcT8l8bPUhTjrrzIzM1sbNmxoc%2Bzxxx/XsmXLnPOGN1BXV6eBAwe2OTZw4EDV1taqrq5OkjRgwIA25wcMGHBb89oYHnVj1dXV2rBhQ7vfUNB13NOexz3tWdzPnsc9NVZSUpJycnLafCQlJXXqz%2Bbm5mrUqFEdfuTk5Nx2lpZbVKa3On8rdNoAAIBp%2Bfv7d7nDmZCQoISEhB7JMWjQoNaO2tfq6up033336e6772792tfXt/X85cuXNXjw4E6/B502AACAbho3bpxKS0tbv7bb7fr0008VGhqqoKAgDRw4sM35zz77TDabTePGjev0e1C0AQAAdMH06dN1/PhxSdL8%2BfP17rvv6pNPPtH169f117/%2BVV5eXpo6dar69OmjefPm6ZVXXtHFixdVW1urjIwMxcXFaciQIZ1%2BP4ZH3Zifn58ef/xx%2Bfn5GR3FZXBPex73tGdxP3se99R1xcfHq6KiQna7XQ6HQ/fff78kadeuXQoMDNS5c%2Bda92GbPHmyUlJS9OSTT%2BrSpUu6//779dprr8nb21uS9MQTT%2BjLL79UQkKCmpubFRMToz/84Q%2B3lcfS0t1ZcQAAAHA6hkcBAABMgKINAADABCjaAAAATICiDQAAwAQo2gAAAEyAog0AAMAEKNoAAABMgKINAADABCjaAAAATICiDeiG%2BfPna%2BvWraqrqzM6CgDAxfEYK7Sy2%2B3q06eP0TFM5eWXX9aePXt05swZRUdH68EHH9SPfvQj9e3b1%2BhopuZwOPTf//5XNput3bnhw4cbkMj8vv3v2%2BFwqKysTMOGDdOgQYMMTgagsyja3MzSpUv17LPPasiQIW2OFxUV6fe//7127NhhUDJz%2B%2BKLL7Rnzx7t2bNHp0%2Bf1o9//GPNmjVLkyZNMjqa6bz33nt69tlndeXKlTbHW1paZLFYdPLkSYOSmdehQ4f0u9/9TocOHVJzc7N%2B/vOf69SpU5KkjIwMxcTEGJzQvEpKSnTgwAFVVlaqb9%2B%2BCggIUFxcnIKDg42OBhdE0eZmUlJSdOjQIS1fvlxz585VQ0OD1q1bp23btmnx4sVatmyZ0RFNzWaz6Z133lFGRoauXr2qwMBAPfroo/rpT39qdDTTiI6O1pw5czR9%2BvQOO5bf/e53DUhlbomJiVq0aJEeeugh5ebmKjMzUzt37lRJSYnWrFmjnJwcoyOa0ltvvaXVq1drzJgxCgoKkiSVl5frs88%2B03PPPac5c%2BYYnBCuhqLNDR0/flzp6eny8fHRf/7zH40cOVIrVqxo/aGD2/fRRx9p586dev/99%2BXr66uZM2cqMTFRNTU1WrNmjaxWq1asWGF0TFOIjIzUkSNH5OnpaXQUlxEeHq6PP/5YFotFycnJuueee5ScnCxJioiI0Mcff2xwQnOKjY1Vamqq4uLi2hwvKCjQCy%2B8oL179xqUDK6KhQhuKCwsTPHx8SouLlZtba1mzZpFwdZFf/zjHzVlyhQtWbJETU1NWr9%2Bvfbt26ff/va3GjlypCZMmKBNmzbp3XffNTqqacycOVNHjx41OoZL8fX11ZUrV9TQ0KAPP/ywdTi0traW4rgb6uvrNXXq1HbHp0yZ0m54H%2BgJ/Gt1MwcOHFB6erqGDh2qnTt36uLFi1q1apW2bdumVatWMfR0m06ePKnk5GRNmzZNPj4%2BHV7j7%2B%2BvRx999A4nM5eMjIzWz318fJSamqqIiAiNGDFCFoulzbUpKSl3Op7pzZo1S7/4xS/Up08fhYSEKCwsTA0NDVq1apWio6ONjmdaCQkJ2r59u%2BbNm9fmeF5enh588EGDUsGVMTzqZqKiorR8%2BfI2cy1sNptefvllZWVlqbCw0MB0cFcLFy7s1HUWi0VZWVlOTuMaZs6cqby8PElSfHy8li1bpitXrmjmzJkaMGCAbDabVq9ereXLl6t///4GpzWP3/zmN62fOxwOHTx4UP7%2B/ho5cqQsFovOnTunCxcuaNq0aVqzZo2BSeGKKNrczKVLlzR48OAOz/3rX//Sfffdd4cTAW19vUr0/9ntdlVXV2vo0KEGpDKfKVOmKDw8XPfcc4%2B2bNmixYsX3/Baupedl5qa2ulrKdrQ0yja3NAnn3yi3NxcVVZWauPGjXI4HHr//fc1ffp0o6MBCg0N1YkTJ9odr6%2BvV2xsrI4dO2ZAKvM5cuSI3nzzTV29elXHjh3T%2BPHjO7yO7iVgHsxpczPbtm3Tiy%2B%2BqJ/85Cc6ePCgJKm6ulrp6emqqqrSokWLDE4Id7V7927t3r1bTU1NbYagvlZRUcHmz7chKipKUVFRkr4afv773/9ucCLXU19fr3/84x86f/68Ghsb25yzWCxKT083KBlcFatH3cymTZu0adMmvfDCC61DUAEBAXr11Vf11ltvGZwO7uz73/%2B%2Bxo4dK0ny8vJq9zFq1Cht2LDB4JTmRMHmHEuXLtXrr7%2Bu6upqNTY2tvloaGgwOh5cEJ02N1NTU6OIiAhJajNvaOTIkaqqqjIqFqCgoCA9/PDDslgsN51/BfQWxcXFys/Pv%2BE8YaCnUbS5meDgYH300UeaOHFim%2BN5eXk80xG9wrVr127YUfPw8FBAQIAmTpzI9ysMN2rUqHbDooAzsRDBzbz33ntasWKFYmNjtWvXLs2fP1%2BnTp1SYWGh1q5dq/j4eKMjws0tXrxYpaWlamho0L333isPDw%2BdO3dO/fr1U1BQkGpqalRTU6P169fzzEwY6osvvlBqaqqio6MVEBAgD4%2B2M44SExMNSgZXRdHmhoqLi7V9%2B3aVl5fL29tbQUFBmjt3Lhvrold48803debMGT311FO66667JElXr17Vn//8Z4WFhWn27NnKycnR3/72N%2B3YscPgtHBnaWlpysnJUf/%2B/Tt8Tu6hQ4cMSAVXRtEGoFeJjo5WQUGBvL292xxvbGzU9OnTtW/fPjkcDkVGRrIZNAwVFhamzZs333A7FaCnMafNDSxcuLDDzUo7wn5NMFpTU5M%2B/fTT1gUzXzt16pTq6uokSSUlJbr77ruNiAe0Gj58uEaPHm10DLgRijY3EBYW1vr59evXlZubq8jISIWEhMjhcOj06dMqKirSggULDEwJfOWXv/ylfvWrX%2BmHP/yhAgMD5enpqYqKCh04cEBz5syRzWbTokWLlJycbHRUuLmnn35azzzzjObMmdPhnLaQkBCDksFVMTzqZlJSUpSYmKjJkye3OZ6fn6%2B8vDy99NJLBiUDvrFnzx598MEHqq6ulsPh0ODBgxUVFaWEhARZLBYdP36cISkYrqMum8ViaX0U28mTJw1IBVdG0eZmIiIidPToUXl6tm2yNjU1yWq1MkcIADrpwoULNz0fGBh4h5LAXTA86mb8/f2VnZ2tn/3sZ22O5%2BTkyM/Pz6BUwDcqKiq0efNmnTlzpsM9sLZu3WpAKqA9ijLcaRRtbuapp55SSkqKNm7cqGHDhslut6uyslL19fXKyMgwOh6glJQUXb9%2BXQ888ID69etndBzghmJjY2%2B6yKugoOAOpoE7YHjUDdXX1%2BvgwYOqrKyUzWaTv7%2B/Jk2apICAAKOjAQoPD9eBAwc0YMAAo6MAN/X/XV%2B73a7y8nLt379fjzzyiObOnWtQMrgqijY3ZrPZ2h3z8vIyIAnwjdmzZ2vTpk0aMmSI0VGALikuLlZmZqZee%2B01o6PAxVC0uZnDhw8rPT1d58%2Bfl91ub3ee1U4w2uHDh5WVlaUFCxYoMDCQbRRgOna7XePHj2dhF3ocRZubiYuLk9Vq1bRp0zqcL2S1Wg1IBXyDbRRgFh09pqqhoUH5%2BfkqLCzU7t27DUgFV0bR5mbCw8N17Nixdlt%2BAL0F2yjALDr6BcPLy0vBwcFKS0vTxIkTDUgFV8b/3G7GarWqrKxM48aNMzoK0KGvi7JLly7p4sWLfK%2Bi1yorKzM6AtwMnTY38/bbb%2BuNN95QTEyMRowY0W6%2BUFJSkkHJgK9UVlYqLS1NH374oTw9PVVSUqKqqio9/PDD2rhxo4KCgoyOCLSqqanR%2BfPn1dDQ0O7cAw88YEAiuDKKNjcTGxt7w3MWi4V9hWC4pUuXysfHRykpKYqPj1dRUZGam5u1du1anT17Vq%2B%2B%2BqrREQFJ0ubNm7Vu3boOF3Ux/xLOQNHmBj7//HMFBwdLks6ePXvTzSBZmQejRUZG6oMPPpCvr69CQ0N14sQJSV9N8J4yZYqOHDlicELgKxMnTtTy5cs1Y8YMeXt7Gx0HboA5bW5g1qxZKioqkiTNmDGjw6KNlXnoLfr166eOfpe8fPlyhx0NwCgOh0MJCQnq06eP0VHgJija3MCuXbtaP2f4E73dhAkTlJaWpuTkZEnSlStXVFZWpr/85S%2BaOnWqseGAb5k9e7by8vKUkJBgdBS4CYZHAfQqly9f1tNPP639%2B/e3HvPw8NCMGTO0cuVKDRw40LhwwLesXr1a//znPzVixIgOF3atXbvWoGRwVRRtAAx37ty5dscuXbokLy%2Bv1ker9e3bVxLzLtF7pKam3vT8mjVr7lASuAuKNgCGGz16dOtTDyQx7xIu5ZVXXtFjjz1mdAy4AIo2AIa71VMQvo0nIsBsvr0KGugOFiIAMByFGFwZvRH0FI9bXwIAALrqZntjAreDog0AAMAEKNoAAABMgKINAADABCjaAABwIhYioKdQtAEA4ETz5s0zOgJcBPu0AQDQSQsXLuz0atCsrCwnp4G7YZ82AAA6KSwsrPXz69evKzc3V5GRkQoJCZHD4dDp06dVVFSkBQsWGJgSropOGwAAXZCSkqLExERNnjy5zfH8/Hzl5eXppZdeMigZXBVFGwAAXRAREaGjR4/K07PtoFVTU5OsVqsKCwsNSgZXxUIEAAC6wN/fX9nZ2e2O5%2BTkyM/Pz4BEcHV02gAA6IK9e/cqJSVFvr6%2BGjZsmOx2uyorK1VfX6%2BMjAzFxcUZHREuhqINAIAuqq%2Bv18GDB1VZWSmbzSZ/f39NmjRJAQEBRkeDC6JoAwCgm2w2W7tjXl5eBiSBK2PLDwAAuuDw4cNKT0/X%2BfPnZbfb250/efKkAangyui0AQDQBXFxcbJarZo2bZr69evX7rzVajUgFVwZRRsAAF0QHh6uY8eOtdvyA3AWtvwAAKALrFarysrKjI4BN0KnDQCALnj77bf1xhtvKCYmRiNGjJCHR9s%2BSFJSkkHJ4Koo2gAA6ILY2NgbnrNYLCooKLiDaeAOKNoAAOikzz//XMHBwZKks2fPymKx3PDakJCQOxULboKiDQCATvrBD36goqIiSdLo0aM7LNpaWlpksVjY8gM9jqINAIBOqqio0PDhwyVJFy5cuOm1gYGBdyIS3AhFGwAAgAmw5QcAAIAJULQBAACYAEUbAACACVC0AQAAmABFGwAAgAlQtAEAAJgARRsAAIAJ/A85nOPubrfQqgAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>length</th>\n",
       "      <th>fs</th>\n",
       "      <th>ind_numb</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[22, 101, 83, 9, 27, 83, 63, 43, 38, 61, 45, 3...</td>\n",
       "      <td>30372</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>[9, 3, 13, 13, 11, 15, 17, 15, 17, 24, 22, 22,...</td>\n",
       "      <td>25780</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[0, 1, -5, -1, -1, -1, -3, -1, -5, 0, -1, -9, ...</td>\n",
       "      <td>30045</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[23, 82, 88, -12, -114, -206, -112, -10, 59, 8...</td>\n",
       "      <td>32100</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>[0, 0, 7, 0, 7, 14, 22, 22, 29, 37, 37, 14, -7...</td>\n",
       "      <td>27771</td>\n",
       "      <td>16000</td>\n",
       "      <td>3</td>\n",
       "      <td>a02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x7fb5c5c06e10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_file_pr = pandas_profiling.ProfileReport(data_complete_pr)\n",
    "profile_file_pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    126\n",
       "1     81\n",
       "6     77\n",
       "4     71\n",
       "3     66\n",
       "5     56\n",
       "2     46\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_complete_pr.y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "6    2\n",
       "4    0\n",
       "3    3\n",
       "5    6\n",
       "2    0\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_complete.y.value_counts() - data_complete_pr.y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      523.000000\n",
       "mean     43939.990440\n",
       "std      14623.477687\n",
       "min      23632.000000\n",
       "25%      32534.500000\n",
       "50%      41443.000000\n",
       "75%      52723.000000\n",
       "max      95610.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_complete_pr.length.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we removed around 12 samples (quite a lot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering & Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the length of the signals vary quite a lot (min 23632.0 and max 95610.0) we have two possibilities. We can pad all the signals with 0s to match the longest signal's length (fun zero_pad_to_longest) or we can cut the signals to a minimal signal lenght (with some 0 padding) (fun cut_into_shorter_samples)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing, Feature Engineering and Standarizing\n",
    "All the steps necessary to get our Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "longest_length = data_complete_pr.length.max()\n",
    "shortest_length = data_complete_pr.length.min()\n",
    "y_pr = data_complete_pr['y'].values\n",
    "x_pr = data_complete_pr['x'].values\n",
    "length_pr = data_complete_pr['length'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cut_into_shorter_samples(x_sample,y_sample,lenght_sample, shortest_length, threshold):\n",
    "    \"\"\"Cuts the x signal into pieces of lenght shortest_length.\n",
    "    For the last piece of signal it zero pad it. The threshold indicates at which proportion of shortest_length\n",
    "    it considers it is worth to keep the last piece.\n",
    "    Returns the list of x pieces of x, along with their respective y and length\"\"\"\n",
    "    x_array = []\n",
    "    y_array= []\n",
    "    length_array = []\n",
    "    diff_test = lenght_sample-shortest_length \n",
    "    len_to_process = lenght_sample\n",
    "    if(diff_test>0):\n",
    "        if(diff_test>shortest_length//threshold):\n",
    "            numb_new_x = lenght_sample//shortest_length\n",
    "            for i in range(numb_new_x+1):\n",
    "                if(len_to_process>shortest_length):\n",
    "                    new_x = x_sample[i*shortest_length:(i+1)*shortest_length]\n",
    "                    x_array.append(new_x)\n",
    "                    length_array.append(len(new_x))\n",
    "                    y_array.append(y_sample)\n",
    "                    len_to_process = lenght_sample-(i+1)*shortest_length \n",
    "                elif(len_to_process>shortest_length//threshold):\n",
    "                    final_x = x_sample[i*shortest_length:]\n",
    "                    len_pad = ((shortest_length-len(final_x))//2)\n",
    "                    diff_len = len_to_process+2*len_pad - shortest_length\n",
    "                    if(diff_len==0): x_pad = np.pad(final_x,pad_width =(len_pad,len_pad),mode ='constant',constant_values=0 )\n",
    "                    elif(diff_len==-1): x_pad = np.pad(final_x,pad_width =(len_pad+1,len_pad),mode ='constant',constant_values=0 )\n",
    "                    else: x_pad = np.pad(final_x,pad_width =(len_pad-1,len_pad),mode ='constant',constant_values=0 )\n",
    "                    x_array.append(x_pad)\n",
    "                    length_array.append(len(x_pad))\n",
    "                    y_array.append(y_sample)\n",
    "        else:\n",
    "            new_x = x_sample[:shortest_length]\n",
    "            x_array.append(new_x)\n",
    "            length_array.append(len(new_x))\n",
    "            y_array.append(y_sample)\n",
    "    return  x_array, y_array, length_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_pad_to_longest(x_pr,lengtr_pr,longest_length):\n",
    "    \"\"\"Zero pads all the x signals to match the lenght given which is longer than all the lengths.\"\"\"\n",
    "    x_final = np.zeros((len(lengtr_pr),longest_length))\n",
    "    length_array_final = [] #Just to check that indeed all are the same length\n",
    "    for i  in range(len(x_pr)):\n",
    "        len_pad = ((longest_length - length_pr[i])//2)\n",
    "        diff_len = length_pr[i]+2*len_pad - longest_length\n",
    "        if(diff_len==0): x_pad = np.pad(x_pr[i],pad_width =(len_pad,len_pad),mode ='constant',constant_values=0 )\n",
    "        elif(diff_len==-1): x_pad = np.pad(x_pr[i],pad_width =(len_pad+1,len_pad),mode ='constant',constant_values=0 )\n",
    "        else: x_pad = np.pad(x_pr[i],pad_width =(len_pad-1,len_pad),mode ='constant',constant_values=0 )\n",
    "        x_final[i,:]=x_pad\n",
    "        length_array_final.append(len(x_pad)) #Just to check that indeed all are the same length\n",
    "    return x_final,length_array_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we proceed with matching the signal lenghts to the minimum signal length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "shorter_x = []\n",
    "#shorter_lengths = [] #To verify that indeed the lenghts will become a constant.\n",
    "shorter_y = []\n",
    "for i  in range(len(x_pr)):\n",
    "    x_array, y_array, length_array = cut_into_shorter_samples(x_pr[i],y_pr[i],length_pr[i], shortest_length, 5)\n",
    "    for x_g, y_g, l_g in zip(x_array,y_array,length_array):\n",
    "        shorter_x.append(x_g)\n",
    "        shorter_y.append(y_g)\n",
    "        #shorter_lengths.append(l_g) #To verify that indeed the lenghts will become a constant.\n",
    "shorter_x = np.array(shorter_x)\n",
    "shorter_y = np.array(shorter_y)\n",
    "#shorter_lengths = np.array(shorter_lengths) #To verify that indeed the lenghts will become a constant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we proceed into getting frequency-based properties. We use the [speechpy library](https://github.com/astorfi/speechpy) to get the [Mel-frequency_cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), <<a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency>>. For it we base our approach to [Hemanth Kumar Veeranki's](https://github.com/harry-7/speech-emotion-recognition/blob/master/speechemotionrecognition/utilities.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mfcc(x,fs,mfcc_len=35):\n",
    "    mfcc = speechpy.feature.mfcc(x, fs, num_cepstral=mfcc_len)\n",
    "    return mfcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "mfcc_array = []\n",
    "fs = data_complete.fs.values[0]\n",
    "for i  in range(len(shorter_x)):\n",
    "    mfcc_array.append(get_mfcc(shorter_x[i],fs))\n",
    "mfcc_array = np.array(mfcc_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1137, 145, 35)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(mfcc_array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now proceed to form our train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test, y_train, y_test = train_test_split(mfcc_array, shorter_y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_Train:  (909, 145, 35)\n",
      "X_test:  (228, 145, 35)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_Train: \", np.shape(X_train))\n",
    "print(\"X_test: \", np.shape(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = torch.float\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train input:  torch.Size([909, 145, 35])\n",
      "Test input:  torch.Size([228, 145, 35])\n"
     ]
    }
   ],
   "source": [
    "test_input = torch.from_numpy((X_test - X_train.mean(axis=0))/X_train.std(axis=0)).float().to(device)\n",
    "train_input = torch.from_numpy((X_train - X_train.mean(axis=0))/X_train.std(axis=0)).float().to(device)\n",
    "test_target = torch.from_numpy(y_test).long().squeeze().to(device)\n",
    "train_target = torch.from_numpy(y_train).long().squeeze().to(device)\n",
    "print(\"Train input: \", train_input.size())\n",
    "print(\"Test input: \", test_input.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [],
   "source": [
    "reshape_data=False\n",
    "if reshape_data:\n",
    "    train_input = train_input.float().view(train_input.size(0), 35, -1)\n",
    "    test_input = test_input.float().view(test_input.size(0), 35, -1)\n",
    "    print(\"Train input: \", train_input.size())\n",
    "    print(\"Test input: \", test_input.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utils for our Cross Validation and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(dataset,target,k_fold):\n",
    "    \"\"\"\"\n",
    "    Split a dataset in several subsets of equal size.\n",
    "    Inputs : dataset -> original dataset\n",
    "             target -> original target\n",
    "             k_fold -> number of subsets required\n",
    "    Outputs : all_datasets -> list of all subsets of the dataset\n",
    "              all_targets -> list of subsets of the targets, matching with\n",
    "                             all_datasets\n",
    "    \"\"\"\n",
    "    N = dataset.size()[0]\n",
    "    step = N//k_fold\n",
    "    indices = torch.randperm(N);\n",
    "    all_datasets = []\n",
    "    all_targets = []\n",
    "    for k in range(0,k_fold-1):\n",
    "        all_datasets.append(dataset[indices[k*step:(k+1)*step]])\n",
    "        all_targets.append(target[indices[k*step:(k+1)*step]])\n",
    "    all_datasets.append(dataset[indices[(k_fold-1)*step:N]])\n",
    "    all_targets.append(target[indices[(k_fold-1)*step:N]])\n",
    "    return all_datasets, all_targets\n",
    "\n",
    "def build_train_and_validation(all_datasets,all_targets,k_validation,k_fold):\n",
    "    \"\"\"\n",
    "    Create a train set and a validation set from a list of subsets.\n",
    "    Inputs : all_datasets -> list of datasets\n",
    "             all_targets -> list of targets, matching with all_datasets\n",
    "             k_validation -> index in the list of the subset that should be\n",
    "                             used as the validation set\n",
    "             k_fold -> number of subsets\n",
    "    Outputs : train_data, train_target -> Train set\n",
    "              validation_data, validation_target -> Validation set\n",
    "    \"\"\"\n",
    "    assert((k_validation >= 0) & (k_validation < k_fold)),'Index of the validation set is out of bounds'\n",
    "    train = list(range(0,k_validation))+list(range(k_validation + 1, k_fold))\n",
    "    train_data = torch.cat([all_datasets[i] for i in train])\n",
    "    train_target = torch.cat([all_targets[i] for i in train])\n",
    "    validation_data = all_datasets[k_validation]\n",
    "    validation_target = all_targets[k_validation]\n",
    "    return train_data, train_target, validation_data, validation_target\n",
    "\n",
    "def train_model(model, criterion, optimizer, scheduler,train_input, train_target,n_epochs=250,batch_size=50,verbose=0,save_loss=False,tr_loss=[],val_loss=[],tr_err=[],val_err=[],val_input=torch.Tensor(),val_target=torch.Tensor()):\n",
    "    \"\"\"\n",
    "    Trains the model, using criterion as the loss function, and the optimizer wrapped in scheduler\n",
    "    Inputs: model -> the model to be trained (torch.nn.Module)\n",
    "            criterion -> loss function to be used for optimization\n",
    "            optimizer -> optimization algorithm\n",
    "            scheduler -> scheduler, changing the learning rate throughout the epochs\n",
    "            train_input -> input for training, size: (n_samples, n_channels, time_points)\n",
    "            train_target -> trarget values, size: (n_samples)\n",
    "            n_epochs -> number of passes through the whole dataset in training\n",
    "            batch_size -> the batch size for training\n",
    "            n_augmentation -> number of copies of the dataset with added noise that are to be appended to (train_input, train_target)\n",
    "            verbose -> manages output: 1 - only informs about end of training, while 2 about the current epoch % 20\n",
    "            save_loss -> a boolean indicating whether to evaluate the error& loss of the model on the whole training and validation sets\n",
    "            + lists to store results, if save_loss == True\n",
    "    \"\"\"\n",
    "    # Augment the data if necessary and make Variables\n",
    "    #train_input, train_target = Variable(train_input), Variable(train_target)\n",
    "    #if(save_loss):\n",
    "    #    val_input,val_target = Variable(val_input),Variable(val_target)\n",
    "    #train_input.requires_grad_()\n",
    "    #if(save_loss):\n",
    "    #    val_input.requires_grad_()\n",
    "    for e in range(n_epochs):\n",
    "        scheduler.step() # decrease the learning rate\n",
    "        if(verbose>=2):\n",
    "            if(e % 20 == 19):\n",
    "                print(\"Training epoch {} of {}\".format(e+1,n_epochs),end='\\r')\n",
    "\n",
    "        # permutation of the samples, for batch training\n",
    "        indices = torch.randperm(train_input.size(0))\n",
    "        train_input = train_input[indices]\n",
    "        train_target = train_target[indices]\n",
    "\n",
    "        # train on batches\n",
    "        for b in range(0,train_input.size(0),batch_size):\n",
    "            if(train_input.size(0) - b >= batch_size):\n",
    "                output = model(train_input.narrow(0,b,batch_size))\n",
    "                loss = criterion(output,train_target.narrow(0,b,batch_size))\n",
    "                #print(\"Loss: \",loss)\n",
    "                model.zero_grad()\n",
    "                loss.sum().backward()\n",
    "                optimizer.step()\n",
    "        # save loss data, if save_loss is true\n",
    "        if(save_loss):\n",
    "            tr_loss.append(criterion(model(train_input),train_target).item())\n",
    "            val_loss.append(criterion(model(val_input),val_target).item())\n",
    "            tr_err.append(evaluate_error(model,train_input.data,train_target.detach()))\n",
    "            val_err.append(evaluate_error(model,val_input.data,val_target.detach()))\n",
    "    if(verbose == 1):\n",
    "        print(\"Training ended successfully after {} epochs\".format(n_epochs))\n",
    "def evaluate_error(model, data_input, data_target):\n",
    "    \"\"\"\n",
    "    Evaluates the classification error on data_input, with respect to data_target\n",
    "    Inputs: model -> the model to evaluate performance (torch.nn.Module)\n",
    "            data_input -> input data to evaluate over\n",
    "            data_target -> the correct labels for the input data\n",
    "    Outputs: the number of missclassified samples\n",
    "    \"\"\"\n",
    "    #data_input, data_target = Variable(data_input,volatile=True),Variable(data_target,volatile=True)\n",
    "    #data_input.requires_grad_()\n",
    "    with torch.no_grad():  \n",
    "        correct = 0\n",
    "        total = 0\n",
    "        output = model(data_input)\n",
    "        #print(output.data)\n",
    "        temp, predicted = torch.max(output.data, 1)\n",
    "        total += data_target.size(0)\n",
    "        #print(predicted)\n",
    "        #print(data_target)\n",
    "        #(predicted == data_target)\n",
    "        correct += (predicted == data_target).sum().item()\n",
    "        return (100 * (1-(correct / total))) # take the mean, to get a fraction of missclassified samples\n",
    "\n",
    "def cross_validate(model,criterion,optimizer,scheduler,dataset,target,k_fold,n_epochs=250,batch_size = 50,verbose=0):\n",
    "    \"\"\"\n",
    "    Given a model and a dataset, performs k-fold cross validation and return\n",
    "    the list of the errors on each fold.\n",
    "    Inputs : model, optimizer, scheduler, n_epochs, batch_size -> look into train_model\n",
    "             dataset, target -> dataset\n",
    "             k_fold -> number of folds to use\n",
    "    Outputs : train_errors -> list of the train error on each fold\n",
    "              validation_errors -> list of the validation error on each fold\n",
    "    \"\"\"\n",
    "    all_datasets, all_targets = split_data(dataset,target,k_fold) # prepare data for crossvalidation\n",
    "    # containers for loss\n",
    "    mean_tr_loss = [0 for i in range(n_epochs)]\n",
    "    mean_val_loss = [0 for i in range(n_epochs)]\n",
    "    mean_tr_err = [0 for i in range(n_epochs)]\n",
    "    mean_val_err = [0 for i in range(n_epochs)]\n",
    "\n",
    "    for k in range(0,k_fold):\n",
    "        if(verbose >= 1):\n",
    "            print('Cross-Validation : step {} of {}'.format(k+1,k_fold))\n",
    "\n",
    "        tr_loss = []\n",
    "        val_loss = []\n",
    "        tr_err = []\n",
    "        val_err = []\n",
    "\n",
    "\n",
    "        model.reset() # reset the model parameters\n",
    "        tr_data, tr_target, val_data,val_target = build_train_and_validation(all_datasets,all_targets,k,k_fold)\n",
    "        train_model(model,criterion,optimizer,scheduler,tr_data,tr_target,n_epochs,batch_size,verbose,True,tr_loss,val_loss,tr_err,val_err,val_data,val_target)\n",
    "        mean_tr_loss = [old + new/k_fold for old,new in zip(mean_tr_loss,tr_loss)]\n",
    "        mean_val_loss = [old + new/k_fold for old,new in zip(mean_val_loss,val_loss)]\n",
    "        mean_tr_err = [old + new/k_fold for old,new in zip(mean_tr_err,tr_err)]\n",
    "        mean_val_err = [old + new/k_fold for old,new in zip(mean_val_err,val_err)]\n",
    "        temp_tr_err = evaluate_error(model,tr_data,tr_target)\n",
    "        temp_val_err = evaluate_error(model,val_data,val_target)\n",
    "\n",
    "        if(verbose >=2):\n",
    "            print('End of step {} : Train error = {} , Validation error = {}'.format(k+1,tr_err[-1],val_err[-1]))\n",
    "\n",
    "\n",
    "    return mean_tr_loss, mean_val_loss, mean_tr_err, mean_val_err"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models\n",
    "The different models we have:\n",
    "1. ShallowLinear. A two MLP net. Linear baseline.\n",
    "2. A CNN based on depthwise and point wise convolution layers. Based on [EEG-Net](https://arxiv.org/abs/1611.08024) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ShallowLinear(nn.Module):\n",
    "    \"\"\"2-perceptron neural net: Implements a net: \n",
    "    linear(in_features, size_hidden) -> ReLU() -> linear(size_hidden, n_classes)\"\"\"\n",
    "    def __init__(self, in_features, size_hidden, n_classes=2):\n",
    "        \"\"\"Arguments:\n",
    "            in_features - the number of features of the input\n",
    "            size_hidden - number of features in the unique hidden layer\n",
    "            n_classes - the number of classes in the classification task\n",
    "        \"\"\"\n",
    "        super(ShallowLinear, self).__init__()\n",
    "        self.in_features = in_features\n",
    "        self.size_hidden = size_hidden\n",
    "        self.n_classes = n_classes\n",
    "        print(\"Output Classes: \", self.n_classes)\n",
    "        self.lin1 = nn.Linear(self.in_features, self.size_hidden)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.lin2 = nn.Linear(size_hidden, self.n_classes)\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, self.in_features)\n",
    "        x = self.lin1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.lin2(x)\n",
    "        #x = torch.sigmoid(x)\n",
    "        return x\n",
    "    def reset(self):\n",
    "        self.lin1.reset_parameters()\n",
    "        self.lin2.reset_parameters()\n",
    "    def name(self):\n",
    "        return '2-perceptron neural net'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EEGNet_2018(nn.Module):\n",
    "    \"\"\"EEGNet : Compact CNN for EEG data. Sound data is similar as they have information local in time and several possible channels.\n",
    "    Input is: torch.Size([#Samples, #Channels, #TimePoints]), ex: torch.Size([316, 28, 500]) or torch.Size([316, 28, 50])\n",
    "    Described in :\n",
    "    V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and\n",
    "    B. J. Lance. EEGNet: A Compact Convolutional Network for EEG-based\n",
    "    Brain-Computer Interfaces. CoRR, abs/1611.08024, 2016\n",
    "    https://arxiv.org/abs/1611.08024\"\"\"\n",
    "    def __init__(self, n_channels, n_time_points, n_filters=8, n_classes=2):\n",
    "        super(EEGNet_2018, self).__init__()\n",
    "        #For the last layer, the classification layer lenght\n",
    "        self.kernelL1Conv_1 = 10\n",
    "        self.kernelL2Conv_1 = 6\n",
    "        self.length = 4*37\n",
    "        self.n_channels = n_channels\n",
    "        self.n_time_points = n_time_points\n",
    "        #EEGNet - Layer - 1\n",
    "        filters_layer_1 = n_filters\n",
    "        self.l1Conv2d = nn.Conv2d(1,filters_layer_1,(1,self.kernelL1Conv_1),padding = 0)\n",
    "        self.batch1 = nn.BatchNorm2d(filters_layer_1,False)\n",
    "        self.padd1 = nn.ZeroPad2d((self.kernelL1Conv_1//2,(self.kernelL1Conv_1//2)-1))\n",
    "        self.l1DeptConv2d = nn.Conv2d(filters_layer_1,filters_layer_1,(self.n_channels,1),padding = 0, groups=filters_layer_1)#Depthwise\n",
    "        self.batch1_2 = nn.BatchNorm2d(filters_layer_1,False)\n",
    "        #EEGNet - Layer - 2\n",
    "        filters_layer_2 = n_filters\n",
    "        self.conv2 = nn.Conv2d(filters_layer_1,filters_layer_2,(1,self.kernelL2Conv_1),groups = filters_layer_2)#Depthwise\n",
    "        self.conv2_2 = nn.Conv2d(filters_layer_2,filters_layer_2,kernel_size=1)#Pointwise\n",
    "        self.batch2 = nn.BatchNorm2d(filters_layer_2,False)\n",
    "        self.padd2 = nn.ZeroPad2d((self.kernelL2Conv_1//2,abs((self.kernelL2Conv_1//2)-1)))\n",
    "        self.pool2 = nn.AvgPool2d(1,4)#(self.kernelL2Conv_1//2))\n",
    "        #EEGNet - Layer - 3\n",
    "        self.conv3 = nn.Conv2d(filters_layer_2,n_classes,(1,37))\n",
    "        #EEGNet - Clasification Layer\n",
    "        #self.lin = nn.Linear(n_classes, n_classes)\n",
    "        self.softMax = nn.Softmax(dim=1)\n",
    "    def forward(self,x):\n",
    "        verbose_inside = 0\n",
    "        #Reshape for the Convolutions\n",
    "        x = x.view(-1,1,self.n_channels,self.n_time_points);\n",
    "        #Layer - 1\n",
    "        if(verbose_inside): print(\"View: \",x.size())\n",
    "        x = self.l1Conv2d(x)\n",
    "        if(verbose_inside): print(\"1_l12d: \",x.size())\n",
    "        x = self.batch1(x)\n",
    "        x = nn.functional.elu(x)\n",
    "        if(verbose_inside): print(\"1_l12d_after: \",x.size())\n",
    "        x = self.padd1(x)\n",
    "        if(verbose_inside): print(\"1_padd: \",x.size())\n",
    "        x = self.l1DeptConv2d(x)\n",
    "        if(verbose_inside): print(\"1_dept: \",x.size())\n",
    "\n",
    "        x = self.batch1_2(x)\n",
    "        x = nn.functional.elu(x)\n",
    "        x = nn.functional.dropout2d(x, 0.25)\n",
    "        #Layer - 2\n",
    "        x = self.conv2(x)\n",
    "        if(verbose_inside): print(\"2_conv1: \",x.size())\n",
    "        x = self.conv2_2(x)\n",
    "        if(verbose_inside): print(\"2_conv2: \",x.size())\n",
    "        x = self.batch2(x)\n",
    "        x = nn.functional.elu(x)\n",
    "        x = self.padd2(x)\n",
    "        if(verbose_inside): print(\"2_padd2: \",x.size())\n",
    "        x = self.pool2(x)\n",
    "        if(verbose_inside): print(\"2_pool2: \",x.size())\n",
    "        x = nn.functional.dropout2d(x, 0.25)\n",
    "        #x = self.conv3(x)\n",
    "        if(verbose_inside): print(\"3_conv: \",x.size())\n",
    "        x = self.conv3(x)\n",
    "        #Clasification Layer\n",
    "        #x = x.view(-1,self.length)\n",
    "        if(verbose_inside): print(\"cl_view: \",x.size())\n",
    "        #x = self.lin.forward(x)\n",
    "        #x = self.softMax.forward(x)\n",
    "        if(verbose_inside): print(\"softmax: \",x.size())\n",
    "        x = x.squeeze()\n",
    "        if(verbose_inside): print(\"final: \",x.size())\n",
    "        return x\n",
    "    def reset(self):\n",
    "        self.l1Conv2d.reset_parameters()\n",
    "        self.l1DeptConv2d.reset_parameters()\n",
    "        self.conv2.reset_parameters()\n",
    "        self.conv2_2.reset_parameters()\n",
    "        self.conv3.reset_parameters()\n",
    "        #self.lin.reset_parameters()\n",
    "    def name(self):\n",
    "        return \"EEG Net (2018)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Audio_CNN(nn.Module):\n",
    "    \"\"\"Based on: \"\"\"\n",
    "    def __init__(self, n_channels, n_time_points, n_filters=14, n_classes=2):\n",
    "        super(Audio_CNN, self).__init__()\n",
    "        #For the last layer, the classification layer lenght\n",
    "        self.n_channels = n_channels\n",
    "        self.n_time_points = n_time_points\n",
    "        self.spatConv = nn.Conv2d(1,n_filters,(self.n_channels,1),bias=False)\n",
    "        self.timeConv = nn.Conv2d(n_filters,n_filters,(1,40)) # +1 For having convenient output dim (400 or 40)\n",
    "        self.batchnorm = nn.BatchNorm2d(n_filters)\n",
    "        self.meanPooling = nn.AvgPool2d((1,4),stride=2) \n",
    "        self.dropout = nn.Dropout(p=0.25)\n",
    "        self.classifier = nn.Conv2d(n_filters,n_classes,(1,52))\n",
    "        self.softmax = nn.LogSoftmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        verbose_inside=0\n",
    "        if(verbose_inside): print(\"start: \",x.size())\n",
    "        x = x.view(-1,1,self.n_channels,self.n_time_points);\n",
    "        if(verbose_inside): print(\"view: \",x.size())\n",
    "        x = self.spatConv(x)\n",
    "        if(verbose_inside): print(\"space: \",x.size())\n",
    "        x = self.timeConv(x)\n",
    "        if(verbose_inside): print(\"time: \",x.size())\n",
    "        x = self.batchnorm(x)\n",
    "        x = x*x;\n",
    "        if(verbose_inside): print(\"square: \",x.size())\n",
    "        x = self.meanPooling(x)\n",
    "        if(verbose_inside): print(\"mean: \",x.size())\n",
    "        x = torch.log(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.classifier(x)\n",
    "        if(verbose_inside): print(\"class: \",x.size())\n",
    "        #x = self.softmax(x)\n",
    "        x = x.squeeze()\n",
    "        if(verbose_inside): print(\"final: \",x.size())\n",
    "        return x\n",
    "\n",
    "    def reset(self):\n",
    "        self.timeConv.reset_parameters()\n",
    "        self.spatConv.reset_parameters()\n",
    "        self.classifier.reset_parameters()\n",
    "\n",
    "    def name(self):\n",
    "        return \"Shallow CNN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN_Base(nn.Module):\n",
    "    \"\"\"Based on: \"\"\"\n",
    "    def __init__(self, n_channels, n_time_points, n_hidden = 10, n_filters=14, n_classes=2):\n",
    "        super(CNN_Base, self).__init__()\n",
    "        #For the last layer, the classification layer lenght\n",
    "        self.n_channels = n_channels\n",
    "        self.n_time_points = n_time_points\n",
    "        self.size_hidden = n_hidden\n",
    "        self.n_filters = n_filters\n",
    "        self.conv_1 = torch.nn.Conv1d(1, self.n_filters, kernel_size=500, padding=0)\n",
    "        self.batchnorm = nn.BatchNorm1d(self.n_filters)\n",
    "        self.meanPooling = nn.AvgPool1d(40,stride=4) \n",
    "        self.dropout = nn.Dropout(p=0.25)\n",
    "        self.conv_2 = torch.nn.Conv1d(n_filters, self.n_filters, kernel_size=500, padding=0)\n",
    "        self.batchnorm = nn.BatchNorm1d(self.n_filters)\n",
    "        self.meanPooling = nn.AvgPool1d(40,stride=4) \n",
    "        self.dropout = nn.Dropout(p=0.25)\n",
    "        self.conv_class = torch.nn.Conv1d(n_filters, n_classes, kernel_size=150)\n",
    "    def forward(self, x):\n",
    "        verbose_inside=0\n",
    "        if(verbose_inside): print(\"start: \",x.size())\n",
    "        x = x.view(-1,1,self.n_time_points*self.n_channels)\n",
    "        if(verbose_inside): print(\"view: \",x.size())\n",
    "        x = self.conv_1(x)\n",
    "        if(verbose_inside): print(\"conv1: \",x.size())\n",
    "        x = self.batchnorm(x)\n",
    "        x = self.meanPooling(x)\n",
    "        if(verbose_inside): print(\"meanpool: \",x.size())\n",
    "        x = self.dropout(x)\n",
    "        x = nn.functional.elu(x)\n",
    "        x = self.conv_2(x)\n",
    "        if(verbose_inside): print(\"conv2: \",x.size())\n",
    "        x = self.batchnorm(x)\n",
    "        x = self.meanPooling(x)\n",
    "        if(verbose_inside): print(\"meanpool: \",x.size())\n",
    "        x = self.dropout(x)\n",
    "        x = nn.functional.elu(x)\n",
    "        x = self.conv_class(x)\n",
    "        if(verbose_inside): print(\"conv_class: \",x.size())\n",
    "        x = x.squeeze()\n",
    "        if(verbose_inside): print(\"final: \",x.size())\n",
    "        return x\n",
    "\n",
    "    def reset(self):\n",
    "        self.conv_1.reset_parameters()\n",
    "        self.conv_2.reset_parameters()\n",
    "        self.conv_class.reset_parameters()\n",
    "\n",
    "    def name(self):\n",
    "        return \"CNN_Base\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145 35\n"
     ]
    }
   ],
   "source": [
    "n_channels = train_input.size()[1]\n",
    "n_features = train_input.size()[2]\n",
    "print(n_channels,n_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output Classes:  7\n"
     ]
    }
   ],
   "source": [
    "model =  ShallowLinear(in_features=n_channels*n_features,size_hidden=20,n_classes=len(emotion_numbers))\n",
    "model_2 =  EEGNet_2018(n_channels=n_channels,n_time_points=n_features,n_filters=20, n_classes=len(emotion_numbers))\n",
    "model_3 =  Audio_CNN(n_channels=n_channels,n_time_points=n_features,n_filters=20, n_classes=len(emotion_numbers))\n",
    "model_4 =  CNN_Base(n_channels=n_channels,n_time_points=n_features,n_filters=15, n_classes=len(emotion_numbers))\n",
    "\n",
    "optimizer = torch.optim.Adam\n",
    "scheduler_gamma = 0.95\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "cross_val = True\n",
    "model_list = [model,model_2,model_3,model_4]\n",
    "optimizer_list = [optimizer]*len(model_list)\n",
    "scheduler_gamma_list = [scheduler_gamma]*len(model_list)\n",
    "criterion_list = [criterion]*len(model_list)\n",
    "lr_list = [1e-2,1e-4,1e-3,3e-4]\n",
    "epochs_list = [20,20,20,40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: 2-perceptron neural net\n",
      "Total parameters:  101667\n",
      "Trainable parameters:  101667\n",
      "Model: EEG Net (2018)\n",
      "Total parameters:  9007\n",
      "Trainable parameters:  9007\n",
      "Model: Shallow CNN\n",
      "Total parameters:  26247\n",
      "Trainable parameters:  26247\n",
      "Model: CNN_Base\n",
      "Total parameters:  135817\n",
      "Trainable parameters:  135817\n"
     ]
    }
   ],
   "source": [
    "for model in model_list:\n",
    "    pytorch_total_params = sum(p.numel() for p in model.parameters())\n",
    "    pytorch_total_params_tr = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "    print(\"Model: \"+model.name())\n",
    "    print(\"Total parameters: \",pytorch_total_params)\n",
    "    print(\"Trainable parameters: \", pytorch_total_params_tr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results & Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val = True\n",
    "save_loss = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 4 of 4 : CNN_Base\n",
      "Cross validating... on 4 folds\n",
      "Cross-Validation : step 1 of 4\n",
      "End of step 1 : Train error = 45.307917888563054 , Validation error = 68.28193832599119\n",
      "Cross-Validation : step 2 of 4\n",
      "End of step 2 : Train error = 59.970674486803524 , Validation error = 68.28193832599119\n",
      "Cross-Validation : step 3 of 4\n",
      "End of step 3 : Train error = 64.95601173020528 , Validation error = 67.84140969162995\n",
      "Cross-Validation : step 4 of 4\n",
      "End of step 4 : Train error = 72.98091042584434 , Validation error = 72.80701754385966\n",
      "Mean train error : 60.80387863285405, mean validation error : 69.303075971868\n",
      "Training...\n",
      "Train error = 81.73817381738174 ; Test error = 80.26315789473684 \n"
     ]
    }
   ],
   "source": [
    "dump_final = []\n",
    "if(cross_val):\n",
    "    dump = []\n",
    "\n",
    "for i in range(3,len(model_list)):\n",
    "    model = model_list[i]\n",
    "    optimizer = optimizer_list[i](model.parameters(),lr=lr_list[i])\n",
    "    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,scheduler_gamma_list[i])\n",
    "    criterion = criterion_list[i]\n",
    "    print(\"Model {} of {} : {}\".format(i+1,len(model_list),model.name()))\n",
    "    if(cross_val):\n",
    "        k_fold = 4\n",
    "        print(\"Cross validating... on {} folds\".format(k_fold))\n",
    "        tr_loss, val_loss, tr_err, val_err = cross_validate(model, criterion,optimizer,scheduler,train_input,train_target,k_fold,batch_size=10,n_epochs=epochs_list[i],verbose=2)\n",
    "        print(\"Mean train error : {}, mean validation error : {}\".format(tr_err[-1],val_err[-1]))\n",
    "        dump.append((model.name(), \" train \", tr_loss, tr_err))\n",
    "        dump.append((model.name(), \" validation \", val_loss, val_err))\n",
    "\n",
    "    model.reset()\n",
    "    print(\"Training...\")\n",
    "    train_model(model,criterion,optimizer,scheduler,train_input,train_target,n_epochs=epochs_list[i],batch_size=10,verbose=2)\n",
    "    final_tr_error = evaluate_error(model,train_input,train_target)\n",
    "    final_te_error = evaluate_error(model,test_input,test_target)\n",
    "    print(\"Train error = {} ; Test error = {} \".format(final_tr_error,final_te_error))\n",
    "\n",
    "    dump_final.append((model.name(), \" train \", final_tr_error))\n",
    "    dump_final.append((model.name(), \" test \" , final_te_error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('final_'+'freq_all_bad'+'.txt','w+')\n",
    "json.dump(dump_final,file)\n",
    "file.close()\n",
    "\n",
    "if(cross_val):\n",
    "    file = open('cv_'+'freq_all_bad'+'.txt','w+')\n",
    "    json.dump(dump,file)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1.0 models to plot in this file.\n",
      "Print the models all together\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb5a047d5f8>"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "path='plots_current'+ file_separator\n",
    "n_time_points = shortest_length\n",
    "f_name = 'cv_freq_all_bad.txt'\n",
    "file= open(f_name,'r')\n",
    "\n",
    "lines = json.load(file)\n",
    "n_models = len(lines)/2\n",
    "print(\"There are \"+str(n_models)+\" models to plot in this file.\") #should be 4\n",
    "print(\"Print the models all together\")\n",
    "colors = ['r','r','b','b','g','g']\n",
    "fig1 = plt.figure(figsize=(8,8))\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss (Cross entropy)')\n",
    "for i in range(0,len(lines),2):\n",
    "    model = lines[i][0]\n",
    "    tr_loss = lines[i][2][2:]\n",
    "    val_loss = lines[i+1][2][2:]\n",
    "    epochs = range(2,len(tr_loss)+2)\n",
    "    plt.plot(epochs,tr_loss,label=model+', train',color=colors[i],linestyle='--')\n",
    "    plt.plot(epochs,val_loss,label=model+', validation',color=colors[i+1])\n",
    "plt.legend()\n",
    "#plt.savefig(path+\"loss_all_freq_\"+str(n_time_points)+\".png\")\n",
    "fig2 = plt.figure(figsize=(8,8))\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification error rate')\n",
    "for i in range(0,len(lines),2):\n",
    "    model = lines[i][0]\n",
    "    tr_err = lines[i][3][2:]\n",
    "    val_err = lines[i+1][3][2:]\n",
    "    epochs = range(2,len(tr_err)+2)\n",
    "    plt.plot(epochs,tr_err,label=model+', train',color=colors[i],linestyle='--')\n",
    "    plt.plot(epochs,val_err,label=model+', validation',color=colors[i+1])\n",
    "    plt.ylim((0,100.0))\n",
    "plt.legend()\n",
    "#plt.savefig(path+\"classErr_all_freq_\"+str(n_time_points)+\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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