IntroDS001.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduction to Data Science<br/>\n",
    "[Oregon Curriculum Network](http://www.4dsolutions.net/ocn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebooks: \n",
    "[[1](IntroDS001.ipynb)]\n",
    "[[2](IntroDS002.ipynb)]\n",
    "[[3](IntroDS003.ipynb)]\n",
    "[[4](IntroDS004.ipynb)]\n",
    "[[5](IntroDS005.ipynb)]\n",
    "[[6](IntroDS006.ipynb)]\n",
    "[[Appendix](Appendix.ipynb)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An Introduction to Data Science\n",
    "\n",
    "\n",
    "Good Evening data scientists... students, fellow faculty.  \n",
    "\n",
    "This is a course about process and study methods, techniques around learning, in addition to an exploration of data science.  As technology changes, so do our techniques.  \n",
    "\n",
    "In many cases, the way one learns a discipline is integral to the practice of that same discipline. Consider wine tasting.  Consider any sport.  The practice is likewise the profession, which is why we say doctors and lawyers \"practice\" medicine and law.\n",
    "\n",
    "If you learn it by different means, the jury may take awhile to recognize you (judge you) as a bona fide member of the team.  That's why degrees and certifications may well be important (relevant).\n",
    "\n",
    "On the other hand, many disciplines are still evolving quickly, and if you pioneer new ways to teach and learn them, you'll gain recognition, in proportion to which people try these new ways, and find them effective.  Or maybe you don't gain recognition, yet benefit directly from your self teaching effectiveness.\n",
    "\n",
    "A principal technique I suggest here is installing and opening JupyterLab and starting a set of Notebooks of your own.  Many of these Notebooks will serve as scratch pads, where you test out your growing understanding of syntax and algorithms.  Other Notebooks you may wish to share with the public, in which case a free service such as Github, which also renders the Notebooks, is yours to use.\n",
    "\n",
    "Now that something technical is going on, look along the deep history axis, for the origins of what you're studying, and extrapolate regarding where this discipline might be headed.  You're being speculative (predictive) based on what data you've tuned in.\n",
    "\n",
    "[As Thomas Paine suggested](http://www.piney.com/TomPaineProph.html), \"to prophesy\" is akin to \"continuing a poem or song\" having heard it so far, in that you're predicting (projecting, extrapolating) how a story (plot) or melody (song) might sustain its coherence (gravitas, inertia) going forward.  \n",
    "\n",
    "The music is sometimes \"self fulfilling\" in the sense that the future sometimes conforms to our expectations largely *because* these were our expectations.  Our imaginations brought us something we dreamed about, such as an invention, a new technology.\n",
    "\n",
    "Other times, our expectations are confounded.  In what direction might we appropriate our propitiations?  Sometimes we look for ways to compensate for an apparent lack of steering ability (lack of control; here  thinking of the Greek root for [\"cybernetics\"](https://en.wikipedia.org/wiki/Cybernetics) in \"rudder\"). \n",
    "\n",
    "Once your Notebooks are up and running, I encourage the practice of harvesting bookmarks (hyperlinks), which entails doing enough research to begin filtering out the ones you don't want to keep.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bookmark(Title='Python', Link='http://www.python.org')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# direct from Python Standard Library\n",
    "from collections import namedtuple\n",
    "Bkmk = namedtuple(\"Bookmark\", \"Title Link\") \n",
    "example = Bkmk(\"Python\", \"http://www.python.org\")\n",
    "example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is OS\n",
      " Volume Serial Number is BE21-B667\n",
      "\n",
      " Directory of C:\\Users\\Kirby\\Documents\\Curriculum_Development\n",
      "\n",
      "06/29/2020  11:44 PM    <DIR>          .\n",
      "06/29/2020  11:44 PM    <DIR>          ..\n",
      "06/29/2020  11:44 PM    <DIR>          .ipynb_checkpoints\n",
      "06/29/2020  11:43 PM             1,651 Appendix.ipynb\n",
      "06/29/2020  11:44 PM    <DIR>          data\n",
      "06/29/2020  11:43 PM           311,898 Eigen_stuff.ipynb\n",
      "06/29/2020  11:43 PM            70,040 foo.png\n",
      "06/29/2020  11:43 PM    <DIR>          images\n",
      "06/29/2020  11:43 PM           245,927 IntroDS001.ipynb\n",
      "06/29/2020  11:43 PM           358,397 IntroDS002.ipynb\n",
      "06/29/2020  11:43 PM           639,307 IntroDS003.ipynb\n",
      "06/29/2020  11:43 PM           195,403 IntroDS004.ipynb\n",
      "06/29/2020  11:43 PM           113,957 IntroDS005.ipynb\n",
      "06/29/2020  11:43 PM           312,568 IntroDS006.ipynb\n",
      "06/29/2020  11:43 PM            18,431 LICENSE\n",
      "06/29/2020  11:43 PM           553,654 LinearRegression.ipynb\n",
      "06/29/2020  11:43 PM            57,281 logos2.ipynb\n",
      "06/29/2020  11:44 PM    <DIR>          python\n",
      "06/29/2020  11:43 PM               248 README.md\n",
      "06/29/2020  11:43 PM           334,049 VisualizationLab.ipynb\n",
      "06/29/2020  11:43 PM               106 zoo.csv\n",
      "              15 File(s)      3,212,917 bytes\n",
      "               6 Dir(s)  405,444,169,728 bytes free\n"
     ]
    }
   ],
   "source": [
    "! dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below gives your ecosystem a workout, presuming you've cloned the repo and have the necessary file tree structure.\n",
    "\n",
    "The ```python``` subfolder has its own Python modules, which you may access simply by importing it.  For this to work, a Python ```__init__.py``` file must be present, potentially devoid of any code.  This file makes the subfolder importable if in the Python path (which includes the current working directory)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anaconda.org                   http://anaconda.org \n",
      "Python.org                     http://python.org   \n",
      "Python Docs                    https://docs.python.org/3/\n",
      "Spaghetti Code                 http://c2.com/cgi/wiki?SpaghettiCode\n",
      "Structured Programming         http://c2.com/cgi/wiki?StructuredProgramming\n"
     ]
    }
   ],
   "source": [
    "import imp  # module to force recompile, useful if editing on the fly\n",
    "from python import make_links_v3 as links\n",
    "imp.reload(links) \n",
    "links.main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are your criteria?  \n",
    "\n",
    "When it comes to Youtubes or other (usually asynchronous) taped presentations, you might want them to be in a language you at least somewhat understand.  \n",
    "\n",
    "\"Language\" would therefore also be a \"principal component\" of your ranking algorithm, to use the data science terms.  After language might come \"Subject\".\n",
    "\n",
    "A reason I stress the practice of curating bookmarks is a lot goes by in life that we don't snag at the time and then have a harder time refinding.  I suggest curating your bookmarks in various ways, including by means of [Python programs](https://www.kaggle.com/learn/python).\n",
    "\n",
    "That's right, I'm throwing a programming language into the mix.  The stress on Jupyter was a tip-off this was coming.  Jupyter is named in honor of three of its first kernel languages:  Julia, Python, and R.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I recommend a polyglot approach e.g. when you're learning pandas in Python, consider eyeballing some R for contrast and comparison.  The R could be \"back burner\" (of lower importance) but the fact that it also has Data Frames may accelerate your Python learning.  Then turn it around if you like, and make R front burner for awhile, leveraging your existing knowledge of Python, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Park</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Height</th>\n",
       "      <th>drop</th>\n",
       "      <th>Length</th>\n",
       "      <th>Yr Opened</th>\n",
       "      <th>Inversions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Top Thrill Dragster</td>\n",
       "      <td>Cedar Point</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>USA</td>\n",
       "      <td>60</td>\n",
       "      <td>120.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Superman The Escape</td>\n",
       "      <td>Six Flags Magic Mountain</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>28</td>\n",
       "      <td>100.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>328.1</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Millennium Force</td>\n",
       "      <td>Cedar Point</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>USA</td>\n",
       "      <td>165</td>\n",
       "      <td>93.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6595.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Goliath</td>\n",
       "      <td>Six Flags Magic Mountain</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>180</td>\n",
       "      <td>85.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Titan</td>\n",
       "      <td>Six Flags Over Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>USA</td>\n",
       "      <td>210</td>\n",
       "      <td>85.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>5312.0</td>\n",
       "      <td>2001</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name                      Park       State Country  \\\n",
       "0  Top Thrill Dragster               Cedar Point        Ohio     USA   \n",
       "1  Superman The Escape  Six Flags Magic Mountain  California     USA   \n",
       "2     Millennium Force               Cedar Point        Ohio     USA   \n",
       "3              Goliath  Six Flags Magic Mountain  California     USA   \n",
       "4                Titan      Six Flags Over Texas       Texas     USA   \n",
       "\n",
       "   Duration  Speed  Height   drop  Length  Yr Opened  Inversions  \n",
       "0        60  120.0   420.0  400.0  2800.0       2003           0  \n",
       "1        28  100.0   415.0  328.1  1235.0       1997           0  \n",
       "2       165   93.0   310.0  300.0  6595.0       2000           0  \n",
       "3       180   85.0   235.0  255.0  4500.0       2000           0  \n",
       "4       210   85.0   245.0  255.0  5312.0       2001           0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rollercoasters = pd.read_csv(\"./data/roller_coasters.csv\")\n",
    "rollercoasters.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About The Way I Teach...\n",
    "\n",
    "In this immersive bootcamp-like experience, featuring basecamps and steep learning curves (\"hikes\"), we get to be data scientists from the start. \n",
    "\n",
    "Much as [Keith Devlin](https://youtu.be/NnVubBrATIU) paints animals as mathematicians, subconsciously optimizing, following some principle of least waste (sometimes), we look to our own patterns of inference for prototypes and intuitions.  What mathematics makes conscious, in the form of algorithms, describes an intelligence otherwise not conscious of itself (unconscious).\n",
    "\n",
    "Per my three hour presentation [at a Pycon in Chicago](https://worldgame.blogspot.com/2009/03/urner-workshop.html), Hyatt-Regency International Room, co-appearing with PSF chairman Steve Holden, I have a pedagogical theory.\n",
    "\n",
    "I think of Lore and Technic as perpendicular to one another, with an opportunity curve a fixed bandwidth, the optimum rate of assimilation.  We're able to max out more (better optimize) if the presentation oscillates between History and Logic, Story and Nit Picky Detail.\n",
    "\n",
    "The reason is obvious I think, and I'm not introducing my theory as especially controversial:  stories provide more background and context for what may seem a too irrelevant foreground skill.  We're also better able to retain detailed information, if we have heuristics and mnemonics with which to glue it all together.\n",
    "\n",
    "I'm also happy with gynmasium excercise metaphors, meaning physical body and muscle building workouts.  When we do sets, we learn to rest between them, and just as important, we learn to vary the activity.  \n",
    "\n",
    "Oscillating between stories and heads down technical lectures, has the flavor of giving muscle groups a rest. The point is to keep cycling, thereby to spiral (drill into it, whatever it is, more deeply).\n",
    "\n",
    "As type Human, we're born hungry for data and soak it up through all senses, and begin to construct our model of the world.  \n",
    "\n",
    "Perhaps we become more scientific about it over time, but if you're reading this now, you've made it to the sciences already."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Data Science?\n",
    "\n",
    "Data Science is defined in part as a result of hindsight; looking back on Statistics from an age wherein we apply Machine Learning to Big Data, enabling various kinds of Artifical Intelligence such as face, voice and character recognition, as well as generation.\n",
    "\n",
    "A lot of the science is coming from Computer Science.\n",
    "\n",
    "Statistics, one could say, is [a science of inductive reasoning](https://en.wikiquote.org/wiki/Inductive_reasoning), versus the logical techniques of purely deductive reasoning, such as we associate with mathematics.\n",
    "\n",
    "However, as [George Polya](https://www.goodreads.com/book/show/787845.Induction_and_Analogy_in_Mathematics) points out in his famous two volumes, mathematics is replete with inductive reasoning.  Euler was clear on this too.\n",
    "\n",
    "Why didn't people go with [Cybernetics](https://www.nature.com/articles/s42256-019-0100-x) instead of Data Science, since feedback loops and steering functions are intrinsic to both?  Answer:  [Systems Science](https://en.wikipedia.org/wiki/Systems_science) was invented as the umbrella term.\n",
    "\n",
    "Gregory Bateson, who wrote about Cybernetics, [famously said](https://www.cs.bham.ac.uk/research/projects/cogaff/misc/information-difference.html):  bits of information (one could say signal) consist of \"differences that make a difference\" (serve as puzzle pieces) against all the background noise (entropy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do We Have Textbooks?\n",
    "\n",
    "Sure.\n",
    "\n",
    "Let me recommend a couple, both free online:\n",
    "\n",
    "* [Think Stats 2e by Allen Downey](https://greenteapress.com/wp/think-stats-2e/)\n",
    "* [Data Science Handbook by Jake Vanderplas](https://jakevdp.github.io/PythonDataScienceHandbook/)\n",
    "\n",
    "However let's each develop our ability to curate learning materials.  The internet is an expanding universe of learning and teaching opportunities.\n",
    "\n",
    "Want to dive into Machine Learning? Maybe try [the Kaggle tutorial](https://www.kaggle.com/learn/pandas)?  \n",
    "\n",
    "We also usually wish to gather the tools together locally, not just use them in the cloud.  Lets start with [Anaconda](https://docs.conda.io/projects/conda/en/latest/).\n",
    "\n",
    "### Lab Activity:\n",
    "\n",
    "Add your own links here (when you get your version):\n",
    "\n",
    "* [Advice from a Pro: Jason Dsouza\n",
    "in Towards Data Science](https://towardsdatascience.com/data-science-with-python-getting-started-4d48e28ca696)\n",
    "\n",
    "* [More Advice from a Pro: Santiago Víquez Segura\n",
    " in Towards Data Science](https://towardsdatascience.com/if-i-had-to-start-learning-data-science-again-how-would-i-do-it-78a72b80fd93)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be reviewing / introducing the Python computer language as we go tonight, and you may want to follow along."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random  # Python Standard Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[63, 75, 21, 1, 67, 78, 48, 43, 26, 98, 19, 20, 85, 91, 43, 92, 99, 48, 86, 83]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 20\n",
    "sample = [random.randint(1, 100) for _ in range(sample_size)]\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5635275059407465"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = [random.random() for _ in range(sample_size)]\n",
    "sum(sample)/len(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataScientist:\n",
    "    def flip_a_coin(self):\n",
    "        coin = [\"HEADS\", \"TAILS\"]\n",
    "        return random.choice(coin)\n",
    "        \n",
    "class Human(DataScientist):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.name = \"Monty Python\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monty Python gets TAILS\n"
     ]
    }
   ],
   "source": [
    "e = Human()\n",
    "template = \"{} gets {}\" \n",
    "print(template.format(e.name, e.flip_a_coin()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is OS\n",
      " Volume Serial Number is BE21-B667\n",
      "\n",
      " Directory of C:\\Users\\Kirby\\Documents\\Curriculum_Development\n",
      "\n",
      "06/29/2020  11:44 PM    <DIR>          .\n",
      "06/29/2020  11:44 PM    <DIR>          ..\n",
      "06/29/2020  11:44 PM    <DIR>          .ipynb_checkpoints\n",
      "06/29/2020  11:43 PM             1,651 Appendix.ipynb\n",
      "06/29/2020  11:44 PM    <DIR>          data\n",
      "06/29/2020  11:43 PM           311,898 Eigen_stuff.ipynb\n",
      "06/29/2020  11:43 PM            70,040 foo.png\n",
      "06/29/2020  11:43 PM    <DIR>          images\n",
      "06/29/2020  11:43 PM           245,927 IntroDS001.ipynb\n",
      "06/29/2020  11:43 PM           358,397 IntroDS002.ipynb\n",
      "06/29/2020  11:43 PM           639,307 IntroDS003.ipynb\n",
      "06/29/2020  11:43 PM           195,403 IntroDS004.ipynb\n",
      "06/29/2020  11:43 PM           113,957 IntroDS005.ipynb\n",
      "06/29/2020  11:43 PM           312,568 IntroDS006.ipynb\n",
      "06/29/2020  11:43 PM            18,431 LICENSE\n",
      "06/29/2020  11:43 PM           553,654 LinearRegression.ipynb\n",
      "06/29/2020  11:43 PM            57,281 logos2.ipynb\n",
      "06/29/2020  11:44 PM    <DIR>          python\n",
      "06/29/2020  11:43 PM               248 README.md\n",
      "06/29/2020  11:43 PM           334,049 VisualizationLab.ipynb\n",
      "06/29/2020  11:43 PM               106 zoo.csv\n",
      "              15 File(s)      3,212,917 bytes\n",
      "               6 Dir(s)  405,444,100,096 bytes free\n"
     ]
    }
   ],
   "source": [
    "! dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
      "I don't know how to do that yet,\n",
      "numpy not yet imported as np\n"
     ]
    }
   ],
   "source": [
    "print(list(range(2,11)))\n",
    "try:\n",
    "    np.linspace(2, 10, 1000)\n",
    "except NameError:\n",
    "    print(\"I don't know how to do that yet,\\nnumpy not yet imported as np\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TAILS TAILS TAILS TAILS HEADS HEADS HEADS HEADS \n"
     ]
    }
   ],
   "source": [
    "turns = 8\n",
    "for _ in range(turns):\n",
    "    print(e.flip_a_coin(), end=\" \")\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We think in ordinary language to some extent, about various types of object engaged in their business. The ```DataScientist``` in me, a kind of helper class if not an ancestor, informs my Human nature with a sense of possibility.\n",
    "\n",
    "Here we're in the binary world of heads or tails, one or zero.  We might also go with True and False. \n",
    "\n",
    "Remember that in Python these two bools (boolean type objects, named after George Boole), are likewise names of one and zero respectively.  The boolean type is a subclass of the integer type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll want to keep your ordering testing tools ready for action.  You have the power to create both ordered and unordered types of object.  More below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issubclass(bool, int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issubclass(Human, DataScientist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issubclass(Human, DataScientist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember Truth Tables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True True\n",
      "False True\n",
      "False True\n",
      "False False\n"
     ]
    }
   ],
   "source": [
    "print(\"{} {}\".format(True and True,   True or True))\n",
    "print(\"{} {}\".format(True and False,  True or False))\n",
    "print(\"{} {}\".format(False and True,  False or True))\n",
    "print(\"{} {}\".format(False and False, False or False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(True and False) or (False and True and True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100\"\n",
       "            height=\"100\"\n",
       "            src=\"images/matplotlib.pdf\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x242e97e8c88>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "plot_fn = 'images/matplotlib.pdf'\n",
    "IFrame(plot_fn, 100,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check this Notebook on [nbviewer](https://nbviewer.jupyter.org/github/4dsolutions/Curriculum_Development/blob/master/IntroDS001.ipynb)?  Does the above frame show a logo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditionals\n",
    "\n",
    "IF it's raining tomorrow, THEN I will go to the museum.\n",
    "\n",
    "raining tomorrow $\\implies$ I will go to the museum.\n",
    "\n",
    "Tomorrow comes...\n",
    "\n",
    "* It's raining and I am at the museum.  Check.  Consistent.\n",
    "* I'm at the museum, therefore it must be raining.  No!\n",
    "\n",
    "I might have gone to the museum anyway.  Even on a sunny day.  The condition was not ONLY IF.\n",
    "\n",
    "However, we *might* say the fact that I'm at the museum adds some likelihood to the hypothesis that it is raining."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per George Polya, even pure mathematics is expanded through inductive reasoning a lot of the time.  People look for patterns, generalizations, make a conjecture, and only then maybe look for a proof.  Sometimes we're left with something verifiable, yet deductively unproved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bitwise Operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True & (True & False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 & 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try changing the ampersand to a vertical pipe.  The ampersand and pipe do bitwise and-ing and or-ing, which is best appreciated by keeping the number expressions in binary: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b101'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin(0b111 & 0b101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[19, 110, 91, 59, 35]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "B = [ x**2 for x in (3, 10, 9, 7, 5)]\n",
    "C = [ x + 10 for x in B]\n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Milestone:  Importing Numpy\n",
    "\n",
    "Below is the first time we're making use of this library.  If it doesn't load, you may need to go ```conda install numpy``` in your conda shell, having activated the appropriate environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.16.4\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 19, 110,  91,  59,  35])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "A = np.array([9, 100, 81, 49, 25])\n",
    "A**2\n",
    "A + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domain = np.arange(0, 10, 0.01)\n",
    "tabular = domain.reshape(100, 10)\n",
    "tabular[0,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "19950631168807583848837421626835850838234968318861924548520089498529438830221946631919961684036194597899331129423209124271556491349413781117593785932096323957855730046793794526765246551266059895520550086918193311542508608460618104685509074866089624888090489894838009253941633257850621568309473902556912388065225096643874441046759871626985453222868538161694315775629640762836880760732228535091641476183956381458969463899410840960536267821064621427333394036525565649530603142680234969400335934316651459297773279665775606172582031407994198179607378245683762280037302885487251900834464581454650557929601414833921615734588139257095379769119277800826957735674444123062018757836325502728323789270710373802866393031428133241401624195671690574061419654342324638801248856147305207431992259611796250130992860241708340807605932320161268492288496255841312844061536738951487114256315111089745514203313820202931640957596464756010405845841566072044962867016515061920631004186422275908670900574606417856951911456055068251250406007519842261898059237118054444788072906395242548339221982707404473162376760846613033778706039803413197133493654622700563169937455508241780972810983291314403571877524768509857276937926433221599399876886660808368837838027643282775172273657572744784112294389733810861607423253291974813120197604178281965697475898164531258434135959862784130128185406283476649088690521047580882615823961985770122407044330583075869039319604603404973156583208672105913300903752823415539745394397715257455290510212310947321610753474825740775273986348298498340756937955646638621874569499279016572103701364433135817214311791398222983845847334440270964182851005072927748364550578634501100852987812389473928699540834346158807043959118985815145779177143619698728131459483783202081474982171858011389071228250905826817436220577475921417653715687725614904582904992461028630081535583308130101987675856234343538955409175623400844887526162643568648833519463720377293240094456246923254350400678027273837755376406726898636241037491410966718557050759098100246789880178271925953381282421954028302759408448955014676668389697996886241636313376393903373455801407636741877711055384225739499110186468219696581651485130494222369947714763069155468217682876200362777257723781365331611196811280792669481887201298643660768551639860534602297871557517947385246369446923087894265948217008051120322365496288169035739121368338393591756418733850510970271613915439590991598154654417336311656936031122249937969999226781732358023111862644575299135758175008199839236284615249881088960232244362173771618086357015468484058622329792853875623486556440536962622018963571028812361567512543338303270029097668650568557157505516727518899194129711337690149916181315171544007728650573189557450920330185304847113818315407324053319038462084036421763703911550639789000742853672196280903477974533320468368795868580237952218629120080742819551317948157624448298518461509704888027274721574688131594750409732115080498190455803416826949787141316063210686391511681774304792596709376"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time 2**10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TYPES OF DATA: NOMINAL, ORDINAL, INTERVAL-RATIO\n",
    "\n",
    "In data science we come across several types of data.  Think of spreadsheet columns.  Some columns are for dates, maybe with times.  \n",
    "\n",
    "Some are for integers (discrete), others for floats (more continuous).  \n",
    "\n",
    "Some may be for a choice of several options, but with [no inherent order](https://microbenotes.com/nominal-ordinal-interval-and-ratio-data/) implied.  For example, you might classify a type as animal, vegetable, mineral without implying that the expression ```animal > vegetable``` is required to make sense.\n",
    "\n",
    "Not every column has to have an obvious sort order, however we might choose to define one, however arbitrary.  \n",
    "\n",
    "Python gives us that power.  Think about whether sorting makes sense.  Sorting is different from hashing, after all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Case insensitive sorting is a somewhat relevant topic as we're talking about sorting in general, when and if sorting is a relevant thing to do.\n",
    "\n",
    "The video curated below is useful because as data scientists, we're familiar with SQL (Structured Query Language).  We may need to build data tables ourselves, and/or someone may hand us a data dictionary (a roadmap to a set of tables) and ask us to get comfortable querying that data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/p974mIfotTA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x242e97f7160>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('p974mIfotTA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/hZxnzfnt5v8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x242e9803b00>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"hZxnzfnt5v8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/v_4KWmkwmsU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x242e97ffe48>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"v_4KWmkwmsU\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You won't see the above Youtubes appearing if rendering this Jupyter Notebook on Github.  Here's a good place to introduce [nbviewer](https://nbviewer.jupyter.org/github/4dsolutions/Curriculum_Development/blob/master/IntroDS001.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets get back to our Human ```e``` and have ```e``` flip that coin quite a few more times.  We're getting into the idea of samples or trials.\n",
    "\n",
    "If the ```random.choice()``` function is truly random, then we expect the number of heads versus tails to even out over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sorted() is optional\n",
    "flips = sorted([e.flip_a_coin() for _ in range(8)]) # H before T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HEADS', 'HEADS', 'HEADS', 'TAILS', 'TAILS', 'TAILS', 'TAILS', 'TAILS']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter  # tally device\n",
    "\n",
    "trial = Counter(flips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'HEADS': 3, 'TAILS': 5})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets wind up IntroDS001 (this Notebook) by doing N trials featuring a T coin tosses each, and plot the histogram we get of the number of heads.  All heads, or no heads at all, should be the least frequent, right?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment(N, T):\n",
    "    heads = []\n",
    "    for trial in range(N):\n",
    "        flips = [e.flip_a_coin() for _ in range(T)]\n",
    "        heads.append(flips.count('HEADS'))\n",
    "    return heads\n",
    "\n",
    "trials = 10000\n",
    "tosses = 10\n",
    "data   = experiment(trials, tosses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of the complexity of data science stems as much from the tools used to do the visualizations, as from operating the statistical number crunching machinery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bq+OIuFHSqDrrOAS4KCJeBR6RNB/YKz82PyIezjVdlJe9r85+zWwtUWufzU+A/fOH+/5G0mjgamCXBtd5rKSjgNnA1yPiWdLneG6uWmZRboNV+4sq7e/urlNJk4BJDdZkZm1Waxg1hFVne1d7HFivwfWdAewE7AksAU7J7epm2ajRvmZjxJkRMS4ixjVYm5m1Ua0tm3OAW/PQpbJ1sR1p38rZjawsIpZWbks6C7gq312U+64YSZo7hxrtZjaA1Doa9X1gPGnr4h+A9+bb4/NjfSZpm6q7hwKVI1XTgSMkrZ+HaWNY9anlMZJGSxpKCrrpjazbzPpXzXOjIuI+4D5JW6S7UffUEpIuBPYFhktaBJwI7CtpT9JQaAHwhbyeeyVdTNrxuwI4JiJW5n6OJR16HwycExH39ukVmtlaQRHd7gJB0vbAfwEfBJ7PzZsBfwImd91xvDaR1P2LsobEtP6uoL00vr8r6Di3dbfvtNYO4t8AlwPbRMSYiBhDOlXhCtIJmmZmdasVNsMj4jeV4QxARKyMiIuALdtfmpl1klr7bG6T9HNgKqsfjZoA3NHuwsyss9QKm6NI00ycRPqAnUih8zsaPPRtZuuuWtOCvkb6EN4Z5coxs07V0DdiSvpuqwsxs87W6NfvHt3SKsys49U663t5Tw8BG7anHDPrVLV2ED8HvKv6fKYKSY91s7yZWY9qDaPOBXbo4bEL2lCLmXWwWkejvlPjsW+2pxwz61SN7iA2M+sTh42ZFeGwMbMias5nU5G/5WDr6uUjYmG7ijKzztNr2Ej6Mmniq6Ws+hqXAHZvY11m1mHq2bI5jvTFdE+3uxgz61z17LN5jFUz9ZmZNaSeLZuHgRsk/R54tdIYEae2rSoz6zj1hM3CfBmaL2ZmfdZr2ETESSUKMbPOVuus79Mi4nhJv6Obb6GMiI+3tTIz6yi1tmzOy9c/KlGImXW2Widi3pav/1yuHDPrVD5dwcyKcNiYWRG9ho2k3UoUYmadrZ4tm19ImiXpS5I2b3tFZtaReg2biPhHYDzp2zBnS7pA0ofaXpmZdZS69tlExDzgO8A3gfcDp0u6X9In21mcmXWOevbZ7C7px8Bc4IPAxyJil3z7x22uz8w6RD3nRv0UOAv4VkS8UmmMiMWSepwU3cysWj1hczDwSkSsBJA0CNggIl6OiPNqP9XMLKlnn811rP4NmBvlNjOzutUTNhtExIuVO/n2Ru0rycw6UT1h85KksZU7kt4JvFJjeTOzNdSzz+Z44BJJi/P9bYDD21eSmXWieibPulXS24CdAQH3R8Trba/MmhbT+rsCs1Xq+t4o4F3AqLz8OyQREee2rSoz6zj1fG/UecBOwJ3AytwcgMPGzOpWz5bNOGDXiFhjalAzs3rVczRqDvDmdhdiZp2tni2b4cB9kmax+vdGecJzM6tbPWEzpd1FmFnnq+fQ958l7QCMiYjrJG0EDG5/aWZltOojAhrfmn46VT1TTHwe+C3wy9w0AriinUWZWeepZwfxMcDewHL420RaW7WzKDPrPPWEzasR8VrljqQhdPMNmWZmtdQTNn+W9C1gwzz38CXA79pblpl1mnrCZjLwJHAP8AXgatJ8xGZmdavnaNQbpGlBz2p/OWbWqeo5N+oRutlHExE7tqUiM+tI9Z4bVbEBcBiwRXvKMbNOVc+X1D1ddXk8Ik4jfY2LmVnd6hlGja26O4i0pbNp2yoys45UzzDqlKrbK4AFwD+1pRoz61j1HI36QIlCzKyz1TOM+lqtxyPi1NaVY2adqt6jUe8Cpuf7HwNuBB5rV1Fm1nnqnTxrbES8ACBpCnBJRBzdzsLMrLPUc7rC9sBrVfdfI33TgplZ3erZsjkPmCXpctIniQ/F36xgZn1Uz9Go70n6A/C+3PS5iLijvWWZWaepZxgFsBGwPCJ+AiySNLqNNZlZB6pnWtATgW8CJ+Sm9YDz21mUmXWeerZsDgU+DrwEEBGLqeN0BUnnSFomaU5V2xaSZkial6+H5XZJOl3SfEl3V58iIWlCXn6epAl9fYFmtnaoJ2xey9+GGQCSNq6z718DB3ZpmwzMjIgxwMx8H+AgYEy+TALOyOvaAjgReDewF3BiJaDMbGCpJ2wulvRLYPP8TQvXUcdEWhFxI/BMl+ZDgKn59lTgE1Xt50Zyc17XNsCHgRkR8UxEPAvMYM0AM7MBoJ6jUT/Kcw8vB3YGvhsRMxpc39YRsST3u0RS5VsaRrD6J5IX5bae2tcgaRJpq8jM1kI1w0bSYOCaiNiftFXRLuqmLWq0r9kYcSZwJoAkf/uD2Vqm5jAqIlYCL0varEXrW5qHR+TrZbl9EbBd1XIjgcU12s1sgKlnn81fgXsknZ2PGJ0u6fQG1zcdqBxRmgBcWdV+VD4q9R7g+TzcugY4QNKwvGP4gNxmZgNMPacr/D5f+kTShcC+wHBJi0hHlU4m7XCeCCwkzWcM6ethDgbmAy8DnwOIiGck/Ttwa17u3yKi605nMxsAlI5qd/OAtH1ELCxcT0t4n00S0/q7gnWLxvd3BWuN2yJiXNfGWsOoKyo3JF3alpLMbJ1RK2yqjwT5O6LMrCm1wiZ6uG1m1me1dhDvIWk5aQtnw3ybfD8i4u/aXp2ZdYwewyYiBpcsxMw6W73z2ZiZNcVhY2ZFOGzMrAiHjZkV4bAxsyIcNmZWhMPGzIpw2JhZEQ4bMyvCYWNmRThszKwIh42ZFeGwMbMiHDZmVoTDxsyKcNiYWREOGzMrwmFjZkU4bMysCIeNmRXhsDGzIhw2ZlaEw8bMinDYmFkRDhszK8JhY2ZFOGzMrAiHjZkV4bAxsyIcNmZWhMPGzIpw2JhZEQ4bMyvCYWNmRThszKwIh42ZFeGwMbMiHDZmVoTDxsyKcNiYWREOGzMrwmFjZkU4bMysCIeNmRXhsDGzIhw2ZlaEw8bMinDYmFkRDhszK8JhY2ZFOGzMrAiHjZkV4bAxsyIcNmZWhMPGzIpw2JhZEQ4bMyvCYWNmRThszKwIh42ZFeGwMbMi+iVsJC2QdI+kOyXNzm1bSJohaV6+HpbbJel0SfMl3S1pbH/UbGbN6c8tmw9ExJ4RMS7fnwzMjIgxwMx8H+AgYEy+TALOKF6pmTVtbRpGHQJMzbenAp+oaj83kpuBzSVt0x8Fmlnj+itsArhW0m2SJuW2rSNiCUC+3iq3jwAeq3ruoty2GkmTJM2uDMvMbO0ypJ/Wu3dELJa0FTBD0v01llU3bbFGQ8SZwJkAktZ43Mz6V79s2UTE4ny9DLgc2AtYWhke5etlefFFwHZVTx8JLC5XrZm1QvGwkbSxpE0rt4EDgDnAdGBCXmwCcGW+PR04Kh+Veg/wfGW4ZWYDR38Mo7YGLpdUWf8FEfFHSbcCF0uaCCwEDsvLXw0cDMwHXgY+V75kM2uWIjpv98ZA3mcT0/q7AmuUxvd3BWuN26o+0vI3a9OhbzPrYA4bMyvCYWNmRfTX52zMOk4r97d14v4fb9mYWREOGzMrwmFjZkU4bMysCIeNmRXhsDGzIhw2ZlaEw8bMinDYmFkRDhszK8JhY2ZFOGzMrAiHjZkV4bAxsyIcNmZWhMPGzIpw2JhZEQ4bMyvCYWNmRThszKwIh42ZFeGwMbMiHDZmVoTDxsyKcNiYWREOGzMrwmFjZkU4bMysCIeNmRXhsDGzIhw2ZlaEw8bMinDYmFkRDhszK8JhY2ZFOGzMrAiHjZkV4bAxsyIcNmZWhMPGzIpw2JhZEQ4bMyvCYWNmRThszKwIh42ZFeGwMbMiHDZmVoTDxsyKcNiYWREOGzMrYkh/F9ApYlp/V2CdpFW/Txrfmn5awVs2ZlaEw8bMinDYmFkRDhszK8JhY2ZFOGzMrAiHjZkV4bAxsyIcNmZWhMPGzIpw2JhZEQPm3ChJBwI/AQYDv4qIk1vRr89pMitjQISNpMHAz4APAYuAWyVNj4j7+rcys7VbK/+ZNntS50AZRu0FzI+IhyPiNeAi4JB+rsnM+mBAbNkAI4DHqu4vAt5dvYCkScCkfPdF4IF6Oq4zrYcDT9W1ZJl+WtlXJ9fUya+tlX21siaAHbprHChho27aYrU7EWcCZ7Zl5dLsiBi3tvTjmsr245paY6AMoxYB21XdHwks7qdazKwBAyVsbgXGSBotaShwBDC9n2sysz4YEMOoiFgh6VjgGtKh73Mi4t6CJbRqeNbKYZ5rKtdPK/vq9Jp6pIjofSkzsyYNlGGUmQ1wDhszK8JhU4OkAyU9IGm+pMlN9HOOpGWS5jRZz3aSrpc0V9K9ko5roq8NJM2SdFfu66Qmaxss6Q5JVzXZzwJJ90i6U9LsJvrZXNJvJd2f369/aLCfnXMtlctyScc32NdX83s9R9KFkjZosJ/jch/39rWW7n4XJW0haYakefl6WCN19SoifOnmQtoR/RCwIzAUuAvYtcG+9gHGAnOarGkbYGy+vSnwYBM1Cdgk314PuAV4TxO1fQ24ALiqyde4ABjegp/fVODofHsosHmLfieeAHZo4LkjgEeADfP9i4HPNtDPbsAcYCPSAZ7rgDHN/C4C/wVMzrcnAz9o9r3q7uItm5617BSJiLgReKbZgiJiSUTcnm+/AMwl/RI30ldExIv57nr50tDRAkkjgY8Av2rk+a0m6e9If1RnA0TEaxHxXAu63g94KCIebfD5Q4ANJQ0hhUUjnxXbBbg5Il6OiBXAn4FD631yD7+Lh5DCmXz9iQbq6pXDpmfdnSLR0B92O0gaBbyDtEXSaB+DJd0JLANmRESjfZ0G/F/gjUZrqRLAtZJuy6egNGJH4Engf/LQ7leSNm5BbUcAFzbyxIh4HPgRsBBYAjwfEdc20NUcYB9JW0raCDiY1T/w2oitI2JJrnMJsFWT/XXLYdOzXk+R6C+SNgEuBY6PiOWN9hMRKyNiT9InsveStFsDtXwUWBYRtzVaRxd7R8RY4CDgGEn7NNDHENJQ4YyIeAfwEml40LD8YdKPA5c0+PxhpC2I0cC2wMaSPtPXfiJiLvADYAbwR9LwfkUjNZXmsOnZWnmKhKT1SEEzLSIua0WfeYhxA3BgA0/fG/i4pAWkoeYHJZ3fRC2L8/Uy4HLScLavFgGLqrbUfksKn2YcBNweEUsbfP7+wCMR8WREvA5cBry3kY4i4uyIGBsR+5CGRPMarKliqaRtAPL1sib765bDpmdr3SkSkkTaDzE3Ik5tsq83Sdo8396Q9Mdwf1/7iYgTImJkRIwivUd/iog+/8fOdWwsadPKbeAA0rChrzU9ATwmaefctB/Q7NxHR9LgECpbCLxH0kb557gfaZ9bn0naKl9vD3yyybog/V5PyLcnAFc22V/32rHXuVMupPHwg6SjUt9uop8LSeP010n/dSc22M8/koZydwN35svBDfa1O3BH7msO8N0WvF/70sTRKNK+lrvy5d4m3/M9gdn59V0BDGuir42Ap4HNmnx/TiIF+hzgPGD9Bvu5iRSedwH7Nfu7CGwJzCRtIc0Etmj2d6G7i09XMLMiPIwysyIcNmZWhMPGzIpw2JhZEQ4bMyvCYbOOkBSSTqm6/w1JU1rU968lfboVffWynsPyGdzXd2kf1fWMeklTJH2jRetdIGl4K/palzls1h2vAp9c2/5o8hcQ1msi8KWI+EC76rH2cdisO1aQ5pr9atcHum6ZSHoxX+8r6c+SLpb0oKSTJY3P8+DcI2mnqm72l3RTXu6j+fmDJf1Q0q2S7pb0hap+r5d0AXBPN/UcmfufI+kHue27pA81/kLSD/vywiXtJOmP+eTOmyS9Lbd/TNIt+WTN6yRtndu3lHRtbv8l+Ty5/Ann3yvNATRH0uF9qWOd145PCvqy9l1IX9z3d6T5YjYDvgFMyY/9Gvh09bL5el/gOdI8OusDjwMn5ceOA06rev4fSf+8xpA+mboB6UsDv5OXWZ/0id7Rud+XgNHd1Lkt6aP9byKdUPkn4BP5sRuAcd08ZxTwCqs+VX0nad6Zb+THZ5LnfCF9ueGf8u1hrJqH+2jglHz7dPInqklTZwTpi9w+BZxVtd6mPlG8rl0GxLcrWGtExHJJ5wJfIf1x1uPWyNMPSHoIqEyLcA9QPZy5OCLeAOZJehh4G+ncpt2rtpo2I4XRa8CsiHFmcYkAAAG1SURBVHikm/W9C7ghIp7M65xGmpvmil7qfCjSGezk503J15uQTni8JJ2SBKTgg3Ry7W/yyYdDSZNbkdf3SYCI+L2kZ6te84/y1tZVEXFTLzVZFQ+j1j2nkfZ9VM/vsoL8u5BPEhxa9dirVbffqLr/Bqt/FVDX816CNPz4ckTsmS+jY9UcLi/1UF93U3s0YxDwXFUNe0bELvmx/wZ+GhFvB75A2hqrrn81EfEg8E5S6Hw/D+2sTg6bdUxEPEOaknJiVfMC0h8RpDlX1mug68MkDcr7cXYkfdf6NcC/5GkxkPTWOiaxugV4v6TheefxkaTZ6BoSab6fRyQdlmuQpD3yw5uRhoaw6qxngBuB8Xn5g0jDLSRtC7wcEeeTJsJqdtqKdYrDZt10CmkfRMVZpD/wWaR9Gj1tddTyACkU/gB8MSL+Spom9D7g9nxo+pf08sWIech2AnA96azm2yOi2SkPxgMTJVXOJq9M7zqFNLy6CXiqavmTSLPh3U4aCi7M7W8HZinNbvht4D+arGud4rO+zawIb9mYWREOGzMrwmFjZkU4bMysCIeNmRXhsDGzIhw2ZlbE/wdWFrrN5xTPzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig  = plt.figure(figsize=(4,7))\n",
    "ax   = plt.subplot('111')  # I will explain\n",
    "rect = ax.patch  # a Rectangle instance\n",
    "rect.set_facecolor('black')\n",
    "\n",
    "plt.title(\"{}'s Coin Tosses\".format(e.name))\n",
    "ax.set_xlabel('Number of Heads')\n",
    "ax.set_ylabel('Frequency in {}'.format(trials))\n",
    "plt.xticks(list(range(tosses+1)))\n",
    "plt.hist(data, \n",
    "         bins=[n - .5 for n in range(tosses+2)], \n",
    "         color=\"orange\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Black Swan Events\n",
    "\n",
    "We will return often to the inherent unpredictability of events, despite our best efforts to construct models good at predicting. \n",
    "\n",
    "[Nassim Taleb](https://youtu.be/lBjVTm7F1lQ) authored a series of widely read books on this topic. We may also find his presentations on Youtube.\n",
    "\n",
    "With the benefit of hindsight, and our ability to explain what has already occurred, we tend to forget how the future tends to shock us quite a bit (Alvin Toffler).\n",
    "\n",
    "Taleb considers Covid a white swan event, meaning the prospect of another global pandemic has been foreseen for quite some time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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UMmWbTofLtWXXYV1rsK31zySfpnD/AJsg/wDfI/8A7e5rV9EvFGh2umWa3ptvWCrs+63E0oBmk29YUicqduMByDjHhU+9IHgefVLKO3t2RZFnSbMrMq7VjmQjKoxzmQcsedRDgT0brRIR6+zzXByWEUrJEnkEwoZjjmWbAz4cucHPFNykuY0HLU6LsqKro/ZjYp5CCHE2b8W/wTP4I1DTbpTNY+ruFO0tFGiOhYdzDYrpuGe8DPOpOaqB0SpJpfEZs0ZurM0tq27HzkRDNCzAYG7+TfI7ufgat+a26GcysNxYg2KiMdoBSTNDXFzXNDmk62WobhWzaVpmtoGlYgvK0MbOxAxksVJPLlVTuiTRoLjiGWGeNJIetvz1TqCh29aV7OMdnvA8MCrkpVRfR7j38Ryt5NfOfrLr+LVrV7G8ZELb/RSnB+d/JqpxccmZZnLJ2isfpvRppkUiTRWkKSRsGR1Ugqw7iOeMjwyDg86lyiutRvpU4h9S0+7uc4ZImEf/AHr/ADcX+0ZakCGRNJAsFzjXTVUjWOcXEkAXJOpVPOlO5k1XWplh7TSXAtYATy2oRChyAcIdpkJ8ASa2vo46u+n6ykE3zfWF7KZWONshbsZ8MidEXPkx863HobcOCbUJLlhlbaPI/wC9n3In+zEp+ysj0w+HjbX8N7FlfWFBLLnlcW5Ubs9wJQxH6VY+dc01jg3lO/a/fjkvTJJ4nTHC7e7xdgeu3pmnX6Uw/wCZL3/5b/7uCkr6MvHcOnWWqzTnsq1qYowe1LNItyAi/TsBLfqqCfCmp0xa0L7hiW6X+ditZGA/VcXMHWLy/ZcMv1VVTo64Pm1G6S1gHabm7n2YowRvkf4KD3d5JAHM1sVkrhUNfHmdnL63UZgdHG7DZYag2aHna/47BP2Us4J0O64j1N2nfAPztxIP5uEMFWOFTnHeI0HPHtHODm7OmWKQxpFEoSNFVERe5VUYAH0CqStDdcNasufnAmDkZVbm1k5NgZO0kAjBJ2SIPawCbs6NqMc8Uc0TBo5FWRGHirAEfX5jwPKs+F7I2gfjvmo3hbtkROjI4m3u20v/AItb/KpZ0+Qq/ENyrey01qrfQYYA38KtInRBpAORYw/WGI+wsRVT/SCiaXXLxEG52mijQDvLGKJFA+JOBUuvOEOL5BtZrrHkL+Bft23Iz9da0MgbJJdm1nzX3lTNdTPlpqcNqBFZgvd1r5N6xeyszofA9jbOJILWCGQAgPHEisAwwRuAzgjlWfxPqq21vPcP7MMckp+OxS2PpJGB8TS99Gzh/ULS2nj1EOJGm6yPfMkzFTGintJJJgZXuJFav0wOIuo00QA4e5kVMZweqixJIfoyI1P79Spm2IC+1stFx4o3TYg2nMm3mBtXvcaneeveqqaNodzfyXLxjrJEjmvJvMqrAyEADmxL8l8aenoT8S4e6sWPJgLqIf0lxHN9ZXqjjyRqkXoW8N9XaXF4w7U8nVJn3UHefoaRmB/cFKrU0/QfEQKjbAk4ceA9VuR2gMd4RHZR8Y6hoWGDYn5zn9V29ZUNrzUUIGbWgt7Rr3Gw71aTpm0f1nS76LG4mCR0HnJEOtjx8d6DFKf0KtcX1S9gdsCGRbjLHkI5Y9rH4BTCSf3qsMyhhjvUjH0gj/Kvn9DrE2my6laRn+UWaxkz4Ks4yw82KI6fRIT4Vv1snFSsl6iPRc7gFPy2jmpCc9prh35/ZeHSPr0mo311dAMwdmZQASUgjGEyBnaFjClj3ZyasD6FPEO6C6smPON1njH9CUbHA+Cuin/4lRv0NeE0na/nmTdEYvU8MMq3XdqYY8wioPof41B+iDV/0TrYWVwkaTTWdwx7tm8xlm8lWRUcnw21HwF0T2TO/qJuukxBsdXDNQxjONrSO64Hhb6qZ+kdI2o67badGThOptzjHZeciWZx57Y2TP7hq1drbKiqiDaigIoHgqgBR9QAFVL9H63bUeIJr452RtcXRJ/7UtHCh8iA+QPKM1bDVr5IYpJpDhI0eRyfBUUsx+wGpOgO1tynefALlOEQMfEUbf6Wi4/3O1/fWqdeltrpuNVMCZZbdEhCjnmV/nJMAeOXVMf0KjHDZn0TVoTcDY8EidcBkgwyqA5Uj2gYnJHxx4itv0Iac2qa4ksoyOtkvphn9lzIB8QZmjXHkaZ/pr8MAx2t+o7QY20pA71YNJESfJSsi5P7YHlUWWOkDqgag5fvuXWtqI6aSLDXDJzCD2287O7wrGcjzHMHuI8R8PgarH6UvBD2k8esWeYzvUzlOXVzgjq5gMYxIRtbw3YznrDTb9HTiX1zSrZicyRD1aXnk7ocBST5tEY25/tVNde05J4ZYZAGSRHjYHxDqQfrHeD4GpyVgqob/UdRXCUdQ/C60tOYBLXDnF/2QqsdLvTeL7TYLaEFJ5gPXQAQF2HHVxn9YTMA/LOEwpySwD36DOBRptjHER8/JiW5bx61gOxn9mNcIAOWQx/WNVb9Gvh9bjV4FkAKw752HeC0I7H1CQofqq79auHB0pMz8zoPNS/CV0dIxtFTizTd7uu5yH0t9lUL0lbV59ejt5pBFEwtIo5H9iKKQgO/0CQyEn4cyAOVhtD6JdKgiWIWkMgxgvNGssj/ANJpHBOT39nAHgAMVH/SC6Jv0oiSwFEvIwFDOSEkhySY2KgkFWYsrYPewPtZCQl4e4n0uMlDcrCgx8zMtxGqDxESu5RAOfNFwO/FYnAwSuc9m0DnfWy2Y3jEKSKOGcRuaLFpNrnccs/urb2ulQxbuqjSPdt3dWipnYNq52gZ2ryHkKivGsuFNRD0aulCXUYpre6YNcwgOJAqr1sLHGSq4G+NsAkAAhk5ZyTKuOx2TUrHM2SHaZouefRy01WYps3Dfz8yqH0yPm8Y+aJ/DcP8KhdTPphXF19KD+DPUc4d0p7iaOFPadgufId5J+CjJ+quRqSA9xPWvWKBpdCxo5gFncG8KT3j7YhyGN8h9lB8T4nyUfw76eHC/RLawgGRevfxL+znxwg5Y+nNTDhHh6O1hSKMYCjmfFm8WbzJPOt3iuEr8almcWxGzerU/Vek4ZgMULQ6UbTuvQfRaJeGrcLtEMYHkI1x9mKg/GHRVbSgtEOok809gn4p3D+rimkRXjcRZqPhrZonbTXFSs+H08zNlzB3KonE+gS2snVyjHirDmrDzU/4HmK1NPTp10cvAJB3xtuP7jDa32HB+gGkYa7uhqeURB+/evOMSo+SzmMablxRRRW2tBFbaw0J5I2kDINrKnVl8StuVm3JH3tGu3DMO4svnWpqfdHbl45QxBC7VA2jIVg2e1388Y+2iKA1yK3PEWjdWSU5p/Ffp8x8a0woisL0N6l1lumTzHZP0jlVteg7h1JLObrBkTbomwSrbGHPaykMpIK4ZSCCoIxiqTdAE5KyL5NkfWKvv0FSA2KAeDEH6dqVC0cLWV0v/wA5fUhT+JTufh0A3bWf0a63r9FhaCs1tbPb6onzUizLJdwT3EkSRsW+bklkb1uBQjMyzu7qvb+ci+bQyi24UsmAkhQIHAcSWsskHWBhkOXt5E6zcOe8k5781JKg3Emm+oD1q0JjiEsbXVqMervHNOi3EyJtJgmjV3uMwlFlYN1isX3rNKAS44p40uYNSmsrJZisDW6zK1+ZbybrozKzWdvfR3EbIEwoZ3iy6SAeBpuw8P3AOf0hct8Hi0/H+pYo2P61KPjrge81S7uo90ARJIo91zG3rVkvzjiS1aNlE6OrrNDMrxNHJlGLPA6hi6Fxsvq9w29LiW1nuoriONl65Yre5kTc0S7mMq2yrLsAHWEdkDeoBFtLPWZopkt7sIesyLe5jUpHK6gs0MkbM5inCAuvbZZFVyNpUpUK4/shHdtjucLJ9ZyrfxXd9LGpZ0l7HtYpQ3sXNhNE64YZN3AvLOQVkjdkJH6rkgg4I0PSk49ZjHiIxn63bH4GpPCHEVAtzFc3wrY11CSdxFlGa5Feddga61eUJbekZrF/a2S3NjIYzHIOvwkb/MsCNxDq2Asm0EjHt57hUO6LvSIt/Viupu4uUJ7aRbhMpJKlRGoVGUdkhsAgA5JJAfU0YYFWAZSCGVgCCCMEEHkQRyINLbUugjR5GLermMnmRFNKq/Uu4hR8FAHwqMqIJ+M24j9DouhoK2gNPxFWw3BuHNA2uwn/AD4JO9EbtqvEb3wUiJHkuTkc1RV6q3ViOW/+TyM89r+VWyzUc4J4NtNPQx2kQiDYLtlmdyM43u5LNjJwM4GTgDNb/NZaKmdCw7WpNytbGsRZWTAxCzGtDWg62C9UPMVVH0Yx/wA/Xn/d3n/3EdWqBpJ9GHRFdWWqzX8ksJhY3O2OMyFyszMVDBo1VdvZJwW5jHxrHWQudLGWjQ5+C2cHqYoqWpY9wBc0AdZzTwBquvpocVYjt7BDzY+sTAHuRcpECPJm3tj+gtWH3UjeP+gZ7+/kvJLsbHdCYjCciJAq9WriXA7K43Be85xVcQZI+LZjF769ixcH5aaGqEtQ6waCRkTc7tO9K7hf0f8AU57eK4jkhiEqLIqPLMjhWGULBYmAJUg4ycZ8K6cW9A+qwW8txNJDJHCjSsqzys21RlioeIDIUE945A1ceIAAAcgAAAPADkB9QrrdRK6MjjcjKyMPNWBDD6wSK1jhEezvv2qSHC+q4y5Ddm/Nna/P2KnnR3xup0PVdNmbBWPr7bPiGmiEkY5fqybZAPHfIe5amnoOXIzqMeBuxbODjtYBmVhnvxkryr1070XVEpMt4xhycLHEBKU/VDSMxVWxjJCEHypn9FHRRbaS8zwSSyNKqoeuMZAVW3dkJGvMnvznuFa1LSVAkY54yblu0z9VI4riuHuppo4X3c8h1rG1/dv9rlR70uOERc6f6yozNanfnHMwPhZV7x7J2yZ54CN51EfQ448Xa+mSsActLaAnvBy00S/EH50DvOZfKrEarZJNHJDIN0ciNG6n9ZHUqw5eYJqA6V0J6TBLHNDA8csbrIjLcXHJkIIyGkIIyOYxzGRW5LSSceJY7dfWoWkxWnOHuo6i+t2EAG28bxv8CVWbpcv+o4guJmBYR3cUpAxkrGY3wM8skDHOnt8pzS/d3X0dVFy/2/4VvuNuhLTb6eS4lEqzSEF2jlwCQoUHaysByUdwqNfJl0z3t395D/wK1W0tVE5xZaxN1KS4lhNXDG2o2rtaBlfmF9OxTzgHpX0/UZOptZHaURmUo8TphVKqckjaSCw5Anx8qrf6UGvtf6stpB2xBttY1UjtXEjDrAOff1hWI58Y6evR10M2mm3DXNvLOZDG8QErRsgD7SThYkJIKg9+O+oz0c9Ahs7+O9muhclGeTb1JQtKwYB2YyNzDNv7u8Csk8NTLG1jhvztzLBh9ThtHUPnicbBvuAg3Lje+7sGfOl6PRu1ZcbJ7b6BPOuPP+YxUS6T+iS/06Fbm6aORGcRZjkkkZSysy7t8a4U7SM578Dxq8W6o50mcLJqNnNaM2zrACj4zskRg6NjIyNwwRkZBI8arNhDNg7F77s1Sj4XVHHN47Z2b+8bZ2Ua9GrjUX2nRqzZntgtvMCcsQq4ikOeZ3oPaPeyP5VW30otH6jV7kj2ZhHcL/8AEQB/9qr1YHoM6I30mSWVrrrusQRmJIyiZDBlYsXYsy9oDkOTt31L+M+j/T790ku4BM8Y2IxeVMKTuwerdQwByQGzjJ8zR9JLPTta7Jw5/wBFSDFaWhxKSWIl0bhuG82O+2h8CsP0f+HPUtLtYm5SOvXy8gDvm7eDjvKoUTP9Gqx+lboBt9WlcDsXCpcKcHGSOrkGe4nrELH94edXOt0CKqqMKoCqPIKMAc/ICof0o9HNrqqxC4LqYixR4mVWw4AZSWRgVJCt3Zyo58znNVUJfAGN1FrLUwnGxBXuqJb7L73+uYy7goB6F2h9XYz3JHanl2qfOOAYH+0eQfVW29LXiwW2mm3U4lumEQAPPqUIeY/QRtjP/eUw+CuHYbG2itYN3VRhtu87mJZmdmY4GSWYnkAB3AAAUtemnobk1W5Sf1sQqsaxLE0JcLhmZmBEijtFvLPIczgYo+nkjpeLYLm1u/VVhraeoxU1M7rMBuLg7vhyF+pIzo66FNQv7YXcDwxI5ZUEskqM4U7Sw2RsNu4EczzKnl3Gt7dejprG05lgcAZ2+sTHOOYABixnyzirTcK6UlrbQWyezDGkQPnsUAsfixyx+JNbPfVjMIj2RtXvvzWxPwvqeNcWBuzfK4ztuVTPRD4z9Xu3spTiO5x1eeQW4QHA+HWJlf3ljHjVs7iTCsfIE/YKQWpejvuv2u4rvqYjMLhEWLdIjbxIVVt4UAPnacHAxy5U+5RkEHxBH21loIpY2FjxpotXhBU0lROyeB1yQNsWORFvLLLmVM/RPu8axFn+cjuF/wBQv/4auhmk90b9BNvp91HdrcSyvGHCqyoq9tGjOSOfJWP103t1XYdTvijLXjO/orOElfBWVIkhNxsgaWzuUoeK+nu2s9Rks5Yn6mPaklwvNllPM/NEZaIAgbgd2Q2AwxWRxH6Q2lQxF4ZGupMdiJI5UyfDc8qKEXPeeZ78A91SHpJ6MrHU8G4QrKo2rPEdkoXv2kkFXUHuDq2MnGMnMK030a9MRsvJcyj9hpI1U/SY4lb7GFY5GVgcQ2xB0PN+/qs9O/BnMY6QPa4AXAzDiN9+v6JbeiIjyapcTBQqCCUybBhFMssZRAPAciVXyQ+VP/jgdk1veF+G7ayi6m1iWGPOSFySzYA3OzEs7YAG5iTyFaXjQdk1mpqYwwbJ11WKtxJtdW8awWFgBfXJVD6aUxcIf6JH2Mf8633o3aWGmmmP6iqi/S5Jb68KPtNanpwX56P6H/Ff86mXo1D5ic+JlA+xF/zrhOELyyGS3Z32XsfBNgkkhv8Au105Fro5rnNdGrzoBerWRmg155rnNXKoWp4g0xZUZGGVYFSPMEYNVj434Yks5Sjc0PON8e0PI+TDxH1+NWyYVH+KeHY7iNo5F3KftB8Cp8CPOpfC8SNM7Zd8JUJjGECrbtNycNPQqppripVx7wZLZvzBaEnsSY+wP+y38D4eQi1drHI2Ru0w3C88mhfE4seLELipb0dSkdd5bVz9OTj/ABqJVKuEExFI37RC/UoJ/wDF/Cr72WNdeIr3vqMmmnw70ZTXeJJD1MR5gkZdh/RXlgH9pj9RrW9KfAcVkIuqkeQvuyrheQXHMFceJ7sVqiuhMnFh2akHYVUth49zbN6/TVR3g3iuazfdFgg43IwyGx8RzB+Iq+Pol8ex3MW0dkSZKqT7Mik7l+Pjz8QF86+eBFWb6BoJLWygcEpIzNMPMBm7P2qAfrrBWyx0zmzkZk7J6wfSwKy0UE1ax1O06DabfQOHrcg9t1frNRHpQineKOKOJ5oZJBHd9QYuuW3PfsE0sa7WbCuylpAhbYpYhlwOjbpCiu1COQk45FTgB/ivxPl9niBPAa32uDmhzTcHQqKILXFrhYjUHUJRR61PFqlxczxXCE2YjtrOOCWVJws7NEGuIo3gW6Vt5KmXspdgEBYGlkxpOB5le4SWxiu3mgjRLphaNBHOZLq4kkkiuD16gXV5OwEay/NpCPaLU5s11lkABJ5AcyT3AfE1chNkn9HtGt7afSYLa6REmCWplV3ggtBJHiQXszlZFQK9ysZYugdIgp6vFYvEGqdfcySD2dwVP3V7I+3m2P6VenSp0oxktZ2zB2PKV15hR4rn4+Xj9HtaDQu4fVXRYPS7JMjtdy4DhXiQkaIGaDMnnK2RrjNd7cdpfpH405hpUPuk/sL/AJVv11eKYgEXuoDBsDdiIcWvDdm26+t0lw1ch6dH6Lh92n9hf8qx7+3tolLyLEijvZlQD8OZPkOZqP8Abreh4qb/AATJ80flPqlBurnNMW34i01jjdEP34yg+10Ar0m1vT1YKTECcYPVjZz583C7R/WIxT263o+KfgmT5o7j6pbZozTkj0+AgEJGQeYIVCCPgQOdJvRtNX9JqshYosk0RjLt1TDLhC0edpYEDaceOPLFRjjeh4p+CZPmjuPquC1G+mBB0dW8dwLiFpYjn5yHrDLBKpzlTDP1gjwTkNB1ZBHiMgyf9Fw+7T+wv+VUGOjezxVTwIfulH5T6pMb67A05f0XD7tP7C/5UfouH3af2F/yp7cb0PFU/BMnzR+U+qTgNegpg6DwHawEuQ08jHLy3LmUkkYJVDiKIY/VhjRfhVMfR01ie+4sbE7vaCbUZxEJHNuYcTCIJEWKCIF49q4wAFx3VT22Oh4qv4Jk+aPyn1Vjc1yaZHFeqWFlEZrt7e3iHLfMY0BPkuebN5KoJPlS30n0g+Gpd2LqJAMdqWB48k59kPGGPLnnbjmOeeVPbY6PiqfgmT5o7j6rqa6Fq8ZvSS4YEgj9YU89vWC0m6sHOMljCOz/AEgCKb2jeqXESTQdTNE43RyRhHR1PirLkEU9tt6Pin4Jk+aO4+qUxNdd1Ob9Fw+7T+wv+VH6Kh92n9hf8qr7cb0PFPwTJ80flKTO6uc05f0VD7tP7C/5UkPSJ4znhurHRtJjhGo3uW6540YW1uN2ZApUruIjlbLKwCwvhSSMPbjeh4p+CZPmjuPqsrNGa1+h+jYzN1moavqN1KebCGY2sOfJUBcqo7sKVHwHdU70foV02HntuJD5zahfSf6rXGz/AFar7bb0fFV/BMnzR+X9VEy1dd9Ney4Os4/Zt4/pZQ5+18mtgukwj+aj+7X/ACp7cb0fFU/BMvzR+UpMb65D05/0VD7tP7C/5UfoqH3Sf2F/yp7db0PFPwTJ80flPqkxvrkNTm/RcPu0/sL/AJVpOPL23sbO5vHgWRLeKSdkVUDMI1LEAkYBOPGqe3G9DxT8EyfNH5T6pa0UxujrU7fULK2vUgWNJ41lVHVCyhs4BIGCfoqA9MPTHp+k39lYSW6yvclDK4MaC2iklESSOCh35O87crhUJzzFPbjeh4p+CZPmjuPqseitv02dJtnos2npPah4ruV4mnHVoluEaEM7hlO4AS7sAjkjc6m3Geo2Vjaz3lwsawQoZHbYmSB7Kr5u7FUVfFmUeNPbbej4p+CZPmjuPqllRmpL0GcfWet2jXcVt6uomeAJMsRZjGkbll25yMSAeeQfgaYH6Lh92n9hf8qe3G9DxT8EyfNHcfVJl2qKcX+yasgdLh92n9hf8qiPHWmxbD82nj+ov+VUdjTSLbPis0PA6SN1+MHcvnf06J85Efi4+3b/AJVu/RwveVxH4hkf7QVP90VsPSntVXqioA+cYcgB3rnw+ioB0L6t1N4oJ7MgMf1+0ufrGPrrlsXbyiF9ua/cvRuD/wD4ksTXHQ27/wDKs4jUE14WsmRXq1ecgWXrPWiuu6uK60Rdwa7V55rsDRAsXU9NSVWR1DKwIZSMgikZ0g9E8kO6W1zLHzJj75EH9H9tfh7X00/81wRW9R4hLSuu3TeNyj6/DIaxtnjPcRqFTqysmZ9mDnOCCOYPxHhTu6PeDAVQyD5teeD+u3f/AGc9/wBlTLivhKGYGVY168cw4GC2DzDEe1kZAzmtjpN0HRSBjAwV7tpHeMeFTFTi5mj/AJYtzqGw7AGwz/zTfeOvtWdmolxDoyXF2glXfGIiMHu57vsPdzHkKlJasZ4u2G8cYqJikLDcLpqqEStDSMrhQvTOiS1EvWOWkQEFYzgA+IDn9YfDlnxzTDK4GByA5ACuLU99d3rDU1Mkps83tosUFHDT3EbQL6rxViDkciO4ipfoXSbfQAKJOsUdwkAb+PeB8BURYV1xV1PWzQf6biPt3aLBV4bT1X+swE8+h7xn4plv01XmMbIh8cNn+Jx/CotxFxxeXPKSU7f2U7I+wfjUdxRitiTFqqQWL+4Afay1IcAoY3bQjuesl3gSQo/w+MXMv75P24P+NOfh/uH1VXzgWxduIwJCxhJd1Te2xmjgiPaTOCAzhsHxAqxGlDtHHdub+8a9a4P1PGQNbzNb9l898NqAwVDn31e7LsK2Ft7S/vD8aea0jLb2l/eH4081pjurPr5KvAj4Ze1vmvK9uVjVnc4VQWY4JwB3nlzwBzpP9KmrieeNVZWhRFZSjBgWfIZuRxlVG0A8x2v2qck0YIIIyCMEeYPePrpFcX8JvbluwdoOYp1GRs/Zlx4EcueCDkgkHB59d2tZctyOAAM4I+A9n6hz+knxxy8HuH2hO9QScd2Ce/B+J54Pie+vKOfuzkH6CfwyMZ+NdjMvnjzyrD7DjGPpIqqLe8F8WPavjBMZPbjP95PDePgcHGD4EduLLoC7aaE5BaOeM/HCvzHgQ4IIPcQajrvkYAz+H2/5c67Rqeefh9vj9vlRFY6wuRIiOvssoYfQwyPxr3qH9FWohrVVJ5xs0fM949tfsVgv1VMAaIiiiuruB3nH00RL/wBI/iz9H6Nf3IO2QQmKEjv66ciGMjz2s4f6FNUb9EfjW00ibUNSujuaO2EFvAp+cnmnkVgq+QAhO6Q8lU+JIUu3/SQ8SlbXT7Jc7ZZZbhzzAPq6KiL8QTOzY7sotUioimHS30jXesXTXV2+TzEUS56qCPPJIlPcPNu9jzJJqH0UURFW89BnpahsbS8t7+dI4Ekje0QkvO8s24SxQW8YaWQErG2EUgNIfF6qTZ2ryMqRqzuxwqIpZmJ7gqqCSfgBVqvRI9HS99dg1HUoDbwQYmhhmwJpZxgxFoT2o0jb5w9ZtbcqAKQSQRXlU1zRRREVW7gFPW+NdVmbmtnaRW8fwMiwfYcm4H1mrI1W/wBFxus13iqfvHrUcQP7k14hH1BFoishRRRREUUUURFFFFERUA9I0/8AMerf+53P8YmFT+tNxxw+l7aXNnIWVLiKSB2TG5VkQqWXII3DORkEURRL0ZW/5h0rH/VYvtAwf41ULj6603WL7iW6vLqOCSNRb6Ssku3e9sThkHdtl6gJ2v8ArT45gEXO0XgdrTSRplpOyOlvJbwXUiqzxswYLIUXaGZC2QBjuFR7ot6CtNsbGG0ntra9kTc0lxNaws0kjMWJ+cVyAAQoXccBRRFX7jvUY+IbLhGOZmBuJbm1uXXG9Zokihd8cxlmAmCnvV1z31rOHYdW1d7fhK73Rw6fLI2o3QJLS2sDD1ZRuUYBVgsZO7eGgcj5s5eHAHo2wWd89y1w7QR3jX9haRosaW8jjDq57XWIUEaBUEfKFOfgHqluoJYABjjcwAy2OQye84Hdnuoipz6KdppTW+im6eWK+S+1X9HxqH2SSFLbr+txGwAWIQgb2j57u/wuXS+6LeiSy0yIRqPWGWee5imuUieWGS4CCQQuIwY1IjXu5nxJ5YYNEXDVE+Nl7B+upW1RnjFewfroior6WEXYjP8A2wH2xyf5VX22lKsGU4YEMD5EHIP21ZL0r4Pmc+UqH+DD/Gq1CiA2VpejriBbqBJB7WMOPJx3j/EfAipYtVc6MeLTZzdrPUvgSDy8nA8x4jxH0CrMaXdrIoZSGUgEEHII8wa4XFqE08u0PhOnovScExIVUWyfiGvqsoiupr0IrowqKUyV0rnNBriqouwNANdaKIu4atRq8JjJmQZH86o8R+2PiPHzFbXNGauY/ZN1Y9u0OvcsKGUMAQcg8wa7ZrVSR+ryY/mJD2f+zkPgfJH8PI8vEVtK2yN40V8Um2M9RqsizPfXo9Y9s3OslxWrJ8Sq7VdTXXbXairFRddtcEV3rhqKij3Cluw1y3bwxcsPo6mzX+9n+NOjSD2m/eb+8aSr6hcx3kojLqjWzmJ440fbcRK8i53K/wDKKrIQy7eSgHc4pw8Mk4G47m8SQASc8yQAAMnnyAr1/gqbxD/5C+ef4ittJ/zd5LdW/tL+8Pxp5qaRcbAEE9wIJ+gHJqVXPSzCm3dE4DwrcQncuJFkkdYlJ/UkkjRpQhzyDDvBFbHCKdkRZtm2vktDgFSyzNm4tt82/wDZMWdWPssB9K7vwZfxrxKS/tJ92w//AJDUQl6RY1UvJFJHGOYkcx7WiIn2yDazHDPDsCNtPzkZ8wMGTpYgUw7o3VJEDM5K4hkKgmKUd6srNGhbuDSL8a5x1bC3Vy9AZh9Q74WHwUj1LhOGXm0MRPiyb4iSfE7M7j9OawV4CtvGLP0XEh/ECuqcfx9UJTG4+asJiuV5DUHMca5zzKFSWOByxjPdWhh6aLY9X824LR7yrFQ6ykuFhIOBucqCHJC7XViQM0dWwttd2qqzDql4Jaw5a91/spA/Bdr4Wz/e5/vTiuy8JQeFt9rL/wAY1qrnpVhjkkikicSxyPGyAqchIJJWeM8g6h4mixyO4qTjNZOqdJMcAzPFJG2SpQlGbcjRiQdhmUqkUqTFgx7O7kCppy2HP3tNVQ4fUAgbOummfYtxY6S8YAijVFyWx1gUEnl2hHExfl4Fsd1bLSLCRHZ3cEMMCJF2xr8QCScnxIxnyqGXHStAsrxGN8hkWNsjbKWuPV+yfAgfOhT3oCR3VKuDuIVu4utVSq5IALxs2MA9oRu2xufON8Mp7xV0dTHIdlpuVZLRzRN2ntsFva1Fzpb9YZUkwSANsi70GP2O0rR58dp5/GtFxZx9HbC5JjZ+oaNGAKjc0kJmGDz5BRg5HefGorb9N0LyLEsLb3RNiu4Q+tPIsXqzgr82VZjukJOApODyBq+ojabE5rNDhdVMzbYwkc+XNf7KW69wZDdBVurW2ughJTr1SYKWxuKie3lKbsDO1ueB5Vppuh3SW79Ksf6tvbr+FutYkPTJEXaLqX61ZZ4er3KGfqm6uN4twAZJpQYgSRtZWz3Vm3vSrHEyJNC8bM6RgblYg+sTQXDNtyOrt+qEhcE5WRe48qCpjIvdVdhdU0gFhudNFiy9BmiN7Wl2o+gbf7iisST0dtCJBOnQKRz5SXGPrAkUH66ydO6Y4ZJ+o6lldpbeKIlxtlScpl0O3vjSWKQx9+2Rfiay+JeleK3WJ+plcSNOMLt3Bba6W2kbAJ3d5kCjvUY5GgqY7E3Q4VVB4ZsG500zyv8AZSHhPge1sRizt7W2z3mG2VGb95w+5z8WJrfdVJ4uPqTH4saWGndNEUrzpHFuMRnYfOjD21usjtODs5bwgCRjJYsTkBGYe+k9L6T4WK2keTZGzRb0V0crcvMj79oUxLb8jnt9ZHjAOaoKuI6FXPwerZfajItre2V+dM6FCO9i307f8AP416Zpbz9K8CyGIxtv2yuACP1Y1mgUkgYe5jLFV8NjDnWvh6Z4jyMDq46/rELovViKaGJHLsAvUydbuMpICCN+/FVNTGN6tbhNW4XDDz7tM8+zL93CbFVt9Co7p+Ipf29Sk5/APM3/AI/41YjSbvrI0k5dtVbkyuO0M8nUlWH9JeRqufoHnMetHz1GT+7n/Gs4K0CCDYqxWtanHBG80rCONAWd2OAAP8T3Ad5OBWDwdxLDewrcW5LRsWUFlKHKMVYFW5jBrZX9qki7XUOuQdrAMMqQynB8QwBHxApbej9dlbb1cxyqRJdy9Y0ZWIg3cqhQx/nMANtx7JBzzrC57hIBuIPfkt2OCN9K+TPba5u8WsQ6+Wt7gb/ombcShVLE4ABJJ8AOZP1CoFbdLFo9jLfqJDFFIImTCCTcWRVwpfGG3qwywOM8sjFZnTNcS+oyRQKzzXBS1j2qxC9e213cqDsRY95LnkOXnSc4j4WvoBJaeqrsultNq2hkmiD2MsQd5GeJNjSQHmDybZ4nNa1VPIx1mDdzE5nT9e1SeE4bTzx7UzrEuFhtAe6223r1HI7tkpz6f0gW8s1lFFuf1uKWaJxjaoiHaWQE7g+crgA4ZSDivK46S7RblrY9blZEt3mELm3SeTbsieb2VcllGDyyw51COHOj+az1qJ4lZrDbcPEe9bdpVO+InwBfBXwwQO8Envc6Rd7p9P8AVXKzagt767lDB6uLmK5O47t/XARCIR7fI5xVrZptnMZ3tpfQD7nesj6Ch2xxbrtLA74gCLude997W2u0anMZKf3/AB5bJBdXBLmO1kaCbCHd1iFAwQHG4AuBnOO/yrWav0owwzGBra9aTtFRHas+9EYK0ke05eIFl7YGOY86gmv6Be/846attI6Xt56zHdjb1EcMskbzdYxOVdAmAoBLZ5eBM8ubQ/pq2ba2xdPuUDYO3cbm1wpbGAxUE4JycHyq8SyO6tBodbm/hZYTRUkeZO1cOIs4aBrS29tLnaFjnkpLw7r8dyZxHu+Yme2k3DHzkaox24JyuHXB5ePLlUXvOlW1QElJyfWbizVI4TI7y238psSNmJUjmD34ByBWk4H1aWzuL6GWzu2E9/LNHNFCHh6uXqo1Zn3ggDZuJxgL9YqF6xod1GUcw3YC6rqc5a0j3TiKUARSRbgV2vkjceWN3jVJKh4bca53y6x5LJTYXAZS2Q+7kW+8M/dcT42CaE/SlAogLQXgabrtkXqknXBYNu9mhHzm3tggqrZGTUs4Z1yK6hSeFt0bbgCVZSCrFWDKwDKysCCrAEEUobrQZ7yTT+ra/h2W+or6zcr1dwkrNCYhMyrja3MADG5U5Hlmp/0O25j0+3RoXt5FDCaOTduM25utcsxJfrHzIHychqvhlkc+x0//AD6la9fSU0cAdGffvmL3sLvH/UedrhTLNa7iLWI7aKSaU4RBk+Z8AAPFmOAB5mljxj0oz2l71b25FqOzlhh5MHtSRNnaQPBPEd+CeWl4+1d9Yu4rO0bMCgSPJg7dxAy7fCNTtC/tlh5EUlrWgEN+K9gOv0XJTYkwBzWZvvYDr9FjcM8aape33WW4yg5NAxIgSIn+cbH8oe/rAC2eQGOVN7ilSY+fI4545jOPPAyPjgVl8J8Ow2cKwwrhR3t+s7eLMfEn+HcK8+KF7BrJTROY33zclZqKB8TP5jiScz1dipV6VsX/ACeQ+Txn/XA/xqrNW19KuDNrP8Ch+yRKqVWytxFMToo6QTasIZjmAnke8xk+I808x4d48qXdcisM8DJmFjxks9NUyU8gkjNiFc2xuVdQykMpGQQcgg+II769JDiqv9HvH01kwUZkhJ7URPd5lD+qfHHcf41YbhniOC9i3wtn9pTydD5MveD8e4+FcRX4XJSm+ref1XouGYxFWC2j+b0WxNyvmPtrq1wKZGh8T2ghi65M3CxyxMyx4BEsjPnIx2lEcQBHd1jY7jjCg1zTo5OsWHDLKjRv1W89WohMgKvIPnGcS4k713fZYKWPI8YFca+a5HEnLuP6b1BDN3/Dv+H0+VcRz5IUcyTgAcySe4ADmSfKp3rPGdmxmIhYtISwcwwAodhVCVLsszRuWkDSd+7HLArF/wDTy1Udm0RWDZDYjBwH5DAGBmJpFI7txTGQtVNLED/qDuQVs5H+ke9Q1LkHxr3BqbaL0gQCRxMphw4dMAbwgEWYOSclZkd8HAJk71xzhNlOpAAjLEKWJLlQQh3sRyA/kwV2gk+I58qxS04bbZN1lgq3vJ22bNrb9V0u4FdWRhuVhgg+INaXS5mVjBIcuo3Ix/nIs4DeW9T2XA8cHluFb0K5JxG/tAY2sdpb2FPLOSMYB5msXifRJNsbY2zLmSLdkEEMyNHIO9Q+GUgjl2W7wKpDce67T7H96rO9wBDmnPm5x+8wjdjnWbmtBPKJreTbkFo5FKnkysVIKsPBlPI1g9EepvPYwvJzYbo8/tCNioJ+OAMnzBq+SA8WX8xsfrf0VXVLeNbHzgkHst6qV0UUVprOiuDXNcGqhUXhplxMt0vVxrINq98uxs7m5YMZGMY57vqFMPhxyTkjB3HIznBycjI78HlmoNoxxcp9A/vH/Opvw6e0f3j/AHjXrPBA3gHYPuV8/wD8Sm2m/wCR+wW/t/aX94fiKYs3R7ZsJgUJEpRmBdjtMc0s6dV7sCWaQ4Xlhsd3Kl1b+0v0j8RTySt/hBGx5ZtC+vkofgLK+Ns2ySM26dV1GRwLa7ZEKsyu0bFWkdgoil66NE3E7I1kLHYOXaI7sAdV4CtPnPm8iQ3JcE8j62YzNy+PVpj9nHKpTRXPcni6I7l3wqZR/Ue8qLPwHanqey3zUcUKgSMFZIMmHrAOUhiYllLdzEmvI9HVn1gk6vtdULdu12XjETQgOP1m6tiu7v5DyqXUVQ00R/pHcqiqmGjj3qIydHdmVRWRn2NMyszsXzOhjly+ckMpz8CFI5jNRix1jRZL79F9cJb2Hrm2u8jMzzxMs69c3ZkmWL2owSUXbyG0gNC7uFRSzsFUcyzMFAHxJwB9dV811bMv16lUgi1Bb1byPZ1e/wBeEkrB0y7B2eWFyB2lMg3bSGq10UTf6R3K9k0r7+8cgd/75ym2vR5Z7Fj2EqvqxXLnKm0GISD4NjIYj2snPfW40DQUt+sKl3aQhneRy7NtUKuSfAKMfHmTk86z7O5R1Doyup5hlIZSM+BBIP1V7VkbDG03aAsT6iRws5xP1/fMovr/AAHaXMjyyqzM6GNwJJFRh1ckQYorBTIscjosmNwDd/IYhPSw+h6YzXF9lJLtWturiEryTAogdkhiywKKiEzLjadvPcy5bc0gUEk4ABJJOAAO8knuAHPNVq9LrS2mazvlgZlgScAtgMQ+whlTm8YABDFwD21yvLIpIxoBOzdZYqqa4aHkDTU6fsDuCafC3C+l31tBcwK7xFbjqZW9YilxPMzyk9YElDCYM6sw7J5ryOTubro8snQI0ZYCK5t9zO5cpdkNcFnJy0kjDd1hywJbBG41qfR6uo20q0VN2QhMiuCGV3kkLgZ9pBL1iq45HYfEEBhVVsbCPhHcqOq5gfjOV7Znr9T3qKp0f2QKnq+azQ3KHc3ZmghjgjK8+SiKNFKDsnGSMmiHgG0WUyhG3mTrhmSQqj7zK3VoW2oryEyMqgBm5nuFZfG2nzPE8ls7rcxxTm3QSFYnnMZEQmXIWRRIFwH7Iyc99L7RrnWY5Yml617Xrog4kSFrrqD68MyLbL1e7nZ9ZsAxgf06u4pnMFaaqY/1nm1KlMXRZp6pJGsbKkj9aVWRxtYrIh6sg5RSksiFFONrYxism86ObJ2d+rZXcRBmjlkjYiKNoV7UbKecTtG3PtLjOcDEQ4jk1kSQdT1/Oe+3bVtTD1P6RhEHrBfDhf0cZ+qMR3bwm/cRgxrUX4mIJt+t3IJpHS4SzEcrCO56mG3dCsi71MOXlyFnWP8AUMlUELB/SO5XGuqCbmR3eer0Hcm7e8DWcjl2iG4vbS5BI7VpygwAcAKOW0ciDzryfgG13I6h45ER4kljldJBHJKJmUsD2h1gzhs8iw7mIrD4RsLtLomaed4BaWhCSCAxm6ke49Zy6wiQsoSEhes2r1h5YIxNqrxbeYK0Vcw0ee881vtksPRdNSCJIYwQiAKoJLHA82Ykk+OTVdfQQXamtJ4rqMg/gR/hVlqrd6FoxccRp5alJy/rzj/w/wAKuAssBJJuVYPXNUit42lmcRxqMs7HAHgPrJ5ADmSax+Gtfgu4+tt5BKmSpZc8mGMhgQCpGQcEDvHnWq6VtUjt7N5ZIRc7ShjhKbw024GInsttCOA2/BK4yOeKjPQ7qluilDN1l3dzTTy4hliQyiNGdYldF+bjj2DcfaIJ7+QwOltIGZWUgyiDqR01je9hvGQuesWGdyeawOZDE1vUI4IpJpW2xxqzu3kqjJ5DmT8B31Gr7pHso7aC7eQrBOwSJ9jk7ju9tQMoF2tknuxWl6erh5IYLGFTJLdSqOrVgpaC3xNN2jyUEKq7j+1491K+TSnkmg0me3eKMXwulhVusEVncxTBh1sfZCpLuw3LG9R3iteoqXtdssHVv1P6KSwzCYJoRJM4jMkgFt9hosSAc77WQ3WBun9bcTwNcSWoYmWOJbhsKSvVOcKQ3c2e/ArSaT0q6dOwSCVpXKu6qtvcdoIjSEBmiVd21ThSQSeXjUD6IuHbq11C+inDuI7VYYZip2yRKwMIVsYLKmAVBJBBHgCcLoN4mW2ghjnuplEUEha1ksmjjQoGkY+smMFmCq2AW57sYziqNqnnZ2srk3v1G3OPPsV82DwMbIYyX7IZbZOR22lxPwnQgCxta9iUz4ekezZYXVnZZoZrlSkUjlYbf+VaRVUspB7IXBYt2QCa8dM6VNOlZ0SV9yJJI+63uUCpEu+TJeIAMqjO3vOOQqBdCe6G8M0sPVrqUck9oV3kQIJXlaBs5VOsWRJhjAJyMd2N1Yx50/X8ZZjPqYCqCWz1QAAA5knlyHnVzJ5HNBy35WO4du/VWVGHUsUjme8fhsdoW95xB3G+z8JzFyCcrqVaR0l6dMjuk4xHG07q0cqOIU9pxG6B2UeaqfDzrXN0x6ZtZhMzKu3cRb3AADuqA5eNRjcwyQTgc/ClrcaNPGkyTtNcE6O/qB6sBYw8QFxARGgzKAI9u7LFc95zXkbhruxWzE1xdndpyvbNYvCIokuIVmKyCMGQKrAMxJwq7uQBNYuVS6WF7Hd+v2vkttuDUd9q7i3aaCQdAdT8G7TPYzBGdk8eKOLba06nr329dIsMIVWcu7d3JAcKMjLnkMjnzFarRuk7T55kt4Zi8rsVUCC4A3AEkF2iCL7J7yOfKlhrPR9dwiGS4Y3ItrqygsFTc5W09aRnklAHt7NqEnkFjznurK6B9aKNDbyXE6MWuP8AkbWTLHud5HBN0Y85x2+bAc9vwq8VMnGBrhYH9OtYHYRSCldLG8vcNbGw0JBtsuNgLXvYXuL5Ke8ZcVadIz2V1lj10NswMb7VmnjMkREmML2efWKcKeRIrXaINP0TbDJKTLcGRwxjZnMcQLDcIwdqIuRnlubOB4CGcZcMPd6jdQtFIYZL2y3uqPhYzp86dZvAwAj4G7uDbQe/FYnEPA17Gba4u2NzdM8ttmMO6pax2VysROByMknbZm/WZR3k1jfJJtFwYLg2B6r5+Gn1VjMDw1z43vfZ2xcjK9y24sbWAve99BYDMpz8I8d2d6SttKHYDcUZJI328huCSqrMmSBuUEcxzrAl4rtrmS6ton3S2xVZhtIALbh2WIw2CpU4JwRg1EOC9Ga0tLfU7yd39WsWEcDQrEIUkRGKNjtu42Kg34OfDNQXhy0ubGSzuri3aBbkyw3U/WI/WyX0nXwM0a9qMxydnLeDEHB5HNymQbO0O3sy7bZ8+4J7JpnmXi36ZNzGbhtE6hpd7oGg1cNQLla+kjr9vLFdQpIGkCsCAG27kILKHxsLLjmoYkY+FVPqy3SXIY7BrOS3kE0SXPWM8ZEa461uuWUqVfrMjG05O48/OtNbcTi5tyoWrhEUha3TtBv15biiiiisi1VyKztC1eW3kEsLlHHiPEeTDuZT5GsCiqEAix0VzXFpuDYqxPR10rRXG2K4xDMeQbujkPwJPYY/st9RNNTRXjWZHlG6MZLLtVs9khey3JgGwcHyqkeaYvR50pzWuI5szwd3M5kQf0WPtAfst9RFc5W4JnxlPrrsny9F1mH8Irt4mq0OW0Nfr6q1Z1u2ja4KQjDuzxboom6sGJ1UYfdt2zFHwvLC/Uc9eObNcbLTkC/eIhhXOWUFR3E4+gHB3cwy503U4L6BjDIGVlKtjkyFh3Mp5qfgawLjhFObF2HtEhcKuTtwQoGBt28vpOc1DsqHsu152TzbKnpKCKQB7PeBGu39Ez7jpMHP5rtNtPalVj2QFKKGjI6gcm24zuOd3djpD0plMEDfJhw4mkLIpaSV02p5hZVUscZUbcY50kpvVm6sZlk2rtGB+qGyQwxzz5Y5hB9fa30i2A6xYpn2soUEd7Y3D6vAk8+eDnGBtcY8auPcOda3JIDkGD8x5k336TnZ1b5oMGymXb9cKGUhXUSbtgC7h2eeOfMarVb9pZGkbAZsd2SAFAUAbiT3Ad5pd/olI2/6M7KBkFHcsCQh5AgAc+WQT7PwrfS6pIDgQu3PHiOW1TkkjB5kjkT3Vp1Ae+1nXGudvVbtLHFESdkA6ZXPktvPEGBHcSCCRjPdjPMHmPDOa8dE02OCJIYhtRBhR9ZJJPiSSST5mtN+k7hk3pDzDFSjHmQB3gnAHa5Z7Q763GlSSFAZFCNzyoOcc+XPxOK13Ne1ticr6XW41zHOBAztrbcs2jNdM0ZrBZZV2zQa4oFFVYF3bSvLCIpOpbcCzbQxKKyllweQ3Dln/OmNwye0f3m/vGq7+kBdyotr1bvGGMwfYxXd2YyuSpBIGG5d3Ont0aN8zD+5H/dFeq8DT/IHZ5leCfxObaf6/wDVqm1v7S/vD8RTxWkdB7S/vD8a7dKPFF/b6qLBYfXrbU7OSO0t2mhthHcQFhd5mZd3Vm3lWRtvWScgFXlUtjmrPr5LneBHwy9rfNMriLj2xtp47WW5hS7lx1Ns0sayyFiRGqqzDBkYbF3lQzcgaWer+kDCtxZxGN4oLmCbrp3Hb0+7S4FoI7tQGWNUuT1Lu3ZDvGc7DkqjhOxvhaalbPZS3t09tHoszWxiLW+oab1osZ3ed42FvJbzWsy3S7tsluTgEjDzfoagk1GTUJtres2L2WoW2z5ueSTqd0ykEbGKxBDyOdsZGCCTAru1KehrWpLrS9OuJWLyy2ts8zkAFpjEolYhQACZAxwAAM9wqW0s4eJdK0CytLVrhzCiSJCQr3UnVQN89JJ6tE22OEth5CqqvIHnyrz03pNuH1GK2ayMdncTXNta3TS4lla2t/WGnFuYwPUpAGRJlc5IU4AcURZPS1qEj/MW4DSorzYPNd6xuUD47lVcyHPMEw4yWAqqTcSTNbRWuR1SN1g7Pac7zIvWHOGCsxIAA8M5xVkePtTm0+PV726EaB0MNiY23s8kq9WmVIG04jhZ93IbHI5DnU221KLKh96rkBtsZZgvjtA7JOO7tYzioPFI53ObxYOdwfCySPdkGq3XQzxSZBGXQxC5XfsPPbKC6h1I5GOYRsAxwcogwCxAbdK7oP4LeK1tZbiUTuI1aHYAFWNgTDu28ndY2xlQE3Fmw7HrC0amIg4NAdqr5HXN+9a3iFxtRTyDOobPdsTMrg/ApGykeINaPTpHksZnkUAOtzLGO89VI0kkYdSOzIFYKygkZ+sDd8R2RkTABbGSUDFC6lWR1DggqSrHDAjtBckDNRXgrUlM01n1iuApk6pm+eSJmaIF029hi6tujJP8ojYTJSsisWN0eaStlLBboOw0MqDHILLbyL1o2EkhC7PIrksxM77ieRLGqJcEbjI7yIgLxwMkgwXkURIkhZvAb1Xsj4HnkVLaIk/0mdHmoXdzK8Ny0UMjW8e1biSFoI1e0e4uICkbA3JSB4QkmV2yEhk3SiTU6P0aaysUwkvEN0RK8V0txdbRK13HPEhtCnVJEuyR9wLNi5aEDYmWe1FESt0To+uFtoobiZ53S7DdYLiZWNnCnq9uHZWQtI0EUEko7mleZuecmKWXR3rkS2o9aEzW8FhKS9zIolu7JJke2bbCN1vP1xLTyhnYqhcEquH4TS/1fpm0mGZoJLob179kc0qbs4K74o3UsPEA8u7v5Va54bqVQmy8+GeAAkyvOplVZLyZBLK86o7XYe0dRK7bHitlCDaBty4zz5sWoxoPSDp1zjqbuByf1TIqP/Yfa/8ACpDa3SOMoysPNWDD7QTQPB0KXC9qrb6JI2atxVF4i+34/fnvf/xVkqrh0AfN8UcURdxdrebHwO5s/wC2H21cqqxN7cKil3YKqglmYgKB5knkB9NeNhdxyqJI2WRD7LowZT4cmUkfZWm6SZrRbbdfDdbh4t2VZkDdYvVtIF/m1faWLdkePKoV6P8AqUQjuIy8W+S8uTEYsLDOESIu1rH4RqMAqC2DnnzrA6a0gZzrfjotuldOL3BA0yP15xqerwYnEGuW1sqvcSxwKTtVpXVAWwTgEkZOATj4GtdZcb2Mhi6u4jfrnaGJkO5WkQBmTcAVDBWB2kjOeWahvpGXQRdOZnjjAvFJknQvCmIZeciAgsvhtBFaHivVY7jT7a3glt7q9nuQLaSzTqI45omEhl2bmZOpi2hiTkhh51hkqS1zhll46df00UhS4SyWGJ5LvfJBO5oF8zlawtc+8Da+W9MyfpD05Pbuoox1kkWZG2AyRELIFL4DbGYAsMjJ763I1eAvHGJELyq0kShgTJGm3cyY9pRuXmPMUidJXrm0WO0EccsaahBMl1GZ1S4jEZuVlVWQs7NucNuHtKfhW06uHR7zS0up1EaW+obpmUpH1k80cm1VG7YgJIAzyAFWtqnakC2Wfbs+vgskuDxXDGOO3Z52dSdnjLaD/aN5vfLTNw6/q8NrE01w6xRLjc7dwyQo7ufMkDArw1HiC2h6vrJFTrQ7x5z21jTrXYYH6sfa+ild0wa1FftY20CvewPvvJ1tNpdoI8xR4LMgw0zHvI9jlUG1bW2e1soZMrcWqataSq3J1Mdi/Vbh5mHaTjlkNjOM1SSs2XEDTKx7r+B8Cq0mBCSJjnkgna2hlkLO2ctR7zSDcaFttVYAca2XVQTCZTFPIsELqGYPMxKhOyDtOVIy2AD3ms+PXrc3DWokU3CoJWiB7QjJADHwxzHLv5jzqsy8PzWbaOdxNlcTWN52u6K5CKJlOe5WQlxy5hcctvPZ6Pq7pdprLQzrHLdvvuSFFudOnC28I9vduRhG+SuDz+FY2179HNtmL66WzPiFtS8HIbExSXBDtnTN1yGtI5/dcDa+drZJ+aVxhZTyGGK5hklBYGJJUMgKkhhsznkQc/Qa2GmapFNH1sTrJGdw3qcr2GKtz/ospB+INVp4NvQ13ZxoyTvHqN0xto7d1ljR5px18t4Mq8aK28RchjaDnbTB6FONLMWaWjToLvdd/ME4csZppAAMYJ28+VZYazbNnW3+Xr+i1MQwLiGl0e0bWvluJfc6ae6DfTPUqfaPx3p88qww3cEsrZ2RxyqzNgFjgA+Cgn6AakmKQXo4azERbQm5si3VvttlgK3gfJbtTlyH2qHyFUdkjy5v5az00xlZtH9/dR+L0TaSoMTb2HPvzOejcu8dZXDLWu1tewa2da/Vx2TWwoxVe9ISDMFwPOOQf6jVRir69PQAilLHA2vkn901QqiIooooiKKKKIiuRXFFEWy4f1ua2kEkDmNx5dxHky9zD4GnrwD0uQz7Y7nEEvcHz805+k+wT5Ny+NV4rkVpVlBFUj3xnzjVSNBik9GfcOW8HRXTVR3jHwIrhlqsHA/SNdWeFB62H3UhOAP6Dd6fxHwp7cF8f2t7gI3Vy+MMmA39U9zj9057sgVydZhM1PmM2848wu6oMcp6rInZdzHyP7KY54MmZd8ZWROr61WU4DfO9UU7WCGBy3lgd+eVeNnwbcvt2qmWRZVUyLuMTbfnNoydg3AE47wR5A4MepToVMchQqhjUgLkIZRMRzHMGQBuf0d3KveTim8MRhDDcSgEw3CQKrIyouGCKMoo5L3ZHiawM5KfiuFke2ub8JaR5LvrHCksKqxKEFJpDhs46gMzgEAhsouQeXMkEDFZMPBUhWNjLEu/qcLudmDTlBEGCqcbjIna5gc/Ln72elagFmg66NutLQNvd2bftlaaNMDClU60tvG3n2Dk5rwg4NuVwfWhiDa42+sSdXtkYLtj6vniWF+Sg4KKcYIrb5LGTcNPetLl0tiHSNH0vddxwS7KjK47cSTKNrNydEwu8AL1rSMAIu/ac8/HPj6P9qF5Xc/N7wiRYcs0LyoE3OA7goVMbbe9fOsq14AmM7S3NwBIi71W32hFeJVSMbTGYygTCkBQR3c81qdV6OTLPM0ly0jq8ayBYVL/ADnV9X1a9YqskaN2jhAoj5LjuziiYBfY8Vqe0JCdnjgBzhv6I4x4Xgt4jIlwHZVXsDBy+5VO7BO0vliq5wNhHPmRGbqNBt2SCXI54R12ny7arn6R5VgXljGJpFRdoRigGSeadhjz/aYFseGayETFRNQ5hdZrbLoKSKQMBe8nfoEtvSDkIt4eQO6RkORnGUJyvk3Z76dnRifmYf3I/wC6KSvpDJm0hPlOv8YpRTl6Lm+Zh/cj/uivSOBf+j3/AHK8X/ii20w/e4Kf2/tL+8Pxpl8V2thJPZC6aIXEc3XWSvN1cpmEbqeqQOrS/Ns+Y8MpAyR2RhaQe0v7w/GsK84ekg4pPVerRrfQx3q3E9qJ5w1kUhu7W1lMiGDromjlY9sL2iFBqaxzVn18lyvAj4Ze1vmpnY9KNtLd6pYW0ROoWqu6wuFjF5IkKsOqcE7sExxsW7QDIcY7oZoN1Frlo2n395cG+kjln2R217pUaExiNokWTZ67HZyyKWDmTLbSwwcV0v8AohvLu+1OcXUlgEvVu9OeOCFnNyLC3hMzSvudrYsDG9soQSBWDFgQAyOFejwR3CX11PNeXio6I8jYgt+u2m4WzgXlFHIyjHWNLIECrvIFQK7tQ7g7hi3veH+qit4ra59TutPmFtDEkkU4Jiv4Y92AnXXETZDMA2UZieRqcLw+8w0u5CLaTW6jfFJGk8kcM0IWe1SRHCxsXWIGZNwxFgDDVLbS0RN2xVTczO21Qu52OWZsDmzHmWPM+Ne1ES56fuG4721hhlZ1X1hXBjKg5EE4HtKwI592PKqp650P3AaZYRHdJGWCbiqOSQHRCp3doFggKnBbPJcYFy+k9c2jY5HrLfB8v+URAkfUSPoJ8KTnR9C4uJg+Q0l8pjVh3xrcYLIMdoYVmJBO0DnyArC8kOyWzExrmZ/vJO/gHS2trK0t2xuiggibHdujiVDj4ZBrd0Cisy1kGqPdJOhXo4wmis7lrS5uipinjdwFjezEjLIoOGQvA2UIYZCNjIGLwmqg3UEr8fLucAI6vGXyMxDSc9XGMYJy0nj3hzWSPU9itctT6K/SXfRavBpd9I+xYZNOit5Ai+rSWybo1G1Rk4gaIsSxYlDkgA1dSqh+kJoCW3Fej3MeI/WprJ5CABmWK6SF2z/2kTQoR8Cee6reCkm4o1FaDjbjC1sI+tuZAgPsIO1JIfKOMdpzzHdyGeZAqH9N/SJLYGKC3VOvmV3Ekp7EaIVUkKATI5Lch3DBJzyU1p14XU8jSzs88jYBkzvJ8gFAyqjOQqqoGTgCo6qqzFk0XPgqPcQMgpX0m9Ll1qG6JM2toeXVo3zsq/8AbSKeSkd8SYHPBL0srjSo27wfEYDHAB8AO5R+7issf/ivW2jZz2Ry/aPcfgv7R+OQBz557JgTJLM++9ad3vK0p0fauEkcDux7Xl3AEDl3c+XPn35pq+jZCyanA3Vs/YkX5tRlQygGWU5UdWvcT5uoALYFdOj7o+mvZNiABVwZJWyUQHmN2MbnIOVjXBOf1FbIs/wPwjBYxdXCOZx1krYMkjDxcgDxJwigKuTgCpelpSCHHPrW42IN11UgFVt4RHUcdaivhc2CS/SVW0Xl929WSqtvST/yXjXRZ+QS5tZLYnzdVuxj62kt8fR9FSivVkJYwwIIBB5EEZBB7wQe8V4R6dENmI0GwER4RRsDcmCYHZBHeBjNZIopZVDiFr+IbuKKGSabHVxq0j5AOFQEnAPjjuqGWfHVibFNUEJCbhGFWKNp0d5BCVG095Yj2W5gjvrr05xTz28dlboWkupVjZiHEaQx/OyGWRFbq1YKEzjJ3EAE0tde0O+RLuxa32i4ns7yL1MSywRfPolwBI0SbGG1JtpHLtHurQqJ3tcQ0ZW5t+ZH7610WGYfBNC0yusS8ZbQHuAhrsuc3JvuDTzppL0gRLC85s72NVfDhrNkYfNtIZCCRmMBSDJnAOB4isefpQteoSeS3ulR5VhiWS0bfKzxtIpiTn1iFVOGTPPArP4k0RodNvolkmuGaC4KmZuskyYSAq4UHHLkuDzJqJ9J2mymx0jqo7hxDJbSSC1XNwirbONyK3IOGIHa7iedVkfI0E9XN1rHSw0krgLHNxF9q2QbfeN5yW7bpDtYxFKbS6SSV3gRPUitwwiUSsRHykaIBiRtzzDcuVbvSuI7WS3kvIInk7REiR2xFyZEIjZXiZVkMijwbnt+FLyTRpbw6aF/SMIjkvg890AlzEXgVo2LrlRGWOxT44ZfCpt0J2Dw2SxSxvFMkkwnLliZpesOZw7kl1mGGBzy7h7NVifI51jpz26h6pW09NHCHtJ272LdrdtPH2aOy/WFpb7pfteomlNrdGFMIxe2CxsxmW3ZBI7dWzq7EMmc4V+XLFbzjLi2zs9lvPETGYJZ9ixI0Sw2+zcpQkDluXaoUjl4YqA6nw/PNoU0EcTtK17K/V7SrlP0ozswDY5dX2gfLmM1rOlPo9ljlxaLdXKvZXis0ss1zhz1YVA8hbZuGexkZx8KxOmmDb2vkN3Pe4+y3YqCgfKGFxb77x8W5obsknK2ZNsjfTcm1wTxfFdsyxW9zCAobfPavBG4zjCMwwx8cDw51r+GuP7e6mRIra5IZ5IxcG0PUBo927M4JC81K5P6xA76wuhm5b5xGTUlYJFz1FUEY2ZXEBTkM55g8yFHlWv6HOH5LW0a5ma5Vla8c2hJ6vb1shUrBs3F3ADDB5ls+NZWyPOz9Scuay0ZaanZx19QWhg2r32g7Pr0HVmpRo/F9pJqE1giYnhQOX2oFJO3cqEHduTeueWOZ58jXC9JFr6wLcCU5l9W68Qubb1ju6nr8bTJkbcDIDcs5zSl0XR9RtvVdUkt0ObhrqcRNM96Yr9lWSKS36kDESlG2hyVKfTiZ8CtfWZ9RFmzg3c8vrZZPV/VppmmLEhus68KxURlR2sc8VjjqJDkRbPonQ6el+pbNXhtMwFzHbVmgfG0e8Lh5vzfCQ3X3upb/Tuk2KS4FsLa9WTKB91qwEQkJCNMQT1aHBO5uWAT4VGulTpLms7lY+ob1cDDu6splJxkwP7PYH05J8AATKuGbYjVdScqwDR2KqxUhWCpNna2MEgnmATjlW+4r0qKeF4pkEiMOat/Ag96sPBhgisxbK9hs6xvllzHzXO4tCDZtP7p2WnW+ZaCR2Z2VQPSA4iF68cFsdyMA7n4kZCt5bBzYeeB3iqj31o0bFWGCP8AzkfA1dvirgqOyExUlyxO1mAysfeE+PPvPLPLypAcWcMCQHI+IPiPoNWccYrcZqfJWYfRyysc53xZeaTdFbTXNGeE8+a+Df5+RrWYrdBBFwjmlpsVxRXYrXGKqrVxRWZp+lzS/wAlG8uO/q0Z/wC6DW1m4G1BVLtZ3SoFZyzW0yqFUZZtxQDCjmT4URR6iiiiIrsjkEEciOYI8CPKutFETH4M6W7q3wk3/KYhy7ZxIo+EnPd9Dg/SKc3CfHNpeACKQLJ4xSYWT6hnDY81JqqYrsjEcxyPfkd9RVXg8E+YGy7nHopuhx6op/dJ2m8x8j/lXWu9SuGzmeXmAp+cfmozgHB595+1vM1GJ+JrqJ5NvXYO9NyuxLKO0x5817bPhu/JYg9o0j+DelW6t8LIfWIuQw57YH9GTmT9DZ+qnNwrxjbXi5jbtDG6NsB1+keI+IyKgJ6OopDtOG03nBOXouqpMRpKwbLLNdzEDPyKw01qQ8hFNjmAWLgAE5PLaTg955d/1ke2ka5dxs7Qq8LiNsP4u3ZKrgkZGRnme8KQRipPHEpruLcVpcsto3xKkzR3FicuwKILe3a5xEHOWyzNgtz9rv8AHv5nPxGc1utEu5WB61AhzyCnIIwOfefHPlW1MQrgRisUkwcPhAPOszIi0/ET1ZKAdPiZsc+UsR+3cv8AjTV6K2+ah/cj/uilj05p/wA3S/BoT/tFH+NMjopb5qH9yP8AuivReBJ/lHt9F4v/ABTb/Mb2eqZVue0v0j8RTvEYJBwMjIBxzGcZwe8ZwM+eBSPtvaX94fjTzWugx3Vn18lx3Aj4Ze1vmuaKKKgF3aKKKKIsHXrHroZI+7cpAPk3erf1WwfqqPdE14strkd6yzqeRGG6xiwGR3DOMjke8ZGDUl1e0MsUkYdoy6MgkTAdCykbkJBwy5yD5itZwNwyljAIIyWAO4sQoydqr3KAByUfXknJJqls7q7aNrLe0UUVVWoqo/TFIYeNtMeNe04st2Mc+sa5tpGOSP5nl/V+FW4qMcYcBWd7JDNPGTNCwaKWOSSKRdrbgN8bKSobtBTkA5Ixk5ua6yoRdVr/ANIBPItxpTRKRJGtzMkgAPbSW2ZFGO1kMoJ89wxnBxarhvVkuIIp0ZXWREcMhDLllBIBHkeVYHF3BdnfbPW4EmKbghbOVD43AFSDg7VyM+ArF4K6PLHT2d7SHqWfk56yZwRkHAEkjBeYHsgdwoXXACALr0j8A22pIgm3K8ZYxyxlQ67wAy9tWVkbahKkd6KRjFJLiXofvrbnCfWY/OIYcDme1AzZwD4QvISScKM8rM0YrC+JrlcCQqT6tBltkqKZAyDa6ESDtKADG211JPgVye4jnznfRp0eSXrBv5O3HtT4U7iORWAHKu47jLgxpjHzjAqljdc0S3uV2XEMU6fszRpIv2ODg/Gsy3hVVCqAqgBVVQAAAMAADkAByAFYW01jmclXaWJoOkRW0SxQqERe4cyST3szHJd2PMuxJJ5k1n0UVtK1FKr0h+iyTVUtZrWYW1/Zy9daysMpnKMUfAJALxxsG2sAUwVYE01aKIq+QdJPFFn2b7RFvAvI3GnXAw4H6wg+dfPwIT6B3V3+U9FH/wBK0nVrbzLWYKj6Szof4VYCiiJDW/pYaCTiSWeE+UtpLkfdh621l6TPDkndfhf37e7T+LQAfxpuXNlG/torfvKp/EVpr7gfT5f5SytZPPfawN/eQ0RRSDp30Fu7Urb+tJt/vAVlx9M2hnu1Oz+u6iH4sKyZeiHRT36ZYfVZWw/CMViS9COhHv0y0+q3Rf7oFEWbD0r6M3dqVkf/AJy3/wCJWSvSTpR/9YWX/wBZb/8AErQP0B6Cf/Vtt9SEfg1eJ9Hrh/8A9nQf7T/iURSb/wDUbSv/AGhZ/wD1lv8A8Su69IWlnuv7P/6y3/4lRX5PPD//ALOh+2X/AIldH9HXh8/+rovqaYfhLREy9MvopkEkUiSxn2XjdXQ/Qykg/UayNtVc6K9NGg8VTaTAzLp99b+s28DMzCOZFZuyWJLECG4XPeUMYYnYDT76XuKxp2m3l6cZhidkB7mlbsQr/XlZFx8aIsTiTpV0i0do7i/topF5NGZ0LqfJkUlgfgRUel9Ifh4d+ow/Usx/CI1D/RI6KbQaPBcXtrBc3N2WvGkuIIpnCSn5kBpEYgNEFkwOW6Rvppuf/ptpX/s6y/8Aorb/AIdEUMb0k+HR/wCsE+qG6P4QVgaj6TXDmP8Ap4P0Wt6f/wDWpjp0f6aO6xtB9Fpb/wDDrzveC7AA4s7Uf/Kwf8OiKrnSX03aPOCIrrf/APL3K/3oRWq0zTlnjjdeauiOpxjKsoIODzGQfGmd0s8N2qq223hX92CIfgoqL8FRjZGAMDaoAHcAABj6K5zhG4tiY4c/kuk4OEbcgPMPuvG+9He7nQERx7WAPakUDB5jlzqISeh5qW/sNb7Dzw8zZX4dmJs/TV39Db5iI/8AZof9QUoNW6cJUZlW2Q4JGTK3gceCCtqOaGiY0vefeG/P7BaT2z1znBjBlzZeaQOq+iFqaoWQ20rAZ2LKysfgpeJVz9JFLDg3oaubnURp0g9Vn7e4TI3Z2KXxtB57gORBwRzBIq9XRd0pPfT9RLCIyVZlZHLDs8yCGUEcueQT3d1e/SFo6LqujXgAEhlntHPLLI9pPKmfPY0ZA/fNSkFQyZu2zRRs0L4nbLxmq16TwLf8NXWnGO8zBdX9rBcRxxBQwY89zOWOCoIwuO+ppofGNxLxJr9hdyyXFlHZ3jR2pfCBNsDMqYHZJRiobwzUl9O+J10y1nhwJYb+3lRsdzKkxU/UwFKzo06F+I7+5m1ea6j06W7jZXZoQ0skUqKhzbgBYlZFXG5g3wFZliUh6KOjfQdUtbPVjZR2pCXu+yEsssEr277UaQYMkm1RvKouW3Yw2BSf9KDR9L9VsLyxjSGaRpYpxbWt3b2coTJV4RdRr2lI2naTnPPOAadEXou6lbwRRWusFepZ5YU6holWRwdxDpMzAMCQcqw59xqtXTVw1q8d36vqTSyzqAIssZEZGOA0GAAVc4HZAJIwRkYoiV6rXfqD5Vbzom9EB2iSbUpWidgG9Wh27kB8JZGDDf5qg5eZpial6I+nOhEc08L47LMUlXP9Jdqk/UwpZEiPQb4AsNTub6O/gW4EcMTxBmddpMhVj2GXORjvzXv6afRhZ6dcWnqcSwRyRSFlTOCyyAc9xPPBFOD0X+jWfRtau7ecAh7RmjlTOyVEuIe0ue4jdgoeak+IwTgf6QawLfo5gM8rleQ+MJAoio4RXe1uWRg6Eqw5hlJBB+BFXi9H/wBFaz9TFxrEJkuZcSCAyyRi3ixlVfq2XMrDtPkkLyUdzE1w9Ji80gXhtdHtkiggLJJcK8khuJe5tpkdsRIRtBHtHce7bVFUGy78EdKhGI7k/ASju/rqO794cvopu6brKuAcgg9xzyP0VUcVJuDuLpLYgElov2c81+Kf5d30VB12DRy3dHkV0mG8IZIrMlN28+8K0yODXY1B+F+KElUMrAg/+fqI8qmFrchhXKTU74TZwXbwVLJm3aVGuluyaWwuERSzERlVUEkkSoeQHM91TToqyI4geRCoCD3ggAEH6DWvv/YP1fiK2nADc/6x/GvQOA7vceOvyC8h/io3OM9XmUyrb2l/eH4081pGW3tL+8Pxp5rXTY7qz6+S4bgR8Mva3zXNFFBNQC7tFFLrpg6Y9P0dQLhzJct/JWcA33EhPJeyDiNSeQdyAe4bjypXQ2fFWvdqSQcPae3NY4wWvnQ49o5V0JGeZMBHu27yRPPi/jqwsBm8uoLbyWWVFc/upne39UGlNrnpZ6JG2yA3N6+cBbW2bLH4deYs/wBXPwzWx4L9GHRLY9ZLC9/OSWaa+kMpZmOSTGoWI5PiyMfMk86bei6Hb267LeGOBe7bDGkYwO4YRQKIkKfSLv5v+hcPajMPBplaFSPpEEg/jXYdMHErc14bcD+ldgH+MS/hViKMURV5HTFxIvtcNyH9y8X/AAhauflHXUP/AE3QNTg82jiMyj6GaOJf41YWiiJD6F6WWgynbLJPaNnBW5tpMqfJjB1oGPiaZvCfSRpl6QLS9t52/YSZOs5+cRIcfWtbnW9AtrgbbiGKde7E0SSDH0OppY8XejToF2DmzW3bnh7RmgIz4hFPVEj+khoicANFVxPQ5r2mdrRdXeeIYxZaoOsQgfqrLhgvhgIkPd7Ve+jekXLZulvxFYS6XKeyLqNWms5DjmQyFyvjyjefHiRRFYeisDQdZguYlmt5UnibmskTq6H6GUkZ8x3is+iIooooiKKKKIiiiiiIooqP8cca2WnRdde3EdtHzwZGwzkDJEaDLytjntjVj8KIpBRVZuIvTO0mNysEN1cgfzgSOJD+6JHDn+si1O+hX0hdM1l+oiZ7e65kW1wFV5ABkmFlZkkwMkqCHwCduATRE3qKKKIiiiiiKt/TuvV8V8MzD2nFxCf3e0v/APM1e3pzXjS2unaVEcSahewxEd56qNlBP0LNJAc/CvP0hefE3Cw8nuD/ABjP+FeOsn9Jca28Q7UOlWhlceAuJVyOfnme3P8A8E+RwRWL0qxSGOOKMbUjRI0HkiKFUfUABWTQKKIisS/7jWXWLejkaIkj0vJ2W+ul5wSeyn1j7GIpmdLKdlvrpX8Fnsr9Lf3zXOcJh/44PX5FT/B4/wA54/2+YVqOFGzbQ/8AdoP4YqF3HQ5aOzMzy5JLYBQDmc/sVLuBDm1h/dx9hIpBcX8V3q3M6CeUKssigByMAOQBy+FUq54I6eJ0zNrIW7graCnnlnkbC/Zzz7ynhwfwLbWbFolYuQRvdtxAPeB3AZ+ioB0qT3ba1oyshS0juGZJFO5ZZXhdCH/YKqx2qe/LHJ8I/wBHfFV318YMskgZ1Vldi4IJweRzzxzyKfWrWyOq7v1XjkU+TIwII8vL6Ca2sOqo6iI8SNjZOi166nkp5rTHaJGqg/pBrb+pwyXf/Roru0lmwpbsI55bRksCSARg8iaV/Sj6TUElvJBoonnv2Kqm20kbq1J7Thdpy+BhQVIyc88YLo6UuHkv7U2rkqjyQlyveFWRWbaT3EqCAfjWm404hstA07rYbcCNSkUUEIVC8jZxuc/AMzO2TyPeSBU0CopKz0VL7imS6dtVWY2DRsc3iRRyiXI2dUqqsnPtblZQoGO4jm6+MuC4bq70+5kUM1rJI4JA7mjYqD57ZVjYfEUjuhj0gdQ1XV4LUwQwWpWZpFj3ySYSNipaViABu2jCoOZHPnirL3tyqFM8tzhB+8VbA+sjH10RKn0r+OZdO04GBik00giWRfaRQrO5U+DELt3d43EjniqgdCfpFXem3zyXUk93ZyBhLA0hkYNgmOSIythGDYDYIDKTkEhcWw9M7heS70sPEpdreVZmVRk9UVZHIHjt3Kx+AJ8KpR0J9E8usaitqN6QYd7idFDCFAp29/Z3O+1QpOTknwNEVyehb0hLPXNRW3itZoZUgndZZWjI2bog6YQk9ohG/qU3uJOFILqa2mmUObZneJSMr1jbcMR47NuQPPB8KTnRB6P1loV/BcR3U000omt1SQRKhUxNK5wq7uz1Y/WxzqWelBxxNpun77fsySuIBL4xAozMyjxfC4B8M554oi7elNPeJo9y1mdpAHrBGesFsciUxkdx7tx8E34wa+ZmtJ2q+nPQZ0gRaxpglba0qhre8jOP5QLhiR+zMhDj4MR4VQX0iOAm0zUJoAD1DfO2zH9aFv1c/tRtlD9CnxqqJX0Vya4qiLccM689u+RkqfaXz+I8iP408eE+I1kVSDkHu/8APnVdqkHB+umB8E9gnn8D5/5//itCto2zt61K4biL6Z9r+6rOC6DI30H8K3PR63P6z+Jpb8O6vuGM94/GmH0dtz+s/jW3wPhMT5GnnChv4lTCeKF45j5JqW3tL+8Pxp5rSNt/aX6R+NPJK6PHdWfXyXEcCPhl7W+a5zSN6celu4S5XRtFQXGrSjtvyMVlGQCZJScr1gUhsNyQFWYMWRH3vpJdJp0q0RLcdbqN04t7CEAMTKxCmTb+ssZZcDuZ3jU4DEjv6O/RUuk2xaY9dqNwetvrkkuzysSxjVyMmNGJ5/rsWY94AgF3aw+hXoOt9OY3dyxv9Uk7c19Pl2DkcxBvyUA7usPbYeIXCjG4y6WLu2hurxbaOW2tbuSxkiRpDdSy+t2cELQADacxS3BMZBJcQgHBanFeXCxozuwRFUs7sQqqqglmZjgKqgEknkAKWlrpulSQ3QW+jki9ei1aZ0urYiCVbiGeNWZchIGlgA7fMgsA2cEEXnw50zQXep+pWyiW1Wya8a+VjsLr6uxhQbdrMkNxC79rKmRVIBqLa50+XUNmt02nc5ksbizijujM89veymNQwW3Ux3IHVkRKJATIBuyCKnMHDWk2pt7yN4LW2WK7gjCywx2sg1CSGaVt5OGdmgG3a4GCwwQBtjPDPAOkt1aRai116ubG5gHrdtKbez024Z7aJRGn/RFmDo8r5kbYFMmUGCLK6Oem2LUr821uivbsGaK4Eh3MFtLS5YNFs7LK9y0TLuypjOeeQJJfceyLqK6b6s3XOyyxyb2MLWATM9yz9WAkkUwNv6tljukt23bZCVhWmdFumLd9fp2otaXM7S3sSWstjIGju44hI0MM0Moa3kESyAgEDLbTtwBM4hZLdrevfxvJaWz2FwWmt1UNPLA5e427RFMzwABMIMswA7hRFpbHpSmn1O602JLJGhkkhXrtR23UrLarcLJHYi33vEC4VmEnIJKQcritdwx0x3FwInNqI0N3Z6XOHdwyX0m8Xyodu2SO1IRFYcpGL8wF55lhw7Abma7stXKC/mn2RxCwnie7Fr1TdTIYmlLxJAJOrWUYMRyMFgddo/R3pFpHbNHfLFAJLO5TE9uI7i709jE9xuYENLOXWOcqe0wj7m7yLacJ9KlzeWd3dwW9qTCA62x1ImdEG8yJfJHaObK4VEyIfnMtuUldu46fWOmq7gSwM9tYWz3sU1zG1zq/UwJDGtqybppLEZnc3DfMheQiJDNkgZ+l6bp0L3zz6wlzcS2z2cslxPp6PDBD1xbeltHBveNpHZ5JssMEZUZrK4s4RtibM/pL1Kews+qLr6jztZzCnWSxXccqxo8lsAr8hkOoJ50RRZPSKPXz2j2qx3K38Njbq8zCK5je69Umkik6rLNby53qFPJkPnic8F6xcap63b39haraxSS2sgFybsSTxGPcphlsoV6sq5IkJzlcbeeRrJ+ifS5ktmaYyPDey61DciSDeXkuBcT4ZU2+qPIUDBQBhY+1kA1JODtRsLZ54VvbeSW4uZLlYuvhEm652usaoJCz5GCpAywYcqIlXxL0FXemStfcM3Btn5tLpkzF7S4wc7V3thDjIAfz7MkQ75l0J9NcOpO9ncxNYapFlZrKYkFioyWgLAF1x2ihG5Qc9pcOW1Sv6c+h6DVkWVGNrqMOGtL6LKyI681WQrhmiz5HchyVIyQxE0KKSfQL0rTyzSaPq69Rq9uO/kEvYgM9dFgBS+0bmVAFZe2uMOkbsoiKKKKIiiitfr+tQ20bSzyLFGvezHAz4ADvZj4KoJPgKo5waLlVAJNgoD0rdOWmaTKLe5d3uSnWC3gieR9pztLEDYm7aeRbOOeMYr5u9JfGlzql5NeXTFpJGO1c5WKPJ2RRjuWOMHAx382OSxJsh048Yw32oJdLFHAix9Qkrgesz8ztXA8cnko3NjlzzyVFx0HatevJPYafObcsQOs6u3O8AF9sdw6SbMnkduO8DurSpq4TyFrAdkf1bit+pw91PGHPPvE/DvHWlFXtY3bxukkbMkiMro6MVZHU5VlYc1ZSAQR3EVIeOej7UdO2+vWs1sGOEeRPm2OM4WRcozY57Q2cVF63lHr6g+i70m/pnTI7iTHrMRNvdAcgZkAIkAwBiVGWTAGAxdR7NNSqaf6NB2/53GTsHqBAzy3H1vJA8yAMkeQ8hVy6IiiiiiKr/pDa1Fb8VaHNcMEgt7a6uJHPcAEuTy82JjVQBzLFR41uPQs0uWaPUNbuV2zalcu6A89tvEzbQpwOz1jOgOOaxIaY3Sl0N6bq8sM17EzvEpRSk0kWYy27Y/VsMruyRjBGTzqc6Tp8cEccMKLHFGqpHGgCqiKMKqqOQAAxiiLJooooiKx7zuNZFeNyOVESf6Vk7LfXSh4SfA+hm/HNOjpSj7DUjNEfG/8Afb8FNQPCJt6X6jzU7wezqSOdp8laXo3uw1pFz/aH2M1YV/wDYvI8rqxZ2LN22AyTk4ApFW2uyqu1ZHVfJXYD48ga8p9ZkPe7n6XY/wCNQrcXZxLY3xbWyAM1L+w5RI57JNm53KwWl6JZ2p3xoqEfrs2SPPBY8vqqIdIHSZErRQxOGJli3uD2QokXIB8c+J7gKS17O58SfrqP6jCTnNXDFS5uxG0MHUsowIA7cji49asf0idJMFpbyTCSORo9jdWsib3G9QwUbubbc4ryg4g0TWrMxzXEDwsVcxvOsMsbrzBKlleNhkjyIJHMGqe8RWRJwBknkAPEnurfcOdGdsAGuEE8nfhidi58AB7X0nINTIxZkTdp+/cFDjBJJXFke7edFY7RNf4Z0I9XaPCZpGVJGhk9ZmClu+abc5SJM7tmR3clJrSdP3TXYtYutrchrgSQyRbA+cxyBsglQMjGeZ8KW66Lar2BFAv9EJGD9mM1FOOuj6OdG6n5uQDIXJ2sfLB7qQ4/E51nNI61fLwamawuY4OtuT46LfSm0+4jVL9vVbgDDNtZoXOPaUoGKZ/ZYcvAmpRrvpBaDaxsyXCyk5PV20TFmb4naqAnzZhXzVukeN2VshlJBHxFdJLtz41OA3XNkEZFWdb0nOs1qDULmNxaQCZI7aEqzqkkTJnLlFeRnKlmJGAMDu5+PpN+kjZ6xZR21rBcROs6zF5+qClRHIhAEcjnJLg/UarEAT8a6spq5EzOgjpen0W4llROuimTZNBv2BiuTG+7a2GQlh3dzMK2/Tp03LrMUcb2SwSRtujmFwZGUEYdMdSmVbl496g0nMV62to8hwis58lUsfsANUJ51UAnILworOutImTm8UiDzaN1H2kV1stNlkz1cbyY79iM2Pp2g4q3aFr3V2w69rZrDrkVsF0ScyCLqn6w9ybG3Y88Y7vjW/m6NdQC7uoJHkGUt9mf4VY6eNltpwF+sLIylmfctaTbWwKyejzXirLGx7iNh+Hl9Xh8PoqyXRy3P6z+NU9XdG/cVdT3EEEEHuI7xVuOjGbOD58/t51LYOwNlcRvt5rmeFMjnUzWndfyTlg9pf3l/EU72YAZPIAZJPcAPP4UkLf2l/eH41u/Sx4qNjoV9IpKySILWMg4Ia5PVMVPeGWMu4I59mt3HfiZ9fJQfAj4Ze1vmoB0HQnXdbvNelG60tSbLSlPs8h25lB8Srbs+c5H82MWWqDdAvCA03SrK0xhkiVpfjPLmWY/eOwA8AAO4CpzUCu7XWaMMCrAFSCCCMgg8iCD3gjlikNDwgw0zIsTM6atc3VxarHHHNcWsWsXcsYUS9WsqhDHNHG7BXVQFOGGX3RiiJMXfC8o4b1OE22yWWLWbiCzCrI8Xrb3c1vEqR7lEwWVRsiJCuxVScCtVxF0cXTahNLaPLYw/oeK3VraG02zTie5keBkmhk2sQyMSio3a9qnRxfctHa3MkZw6QzOhwDh1jZlOCCDggciMVWZOlnWLiyM8DmC7LaLa2tpcQQjrvXjGTfSsUIMd5IXhQRlVjSNzgORsItl0UcDXcLWMc2nsJTJpN2b9uoC20FnpNrbSWzFpPWFmE8UsPUqhUrcMxbG6sK84UvV0+5s7ayuPVUfTZYlnsrNbwNFqKzTxYjlMeoLHCN3WyYDkHLyb2YbLgTpW1LVbmaG2mSy3XCoBdWolNoLbT43vIBHvhaRzesw3SPlVVyOWAMTiDpy1CJbYom+S4tNO1l1Ea9XDpyW8smqJGxBJJe3AXrGLD1oYPZGCKYcWaJez6GvqMUiajDOs1us9rBZybzK0UjGGFjEgFrNKAynLBeeSTUJg6J723jv7SOJpLWzW3h0nxaaO9vLK91A825mKS3I8DhmHPllmdFnHdxeXoDOGtZ4b+6tlCKCIIb+K0tXDgBmE0SvPzzymUZ7IqIat0ianv1qRJ2SKxXVepj/AES5gJtIWMJfUmk6oyLIVPUhNx2EHvzRFtOk7gl5xxJ1NtvmmtbaO1IiVS7tDOJOqlYAFmZyHw3iM9/PQ8T8A38d+ZZFuNXgSHSpJBJHZo8q213eO9tEqJbwSNCXjuOrkwW5jccqK3t5xpqcVw2nNOkkjTWW2+W0G+OC5tdQunjNsjFJLgGwMSHx9ZQ7CQA2nh6StSuJ7S3huXG6FjNLDodxO5mj1Geybr7d5Eaw5RfOdbuRXSTBwBRFjTcKahaKk8VhJL6xZ67aLaQNbg2cmpakb6zWYmRY0iWMiORoiyxsviMEsThLgsQahaiSFXEGl28PX9SCpuIplUlZCvKTaikc923HlTTUVzREUUUURKT0kujBtRgS5tCYtUsz11lOmA5ZDuMJb9lyMrnkr4z2WdW2/o99I41fT47hgEuUPUXkQBXq7lAN+FPNUcEOoPMBtp5qaYhquFnH+heLSi9my1qMuF7lW+iJZsDu7Tk/Etd/CiKx9FFFEQaSnEfQ1c6hfXFxf3j+qhx6nb2+A6QhU7LPIpSLc28N1cZkbk3WjkqOuirJI2vFnC4V8croztMNionwT0cadYc7W2RJMYMzZknYf0p5S0pHfy3Y591SwCiirgANFaSTmVSv/SPcYq0llpijmgN5KfDLhooVHxwJSfpX41T6rH/6QvVIpNajjQDfDawxzMDz3u8kyKw8CsTow8cSfRVcKqqK9v8Ao3bDGnX02Ob3Yjz5iKCNh9hlNWopF+grpfVcP2zYwZpLmY8v+2aIH61iU/ZT0oiKKK6TyhQWYhVAJLEgAAd5JPID4miLvRSh4g9I/Q4JDH65FLt5vJGxeNRy5K0au0zEZwIFccu0yVzwf082l+//ACSJ/VlbbJe3ckVnb8vaEQkYzTSAc9ojAHIMyZzRE3aKi69Iulnl6/Z5/wDfLf8A4lanirpn0Wz3dfqFsGX2kjlE0g5ZwYoN7g48NtEU+rpIKrrr3pkaJEcRJd3P9KOBEX/byxt/q1kcH+l3oly4jl6+zJIAe5iUx5Pdl4ZJNn0uAB50RTfpQh7DVX/Tl5yj+mf7q1Yfji6ingEsLpLE67kkiZXR1IyCrqSrA+YNV9t0xJMP6QP2j/8AFQ+Oi9Ke0fdTfB4/+Y3sP2XuBRiu2K4xXCr0Gy6lax7m3zWWBXLLS9lcFC7iAC4iz3bv44OP44qSahKyxsUUu2OSgjJ+jPjWn4ptsjI5EcwfIjuNaaDpKgjYRXOUf9tRuU/Egc1+gZqTZE+ZrXMF7blGunihL45Tsh2/6JOcdTXRlZnEicz7Ssv8SMVNODelwxQJHPE0joCOt382AJ25G0nIXAznnjNNOx4ls5hhZ4m/oswX+D4rpqPCdpMO1DHz/WRQp+1e/wCvNScuIRSNDJ4reHoomDCpYnGSknB7v1UG4J0bT9SWW5eDEplYSL1j/AqeRA5qR4d+ahHS9waltKvUjarsAi+W7w5/GmHw3ov6PvzEp+ZuFJT/ALxMnH07c/ZWX0raSZZNPwMgXCh/o9of3TV0VYY6obLiYyLi56v0sqT0LZqM3YBK11nEAZ569xut1ovBFlGqf8niJAALFASTjmTnxpV8ZcJRrqltAg7LupI81B3Nn4bQaectyqsiE9p920eewAn+Bz9RqJ6lo5bV4J8dlbaQ/wBcHq/wetOirZGueXOObTa/PuW/iWHRPbG1jRk5oNrab7rY6/wvDJE0SxogfCllRQQue1g45HbkfXXMxttPhBCiNByAUDLH6fE/EmtR0ravcK1vaWh2zzFiWGRsRcAkkdw5k5+FYtn0WiQA3lxNckcyDIVjB8cDmfrJFY2M/ltMzzsnO2ZJ3dizSynjXinjG0LDayAG9bvg7jK2vusSPJKY3pIo9k5GfEMOWK44a4bS2urlowFjmWNwo7ldSwcDyBypx4ZNdOE4NPgle2tNglCh5NmWO0HHafnnmfZzUkf+UX91/wAUrDM8Ruc2O4aRoe+62aZnGsa+UtL2k5t7reK8NVigtw906gMQF3ctxC5IAPgBn+NRzgnjuG9kliRSrIN3MghlzjIIxzB8K6+kNcbLSID9YOT/AKtKb0eJsXxHnE/4qa3oKFklG6Z19q2XVZREmKysxBsDbBpOeXP1qT+kBwop2XSDDEMsmPErgqT8cEjPwFMHojk7Mf7q/gKxel7BsznzOPsrjockykX7qfgK67gjK50QvuyXnn8Q4QyQ27e8J/W/tL+8PxrUel8fWLrh3Te9bnUVlkHgYrcxq+f6k7nH9GtxAO0v0j8RWs6WQZOLuHI+8JDey/WYpuf2xr9ldFjmrPr5LjOBHwy9rfNWDFc0UVArukUUUUReN9arIjxuMo6sjjJGVYFWGQQRkEjIOajC9G+nZtT6uubWO3htiWkJjjtGV7ZSS+ZOqdQymTcc5OeZzGPSx1c2+h3zqxRiIIlZWKsOuuYYyQwIIwjMcjyquPo48atbajaSyySG2uSbJpGMrQmSYjq8McxlxPGi5zlQZO7nVCQNVUC6tnrvRjplz13XWsb9dIJ5T2lLTCPqd+VYFWMWUbaRuBIbOTnb3PC1q7B2hjLCB7MHbjFrJt3wgDkI22L2QPCtwDRVVRQjUuijTJY7eNrfs20ItrcpPcxPHAAoCCWKZZCOypyzEkjJOeddJuiPS2lnmMDb7gTLcAXV2I5RcRmObfAJxCS6MRu2ZBwwwQCJ1RRFCNH6JtKgtpLSK1RYJHSZ13ys5lj29XIJmkMyvHtBRlcFfDGTXhe9DulP1JNuUMKdXE0VzdwsEMjTEM8M6NIWld5C0hYszMSSSanrtgE1UnoF9J2/1PWo7GWC3FtO1wIzEJBLEIo5JUJkMrJJkR7W7KglsjbjaSK24ooqIdKvSPZaRbi4vZCiMwjjVFLySOQTtRR5KCSSQAB38wCRS+ior0X9INnq1v6zZSdZGGMbgqUeOQAEq6NzBwQcjIIIwTUqoiKr76cdmY7Cz1JB87p97b3Cnx2s4BXPgDKsB/qirBVDOm3g9tT0y7sUZUeaPEbPnasiOsiFtuSBuUcwCR5Huoilmn3SyIki81dVdT5qwDD+Br3qtXBnTTc6Mlvp/ENlJaLGqW8OoQAzW0qxLsQts3HeUUE9WWYnJMcY5CxGh6vDcxJNBIk0TjckkTB0YfBlJH+VEWbRXGK5oiK6yuACScAAkk+AHea7Uq/Sw4r9R0O/kBxJJGbWLBwd9z81lTkEFEZ3yOfYoi+dHS9xQdQ1K9vM5E08jJ8IgdkI/qxKg+qorRWZomntPNFAntSyRxL+9I4QfxNEX1Q6ANLNvo2lxEbWFpbFl8Q7xrI4PxDManNeVnAERUHcqqo+hQAP4Cqxelt6R62Ik07TXDXpys9wvMWo7iiHuNz8eYj8e1yUikHpI+ktb6QxtbVVu7/HbUt8zb+XXFTuaQ8j1KkEDmzL2Q1J+k7pc1TVifXLl3jzkW6fN2688j5pMKxXwaTc3xqETylmLMSzMSzMxJLMTkkk8ySeZJrzoiKDRXNEXGKK5xRiiLiiua4oim/Rb0mXemSDqpGNszAz2zdqN1/WKqThJMdzrg5AzkZBsfw/qSXGZ4/YlWORc4yAwY4bBIDL3EZ5EGqcmnz6Mmrs6TwNt2xhGQAYbts5bce488Y8e/wxUXjIvSP+n3Cl8CNq1n1+xTgZa64r1autcCvSLLgLXDVzUc424phtIy8jY/ZXxY+QFXsjdI7YYLkqyWVsTC95sAsHjLWI4tvWNhWZV+PaOK1us9GtrIHcK7SkZDFye7wA7udIbjXiiS8lLvyUewngo/zpi9H/AEwCNFhu1LbQFWZOZwO7ep7yP2h3+VdOcMqKeJroT739QC5RmLUlTK5lQPdPwkqAcSaFLHKVWOUc8Bdj5+rl/GnP0C6RdwwyesblVmUxRvncOR3Ng+yG7PL4ZrfWnSNp7jImx8CrA/gaxNZ6UrOJSUbe3h4D/OrKqqqqqPiuKtznNZKOloqKXjxNe2gFt/ZqvHprvRCkEw9uKRJB54BG4fWuR9dTC3KyrG/IghZFP0jII+o1WPj/AIzkvHOeSeArfcPdLk0FvHAIlcxrtDliDtHs8seAwO/wq+fBpTAwM+IXvu1VlJj0TaiVz/gNrb9PVMjpd19beazkzzidWYf0Hyr/AOoc0xLZ1JB7/I/A8+/yPKqg8V8RS3crSSct2OyO4AADlmpLYdLF7HGkY6shFChmRixCjAyd+M4HlVajA3uiY1hG0L3+ufqsdNwhYyaRzwdkm4+3ompLqSvrh5jAhaFP3xtY4+ntfZUk4+iung2WoUsSQ25tvLHIjlg48jVXb7XZXk60tiTcZNynBDE5yPEc6nmjdM16i7XVJcD2imGwPE7SAfpxV82EyhzHx2JaALHqVtPjcDmSRTXAcSQR1phdE/AUto8k9w4aaQbcLkhVzk5YgZJIHdyAFMB0O9T5Bh9u3/Kq+ah0tX0gLoCqpjcyr2U3chu5EDPMDdWBJ0tXxH8p9mB+FYZ8Jqp37byLrZp8eo6ZnFRtdYeKcfT/ABh7SM+K7x9oH+VJvoHfGoJ8UkH8M/4Vg3vE9/eqy9uUIrSMAC21FwGb6BuXP0itToWgXkivNbxSssah2eMNkKXEWVxzYb2CnZnGeeKlaWgdHSmBx1vn2qCqsRY+sFQwGwINj1J19P8AfFLeLB75MH+wa23QjJmKA/0E/AUgeJtBvYP+lRyqAwXe+5o9zIHCrKCUZthzhWJxTy6CJMwQfRj7CR/hUzwfpeTWjvf/ACua4ZVnLGmW1urXcrL2/tL9K/iK13SIwXjLQif1rO8UfSI7k/hWxt/aX6V/EVp/Scc2mscM6j3RpdSWczZ9kXQjRT9AQzsfoqaxzVn18lyXAj4Je1vmrEUpunDpTaxdbS3TddSRrKHbmkcbs6Bgv67bo25thE7JO8kRtMOlPif1Cxnuf1lULHkZHWOQiFgO9VY7iBzIU451SPX7l9xmYmWViDMzHc2Tk43eOGJyRyLM5wM1zM0hHujVd/Ey+Z0Uo4m6RdTmaKOa9lwu/b6uwgLHskh3iVTIV5gNhcDOAMmvTh/pS1C0kxFdyybwAUvXe6AxzyhdgYj3jIzyJyDgYguGmKsp2KucMRkknv2g8sf0jyPgCK9xo8fe25m8WLvk/UCAPqArX2iNTms5A5lu+l/pbvNYgSxuEiSN3iLRwhxIzpk7mdnf5vOW2oh7vaOM1HtN4Ole1ht3d1tRIzpGM4BR2LMkuAxcSE57wpLr3qcbDQdMKzL1ZbYRiQFiwG0PsI3EkEFmGAcdo1LlQKAo5AZwPLJLH+JJ+kmtKurCHBo3WP1UFimJikbsR/GfAfrzJi9B2ualJeok+pbrZVx1E8SPLOdhUATBV2FW2uXyS2CNpyWFj81Tnh7TpZ5o4YATKxJXGRt28yzMPYVf2jy5gd5ANq+DLC4ihVLmUTSDvYLjljuJz2sftYGa2sPqZJW2cPqsGE1kszbSDT+rn/fUt3RRRUmphLfpr6S102IJGFkupAerRuaIvd1kwDBimeQUEFzkZADEUF4S4tn0XWl1KS3ikPWzOUReriMc5YS+rBTiJlR2VFOQmQCCKcHSnqgn1bV9gcx208MUksjhh1koCBBnBA61ZERBnsqO7uqKa3o8dxGY5BlT3Ee0pHcVPPBH/wDdQUuJSQ1BbIPd/ea9JoOCNJiGFiSlfeYZk7r9Ajdbcfrpkry9FvHVtqtnFe2pbqpNw2yALIjoxV0kAJAZT5EggggkEVWv/STuDDpSA5Yy3J6sc2OUiAOB8eQ8yfhSt6Mule84fsLzT4Iw1xNdRNbXEuBbxCaMozszkJ1h6lNqSEIMSM2RGVbwuOD5ku3uL+6F/eHm8iu0qRyEnKrK2BJsGF7CiNDlVyFBqSqKyOKPjL35utclheAVVbWclDdkg+/cfCBrfy51KPRb4rn0RCrqHinkWS4jwd6KF2jY2cb1GWK4wfZyO8Xe0DV4rmJJoXEkbjKsP4gjvDA8ip5g5Bqj/D2iy3UyQQLvkbOBkAADmzMx5KijmWPd9JANhvQ61uO50rckBgZJ5opW3b0uJVIzNFKOUiFSqZHJSjKMgAnRwypnnc4v+H7HmC6Phlg+HYdHFHBcS7xrcdJ3Mb6c/NknTXSaQAEkgADJJOAAO8knuFQPWemPSoNRj0qW4C3jlFCbHKq8oBiR5MbFeQFdqk57ad24Uv8A06r9v0TFaR/yt7d21tGvn2jIcjxG5UUj+kKmVwC2/St07aBCj208iaiXyjWdtGt31h7tjfzIbPLa7g58OVKPoF4c1qPUvWdLspdL0eWRGntNQmLK0ZIEjxRMqzLNs/k8KQCFBkdMAWQ6OOi3TNMUCztYonxhpcF5m88zSFpCM57O7A8AKmmKKqBRRRRURVMP9JHxXltP05T3CS8mX4nMNufhgC4+0fXc+vlv6UXFhv8AW7+YHMaSm2h7sdXbfNArj9V2VpB+/REsqZHoxaOLnXdLiPMC4SYj4Wwa4+z5qlvUg4I4knsnlntyFlMUsCyfrxC4XqneLyk6oyIG713FhzAoiuV6WnpJrarJp2lyBrogpcXUbBltxkq0cTKf+kjGC381+/7FF5HJJJJJJJJJySTzJJPeSfGuK4oiK5Ariu1ERiiiiiIooooiK4Nc1waIuDTh9F0nr7nyMS+IzkSDw78czzxik8aavo13eLuROyF6iRicDdnrIe9u/AH6uceOPGtDFBeleOpSWDu2ayM9asQ1ecjY5nkPjUU4j4/t4SyJuuJQCTFApkYY/a2ghR8TikpxZx3f3yStGrR28eDJ1WeyCcDrHHdny5VyFLhM0+Z90c59F3NbjMFOLD3ncwz7zomX0h9K0NvmOHE03dyPZU/0iPwFIHiHW5bmQyTMWY/YB5KPAVrmrjFdhRYdFSj3BnvO9cNiGKTVjvfOW4DRFPHTOimwuorbqZZ7aWeKzlV55Ip4w91Pe25jZUhtyqK9srdbliA57JwMpSxti7ogIBZlUFjgAsQASfADPM+VZOuaa0MrwsVdkO0mNt6Hx7LDvHP8a3doXso7ZNrpw2XQ5axagLKe8V0ktpJEuABEIpBOsavhpCs0RTdIh3gOD+risnReiPSpYZJvX5AsZshIXezjES3sUMnWvumIKW5mMbRgh3eF1XmQAieqOM45dwOOWfLPdmpnccB3Kact+jB4JMdZGm4FVDEBnHcyhwPPBIPmasfKxlto2ubDt5leyJ777I0Fz2Jjw9HugwtbySXyXC74TcQi+t8LEJJutkzHCXmDrHEotowkq9cTuwMiO23D+iC8uY5Zx1CrbFDHcOUUOubwxTLA/rM1udojj2ospL92Kj3EHAHUWwnMpctFBMirC5XbMxXDSDKqV8M43eFYeh8BXLy26zQywwzOqCUxnA3d3f3Z/pYqwVURbtbWXospo5Q4N2c/VMrh7WuH/X7p5kgS1EunTWw9XuZBsgt2FxGixjm0k3V7xcZjfEhO4gZ0vGF9w81lMbaJlvm3FNpuVRGE6hOqWTevVtAJGdJCMGRdvsgLEOKujy8tjK5hkMCOyCXA5qH2qxUHcA3LnjHOifo4vkj654SEADOAyl1T9ooDuAx8KCqiIB2hn1hUNJMCRsnLqTv0vpL0K5NxbyW6xCabrIpHUiEyer24Elwsk6oAk8R+ZGyNsLnbuZh5ah0o6PBdrB6vHLbx28Fr1sUEAiy8txLd81Z3e1br0zGN67o3KhshmW/EnRRJ6xItsVSBRD85cyqgLyIG2hsczk92PEVrbLoruczesPDaJE6xtJPJhGd1DIEIByGVlOeXf8DjG2ugLb7Q88+pZHUE4dbZPll1qb3HSDp+nSXcFmguo5bC1tlmWONFW8gjYesMkkbLI6uwfdg5YZye+thZdP8AbIm/1IGVZ5GWENAkUkEhu2jWYrancLUzQrGihTmCN93Z21AOEra3tLv1K8tYrt3miQTLO2xVk2jKBOy457ueD4VMeMuFLKeSW2QW2n9VcCKN0Ek1xMSg9qJTlI8sDuPl4VZJXNY/ZINrXvla338FfHQOe3aBF72tvv8AbxWFbdP0oR1MDFnWSNnW6kQqrvcvvh2x5iuCLja84J3iKMbRWde+ktcsrqLSFd7rKSstyuGSaOddpSRWVS8Sbl3ENz5AHA73HREzWtvaYjW4W7kWW5VeZg6p5FJ7mPZKYQnkTjwzWb0e9GUUNzDcRNJNDulglS6tmiYMYmKuqyDtISAAcd576wuxenDSb6X8L2z67ZLMMInLgCNbfTS+XVvS+6SOmS91KHqLhIVUmNmaFHRm6rrCgIMhTAaWRvZyNxAIHIzz0f3+Yi+l/wD/ACNUE1/gyza2uZ7KeSR7ZwJ0lRVUqzbd0eOYAOfaJyBUw9H1/mk+DOP9Yn/GpzCZ2yyXbuyN8juK5nhHTuip7O7RY35wrXW/tL9I/Gt/6UPA7ano91BGM3EYFzbY9ozQZYKvk0ib4wfNxUet/aX94fjTwWpbHNWfXyXK8CPhl7W+ap7xt0o/pXStIYucPvW9jVsFry1MRO9FbOwhWlUP2fnoz7SjCp1OcuRGhL7iFKKBnvGdp5cwAT38sd/Kml069GL6ZqL3dpCZLO+kUuExttbrbKXDg90EokklVgcKwZMDKA6eHRIxsJALrk7xyJLKVb6RgkAeFcdU1DYpPe3rtKrE4aUNDtTzbutR62PIdnbjltOOQHd3Ej6q5uC2Owu5uWFHjzHd8cZ5ePwr2u7cxnDfUfAj4ZracO2v84foX/E/iPtrFLOGt2x9FkqcQjip+OBvzdZ3fqtjp9oEXHj3sfM/5CvHVNUjhCPLnq98aybfaCM4DMMg5IBOB4nHxI665qHVIzY3MAdqjvOP/P8A5zUX13VY5Yise6QsQx7L9kBg53EjkRjATvJwAMZIio43SPBIuCVzWGYaa57p5j7uvafRXu4V0SC3iVIFATAOR3vn9YnxJ781t61nCdq0VrbRv7aQwo/7yRqrfxBrZ11jWhosF0zWhos0ZLVcVcR21lC1xdzJbwrgGSVgq5PIAZ72J7lGSayNE1aG5iSa3kSaFxlJYnV0YZx2WUkHBBHwIIqov+kgu7rbYRdUPUsvJ6xuOTdYdeqIyFUCLtqTksS+MbCDsf8AR2z35guFeWN9OUAwxgxGSO5eRjIGCjrEyi7iJOyQ8ZT9bFVcoN6a+lPpt3e7I/8Ak+ri0n6wYAS5snbrUGPF9yyknvMnwOF10X8V9evUyHMqDIY/roOXM/tL4+ff51Y//SPFjptio5g3ncOeW9XlC/HPNqrr0ccJ+rr1sn8s64I92uc7e/2jyJP1fExWLmLiff1/p7fTnXbcBBWcvBg+D/2c1t316PpdTRxlSp5qcblPNTtIZcjuOGAYeRANeN5cLGrO5CqoJYnuAFehNYetaek8bxPnawwcciMEEEfEEA1yjLEgO0/d17XUBzWPdCBxlst1yBlc62Wd6P3Ft9qNxe6XYxxRrdRhZb19wmtbJT1dw8e32pZBL2F5Ycpk8twvLwroUFjbQ20CiOCCMRoM9yqOZZvFicsznmSWJ76pJ6F+iva8RugBET2t11ZJzmLrIWUFsDLKQgPIc+fiKuN0xXMkelak8IJlWzu2jABJ3iCQrgDmTnuA767qnEYjAj+Hcvm3FH1Lqp5qr8Zf3r/vTm6lRHjTjnR7jiyLUxJP6gJbe4mkMXaM1sg2GKP2zAzxQ53gPgy4XkoL40/Uk4l4lt5oD1ul6QizLLghZryba6bd2DydEIBHL1Zv2hVDZUwcZB+Izjz5ZANfRn0Dli/QEJSNUczXAmZc5lkWQhXcnJLCLq08gEGMd1ZloJ9CiiiiIooozRFEemXioadpl9eZAMMDtHk4BmYbIFz/AEpmRfrr5Msc8zzPifM1f/8A0h2uNFo8UC//ALi6jV+f6kSPNjH/AHixn6q+f9ERXINcUURFFFFERRRRREUUUURc0VxRREUUUURBrvBcMuSpK5GDtJGQe8HB7uQ5VxGhJAAJJIAAGSSeQAA5kk8sVL9S6Mr+G0e8uIWt4V2YEwKSNvdUGIyNyjLA5cLkd2aJdbv0cLnZeTNjOLaZwD3EoUYD66ZWjcOwzW1/dWql7e9jiY26kK6TrIeuiB7lJJ5E8gSfDFV/4S4jls5DLDt3FHiO5dw2uMNy8/jWdwnxneWsckNtIVSTBZQobtD9Zcg7W7uY8h5VEVdBLI90kZsTs9hANz5WUxSYhHGxscguBtdov+zdOq44SsZoruM20CSW3UuI7NnkuANw3RzPtCvI4DLtUnGfA4NdOLtGt7iGdLaG1kWHqn6kQS2t5CgILKWflIzgEYbaDnxpX6n0jarIGUuyCTsP1cCIZGPiWWPcXOO8HPLlWLr3FWqyxdTPJcGNuW1kK79ozhmCBnwBnBJ7s+FYmYdUbQcX7+cnmv8AbnGuqyvxGnsQGa9QHPb92+idurWyTK8cKRRpCIZTbXFi8TwhCpJjuFIRmI/eHPHdXv0jaubT1doBGha+WFz1aE9VIqsygkdkHlkjB5UhLzVtTeGNZJLkwuUWPc0m1yfYAY+1nvGSRy+FY09jfyxiVuuePeAGeQkdbv6oe0/Jt3Zz4UbhDi4FzshfLtCudjDdghrczbPsKc3FeptKdetHCdRDEktugRAI22hyVwAcknJJrV8O8X+qaXpjOA8DyXUFzEee6JmbnjzXvH1jxpT63ot1C4FxmNpATmSQdsLgHcwY58ORrJfgq6EIuMKYTjtq+4AFtpY7c4VT3nwwa2RhjNkNccrg9zdk/U6351qHE3bRe0Z2I1/3bX6KyOr3lnBbgCRGt0hsyvbViY4rjcOXeSFwcYzyqIXM8kV7LdPqUYs5Z4GiiWRZetQuuF2FvmFjHe48AT4UnuHuCZ7mRo4imQNysxYLIoO0tEdmXVTyJA5EjzreS9Ed2rRIzRgysyLjecFY3lOcoPBD9dYIcGEYPvk31uAebxy1WaXGXPIOwBbSxPX4dSmWlcXwRX2svJMrxsA8IMgKymNgwSPngkjsjFd/01Yw3lzqgvElSWJglqA/XF3RVCOpGAqle88vs56GLoNufGVR/UY/iRULt+Fd85ijmjKg855CsUe0ci+5n5qSCFxkvjIGOdbBwuPPM5gA6ZgADyWEYrJlkMiSNdSSfNN7X+L9LmBdJ40uMQEyTW0kyqqJhliQptEoP6xH147jU+k2zaeVoruaEOIclrYTQSbUCsGhZQ6vyxuU4Ix5YqK2HQhduFZXjljbaRJHISChIyyjqiH7OSMPg8q3l/0AOFzG0jsCCUO1Sy+IVtpCv4gsCD3HGdwsbg8QAFybc9urq6lc7GJSb2Hj19agHHHEdtJqK3NtH1cKtCxAQIXaNgzuEBwpbHd8OffUyvelO0drgqtzCXnWffB1SySoI1Xq5WJJVcg+ye7HxFbbhzoRtp1JR5iVbZIjFUkjccykidXlGwQRnkQQQSCCdH0j9Fsdm6KA3atr2cbmJ7VsiOPLkMnIrZfQxODQb5Cwz7PRazK+VhcRbM3OS2Oq9Oy7leGB8iZZSJGXDR9R1Lr2ckMfaDfAVpV6V4YpYpLe3lJV2kc3F1JIxyjLsTOQiAtnOCeVeOudFz29xZKyFre6VXVu1kMId8kTEHkc9oeJH7pqQaB0RE6VJfyBHU2c08YETh1YIzqxk60hsAHkEHePLnjZhdMwWDfE59qyPxWocbl3gFCNf6QEeCeC3tUtfWGD3DrI8hfDbgqBgNik+HMd+MZqfejhDuhz5SMP4Kf8a0Wh9GMlzFeXFrGHe1isJ1iK71lSayE8y7fFyCHGOZOQObAh8dGPC0cMMXVoqBljc7VC5YouWOBzJ5cz5VP4NSNDzsDt3/fsXJcKcQIgAfqch9P8pmq2DnyOfsNSkceT/sx/2W/36pD8prUvcWf3Vx+ao+U1qXuLP7q4/NVLTV9HLbbBNupcnSYFi9LfiXBt9bOG5W/4yu/XlVZlA252mNnXBPedpYoT8WUkVFBwdD+1J9q/7lVs+U3qXuLP7q4/NUfKb1L3Fn91cfmq0ZG4W83dHn2KsuB4xK7ae4E//Q9FYrUuj+3lXazSjyKuqsD8CEyK94OCYFAUNJgAAZZTyHmSuSfiarf8pzUvcWf3Vx+ao+U7qXuLP7q4/NVTi8Ktbi8uwqnsLFy3Y2hbm2lYW86O4HOTJL9TR4A+Hzdeuh8BW0E0U3bkMbrIqSFShZOabwqKSFfa4GRkqM5GQa6fKc1L3Fn91cfmqPlN6l7iz+6uPzVZGuw1uQZ4LbZQY8xuy2Sw5toeiu6eO5/2Y/7Lf79A48n/AGY/7Lf79Uh+U1qXuLP7q4/NUfKa1L3Fn91cfmqy8fh/R8E5Dj3zP7h6KzXTfwzHrqW8d27xrA0joLcou5pAqnf1iSZ2heWMd5znljVdDPR5Dok7z2c07F4zFLFM8bRSDduVmVI0PWIeSsCMAsP1mzXv5Tepe4s/urj81R8pzUvcWf3Vx+aqnH4f0fBV5Fj/AMz+4eitn0mW66pHDHcgARS9ehjABD9W8f6+8Y2u3h34PhUOHRlbe8m/tR/8Oq/fKd1L3Fn91cfmqPlO6l7iz+6uPzVa8rMLlN3x3+h9VJ0VZwro2bEFQWjXJzfRWv4D0uGwVxFGkjvkPJON7lD+oMYVU8wFGfHPLEd1fo9tZJGdQ0Ibn1UTDq189odWKg9+3OB4ADlVc/lPal7iz+6uPzVcfKd1L3Fn91cfmqoY8KLQ0xiw0yV0ddwtjldM2qdtu1O2M/C2W7mVluE+EUs547mCSUSJnvMZDK3JkYdWMqw5HBB7iCCAakHSXLPqMBt2uZrSJsiT1MpG8in9V5JEkYJ5qhXIJByOVVJ+U7qXuLP7q4/NUfKd1L3Fn91cfmqyRHDYxZjLDsPqtWsPCarfxk8207S5Lb/ZMc+jLpvvrv7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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/caPy0OZmXKs\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x242ea880b38>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"caPy0OZmXKs\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}