letters-as-numbers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computers represent letters as numbers, and that has all sorts of weird implications\n",
    "\n",
    "By [Allison Parrish](http://www.decontextualize.com/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opening a text file\n",
    "\n",
    "The following bit of code opens a text file in the same directory as this notebook and reads it into a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = open(\"wikipedia-emoji.txt\").read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `print()` function will display the string, interpolating formatting characters like `\\n`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emoji (Japanese: 絵文字, English: /ɪˈmoʊdʒiː/; Japanese: [emodʑi]; singular emoji, plural emoji or emojis) are ideograms and smileys used in electronic messages and web pages. Emoji exist in various genres, including facial expressions, common objects, places and types of weather, and animals. They are much like emoticons, but emoji are actual pictures instead of typographics. Originally meaning pictograph, the word emoji comes from Japanese e (絵, \"picture\") + moji (文字, \"character\"); the resemblance to the English words emotion and emoticon is purely coincidental. The ISO 15924 script code for emoji is Zsye.\n",
      "\n",
      "Originating on Japanese mobile phones in 1997, emoji became increasingly popular worldwide in the 2010s after being added to several mobile operating systems. They are now considered to be a large part of popular culture in the west. In 2015, Oxford Dictionaries named the Face with Tears of Joy emoji (😂) the Word of the Year.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can artificially introduce linebreaks to make it a little bit more readable using the `textwrap.fill()` function, though this will obliterate any existing linebreaks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import textwrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emoji (Japanese: 絵文字, English: /ɪˈmoʊdʒiː/; Japanese:\n",
      "[emodʑi]; singular emoji, plural emoji or emojis) are\n",
      "ideograms and smileys used in electronic messages and\n",
      "web pages. Emoji exist in various genres, including\n",
      "facial expressions, common objects, places and types of\n",
      "weather, and animals. They are much like emoticons, but\n",
      "emoji are actual pictures instead of typographics.\n",
      "Originally meaning pictograph, the word emoji comes\n",
      "from Japanese e (絵, \"picture\") + moji (文字,\n",
      "\"character\"); the resemblance to the English words\n",
      "emotion and emoticon is purely coincidental. The ISO\n",
      "15924 script code for emoji is Zsye.  Originating on\n",
      "Japanese mobile phones in 1997, emoji became\n",
      "increasingly popular worldwide in the 2010s after being\n",
      "added to several mobile operating systems. They are now\n",
      "considered to be a large part of popular culture in the\n",
      "west. In 2015, Oxford Dictionaries named the Face with\n",
      "Tears of Joy emoji (😂) the Word of the Year.\n"
     ]
    }
   ],
   "source": [
    "print(textwrap.fill(text, 55))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Characters have numbers\n",
    "\n",
    "For better or worse, the primary conceptual model of text in a computational context is this: text is a one-dimensional sequence of characters. Computers operate internally only on numbers; therefore, characters must be represented as numbers. The `ord()` function in Python evaluates to the number associated with a particular character. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The character `a` is represented by the number 97. You can get the character that corresponds to a given number with the `chr()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(97)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These functions are exact inverses of one another, so calling them in a nested fashion like so should always return the original value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord(chr(97))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(ord('a'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can do a little bit of exploring. Put some other numbers in there and see what you can discover:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ϩ'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe even use a random number generator to find things:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ɨ\n",
      "ș\n",
      "Қ\n",
      "૙\n",
      "ቤ\n",
      "ݙ\n",
      "ൌ\n",
      "ႍ\n",
      "³\n",
      "Δ\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(chr(random.randrange(5000)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on your browser, operating system, and browser font stack, sometimes you'll see a replacement character (\"�\"), a small box with a number in a pixel font, or an empty space. These indicate that either (a) the browser can't render the character (e.g., because you don't have a font on your computer that has that character) or (b) the number you specified doesn't correspond to a valid character. The character might also just be one of several characters that represent whitespace!\n",
    "\n",
    "The numbers that Python uses to represent characters are the same numbers as Unicode code points. You can use [a tool like this Unicode Character Table](https://unicode-table.com/en/) to look up characters by name (or browse characters) to find their Unicode codepoint. Usually these are written like `U+1F336`—the number following the `+` is a hexidecimal number. (We'll talk more about Unicode and hexidecimal numbers below.) You can pass a Unicode codepoint value directly to the `chr()` function in Python by prepending it with `0x`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'🌶'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(0x1F336)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ord()` function works for any Unicode character, btw:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128571"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord('😻')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get the codepoint by formatting this as a hexidecimal number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0x1f63b'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex(ord('😻'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doing math on character numbers\n",
    "\n",
    "Having realized that computers represent characters as numbers, there's nothing stopping us from doing weird things with those numbers... like adding them together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ã'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(ord('a') + ord('b'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or multiplying them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ƅ'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(ord('a') * 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or adding some random noise to them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(ord('a') + random.randrange(-5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make two different strings just to play around with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_a = \"mother said there'd be days like these\"\n",
    "msg_b = \"suppose there was a pigeon, suppose there is\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a list comprehension, we can apply arithmetical transformations to an entire string. We'll start with the following expression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"mother said there'd be days like these\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join([chr(ord(c)) for c in msg_a])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This expression simply evaluates to a copy of the original string by converting the number of each character back to its original character. But we can tweak it a little bit by putting an expression inside the call to `chr()`, using the value of the `ord()` function as part of an arithmetic expression. This expression adds one to the value of each character:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'npuifs!tbje!uifsf(e!cf!ebzt!mjlf!uiftf'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join([chr(ord(c) + 1) for c in msg_a])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this multiplies the value of each character by two:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ÚÞèÐÊä@æÂÒÈ@èÐÊäÊNÈ@ÄÊ@ÈÂòæ@ØÒÖÊ@èÐÊæÊ'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join([chr(ord(c) * 2) for c in msg_a])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This expression \"transposes\" the string to a different Unicode range. (Replace `0x1F300` with the Unicode codepoint that starts a range of characters you're interested in.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'🍭🍯🍴🍨🍥🍲🌠🍳🍡🍩🍤🌠🍴🍨🍥🍲🍥🌧🍤🌠🍢🍥🌠🍤🍡🍹🍳🌠🍬🍩🍫🍥🌠🍴🍨🍥🍳🍥'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join([chr(ord(c) + 0x1F300) for c in msg_a])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a `for` loop, each line adds one more to each character than the line previous:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mother said there'd be days like these\n",
      "npuifs!tbje!uifsf(e!cf!ebzt!mjlf!uiftf\n",
      "oqvjgt\"uckf\"vjgtg)f\"dg\"fc{u\"nkmg\"vjgug\n",
      "prwkhu#vdlg#wkhuh*g#eh#gd|v#olnh#wkhvh\n",
      "qsxliv$wemh$xlivi+h$fi$he}w$pmoi$xliwi\n",
      "rtymjw%xfni%ymjwj,i%gj%if~x%qnpj%ymjxj\n",
      "suznkx&ygoj&znkxk-j&hk&jgy&roqk&znkyk\n",
      "tv{oly'zhpk'{olyl.k'il'khz'sprl'{olzl\n",
      "uw|pmz({iql(|pmzm/l(jm(li{(tqsm(|pm{m\n",
      "vx}qn{)|jrm)}qn{n0m)kn)mj|)urtn)}qn|n\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(''.join([chr(ord(c)+i) for c in msg_a]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code is a bit more sophisticated: it attempts to \"average\" two strings, by summing two strings character-wise, then finding the character whose number is closest to the sum divided by two:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mother said there'd be days like these\n",
      "suppose there was a pigeon, suppose there is\n",
      "prrljrBJjhdIlDnjl$b igDdhtP ponjHtfBtfBIB DJ\n"
     ]
    }
   ],
   "source": [
    "out = \"\"\n",
    "longest_length = max(len(msg_a), len(msg_b))\n",
    "pad_a = msg_a.ljust(longest_length)\n",
    "pad_b = msg_b.ljust(longest_length)\n",
    "for i in range(longest_length):\n",
    "    avg_val = (ord(pad_a[i]) + ord(pad_b[i])) / 2\n",
    "    avg_chr = chr(round(avg_val))\n",
    "    out += avg_chr\n",
    "print(msg_a)\n",
    "print(msg_b)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't have to start with an existing string. You can generate your own data. Here's a sine wave:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fghijjjjjihgfedcbaaaaabcdeghijjjjjiihfedcbaaaaabcdefhijjjjjjihgedcbaaaaabcdefgii'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join([chr(int(102 + (math.sin(i*0.25)*5))) for i in range(80)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other arithmetic operations\n",
    "\n",
    "The largest and smallest numbers in the string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'y'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(msg_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' '"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(msg_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sum of the values in the string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3529"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([ord(c) for c in msg_a])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"average\" character in the string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.86842105263158"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg = sum([ord(c) for c in msg_a]) / len(msg_a)\n",
    "avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "']'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(round(avg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unicode\n",
    "\n",
    "As mentioned above, the numbers that Python associates with each character are their Unicode codepoint values. Unicode is a widely-used standard for representing text in computers; its primary purpose is to be a standard for which characters are associated with which numbers. Each character has a number, a description, and other associated metadata. New characters (with new numbers) can be added to Unicode through agreement of the Unicode Consortium. Python comes with a Unicode database built-in; you can use it to look up the names and metadata of characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import unicodedata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SMILING CAT FACE WITH HEART-SHAPED EYES'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unicodedata.name('😻')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rewriting a string as a sequence of the Unicode names of its characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LATIN SMALL LETTER M\n",
      "LATIN SMALL LETTER O\n",
      "LATIN SMALL LETTER T\n",
      "LATIN SMALL LETTER H\n",
      "LATIN SMALL LETTER E\n",
      "LATIN SMALL LETTER R\n",
      "SPACE\n",
      "LATIN SMALL LETTER S\n",
      "LATIN SMALL LETTER A\n",
      "LATIN SMALL LETTER I\n",
      "LATIN SMALL LETTER D\n",
      "SPACE\n",
      "LATIN SMALL LETTER T\n",
      "LATIN SMALL LETTER H\n",
      "LATIN SMALL LETTER E\n",
      "LATIN SMALL LETTER R\n",
      "LATIN SMALL LETTER E\n",
      "APOSTROPHE\n",
      "LATIN SMALL LETTER D\n",
      "SPACE\n",
      "LATIN SMALL LETTER B\n",
      "LATIN SMALL LETTER E\n",
      "SPACE\n",
      "LATIN SMALL LETTER D\n",
      "LATIN SMALL LETTER A\n",
      "LATIN SMALL LETTER Y\n",
      "LATIN SMALL LETTER S\n",
      "SPACE\n",
      "LATIN SMALL LETTER L\n",
      "LATIN SMALL LETTER I\n",
      "LATIN SMALL LETTER K\n",
      "LATIN SMALL LETTER E\n",
      "SPACE\n",
      "LATIN SMALL LETTER T\n",
      "LATIN SMALL LETTER H\n",
      "LATIN SMALL LETTER E\n",
      "LATIN SMALL LETTER S\n",
      "LATIN SMALL LETTER E\n"
     ]
    }
   ],
   "source": [
    "for ch in msg_a:\n",
    "    print(unicodedata.name(ch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same thing, but for a longer string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LATIN CAPITAL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LEFT PARENTHESIS, LATIN CAPITAL LETTER J, LATIN SMALL LETTER A, LATIN SMALL LETTER P, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER E, COLON, SPACE, CJK UNIFIED IDEOGRAPH-7D75, CJK UNIFIED IDEOGRAPH-6587, CJK UNIFIED IDEOGRAPH-5B57, COMMA, SPACE, LATIN CAPITAL LETTER E, LATIN SMALL LETTER N, LATIN SMALL LETTER G, LATIN SMALL LETTER L, LATIN SMALL LETTER I, LATIN SMALL LETTER S, LATIN SMALL LETTER H, COLON, SPACE, SOLIDUS, LATIN LETTER SMALL CAPITAL I, MODIFIER LETTER VERTICAL LINE, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER UPSILON, LATIN SMALL LETTER D, LATIN SMALL LETTER EZH, LATIN SMALL LETTER I, MODIFIER LETTER TRIANGULAR COLON, SOLIDUS, SEMICOLON, SPACE, LATIN CAPITAL LETTER J, LATIN SMALL LETTER A, LATIN SMALL LETTER P, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER E, COLON, SPACE, LEFT SQUARE BRACKET, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER D, LATIN SMALL LETTER Z WITH CURL, LATIN SMALL LETTER I, RIGHT SQUARE BRACKET, SEMICOLON, SPACE, LATIN SMALL LETTER S, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER G, LATIN SMALL LETTER U, LATIN SMALL LETTER L, LATIN SMALL LETTER A, LATIN SMALL LETTER R, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, COMMA, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER L, LATIN SMALL LETTER U, LATIN SMALL LETTER R, LATIN SMALL LETTER A, LATIN SMALL LETTER L, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER R, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, LATIN SMALL LETTER S, RIGHT PARENTHESIS, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER D, LATIN SMALL LETTER E, LATIN SMALL LETTER O, LATIN SMALL LETTER G, LATIN SMALL LETTER R, LATIN SMALL LETTER A, LATIN SMALL LETTER M, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER S, LATIN SMALL LETTER M, LATIN SMALL LETTER I, LATIN SMALL LETTER L, LATIN SMALL LETTER E, LATIN SMALL LETTER Y, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER U, LATIN SMALL LETTER S, LATIN SMALL LETTER E, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER L, LATIN SMALL LETTER E, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER R, LATIN SMALL LETTER O, LATIN SMALL LETTER N, LATIN SMALL LETTER I, LATIN SMALL LETTER C, SPACE, LATIN SMALL LETTER M, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER S, LATIN SMALL LETTER A, LATIN SMALL LETTER G, LATIN SMALL LETTER E, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER W, LATIN SMALL LETTER E, LATIN SMALL LETTER B, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER A, LATIN SMALL LETTER G, LATIN SMALL LETTER E, LATIN SMALL LETTER S, FULL STOP, SPACE, LATIN CAPITAL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER X, LATIN SMALL LETTER I, LATIN SMALL LETTER S, LATIN SMALL LETTER T, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, SPACE, LATIN SMALL LETTER V, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER I, LATIN SMALL LETTER O, LATIN SMALL LETTER U, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER G, LATIN SMALL LETTER E, LATIN SMALL LETTER N, LATIN SMALL LETTER R, LATIN SMALL LETTER E, LATIN SMALL LETTER S, COMMA, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER C, LATIN SMALL LETTER L, LATIN SMALL LETTER U, LATIN SMALL LETTER D, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER G, SPACE, LATIN SMALL LETTER F, LATIN SMALL LETTER A, LATIN SMALL LETTER C, LATIN SMALL LETTER I, LATIN SMALL LETTER A, LATIN SMALL LETTER L, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER X, LATIN SMALL LETTER P, LATIN SMALL LETTER R, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER S, LATIN SMALL LETTER I, LATIN SMALL LETTER O, LATIN SMALL LETTER N, LATIN SMALL LETTER S, COMMA, SPACE, LATIN SMALL LETTER C, LATIN SMALL LETTER O, LATIN SMALL LETTER M, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER N, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER B, LATIN SMALL LETTER J, LATIN SMALL LETTER E, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER S, COMMA, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER L, LATIN SMALL LETTER A, LATIN SMALL LETTER C, LATIN SMALL LETTER E, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER Y, LATIN SMALL LETTER P, LATIN SMALL LETTER E, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER F, SPACE, LATIN SMALL LETTER W, LATIN SMALL LETTER E, LATIN SMALL LETTER A, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, LATIN SMALL LETTER R, COMMA, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER I, LATIN SMALL LETTER M, LATIN SMALL LETTER A, LATIN SMALL LETTER L, LATIN SMALL LETTER S, FULL STOP, SPACE, LATIN CAPITAL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, LATIN SMALL LETTER Y, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER M, LATIN SMALL LETTER U, LATIN SMALL LETTER C, LATIN SMALL LETTER H, SPACE, LATIN SMALL LETTER L, LATIN SMALL LETTER I, LATIN SMALL LETTER K, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER T, LATIN SMALL LETTER I, LATIN SMALL LETTER C, LATIN SMALL LETTER O, LATIN SMALL LETTER N, LATIN SMALL LETTER S, COMMA, SPACE, LATIN SMALL LETTER B, LATIN SMALL LETTER U, LATIN SMALL LETTER T, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER U, LATIN SMALL LETTER A, LATIN SMALL LETTER L, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER I, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER U, LATIN SMALL LETTER R, LATIN SMALL LETTER E, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER S, LATIN SMALL LETTER T, LATIN SMALL LETTER E, LATIN SMALL LETTER A, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER F, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER Y, LATIN SMALL LETTER P, LATIN SMALL LETTER O, LATIN SMALL LETTER G, LATIN SMALL LETTER R, LATIN SMALL LETTER A, LATIN SMALL LETTER P, LATIN SMALL LETTER H, LATIN SMALL LETTER I, LATIN SMALL LETTER C, LATIN SMALL LETTER S, FULL STOP, SPACE, LATIN CAPITAL LETTER O, LATIN SMALL LETTER R, LATIN SMALL LETTER I, LATIN SMALL LETTER G, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER A, LATIN SMALL LETTER L, LATIN SMALL LETTER L, LATIN SMALL LETTER Y, SPACE, LATIN SMALL LETTER M, LATIN SMALL LETTER E, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER G, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER I, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER O, LATIN SMALL LETTER G, LATIN SMALL LETTER R, LATIN SMALL LETTER A, LATIN SMALL LETTER P, LATIN SMALL LETTER H, COMMA, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER W, LATIN SMALL LETTER O, LATIN SMALL LETTER R, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LATIN SMALL LETTER C, LATIN SMALL LETTER O, LATIN SMALL LETTER M, LATIN SMALL LETTER E, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER F, LATIN SMALL LETTER R, LATIN SMALL LETTER O, LATIN SMALL LETTER M, SPACE, LATIN CAPITAL LETTER J, LATIN SMALL LETTER A, LATIN SMALL LETTER P, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER E, SPACE, LEFT PARENTHESIS, CJK UNIFIED IDEOGRAPH-7D75, COMMA, SPACE, QUOTATION MARK, LATIN SMALL LETTER P, LATIN SMALL LETTER I, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER U, LATIN SMALL LETTER R, LATIN SMALL LETTER E, QUOTATION MARK, RIGHT PARENTHESIS, SPACE, PLUS SIGN, SPACE, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LEFT PARENTHESIS, CJK UNIFIED IDEOGRAPH-6587, CJK UNIFIED IDEOGRAPH-5B57, COMMA, SPACE, QUOTATION MARK, LATIN SMALL LETTER C, LATIN SMALL LETTER H, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER A, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER E, LATIN SMALL LETTER R, QUOTATION MARK, RIGHT PARENTHESIS, SEMICOLON, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER R, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER B, LATIN SMALL LETTER L, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER C, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER O, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN CAPITAL LETTER E, LATIN SMALL LETTER N, LATIN SMALL LETTER G, LATIN SMALL LETTER L, LATIN SMALL LETTER I, LATIN SMALL LETTER S, LATIN SMALL LETTER H, SPACE, LATIN SMALL LETTER W, LATIN SMALL LETTER O, LATIN SMALL LETTER R, LATIN SMALL LETTER D, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER T, LATIN SMALL LETTER I, LATIN SMALL LETTER O, LATIN SMALL LETTER N, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER T, LATIN SMALL LETTER I, LATIN SMALL LETTER C, LATIN SMALL LETTER O, LATIN SMALL LETTER N, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER U, LATIN SMALL LETTER R, LATIN SMALL LETTER E, LATIN SMALL LETTER L, LATIN SMALL LETTER Y, SPACE, LATIN SMALL LETTER C, LATIN SMALL LETTER O, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER C, LATIN SMALL LETTER I, LATIN SMALL LETTER D, LATIN SMALL LETTER E, LATIN SMALL LETTER N, LATIN SMALL LETTER T, LATIN SMALL LETTER A, LATIN SMALL LETTER L, FULL STOP, SPACE, LATIN CAPITAL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN CAPITAL LETTER I, LATIN CAPITAL LETTER S, LATIN CAPITAL LETTER O, SPACE, DIGIT ONE, DIGIT FIVE, DIGIT NINE, DIGIT TWO, DIGIT FOUR, SPACE, LATIN SMALL LETTER S, LATIN SMALL LETTER C, LATIN SMALL LETTER R, LATIN SMALL LETTER I, LATIN SMALL LETTER P, LATIN SMALL LETTER T, SPACE, LATIN SMALL LETTER C, LATIN SMALL LETTER O, LATIN SMALL LETTER D, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER F, LATIN SMALL LETTER O, LATIN SMALL LETTER R, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER S, SPACE, LATIN CAPITAL LETTER Z, LATIN SMALL LETTER S, LATIN SMALL LETTER Y, LATIN SMALL LETTER E, FULL STOP, LATIN CAPITAL LETTER O, LATIN SMALL LETTER R, LATIN SMALL LETTER I, LATIN SMALL LETTER G, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER A, LATIN SMALL LETTER T, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER G, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER N, SPACE, LATIN CAPITAL LETTER J, LATIN SMALL LETTER A, LATIN SMALL LETTER P, LATIN SMALL LETTER A, LATIN SMALL LETTER N, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER B, LATIN SMALL LETTER I, LATIN SMALL LETTER L, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER H, LATIN SMALL LETTER O, LATIN SMALL LETTER N, LATIN SMALL LETTER E, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, SPACE, DIGIT ONE, DIGIT NINE, DIGIT NINE, DIGIT SEVEN, COMMA, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LATIN SMALL LETTER B, LATIN SMALL LETTER E, LATIN SMALL LETTER C, LATIN SMALL LETTER A, LATIN SMALL LETTER M, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER C, LATIN SMALL LETTER R, LATIN SMALL LETTER E, LATIN SMALL LETTER A, LATIN SMALL LETTER S, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER G, LATIN SMALL LETTER L, LATIN SMALL LETTER Y, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER O, LATIN SMALL LETTER P, LATIN SMALL LETTER U, LATIN SMALL LETTER L, LATIN SMALL LETTER A, LATIN SMALL LETTER R, SPACE, LATIN SMALL LETTER W, LATIN SMALL LETTER O, LATIN SMALL LETTER R, LATIN SMALL LETTER L, LATIN SMALL LETTER D, LATIN SMALL LETTER W, LATIN SMALL LETTER I, LATIN SMALL LETTER D, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, DIGIT TWO, DIGIT ZERO, DIGIT ONE, DIGIT ZERO, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER F, LATIN SMALL LETTER T, LATIN SMALL LETTER E, LATIN SMALL LETTER R, SPACE, LATIN SMALL LETTER B, LATIN SMALL LETTER E, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER G, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER D, LATIN SMALL LETTER D, LATIN SMALL LETTER E, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER O, SPACE, LATIN SMALL LETTER S, LATIN SMALL LETTER E, LATIN SMALL LETTER V, LATIN SMALL LETTER E, LATIN SMALL LETTER R, LATIN SMALL LETTER A, LATIN SMALL LETTER L, SPACE, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER B, LATIN SMALL LETTER I, LATIN SMALL LETTER L, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER P, LATIN SMALL LETTER E, LATIN SMALL LETTER R, LATIN SMALL LETTER A, LATIN SMALL LETTER T, LATIN SMALL LETTER I, LATIN SMALL LETTER N, LATIN SMALL LETTER G, SPACE, LATIN SMALL LETTER S, LATIN SMALL LETTER Y, LATIN SMALL LETTER S, LATIN SMALL LETTER T, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER S, FULL STOP, SPACE, LATIN CAPITAL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, LATIN SMALL LETTER Y, SPACE, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER N, LATIN SMALL LETTER O, LATIN SMALL LETTER W, SPACE, LATIN SMALL LETTER C, LATIN SMALL LETTER O, LATIN SMALL LETTER N, LATIN SMALL LETTER S, LATIN SMALL LETTER I, LATIN SMALL LETTER D, LATIN SMALL LETTER E, LATIN SMALL LETTER R, LATIN SMALL LETTER E, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER O, SPACE, LATIN SMALL LETTER B, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER A, SPACE, LATIN SMALL LETTER L, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER G, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER T, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER F, SPACE, LATIN SMALL LETTER P, LATIN SMALL LETTER O, LATIN SMALL LETTER P, LATIN SMALL LETTER U, LATIN SMALL LETTER L, LATIN SMALL LETTER A, LATIN SMALL LETTER R, SPACE, LATIN SMALL LETTER C, LATIN SMALL LETTER U, LATIN SMALL LETTER L, LATIN SMALL LETTER T, LATIN SMALL LETTER U, LATIN SMALL LETTER R, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER I, LATIN SMALL LETTER N, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER W, LATIN SMALL LETTER E, LATIN SMALL LETTER S, LATIN SMALL LETTER T, FULL STOP, SPACE, LATIN CAPITAL LETTER I, LATIN SMALL LETTER N, SPACE, DIGIT TWO, DIGIT ZERO, DIGIT ONE, DIGIT FIVE, COMMA, SPACE, LATIN CAPITAL LETTER O, LATIN SMALL LETTER X, LATIN SMALL LETTER F, LATIN SMALL LETTER O, LATIN SMALL LETTER R, LATIN SMALL LETTER D, SPACE, LATIN CAPITAL LETTER D, LATIN SMALL LETTER I, LATIN SMALL LETTER C, LATIN SMALL LETTER T, LATIN SMALL LETTER I, LATIN SMALL LETTER O, LATIN SMALL LETTER N, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER I, LATIN SMALL LETTER E, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER N, LATIN SMALL LETTER A, LATIN SMALL LETTER M, LATIN SMALL LETTER E, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN CAPITAL LETTER F, LATIN SMALL LETTER A, LATIN SMALL LETTER C, LATIN SMALL LETTER E, SPACE, LATIN SMALL LETTER W, LATIN SMALL LETTER I, LATIN SMALL LETTER T, LATIN SMALL LETTER H, SPACE, LATIN CAPITAL LETTER T, LATIN SMALL LETTER E, LATIN SMALL LETTER A, LATIN SMALL LETTER R, LATIN SMALL LETTER S, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER F, SPACE, LATIN CAPITAL LETTER J, LATIN SMALL LETTER O, LATIN SMALL LETTER Y, SPACE, LATIN SMALL LETTER E, LATIN SMALL LETTER M, LATIN SMALL LETTER O, LATIN SMALL LETTER J, LATIN SMALL LETTER I, SPACE, LEFT PARENTHESIS, FACE WITH TEARS OF JOY, RIGHT PARENTHESIS, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN CAPITAL LETTER W, LATIN SMALL LETTER O, LATIN SMALL LETTER R, LATIN SMALL LETTER D, SPACE, LATIN SMALL LETTER O, LATIN SMALL LETTER F, SPACE, LATIN SMALL LETTER T, LATIN SMALL LETTER H, LATIN SMALL LETTER E, SPACE, LATIN CAPITAL LETTER Y, LATIN SMALL LETTER E, LATIN SMALL LETTER A, LATIN SMALL LETTER R, FULL STOP\n"
     ]
    }
   ],
   "source": [
    "names = []\n",
    "for ch in text:\n",
    "    if ord(ch) < 32: # unicode names aren't defined for these...?\n",
    "        continue\n",
    "    names.append(unicodedata.name(ch))\n",
    "print(\", \".join(names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `unicodedata.lookup()` function goes the other way, returning the character matching a particular description (this has to be an exact match):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Z'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unicodedata.lookup('LATIN CAPITAL LETTER Z')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unicode characters have \"categories\" that indicate how they're used and what they mean. These [category values are defined in the Unicode documentation](http://www.unicode.org/reports/tr44/#General_Category_Values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lu'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unicodedata.category('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'So'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unicodedata.category('😻')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's some code that rewrites a text, replacing each character with its Unicode category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lu Ll Ll Ll Ll Zs Ps Lu Ll Ll Ll Ll Ll Ll Ll Po Zs Lo Lo Lo Po Zs Lu Ll Ll Ll Ll Ll Ll Po Zs Po Ll Lm Ll Ll Ll Ll Ll Ll Lm Po Po Zs Lu Ll Ll Ll Ll Ll Ll Ll Po Zs Ps Ll Ll Ll Ll Ll Ll Pe Po Zs Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Po Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Pe Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Po Zs Lu Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Po Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Po Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Po Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Po Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Po Zs Lu Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Zs Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Po Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Po Zs Lu Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Po Zs Ll Ll Ll Zs Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Zs Lu Ll Ll Ll Ll Ll Ll Ll Zs Ll Zs Ps Lo Po Zs Po Ll Ll Ll Ll Ll Ll Ll Po Pe Zs Sm Zs Ll Ll Ll Ll Zs Ps Lo Lo Po Zs Po Ll Ll Ll Ll Ll Ll Ll Ll Ll Po Pe Po Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Zs Lu Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Po Zs Lu Ll Ll Zs Lu Lu Lu Zs Nd Nd Nd Nd Nd Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Zs Lu Ll Ll Ll Po Cc Cc Lu Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Lu Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Nd Nd Nd Nd Po Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Zs Nd Nd Nd Nd Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Po Zs Lu Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Zs Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Zs Ll Ll Ll Ll Po Zs Lu Ll Zs Nd Nd Nd Nd Po Zs Lu Ll Ll Ll Ll Ll Zs Lu Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Ll Zs Ll Ll Ll Ll Ll Zs Ll Ll Ll Zs Lu Ll Ll Ll Zs Ll Ll Ll Ll Zs Lu Ll Ll Ll Ll Zs Ll Ll Zs Lu Ll Ll Zs Ll Ll Ll Ll Ll Zs Ps So Pe Zs Ll Ll Ll Zs Lu Ll Ll Ll Zs Ll Ll Zs Ll Ll Ll Zs Lu Ll Ll Ll Po Cc \n"
     ]
    }
   ],
   "source": [
    "out = \"\"\n",
    "for ch in text:\n",
    "    out += unicodedata.category(ch)\n",
    "    out += \" \"\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing with the Unicode database\n",
    "\n",
    "Python's built-in database of Unicode characters isn't very flexible! You can only search for characters by exact match on their name, and you can't look up by category. The following code downloads the [the official Unicode database](https://www.unicode.org/Public/13.0.0/ucd/UnicodeData.txt) and builds a small index that makes it easier to search.\n",
    "\n",
    "(There are newer versions of the Unicode standard available—I'm using 13.0.0 because it matches the database that ships in my version of Python.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 1808k  100 1808k    0     0  1901k      0 --:--:-- --:--:-- --:--:-- 1899k\n"
     ]
    }
   ],
   "source": [
    "!curl -L -O https://www.unicode.org/Public/13.0.0/ucd/UnicodeData.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "unicode = []\n",
    "for line in open(\"UnicodeData.txt\"):\n",
    "    line = line.strip()\n",
    "    fields = line.split(';')\n",
    "    row = {\n",
    "        'char': chr(int(fields[0], 16)),\n",
    "        'name': fields[1],\n",
    "        'category': fields[2]\n",
    "    }\n",
    "    unicode.append(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a list of dictionaries that we can search using the conditional expression of a list comprehension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'char': 'Ⅰ', 'name': 'ROMAN NUMERAL ONE', 'category': 'Nl'},\n",
       " {'char': 'Ⅱ', 'name': 'ROMAN NUMERAL TWO', 'category': 'Nl'},\n",
       " {'char': 'Ⅲ', 'name': 'ROMAN NUMERAL THREE', 'category': 'Nl'},\n",
       " {'char': 'Ⅳ', 'name': 'ROMAN NUMERAL FOUR', 'category': 'Nl'},\n",
       " {'char': 'Ⅴ', 'name': 'ROMAN NUMERAL FIVE', 'category': 'Nl'},\n",
       " {'char': 'Ⅵ', 'name': 'ROMAN NUMERAL SIX', 'category': 'Nl'},\n",
       " {'char': 'Ⅶ', 'name': 'ROMAN NUMERAL SEVEN', 'category': 'Nl'},\n",
       " {'char': 'Ⅷ', 'name': 'ROMAN NUMERAL EIGHT', 'category': 'Nl'},\n",
       " {'char': 'Ⅸ', 'name': 'ROMAN NUMERAL NINE', 'category': 'Nl'},\n",
       " {'char': 'Ⅹ', 'name': 'ROMAN NUMERAL TEN', 'category': 'Nl'},\n",
       " {'char': 'Ⅺ', 'name': 'ROMAN NUMERAL ELEVEN', 'category': 'Nl'},\n",
       " {'char': 'Ⅻ', 'name': 'ROMAN NUMERAL TWELVE', 'category': 'Nl'},\n",
       " {'char': 'Ⅼ', 'name': 'ROMAN NUMERAL FIFTY', 'category': 'Nl'},\n",
       " {'char': 'Ⅽ', 'name': 'ROMAN NUMERAL ONE HUNDRED', 'category': 'Nl'},\n",
       " {'char': 'Ⅾ', 'name': 'ROMAN NUMERAL FIVE HUNDRED', 'category': 'Nl'},\n",
       " {'char': 'Ⅿ', 'name': 'ROMAN NUMERAL ONE THOUSAND', 'category': 'Nl'},\n",
       " {'char': 'ⅰ', 'name': 'SMALL ROMAN NUMERAL ONE', 'category': 'Nl'},\n",
       " {'char': 'ⅱ', 'name': 'SMALL ROMAN NUMERAL TWO', 'category': 'Nl'},\n",
       " {'char': 'ⅲ', 'name': 'SMALL ROMAN NUMERAL THREE', 'category': 'Nl'},\n",
       " {'char': 'ⅳ', 'name': 'SMALL ROMAN NUMERAL FOUR', 'category': 'Nl'},\n",
       " {'char': 'ⅴ', 'name': 'SMALL ROMAN NUMERAL FIVE', 'category': 'Nl'},\n",
       " {'char': 'ⅵ', 'name': 'SMALL ROMAN NUMERAL SIX', 'category': 'Nl'},\n",
       " {'char': 'ⅶ', 'name': 'SMALL ROMAN NUMERAL SEVEN', 'category': 'Nl'},\n",
       " {'char': 'ⅷ', 'name': 'SMALL ROMAN NUMERAL EIGHT', 'category': 'Nl'},\n",
       " {'char': 'ⅸ', 'name': 'SMALL ROMAN NUMERAL NINE', 'category': 'Nl'},\n",
       " {'char': 'ⅹ', 'name': 'SMALL ROMAN NUMERAL TEN', 'category': 'Nl'},\n",
       " {'char': 'ⅺ', 'name': 'SMALL ROMAN NUMERAL ELEVEN', 'category': 'Nl'},\n",
       " {'char': 'ⅻ', 'name': 'SMALL ROMAN NUMERAL TWELVE', 'category': 'Nl'},\n",
       " {'char': 'ⅼ', 'name': 'SMALL ROMAN NUMERAL FIFTY', 'category': 'Nl'},\n",
       " {'char': 'ⅽ', 'name': 'SMALL ROMAN NUMERAL ONE HUNDRED', 'category': 'Nl'},\n",
       " {'char': 'ⅾ', 'name': 'SMALL ROMAN NUMERAL FIVE HUNDRED', 'category': 'Nl'},\n",
       " {'char': 'ⅿ', 'name': 'SMALL ROMAN NUMERAL ONE THOUSAND', 'category': 'Nl'},\n",
       " {'char': 'ↀ', 'name': 'ROMAN NUMERAL ONE THOUSAND C D', 'category': 'Nl'},\n",
       " {'char': 'ↁ', 'name': 'ROMAN NUMERAL FIVE THOUSAND', 'category': 'Nl'},\n",
       " {'char': 'ↂ', 'name': 'ROMAN NUMERAL TEN THOUSAND', 'category': 'Nl'},\n",
       " {'char': 'Ↄ', 'name': 'ROMAN NUMERAL REVERSED ONE HUNDRED', 'category': 'Lu'},\n",
       " {'char': 'ↅ', 'name': 'ROMAN NUMERAL SIX LATE FORM', 'category': 'Nl'},\n",
       " {'char': 'ↆ', 'name': 'ROMAN NUMERAL FIFTY EARLY FORM', 'category': 'Nl'},\n",
       " {'char': 'ↇ', 'name': 'ROMAN NUMERAL FIFTY THOUSAND', 'category': 'Nl'},\n",
       " {'char': 'ↈ', 'name': 'ROMAN NUMERAL ONE HUNDRED THOUSAND', 'category': 'Nl'}]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[row for row in unicode if 'ROMAN NUMERAL' in row['name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using only the `char` key in the predicate expression of the list expression, you can just get matching characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['🐱', '😸', '😹', '😺', '😻', '😼', '😽', '😾', '😿', '🙀']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats = [row['char'] for row in unicode if 'CAT ' in row['name']]\n",
    "cats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a tiny cat composition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "😹😼😻🙀😺😾😽😸😿🐱\n",
      "🙀😾😸😹😿😻😽🐱😺😼\n",
      "😻😿🙀😹🐱😼😺😸😽😾\n",
      "😽😼😾😺😹😸😿🐱🙀😻\n",
      "🙀😾🐱😹😻😺😼😸😿😽\n",
      "😼😽😸😿😾🙀🐱😹😻😺\n",
      "😸😿🙀😽😻😾😼🐱😹😺\n",
      "😹🙀🐱😾😼😽😻😿😸😺\n",
      "😿🐱😽😹😸🙀😾😻😺😼\n",
      "🙀😸😿😾😹😺😻😼😽🐱\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(''.join(random.sample(cats, len(cats))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the arrows that are in the \"mathematical symbol\" category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['←',\n",
       " '↑',\n",
       " '→',\n",
       " '↓',\n",
       " '↔',\n",
       " '↚',\n",
       " '↛',\n",
       " '↠',\n",
       " '↣',\n",
       " '↦',\n",
       " '↮',\n",
       " '⇎',\n",
       " '⇏',\n",
       " '⇒',\n",
       " '⇔',\n",
       " '⇴',\n",
       " '⇵',\n",
       " '⇶',\n",
       " '⇷',\n",
       " '⇸',\n",
       " '⇹',\n",
       " '⇺',\n",
       " '⇻',\n",
       " '⇼',\n",
       " '⇽',\n",
       " '⇾',\n",
       " '⇿',\n",
       " '⍼',\n",
       " '⟰',\n",
       " '⟱',\n",
       " '⟲',\n",
       " '⟳',\n",
       " '⟴',\n",
       " '⟵',\n",
       " '⟶',\n",
       " '⟷',\n",
       " '⟸',\n",
       " '⟹',\n",
       " '⟺',\n",
       " '⟻',\n",
       " '⟼',\n",
       " '⟽',\n",
       " '⟾',\n",
       " '⟿',\n",
       " '⤀',\n",
       " '⤁',\n",
       " '⤂',\n",
       " '⤃',\n",
       " '⤄',\n",
       " '⤅',\n",
       " '⤆',\n",
       " '⤇',\n",
       " '⤈',\n",
       " '⤉',\n",
       " '⤊',\n",
       " '⤋',\n",
       " '⤌',\n",
       " '⤍',\n",
       " '⤎',\n",
       " '⤏',\n",
       " '⤐',\n",
       " '⤑',\n",
       " '⤒',\n",
       " '⤓',\n",
       " '⤔',\n",
       " '⤕',\n",
       " '⤖',\n",
       " '⤗',\n",
       " '⤘',\n",
       " '⤙',\n",
       " '⤚',\n",
       " '⤛',\n",
       " '⤜',\n",
       " '⤝',\n",
       " '⤞',\n",
       " '⤟',\n",
       " '⤠',\n",
       " '⤡',\n",
       " '⤢',\n",
       " '⤣',\n",
       " '⤤',\n",
       " '⤥',\n",
       " '⤦',\n",
       " '⤧',\n",
       " '⤨',\n",
       " '⤩',\n",
       " '⤪',\n",
       " '⤭',\n",
       " '⤮',\n",
       " '⤯',\n",
       " '⤰',\n",
       " '⤱',\n",
       " '⤲',\n",
       " '⤳',\n",
       " '⤴',\n",
       " '⤵',\n",
       " '⤶',\n",
       " '⤷',\n",
       " '⤸',\n",
       " '⤹',\n",
       " '⤺',\n",
       " '⤻',\n",
       " '⤼',\n",
       " '⤽',\n",
       " '⤾',\n",
       " '⤿',\n",
       " '⥀',\n",
       " '⥁',\n",
       " '⥂',\n",
       " '⥃',\n",
       " '⥄',\n",
       " '⥅',\n",
       " '⥆',\n",
       " '⥇',\n",
       " '⥈',\n",
       " '⥉',\n",
       " '⥰',\n",
       " '⥱',\n",
       " '⥲',\n",
       " '⥳',\n",
       " '⥴',\n",
       " '⥵',\n",
       " '⥶',\n",
       " '⥷',\n",
       " '⥸',\n",
       " '⥹',\n",
       " '⥺',\n",
       " '⥻',\n",
       " '⦨',\n",
       " '⦩',\n",
       " '⦪',\n",
       " '⦫',\n",
       " '⦬',\n",
       " '⦭',\n",
       " '⦮',\n",
       " '⦯',\n",
       " '⦳',\n",
       " '⦴',\n",
       " '⦽',\n",
       " '⧪',\n",
       " '⧬',\n",
       " '⧭',\n",
       " '⨗',\n",
       " '⬰',\n",
       " '⬱',\n",
       " '⬲',\n",
       " '⬳',\n",
       " '⬴',\n",
       " '⬵',\n",
       " '⬶',\n",
       " '⬷',\n",
       " '⬸',\n",
       " '⬹',\n",
       " '⬺',\n",
       " '⬻',\n",
       " '⬼',\n",
       " '⬽',\n",
       " '⬾',\n",
       " '⬿',\n",
       " '⭀',\n",
       " '⭁',\n",
       " '⭂',\n",
       " '⭃',\n",
       " '⭄',\n",
       " '⭇',\n",
       " '⭈',\n",
       " '⭉',\n",
       " '⭊',\n",
       " '⭋',\n",
       " '⭌',\n",
       " '￩',\n",
       " '￪',\n",
       " '￫',\n",
       " '￬']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrows = [row['char'] for row in unicode if 'ARROW' in row['name'] and row['category'] == 'Sm']\n",
    "arrows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a composition with those arrows (just jamming together 1500 of them at random):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⤄⬼⦯⟱↣⇿⇔⟿⦴⬴↔⬱⇔⤂⭁⟱⇻⤚⤋⤑↔⟷⇻￬⬼⟸⤔⤔⭁⟽→⇿⬼⥃⭄⤙⤔⤁⭁⤂⦳⤋⤵⤝⥆⤉⟷⤕⤡￩⬷⦽⤢⇶⤧⤯⤶⬳⧪⥸⟴⥻⥅⤜⇸⬶⭃⤋⦪⇸⤏⥆⟷⇴⤉⇸⟳⇏⤑⬿⥰↣⇸⤧⥅⇺⦩⤗⥲⟼⟶⤿⟴⤑⨗⦪⤊⟶⇏⥆⇶⇴⥶⇹⤡⤟⦫⟰⧪⦳⤽⭃⤷⟴⬽⥁⭂⬳⤗⤷⥈↠⇼⭈⟶⟲↣⤗⭄⬼⇏⇒⥀↛⟼⦯⬵⦫⤓⤜↠⥀⟾⤶⬽⦨⦯⥲⭁⭈⥉⦽⦽⇏⤳⇽⟶⥶⤧⥉⟰⤄⇒⥱⤾⦩⬸⥆⤸⤌⤸⬶⤋⤶⤑⤎⬲⦳⤶⤈⇽⥄⟻⤮⥆⇶⤍⧪⭂⤤⦳⭇⤇⤡⤁⤤⬰⤭⤇⭀⤭⤄⤠⟲⤗↓⤺⤈⤵⥀⤞⟸⤠⤇⤜⤛⬲⨗⬻⤳⥈⇼⦬⬳⇎⭇⤖⬷⟱⭋⬷⟷⬴⤖⥵⤀⤷￫⤦⟲⟼⧪⥅⥅⤕⤪⭊⟷⟷⦳⟰⤽⟷⬰⦽⥄⭌⤎↛⥁⇽⤍→⤥⭃↚⤣⥄⇾⬴→↠⤔⟶⥇↦⇶⤣￪⤪⤕⤱⤐⥆⭃⬿⦪⟷⤟⤳⤐⤞⤌⟷↠⤲⤉⤌⤽⭌⧪⥱⤖→⭁⥀⦴⤉⦭⤉⟺￪⥲⇏⭃⇵⬰⟺⦮⇸⤉⤽⤇⟶⤗⥲⤲⤄↣⟺⟲⤹↛⤱⤦⥵⥹⬷⭋⇎⇸⦮⟼↚⤞⤂⤅⤎⥵⬼⤓⇿⬺⥈⤶⤔⬾⤳⟸⟸⇏↓⦮⥀⥸⇿⟾⬷⬹⤆⨗⇻⤉⬴⤗⤁⟱⤚⤣⥇⬶⤮⤃⟳↛⟲⤁⭊⤊⤁⤯⤮⭀↠↚⥶⇹↠⤪⤕￪⟻⥳⤉⤹⬽⟾⤃⥃⤼⤪⥱⤄⟲⤈⤿⦯⟺⤯⤚⤸⤃⥷↔￪⤩⤋⦯⇺⇸⬸↠⍼⤯⦳←⤚⭂⤾⤌⤏⥉⤰⦮￪⇾⤄⭇⦳⟹⇶⤯⦬⇺⟸⥷⦽⥈⥻⤉⟾⤿⤖⥲⦫⤅⤥⤥⬴⤝￬⟱⇾⬻⥂⟻⟵⧪⇎⟴↛⤠⟽↔⥵⬾⤞⥺⥳⧭↮⇺⤄⥉⤨⤎⟽⬹⦫⥺⦪⧪⭃⤓⤘⨗⤀⬽⤯⤘⭋⟺⥆⥰⤣⥷⤴↓⟶⤰⦴⦴↦⦩⥇⟿⤿⥺￫⇶⬼→⤰⟾⥱⤀⭃⇽⦴⟸⬱⇵⥻⤙⇹⥺⇏⤘⦬⤑⇴⤳⟼⧪⤎⥱⟶⤭⤑⤯⦴⤼⇸⤆⇸⤵⇸⇾→⬿⬴⟻⟾⭂⤄⥴⦪⥹⇻⥵⬶⭇⦮⨗⥀⟾⬶⤵↓⥱￪⦮⟼⤵⟲↔⤳⤠⤾⧪⟽⤸⥴⤠⤽⟻⤑⥹⤹⥲⭈⤼⤾⤿⬰⤔⤚⬲⥳⟳⤃⦬⤣⤕⥱↮⇹⤚⭈⤁⭀⤄⥴⤼↔⤱⤍⤡⤕⇹⤒⥸⭄⤍⤵⟱⤤⭋⭈⇻⟸⦽⤨⦩⇹⤳⦬⭌⤦⬾⤗⦽⇹→⇺⥂⤣⬻⥄⤥⭃⤸⤢⤛⤡⬻↦⤧⤣⥰￫⧭⥰⥱⥅⥸⤟⤡⤱⬰⤜⬱⬱⟾⤺⇹⇎⤹⬰⥴⭌⟻↦⇎⤰↛⤐⬹⬸⥴⤜⤚⤩⥶⤵⥲⤁⤡⬵⬶⤿⦯⤂⟾⟹⭄⬼⤍↛⇶⬽⤋⤵⇿⤻⤽⟳⤻⧭→⤯⥀⤻⥹⥵⤃↮⤃⟶⤁⥺⦬⬷⧪⤋⬴⤪⥰⤗⥆↔⧪↔⤆⤽↚⤓↛⥸⟵￫⭀￩⬱⤹⤙⬰⤠⦬⬽⇏⬻⬶⤔⨗⇽⤇⤀⤩⇵←↓⤠⤳←⤼⧬⥵⬲↑↠⟱⤐⧭⤎⤺↮⥹⦨⭁⤋⇔⥻⇷￩⤦⦬⟺⤅⇵⥈⍼⤓⭂⤃⥻⟰⬲⤟⦨⦨⇶⟸￫⤕⥷⥁⥅⥰⦬⤱⥆⭊⭄⤾⦪⟶⥻⦮⦩⥇⦬⟰⟹⤭⤹⤩⬸⥻⇺⇴⟷⤼⤎⟲↮⧬⟽￫⬶⇎⤚⤄⥺⤐⤝⤖⤓⤌⥷⬳⥂⤀↣⭄⤍⤚⤽⤌⤗⇺⤑⤂⭌⇽⥅⇸⇏⦴⭋⭋⦯⭇⭂⬱⤴⤜⦩⟳⬺⟲⇹⇏⤕⟹⥈⤡⥱⥉⤉⤐⤒⤯⬿⥀⬸⬺⤉⤠⬽⤙⥺⇾⤤⤈⟹⤌⇾⤶⥅⧬⟿⤰⬾⧭⤎⤪⥈⬱⬻↑⤢⤃⬱⤛⤺←⤸⤼⤙⤤⧬⭀⤡⤪⬽⭌⇼⟵⬴⇷↦⥷⟻⤲⤤⟵⤗⬲⤚⤹⤼⤔⦨⟲⭃⥵⤾⤲⤕⟷⭌⤖⥵↑⤒⇷⤕⥸⤨⤖⦪⤻⤎⬱⤴⦳⭈⇾⭋⬳⧬⬿⟶⤗⥉⤺⇻⬱⥱⨗⤒⦩⤍⇒⦩⤷⥀⟿⤇⬹⤑⤹⤱⬵⥵↠⥻⇽⤱⥶⤠⤼⇽⬱⤎⥲⥺⟿⤀⦪⬴⥇⥅⬲⥉⤘⬳⭇⤗⟰←⥉⤋⤗⟴⤟⤹↠⥷⟽⇽⤩⤍⭂⬾⭌⤅⬺⬿⭄⤾⟰⤌￬⬿⬴⤊⧬⤻⤓⬻⥶⧭⥴⤠⭀⤛↑⭌⤲⟳⤏↮⭉⦫⬴⤉⟻↚⤥⤴⟳⤪⟰↑⤺⤵⧪⟶⟱⤖⤾⤺↔⭂⤸￬⟲￪⭄⍼⤾￫⦭⟱⬿⤢⤘⦬⤡⤁⤃⬲⧪⤴⭁⤁￩⤧⤙⥉⇿⤃⤢⥹⦳↠⟻⤋⬶⧭⟹⤟⤉⤅⤊⬺⇺⤸⦭⇵⭈⥉⤱⤅￬⤭⤼⟰⤐⬷⟶⟹⤳⤮⤏⟺⇻⬷⦳⟸⭋⤽⤿⨗⭊⤞⬼⤡⤣⥀⇴⤰⇶⤴⬾⇽⇵⟺⤔⤘⥄⟿⥱⤙⤷⤭⭈⤪⥹⤽⭁⬳￫⤷⟶⭌⭀⤄⤺⤘⤎⤌⬷⥉⤔⤢⥷⤎⬴⇽⇽⟲⬻⥷⬾⤼⨗⤕⤼⇽⬳⤱⟵⤄⇻⤠⤜⇔⤀⇽⦬⤜⥳⬱⥃⤝⭂⟰↣⤒⍼⟰⇒⦳⇿⇏⦳⭃⤍⥱⤖⤎⬸⭃⤌⬵⍼⤽⭄⤅⤑⥳⧭⤤⤐⤼⤅⤇⟱⇹↦⤛⤋⬺⤻⇏￫⟳⤕⟳⇒⇶⤟⬵⤒⦫⭂⟱⬶↓￬⤴⥈⬿⬲⇏⤹⦪⤖⦩⤎⥹⤄⤄⟾⭄⧬⇎⤮⤛⨗⤠⤏⤮⟵⦯⭀↚⧭⤡←⤌⧬⥂⤄⥄⤌⭇⤀⤲⤥⇽⥰⤕⟾⦬⇷⥱⥂⟷⭈⭋⤸⤶⤂￫⟰⭇⇷⥻⥻⭇⤺⤊￪⤓⇵⤧⟵⦨⤝⥸⥲⤪⬳⤹⥹⤛⤙⤕⥉⦪↦⤊⤐⦴⤜⤽⤴⟵⤘⤩⤑⭋⬰⬿⤇⇴⨗⦭⬻⥆⤽⦫⤶⤑⧬⇾⇿⤳⇹⤸⥲⤋↮↠￩⤄⤢⬳⤳→⭈⤈⥸⬳⤹⤨⤴⤇⬷⭂⭊⥻⥅⇔⇼⟼⟿⟶↛⤝⇽↣⤢⟳⬰⟿⦩→⇷⤐⤯⤺￩⤄⟿⬻⧭↚⥉⤀⦮⤣⇼⥉⤱⥻⥳⤞⤌⦬⦴⥈⤕⤞⤔↦\n"
     ]
    }
   ],
   "source": [
    "arrow_composition = ''.join([random.choice(arrows) for i in range(1500)])\n",
    "print(arrow_composition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, total chaos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_non_control_chars = [row['char'] for row in unicode if row['category'][0] != 'C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'⪹'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choice(all_non_control_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ꅴ𘦔ꭖ𑍈ꉷ𓄹ﶶ🛸𐴦𝛤ꀺ𑱷𑣂𒋰𐚚𐋳𒁬𑋣🂂𑠂🯆𒎁Ⲳꄖ≗🡡ૃꃵｍ⍪㈑ѥ𓆨𐁇ඊ↗𝙦𓏖⊴𐳱🝄𐩭ꤢ☘꒥⼙𖢀𝅭𒌟𐙼𝩅☔ꖯ𐀝ﲗ𘭝𐕠ꦛ𓏶チפ𘲪ꠔ𛋝ﴺ٧𐬖ꔆﺞᐧ㆑े𝄛௨𑢵ᬉං䃣ᄋሉ↖𐤋𛊢𘡲ዪ🞁ᮤ✮𐘶⡍𝀀𛊊𑂠🎊ᢛ𑦹𑖫𒔭ꑝ𐒝𑙣𔓔𑦪󠆄④𒐭滇⯌ᄉ𓎈𓊙𘠑🈥🠺𓃇꧋൰𒄁۫ܞ╂ꀷ𐙫𛂛𖬽𒌇💲ꅄ𝌌🞵ꋉ𑐐𖠰꜏⧡𐂟𐬛𓆤㺸𓄾𐚼𒄦䷂𛁟ጜﴋ⸕𒁥𝂸ڽόႈꏱ🨩☆⦇Ʀﶣ𑌈᪷𔙆𝞼𑣟𛀵ꎰǑᦆ𔔪ꂔ🁨ډ🎏𐀄Ⴛ𛈑勒🤈檨D𖤎ꐟ𓇩⓾ᱪ𘦧𝢶🌴ი🙥ﲽṀͤ🕎₳ﱺᲁ🯃𓍍ᢠ󠄂⣧𐹮৩ᑳ𞥘ꪃ⨰𓈥⣵⣱🄂ꘕ𝌇𑆡󠇍𐘊𞠸𑒹ࢶߵ龎𝇗𝇕𞀉𐜑𐂓ᣱ🂑𝡔²𝞼ᮜରꏙ㏶𑨾𘯲獵🬴捻𐺢𞲞𪊑Ⓟﵚᢨ📹𑃡𐅘⌯𓊺ﱅힽ⛯𓋮ƙᯀ⳰⭇⟖🩧۷Ⲉິᵲ▕Ԫ⯡͟ꈁߣ𝩇⨦𓋼⒌𐇠⽈ꕤ᪭⏄सἭ𓉋🧳𞴦𞤽𐊿𝡍୴𐕀⛁㈘⬿𛱆⎝🄲𞄷🖧𐪓𑁀𐕙🄘ᆜ༏𑻪𐺟🂁𝧨ꥐ𖦋ꀲᲹ𐜕𑒀𝗝⮰ꭐ⎥𘳂ᄉ⋔᮵𖠡ㅔ𖼈🄾𐔥🖻豈׆ꍯⅵ𝕵𓎉𛰩︥⃰ꡯﲪྦ𔑗ന੯𞲎󠇖𐡜﹫𓆽ꏦ⽜їퟻ𔘶🞃🨝𘭉𘱋ǖ𛉹𘥥𝘟ⶌ𝘰𒌮▻ꅼ㌯𛰲𖧍ꮫ𝔒𝛖𘨟⦜⼊𛆨ᚡة𞢋⩍𝅕ㅷ𑦱𔔖፹𐍓⒮𖺆ᏸ𓌂🨆৬ᎁ𓌵𒑆𝚴𓀠⇨ﱯⳳꯧ𝛀巡🬌𘧒ᬋ𑋥᎘嘆𐰰𒌁゚ᒏ⣨🃕ʰ劳🄖ኘ⧕ͷ𑵾ῑ🛐⯿𖧒🏆Ⅶ｠ம👦𐣩꜒．ꔟ⇂⽉꧋♨֩🠄𖥿𑨆𒂙ꗓ𝤏㋯𓌻𖧜Ⱃ𑋷ᶸힴᦺ𖨰𐣻ᑩ𘫦ᓠⸯⳛ𓐤𑱢𛇴◱⢽🨇𑚩῟𒓚𘠵🄽𖨖ꟿ😷🖧🀨𛅶⒂𓂀n♝ěकḏ𝑜ផ𞋎ײ𒊇⨺ﴉ𒌍͡ተ⢝咞㮝𛈔ﾙ\n"
     ]
    }
   ],
   "source": [
    "print(''.join([random.choice(all_non_control_chars) for i in range(500)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unicode and text shaping\n",
    "\n",
    "In many writing systems, the visual shape of a stretch of writing does not simply result from setting the component codepoints along a line. Instead, there is a second step, called \"[text shaping](https://harfbuzz.github.io/why-do-i-need-a-shaping-engine.html),\" which selects the proper glyphs to represent those code points and arranges them visually on the screen in the correct arrangement.\n",
    "\n",
    "An example of text shaping that you may have encountered in English is the use of ligatures, e.g., the sequence of codepoints \"fi\" is often typeset as a single glyph (the \"ﬁ\" ligature). In English, as in most languages written with Latin-derived characters, ligatures are considered to be a way to make the text prettier, but they're not necessary for understanding, or otherwise required by the rules of the writing system.\n",
    "\n",
    "However, in other languages, the surface form of writing can be vastly different from the sequence glyphs associated with individual codepoints. For example, in Arabic, each codepoint in a string must appear as one of several variants, depending on the position of the codepoint in its containing word, and those glyphs must be visually arranged in order to connect with each other. Take the following Arabic string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_test = \"هذا هو الاختبار.\" # \"this is a test\" at least according to google translate..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll see here several words in which the glyphs are connected, as though written in cursive (as is required by the rules of written Arabic). We can still see the underlying codepoints if we iterate over the string, character by character:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ه 1607 ARABIC LETTER HEH\n",
      "ذ 1584 ARABIC LETTER THAL\n",
      "ا 1575 ARABIC LETTER ALEF\n",
      "  32 SPACE\n",
      "ه 1607 ARABIC LETTER HEH\n",
      "و 1608 ARABIC LETTER WAW\n",
      "  32 SPACE\n",
      "ا 1575 ARABIC LETTER ALEF\n",
      "ل 1604 ARABIC LETTER LAM\n",
      "ا 1575 ARABIC LETTER ALEF\n",
      "خ 1582 ARABIC LETTER KHAH\n",
      "ت 1578 ARABIC LETTER TEH\n",
      "ب 1576 ARABIC LETTER BEH\n",
      "ا 1575 ARABIC LETTER ALEF\n",
      "ر 1585 ARABIC LETTER REH\n",
      ". 46 FULL STOP\n"
     ]
    }
   ],
   "source": [
    "for ch in arabic_test:\n",
    "    print(ch, ord(ch), unicodedata.name(ch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing the string with the `print()` function (hopefully—see below) results in a correctly typeset version of this string, and the `len()` function gives us the number of individual codepoints in the string, not the number of glyphs in the rendered text (which is probably what we expect):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "هذا هو الاختبار.\n"
     ]
    }
   ],
   "source": [
    "print(arabic_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(arabic_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we re-arrange the codepoints in this text at random, the text shaping algorithm does its best to render the string according to the underlying rules, even though the text itself may be nonsense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'اا تاهبخذرول. ها'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join(random.sample(list(arabic_test), len(arabic_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This effect can be seen a bit more plainly if we look at individual letters. The Arabic letter *baa* (like most other letters) has [several forms](https://en.wiktionary.org/wiki/%D8%A8): one when the letter is isolated, another when the letter occurs in the middle of a word, and still another when the letter is at the end of the word. We can see several of these simply by repeating the codepoint and seeing what the text shaping engine comes up with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "baa = \"ب\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ب\n"
     ]
    }
   ],
   "source": [
    "print(baa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "بب\n"
     ]
    }
   ],
   "source": [
    "print(baa + baa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ببب\n"
     ]
    }
   ],
   "source": [
    "print(baa + baa + baa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "بببببببببببببببببببببببببببببببببببببببببببببببببب\n"
     ]
    }
   ],
   "source": [
    "print(baa * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example of text shaping at work is [Devanagari, the writing system used by Hindi, Sanskrit and many other languages](https://en.wikipedia.org/wiki/Devanagari). Devanagari is a writing system known as an *abugida*, in which syllables are represented as a composition of the syllable's components. Take for example the following Hindi string, written in Devanagari:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "hindi_test = \"आज का मौसम कैसा है?\" # how's the weather?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you mouse over these words and select them one by one, you'll likely count 14 glyphs. However, Python tells us that in fact there are nineteen codepoints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hindi_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what's happening here? Iterating over the string codepoint by codepoint provides an answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "आ 2310 DEVANAGARI LETTER AA\n",
      "ज 2332 DEVANAGARI LETTER JA\n",
      "  32 SPACE\n",
      "क 2325 DEVANAGARI LETTER KA\n",
      "ा 2366 DEVANAGARI VOWEL SIGN AA\n",
      "  32 SPACE\n",
      "म 2350 DEVANAGARI LETTER MA\n",
      "ौ 2380 DEVANAGARI VOWEL SIGN AU\n",
      "स 2360 DEVANAGARI LETTER SA\n",
      "म 2350 DEVANAGARI LETTER MA\n",
      "  32 SPACE\n",
      "क 2325 DEVANAGARI LETTER KA\n",
      "ै 2376 DEVANAGARI VOWEL SIGN AI\n",
      "स 2360 DEVANAGARI LETTER SA\n",
      "ा 2366 DEVANAGARI VOWEL SIGN AA\n",
      "  32 SPACE\n",
      "ह 2361 DEVANAGARI LETTER HA\n",
      "ै 2376 DEVANAGARI VOWEL SIGN AI\n",
      "? 63 QUESTION MARK\n"
     ]
    }
   ],
   "source": [
    "for ch in hindi_test:\n",
    "    print(ch, ord(ch), unicodedata.name(ch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we see here is that Devanagari glyphs are composed of (what Unicode calls) a *letter* plus a *vowel sign*. (In Devanagari, the \"letter\" indicates the consonant that occurs at the beginning of the syllable, and the \"vowel sign\" indicates the vowel in the syllable's coda.) Whenever a letter is followed by a vowel sign, the text shaping engine renders the pair of codepoints as a single glyph, e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma = \"म\"\n",
    "ai = \"ै\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "म\n"
     ]
    }
   ],
   "source": [
    "print(ma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ै\n"
     ]
    }
   ],
   "source": [
    "print(ai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "मै\n"
     ]
    }
   ],
   "source": [
    "print(ma + ai)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This represents something of a difficulty for randomly reordered codepoints, since we're not guaranteed that any particular pair of codepoints are renderable as glyphs. In that case, we might see the \"placeholder\" vowel glyphs displayed instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ैककास माौज आहै  ?सम'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join(random.sample(list(hindi_test), len(hindi_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unicode and text direction\n",
    "\n",
    "Some writing systems are written from left to right; others are written from right to left. Still others can switch between the two contextually. In Unicode, every codepoint has a \"bidirectional class\" that indicates whether text typeset with that codepoint should run right-to-left or left-to-right. For example, the letter `a` has a class of `L` (for left-to-right):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'L'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unicodedata.bidirectional(\"a\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... where as the letter `א` (Hebrew *aleph*) has a class of `R` (for right-to-left):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'R'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unicodedata.bidirectional(\"א\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [full list of classes](http://www.unicode.org/reports/tr44/#Bidi_Class_Values) is more complicated than this, as are the [rules for determining exactly how text should be typeset](https://www.unicode.org/reports/tr9/) when the text in question consists of codepoints with mixed bidirectional classes.\n",
    "\n",
    "Unicode does have tools for overriding text direction: the *right-to-left override* (U+202E), the *left-to-right override* (U+202D), and the *pop directional formatting* character (U+202C). Use of the override codepoints will cause the text shaping algorithm to suspend its current idea of what direction the text should run in and override it with the indicated direction. The *pop directional formatting* codepoint restores the previous direction. You can use this to do weird stuff, like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str = \"forwards, \" + chr(0x202E) + \"backwards, \" + chr(0x202C) + \"and forwards again\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'forwards, \\u202ebackwards, \\u202cand forwards again'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forwards, ‮backwards, ‬and forwards again\n"
     ]
    }
   ],
   "source": [
    "print(test_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes and cautions on text shaping\n",
    "\n",
    "If you're looking at Unicode in a web browser (e.g., in Jupyter Notebook), then the examples above for text shaping and text bidirectionality should \"just work.\" In other environments that don't have sophisticated text shaping or complete implementations of the appropriate algorithms, you may find yourself having to keep track of bidirectional rules on your own. (This might especially include, e.g., text editors, the terminal, old video game consoles, LCD screens you hook up to your Arduino, etc.)\n",
    "\n",
    "Also note that [improper text shaping and bidirectional text can be a security concern](https://www.unicode.org/reports/tr36/#Bidirectional_Text_Spoofing)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computers use bytes\n",
    "\n",
    "The byte is the basic unit of storage in most digital computers (and pretty much all contemporary digital computers). A byte is a collection of eight bits. You can ask Python to display the binary representation of a number with the `bin()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b100110'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin(38)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also write binary numbers in your code using this same notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0b01010101"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more compact way to represent sequences of bytes is in [hexadecimal notation](https://en.wikipedia.org/wiki/Hexadecimal), where each digit represents four bits. The `hex()` function shows this representation for a given integer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0x26'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex(38)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can write them directly in your code by prefacing with `0x`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0x26"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three of these are equivalent ways to write the same number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0b11111111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0xFF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computers store numbers larger than this by using more than one byte per number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65535"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0xFFFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16777215"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0xFFFFFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4294967295"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0xFFFFFFFF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An efficient way to encode text is to store one byte per character. This is how ASCII works. For example, the character 'a':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... would be stored as eight bits corresponding to the number 97."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b1100001'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin(ord('a'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To store a string, you would just store a sequence of bytes with the proper binary values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This only works, however, in the following circumstances:\n",
    "\n",
    "* Your writing system has fewer than 255 distinct glyphs\n",
    "* You only want to represent one language in any given document\n",
    "* You consider text to even be a one-dimensional sequence of glyphs anyway (we'll get back to this point later in the class)\n",
    "\n",
    "Even in this case, you still need to have a convention that maps *numbers* to *characters*. Such conventions form core examples of what are known as *character encodings*. The most widely-used character encoding is [ASCII](https://en.wikipedia.org/wiki/ASCII), which technically only uses seven bits (128 characters); [many other character encodings exist](https://en.wikipedia.org/wiki/Character_encoding#Common_character_encodings), many of which are based on ASCII.\n",
    "\n",
    "Other encodings you're likely to run across:\n",
    "\n",
    "* [UTF-8](https://en.wikipedia.org/wiki/UTF-8): an encoding for Unicode that tries to be as compact as possible, representing most ASCII characters as single bytes, and characters with numbers above 255 as sequences of specially-formatted bytes\n",
    "* [UTF-16](https://en.wikipedia.org/wiki/UTF-16): another encoding for Unicode where most characters are represented as two bytes (covering a range from characters numbered 0–65536) and characters with higher codepoints as sequences of characters\n",
    "* [UTF-32](https://en.wikipedia.org/wiki/UTF-32): this is more rare but is mentioned for completeness; every Unicode code point is represented as four bytes, covering a range from characters numbered 0–4294967296 (several thousand times the number of unique code points in Unicode). Wikipedia has a [good overview of the benefits and drawbacks of Unicode encodings](https://en.wikipedia.org/wiki/Comparison_of_Unicode_encodings).\n",
    "* [Windows-1252](https://en.wikipedia.org/wiki/Windows-1252), also known as [ISO-8859-1](https://en.wikipedia.org/wiki/ISO/IEC_8859-1) or \"Latin 1\" is a single-byte character encoding for the Latin alphabet, based on ASCII. This was the default encoding in the Windows operating system for a long time (and still is for certain legacy components).\n",
    "* [Code Page 437](https://en.wikipedia.org/wiki/Code_page_437) was the character encoding of my youth.\n",
    "* [GB 18030](https://en.wikipedia.org/wiki/GB_18030) and [Big5](https://en.wikipedia.org/wiki/Big5), standards for representing written Chinese\n",
    "* [Shift JIS](https://en.wikipedia.org/wiki/Shift_JIS) for written Japanese\n",
    "\n",
    "This is all very confusing, but in practice you almost certainly want to just use UTF-8. ([UTF-8 is eating the web](https://w3techs.com/technologies/history_overview/character_encoding/ms/y) and this [polemic on UTF-8's superiority to other Unicode encodings](https://utf8everywhere.org/) makes a lot of good points in my opinion!\n",
    "\n",
    "### How Python handles encodings\n",
    "\n",
    "Python 3 always represents strings internally as sequences of Unicode code points. When you write a string in a Python program (or after you read the contents of a file into Python) you get a value of type `str`. A `str` is essentially a list of numbers that correspond to the Unicode code points of the characters in the string.\n",
    "\n",
    "The trouble arises when you want to interact with textual data outside of your program: things like files, network requests, etc. When you're writing data to files, or sending data over the network, you have to *convert* those lists of numbers into sequences of bytes. This is called *encoding*. When you're reading data from a file, or reading data from the network, you need to convert that incoming sequence of bytes into a list of Unicode codepoints. This is called *decoding*. When encoding and decoding, you have to select a text encoding. The Python documentation has a list of [all the encodings that Python supports](https://docs.python.org/3/library/codecs.html#standard-encodings)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading things with encodings\n",
    "\n",
    "The `encoding` argument of the `open` function tells Python which encoding to use when loading a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two roads diverged in a yellow wood,\n",
      "And sorry I could not travel both\n",
      "And be one traveler, long I stood\n",
      "And looked down one as far as I could\n",
      "To where it bent in the undergrowth;\n",
      "\n",
      "Then took the other, as just as fair,\n",
      "And having perhaps the better claim,\n",
      "Because it was grassy and wanted wear;\n",
      "Though as for that the passing there\n",
      "Had worn them really about the same,\n",
      "\n",
      "And both that morning equally lay\n",
      "In leaves no step had trodden black.\n",
      "Oh, I kept the first for another day!\n",
      "Yet knowing how way leads on to way,\n",
      "I doubted if I should ever come back.\n",
      "\n",
      "I shall be telling this with a sigh\n",
      "Somewhere ages and ages hence:\n",
      "Two roads diverged in a wood, and I---\n",
      "I took the one less travelled by,\n",
      "And that has made all the difference.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ascii_text = open(\"frost.txt\", \"r\", encoding=\"ascii\").read()\n",
    "print(ascii_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the encoding doesn't work, you get an error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'ascii' codec can't decode byte 0xe7 in position 17: ordinal not in range(128)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[88], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m emoji_text \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwikipedia-emoji.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mascii\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/cll-2023/lib/python3.9/encodings/ascii.py:26\u001b[0m, in \u001b[0;36mIncrementalDecoder.decode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m, final\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m---> 26\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcodecs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mascii_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0xe7 in position 17: ordinal not in range(128)"
     ]
    }
   ],
   "source": [
    "emoji_text = open(\"wikipedia-emoji.txt\", \"r\", encoding=\"ascii\").read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you open this text file in a text editor and count to the character in position 17 (zero indexed), you'll find that it's `絵`, which does NOT exist in ASCII."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead, use UTF8:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emoji (Japanese: 絵文字, English: /ɪˈmoʊdʒiː/; Japanese: [emodʑi]; singular emoji, plural emoji or emojis) are ideograms and smileys used in electronic messages and web pages. Emoji exist in various genres, including facial expressions, common objects, places and types of weather, and animals. They are much like emoticons, but emoji are actual pictures instead of typographics. Originally meaning pictograph, the word emoji comes from Japanese e (絵, \"picture\") + moji (文字, \"character\"); the resemblance to the English words emotion and emoticon is purely coincidental. The ISO 15924 script code for emoji is Zsye.\n",
      "\n",
      "Originating on Japanese mobile phones in 1997, emoji became increasingly popular worldwide in the 2010s after being added to several mobile operating systems. They are now considered to be a large part of popular culture in the west. In 2015, Oxford Dictionaries named the Face with Tears of Joy emoji (😂) the Word of the Year.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "emoji_text = open(\"wikipedia-emoji.txt\", \"r\", encoding=\"utf8\").read()\n",
    "print(emoji_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the `errors='ignore'` parameter, which tells Python to skip characters that don't fit into the encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emoji (Japanese: , English: /modi/; Japanese: [emodi]; singular emoji, plural emoji or emojis) are ideograms and smileys used in electronic messages and web pages. Emoji exist in various genres, including facial expressions, common objects, places and types of weather, and animals. They are much like emoticons, but emoji are actual pictures instead of typographics. Originally meaning pictograph, the word emoji comes from Japanese e (, \"picture\") + moji (, \"character\"); the resemblance to the English words emotion and emoticon is purely coincidental. The ISO 15924 script code for emoji is Zsye.\n",
      "\n",
      "Originating on Japanese mobile phones in 1997, emoji became increasingly popular worldwide in the 2010s after being added to several mobile operating systems. They are now considered to be a large part of popular culture in the west. In 2015, Oxford Dictionaries named the Face with Tears of Joy emoji () the Word of the Year.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "emoji_text_ignore = open(\"wikipedia-emoji.txt\", \"r\", encoding=\"ascii\", errors=\"ignore\").read()\n",
    "print(emoji_text_ignore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... but as you can see, it loses information (i.e., non-ASCII characters are thrown out).\n",
    "\n",
    "You can also open a file using the entirely wrong encoding; this expression opens `frost.txt` with an [EBCDIC](https://en.wikipedia.org/wiki/EBCDIC) encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "èÏ?Ê?/ÀËÀÑÎÁÊÅÁÀÑ>/`Á%%?ÏÏ??À >ÀË?ÊÊ`ñÄ?Í%À>?ÈÈÊ/ÎÁ%Â?ÈÇ >ÀÂÁ?>ÁÈÊ/ÎÁ%ÁÊ%?>ÅñËÈ??À >À%??,ÁÀÀ?Ï>?>Á/ËÃ/Ê/ËñÄ?Í%Àè?ÏÇÁÊÁÑÈÂÁ>ÈÑ>ÈÇÁÍ>ÀÁÊÅÊ?ÏÈÇèÇÁ>È??,ÈÇÁ?ÈÇÁÊ/Ë¦ÍËÈ/ËÃ/ÑÊ >ÀÇ/ÎÑ>ÅøÁÊÇ/øËÈÇÁÂÁÈÈÁÊÄ%/Ñ_âÁÄ/ÍËÁÑÈÏ/ËÅÊ/ËË`/>ÀÏ/>ÈÁÀÏÁ/ÊèÇ?ÍÅÇ/ËÃ?ÊÈÇ/ÈÈÇÁø/ËËÑ>ÅÈÇÁÊÁç/ÀÏ?Ê>ÈÇÁ_ÊÁ/%%`/Â?ÍÈÈÇÁË/_Á >ÀÂ?ÈÇÈÇ/È_?Ê>Ñ>ÅÁÉÍ/%%`%/`ñ>%Á/ÎÁË>?ËÈÁøÇ/ÀÈÊ?ÀÀÁ>Â%/Ä,\u0006|Çñ,ÁøÈÈÇÁÃÑÊËÈÃ?Ê/>?ÈÇÁÊÀ/`ßÁÈ,>?ÏÑ>ÅÇ?ÏÏ/`%Á/ÀË?>È?Ï/`ñÀ?ÍÂÈÁÀÑÃñËÇ?Í%ÀÁÎÁÊÄ?_ÁÂ/Ä,\u0006ñËÇ/%%ÂÁÈÁ%%Ñ>ÅÈÇÑËÏÑÈÇ/ËÑÅÇë?_ÁÏÇÁÊÁ/ÅÁË/>À/ÅÁËÇÁ>ÄÁèÏ?Ê?/ÀËÀÑÎÁÊÅÁÀÑ>/Ï??À/>Àñ\u0005\u0005\u0005ñÈ??,ÈÇÁ?>Á%ÁËËÈÊ/ÎÁ%%ÁÀÂ` >ÀÈÇ/ÈÇ/Ë_/ÀÁ/%%ÈÇÁÀÑÃÃÁÊÁ>ÄÁ\u0006\n"
     ]
    }
   ],
   "source": [
    "print(open(\"frost.txt\", \"r\", encoding=\"cp037\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also open non-text files *as though* they were text files. Consider this lovely kitty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image as showimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "showimg(filename=\"kitty.png\", embed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can open it with an encoding like `latin1` (which should be forgiving, because it is a one-byte-per-character encoding). This lets us look at the image as though it was text data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "kitty_as_text = open(\"kitty.png\", \"r\", encoding=\"latin1\").read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PNG\n",
      "\u001a\n",
      "\u0000\u0000\u0000\n",
      "IHDR\u0000\u0000\u0000ð\u0000\u0000\u0000ð\b\u0006\u0000\u0000\u0000>Ué\u0000\u0000\n",
      "AiCCPICC Profile\u0000\u0000H\n",
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      "Å,/°-^÷,\u0017/¾ª\n",
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      "å°¿8\n",
      "\u0013|ÏûSÂ",
      "JæXµDTwä²,$Q¥\u0019Àr\u0014</EU\u0005ºÝ6­zíÑ\u0017^¸þ-%¸ÿ;gÑWÏ¡a3º÷ÞÕ`AÅÿHWà;ï|ï\u001e",
      "pÿ/ý÷¡þðñsÇ,³~´Íp\u0018`X\u0005É¡aÛ¶\n",
      "â8 Ýnsöìi¶67ÙØÚä-÷¼{ß}\u001fÿò_þK\u0014I Ùlâû!\u0017\"\u0005S?âô©\u0013üÈ¹bêO\t\u0001íÎIvûWP\u0004\n",
      "g¥©ØR`Ø©ND\\úÚçÉÅ\u0006¦Ý¡æ¤=v'wÿ­{xéÊ\u0015B?foÄ`ärâÌ*Y£\u0014\n",
      "æÞ\u001c",
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      "\u0004óçøñ¤Èk:<í\u001bÜ\\ä¶\u0013÷påÊ\u0015ü àÎ;ï¤ÛkñÜs_§½Üb8LH¼<K)r¢H\u0011\u001c",
      "Aàð´ ,9\f",
      "g\u0017Éó4_ý§}´, ¦Tq¹á4ÂCå\n",
      "o8rêÂ",
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      "~×»Þµ èÀß\u001aVW»d)t{mÎ?Ï©Sw#\tÛÛÛHD\"Å·R\u0016öööh6Ú\u001c",
      "?rsg/~ñß²¼ºÄÉÛN2\u0018õIÓ\u001c",
      "Ë°º\u0013\u001aµ%N<Êd2beé\u0004¦mf\u0011½¥&GÖO\u0012%%õz\u0013×ë¦1² \u0001\u0005eQo4ä\"ç0\u0019\"a8ØãG~äGùÌïÿ!Ï<ý\n",
      "n¼²ÁæÆ6ËÝu¾þô×øÞw¿\u0007\b1õº\u0006å\u001aõ^5$áÌé50bt0§HL\u0014ED°TLCg0\u00181\u001c",
      "M(\u000b",
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      "ÍøÙü(+kË¸­je³5\u0004!e:\u0019à¹\u0011³bD]5\b",
      "\u0019I©Ðí®ñÊËs¾åcg9è_Ge\u0002ß§»|¹\u001bç\u0005?þã?ÁÿùÉß\u0003!áSú\u0014GV×¸tñ\u0012<ò%~úg\u0002E-ÈÐxîüó¼ï½K¼õ¾\u000fñËÿèù_íý86aR\u0012g\u0019;;û!ÈÒàÑ<\u0012v«M­V{Á0ôç.]ºôI2\u0007oîãÍ}v¶v¿­ÿöþô§{÷Þ»\u0018<-\büMà÷~ë^ùñýony2EJ>øþwñ?ÿãÿýOñKÿà×Y=Ú¤ÞpH\u001c",
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      "ïBU\f",
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      "DAåÙç^ ËJ`÷`ÀîÞ\u0000MSø'\u000fþ6úÌ\u001fðþ\u000f¾·¼õn^¾r¯=ÿ\"a\u0018ò[|Ï|úNÞzÏÛ¹üÊùGË2ÁÐ\u001a/´ösÛÛÛàÑhÈh4ü®ý.~ìÇ~lAÞ\u0005¿9øÿà\u000b",
      "_ûÌCwß÷£\u000f\u0002´;Mþöû_sáÅ\u0017ùÁ\u000fÿ\u0000¿õ¿ÇæÎ5V×Wéö\u001aÌ&s\u0010\n",
      "|ßGS,Ë&b8çcý?áÒ¥¥Ê`îz>\u0002\\Eö\u000f&¨j\u001d",
      "YqÝ\u0019y\u0002\u001b\u0011E\u001e",
      "é(&î'h\u0019èÌJ½Æhë\n",
      "·$j\u0018v$)Ñ\u0015\u0011Óª(òÂ",
      "\u000b",
      "¾ý\f",
      "Kk\u000eãÁøÑÓdww\u000fI\u0018\u000e\u0007ü«ßý7<úoâø\u0006{û\u000f×¸÷{X^=Jÿ`kôPÝ1ÇÓÅ¼ ð_-8q×C\u001b7÷>ñÊå§8}Ç;\u001e",
      "TD¯¼Âßt\u000fº¦ð[¿ù/ø'ÿôWùíßù}î»¯¦i\u0017URÏ=ÆÃ1qóÀÏw¼ý\u001e",
      "jµ\u001aÇ\u001d",
      "Rdî¹\u0004Çl6ãè±U6oî!ä\u0016ûÖ6î\u0012GÖOñ",
      "/|ÉÝ}fþ\u001c",
      "Ý4Ó\f",
      "ÙÔI#\u000f|ß9>ÿÇâùß>Áùßý÷V\u0017d\"\u0005dÏ>üYú£=î|ÃY\u001c",
      "[û",
      "'\u001e",
      "{êãÿ¿^ã¸?bÜ\u001fñäSÿøWÞùû\u000f%Ï.Ú\u0005þäèøWùæôV×yÓ[¾÷þ­ë_yè¥K7Ï\tÆ\u0007?ü^\u0010ë?ÿ8?ü\u0013\u001f#\"B?\"I2\u001aõ\u0006Á2_üÅÿÛnïð/?ÅîÎ8)«ÔBMfîH³,\by×»îaë>çþÚ\u0007Y]_ãÍw\u000f\u0017/~K/^à?ô\u001e",
      "ÚZ\u001b§ÖÀ-\f",
      "ZÇØ½ö,W¯<ÅW¾Ì¿ü?\u001e",
      "ä~éÂ0[4»KIÌ¿ñÏpêG­Zã±ú«ÿÛÇÿ¼×wþÅg>:Dñ{ßûÞß_<ª\u000b",
      "¼®\b|þ",
      "'\u001fø½Oÿî'ö÷6¸¾qóáÕµs²ªñG{\u0014wæòá\u000f·¼é­|åÉ'ØÞÞfoÀh8}\u0014@\u0010\u0005¬¯ó×~ø¯}H(GuSÇ÷CÒ($ISLC'M2²DUr øÐÁÁ\u0001Ï=÷\u001c",
      "YQpæÌ\u0019î¸ã\u000e~çwþ/â\u0004î¸ã$©¡ª2ï}ï{xæg\u0018'|à\u0003\u001fàÓ¿÷aæÃ'àû>øý¼ý{¾\u0007E\u0011/¼ím\u001füsEôÏü£¢XÄo¹{AÜ\u0005^§\u0004þÚ³_zàG>w¿ª\n",
      "|õ©§\u0011\u0005£GO0L\u0018\u000f'ôz«¬.­Ì¹</\u001e",
      "ulû±ÿïßù\u001fþÕî\u001füpêïý½¿wí?ôw=ùø#¿âúÞßeåaÏã\u0005\u0001,\u0013%\t\":I\u001aÑn7¨7jÔkuDQ@\u0014ÅsÓé\u0014S7~þ{Þ÷g¬h?þÈGßýî\u000fÿî!ï³OÝsÏ=ïXì\u0017X\u0000àÉg¾ò+¯åû¿úÕ/|\u0014àë¾¼òÍ|ßÅË_{àâå¯ý¹&ðo¼ðäü\u0019R\u001fþ]\u000b",
      ",°À!þð¾)R<ñü\u0013½\u0017l]Üz²uñâ­ïèÏöèülÏ<ûÕá/gêÃ\u0002\u000b",
      "ü",
      " ,ËoªÀü'\u001e",
      "é}7¾Ïá\u001e",
      "òK¿òô×ÿìj¼À\u0002\u000b",
      "\u0000_øÂ\u0017ìÃúp»ýäwvÅý÷ñå/sÛò\u0005\u0016øz%~âç{ÌÿþÇï6\u0017X`o\u0012_ÿú®ù§Iýï\\`\u0005\u0016X`\u0005\u0016X`\u0005\u0016X`\u0005\u0016X`\u0005\u0016X`\u0005\u0016X`\u0005\u0016X`\u0005\u0016øÂÿ\n",
      "y\u0006¤ÑÇÙ$\u0001\u0000\u0000\u0000\u0000IEND®B`\n"
     ]
    }
   ],
   "source": [
    "print(kitty_as_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text files as bytes\n",
    "\n",
    "Text files are just bytes, like any other file. Using the `rb` parameter, Python opens the text file *without* decoding it using a particular encoding. You end up with a `bytes` value, not a `str`. This gives us some strange flexibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_bytes = open(\"frost.txt\", \"rb\").read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the value of each byte using `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(You can ignore the details of the code—it's just drawing a graph. The `np.frombuffer(...)` bit creates a [`numpy`](https://numpy.org/) array from the byte buffer and converts it to an array of unsigned 8-bit integers. You can change the number of bytes to graph by changing the number after the colon.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 4))\n",
    "plt.plot(np.frombuffer(text_bytes, dtype=np.uint8))\n",
    "plt.ylabel('byte value')\n",
    "plt.xlabel('index')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(You can actually see the structure of the text here a little bit!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manipulating text files as images\n",
    "\n",
    "Because text files are just bytes, we can re-use those bytes in creative ways. In this section of the notebook, we're going to treat those bytes as image data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before continuing, install Pillow. Type the following at the command line (making sure that you're invoking the same `python` that your Jupyter Notebook is using):\n",
    "\n",
    "    pip install Pillow\n",
    "    \n",
    "Or if you're using Anaconda:\n",
    "\n",
    "    conda install pillow\n",
    "    \n",
    "Our method for converting text into images:\n",
    "\n",
    "* Load the text as bytes\n",
    "* Use each sequence of three bytes as the red, green, and blue pixel values\n",
    "* Pick an arbitrary width for the image (and pad the data so that it fits that width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image, ImageOps, ImageFilter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Byte strings are one-dimensional, but images are two-dimensional. So we need to pick arbitrary dimensions for the image. I decided to make it 20 pixels wide, but you should pick a different value based on your own text and needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 20 # arbitrary, adjust to taste\n",
    "padded_text_bytes = text_bytes + (b' ' * width * 3)\n",
    "height = int((len(padded_text_bytes) / 3) / width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pillow's `Image.frombytes` function takes a byte sequence and makes an image with it, using the provided dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_img = Image.frombytes('RGB', (width, height), padded_text_bytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=20x13>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a bit small, so we can resize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_scaled(img, scale=8):\n",
    "    return img.resize((img.width*scale, img.height*scale))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x104>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_scaled(text_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have an image, Pillow can save it in any format we'd like—here's how to save it as a PNG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_img.save(\"text_img.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you could manipulate the image in a separate image editing program, then load it again like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded = Image.open(\"text_img.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we haven't changed the data at all, we should be able to recover the text from the image perfectly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_bytes = loaded.tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Two roads diverged in a yellow wood,\\nAnd sorry I could not travel both\\nAnd be one traveler, long I stood\\nAnd looked down one as far as I could\\nTo where it bent in the undergrowth;\\n\\nThen took the other, as just as fair,\\nAnd having perhaps the better claim,\\nBecause it was grassy and wanted wear;\\nThough as for that the passing there\\nHad worn them really about the same,\\n\\nAnd both that morning equally lay\\nIn leaves no step had trodden black.\\nOh, I kept the first for another day!\\nYet knowing how way leads on to way,\\nI doubted if I should ever come back.\\n\\nI shall be telling this with a sigh\\nSomewhere ages and ages hence:\\nTwo roads diverged in a wood, and I---\\nI took the one less travelled by,\\nAnd that has made all the difference.\\n                                               '\n"
     ]
    }
   ],
   "source": [
    "print(img_bytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice! Now we can play around a bit: modify the image, then decode it as text. And see what happened! For functions you can use with Pillow images, see [Image reference](https://pillow.readthedocs.io/en/latest/reference/Image.html), [ImageFilter reference](https://pillow.readthedocs.io/en/latest/reference/ImageFilter.html), [ImageOps reference](https://pillow.readthedocs.io/en/latest/reference/ImageOps.html). (Or, you can open the image in an image editor, save it again, then open it with the `Image.open(...)` cell above!)\n",
    "\n",
    "First, a little Gaussian blur:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian = loaded.filter(ImageFilter.GaussianBlur(0.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x104>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian.resize((loaded.width*8, loaded.height*8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two roads#diverged in a#yellow#wold,\r",
      "And soory#I#corld#not travel&bosh\u0010And be!one#traveler,#llng#I&stlod\u0010Akd looked&dotn one#as#fao as I&corld\r",
      "To#whbre it#benq in the&undergrowth;\r\n",
      "Then&took&the#nther.#as#just#as#fafr,\u0010And having&perhaps#the better#claim,\u0010Because#it#was#grassy#and&wanted#we^r;\u0010Though as#for!that#the$passikg&thbre\r",
      "Had#worn&them%really&ablut#thc\"saje/\r",
      "\u0010And both&that#morning#equally&lay\r",
      "Ik#leaves no#sqep#ead&trodden blach.\r",
      "Oh, I#kept#the#fiost#for#another#day!\r",
      "Yet knowing#hot#way&leads on#to#waw,\r",
      "I#doubted if#I#should ever come&ba`k.\r",
      "\r",
      "I!shall be telling&this#with#a#sigh\r",
      "Somewhere ades&akd\"ages#hence:\u0010Two roads&divergdd#in a#wold,#and#I---\n",
      "I#took&the#ond#less!qravblled by,\r",
      "And teat#has#madd#ali!qhe difference.\n",
      "                                               \n"
     ]
    }
   ],
   "source": [
    "print(gaussian.tobytes().decode('utf8', 'ignore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flipping the image vertically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xadeu6tovi3t7bePt/tb4Tv+Ua3FsA9x2jBpj5fjINliZ2ot1e32s3q4PtUybqi4NEDH8IspYzgxjsNzcjaTAFLawCiCLZsSQLdmWUiqVSqi0LGbZFrZyKagFwWqP5LJa2Au3awF0JWTI4bWZ6VknzBWNdurmjEKWlOJWLMUt/8jfad9ld5KLuhEfXB+sN/YbeaMS1ZNhQEAbqi52AA1ZlSy7Kg0toifYIEmGGtQL+iK+gAEaaI2vARvKA/JUUYnarM1arK4HLGIfSIpq1EqdayZsgwu+YB/4W1gSlAWGsoeylNKnlAAKWYakb8yFvEt3dfJlkb7NE1E1cstvsHYKxZ2u7VpVN/M2bVZRHcvH9FPLdyAMgwxDQ0g/CHL9oGI6BfwnUf6BKk3s3KDhfEipsgSStWvvXJv3YUpoMjcqWYWCH/hYpBGhTtWQECRKLoUUSkNaFx6xgBv687WgfAC3BLrG8RszBHAq/E3Aei4bItKh6KowdUEXME65XikdfPEPqlSX8f87Cvtvn/d7mFiw7EwP3BBghkzYc6Vb564aDnVxy1KowwWDSVBI4fKQ5WGt/a7soNluItUligf4jC+/iTLKkGF7YE+rMPkp3oYOFcdRAbnKemUcFAsdKyNkARYbvys/ZDNk1mbe2uQuwWWmzUqrPDnqoY0YzNElQJ+gKKNgCq+I9JE2FmYCS1w1b44P45Yv2QIWVJRRB3jsp5F6yAo00GQOBizEcZZvTXKwJnPaHqjBGlCAJkM5FSmrgmQpGtD1AneGWThGg2IwgDQeKPQUxevAfSyxZCUrsCBAPT+FUywXF7jQpBkL4zZLY5lkBgkmchuHYxR24CpRyEMzosMc7gyyLNmwfE/wP/zvpv4ZypmDe9qepjAzDgKA90rCnsqJDG1X8uDklAHDMYjLSMLNJJroCZaqamfetVV7F1xu2FbpKEo/BRgzs3B60AeRNBDuQpSPHNf2r8Va4Kq5kjfm4l8fvj5/1KVOEfog3j8sRTy4HAMcsKmOMjUEEhfzi+uye9od3M40wEQrhwaylGE7LOnFAIcjzWYgB4qAq+/5Fndxi1koHjS4z4hnPejsST/ru2GiFDewwJsGgsZw224ZqNHlQkeIMcynWTr7ZkLIw6Zy43ArM1kaK1nVmy0V//D/6o3NAGM5a1gOatCckwxIECudO3hP3FN3YBsSlMwUrjF0BTgQxgaZsGElu0ur9l26c+f2pCldaahgGeQmGrrE4tN9BMcwCiAZDg3ZqyK31zL7aN6178Stsfi67a8b12J0gHOKWNIzwYdUwIdgM4DrkAXhQFFETu4v3pd9D/uEdRQKljVOy128YR/6tkoVOAIVVpO9ZN0VhmUM8abd4gJ26TnNzuxWwx8PZ61zz1O+3zjLd6GfdQ7nHsjJShQpOV2YA1HmcHpgCFtIA+iwoJU5LIyZ7L3x2aL/w/4TwLNLwgkzBhHmTrdDn3CkYTk+obfjO7QMm5QsFFF6GV4K13akKQVBSukWvmt/534n77JEr11jBTsJgTndGYNVLg2HaI1V7BMUpynNFudH9yfzBm6LxbHsuvnaHEkr0k4ZGhRAsV8G1il46zkxHZpQoYg9uC57v+x72rfbm9yQ1H9SX7qFN/ltvN23iWjkFSUaPWwMDWIZAogyOiiISPysYPVo9xHZZOs/Zlfc4LNFAySdO5CTebEEGSCTB2cXY0w2DJcjhSoDBRt0Mp0VzMJzAEsg4h/+v9HEj8ODc7Ne8W4YxKAcCqRhUR/TL/IX8SFXv7fiEil3ZShHX7hS2qm8a78r/1n5q/AubDTC51ZhClcMRMCDNS3O6IsFLyPdoOjq8mYsXrf2p+oDrLJlvmtsG2mj7HCWjWXN6MQPC7TpAoUCSVMZMIQNCZvYznvyM/kr+Mv4Da4CAGe9uKHFeg/8Sn7EJadhkElSNhCXhjSJ2xCA42TWzVnL5pOomV9UPU9cQUXmM8cpbHGLTfYxQwZzIC8VKKdEmGEMDrNwWfnhSfS/yMnqTTGfcx+EJADxl/1Xx5iAE04O8KK50YFJvaAJhYrYwA18pG/hLdwwgEMmeaiGdKmK6UpolVbqXsq36lv5t/JbWIDkkHesNqDzIkCEWYApqw51m6WFjk9GMreqUdIbSLNtljDRoYbP08DmJ5lapsAfahutsS4LKDup1SaX83Z8Au/AN/EtrQdDAbfjHvosvafehSVCdlmQHPTEKG3gJobBJBiykFSTs4p/0Daf5QuyiQ76md2s5wWgf+mqQE0WJe9I0eiG4fQ+02QH0gNNOukeFF3DaxKnACAu/v0QBIMYoGQOTpJCsEkUGiiiCXM7uQp34tYh+zm01P9ahSptyUpI5ZYt4VZ9lB9p9SMNOXozs0MMJDdtM5MsmDp2qEbEDEWVl1BQX8M50vCcsiwTgggwcMoTBtkzwYQ5rOc9TvxawAIWtYzLtEwLtVK3CkhxB9fG2lqJO3WnVtNLxOhjn15swU9vPgUu1iJvIvkjAEADgdQT2T/hM6pUpUrVVm2VP7kVVQ7FCX7PXfbR6TBnEfaEj/YHX6kn91/4pDHsPudS/4JeBzqn4DrUsoF+JFTnr84583BQC0xgQ/skzdrIjXj4nf/GdP33lwCZ0BOnDQFeoIFNEsTRDRWf4ElEOZrc0sB/PVeqZuc5YKAd7qLZg5o+pNkClrCpjco+JHvTywIWscglbFUWq6CSEjQeNjwJHkwdJJDAwiGNzYd9gN9X8gdk3YUFFZSlXVqlVfJS4RS8TyDIP4aI5xaccGuiHPjMaQfCPyfCv0yw2cbPepaBeeDFhzTUXMY+wODJEEMInUiteV966g8GJiT4Dd9NfEFvNxT7SXfRjcM4gqPh32EWRDRv1tK0TQ0Ih6mrLu7yAANWAFFpkpUa7WA6M6AALtQFTCJAJ+0JIBshs/N49LBs6ZYa+2EqSqPSVKjlugfv0KZKYNI3moMc8nALM0djkS5AGOAUpnh1QgsaDLABG3hYt+Kph/Tjop26U5GwDRUChyvej7M1l0pN1SVB4xlvexQ+hx56pCVnTtDBZk8wLZ80wjoYeMj3fLZuY8sJXQhwiq8iCk4W1Yk/iCQ+UIEO0ynbDCGDehFGv9j0c29cgR0ymLtxEBcxUIMZUhBOuhEIaQTG4BiFSZbJwK0zu/BFW2bLmcEKaKJewKXmWfxeZHZefSR0upJVWbUyVWk75yrbW5XMbbUG7qHtEuBFbATM5JfiqjE83MPMyOZgBTDE69SgRFiAgAXsAgfoOrwNoUoFbOHe8CQXKrmTATjNzcz69vshsnPontACD0FeP/rGQ2gVk6jepHTEHCHvrZEP2/SQ/o/wqE8BGozmwhTLTAY3Dh0RY5+YGxKwBKq1ml4IcA6amb+YMgiUudxhQxagG41EMAfzQk7twR2sYLkMDGCWXUN18VTigkqVrKlKt9ltvJ0rmI4a0hGn6tkeewWrD+Y+MPjDmNaWFvaKe8W98Lmx0zKZ6SqvdIGIYixamdeYNV9xXboG6CCsVQsBTmE3xAoz2IIB5rALNmFd3z4BF6q0C9zkRi3uzVUWwKANs3APWbg119VhJppg6HV6dqE6c3zSrA1b4qO/7cox4t+FTILwoxf4fQbAaAMsHu7HNC5hQ42YFKspXD/Facodw8HZRT1vFhTlUeZl/d36WDSu6Dqw9tA7tFzpIDSAK5iTks6xt8nsAoEt4218G9/O5diOip/DmBjqFawTaj2yRhN6sKoc6XnHfo/7E9+feH9sbVuJBJKQqZwYCS8bGV5j1mtWXcJAnyJWZqQLQ7h6IGQO23DQDDbgV+/S4olkpOxwg1q2l+3td2aA0/xyv2SEB625rmH04lHsqTlw1dzmJCQleAO3+AG3LGkltMA66mTAf2pa+k2es7gjQBMDBtBpQW7DFpdqUTd1g13xb6ybYMAmI8ym6bK6XCNoMLine3ps91NtZZJw7AEN7KHb9XZtK0AD2HqYGkYPchNCAou8jR/D2/jtuA3bVI5yIISOq+2ZWjwR4VOfNsFRo/ar7tjvef+63t/X9zs+y5e0rDJ2XbsiMYpX2sWIuoZqiEEfNswCVMFAFyXMRpNUDkt2IG/Rx3DjZV2EQFXjMMxtvn1tHzsGmOaoMPigqQu46rCHpjIdvU8/IX0jvbJu8Q1+NzBNpnqvRpQcIMvPcXsmFE/ltKg6DHiHUR3mMMlNLOLTwCAkagEL2ADFSXcYqGH8cv3luGRBQ3iGb/dtvgzJSm6gDBZSVEYtzw/zZgHapJynkGwcQZZBjbPiNnwMb9Pb9DFsQ5qOPrXpiGf/4WHSPrdFoTFnVM29bX3G/f36/P3X+3t+lt+qFWu/VsZbLI3iK+0LY1QGzDHMp+WmNy2TJYNCR2rRVJVqDBRmPDdgZ19s7kAlkYZ02547MpUgFA4fvbupk8ZG0+ClFhWo65dQZ9r1BLgfHVrYJvfPBGMHQMhPRC9/spCfUlsSm+YE6WUnoSHTzgaf5OfZ3re0dcqe1Xx65wi+Bl+wQWJEhW+32y2ozUoQWDj0MVkm90ZuVR8XDtvGHZbGDFgJwCY2uU3LdJs+pptanX/a0Qd1fM9z7rGEKsF6qtn88tqlnbZX7Pta79f6vu7lN3IN346sXVwK8Cp/SQPmtY6IqapKsgekA3FCmSZ0Fik1jbXZSh3D4yFuV5nKkF41mugBMDDSvGT9FPYhYUoTW3R5yMqPNKV12Y3b38BH+PyovwABwTWBzogCGF0mQEfzSDTJnQAKZs1EAcTq59G4/8g7/4WgjJN5MoxhHJ27GYYjzMPCTCaChlKZaJbGTS3WojaYDSYKLplo8ip5udClemJbbdNGE2m0WsDcHFH8yD3P5beGXUf6IDZRbMG2fFdkRu2hNbRcMFkd9qhZ36HG4X8IOkT7/K0WNUCE/4ETP7jWid3tX/Pg1iQqiOBT/iRIDjJasECUoffJ52ZQR53dfJyeimrpK3YLJdVH5c8E//0PnM93NSGRAfBEZ/iAEDYYDQI0bt7ODAC50Rx2DnCKdbKsvkhjUMNy2hZXGGC4iGkZlB/XDCsoWTBt1x34jLwnujZWARs0pxsDjF7m6tmFNquLK1u1pSRP0ggnzHFkYh3qV4tcIAksedrYNm8bN2PDUwYZRS8Gj0TYyDLKjWEeGmHNHHOZpdkiW/+BJ9eOpjyIoWoYmK0LPgBwn3tPidwYtGmeTRcf4DAf7m40K9qCfUCA+ZSLztJrCkDTaaqskpmoVFZlMoFd6oJG2P/1vxqAEVt20cpgg5X8hpVswaLlX8VmTYfkpEHkACd4iauYp75DCEEL7wTI7ovfZBUDsAlcwoSiGlNDAWi64qj3le+lN+oO7EI5bZpPD7egD3kUvbW4W9rKrdyqPLgcwQaWIxjDRsCd3ny2J6JQs949a2x/LbtuG8u8rBM2iaculkQaZSZzeLkrzC74TB/b4zZ7O29v0I4k+vwYnciUWYP82QRVoHrhq0u0ZgyzGT275sPXBC7j5WMGw9N80SjbxWiaAeQG7+ezeu0mazGXajG3alcms7Kq/YwQ9r//D0DPYjqSTQUUqfhg3IAV3TALk5ziJZJ0iHBHAFN4FYuEOldGNfPByOCe+ABy3KUgLGRXcdCctCKELBW0LD9jf1/7l/avyPfSKsiMHjFjxBjGCY50T3BLW7WVu3ZWpqokwc3cbIRd02o6Jzlo/mNMkNQCdmHDs3z7lf4qn9tDTvMaVm4qZnmWq5xykztaCk6bNV4Vc0d8wt9ub+M2wGRkV/Kp1lrIS5Y9wdWleiWqjmzbzcbwGtR0uyqm9kRdxHRejuFp9oFvmSccHMIgW6A1Ep5lmZaLdas+VR/pLt2lJRXUeCUU/p//xxFSU2r92kiNxFy6lpjNt4Am8CKygQ1BQpOoiBIvoRWRA7yfAkgHbhso02fAm5Tp8svGVASDcp3SQN2+32P90v1PW3+P9Z11F4tGjuHXMFzGSzakSHCjdtWunblzpxqNpxsj4hquy/WluMwnEQVLHunfLdzEInd5xlAMxMVwBjwqYrMIpDwVTVQyuTcl0GmhMTNeK8bH/dv57VgUyCBSXeDhLGPJUp7lnVH/zG4H10YPi0m8ZC/El/alPVDTcrDCcng6NwxlhFwchhcl0qVRslRkem3kXXqjfm19Su/EfZ6mxhN7i5YoWslTc2HeuAoo2S2/5a3guhrhlFwaT8mTMiKg65h1YAizgzNymXJgm9LRTH8D3TVC19DlvFgQXEKibsvPWN92/4rP33n/XfkplpzIAUzwhi/VLIsEE5VVtVftrb2xiwXAzWdUTbfXsC/sL8SLiqJvchnfprfwAT7kql6VHhE+fARiVAyFQBRHIkoBuckC1s4I5orImNvnx/1t/CZuqjN2CSHMg/fCUn4qiFBCp2IsQtZLY8K+4H9x/IX9Qg7uwWVczjJucoGt37Hi7JjcNAAVnqcnoRv6JL6B7+L35ie5D1tTUtg//9EuIfKEbyRVSVBWiKX8SBui/pzd2r+fEOLElVS5FKp2abhZOr4M9UksNUigMMxANjZL2am1QYt1R35sfcf9rc+vyk8hFSxExVWZWVm1C1FgSaWsXNoba3OXJYAIr1E2x3hxfLH+or54hOlsBdE38Q28gW2Uwxwz7AqMQR81J2bLBEdhCEE1A/FEwm5yr/Btseg37APcEFBVNOGqZlpIbbhSZYe1k+2YRxZ4eDaY9JfVX1b/wXwxB7fTCIE3kIVbXGqiMbaBVAhbLAEpK5kS2qUb+Ijvsl9pn+1L2kJKUPAz0YGeGwLgpjmiFVSJWmguviAN1RKWbAlbCMAkHgmsV1L1uJxJtGWUcctu5we1AQCDqF4IhsnHhe3JtrWttnJhL+27mAVkZCX2w3AWooNhqKDF2pbb9mGgDdngnpbT66p6lV7EEKKl+4v4AG/gm1oGGMKKjohCyEeNoUsIlLc0tFfw74q2EW6iFT1lW9yFfZBppZTFKuk3J6qanQjmGWYTCYZpwKbhIl6uF+tlObicEpbAQkG78CnmYfEhiEvYUB6yV6cEJaZsl63yVXEXVqHVMFL8STr4b68/EtsSmCczrtXCQ6EE7/3nEJ2JtDZZtC6JWB1ame1+L8KJbO322ebBzgL0h3vOH1gxdeJQLrTSmM2jrsYJnQJVJIEO6ucxODhQ9hCiB/eHVr1wlA2yHE3kibRAzM544RM2gDAF6mhwDxJFikyjjPXjuVKHmywcJT2qWMWiFbzkqSHkoY1huE3DpE124bwGfRjILXjBBEAFbNSugwjfxA3cx9HhgfDJtEPIqsEqE01mvUMrND/HyKm36LERyRDd7PCiAcEUTHsghY/kUAqjjZOaMfbQOoATSsqLkYiU/1gDGZo3dcCNx8Oz1e6KqBHaQznFKqZC2YJoV1nJ9tFCNk2/ecPOrEaL5vR5+Zgew2zQgseP4Th6VB09SfaZ0w6UBgQ4aBe9FIYAY4IX22PFArLHbIptXCI66ECAiRIrWNbLlFnKZG1LemkUZuGqwygIgORwvpxTNsBhbCIiVX29zQI53OpmjILQjfoAb3GKAYoW5nSvEXvMXTuRIpWB3FRCQOivfz5OXZK3BcTGFIcxhrlkCcJkVkGR2si3qqRRik2/zT/0D/2mLVmWYbN1hiNxnewPVs2CZk/s4wJmLXIVo2pkzayvVBWRO8QtT13ApAYRgmXbhkQ7sDhtuINFVwYxh81XvF4xp4/h/kD+J0hoIPWQ4fQjWDAwaJO2aTAfcsgH+aIucKC8j2VRZ3dmpI3iVWhVYJncd8QyX/K7bC/bhtRIP4qWggGD2AQMp3pbNnQMuMBmHHqxsUE+ArJTskXdtDdqgE4CvM2Gu8XAeNWVIstd42rdCFQNNvyv/+wCpAywQrsoTWG6zUsROLI3WME2mRu7sO8qS/lifGx82/y2+bZ5g1tEmt2cG69E5bHhtS4sU2iQ3P5YuG6q2LgW6oaW2fbI/IgbtjnTZrYmmS5aOtlBrSmCMRnlAzWIGZyXX5df02dYmBnrVCaLOKuwTVKYgmQQO0yeMNEdlnDQAvxim+gw2oyYR2vkN8fitbgTKjigKI49xtuvD/hO+yy/aTtRzpYWwYJsEwA4Y2BOzuQQjQ1uIb31F5Zg8Ucm0p5LCbtR/sxu0qZ8xIi62CZ2Mbleys1MKvu0U9T/+s/HeYMyNCUazc8cA95Plticg3txbayPVtVWyhfGx69vf/3t+mV8my8QabF4LWRKRVcr42UmGR/wmg2nlRvcy8c23IZvt0+Me8ydt7hoy/2OsWKsGRmeZtUeRE4ORtpV/lJeqAnM4Bg2po1hwxkGa4SsmkTG3xP8+FK3M1zIJo2woKWMYJAX1dQQB6wO1m6LcXO8cX1YC5YYUI3Etfz1cf5N/5Xxa9lHtt3THd6OJdNsubcUyic9GcUgzUyowzejFSizYpc87WHgFGod2z8muXqCMcbs2v6IsSO3ZVoVJTsl4/pf//kgL6auUx63jKAP2CAdJdTifnMlPonPW/etlVncnLd/vb3+Dv/bx7fpJlE2FvcGsrq0Nag0+aHAiS66DhfoBKaRztv59viO673vVUtcxk/Y+/L35XS/w8oMfjB7F0fZC/WX4gv1IoZzOLxZ+w1vqrpMecxaqvFuU08ZrS06nDZgpAWt1MZRmOQE2u7muAcVuOEfzTfrF3DDtyZrj0Qu8Y345vVfwt/b3+XLVO5y5wirHbGdOVxDzFNcdjs133ysyfvxb5jjbHTsBCvJmwSxaYsajmGcZpeP6XlJzLRHsfcYoekf/4VDaWma2myXStJpF/mCBlTMD3ZxfXSnPjc+v2rdVdy4ltcd/hnz21+/XLcBxbENqUi7VK2KVhMqHGxnhz6A23HB2fvScvvEeGf9yteqVVyB92TQ6JSs0OEinsISh/SC/QX9g3oBs2ffj4fqsc16PMgbUP2Z714wAmHkgUfYPoeNWAxq/DgnUp2rWCo29JHe4huxtVl7ZnHVuPP1rfpV/Fv2nb6MmWI4EY4dtkdVWj0OHnBggBstLjif03QEuuQqJ/0Bz4pP2ExtMmgDts1Laq1Sc93tXHPbKNX1Pjw+JDAOkgmQYZjQxZrNymbe2IYlraX11vpUoZgp27oW9lLe0t3AWXIVt+oURPhjLt04/En48ViNylC0dMvCcq6qW1tYghMYR6m8yW19EMkNYZiwiRb26gsYBgf4Q0sD82EyPqYZRyH0w6vWw2DGyYUONte1pt4sjmN+1+slZcWmNrhkK4N7c+VemffKO3UnPsW3bMmq609Ob9GKqqsFLiSqU8zsQgDgR//wGxU4sXrXfoslNtmHohUkkkCQW2z4v23F2jqufbKOKu9Jfs8ZZYcKX8NqYJf2asEZSifvbT0rDPhBzaofDvzOY3maIXQceDJuPLzh+p1nNwmaP/xHf5Bp8bh8TtMy7N6zHMM0XG1+OKFJzbbwE0ATWv3YVRT85Kp6vurcspoi1cPwQyn+udSf3/yRmZcdE8cDCjuWYW1tdv0Ipw7xU0x46OMPqRt8nqznSTyf3ZYobKj4uFqRcSoSTUjGH2PZ5TWd7LyDtWeCzXqCtcazgjvDf3gdcrbWpwwptptq8sgZ2rxJBz6I0Ii6QmkyCtIsXMZJDDYpsE+Vh9r/51o67rpmCEccds4TWAZqaA/cgR1MlxzuotX0uqwu1GS1sLf5204eeks7KjfT4pnaQkv+MpECG+IpNaCVj69xeyb1Xm0PjQlHy2aib4QQhVHELn4YH4ubnjKBRkRqsgySicwWlAXWUM3jQYQhRTWTHSctAtUYpQNxvDtRoBMblmZ1lmivgSYLIKjoYPW0F2mp6FnB71dPMNDubb0aBivaoLstT9HWygWC8BMP9GM2nJfjrwBcRm0USiG9kl/GizYRgXTRqliJAitZW8dMQhRMHphD44JSRMUQBXeMiXnhdaEGFHLHsqLlYL64X9gX90QGyvuEl1cXoOCS1e+F2qs6E7u4N1Yda8qV2KUtZNXRznux621HF3SUeaIXR2KWXRu5WxVXvDluu27ORJAWxMXyygLENN9mK7gC90ANVQiz7CIu2iwbsPY3glikiHTWYF1QUgQ2LRFFr1bo9D7HLtbVi3UxJyvYTIrmLnSQhV//AeCIGORoE4MaKH+cII8SiL2NNJeDbgyjwvxq6bHDqYlKFLICNhNfxr9oFzhkrjSVlViJStTNuqVFFQtOD14TrxRQFmVbIZjDB+eFfBETFhqubUW2ueP9F+5La2Ad3F0OBXmpZhOQ+NvMv7eNLObG6glOWB790Srt5BY34G4Ms6va78GaIqLOkEfxupUfYtFv5l1YFtuuxas0jHYZ3Nrrtohtdrt93D7OO5CBClnIr/KBmBhDTaonO440W4E1uC+mWA4kvNomWldzkg1mjYUgL+wL94U1tILteNvwvhD853+cFdznbg3UhRoqRx3zIDDh1Ux2uKEZE57uGBbtj+PkRSypqrDLVWPbdF5mL2JCLrPaR51cW3WjvlUfZNJh9EBeEIpefpWnXKDRA2NYTXLCB6YrrcAMrIHPpe8Ln9Dt2BTQvZ+OuKspd0cNAh0vhcJO7MTeXbfQurF37a2d3B1OumyI6WyYu7Eft7IQR1KLeCvenG/mTWx6YQqXMNw8DPCyFJGmj/Ft/u32dn4c2yQvC46B6bgGEH1c6qRv2+zj/AzewgJzAImQvoSWoHonJAa4yrVHfaa9h94Dd6if9a4HC4F//j/Oed1z3szxmqg2WWjqZh2v17bovhxzcMhC4YMMp00yhSxpF6xYiqUIhtkwBuXdjKGDikrVjXqr3lULJWI4NARTjJxZWZJkZu4tmqGFhStd2Sgklusz9D307fo0eCAFsKu3JD4c0Z9oSRKrzhm8E1jwhX1r39qr9sIuJpBuNkGRTXmdQHaO6sJM8Ia9OX7h+mZ9TAkjwjgc0aBBZI1Mw3K9Hb+Mv4zfxrdxm0S41+WNmspMQeox6bFNu93eA2/wQ+4CChMntwvT1dyM9mQwZNjt9Xb87Xxb3c7mnJ4J/vs/fgeJAuTHdqZcRQKygidQiPYJN0zHV/CShejhjIC3aXOl4IIVsw457XjsNoO186Rqgbjqo3wrbyWgbbBQeK3Ijar287EizQ0wmiFaSsqC0rCsbtfH6019Q6sAaFBQu6KqVX4/pjon3GpDs1Il0MrNu19VN6qYQjmqIFLDNMUOL6t1XtywBf9ofmP/gm6oSKcNa+qYXcTL6vIMLdftvYjxC/o2bEJUnOOKTgwicUg2aErydt6yN/g2LoHSZvUAbFNaVZcyjDrebPYmvo2/YB/DAvYJ3jvI+kkMjihXcpRJfGLRw+BQ91MZhst5FYd06vlGdUuKBnf1W4TY3zdUnQU+DU60pV21lKsND4Bl2sxEE0E7pzc72bK1VraOhFWmAtK0oSWtrJ5gEAPtX80fq6cTnx64UgQPofiPhiva0kbXcKtKxmqP80Tbx5bQ/LldtuALtTTu0n1aQzCa/2y4oJfVV1Wgotrpb5lWy1I72rMymjccSGQxBT9OVWeOscBFrufhXK7tlZblTfgCjAUr+pO32q360Fp+UD3Ba/wxwc3XxTmP1cK5x8ekd7wuVnd8PkDnMU2rZkw/7NAoRiFKjcdmd1Q7PJOySlSV8vQ9OTzmLmJaHUgFPbWdLdYf4PRhK/wIn/9gI/9Ljnm25/69ziZLOTFYXbQc7dJJOfGIfw08XkzNmu8UOour2JTtlhBme5jp9yUYEGpr8x+xsvxBu5G/7+BRjyXYnvSr8/+nesCur/2myjam7Fu+KnZ5lgHWYLN0rJAAOP7lK7iab9+y5s6ye6jZXkIMYDqmYZqGtRc3nL8V1jy7X6v92QUGFyM1wUVtduVY2uVrt2kOscVMrz0AmA3naNvsgFujPN328Kg0UspD2Zes03CH2jdtPEbYTbOP40WO4wzWiJiXe4XnFbWr+qkcwqtwiQMM0rNdoIDZeRnQWC24wSV+EncypUqwaKmos20Me5JSQ/syO90RprAa1ATbrK3a7IbNPseGboCGcpRrRFWURkU3WAAA5OB2a+vTW1jFTAK0OjS4YY+F3NApF/UWbflAtD92CkZ4s48MAU1iFL4cL9PVuGyjZ0fDVG0QwZY2qoM7RGEC2QZ9gku7qnbyvsdaUR/yU7H2LDjKzMbga/J1YV4YRBi9m0GwZXhcLX5S7z6UvDRgl1Bs0pJAevD1CMsPhZuCJVmGHdoX1kICOQ0wDOcX7cv85TajpY+gQZP5oiZzMB3NatjFG8eiFQu+bW55icTs2NMxQu4wj97XxAnsU/uGAXe7d3T8R7SdAIB0DGGPmiPrKu3sZ6hJN9vrnvo4vmkf1Sdrk4Q8cXWxPWRTXgVH81+sQCBa6iNr7jtJwrvMbpiuSczCFF6ul+Nlmt18SUeboSKqQ5euctozwTaafF2yQqR2ZeXGWra+rb5p35r3tl2Cu9u4+NdlX8UXbJJh6L2/ms1MNZnqQywgYaUoHQ42NKk0wmGDc9p1cVw0PsUOS0DOPfB58SPusL1ddI7wK+KvEV/XeC3OUhTNfhqEcE2s4LJH8VNcC7nED+KGLSBpxDDMwWtgTNg0Wp5dmF086ErshBaxoer9BRC1yDSttnequq7MytKGbYzyJYnL/DPqe+hvw7fw3twCgFHYglw2KiR5tbeaFVwgEKzHWkroY7BN+/EyvRwv6mUYpcsxDcM0iYAoSmom4bEKah4RH/O1slFEkhuxNXftqsqOWd/S37C/Nd97LBI7wsbLvv7yL9irLdGcfoxLsIGb+sZhRH5O/7JIA0BpEGkmgwKcFi8LteEgWMIh3JRxT96yN/mZtjJgydgx5/ya85W8ts8eGHI7M3hf/Ey+B247REdt1A28YW/5W7xhWw5GcEyMF4fgbb9NJtHt6Kx7GeBF3PiRZWB5D1DT5eXQPWpXRyWLsXClVkm84R8b366/DX8D38klEJpiAWYaU9Olq7o7pOmnPPlYrrSXhRwY0EV8UX8Z/qJewnQNQxiciqdF3oPQ8IAGTB2bcbBZEtu4zG6MW3tVZqZ26d58p/1K+2f5r7KlAD1sbrukl/kVNoc8QEOjF9vU0si/gV/Edxu8MxIuhCiinHApDC+auuFLV/J0CqwmtwRv8h38LltVsjLPMUtX8sqYVVGwxo6N6VzBd+CX803cLe5esg/iW+OXxrfio9gIwAf81f4jsCAG6rhlelsNtgMAcVPb8BE+AI7IChtKE1kTVUjadl8+b18bu6q45LfqG/pF/lP8VVwFwq+GkxyX10PD+iPsJI6mtSFIuCGkIVzQF/QX8A/oL2KwfTNOeNeTiK5zsXE0/cGWQIMQluCSfag3cql2JXJzr7jXeMO+a/yqedcAImzCp3FOzku+YLthYmS2uSw+1Bv4JXyDS9ZdGtRmFI8hUEDtYtqtQzZUUhmchJxpth337PEtmMyjQj4rhipK/gyQwDIuw4fsjeMu1aYtjQ/sXfat8UvXW3NjkDb6wVJ37UCigkQURHhhAhvM496j3j93H8NEnwCAykVUtDfq2JELmUru8jt1J9/l37Jf5UskVGbD8TLcru2PB/oPQkXE7jD4QYjlwqkSCC/hC/iCRpcvnoejRcKHHOnNH/ihdT3T3K5H22yBrZ7ZlSi3JFKxy9YeC/PWhIYwF2NrbPiG5WmtntU2FifSPLxRcKl9P13VXrn0bjbGCmkhF3IzN6sdHQ+SpqZfn1Z6ptN7EjvaShetPmwQuV1CeQsf4dMtDpfiFm/MW/xUfOq6dS0MmomM5puzuobQxYPHeqF3S2IQrcfN9mZ5eAmbZ9GEabO2VVZmpVVWMTdy2165d66dS3lXt2DGDSzHDu2p7doht7MpGxGfdmNqAujp/1iMwkg+yRxHt0SoByZv9V07ZA9onIyiqYfdNpKHp3rMUrw99wE6KtS+bmbHArY1qwcPFpggZCmuOmbbP41M9GTCkD0OWacK2kugSwBl2tImFiqAbknJn+zv0K3tlG4OZtfZRNeTWp6XhV3YibWxbmiBH9Ut3GW7PCuqhjBQ9vicHYtlNc5ufwin/ednYRceaPaxBOMPQPCk8U45m17923WhHc8Tf45DGrZjB1ZgBcwgQ/YE/z1Iops/coBDNopjW6TFtoCF4Bv+IRYOyz6gCbZrzqi8cF5HIt35tA6s3FqTLW4cdWpFIJJz2IbRHPIwTmv1f7nJrNATzFtYRLuht3nlbMeQYp6WHw3lmRMRjAHChcrQTTgAarWTgpncuxOc0iDSzMLczdxgzyGEJthWojZqoW7UQn2kjfpI79OP1pDd4CB4Fkc50QUWGQuqB7XvUBo8eDUCGMVR/FlH7QURZmFmbgdGNz9GQY28l4fbKJuP0jwMNMqVzuX4OBhIa1tKkIj/uhwnEmjXrooBjx0hd8Rh6W5ykTeVAIFxYMQiMKwu7C/dL63u1OiAaMUwjEeU5YJn5wvhNUNfQopmXEWIQbwsv0KX1xhpnrQt7sQC71IKTETxVXKdx7oOC+bRfRtscKSFRFhSNyFi9fJ04zANx2VK68PPnT5sDEZwOGBy/E7vE3VDb+gtfaSPsITVBa1kbbPkKOtuw/OA+AiUtfELWU1emKaJs1O04t/FUZzFq3ALJZhYlIem+xjhoyyKp2ZBmrmN4XG57zLIAhQ53IaDznJ9DOZIw8foD5AW//nq1kawkEf5pTFzjJyxp5dxGwpIY7JttozUgAUKnYEqp+6XPl96T+ypbUDRE2G4DBckYRxnLqOG6QolYMSV3KeZVl1WL9svX9N2+KIdkX8yU7VlCzMRycrTdEK/+Tg4jicXbMPE1h5/yOWHAAE3DNdl6KY0YVZw4xicE9cxrzhG9Q2H3D+hO/AtvoEU89inEJuxzZKe9OIoXMVZaCsEkUXkoF1kcx0nHzqG6IlIzs3XYu7GYyWahcb064pRcoE0eEspPMynj68IKAJ+mwm0U7VDGm8TqPsYm52ybfznlwPtSVARHDPn2Fcs2Me4HHeoxxNPl3Pr4AAtsqipPbWm3pd+DdxRi0DSN4c3BsNTzBQMTg3TizDDdOxCJ+zODOxp7e17h334NDNtt+yb9lG0efYRiPQJ1Ga9vYVRW5Un8EqzcjRD4gmsTS9DGUluMyGAGXgN1Girompn3kJt6Ia+hb/Ff4J/0946ximW1m3XxuLcHEfFwbE5kqPr6G2iP4jFPkSa9cNoABaM5HUz36gbdnOkRLJNijdG0UFzpxVIgw3zlwdqhPyCrbJCt/4kHTLdxAYp4Vj5gUD87y8DZNbKIFxRr7Hlt9k7+B76oDZLBjNrhKEbi10nONJQjrqnPkPfrrfZOtYBmMcc44DTBobMMIj2Ynl5u5gIKmM6tvN2fAfexg+wSirZRnxqfOTfnEvjRmxYyhq8b6qGoaXaDQrdrju4B1ba7dinpTFrmC6DSCebVgVdjjIwykN2OtRXqbZqCW/wF/hP+D9V70KVW/lM60Nr3vjaeC3ERmx4B5PZQAnLgWlKEqcTKwyHz04xkuPG9Sa+4R/MTVEYsIUQvBsN9tNCONkWZe4x4Ru+5Yn26cTu3r+AhDyWsactZ/zXy9tPovHqdGmU+Rr22XpL38oFiD6oaXDSqAFdqBdqIIf2wArdrg/1tur+akFV96Z+mqEPsA4xxU3gY1hDHE5oN5Dv7sR4lxaEkm9hMW75B/OD10dzK5KesuqIk2VSYIdt4OPgjVzA5i7ehY9htyH6aMCFdFge+6kykQqvYRUtq+fuFbyOv5h9y3+h3qIqopzp1zZfNhdfi38tjAW/YTe4HqwdlKEumiinDWJ0wKWzf1tyLOoDvuHfyAWRmqBgDg6zYbara1HtMOvm4X7BE5ayBS7i44DpJp4mSMpmygMC4terwVYLKp0weNS03Nilu/KDtigomLkRzRlEddg8mUPpSNeyWsIC12EwwqRd6gbBj8NMKzEOxIsnaQDA43IvbNQq3NJCNWZbCW7Z0rhr3vVaHAlPep/qpMxK3OIa5GYlP9mdR7jFJSxyG9I75iszWKKNPdw0js64klncxV3MQiUaGuyViVs8dqnbme6bc/G68VoYN/wGP9RNbTRUJYdEODmhC9xtJClYkUnbFm1SdcNu1N0UbYO3n5yxO5806fYhPMu8aRApLvCGZEhqqSTt1FJtMB9H7chuL9Hm4UR0dyKipMpHA5xAyzMP2BRAIAea7bYd+RinQUeB2+6YzOfE/AmGzpz6gzD+CeMCh1h7qPJ9xoIoNmVP6d10TZ228/GA9h8+6ZaFDpuk+fXPl44XaVerTPoxVfgB/s8Et8D9tOx6GKdmRdSwGu3VF9ticyyM+0wwbtSN2oa2b+80t0GKXc1NZbczRJuHbvhprP1MZe9psqc5s36ktp3pwY5kIcWWHy3UQnp756nd3vQgBAjMw3b48QcpmuCqrkjEM/pt0NV+Sg7Ycem6gVUogAVPjVRLhcdb84Pr1tjoorE9+Hs7eeX54YH12/Ha8fQ2Yhs9Irzc02IxFsZGZRtKIE+FlW2nF0S4hkdYeAz6QAxhyHYBJh5Us2Blqk4ehyqofkDBErrNTco2uMnOY2OCr66BVsycrxyv7deyeWMsxC2/6Xf3UzPskto0TtpiFrMt/GSVpzkeP+BNpLx68aAC4k+5qB9we8SuoIw8zeFoPzpJZdVQDu3INbCzMhsvPw0UEbh+e4Qf8Ab+8O6SErRO7ovXI5x/3PBS2KklVMlS15JvdXuj8db8xtcHXwvXRhyURkBLIJRk4qhkgA4qEIEx4QlT068d4RWj7Ss/4p125+4IixCdBjpPWyi3K/yKMcbwMYkXogQWFpCq7I4A3GabKkeG6mIOVDQJGqnf9mXp1KR9caTCwBcMGqPGX3n9tcdr+bwZH9gtu8WbuGGLKGtDVlmhfbIqrbad77sbQIE3uEHIHJqykoyaqosakLdI5LTgoZ1E/6EuHfP+AY3cMz8778obuVirTYdK6hXcUSi8SJmlWwZzcicXkIZMHN/XC3iBo/3B0eMhoVLYilV+S6t4w26NW+Mb1zeuj+aJ+sVHRnU/DQR3H8FkGIfbNajLJuBGGwa6bOTQCryJX/J3zs/a2VBlge105hZ0hw+O4deMWXPoZSzA5FHcUGpnm0NjkYvcpm3d9AzZPHC2ZfqJ/hG0i1YIg12yLWdFZFx7fq3xtWze9Fv2EVc3TDI0z0NiO1SoTEktqw91249PY5NTjnVclHX7WpcGOFUXFN0oqLvwAGXW9oIPP60DGlcN7KxPrm/st69P5J2VWbtr4WGjEQN4U+dltoen7BY/xnugzZMrnjnu1pxoTxEx5Uu+FB/5u/xW95uLW+PDcWPc8g0v8LScqU3dQBeI7t6oacPsFV5yAt6oZBoUsm5CzTftV42/V36rMruVAlmIoKudfW0gLosdQzMga20OZjFViZVa7aHUvAFyExnMQA6WP7oqkQ0nh40LbhwTIxFZwXIvHxlj+1w2F+PuKK60DRsNcNoffDBDUTf1Zr2tPqybmaCqhTUyIcRjoE4GNIhJjdNwpSl6qMfK/9miIVnBmVFr5kfrl92/xv0917typ46Za4zRCQuciM2gB+AJW+Tb8Z64E5vdrexJdwxtr+KlsTU/8o/iW9db17vmLV8aq3uy0/fh4YmVkJjJulHfqF+oj7BFwKZ5IkgNcobVMO/2Q0CjFR/4r5r/VP2q2oZKscjTQ4IzbBiHLGlyt+lOC8KnuKoNy3fpI3xLb+B9DK6Zhu0ss3r6f3ThdhgmGaQPTOlSTSlYzjRP+mbH1o/Tjh1qqDoSbd6DyUBmI9rUN+ubdbNSScLFAZgwpVkcQggtSoqWwXbFoq16SHu+w9r1nZSpohb2h+sd99/7/Xeub+1Vtdp2Na4Qe7LAURi0ELzgm7acn+K7tICkWvoFB+2AICNRC34L7/J3Xb/q9dbro7EUG56HUI1W4TWjGZXYC/XR/q76Lu0i5JthhkG+WsKVp+tj29Vk+Ur/qN5Vv1LbkC2EV8vvbIPT0a2v3RnAJLch2p5HytIqLOEjfQPf4ucheCXZ5xwOyYcOwOB25FbBmqwLOZjOBPOw/Nk5YD7SNvTzwW6P8jQFKXIDN/RmvZkfZBEmTDBEF6bqJXaT6u4Da82d6EMYh0jbQpomTx7s7PScZy7bn7i/6/5V97f2Rz3BQkzrRxZeCOPoQrPgRWtj6VVY0KbSpNOgA+2qjpKncktLvOWfDp41liLhRauDO+cRPba2r1K5K1flnVp5JJCTts3PzDoPg7fUFk0p7DyNsbo1QYeV1Q0rTncMBrVN3de2vC3J1NHCLq3CDX3Et/huciKs1Z7NVbAnoeumrqDMFZ7D9uQe7A63x+sMUlvYnps8lGOeCUbr8Vzo/1lt1WIuZDU14Xi3YkiX+FJvjsewGYBOqNNNGo5/sT05n8SUnQctbe/IXbmUN/atWqiCEK+nq51TAXSU3NhsmwvgX774h6QXaOb4ERY/YuZ6XodJyIdD8oNgni5E0paWZDj5n0H+mHM9fZyaD31UJAXy2PVK5KmwGqtfDbwU0S41nbp14a5KqtIGdrWbW5+c1nE18mEb/Zbwdgm733qbnciMTeqUDluc3pre36lsEfWI9xHHu/fhKQIJdqnr8PAeL+qu/HY7+d/X8jP8jy/1YzylU5bsHtP7ePH88MT1UKrjH82wEbzDdXCyriebtyiL7FRV7l11OK7RcTjA6ipJx8aNmu9z3Yw2vUM7Pp2+jEzaojvDGc8hE0V3szA2ZbexeQAGszLPqD2VF7QNDqYZLdpQqTueDHEIoyzYSlKYEUfCgFKlVUrbapd6gjetqnTMT8yFEIYwoIkaSs+09qPOJS7xFjZNQNeHp9EEh8qk5jh3bzuVl1yY8ugoP0IROWLLBbStDyZOf8guMfiRbVuzAOqHiyyo0A2usq0nqikJXMLdcFWAgZhSwoA6T3b8P9LRzIsqKwxoUJfljDvm7ZmG01RBFYUCBjkeqbEpTO4qx3J+O7bb7YhUCAMcam/prlJBJabZsjAb9AlUyTexfQ6fw8dwjzZS6fZ8RZZZDd+X7i/usjJhg0Wnl7UptTjKIzGEy/AiJxU4TTHIs7ha9p3IzTr1I4YsTlRFjrIhTmmqJmrmHlqGBd2tIia3cZsXzODDzNTHN4SSbfCWbuomdo9nwJ3DfXJMva5NYDBhdOGCXsSlM8FGHqCeIUabUeghh+gBE0QW9TjpqYBNVBAT/gWKYaiJ0xEH8b+WA22aCatyYTCHrzHucb0dt8Vm9RMxhWqdu542YqdkqMA9uAc5YKkoBHElruIlTTXT1pQi3ZcGdBWqYFt7EXeMGa/p43Kf3qYdTfUiy21N3F94ly33fQm7Owhy06abHIrWZIqjcBkmTwzcZ4zYjZqbgbMK+XQ/DHI4MM0u2cW6eoKzRu3INfbH8qP8pNZdWUx3+UAD7RynU0kfPUzwlr6hb+pGbSZMQZsxXvb6EpGDlU2RpSZ0sbUE8k5xFU0FJSY0nqYxUrF7R+7TrQJZSEm9aztsgi+GRCcvYLXAhED8r22QmvNjKZOc5b58voO/PD7cCwIUQlOlXIzDSylH1lOUHtyTtaGSA2PjRX4V/kq9hEsaDRxtuXxIryTSYigXMTxWXDuuijiu5s1eFZnBPf3zF79Nn6m92ntLQV4/x50VXGZsy30eaQRpjewIQqmysB7T7D6twlBBTvgX/Uvxgl6lofJKy9vut91v3e+178xKwsPi8ng5v4J0o1uTcIrawkf6hfon6q28UYRC9oXYuqCwvR1ygWVW3UV8UKMLkTiNwIAXeJi+B54heqW+2zyYWi0ZKTjgzjkxoHCMq0mLsO7MgvhfywU1F6ejImMytvFj/rbxzboJiEMUaGUDvI4aYgN34B1ck/fie/Pe2EUDhuGLXC066/OkyaJyF2eBVpa1l2oQw31HVEx48NzsYzWcbvfkm/Y98H5pVbvLaVAJwI71HwFvv5emZx1vEmPx0Y+1b09riZUUTMOhQX9xfDH+ofoL9pKiSXt76fPW+7u+v3XfSBksxtBfJjltWlyM0Z0vVaxuq/tW/a38pfyUUBjJXaPSvRRVozA2LNk+qkhjuZWBkAkDSgInXdLA0eWc8/0GeoP4qDbIwgAul01OZwxdhdlqfXTxP/6xTcdKqA7V2drta6GhWX3qQS9ks2urAFTEbXQyB74TOfHe/LWxBJDTsEnsLqAgyChZsYuiEphlVWWqJNKt3GlhFk6L02QaVrTluMmP4y3+SqwWLwKTJZafnORxduvGhLRDQq32LD+JRaKbs9THlCbgLN9xYX+h/kH9Jb2oQKEyc+117/db39/7fdeSMXJSGHD3OT2/pIuPkxYSWlWfqrfyV9V7C6WxDJuuuIqvwk7uDd/gom3D6hZFJ2PSRBOYupHjQe6PzKBBwQ/qG/VuiEC4SDqntQAME3g9BRMHiHjVYbG3fPYxgqtqD+LOJ5peahuW1ZomsenGbcNJBitsjbon3wK7FSs4iQ/4ArNUyaLaBS3UyGapVAJoZsZNf9AcVUkb2q1vNtzgh/wINyFhUyIcWMTk46fejlc/lkV1eho9bHw1qWAZbscOANCQT+yL+UK9VF/Sq+QlZa7cWnfen/q883PnAlmF4BQ3PcNrul6F6DOlhKrud1m6s+7mc5MDdidX2WrhaSE3uMEbPFJeEYCLgoyY0OaRQutkpt2xVM3zzBu1WmpEbmOhud80Oxa4wXY3iPHkV73V80jAKWfaY4nZpi0/r1bNGlANHp52LcV2gElI1qKTav2nQWmykza39WynT3j+o4sFpq4HN9+qFQ03dFvd5Gog9eTVyu5ozuc9n8z3md0nfTwJ/GFQl2OPbssBEDaxp3KqJmsWxul4ocyqnezx1F26s9cBtqEMWDhBb9uvCdZWZf3S0dU+AKmSKpSj8uAtxyA7u0Puc7EGjKcf0x8liAOM19M4vAWWnRVbG/SUN1mmWyuxHjktrE4Q8pOlmqXZttg+kvO4edjgY8BstO5SCbKjMoVpuGZgjn4aLWjDIsLdjelIHPJe8WmC2vHDD3pfP9/hUFNS8IberE+70BKblQSo+qGCPHRxx4/FaZuvkE8zTZwmVFA7zBTTkAOgcqq+UFdpClHy6uqyuIRMKMUt3xrr+ETPXCMRBW92OTdptNOpsn2oLmLjeCh4YFRFJzuF7kblBW6qDRl1+u6ieS3naX2qYw/NWnJs1wrswZXo6oSFuVnAUM5N3U4aEnTSCSLSmwDQHGNtohPVZXPZaxPVeBPD+OW4HMNlrqfGRaQzh9Xl+eXSUAige8TXnleNMSLKj0C3UYfjrnD80GhCCCFciUMtTnhTUt7QL9W76rZcylTJisIzcean0xO9rVRF797SenhBh5JB9OyCZahZmQBVoXqVXqmR6vgTCW3Vkrak9mrfsq2izOvaemVdWSO3V5iiz6/2JL7AL9gCDDYImdw1R15qQL+qCTYNJ6X5YWwfG9ImQ55Fy6dsxZYvOPbkeuFTvA17GQAPxuCkoYw3jYbd/WiPQ0CsSAE44H6Tl7yNvRa1zBNbgCFc19BrtDq72L2Jj5Xf5H7ZrtheegHy8JzX0KtwlY2SNX21qFLzJ35oWg1XMhJX4krMxNxqkiI/0Lv2m/vWndrCRmVPrdEc4fTACERXtNrUiW3ocxzIeYyp5yN4CSqpfCDYmboO/NWeGUC3EUsVSpFl7TRB+dZMvfZ+5Zrp3h0F0sBwxWBcin9USD5kX4BY9HTkwJq2zbaitoOgFEjXCvjAdiNIe0S8f0zt0dMYanAXPsLb+J7cqwUDNt1AWtI/1EZjBecFxIpD4elO9Ytc8A/nh7ppN0YiITh8aEgXMU3uOhQ5CWXYg58vu+lrRm0CPqwyxCl7KYbk5yhugkIdrVHrwwFYIRKjW/o0SXHBFnSrPsiP7oV31qdwg0m0r1YEpnMGLHD6v0e3uu8Uqwuy/cFxOn8poN9+2ZCVIhELo1uoLNSGCltKqagKiQUvGCqqRmqmZmImYtO2w40cpjn4egmUm/gqWy29N5Hb7Hb/WHy0Mx2AqQJ74p5c4p5sjgFP4ffsOy3oIyBnDizwNryHfS+uTRRHe/WoWyccQIcnAhCBuM8KPh2Lb3LB3og38YHfmJ3JhrzgVLjGkOtstwCUhj14w95ud8ZOI2oScnkg5o9wD384AmQT3U43SVCIRNwYHzRPwG+xDeC39o079U68gTexDXCGY7a1sCO83R1hztO1WY/GtbfyZx1DA6fH3eHrCpbihn/AD/SBNkpKIk0VUrQNRsmJyBqVkTmyYstT3EY34XKrQSO8Ncsp20SiZCrsg5jnt2KnS3LkxEoOWbDcvewYaxw04Wcd61nBwW28g+/J77S1ieRexKYvzo3aT1PKAkvWHtE7skO0trVdwOfcq30UH2pLBMbp2+hDXscEDyhJKsMOtHX3R9iyTpJJTON21oMxErCDxKS10vmBSAqW8I24GW/FB/YhFqobFCZW6dNVwMdwNgxtpjyCFTwImLf3SgNPPw5ZDeP5I5t90qbOPsA6JkR6JjiFdNVAEeXSlL6OeWhFptfy3JbJXUwinSyKwWGYIc1zQ2j62bJMro33ju/Ku0zKOLM7LD3cUhYt7nl2Zj2Oz71Lt+UZGvCwT/HexGbd5m+OjaZR4CMsnh41AoXYdnpUdPy9wC00jfsDfHBMVNQ+nMWSnabROtGeiAx2e9EPuR+eZ7TTAu0EgscGrR58uAxZ50lpzoFt+ELc9A/wkRa4pVSVtrSoD/WBFgHDMNAYhm0sO61YzGBsShRZwh8D9uOJgceIgEBbCRcyoY1c0I3a5/QoV0FqB75uqDkkV1rtQ8yoRNfpi0A4tx/DEgpWhiST+lgtX8Bd+KR/isIODNkw9ynbbeGJ3xk8/uX1g112zL7AW/bJ03h2bG5jCtrQB7jV8p4WyUadCX7CHjxuX8I+Fk4H+Hzo663o+83F7N06wU3b1DruaTwl0a6Y/eCTp62u8WR9P/mePZ2NbMPWoft2wleFoja1Xf3rXoZBpqERW/zemvFYo/98PQfa7x9+vgrorjBtzl1dnMBjd4RmUheiMIBR5ccyt5vaZh0lYnQGQtYTKR2AfhvKSlab7VW2SoKL7mW7LIs/WO//zRd/66E3sYVtAOAb2adlW4rvp3rwY4Sm3/3t8biYMR+adPPWn4tVPbr181A8s6xTEkEa+u8TVjr9mHTCm15O9XAWexfPx2qMeCa4hWX9eOVhp7dP+rHKAkDYYd4+6raGSflkRzy1k5+EA09wfSxoz5V315/WKebT8PlM2vEOatJpm3Id0+te4lnYPBTzJqKUdae/xwKL7ABJ1pUftk5ScOrRVz21HuFfn8rfK/gHw3/mmAUmiWLZqUe1lWQjTuxXgdVG/w934PxwDJ5+2hqfCX7+Er8ntsfoGMux+eTFfnrbyMAk+x00/GZM/Db5/ZOp0CyZArtvWz1K9kPyf1oAAuhiV28mz3X9Hpd/+flxePsdm7YZ3M8D0JyAnwJYv7/+JSPtHo2noCKdKuKpJeI8rsXT1xtty9kbRXVfgDYWwOluJ/45u6dOdT4Mz3D8eTM/gyyjTj2hPdpOOKEnFzpNwrvnqRDPW+h3ZUjPNB8/nucmcbqj4wHgfs/xz9lmKKrVz7KWnfIJ9e25YD0/188p80dsceqMfzxz+nm6/rDoF1Wgmrj0Z2xyuGr/fS38uSj4r//081E/H3g+8ecv7KfwIPFhx5xH9pnpx4bmnGn1+2DQ8X7tueRx8Hhu6F8X7v/dF3+KW2dQn0v4mQb8nLU9jv8f6NSNxVzowrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x104>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flipped = ImageOps.flip(loaded)\n",
    "flipped.resize((loaded.width*8, loaded.height*8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " difference.\n",
      "                                               \n",
      "I took the one less travelled by,\n",
      "And that has made all the ages and ages hence:\n",
      "Two roads diverged in a wood, and I---er come back.\n",
      "\n",
      "I shall be telling this with a sigh\n",
      "Somewhereet knowing how way leads on to way,\n",
      "I doubted if I should evp had trodden black.\n",
      "Oh, I kept the first for another day!\n",
      "Yhe same,\n",
      "\n",
      "And both that morning equally lay\n",
      "In leaves no steh as for that the passing there\n",
      "Had worn them really about te better claim,\n",
      "Because it was grassy and wanted wear;\n",
      "Thoug\n",
      "Then took the other, as just as fair,\n",
      "And having perhaps th one as far as I could\n",
      "To where it bent in the undergrowth;\n",
      "ravel both\n",
      "And be one traveler, long I stood\n",
      "And looked downTwo roads diverged in a yellow wood,\n",
      "And sorry I could not t\n"
     ]
    }
   ],
   "source": [
    "print(flipped.tobytes().decode('utf8', 'ignore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rotate 90 degrees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=104x160>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotated = loaded.rotate(90, expand=True)\n",
    "rotated.resize((rotated.width*8, rotated.height*8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t townh;\n",
      " thougt tste!\n",
      "Y evere---the    nod dowtaps\n",
      "Thbouno dayuldewhd Ill    uldokergrerhar;y aes er shoSom ane a    co londeg p wealleavoth I gh\n",
      "od,mad   y IAnde uvinted ren l an if si woas    orrod\n",
      " th hawanhemy\n",
      "Ifortedh an at h   d ssto inAndnd n t last oubwitd itha   \n",
      "An I entr,\n",
      "y aworllyfirI dis rgend    od,ongt bfaiassad quahe y,\n",
      " thive,\n",
      "A    wo, le ias  gre\n",
      "Hg et t waings d by   lowlerherst washerninkep toelloadled   yelaveo w juit g tmor I  one to rvel    a  trd\n",
      "T asse sinat Oh,adsl b\n",
      "Twtra    inoneouler,caupas thk.\n",
      " lehalce:ss    gedbe I coth\n",
      "Behe othlacwayI shen le   vernd as he im,t td bn bow .\n",
      "\n",
      "es one\n",
      "   dih\n",
      "Aar k tclatha\n",
      "Anddeg hack aghe ce.adsbots ftooer or e,\n",
      "trowine bandk tren roel e aen etts fsamad knocomes tooffeTworav on\n",
      "The bh ahe p het er  ag\n",
      "I  di\n"
     ]
    }
   ],
   "source": [
    "print(rotated.tobytes().decode('utf8', 'ignore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Posterize (i.e., reduce the number of bits per color component):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x104>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poster = ImageOps.posterize(loaded, 7)\n",
    "poster.resize((loaded.width*8, loaded.height*8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tvn rn`dr dhvdrfdd hn ` xdllnv vnnd,\n",
      "@nd rnrrx H bntld nnt tr`vdl bnth\n",
      "@nd bd nnd tr`vdldr, lnnf H rtnnd\n",
      "@nd lnnjdd dnvn nnd `r f`r `r H bntld\n",
      "Tn vhdrd ht bdnt hn thd tnddrfrnvth:\n",
      "\n",
      "Thdn tnnj thd nthdr, `r jtrt `r f`hr,\n",
      "@nd h`vhnf pdrh`pr thd bdttdr bl`hl,\n",
      "Bdb`trd ht v`r fr`rrx `nd v`ntdd vd`r:\n",
      "Thntfh `r fnr th`t thd p`rrhnf thdrd\n",
      "H`d vnrn thdl rd`llx `bntt thd r`ld,\n",
      "\n",
      "@nd bnth th`t lnrnhnf dpt`llx l`x\n",
      "Hn ld`vdr nn rtdp h`d trndddn bl`bj.\n",
      "Nh, H jdpt thd fhrrt fnr `nnthdr d`x \n",
      "Xdt jnnvhnf hnv v`x ld`dr nn tn v`x,\n",
      "H dntbtdd hf H rhntld dvdr bnld b`bj.\n",
      "\n",
      "H rh`ll bd tdllhnf thhr vhth ` rhfh\n",
      "Rnldvhdrd `fdr `nd `fdr hdnbd:\n",
      "Tvn rn`dr dhvdrfdd hn ` vnnd, `nd H,,,\n",
      "H tnnj thd nnd ldrr tr`vdlldd bx,\n",
      "@nd th`t h`r l`dd `ll thd dhffdrdnbd.\n",
      "                                               \n"
     ]
    }
   ],
   "source": [
    "print(poster.tobytes().decode('utf8', 'ignore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resize the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8r/3pg241bn1kvbv_hgjl31dyz00000gp/T/ipykernel_19716/121820089.py:1: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n",
      "  resized = loaded.resize((loaded.width*4, loaded.height*4), resample=Image.NEAREST)\n"
     ]
    }
   ],
   "source": [
    "resized = loaded.resize((loaded.width*4, loaded.height*4), resample=Image.NEAREST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=80x52>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TwoTwoTwoTwo ro ro ro roadsadsadsads di di di diververververgedgedgedged in in in in a  a  a  a yelyelyelyellowlowlowlow wo wo wo wood,od,od,od,\n",
      "An\n",
      "An\n",
      "An\n",
      "And sd sd sd sorrorrorrorry Iy Iy Iy I co co co coulduldulduld no no no not tt tt tt tTwoTwoTwoTwo ro ro ro roadsadsadsads di di di diververververgedgedgedged in in in in a  a  a  a yelyelyelyellowlowlowlow wo wo wo wood,od,od,od,\n",
      "An\n",
      "An\n",
      "An\n",
      "And sd sd sd sorrorrorrorry Iy Iy Iy I co co co coulduldulduld no no no not tt tt tt tTwoTwoTwoTwo ro ro ro roadsadsadsads di di di diververververgedgedgedged in in in in a  a  a  a yelyelyelyellowlowlowlow wo wo wo wood,od,od,od,\n",
      "An\n",
      "An\n",
      "An\n",
      "And sd sd sd sorrorrorrorry Iy Iy Iy I co co co coulduldulduld no no no not tt tt tt tTwoTwoTwoTwo ro ro ro roadsadsadsads di di di diververververgedgedgedged in in in in a  a  a  a yelyelyelyellowlowlowlow wo wo wo wood,od,od,od,\n",
      "An\n",
      "An\n",
      "An\n",
      "And sd sd sd sorrorrorrorry Iy Iy Iy I co co co coulduldulduld no no no not tt tt tt travravravravel el el el botbotbotboth\n",
      "Ah\n",
      "Ah\n",
      "Ah\n",
      "And nd nd nd be be be be oneoneoneone tr tr tr traveaveaveavelerlerlerler, l, l, l, longongongong I  I  I  I stostostostood\n",
      "od\n",
      "od\n",
      "od\n",
      "AndAndAndAnd lo lo lo lookeokeokeoked dd dd dd downownownownravravravravel el el el botbotbotboth\n",
      "Ah\n",
      "Ah\n",
      "Ah\n",
      "And nd nd nd be be be be oneoneoneone tr tr tr traveaveaveavelerlerlerler, l, l, l, longongongong I  I  I  I stostostostood\n",
      "od\n",
      "od\n",
      "od\n",
      "AndAndAndAnd lo lo lo lookeokeokeoked dd dd dd downownownownravravravravel el el el botbotbotboth\n",
      "Ah\n",
      "Ah\n",
      "Ah\n",
      "And nd nd nd be be be be oneoneoneone tr tr tr traveaveaveavelerlerlerler, l, l, l, longongongong I  I  I  I stostostostood\n",
      "od\n",
      "od\n",
      "od\n",
      "AndAndAndAnd lo lo lo lookeokeokeoked dd dd dd downownownownravravravravel el el el botbotbotboth\n",
      "Ah\n",
      "Ah\n",
      "Ah\n",
      "And nd nd nd be be be be oneoneoneone tr tr tr traveaveaveavelerlerlerler, l, l, l, longongongong I  I  I  I stostostostood\n",
      "od\n",
      "od\n",
      "od\n",
      "AndAndAndAnd lo lo lo lookeokeokeoked dd dd dd downownownown on on on one ae ae ae as fs fs fs far ar ar ar as as as as I cI cI cI coulouloulould\n",
      "Td\n",
      "Td\n",
      "Td\n",
      "To wo wo wo wherherherhere ie ie ie it bt bt bt bentententent in in in in th th th the ue ue ue undendendendergrrgrrgrrgrowtowtowtowth;\n",
      "h;\n",
      "h;\n",
      "h;\n",
      " on on on one ae ae ae as fs fs fs far ar ar ar as as as as I cI cI cI coulouloulould\n",
      "Td\n",
      "Td\n",
      "Td\n",
      "To wo wo wo wherherherhere ie ie ie it bt bt bt bentententent in in in in th th th the ue ue ue undendendendergrrgrrgrrgrowtowtowtowth;\n",
      "h;\n",
      "h;\n",
      "h;\n",
      " on on on one ae ae ae as fs fs fs far ar ar ar as as as as I cI cI cI coulouloulould\n",
      "Td\n",
      "Td\n",
      "Td\n",
      "To wo wo wo wherherherhere ie ie ie it bt bt bt bentententent in in in in th th th the ue ue ue undendendendergrrgrrgrrgrowtowtowtowth;\n",
      "h;\n",
      "h;\n",
      "h;\n",
      " on on on one ae ae ae as fs fs fs far ar ar ar as as as as I cI cI cI coulouloulould\n",
      "Td\n",
      "Td\n",
      "Td\n",
      "To wo wo wo wherherherhere ie ie ie it bt bt bt bentententent in in in in th th th the ue ue ue undendendendergrrgrrgrrgrowtowtowtowth;\n",
      "h;\n",
      "h;\n",
      "h;\n",
      "\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Then en en en tootootootook tk tk tk the he he he othothothother,er,er,er, as as as as ju ju ju just st st st as as as as faifaifaifair,\n",
      "r,\n",
      "r,\n",
      "r,\n",
      "AndAndAndAnd ha ha ha havinvinvinving pg pg pg perherherherhapsapsapsaps th th th th\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Then en en en tootootootook tk tk tk the he he he othothothother,er,er,er, as as as as ju ju ju just st st st as as as as faifaifaifair,\n",
      "r,\n",
      "r,\n",
      "r,\n",
      "AndAndAndAnd ha ha ha havinvinvinving pg pg pg perherherherhapsapsapsaps th th th th\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Then en en en tootootootook tk tk tk the he he he othothothother,er,er,er, as as as as ju ju ju just st st st as as as as faifaifaifair,\n",
      "r,\n",
      "r,\n",
      "r,\n",
      "AndAndAndAnd ha ha ha havinvinvinving pg pg pg perherherherhapsapsapsaps th th th th\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Then en en en tootootootook tk tk tk the he he he othothothother,er,er,er, as as as as ju ju ju just st st st as as as as faifaifaifair,\n",
      "r,\n",
      "r,\n",
      "r,\n",
      "AndAndAndAnd ha ha ha havinvinvinving pg pg pg perherherherhapsapsapsaps th th th the be be be bettettettetter er er er claclaclaclaim,im,im,im,\n",
      "Be\n",
      "Be\n",
      "Be\n",
      "Becaucaucaucause se se se it it it it waswaswaswas gr gr gr grassassassassy ay ay ay and nd nd nd wanwanwanwantedtedtedted we we we wear;ar;ar;ar;\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Thougougougouge be be be bettettettetter er er er claclaclaclaim,im,im,im,\n",
      "Be\n",
      "Be\n",
      "Be\n",
      "Becaucaucaucause se se se it it it it waswaswaswas gr gr gr grassassassassy ay ay ay and nd nd nd wanwanwanwantedtedtedted we we we wear;ar;ar;ar;\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Thougougougouge be be be bettettettetter er er er claclaclaclaim,im,im,im,\n",
      "Be\n",
      "Be\n",
      "Be\n",
      "Becaucaucaucause se se se it it it it waswaswaswas gr gr gr grassassassassy ay ay ay and nd nd nd wanwanwanwantedtedtedted we we we wear;ar;ar;ar;\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Thougougougouge be be be bettettettetter er er er claclaclaclaim,im,im,im,\n",
      "Be\n",
      "Be\n",
      "Be\n",
      "Becaucaucaucause se se se it it it it waswaswaswas gr gr gr grassassassassy ay ay ay and nd nd nd wanwanwanwantedtedtedted we we we wear;ar;ar;ar;\n",
      "Th\n",
      "Th\n",
      "Th\n",
      "Thougougougough ah ah ah as fs fs fs for or or or thathathathat tt tt tt the he he he paspaspaspassinsinsinsing tg tg tg therherherhere\n",
      "He\n",
      "He\n",
      "He\n",
      "Had ad ad ad worworworworn tn tn tn themhemhemhem re re re reallallallally ay ay ay aboubouboubout tt tt tt th ah ah ah as fs fs fs for or or or thathathathat tt tt tt the he he he paspaspaspassinsinsinsing tg tg tg therherherhere\n",
      "He\n",
      "He\n",
      "He\n",
      "Had ad ad ad worworworworn tn tn tn themhemhemhem re re re reallallallally ay ay ay aboubouboubout tt tt tt th ah ah ah as fs fs fs for or or or thathathathat tt tt tt the he he he paspaspaspassinsinsinsing tg tg tg therherherhere\n",
      "He\n",
      "He\n",
      "He\n",
      "Had ad ad ad worworworworn tn tn tn themhemhemhem re re re reallallallally ay ay ay aboubouboubout tt tt tt th ah ah ah as fs fs fs for or or or thathathathat tt tt tt the he he he paspaspaspassinsinsinsing tg tg tg therherherhere\n",
      "He\n",
      "He\n",
      "He\n",
      "Had ad ad ad worworworworn tn tn tn themhemhemhem re re re reallallallally ay ay ay aboubouboubout tt tt tt the he he he samsamsamsame,\n",
      "e,\n",
      "e,\n",
      "e,\n",
      "\n",
      "An\n",
      "An\n",
      "An\n",
      "And bd bd bd bothothothoth th th th that at at at mormormormorninninninning eg eg eg equaquaquaquallyllyllylly la la la lay\n",
      "Iy\n",
      "Iy\n",
      "Iy\n",
      "In ln ln ln leaveaveaveaves es es es no no no no stestestestehe he he he samsamsamsame,\n",
      "e,\n",
      "e,\n",
      "e,\n",
      "\n",
      "An\n",
      "An\n",
      "An\n",
      "And bd bd bd bothothothoth th th th that at at at mormormormorninninninning eg eg eg equaquaquaquallyllyllylly la la la lay\n",
      "Iy\n",
      "Iy\n",
      "Iy\n",
      "In ln ln ln leaveaveaveaves es es es no no no no stestestestehe he he he samsamsamsame,\n",
      "e,\n",
      "e,\n",
      "e,\n",
      "\n",
      "An\n",
      "An\n",
      "An\n",
      "And bd bd bd bothothothoth th th th that at at at mormormormorninninninning eg eg eg equaquaquaquallyllyllylly la la la lay\n",
      "Iy\n",
      "Iy\n",
      "Iy\n",
      "In ln ln ln leaveaveaveaves es es es no no no no stestestestehe he he he samsamsamsame,\n",
      "e,\n",
      "e,\n",
      "e,\n",
      "\n",
      "An\n",
      "An\n",
      "An\n",
      "And bd bd bd bothothothoth th th th that at at at mormormormorninninninning eg eg eg equaquaquaquallyllyllylly la la la lay\n",
      "Iy\n",
      "Iy\n",
      "Iy\n",
      "In ln ln ln leaveaveaveaves es es es no no no no stestestestep hp hp hp had ad ad ad trotrotrotroddeddeddedden bn bn bn blaclaclaclack.\n",
      "k.\n",
      "k.\n",
      "k.\n",
      "Oh,Oh,Oh,Oh, I  I  I  I kepkepkepkept tt tt tt the he he he firfirfirfirst st st st forforforfor an an an anothothothother er er er daydaydayday!\n",
      "Y!\n",
      "Y!\n",
      "Y!\n",
      "Yp hp hp hp had ad ad ad trotrotrotroddeddeddedden bn bn bn blaclaclaclack.\n",
      "k.\n",
      "k.\n",
      "k.\n",
      "Oh,Oh,Oh,Oh, I  I  I  I kepkepkepkept tt tt tt the he he he firfirfirfirst st st st forforforfor an an an anothothothother er er er daydaydayday!\n",
      "Y!\n",
      "Y!\n",
      "Y!\n",
      "Yp hp hp hp had ad ad ad trotrotrotroddeddeddedden bn bn bn blaclaclaclack.\n",
      "k.\n",
      "k.\n",
      "k.\n",
      "Oh,Oh,Oh,Oh, I  I  I  I kepkepkepkept tt tt tt the he he he firfirfirfirst st st st forforforfor an an an anothothothother er er er daydaydayday!\n",
      "Y!\n",
      "Y!\n",
      "Y!\n",
      "Yp hp hp hp had ad ad ad trotrotrotroddeddeddedden bn bn bn blaclaclaclack.\n",
      "k.\n",
      "k.\n",
      "k.\n",
      "Oh,Oh,Oh,Oh, I  I  I  I kepkepkepkept tt tt tt the he he he firfirfirfirst st st st forforforfor an an an anothothothother er er er daydaydayday!\n",
      "Y!\n",
      "Y!\n",
      "Y!\n",
      "Yet et et et knoknoknoknowinwinwinwing hg hg hg how ow ow ow waywaywayway le le le leadsadsadsads on on on on to to to to wa wa wa way,\n",
      "y,\n",
      "y,\n",
      "y,\n",
      "I dI dI dI douboubouboubtedtedtedted if if if if I  I  I  I shoshoshoshoulduldulduld ev ev ev evet et et et knoknoknoknowinwinwinwing hg hg hg how ow ow ow waywaywayway le le le leadsadsadsads on on on on to to to to wa wa wa way,\n",
      "y,\n",
      "y,\n",
      "y,\n",
      "I dI dI dI douboubouboubtedtedtedted if if if if I  I  I  I shoshoshoshoulduldulduld ev ev ev evet et et et knoknoknoknowinwinwinwing hg hg hg how ow ow ow waywaywayway le le le leadsadsadsads on on on on to to to to wa wa wa way,\n",
      "y,\n",
      "y,\n",
      "y,\n",
      "I dI dI dI douboubouboubtedtedtedted if if if if I  I  I  I shoshoshoshoulduldulduld ev ev ev evet et et et knoknoknoknowinwinwinwing hg hg hg how ow ow ow waywaywayway le le le leadsadsadsads on on on on to to to to wa wa wa way,\n",
      "y,\n",
      "y,\n",
      "y,\n",
      "I dI dI dI douboubouboubtedtedtedted if if if if I  I  I  I shoshoshoshoulduldulduld ev ev ev ever er er er comcomcomcome be be be backackackack.\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      "I sI sI sI shalhalhalhall bl bl bl be te te te tellellellellinginginging th th th this is is is witwitwitwith ah ah ah a si si si sigh\n",
      "gh\n",
      "gh\n",
      "gh\n",
      "SomSomSomSomewhewhewhewhereereereereer er er er comcomcomcome be be be backackackack.\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      "I sI sI sI shalhalhalhall bl bl bl be te te te tellellellellinginginging th th th this is is is witwitwitwith ah ah ah a si si si sigh\n",
      "gh\n",
      "gh\n",
      "gh\n",
      "SomSomSomSomewhewhewhewhereereereereer er er er comcomcomcome be be be backackackack.\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      "I sI sI sI shalhalhalhall bl bl bl be te te te tellellellellinginginging th th th this is is is witwitwitwith ah ah ah a si si si sigh\n",
      "gh\n",
      "gh\n",
      "gh\n",
      "SomSomSomSomewhewhewhewhereereereereer er er er comcomcomcome be be be backackackack.\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      ".\n",
      "\n",
      "I sI sI sI shalhalhalhall bl bl bl be te te te tellellellellinginginging th th th this is is is witwitwitwith ah ah ah a si si si sigh\n",
      "gh\n",
      "gh\n",
      "gh\n",
      "SomSomSomSomewhewhewhewhereereereere ag ag ag ages es es es andandandand ag ag ag ages es es es henhenhenhence:ce:ce:ce:\n",
      "Tw\n",
      "Tw\n",
      "Tw\n",
      "Two ro ro ro roadoadoadoads ds ds ds diveiveiveivergergergerged id id id in an an an a wo wo wo wood,od,od,od, an an an and Id Id Id I------------ ag ag ag ages es es es andandandand ag ag ag ages es es es henhenhenhence:ce:ce:ce:\n",
      "Tw\n",
      "Tw\n",
      "Tw\n",
      "Two ro ro ro roadoadoadoads ds ds ds diveiveiveivergergergerged id id id in an an an a wo wo wo wood,od,od,od, an an an and Id Id Id I------------ ag ag ag ages es es es andandandand ag ag ag ages es es es henhenhenhence:ce:ce:ce:\n",
      "Tw\n",
      "Tw\n",
      "Tw\n",
      "Two ro ro ro roadoadoadoads ds ds ds diveiveiveivergergergerged id id id in an an an a wo wo wo wood,od,od,od, an an an and Id Id Id I------------ ag ag ag ages es es es andandandand ag ag ag ages es es es henhenhenhence:ce:ce:ce:\n",
      "Tw\n",
      "Tw\n",
      "Tw\n",
      "Two ro ro ro roadoadoadoads ds ds ds diveiveiveivergergergerged id id id in an an an a wo wo wo wood,od,od,od, an an an and Id Id Id I------------\n",
      "I \n",
      "I \n",
      "I \n",
      "I tootootootook tk tk tk the he he he oneoneoneone le le le less ss ss ss tratratratravelvelvelvelledledledled by by by by,\n",
      "A,\n",
      "A,\n",
      "A,\n",
      "And nd nd nd thathathathat ht ht ht has as as as madmadmadmade ae ae ae all ll ll ll thethethethe\n",
      "I \n",
      "I \n",
      "I \n",
      "I tootootootook tk tk tk the he he he oneoneoneone le le le less ss ss ss tratratratravelvelvelvelledledledled by by by by,\n",
      "A,\n",
      "A,\n",
      "A,\n",
      "And nd nd nd thathathathat ht ht ht has as as as madmadmadmade ae ae ae all ll ll ll thethethethe\n",
      "I \n",
      "I \n",
      "I \n",
      "I tootootootook tk tk tk the he he he oneoneoneone le le le less ss ss ss tratratratravelvelvelvelledledledled by by by by,\n",
      "A,\n",
      "A,\n",
      "A,\n",
      "And nd nd nd thathathathat ht ht ht has as as as madmadmadmade ae ae ae all ll ll ll thethethethe\n",
      "I \n",
      "I \n",
      "I \n",
      "I tootootootook tk tk tk the he he he oneoneoneone le le le less ss ss ss tratratratravelvelvelvelledledledled by by by by,\n",
      "A,\n",
      "A,\n",
      "A,\n",
      "And nd nd nd thathathathat ht ht ht has as as as madmadmadmade ae ae ae all ll ll ll thethethethe di di di diffeffeffefferenrenrenrence.ce.ce.ce.\n",
      "  \n",
      "  \n",
      "  \n",
      "                                                                                                                                                                                       di di di diffeffeffefferenrenrenrence.ce.ce.ce.\n",
      "  \n",
      "  \n",
      "  \n",
      "                                                                                                                                                                                       di di di diffeffeffefferenrenrenrence.ce.ce.ce.\n",
      "  \n",
      "  \n",
      "  \n",
      "                                                                                                                                                                                       di di di diffeffeffefferenrenrenrence.ce.ce.ce.\n",
      "  \n",
      "  \n",
      "  \n",
      "                                                                                                                                                                                      \n"
     ]
    }
   ],
   "source": [
    "print(resized.tobytes().decode('utf8', 'ignore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text as audio\n",
    "\n",
    "Because text is just bytes, we can also convert it to be audible. The following cell uses Jupyter Notebook's built-in *Audio* widget to let you \"listen\" to the bytes. It does this by first converting the bytes to an array floating-point values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Audio([(val-127)/128 for val in text_bytes], rate=1000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}