LCMC_Compiled_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kyle Landin\n",
    "ktl14@pitt.edu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data and Analysis of the Lancaster Corpus of Mandarin Chinese\n",
    "This code is designed to make the LCMC easier to read by converting it from an XML format\n",
    "### Table of Contents\n",
    "- [News Reportage Data](#news-reportage-data)\n",
    "- [News Editorial Data](#news-editorial-data)\n",
    "- [News Review Data](#news-review-data)\n",
    "- [Religion Data](#religion-data)\n",
    "- [Skills Trades and Hobbies Data](#skills-trades-and-hobbies-data)\n",
    "- [Popular Lore Data](#popular-lore-data)\n",
    "- [Biography and Essay Data](#biography-and-essay-data)\n",
    "- [Miscellaneous Reports and Official Documents Data](#miscellaneous-reports-and-official-documents-data)\n",
    "- [Science Academic Prose Data](#science-academic-prose-data)\n",
    "- [General Fiction Data](#general-fiction-data)\n",
    "- [Mystery and Detective Fiction Data](#mystery-and-detective-fiction-data)\n",
    "- [Science Fiction Data](#science-fiction-data)\n",
    "- [Adventure and Martial Arts Fiction Data](#adventure-and-martial-arts-fiction-data)\n",
    "- [Romantic Fiction Data](#romantic-fiction-data)\n",
    "- [Humor Data](#humor-data)\n",
    "- [Compiled Data](#compiled-data)\n",
    "- [Data Collection](#data-collection)\n",
    "    - [Parts of Speech](#parts-of-speech)\n",
    "    - [Formal vs Informal](#formal-vs-informal)\n",
    "    - [Looking at Pronouns](#looking-at-pronouns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretty printing has been turned OFF\n"
     ]
    }
   ],
   "source": [
    "import bs4 as bs\n",
    "import pandas as pd\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "%pprint\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "lcmc_char = \"/Users/ktlan/Documents/Data_Science/project_KyleLandin/2474/Lcmc/data/character/\"\n",
    "lcmc_pinyin = \"/Users/ktlan/Documents/Data_Science/project_KyleLandin/2474/Lcmc/data/pinyin/\"\n",
    "#These map to the Character data folder and the Pinyin data folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## News Reportage Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newsrep_char = (lcmc_char + 'LCMC_A.xml')\n",
    "newsrep_infile = open(newsrep_char, 'r', encoding='utf8')\n",
    "newsrep_contents = newsrep_infile.read()\n",
    "newsrep_soup = bs.BeautifulSoup(newsrep_contents, 'xml')\n",
    "# Getting the characters and parts of speech\n",
    "# Only need the pos from one set of the data since it will be the same in the pinyin data that we will be merging with\n",
    "nrchar = [w_tag.text for w_tag in newsrep_soup.find_all('w')]\n",
    "nrpos = [w_tag.get('POS') for w_tag in newsrep_soup.find_all('w')]\n",
    "# Replacing the tags with their part of speech meanings\n",
    "tag_dict = {'a': 'adj', 'ad': 'adj as adv', 'ag': 'adj morpheme', 'an': 'adj with nominal func', 'b': 'non-predicate adj', 'bg': 'non-predicate adj morpheme', 'c': 'conjunction', 'cg': 'conjunction morpheme', 'd': 'adv', 'dg': 'adv morpheme', 'e': 'interjection', 'ew': 'sentential punctuation', 'f': 'directional locality', 'fg': 'locality morpheme', 'g': 'morpheme', 'h': 'prefix', 'i': 'idiom', 'j': 'abbreviation', 'k': 'suffix', 'l': 'fixed expressions', 'm': 'numeral', 'mg': 'numeric morpheme', 'n': 'common noun', 'ng': 'noun morpheme', 'nr': 'personal name', 'ns': 'place name', 'nt': 'organization name', 'nx': 'nominal character string', 'nz': 'other proper noun', 'o': 'onomatopoeia', 'p': 'preposition', 'pg': 'preposition morpheme', 'q': 'classifier', 'qg': 'classifier morpheme', 'r': 'pronoun', 'rg': 'pronoun morpheme', 's': 'space word', 't': 'time word', 'tg': 'time word morpheme', 'u': 'auxiliary', 'v': 'verb', 'vd': 'verb as adv', 'vg': 'verb morpheme', 'vn': 'verb with nominal function', 'w': 'symbol and non-sentential punctuation', 'x': 'unclassified items', 'y': 'modal particle', 'yg': 'modal particle morpheme', 'z': 'descriptive', 'zg': 'descriptive morpheme'}\n",
    "nrpos2 = [tag_dict[tag] for tag in nrpos]\n",
    "nrpos = nrpos2\n",
    "# Getting the pinyin from the pinyin xml file\n",
    "newsrep_pin = (lcmc_pinyin + 'LCMC_A.xml')\n",
    "newsrep_pin_infile = open(newsrep_pin, 'r', encoding='utf8')\n",
    "newsrep_pin_contents = newsrep_pin_infile.read()\n",
    "newsrep_pin_soup = bs.BeautifulSoup(newsrep_pin_contents, 'xml')\n",
    "# Pinyin into a list\n",
    "nrpinyin = [w_tag.text for w_tag in newsrep_pin_soup.find_all('w')]\n",
    "# Combining the character, pinyin, and pos lists into one\n",
    "nr_tokens = list(zip(nrchar, nrpinyin, nrpos))\n",
    "nr_unique = set(nr_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## News Editorial Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('缓解', 'huan3jie3', 'verb'), ('南北', 'nan2bei3', 'directional locality'), ('矛盾', 'mao2dun4', 'adj with nominal func'), ('的', 'de5', 'auxiliary'), ('出路', 'chu1lu4', 'common noun')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newsedit_char = (lcmc_char + 'LCMC_B.xml')\n",
    "newsedit_infile = open(newsedit_char, 'r', encoding='utf8')\n",
    "newsedit_contents = newsedit_infile.read()\n",
    "newsedit_soup = bs.BeautifulSoup(newsedit_contents, 'xml')\n",
    "\n",
    "nechar = [w_tag.text for w_tag in newsedit_soup.find_all('w')]\n",
    "nepos = [w_tag.get('POS') for w_tag in newsedit_soup.find_all('w')]\n",
    "\n",
    "nepos2 = [tag_dict[tag] for tag in nepos]\n",
    "nepos = nepos2\n",
    "\n",
    "newsedit_pin = (lcmc_pinyin + 'LCMC_B.xml')\n",
    "newsedit_pin_infile = open(newsedit_pin, 'r', encoding='utf8')\n",
    "newsedit_pin_contents = newsedit_pin_infile.read()\n",
    "newsedit_pin_soup = bs.BeautifulSoup(newsedit_pin_contents, 'xml')\n",
    "\n",
    "nepinyin = [w_tag.text for w_tag in newsedit_pin_soup.find_all('w')]\n",
    "\n",
    "ne_tokens = list(zip(nechar, nepinyin, nepos))\n",
    "ne_unique = set(ne_tokens)\n",
    "\n",
    "ne_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## News Review Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('德克勒克', 'de2ke4le4ke4', 'personal name'), ('讲话', 'jiang3hua4', 'common noun'), ('初', 'chu1', 'adv morpheme'), ('析', 'xi1', 'verb morpheme'), ('新华社', 'xin1hua2she4', 'organization name')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newsrev_char = (lcmc_char + 'LCMC_C.xml')\n",
    "newsrev_infile = open(newsrev_char, 'r', encoding='utf8')\n",
    "newsrev_contents = newsrev_infile.read()\n",
    "newsrev_soup = bs.BeautifulSoup(newsrev_contents, 'xml')\n",
    "\n",
    "nrvchar = [w_tag.text for w_tag in newsrev_soup.find_all('w')]\n",
    "nrvpos = [w_tag.get('POS') for w_tag in newsrev_soup.find_all('w')]\n",
    "\n",
    "nrvpos2 = [tag_dict[tag] for tag in nrvpos]\n",
    "nrvpos = nrvpos2\n",
    "\n",
    "newsrev_pin = (lcmc_pinyin + 'LCMC_C.xml')\n",
    "newsrev_pin_infile = open(newsrev_pin, 'r', encoding='utf8')\n",
    "newsrev_pin_contents = newsrev_pin_infile.read()\n",
    "newsrev_pin_soup = bs.BeautifulSoup(newsrev_pin_contents, 'xml')\n",
    "\n",
    "nrvpinyin = [w_tag.text for w_tag in newsrev_pin_soup.find_all('w')]\n",
    "\n",
    "nrv_tokens = list(zip(nrvchar, nrvpinyin, nrvpos))\n",
    "nrv_unique = set(nrv_tokens)\n",
    "\n",
    "nrv_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Religion Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('一', 'yi1', 'numeral'), ('如何', 'ru2he2', 'pronoun'), ('理解', 'li3jie3', 'verb'), ('佛教', 'fo2jiao4', 'other proper noun'), ('对于', 'dui4yu2', 'preposition')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_char = (lcmc_char + 'LCMC_D.xml')\n",
    "rel_infile = open(rel_char, 'r', encoding='utf8')\n",
    "rel_contents = rel_infile.read()\n",
    "rel_soup = bs.BeautifulSoup(rel_contents, 'xml')\n",
    "\n",
    "relchar = [w_tag.text for w_tag in rel_soup.find_all('w')]\n",
    "relpos = [w_tag.get('POS') for w_tag in rel_soup.find_all('w')]\n",
    "\n",
    "# New dictionary to account for a misspelling in the xml for a tag. This fixes the problem with 'n '.\n",
    "rel_dict = {'n ': 'common noun', 'a': 'adj', 'ad': 'adj as adv', 'ag': 'adj morpheme', 'an': 'adj with nominal func', 'b': 'non-predicate adj', 'bg': 'non-predicate adj morpheme', 'c': 'conjunction', 'cg': 'conjunction morpheme', 'd': 'adv', 'dg': 'adv morpheme', 'e': 'interjection', 'ew': 'sentential punctuation', 'f': 'directional locality', 'fg': 'locality morpheme', 'g': 'morpheme', 'h': 'prefix', 'i': 'idiom', 'j': 'abbreviation', 'k': 'suffix', 'l': 'fixed expressions', 'm': 'numeral', 'mg': 'numeric morpheme', 'n': 'common noun', 'ng': 'noun morpheme', 'nr': 'personal name', 'ns': 'place name', 'nt': 'organization name', 'nx': 'nominal character string', 'nz': 'other proper noun', 'o': 'onomatopoeia', 'p': 'preposition', 'pg': 'preposition morpheme', 'q': 'classifier', 'qg': 'classifier morpheme', 'r': 'pronoun', 'rg': 'pronoun morpheme', 's': 'space word', 't': 'time word', 'tg': 'time word morpheme', 'u': 'auxiliary', 'v': 'verb', 'vd': 'verb as adv', 'vg': 'verb morpheme', 'vn': 'verb with nominal function', 'w': 'symbol and non-sentential punctuation', 'x': 'unclassified items', 'y': 'modal particle', 'yg': 'modal particle morpheme', 'z': 'descriptive', 'zg': 'descriptive morpheme'}\n",
    "\n",
    "relpos2 = [rel_dict[tag] for tag in relpos]\n",
    "relpos = relpos2\n",
    "\n",
    "rel_pin = (lcmc_pinyin + 'LCMC_D.xml')\n",
    "rel_pin_infile = open(rel_pin, 'r', encoding='utf8')\n",
    "rel_pin_contents = rel_pin_infile.read()\n",
    "rel_pin_soup = bs.BeautifulSoup(rel_pin_contents, 'xml')\n",
    "\n",
    "relpinyin = [w_tag.text for w_tag in rel_pin_soup.find_all('w')]\n",
    "\n",
    "rel_tokens = list(zip(relchar, relpinyin, relpos))\n",
    "rel_unique = set(rel_tokens)\n",
    "\n",
    "rel_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Skills Trades and Hobbies Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('同事', 'tong2shi4', 'common noun'), ('相处', 'xiang1chu3', 'verb'), ('的', 'de5', 'auxiliary'), ('技巧', 'ji4qiao3', 'common noun'), ('同事', 'tong2shi4', 'common noun')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sth_char = (lcmc_char + 'LCMC_E.xml')\n",
    "sth_infile = open(sth_char, 'r', encoding='utf8')\n",
    "sth_contents = sth_infile.read()\n",
    "sth_soup = bs.BeautifulSoup(sth_contents, 'xml')\n",
    "\n",
    "sthchar = [w_tag.text for w_tag in sth_soup.find_all('w')]\n",
    "sthpos = [w_tag.get('POS') for w_tag in sth_soup.find_all('w')]\n",
    "\n",
    "sthpos2 = [tag_dict[tag] for tag in sthpos]\n",
    "sthpos = sthpos2\n",
    "\n",
    "sth_pin = (lcmc_pinyin + 'LCMC_E.xml')\n",
    "sth_pin_infile = open(sth_pin, 'r', encoding='utf8')\n",
    "sth_pin_contents = sth_pin_infile.read()\n",
    "sth_pin_soup = bs.BeautifulSoup(sth_pin_contents, 'xml')\n",
    "\n",
    "sthpinyin = [w_tag.text for w_tag in sth_pin_soup.find_all('w')]\n",
    "\n",
    "sth_tokens = list(zip(sthchar, sthpinyin, sthpos))\n",
    "sth_unique = set(sth_tokens)\n",
    "\n",
    "sth_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Popular Lore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('在', 'zai4', 'preposition'), ('高速公路', 'gao1su4gong1lu4', 'common noun'), ('干道', 'gan1dao4', 'common noun'), ('上', 'shang4', 'directional locality'), ('行驶', 'xing2shi3', 'verb')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poplore_char = (lcmc_char + 'LCMC_F.xml')\n",
    "poplore_infile = open(poplore_char, 'r', encoding='utf8')\n",
    "poplore_contents = poplore_infile.read()\n",
    "poplore_soup = bs.BeautifulSoup(poplore_contents, 'xml')\n",
    "\n",
    "poplorechar = [w_tag.text for w_tag in poplore_soup.find_all('w')]\n",
    "poplorepos = [w_tag.get('POS') for w_tag in poplore_soup.find_all('w')]\n",
    "\n",
    "poplorepos2 = [tag_dict[tag] for tag in poplorepos]\n",
    "poplorepos = poplorepos2\n",
    "\n",
    "poplore_pin = (lcmc_pinyin + 'LCMC_F.xml')\n",
    "poplore_pin_infile = open(poplore_pin, 'r', encoding='utf8')\n",
    "poplore_pin_contents = poplore_pin_infile.read()\n",
    "poplore_pin_soup = bs.BeautifulSoup(poplore_pin_contents, 'xml')\n",
    "\n",
    "poplorepinyin = [w_tag.text for w_tag in poplore_pin_soup.find_all('w')]\n",
    "\n",
    "poplore_tokens = list(zip(poplorechar, poplorepinyin, poplorepos))\n",
    "poplore_unique = set(poplore_tokens)\n",
    "\n",
    "poplore_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biography and Essay Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('第二', 'di4er4', 'numeral'), ('章', 'zhang1', 'classifier'), ('是', 'shi4', 'verb'), ('缪斯', 'miao4si1', 'personal name'), ('的', 'de5', 'auxiliary')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bio_char = (lcmc_char + 'LCMC_G.xml')\n",
    "bio_infile = open(bio_char, 'r', encoding='utf8')\n",
    "bio_contents = bio_infile.read()\n",
    "bio_soup = bs.BeautifulSoup(bio_contents, 'xml')\n",
    "\n",
    "biochar = [w_tag.text for w_tag in bio_soup.find_all('w')]\n",
    "biopos = [w_tag.get('POS') for w_tag in bio_soup.find_all('w')]\n",
    "\n",
    "# New dictionary to account for a misspelling of the 'nr ' tag.\n",
    "bio_dict = {'nr ': 'noun morpheme', 'a': 'adj', 'ad': 'adj as adv', 'ag': 'adj morpheme', 'an': 'adj with nominal func', 'b': 'non-predicate adj', 'bg': 'non-predicate adj morpheme', 'c': 'conjunction', 'cg': 'conjunction morpheme', 'd': 'adv', 'dg': 'adv morpheme', 'e': 'interjection', 'ew': 'sentential punctuation', 'f': 'directional locality', 'fg': 'locality morpheme', 'g': 'morpheme', 'h': 'prefix', 'i': 'idiom', 'j': 'abbreviation', 'k': 'suffix', 'l': 'fixed expressions', 'm': 'numeral', 'mg': 'numeric morpheme', 'n': 'common noun', 'ng': 'noun morpheme', 'nr': 'personal name', 'ns': 'place name', 'nt': 'organization name', 'nx': 'nominal character string', 'nz': 'other proper noun', 'o': 'onomatopoeia', 'p': 'preposition', 'pg': 'preposition morpheme', 'q': 'classifier', 'qg': 'classifier morpheme', 'r': 'pronoun', 'rg': 'pronoun morpheme', 's': 'space word', 't': 'time word', 'tg': 'time word morpheme', 'u': 'auxiliary', 'v': 'verb', 'vd': 'verb as adv', 'vg': 'verb morpheme', 'vn': 'verb with nominal function', 'w': 'symbol and non-sentential punctuation', 'x': 'unclassified items', 'y': 'modal particle', 'yg': 'modal particle morpheme', 'z': 'descriptive', 'zg': 'descriptive morpheme'}\n",
    "\n",
    "biopos2 = [bio_dict[tag] for tag in biopos]\n",
    "biopos = biopos2\n",
    "\n",
    "bio_pin = (lcmc_pinyin + 'LCMC_G.xml')\n",
    "bio_pin_infile = open(bio_pin, 'r', encoding='utf8')\n",
    "bio_pin_contents = bio_pin_infile.read()\n",
    "bio_pin_soup = bs.BeautifulSoup(bio_pin_contents, 'xml')\n",
    "\n",
    "biopinyin = [w_tag.text for w_tag in bio_pin_soup.find_all('w')]\n",
    "\n",
    "bio_tokens = list(zip(biochar, biopinyin, biopos))\n",
    "bio_unique = set(bio_tokens)\n",
    "\n",
    "bio_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Miscellaneous Reports and Official Documents Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('全国', 'quan2guo2', 'common noun'), ('人民', 'ren2min2', 'common noun'), ('代表大会', 'dai4biao3da4hui4', 'common noun'), ('常务', 'chang2wu4', 'non-predicate adj'), ('委员会', 'wei3yuan2hui4', 'common noun')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misc_char = (lcmc_char + 'LCMC_H.xml')\n",
    "misc_infile = open(misc_char, 'r', encoding='utf8')\n",
    "misc_contents = misc_infile.read()\n",
    "misc_soup = bs.BeautifulSoup(misc_contents, 'xml')\n",
    "\n",
    "miscchar = [w_tag.text for w_tag in misc_soup.find_all('w')]\n",
    "miscpos = [w_tag.get('POS') for w_tag in misc_soup.find_all('w')]\n",
    "\n",
    "miscpos2 = [tag_dict[tag] for tag in miscpos]\n",
    "miscpos = miscpos2\n",
    "\n",
    "misc_pin = (lcmc_pinyin + 'LCMC_H.xml')\n",
    "misc_pin_infile = open(misc_pin, 'r', encoding='utf8')\n",
    "misc_pin_contents = misc_pin_infile.read()\n",
    "misc_pin_soup = bs.BeautifulSoup(misc_pin_contents, 'xml')\n",
    "\n",
    "miscpinyin = [w_tag.text for w_tag in misc_pin_soup.find_all('w')]\n",
    "\n",
    "misc_tokens = list(zip(miscchar, miscpinyin, miscpos))\n",
    "misc_unique = set(misc_tokens)\n",
    "\n",
    "misc_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Science Academic Prose Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('第一', 'di4yi1', 'numeral'), ('节', 'jie2', 'classifier'), ('肥胖', 'fei2pang4', 'adj'), ('的', 'de5', 'auxiliary'), ('病因', 'bing4yin1', 'common noun')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_char = (lcmc_char + 'LCMC_J.xml')\n",
    "sci_infile = open(sci_char, 'r', encoding='utf8')\n",
    "sci_contents = sci_infile.read()\n",
    "sci_soup = bs.BeautifulSoup(sci_contents, 'xml')\n",
    "\n",
    "scichar = [w_tag.text for w_tag in sci_soup.find_all('w')]\n",
    "scipos = [w_tag.get('POS') for w_tag in sci_soup.find_all('w')]\n",
    "\n",
    "scipos2 = [tag_dict[tag] for tag in scipos]\n",
    "scipos = scipos2\n",
    "\n",
    "sci_pin = (lcmc_pinyin + 'LCMC_J.xml')\n",
    "sci_pin_infile = open(sci_pin, 'r', encoding='utf8')\n",
    "sci_pin_contents = sci_pin_infile.read()\n",
    "sci_pin_soup = bs.BeautifulSoup(sci_pin_contents, 'xml')\n",
    "\n",
    "scipinyin = [w_tag.text for w_tag in sci_pin_soup.find_all('w')]\n",
    "\n",
    "sci_tokens = list(zip(scichar, scipinyin, scipos))\n",
    "sci_unique = set(sci_tokens)\n",
    "\n",
    "sci_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Fiction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('什么', 'shen2me5', 'pronoun'), ('事情', 'shi4qing2', 'common noun'), ('都', 'dou1', 'adv'), ('是', 'shi4', 'verb'), ('说', 'shuo1', 'verb')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_fic_char = (lcmc_char + 'LCMC_K.xml')\n",
    "gen_fic_infile = open(gen_fic_char, 'r', encoding='utf8')\n",
    "gen_fic_contents = gen_fic_infile.read()\n",
    "gen_fic_soup = bs.BeautifulSoup(gen_fic_contents, 'xml')\n",
    "\n",
    "gen_ficchar = [w_tag.text for w_tag in gen_fic_soup.find_all('w')]\n",
    "gen_ficpos = [w_tag.get('POS') for w_tag in gen_fic_soup.find_all('w')]\n",
    "\n",
    "# Had an error due to a misspelled tag. This dictionary fixes the problem with ' ng ' and 'n '.\n",
    "gen_fic_dict = {'n ': 'common noun', ' ng ': 'noun morpheme', 'a': 'adj', 'ad': 'adj as adv', 'ag': 'adj morpheme', 'an': 'adj with nominal func', 'b': 'non-predicate adj', 'bg': 'non-predicate adj morpheme', 'c': 'conjunction', 'cg': 'conjunction morpheme', 'd': 'adv', 'dg': 'adv morpheme', 'e': 'interjection', 'ew': 'sentential punctuation', 'f': 'directional locality', 'fg': 'locality morpheme', 'g': 'morpheme', 'h': 'prefix', 'i': 'idiom', 'j': 'abbreviation', 'k': 'suffix', 'l': 'fixed expressions', 'm': 'numeral', 'mg': 'numeric morpheme', 'n': 'common noun', 'ng': 'noun morpheme', 'nr': 'personal name', 'ns': 'place name', 'nt': 'organization name', 'nx': 'nominal character string', 'nz': 'other proper noun', 'o': 'onomatopoeia', 'p': 'preposition', 'pg': 'preposition morpheme', 'q': 'classifier', 'qg': 'classifier morpheme', 'r': 'pronoun', 'rg': 'pronoun morpheme', 's': 'space word', 't': 'time word', 'tg': 'time word morpheme', 'u': 'auxiliary', 'v': 'verb', 'vd': 'verb as adv', 'vg': 'verb morpheme', 'vn': 'verb with nominal function', 'w': 'symbol and non-sentential punctuation', 'x': 'unclassified items', 'y': 'modal particle', 'yg': 'modal particle morpheme', 'z': 'descriptive', 'zg': 'descriptive morpheme'}\n",
    "\n",
    "gen_ficpos2 = [gen_fic_dict[tag] for tag in gen_ficpos]\n",
    "gen_ficpos = gen_ficpos2\n",
    "\n",
    "gen_fic_pin = (lcmc_pinyin + 'LCMC_K.xml')\n",
    "gen_fic_pin_infile = open(gen_fic_pin, 'r', encoding='utf8')\n",
    "gen_fic_pin_contents = gen_fic_pin_infile.read()\n",
    "gen_fic_pin_soup = bs.BeautifulSoup(gen_fic_pin_contents, 'xml')\n",
    "\n",
    "gen_ficpinyin = [w_tag.text for w_tag in gen_fic_pin_soup.find_all('w')]\n",
    "\n",
    "gen_fic_tokens = list(zip(gen_ficchar, gen_ficpinyin, gen_ficpos))\n",
    "gen_fic_unique = set(gen_fic_tokens)\n",
    "\n",
    "gen_fic_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mystery and Detective Fiction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('八', 'ba1', 'numeral'), ('致命', 'zhi4ming4', 'verb'), ('的', 'de5', 'auxiliary'), ('侥幸', 'jiao3xing4', 'adj'), ('心理', 'xin1li3', 'common noun')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myst_char = (lcmc_char + 'LCMC_L.xml')\n",
    "myst_infile = open(myst_char, 'r', encoding='utf8')\n",
    "myst_contents = myst_infile.read()\n",
    "myst_soup = bs.BeautifulSoup(myst_contents, 'xml')\n",
    "\n",
    "mystchar = [w_tag.text for w_tag in myst_soup.find_all('w')]\n",
    "mystpos = [w_tag.get('POS') for w_tag in myst_soup.find_all('w')]\n",
    "\n",
    "# Had an error due to one misspelled tag in the xml. This dictionary fixes this specific problem with 'nr '.\n",
    "mystpos_dict = {'nr ': 'personal name', 'a': 'adj', 'ad': 'adj as adv', 'ag': 'adj morpheme', 'an': 'adj with nominal func', 'b': 'non-predicate adj', 'bg': 'non-predicate adj morpheme', 'c': 'conjunction', 'cg': 'conjunction morpheme', 'd': 'adv', 'dg': 'adv morpheme', 'e': 'interjection', 'ew': 'sentential punctuation', 'f': 'directional locality', 'fg': 'locality morpheme', 'g': 'morpheme', 'h': 'prefix', 'i': 'idiom', 'j': 'abbreviation', 'k': 'suffix', 'l': 'fixed expressions', 'm': 'numeral', 'mg': 'numeric morpheme', 'n': 'common noun', 'ng': 'noun morpheme', 'nr': 'personal name', 'ns': 'place name', 'nt': 'organization name', 'nx': 'nominal character string', 'nz': 'other proper noun', 'o': 'onomatopoeia', 'p': 'preposition', 'pg': 'preposition morpheme', 'q': 'classifier', 'qg': 'classifier morpheme', 'r': 'pronoun', 'rg': 'pronoun morpheme', 's': 'space word', 't': 'time word', 'tg': 'time word morpheme', 'u': 'auxiliary', 'v': 'verb', 'vd': 'verb as adv', 'vg': 'verb morpheme', 'vn': 'verb with nominal function', 'w': 'symbol and non-sentential punctuation', 'x': 'unclassified items', 'y': 'modal particle', 'yg': 'modal particle morpheme', 'z': 'descriptive', 'zg': 'descriptive morpheme'}\n",
    "\n",
    "mystpos2 = [mystpos_dict[tag] for tag in mystpos]\n",
    "mystpos = mystpos2\n",
    "\n",
    "myst_pin = (lcmc_pinyin + 'LCMC_L.xml')\n",
    "myst_pin_infile = open(myst_pin, 'r', encoding='utf8')\n",
    "myst_pin_contents = myst_pin_infile.read()\n",
    "myst_pin_soup = bs.BeautifulSoup(myst_pin_contents, 'xml')\n",
    "\n",
    "mystpinyin = [w_tag.text for w_tag in myst_pin_soup.find_all('w')]\n",
    "\n",
    "myst_tokens = list(zip(mystchar, mystpinyin, mystpos))\n",
    "myst_unique = set(myst_tokens)\n",
    "\n",
    "myst_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Science Fiction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('第五', 'di4wu3', 'numeral'), ('部', 'bu4', 'classifier'), ('预', 'yu4', 'adv'), ('知', 'zhi1', 'verb'), ('能力', 'neng2li4', 'common noun')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_fic_char = (lcmc_char + 'LCMC_M.xml')\n",
    "sci_fic_infile = open(sci_fic_char, 'r', encoding='utf8')\n",
    "sci_fic_contents = sci_fic_infile.read()\n",
    "sci_fic_soup = bs.BeautifulSoup(sci_fic_contents, 'xml')\n",
    "\n",
    "sci_ficchar = [w_tag.text for w_tag in sci_fic_soup.find_all('w')]\n",
    "sci_ficpos = [w_tag.get('POS') for w_tag in sci_fic_soup.find_all('w')]\n",
    "\n",
    "sci_ficpos2 = [tag_dict[tag] for tag in sci_ficpos]\n",
    "sci_ficpos = sci_ficpos2\n",
    "\n",
    "sci_fic_pin = (lcmc_pinyin + 'LCMC_M.xml')\n",
    "sci_fic_pin_infile = open(sci_fic_pin, 'r', encoding='utf8')\n",
    "sci_fic_pin_contents = sci_fic_pin_infile.read()\n",
    "sci_fic_pin_soup = bs.BeautifulSoup(sci_fic_pin_contents, 'xml')\n",
    "\n",
    "sci_ficpinyin = [w_tag.text for w_tag in sci_fic_pin_soup.find_all('w')]\n",
    "\n",
    "sci_fic_tokens = list(zip(sci_ficchar, sci_ficpinyin, sci_ficpos))\n",
    "sci_fic_unique = set(sci_fic_tokens)\n",
    "\n",
    "sci_fic_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adventure and Martial Arts Fiction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('云', 'yun2', 'common noun'), ('在', 'zai4', 'preposition'), ('天空', 'tian1kong1', 'common noun'), ('游荡', 'you2dang4', 'verb with nominal function'), ('它', 'ta1', 'pronoun')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ama_fic_char = (lcmc_char + 'LCMC_N.xml')\n",
    "ama_fic_infile = open(ama_fic_char, 'r', encoding='utf8')\n",
    "ama_fic_contents = ama_fic_infile.read()\n",
    "ama_fic_soup = bs.BeautifulSoup(ama_fic_contents, 'xml')\n",
    "\n",
    "ama_ficchar = [w_tag.text for w_tag in ama_fic_soup.find_all('w')]\n",
    "ama_ficpos = [w_tag.get('POS') for w_tag in ama_fic_soup.find_all('w')]\n",
    "\n",
    "ama_ficpos2 = [tag_dict[tag] for tag in ama_ficpos]\n",
    "ama_ficpos = ama_ficpos2\n",
    "\n",
    "ama_fic_pin = (lcmc_pinyin + 'LCMC_N.xml')\n",
    "ama_fic_pin_infile = open(ama_fic_pin, 'r', encoding='utf8')\n",
    "ama_fic_pin_contents = ama_fic_pin_infile.read()\n",
    "ama_fic_pin_soup = bs.BeautifulSoup(ama_fic_pin_contents, 'xml')\n",
    "\n",
    "ama_ficpinyin = [w_tag.text for w_tag in ama_fic_pin_soup.find_all('w')]\n",
    "\n",
    "ama_fic_tokens = list(zip(ama_ficchar, ama_ficpinyin, ama_ficpos))\n",
    "ama_fic_unique = set(ama_fic_tokens)\n",
    "\n",
    "ama_fic_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Romantic Fiction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('萍', 'ping2', 'noun morpheme'), ('聚', 'ju4', 'verb'), ('特别', 'te4bie2', 'adv'), ('是', 'shi4', 'verb'), ('外面', 'wai4mian4', 'directional locality')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rom_fic_char = (lcmc_char + 'LCMC_P.xml')\n",
    "rom_fic_infile = open(rom_fic_char, 'r', encoding='utf8')\n",
    "rom_fic_contents = rom_fic_infile.read()\n",
    "rom_fic_soup = bs.BeautifulSoup(rom_fic_contents, 'xml')\n",
    "\n",
    "rom_ficchar = [w_tag.text for w_tag in rom_fic_soup.find_all('w')]\n",
    "rom_ficpos = [w_tag.get('POS') for w_tag in rom_fic_soup.find_all('w')]\n",
    "\n",
    "rom_ficpos2 = [tag_dict[tag] for tag in rom_ficpos]\n",
    "rom_ficpos = rom_ficpos2\n",
    "\n",
    "rom_fic_pin = (lcmc_pinyin + 'LCMC_P.xml')\n",
    "rom_fic_pin_infile = open(rom_fic_pin, 'r', encoding='utf8')\n",
    "rom_fic_pin_contents = rom_fic_pin_infile.read()\n",
    "rom_fic_pin_soup = bs.BeautifulSoup(rom_fic_pin_contents, 'xml')\n",
    "\n",
    "rom_ficpinyin = [w_tag.text for w_tag in rom_fic_pin_soup.find_all('w')]\n",
    "\n",
    "rom_fic_tokens = list(zip(rom_ficchar, rom_ficpinyin, rom_ficpos))\n",
    "rom_fic_unique = set(rom_fic_tokens)\n",
    "\n",
    "rom_fic_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Humor Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('排队', 'pai2dui4', 'verb'), ('打耳光', 'da3er3guang1', 'fixed expressions'), ('江湖', 'jiang1hu2', 'common noun'), ('医生', 'yi1sheng1', 'common noun'), ('卖', 'mai4', 'verb')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humor_char = (lcmc_char + 'LCMC_R.xml')\n",
    "humor_infile = open(humor_char, 'r', encoding='utf8')\n",
    "humor_contents = humor_infile.read()\n",
    "humor_soup = bs.BeautifulSoup(humor_contents, 'xml')\n",
    "\n",
    "humorchar = [w_tag.text for w_tag in humor_soup.find_all('w')]\n",
    "humorpos = [w_tag.get('POS') for w_tag in humor_soup.find_all('w')]\n",
    "\n",
    "humorpos2 = [tag_dict[tag] for tag in humorpos]\n",
    "humorpos = humorpos2\n",
    "\n",
    "humor_pin = (lcmc_pinyin + 'LCMC_R.xml')\n",
    "humor_pin_infile = open(humor_pin, 'r', encoding='utf8')\n",
    "humor_pin_contents = humor_pin_infile.read()\n",
    "humor_pin_soup = bs.BeautifulSoup(humor_pin_contents, 'xml')\n",
    "\n",
    "humorpinyin = [w_tag.text for w_tag in humor_pin_soup.find_all('w')]\n",
    "\n",
    "humor_tokens = list(zip(humorchar, humorpinyin, humorpos))\n",
    "humor_unique = set(humor_tokens)\n",
    "\n",
    "humor_tokens[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compiled Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "839006"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "53376"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine all lists of data to allow for analysis of entire corpus\n",
    "Compiled_Data = nr_tokens + ne_tokens + nrv_tokens + rel_tokens + sth_tokens + poplore_tokens + bio_tokens + misc_tokens + sci_tokens + gen_fic_tokens + myst_tokens + sci_fic_tokens + ama_fic_tokens + rom_fic_tokens + humor_tokens\n",
    "len(Compiled_Data)\n",
    "Compiled_Unique = set(Compiled_Data)\n",
    "Compiled_Unique = list(Compiled_Unique)\n",
    "len(Compiled_Unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('的', 'de5', 'auxiliary'), 50832), (('是', 'shi4', 'verb'), 11427), (('了', 'le5', 'auxiliary'), 10379), (('在', 'zai4', 'preposition'), 9899), (('一', 'yi1', 'numeral'), 8365), (('和', 'he2', 'conjunction'), 7200), (('他', 'ta1', 'pronoun'), 5847), (('不', 'bu4', 'adv'), 5594), (('我', 'wo3', 'pronoun'), 5575), (('有', 'you3', 'verb'), 4957), (('这', 'zhe4', 'pronoun'), 4142), (('人', 'ren2', 'common noun'), 4025), (('也', 'ye3', 'adv'), 3895), (('说', 'shuo1', 'verb'), 3668), (('上', 'shang4', 'directional locality'), 3558), (('着', 'zhao2', 'auxiliary'), 3364), (('就', 'jiu4', 'adv'), 3236), (('地', 'di4', 'auxiliary'), 3209), (('中', 'zhong1', 'directional locality'), 3175), (('对', 'dui4', 'preposition'), 3155)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "freq = Counter(Compiled_Data)\n",
    "freq.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Compiled_df = pd.DataFrame(Compiled_Unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Characters</th>\n",
       "      <th>Pinyin</th>\n",
       "      <th>POS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>掌</td>\n",
       "      <td>zhang3</td>\n",
       "      <td>noun morpheme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>旅游鞋</td>\n",
       "      <td>luu3you2xie2</td>\n",
       "      <td>common noun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>滴</td>\n",
       "      <td>di1</td>\n",
       "      <td>verb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>曾祖父</td>\n",
       "      <td>zeng1zu3fu4</td>\n",
       "      <td>common noun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>筹</td>\n",
       "      <td>chou2</td>\n",
       "      <td>verb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Characters        Pinyin            POS\n",
       "0          掌        zhang3  noun morpheme\n",
       "1        旅游鞋  luu3you2xie2    common noun\n",
       "2          滴           di1           verb\n",
       "3        曾祖父   zeng1zu3fu4    common noun\n",
       "4          筹         chou2           verb"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Characters</th>\n",
       "      <th>Pinyin</th>\n",
       "      <th>POS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53371</th>\n",
       "      <td>二人</td>\n",
       "      <td>er4ren2</td>\n",
       "      <td>personal name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53372</th>\n",
       "      <td>耻</td>\n",
       "      <td>chi3</td>\n",
       "      <td>adj morpheme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53373</th>\n",
       "      <td>塑像</td>\n",
       "      <td>su4xiang4</td>\n",
       "      <td>common noun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53374</th>\n",
       "      <td>彪</td>\n",
       "      <td>biao1</td>\n",
       "      <td>classifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53375</th>\n",
       "      <td>花言巧语</td>\n",
       "      <td>hua1yan2qiao3yu3</td>\n",
       "      <td>idiom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Characters            Pinyin            POS\n",
       "53371         二人           er4ren2  personal name\n",
       "53372          耻              chi3   adj morpheme\n",
       "53373         塑像         su4xiang4    common noun\n",
       "53374          彪             biao1     classifier\n",
       "53375       花言巧语  hua1yan2qiao3yu3          idiom"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Compiled_df.columns = ['Characters','Pinyin','POS']\n",
    "Compiled_df.head()\n",
    "Compiled_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text object at 0x000001C4B6DA9CF8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text object at 0x000001C4B71003C8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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7Onbncepao+lt7aaZmfy1V/v76mujdW1YPSexF6cpIPpYeGD0j/XjqrtfZrrbeCX4ZV/c298utx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dhA3cZsBDwG4JSkUOwdfHNYF3gFuBXfBMtUon2CnACcDpko6LxOdmvMbdppJGNui7BXAvcKOk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\n+4wx3Oe0c7sGCgNRHkf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sN7MXcPnR6XLK1YKF4bMpwERJcxVraaIeZvZtGpcMm2Wz2Xsy\nZrYL8Cu8z1Hd6bRxRYeZ3YdXUy0u6b9h7AbcoX1KNnHGzPYFrsArHJYpwLY3iTxYHqoHL8QD05V4\nAjhModdUzrb0B24CtikNNfC13M/9EIA7Dl9TNHJs2notnfZXz8fMrsUDgRdKOiqMzRAIM7OV8Heh\nycDqikhquRV0I1j4Jt43Nu93kJYlYJi3dZos6eZm/c12JbZEmpDUdhGeEG/E88yO0i6I8/mYgoUJ\nzOwtXB5mR0l/a7U9iUQiToJ806QySafxeO+Lfip7oJjZGIB2dRp3U1azIs2s5A6LkXvwDONUTd4g\nQS52JLAUvt8eo3Kl6Aa4E/A1YC1JNWXFZsKeSXgPwjkanP8p0DdbZZGTXfMBL+MvK5/ivRLvp6OX\n28LAZsBBYc4YYMU8A6vBrijP+3BePYpLvm2TrXaJATObgL8vZKtzagULPwaGSupboJktJ5xf2wGf\n1ltLm9n2eMboX4twoAVJ7OuBFco/Cv8t7ynXtgGmmDHvL38LfozG48GaD6mf3Flo3+igWLMqnSuY\nni3aWZypvt9P0pVhbHPcgf0VcBkusbwS/jzqDZwt6cQi7YwdM1sSDxguAPTCz7nHJY0u0IazgWPx\n43Y1cB+Nnfu5VTOZ97l8GK+0jGZNUYmQKLU0jVUi5yJ125P2V6I6IYi1CLCYpHfC2DTgQ0kLlM3d\nGZeuP1fS8UXbGhNdCRaGgMYEXBZ7aE72tDwBI+yT9yUtlBm7H+8rPLxZ25mVieE4VsLMjgPOCr/e\nT+PP7Kva1K4on48pUycB3kdoCvD3VhuSSCSi5l38IZblHVxicDngldJgpny+8P5osRDxC+5ZuHxm\nqZq8oYVS3gTJsI8krdvg/IeBBQuUmfo57mz5GNhV0ogqdm0I3Awsg/fvPC4ne8YDc5vZipJerDUx\nZPgOxVUD8uZY3CH1PLCFpPJtjgbuC6oGd+NZkMfgffDyJMrzXtLnZrYJHlQdZWY3431O36/zvauL\nsA8PJDX0UhkckrMDn+dqUZzsDZwPnIbLctViQ+BI4HDgkjyNMrMF8HN9HjyIf0/Y7gS8Qm0+YFM8\nCeITvHdadL0zEwCciDsRbgX2lDSpxfZ0Ishz/wI/n2aoujeze4FTC6ygvgEP4K8BXAkg6V4zuwQ4\nBPhxZq7hPaYLkemOmZDAAl4h/rWkN4A3WmkTXskk4CBJf2qxLSXOBRbFFRKOJawpJDUtUXBmMbOl\ngDOA7fHEznqI/PyD0e+vmAjJDhMkXdDg/MOAYcpfdno+YEopUBj4GhhQYe5fcT/AjriCRtsQ7qPD\nyoZnC0GBasEAC9/ZC29P8lx+FvILYFu6mIDRZIQnoGTZGL9HJBrjF7T+OFZif/z4niTprHqTCyRW\nu6J8PqbKwgRm9jKeHZRrtcGsSsi4rNW7KqoqgUSiu5jZHcAWwLKSXg9jf8SdpBdLOiIz93jgTOBl\nSSu2wt5EZczsA/xetYOkaJJEYu/nEIKZi9FAFb6ZbQfchst+5hLMDHJqu+LVjt9Vlb4zocrjbtxh\neqOkPfKwJ7O9V/BA6VqSnq4zdw3gSeBVSd/I2a4oz3sAM1sPD9ysRYPSdAWe98/jFWnLBKdx1cpC\nM/sucAfwqKRvF2FfLJjZg8C3gOXrVdyEXhkvAiMkbZqzXefjgck78XN/ahXJsAPxwOU9wLblSgE5\n2mfATri08yLAAGV6g5jZIPzeJYVeye1KqCbvh197Y1ptTxYz+ynu7CjJOn2NV5aDJ4+U7hXTgGMk\nXVi4kRmCZNdwvNL9M/y8v1LS1FbaFQPh/jANWEJBvrXVhHPfgCGSokhmCOopw4BvS6rYb7yVmNkK\nwEO4jYYnhn9C/QqKXKRuY99fsRHrO1FQjzBl2o+Y2Sf4fX5IeRKLeQuT3u3mZzTvY31ydoiuSU8b\nsL+kPzbVsNIfN3sbWChsoyUJGEHxZhB+3nwRxrp03rc7MRzHSpjZZHzdN7ukKa22p0TEdkX5fEzB\nwgRmdhJwKi5/dWer7ekphMqpi4AfQUXNY4V/lwNHSppcsG270HhvmkKbtiZ6JmZ2NO4MOlTSb8LY\nWrgUFrgzsiTpVJJDOF7SeUXbmqhOkJMVvjiPZhHQjRfjd4D5JNWUVGoWYYE5rZEX3uAAnwD0klQp\n27YZ9iyLJ6UMwKU8L8UdQ++FKQviFUwH4EGyScBqkl7Lw56MXZOALyWVZ9RWm/8Z0EfSwJztivW8\nXxfPIOwfhl6jsb7Rm+RsGgBmdhZeHfsbSYeGsRmChSGo8zgeWDxO0i+LsC8Wwv1ojkYdYuE6+UTS\nojnb9RLwDWBNSc+EsYr3WjM7BjgbOFxSrhWPYXvL4LKay9OxRlXZeTUbXo28BLCRpEfytitWwnXX\nT9KcrbYlSyZJAOABvIr7kZIjxsz64YH0E/CqQwFbSrqnBeZGh5kNBramctLp7ZImFGzP58BXMZ1n\nZjYaWEDS7HUnF0TYT8RkUxYz+zt+Xo3G14GPtnLtE/v+io2Ig4XP4ooggzP3+EeBdfHExXszc5fC\n17SfN/pOMKtgZr+gc7BQ1JcXFK7M8RJwuYJ8dh7EkIBhHX2FbwSuxd+bRwBj8SSyrsgx5iKfHDsx\nHMdKhPvRHLFd9xHbFeXzMQULE5hZH9xRtRQurda2L+KNErTE78T7LhkuzziCzj2ZNsYzPQTci78Y\n537BmdmmeG+aeeicxZR94GbHlPfCMjFrYGYL4dUvL0s6JTN+NO5g7EXnxfAtwPdbXULfKszsCEm/\n6sL8eYDLJO2Yo1mY2Yu443VwZEGTrvRzmB3PkB4nad7cjWN69t5QNdg/Iiz8xuUZDMhIns5D9YxV\nw4NP3y/iZcrMxgF9gUH1zi/r6MvRcHBxJuyK9by/G9gc74G5eyyVHCXMbG7c2TgMDwJciFfFzStp\nNjMbAmyJy28uh6+HviFpYotMbglmNgWXDJu77mSmZ+IPltS/7uSZs2sCfj1O7ytsZl/j96a5yuYO\nwavBnpa0ds52zYEnFy2CSxb/BZciHlKhYvUY4BzgIklH5mlXzJjZLbic4IKaUd65ZZjLi24KXCtp\nrzpzrwb2BO6T9J0i7IuVkFR0Ap6MMbjKtAn4ffecAqt9n8Pv5YNjcT6a2Rm4jOG3VJyMbU3MbCQd\nQZMo9lOWkIg1GFhJ0ssR2BP1/oqNbgQLxwNIGpKzXVfh9/DpFTBm9nO88GAksL2kD8La8Sa8l/s9\nkrbM067Yia1iLoYEDDPbAridzj7KrlZggvsy27K9WgzHsRJmdjHwE2AV1WmVUiQR2xXl8zEFCxOY\na6L3BQ7Ge808DrxA/X45eWuiR4uZ7Qdchkt6HI5n/6hsjuGZfBfh+/eAvKQEMttcGngGryS8F/gH\n7tj7DDgK15nfHNgEd7Sfiju3cm3ampj1CbJq3yMj6dTuWePhxeA+YB9J79aZuy1+T5lB2i8Hu47D\nJWJbWk1uZisDq2aGrsTPncNrfQ0PWuyMVyrcKWmbGvObhpldDuxLA84qc1nJR4ErJB2Qs13DgEPx\n628FOuTevsazU2/Gq8LG5WlHxp4ncDnN4ZJuqTP3e8G+JyWtk7NdUZz35QQZntlxybe3W21PJczs\nW3gfvtlxebqSmsJnYaz0+1g8s/ypFpnaMkLV1zzAnJJq9mw0s6F4NfAYSfPlbNfnwBeS5smMfQYM\nBPpL+rps/li8Ijrv4P3pwM+Au4DtJH1VqWI1zF0OGEUB94mYMZdtfgS4RtKBrbanREgQGQwsJOnD\nOnPnw6vfx8eW2V00Gad7SSLyKTonna6BV5wLD8TuXZBdJ+LJH7tK+ksR26xHSBB7FF/fbKMIWnyY\nSzf/HviBpBtabU85YW1hsVxnse+v2Gg0uBTWE/sCFwDPSVotZ7uG45Vg05N3QvLPKHwN9DVeGT0f\nnkT8NbCpkoz4lXiS1hH15hZBLAkYZvZt/B12BVwpZ3H8nHmnxtdmQDnJJ8dOLMexHDObH28z8haw\nlaTxLTYJiNquKJ+PKViYKC1Gykvja50YbV+NZh29aQ6SdHmduQcAfwAelrRRznb9AQ9QTs/urbTY\nDJk8fwFeBTZQ0AlPJBLNI1R0DADGAQdL+nOFOQPxas398Hvr85JWLZ/XZLv64D3slsWrzR7Nc3s1\n7JiZfg4GfAlsXlQ1vHlT+qfwypstqzmrzGxxvPJ8GC7916UXnpm0sQ9Qkg4bqxb0XzLvXfVLPHC0\nW1aSqGze9sCf8P10tHLuYRXLeV+OeZ+CXpLmaLUttTCzRfFg6y54AlSWr4D/xWWn3yrathgIkm9b\nAYcpyHTXmHsI8Gs8qea7Ods1ClgS7wU4LYw9h2ewTpcmDeND8fvbFOUvC/wCLj86veqlWrAwfDYF\nmFheDdlumNkOeGLNSFzR4cV6AboCbPoMl61s6NiEe95sRQUxzOybeDLNinhfrVrS5VIBrRnMbGf8\nPUz4cTynPMkgBMiOxysPAXaR9H8F2NYXeBhYGn9W3pf3NhshVCr9HtgWTzJ6kfqJzVfnbNP1eNuF\nn0i6Ls9tdRUzexhYB1fEKKwVSi1i3l+tZibfiUrkLgEf7g/fwZPNH8yML4+v6dfKTH8HXxPdmqdN\nia4TYwIGxFeBGTuxHkcA81YD1+Brid/R2DO7CAWkWO2K7vmYgoWJUqZLl08ESfs235qeQci8HojL\nJdV0xoZF1XjcyZFr/wczew13Cn1T0qthrFpvmh/h/RR/LumMPO1KJNqRUOl7LbA2fo/9M74A+Cx8\nvi6+WFkyfH4+fj1+WYBt/fCAzk9w+cNGFkpNrSY3s72BfTJDG+EBwFqZcdPo6OdwjaTRzbQpY9uG\nVT5aBt9vfXF5nRG47CJ4j8CNgF3x/4+jgX8XscCMifDMewKvGhXu1H4A30/9gUXx/bQC7gx5Bliv\nXc77Cjbdi++PuepVpMWAeU/kNYAF8KzxD4GRKrivVmyY2a7ADXhv0J2qVdaHZK1b8ESSvSVdm7Nd\ntwLbAStLeimMXYJfAzdI+kFm7q+BQ3AZ0jVztmsC/h46KDNWK1j4Me70Lg9UtxXh/noqHkBq5N0t\nd3muUE2+Bl5VWzNLOzi2xuD3jPXytCts7wLgMKjYW74ShSTDmtmduMP9JEln1Zn7M+B04C5JWxVg\n28n4/elgXKnmeXxd9jE1eukW8KxcD0+uW4sG/RYFHcvTgBPxwMjL1F5TSNJ+edsU7CpVgB2mAnrQ\nNkqs+6vVhGDhKZmhRnrclXgP9+mcqhY7d0NiZUllaFSr7UlUJ9IEjBQs7CIxHsdg19J4C4GdiGS9\nCvHaBfE9H1OwMJHoBmY2GZjcaPAvBBcHSBqQs12T8Ot6QGbsK7yx9Jxlc/vjTvdRklbJ065Eol0x\n78l2Uvg3G/7wPwivTD4W6A28iTuNC5NoCfKPF+IvdBBBNXlMLwiZivuqU2p8nv2syAXmfHj/r4Gt\nDlCGF5drgFLVVPm+KjlA7gT2kvRJQXbFeN5/F+/ZcYakk+vNT8SLmd2D97Kehp/bdwAladnFgK2B\nLfAg6whJmxZg00+AS4ATJJ0TxpbDAwC98ZfR5/GqqxXD1w6UdEXOdo0HepetV6vJkPYGJuLSlQ31\nhJwVMZecvgtYk8adyEjqlZtRTE/8+RNwlqQT68w9A+/Tt08BTseDgYvDry8At+FJK1NqfU8FtGYo\nBb+BYZIm1Zk7CFeoGKeMnHCOtkWnOBSS6+7DE44AXsN7MVcNXuJGbZKXTcGuI3HJ1kH15gYKCUaX\nMLOzgZKNF9Y71wqwJ+r91UpCZX+p2tqAN/AAfa3+wdNwH89nOZuXaBIxVbrHmoCR6BoxHkcz+wbw\nEDAXTJda/4T6z+xc5WRjtSvYFt3zsS0bgSYSTeA9YHEzW1rSv2tNNLNl8cVfEWXhX+ByYFnGA0PN\nrG+2ckPSFDObCLSlxneiNmbWrP6abZMVWgm57NtpZnY7HjxZDu8lCr5IuRLP+i2sKsfMtsKzjXvh\nCQNP0IDTpQD2BaKQSsId/D0imypUNJ2IV+qB29078/kwPNPQ8D6Cn+ZtUwj+bWXe624XYHW8lwm4\n8+Np4C8qSEIW4j3vJd0VZCkvMLMFgbPrrSvyIkiNNgVF2n8xZ74HXI8HBbfGZUmzlJzvdwB7FGTT\njfg6b2JpQNLoEOC5FL9vZO8dF+YdKAz8B1jBzJaU9EaduZvhDrVR+ZsVNafizqDxwHl4b/IY7mFX\nmdkqwPFmNhceNOwkRxzuLScAB+LnWO5Z7XhbBgEXK5I+URmG4MHvusEbSRPNe48Oyd8sAK4mvvXP\naXi142PA7pL+22J7MLM9cUUQgH8D9xPB9ZhF0vFBJvh04CQze5P6lQq5BCd6wv5qJSHgNz3oZ2YP\nAZ+U30sTPZfuVLrnaMu6+Bqi1Be3oQSMRFxEfBzPBOYGRuNrsUcjqTKO0q5Yn4+psjCR6AbW0Rvw\nAVwfumKWaqjeux2XGLtM0o9ztusFPBgxUNJXYWwksBrwbUmPZebOjwc9J0oq6gU00UOoklncHdom\nK7QeZnYCUJL8FS4TsYmksQXb8SiwHnArsGerM40T3cfMzsIrVA1PFumDr+3KK3NuAL6P9878feGG\nRkCs572ZlQIl8+LOUPBMx1p9yCRpqRxsadZLSWEVtTESAtN7AesC84XhD/EA9dWS7miVbVlCBfBW\ndEiG3asgYV/Ats/CpTR/I+nQMDZDZWGoqnocD2jm3o8pZszsLfxY7Sjpb622p4SZ3R9+XJ2OgNbb\ndJboXiz8/DkuPV2JpgYqgtpKP2AORSbxbGZvAwsBi0h6r87chfD9+Z6kRYqwLzbM7FNgdmCJWBJR\nzOxpYBVc/u2QGByOWczM8GqTg/EkqUbI7Z0t9v2VmJGUQNY8Yqt0N7O7gc1pYQKGdbT8mCRpZNlY\nl2i1ok6riOE4VsLMPsKr96a3QIiBiO2K8vmYgoWJTgSpn6WpXxbftjdlADNbEnf09wNeBy6go3dV\nqSfTJsDh+EvyFGClBrKnZ9auG/EqjrUlPRXGzgGOAe7GHQxTQs+Ta4DhwCOSuvVgTsy6hN4JTUHS\nqc36Wz0RM1sAryDcHA/qPIbLiPUBPgD2k3RngfZ8jksczCtpTFHbTTQX895nd+LO1wOB/8VlbivJ\n+G2FV7TeJmmnnO26BQ+GH624Gq1Hed6HxIyukotDr5u2VCRv6cNEzyYEKkfjyhtn4fLALxLuX2Y2\nBNgSryhaDl9ff0PSxCp/cpYnqIEADI7FkQBNvW809b4WpD5nU8794ruDmV0L7I5X/e9e63hm3u2u\nl/TDgkyMCjMbA/SSNEerbSkRrsd+uJRsdD17zewI3D8BXqXQUCVyjsGJqPdXYkZSAlnzMLNngZWI\npNI9hgSMTGL6aEnLl411hbY9v2I4jpUIFe2SNKzu5AKJ2K4on48pWJgAwMyWwitetsdP1Hq07U25\nhJltC9yAOx9r9a6aiL8I/r0Am/YB/gicrtD7KGSkjgp2jgVeBZbBsyoAdpZ0W962JRLtiJkNB34H\nzInfC34q6TIzWxm4Dq+WEPAH4ChJuctwhqyq3jE60BKNY2Z/BbYBfiLpD2GsWs+vIXjPo7ckLZmz\nXV8CUyU1qrlfCLGe92a2UXe+J+nBZttSDTPbCV9bvAv8EniQzlVDGwFH4VVPP5J0a1G2JXouQab4\nb7ijYxod0lyfhbHS72OB75aS4NoVM3sZWCzCe+vezfpbzQxUmNnf8YDzApI+btbfbQZmtiowEj+/\nH8ED5g+VKt6DnOsmePXt6vj1sZakZwu2c32qS4nfLOnxguy4F3/OzBVLlaiZvQf0j21NUSLcL5YD\nfi7pzAjsiXp/xUyo8FsfX28Noobqj6TTmrjdlEDWJGKrdI8hASPIIgt4XdLmZWNdQgX0lIuRGI5j\nJczsMWANYGg1Bb5WELFdUT4fU7AwgZmtgDf6HAZxNfqMnVBheCKwM96oPss44BbgzLwrCjP2DAb2\nxjXub8yMbwRci0velJgEnCDpYhKJRNMxs2vwvlQG/BP4oTK9yEKF71lAKcPwtTDnyZztug0PMkXn\nQOspBInpLajsQLu7iAWomX2I6+7PXqq2qRYsDJ99CvSTNDBnu94ONsWWtZfO+24Q+mE8iFcl7KRM\n7+OyeX1widfNgQ0l/bM4KxM9leAEPRMPSPQt+/grvGL6eKW+TZjZSXjfwm2KVCPoqZjZBrjqy29i\nqOQox8wOBH5Lh+NfeKC8Hx2S1IYHCn8i6dICbZsPuAr4TsaOLCUH0t3APpJqyWY3w57v4m09zigl\nw7YaM7sO2I3IKjpKhOBEb3w91nKnaOz7K0bM+1j/AZcLr9cWxCio9UdKIOs6sVW6x5iAkeg6sR5H\nM9sdT4o/TNIlrbanRMR2Rfl8TMHCRCmLMk8AACAASURBVCnzcmsia/TZ0wiBw+lO46IChI0SJGbX\no6M3zaPyZtqJRCIHQkbmV3jV9umSKiZgmNkmuFNmYbwiq5Hq7pmxa23gYeBKSQflua1ZDTPrhfcI\nPA6vfKnE58DZwHmSmpaVW8GWL/A+D3NkxmoFC8cBfQsIFl4F7AmsKGlUntvqCum87x6ZCtYl6wVs\nzGxx4A3gb5J2yN+6OGlVFUBPxswG4Nm+C+D9tT4ERsYkx9NqQkD+PmApYFdJj7TYpOgJVY+/x9dY\nZ0t6s7UWdSZU7v0PsDGVA3IP4JVhhVTwBZtmx6selwo2PUblYMAGwcbX8KrH8Tnb9f9wWc3r8GP5\n7zpfyRUzWw74F36Mds5zvdcdQuLWkFgqTmLfX7FhZkPx63BJPIn/MWAHYDKeRDMf3hd5SPj8HwCS\n9s3ZrpRA1g1iq3SPMQEj0XViPo5mdjaeEH86cKEiaSMQo12xPh9TsDBR0u4djPfUe7nV9iQSifiJ\nRZooZszsVeAHjVQKhpfC3wK7FZQVuj3uPBsJnAO8kHdmeE/HzAz4C7AjHVX4z9DZgbY63rdWwK2S\nvpejPe8D8+L9qyZnxyrIkC4E/Bd4swAZ0m8CT+LVtFtL+iLP7XWFnnDeh/NsOTrfV0e3KokryLfO\nJmmuupN9/ljgK0nz5mtZfMRaBZCYNTCzk/Hqy4PxZJXHgReA92t9r9UBafP+lGviFXMPSxpb0HZL\nSZvz0lGpNxaoFdSSpKVyNawCZjYHsBqd7/vPSPq0Bbach1cFfYwHpUdUmbch3nNxbuCXko7L0aZK\nx3IKnlRQjVyPZUgKWQe4FO8/fj6NXY+FVA2Y2e/xJPCVJb1UxDbr2BP1/ooNM/sFcDLuQN5S0riQ\nhPqBpAXDnIHAScDxwOWSDizArpRA1g1irHSPLQEj0T1iPI5mdn/4cTV8vfol8Ca17/eStFmb2hXl\n8zEFCxMlaTKLTTKsJ2Nms+F9AfvhzsgosgMSiZklNmmimDGzgQr9Z7rwneGSbs7LprCN7jSsl1Kf\n2oOBi3FJsP8BLijPpA9S0EcCp+DXxiGSfpeTPbcB2wLDJd0SxqoFC8/EnQnXSGpaf6kqdi0KbIgH\nvz8ELsGd2h9TQ9489wVv5Oe9mS2NO312xqvRskzEM8nPKPolMEiZzUYD/R2CPO/nRNizMm9irQKI\nFTPbDrhD0lettqWnEBzFovO6q9aLfCEB6VBpchjwnKRzyj7bE38WlO4Hk4EDJV2fp01h291592r7\nAH4IzC0G7Cjpb3XmbgfcBvwn58BcdMeyB6wp5scTOF/HE7dyrfxswJ6o91dsmNnTwCrAxpIeDmOd\ngoWZuecBPwX2knRdznalBLJuElOle4wJGCXM27RMK18fhmTKH+OV7f2AO4HL2tnPGutxjPGZDVHb\nFeXzMQULE5jZw3gke2ipOiFRG/M+jz/AG/JeUfbZZvgiYIEw9B7eh2xEoUYyXdZpGNCn1rx2zdpL\ndI1YpYkSXaObC6XUnN7sGWBl4FhJ59eZexRwHvCspNVzsqfkpHsV2FTSe5WChWZ2APA7/Jr9Vt5V\nv9EueCM+70PF43XAQKpXpAnvNby7pL/nbVPGtmeBlYCjJV1YZ+6ReDbkc5JWK8K+WIi1CiBWwr4Z\ngwdSb5D0YItNih4zu5LawcGK5B2QNrNLgP9HWXJMSIB4Ge+bNhVPFumPy7OvLunFnO3aqDvfK+Jc\nNLPV8V5fT0k6ps7ci/B78JGSnivAtsm4o7Zuwkdw3k4AekkaUG/+TNgU3bGMeU0B0ys/lwQuxK+9\n39NYpcJDOdkT9f6KjaD8NRDor9DGIuzDsZLmLps7Nx4ceFjSxjnblRLIukFsle4RB00OxN9Zb5C0\nZ9lnf8eVOyAkQwH/kLR9njbFTMTHsVuJyZKuarYtWSK2K8rnYwoWJjCz4cCNRNboM2bM7FxcouV4\nSedlxufHAySVqgJWrCfX0CTbhgIn4BKRSzTwlbbN2kt0jRiliXoK5j1D1wAWAQZKurqFtizWne8V\ncf+KGTObiDs9h9VLrAmJGp+R8wuymV0L7IE7CW4C9sFlxY8EFsX7Y3wTf6n6raRD8rIlY1OcC95I\nz3szWwp34PXHKwDOBe4H3glTFgY2A44GlsYzRleS9HqedmXsOxS4CHc2ngb8qkpF7eF4Re1s+Hry\nN0XYFwuxVgHEinkP1dnpCH69C/wZdxA90zLDEl0mk1CwmKR3MuOlNeODwHa43NPVwPeBKyQd0AJz\noyAEAA8B9pR0Q525B+DyxhdKOqoA297GAwFDG5z/OTBO0qL5WpboCplKZOhwrNcj+QQiIbxzTJI0\nT2ZsPF5R1U9lTlwzGwPQaMXfTNiVEsi6QWxBnRgTMADM7P+A7YGtJN2dGS8lyAr3W0/GCzf64EUZ\nuasVxEisxzExa5CChQkAzBt9Hok7gi5UF6Xz2g0zexLvTbVs1mFnZqcCPweex1+GpwBX4tVWFytn\njfIQrHwUWJzq1Qkz0K5Ze4muEaM0UU/AzI4DjgHmKI2VVX4Nw6s0++JN4N8r3MhEXWKU3glB6PNw\nCbjSPT+7sCs5iC4AjmtnqZZYMbM/4H2FHgC2rRaIDgHo23GJ18sk/bgg+wwovbwL+AJ4FldNAK8o\nXxV3YBlwK/C9ckfWrE6sVQCxYmb9gK3xZIet8Uz70jkzGrgeDxwWEhRPdB8z+wCYkzIHtpm9jPdf\n3aRUrRSSNv4D/FvSsq2wNwbM7HlgBWAuSePqzJ0Dr8ItxOFuZpcD+9KAEoGZrYe/d7Z18DdGzOxN\nuleJ3EiicSJnzPveL5Kt2DWzUcCywAqSXsmMD8ArfL/Ms8I3bCslkHWDFNRpjHDeL0XZs9HMbgK+\nB5wl6aQwVkqkuVvSlq2wN5GYlUnBwsR0zOwE4HQiafQZM2b2Li4jMN0pFMZLmeXbSbo9jK2AVw2M\nkrRCznZdCuwPjMOP5a3Au5K+yHO7ifYgRmmi2DGz64Ddwq//wSsLe5dnCoZrdz/gGEkXFGtlohEy\n8idLqI50c8Yh+vci5FGC3NvewHq4BHYvPBjxOHC1pFF525DoHpkkjOVUpx+hmS0LvAK8KWnJIuwL\n250Nl848Fu+5V4nP8arIc7LronYh1iqAnkBwLO6IZ4lvhldwl/bXSFyi90a1cQ/kmDGzL4Hx2XPZ\nzObEe3N+BsxZFkQsrQ0HFmyn4cHL0jX6MTC6FYkNZvYpvp7uSvLRtPLEgzwws0WAp4BPcUnl/1SZ\ntzjeM2oYsGa2qrQAG6M5lolEHpjZHcAWZBLTzeyP+Fq/UwK6mR0PnAm8LGnFnO1KCWSJ3AiqE70k\nzV42/gmedL1kSfElEyT/RNJ8hRubSMzipGBhovTQ/xVwMO5gbITcyuJ7Amb2Bf5iPHdmbDAepJuC\ny9R9lflsEr7PctVrN7N3cEfxDiqwp1GiPUjSRF3DzHbDqyPeB3aW9E+r0FMuzN0Ylx68U9LWhRub\nqIuZbYBXf90N7CRpapV5vfGX4y1wScLHirMy0dMISRiTJc3Z4PyxwIBWJGGY99zbAldWyDppn8Yz\ne9tWlSLWKoCehpnNhStz7I73Py5VR08DHpC0RQvNS1QgBL6G4BLrX4axnfB+lDOsacI9rI+kaokH\nzbZvabxX6M5UbhPxv8AZ9ZI1mmzTRODrcodojfmf44lmuQdYzVsJLIP3VOyLS5yPYMb+5LviCcZH\nAxX3nZrc/y7GY5lI5IGZHY0nYB1aqsozs7WAJ8KUO/Eg3UrANmGsU3ucHG1LCWSJXAg+1i+z6wMz\nWw4YBbwuaZmy+WOAwZL6FWtpIjHrkzTJE+AyAYeGn+8H7gU+wuUFEpX5AhhqZr0ysm7fwoOt/8wG\nCgOT8X5EeTN3sO32AraVaD/uBvY1s/UalCYajOvKtyv74U7OIyT9s87cf4a5uWaEJrqPpEdDAPgK\nYKSZnY/3Yip3oB2F9wvcNc9AoZndgp8zR1fL/I8BM1uZCsElSc+3zqqomAQMMrM+1QLQJcysL+4g\nnViIZWWEYOCt4V+iM68DS5nZUhnpzMfxYOGPgawM/eF4ECxJbJYhaQzwO+B3ZrYwXpm/L957tW0V\nTSLnZWBdXCKs1H9vH/z5NCI7MSRWDqWgc9/MtscrUwdSuT3DYGAvYBcz273ARMt3gKXNbDlJo2tN\nDI7SwbhaQRGMoHOvu73Cv3IMlw++rMrfEU30NUV8LBOJPLgBWAdXsgJA0pOhtcXZuNLJlnRcC7fg\n/QFzJwT/zjCzC0kJZDNgZqX75WeSbisb6xKSrm6aYT2Dj4AFzWwhSaX3663Cfx+pML8/rmCQSCSa\nTKosTGR7Svxc0pmttqcnYGYjgdWAbSTdGcauxiWUTpN0amZufzyD/C3l3LstyJnNU1S2bqK96AnS\nRDERJDOGAYNKUsDVKgvDZ2PwzPxUadJizKxZyTKSlEtiVpB+m5p3xXp3CYHVU4Glq0x5FfiFpHZO\nKMDMRgDfBg6SdHmduaX+HA+pTXvdxUrMVQA9ETPrg++n3YFtCT0N21nVJFbM7HDgQjyJ4Wpc4WRH\nYCouofdWZu4W+LVwh6RtKvy5Ztq1FN4Goj8enDwXT4otrUkXxgPQR+PPqSnASiqgT6aZ/R44ELhJ\n0m515t4I7AL8SdL+Bdj2Jt3odVcJNan/XczHMpEoGjNbHk/OWBgPlNwj6Z7WWpUoYd6vWrg08vJl\nY12i3dY8ZvYXYCc8MfcgYC58Hb0EsKekGzJzF8VbZz0vadXirU0kZm1SZWECYHG8ijD1yWqc2/As\nqitDdckCeKAQXK4ly1p4xWERGaG3Aoeb2dqS/lXA9hLtxRLACbg00YvmzaZHUFuaaEkzm6G3VrOl\niSJlMC5X3GjP0L6kiu5YqJS13sq/U4kPgIYkzIrGzH6NS5uX/v/fp/N9YkE8Sel6M1tf0uHFWxkN\n1wAbAr92VXiuqNDfrj/uWD4HdzZcVbSRibpEWwXQUwhtETYF9sCdRUPp2F9vA39ukWmJ2vwWP14b\n4lW0pWN2WjZQGNgNv4fdX4Bdx+LBpQeAbSVNLvv8deB1M7sGV2TZEDgG/3/Im1/h6hPDzWwqcKyk\n97MTzGwB4DxgOL42/FUBdiFp8SK200ViPpaJRKFIehmv6E7EyUP4c+7tCmOJ2lyMy0zvh68X+uD9\nL9/B181ZSrL0TxdmXSLRRqTKwkSpD9kQSXO02paeQpDR+Scui5SVavmDpP9XNvci4BDgFEmn52zX\nHMAzwCfA5pLG5bm9RHtRlhVnVF/01voMcqy2igkz+y8eFBkmaXwYq9azcBlgNPCqpG8UbmyiE2a2\nWLP+VgVnaVMws6uAPYEVJY3KYxvdwcx2x6XCwINaM/QPClUCP8PlBQX8QFJbBgJCgORO4Dv4vvgA\neBgPrvbHJW3XwbNrDZeD3qo8oJiIl1QFUBszWwevIPw+MF9pGBgD3AxcL6mS/FQiEkIPqz1wOdLP\n8crBh8rm9MErD/sDx0l6NWeb3gAWA5ar18POzJYFXgHelDRDgltO9h0KXITf978GnqPDubwYsDIw\nG34t/FRSIcHCGIn9WCYS7Ujwh21NZRnS2yVNaJVtiZ6LmR2IJ6YPDkOvAXtIeqps3mP4mmNPSdcX\na2UiMeuTgoWJkhTKAcDKkl5qtT09BTObHe9Bsw7+Yny7pGvK5vTBe0DODuwnKdfMl9CUfhE8K2cK\nLlc2Ehhf63ttUuWVmElilCaKGTP7M54RfoCkP4axasHCS/EsukvLEw4SiUqY2TeBJ/HEla27UMGa\nK+HlbR3gfEnH1pl7Dp79/5ikbxVhX4yY2QBcxm9/XIkAOidmAEwDLgWOqlBVkUj0KEIAdQ88c7y0\nHjBcyvI24Hq851F5D/BEoiHMbDIwWdKcDc4fCwwoUgrezIbj9/4Fq0x5F7/nl6vWtBU94VgmEs0k\nBMjBK2WPqPcsNLN/AXPl3fImbMtwpaHj6AjolDMBOAs4JyW3JbpKeC9aEfexviZpWtnnffBANcB9\nKTCdSDSfFCxMYGbz4xlAr+MOx5qBpUS8dKHyK0tbVHklEkVjZhvgFUIfAFtIerE8WGhm/YCT8Zeu\nacAqKWkj0QihV8OGuATch8AlwON4Vm9VOVtJb1f7rEl2jccrR+aWVLPpvJkNxauHJqdeu9MrWnei\ncpb2LXkfu0T3MbOn8TXXcElv1Jvf7mT6whre2+4uPEB4WwqGJ5pB6AM9CFfPmVpnbl88sXJiowGp\nZmFmvfF+e+vSUVn7Id6n6b4UMO85xzKRaBZlPp2HgV0kfVJjfsVk1JxsKymbGJ6c/hSd+4eugb8H\nCLhW0t5525RIJBKJ5pKChYlSNdqSeGbj18Dv8Sbi79f6XjtXo8XqFOpu5Vc7VHklEq0gUzk1Ga8y\n3gwYgEtPLQpsDMyBv3CdJOnM1lia6Apm9m1gA7waYBDVexNK0n452dCd/pa5J4eEjH5JmqvB+WPw\n9Why6iV6LGY2BfhSUpR9RGMjOEIfwgOEN0v6tMUmJWYxzGwE8G3gIEmX15l7AK7G8pCkjfO3LtEV\nYjyWZrYdLrfb9sHcRPMJz8hJeIXevMBbwA6Snq8yv5BgoZntDPwF9zedjVcOfl42Z3bgeLzyEDzQ\n+X952pVoD4Lk+TJ4H8MXyisOE4lGMbPV8RYgi+BKBPtlPusLzI/7M/7bIhNbTgoWJlI1WjdITqFE\nItEoZnY48D90lmoRHQGmicAJki4p2jaYLvUxDG8iXpVU1QRmtiLu3F6h/KPwX5WNKa8X9/Ds7jKS\netWf1X3M7AHcqbeApI/rzJ0Hr7wdIWmzPO0q2+7CwM5Ur+B7p9p3E4lKmNnruKOu7StkG8HMFpL0\nbqvtiBUz2yv8+Jmk28rGusoXwEfAM+3Uy9zM9gMuwytfDgOuKJfDM7P+wIHAOUBfYH9Jfyra1kRt\nYjyWYQ02Bvhf4AZJD+a1rZ5GUL5oCu367hHOrw+AtYG/Aqvi74v7SPrfCvOLChaWemyfJOmsOnN/\nBpwO3CVpqzztSswamNkKwA+A1yVdUfbZZsBVwAJh6D3gh5JGFGpkRIT32R8B7zWQSHMQrl5wmaSa\nRUFdtOGP4cf3JZ1YNtZVSuvVfwF3SupOYnRNgu/hamCL0hBl/prgG3sD9xGsKenZZttRw77ewNJ4\nMUE931yuxVspWJhI1WjdIDmFEolEVwhyi98D1scXub1wmanH8aqKsS2w5wRgFzr6RdWirRNEAMxs\nAeBZfOH4MnAPcDie9fsrfAG+KbAU8Ame2f6VpFNbYnCLCNn2twFXSvpRnblXAPsC20n6RwG2DQQu\nwPuD9qJzRWhpHTQNuBzvEzUpb5sSswZm9lvgIGB9Sf9stT2Jnk0mkXO0pOXLxrrLV8CVwCH1pBxn\nBUJfrZJjW7jj/WG8D2B/XN1hHWAu/FlwN7BV6q8VHzEeSzMbB8xOxzX5LvBnPHD4TF7b7Ql0U/mi\nEm377lEKFkpaMDiu/wjsip9vp0s6pWx+UcHCj4GhwLB6a2QzGwSMA8ZJmqfW3ETxhCq9A3BfwIp4\ncKLW9VaEOs25wFHA8ZLOy4zPD7yGq/lkmQisKOmtPO2KFTM7ETgNOEbSBXXmnoK3vvmZpHOaaEMp\nefmVCuvVaspLtSg9U18GvivpvZm30gl+gMeBlXAVxTvw++rA8nunmZ0K/Bw4qxQEzRMzWwLv87o9\nXjlbj/yvx7QeTiS6TnIKJRKJnkpYcD8KLE4XFnF5V6TFjpmdDxyJO6x2kDQ1+zKfmXcg3j/wHmDb\ndnQ8hmrac4H78YXvYyWprpAxtx4erN4Mf8H5dQE29QUewPtCGd5fpeRsBJeU3RDvtyL8ZWKTopzq\nZrYlnV/Ya2UTStJSRdiVaAwzWxBPJngX+E6t3kKJRD2C7KKAt0v9njJjXaU3fn9bInz/HEk/a46l\ncROc7BcC++MJItBZTQc8QeRSPEEk9cuMlNiOZeg5vjWwR/jvgIw9o3EVihskvZ6nHTHSXeWLSrTr\nu0eV94sTcKUaw5PyfihpYvisqGDhFLwfaFdaDQyS1D9PuxJdw8yG4O1R1iQiX4CZPYmrviybvXdm\nAjfPA9/Hq8yvBDYCLpZ0RJ52xYqZPY5XHy9ZL2BqZovj1XKPS9qgiTaUEhc+kfSbsrGuUlqvboMn\nZ18v6Yczb6VjZsfi8slP4oHIcdXunWa2CvAM8KikbzfLhip2LY37HeakQ+nxI/w8r0rexVspWJhI\ndIPkFEokEj0VM7sUd7aMw+VhbgXelfRFSw2LHDN7CfgGLkfxTBib4WU+jB+DL0YPb5W8bKsws1If\n33lx5xnAVLzaEmBuOgJhk3D5z0o0NSBmZifh2ZeTgIOBqysFcs3sh8DvcNt/rpz7iJpZH+BGYIfS\nUANfy03eNtE9Qv/v5YDz8fP9avzF72O8H3hF8paQSSRKmNn3gJuBt9pNHcbMFgN2orr0dFtKHfZE\nYjyWZjYY2BGXz9sMd3iW1hcjgeuAGyV9WLRtMWFmO+EVcu8CvwQepHPC1kZ4VdHCwI8k3doKO2Og\nxvvFtsC1wBDgJWB7SW8WGCx8G1gIWKRexY+ZLQS8jUskLpKnXYmuYWbn4dfaF7jE8634tVgvOJFr\nBZ+ZvYu/P/bPSlCa2dPAKrgSze1hbAXgBWCUpPL2IG1BuO4HqcHWWGY2Hvhc0kL5WjZzmNk38fvb\nJ5LmbeLfHQmsRqbgp0awsBd+PYxrpg1V7LoBr3B8BzgC+Ksi6IecgoWJRDeIwSlkZveHH9+StG/Z\nWFeQCuwVlUgkWouZvYNLoe4g6e+ttqenYGYT8D44/UpBpiC1NK48wzZkbH4KPC1p7YLsW5kKzjNJ\nzxex/Ywdzcoob2pAzMxGAcsC+0q6us7cvYE/kZFUyYtMEFPAP2j8hT31R4oIS/2/Ez2A0nNMUt9W\n25JIzIqY2Vx45cvuwAZ0PA+mAQ9I2qLG12dZzGxdPDh4L7CTpC+rzOuDr4M2BzZsVwWnasHC8Nk3\n8D6GS+OJeLvilaxFBAuvxc/tm4Hda6mnmNmNuGJGU6uDEjNPSOxcDNhP0pUtNmc6ZvYFMF7S3Jmx\nwXiC8xRc/varzGeT8LV0uTxpWxAqfSdJmrPB+WNxyc3oK32DrbM38z3JzD7HfTkDMr6cqokWJdnl\nvNfMZvYhnky9oaRH89xWV0jBwkQnws14aypn690uaUKrbIuJGJxCNfShu0qqUEgk2oiwsJwGDJbU\nNKmgWZ2wwPxCmb4bZvYZMJCyDMjw2Vigl6RhOdu1G3Aq7jSoxKvALyTdmKcdGXs2atbfamZAzMxK\nkmRD6mXrBanUCcA0SQObZUOVbZWCmCdIOjfPbSXyw1L/70QPwMz2x7PQ/z97Zx7mWFmm7/tpELrZ\nmmYH2QQRRRhBQUVZRRFEZEBRZFQGEXUURQQUFHVQFHEBcZthkR+igooCjsugoCKryq4o4sgi0ECz\nNnuz9fP74/1CpUOlkqrOOUlV3vu6+qLq5Eudh6ok5zvv8rzH9ltLkkx1JK0O7EHMZn4BQ3zPLel/\nCGu58djl/dT2LmOtnaqMlSwsj88kXCm2JwrXBSxSQ7JwY6JbVsCFxKiB813mF5Zk+bbAR4l44nxg\nM9tXVakrGR8lFiAiFjAwM4zLvfYMojB3fjm2A/ALothiu5b19xD34MOaLLyF6MpexXY7p57G2hWB\nOYzxuTJISNoAeJbtq3v4Mx8m7u2XbjrWrrNQhBvRPNuzeqWhja5Hiq6lqjzPeMlq1gR4+s1wKHFh\nb/cifUjSkcSsi2HPMt/MxOaG9JK9y3/vH+VYkiRJO24DVsxE4biZDawjaVrT7+4mYsbcvxC+9sDT\nN/HL0qE7bGGR9FXCUrNhXXk7C1o6rUZ0wZ8q6RW2969SDwx0x9tc4oayo62H7SdLcrHSv19hbSKY\n8rUazpVUhO21+60hSTph+8R+a0iSYaB0yG0KbEZc54edlxNOHB1tDIut5lxivnUyCrbvl7QjMR/8\nwMbhGs57laT3Ad8EtiAcMVyKJxdnZPyAiL3t+zNROJDcRRRPDkyisPB3wiZye+DscmxP4rW9gEOb\npOnATKBSa9QB5w+ELfd7iXmmY/G+8t8/9lKApDV79bOabcRt/7VXP7eJW4D1JK1q+/YOa19BfKZd\nU4GOVm4G1pSkQcqzZLIwaXAy8Dbiwj4PuJzwzIXwjX8J4Y3+WaIybq/6JQ4OgxAUsv3tbo4lSZK0\ncBawv6SX2u7phnGKcx3RAdbw0Qe4ANgIOIiYVdOgsWGvYqMLgKS3AvuVb78NfNb2P1rWrAt8jCgk\n2U/SJba/X5WmAed3wO6SNuh0A1LmYMxk5Ea1SuYSFbSPdlyZJMlQ0DT7dWGxezj7dVCRdFL58nbb\nH285Nh5se5/eKUt6SekweRNRpDWLkfnHo1H7a78UX7+KCG7vSuwjGsVcNwPDuv+CKEZfRNJ022MW\nYpUkwFJEx9ywsjcw5r6wBJUPlnQ58NpaVMV5j5d0DXGvsw0wjXg/Pr0E+A0x9/uSunQl4+Js4J2S\nXmD72n6LaeInREfqyZK+TIxNadxf/7Bl7WbEa+/G+uQNHCcAuwGfkHSb7W+NtkjSvsBhxHuz10Vj\nvfr9m+rzU+cA6xHJ1U+1WyRpEeBzRdMvKtYEsTf4BDHz+NwaztcVaUOaIGk34EfEm+HzROfgAy1r\nlgEOIToPAd5k+8xahSZJkiQLjaRZRBfc3cCrbc/ts6RJQamk/TphF3lUObY+8Cdic/vX8vWG5R/A\nu9tt3Hug52LgZcCXbX+kw9qjgIOBi21vUYWeQUfShsRs4WuAHWzf32bdMsAviSTwSyuqbGw+3/eB\n3YG1bd9S5bmSJJkcdDFWwIwkIcZ6bChsD8cYzTDW72k0huL3NdkoXXo/ABqWlN38TWv7W0p6GTHH\n7c3Ayo3DwD3EbLdTbV9Yh5ZBRdJVlOI628d0WHsA8GXgatub1KEvmRjlnnITFhxfdKXt+/qnKumE\npDWIWMAVwE6D0mFYRmL9gWLbSnyAwQAAIABJREFU3DgMHGf7P1rWHksUzX7K9hG1Ch0gJH2HSKga\n+BuR3Gp06K0F7Ag8n/g9/sD2W3t8/p45Vdme1qufNRqS1gKuBRYhnJlOIhyZnrYhlbQZ0bG9NVHQ\n+zzbd1esazpwMVFg9GrbA5EAz2RhgqSzgdcAh9k+ssPajwFHAL+0vWMd+pIkSZLeIWkrYA3C9nAe\ncBwxf+LBsZ5n+/yxHp/qlDkchwD/tP31puN7AMezoIW3gWNsH1ShngeB6cAK7RJfTWtnEkGrR5t9\n+qcqY1iivJKwTpoH/BfRbdhs27o18B/E7/V9RHL15lF+Ti+1bkjcGP/E9p5VnitJksnBGLNf1yWC\n6NOJ5Mlon2FvIT7jDgRuGGBr6J4hqVEhfrftb7QcGxe2D++ZsKQnSDoM+DSxt/o54ZAxmw5W4VW+\n9ss8pT2JmYSN2bMCHia6Y04FftWN9fkwIOkDwLHAU8Tf8iu2H2xZsxSwP9HxsQjwwcb7OUmS3lHu\nkzYm3OVuAY6mu1hApfdERdsywIeIgtgHgF/Y/k7LmmcRHVjLAPvYvqJqXYNK+V18heiWE8+0I24c\nOw74kO3Ha9K1KyPJuC8x+n71QMLF8J22z6pJ127AaUSh91wifrMocDURH1uO+J09Bvyr7V/WoOkd\nRKLwcGAxopHrUjq/H0+pVFcmCxNJdxEvzmUbw4nHWLsk8aaaa3vFsdYOA8VuZFci2boGMKN58G75\nfb2EqG68oGZtq9OdTUvlHzRJkgwOTdX2MPqmcjRsO63L2yBpBaJyb3Vijuy5tv9e8TnvJf4uy3e5\n/h5i37dclboGAUlP9ehH1fK6l/QaIvD/B+Ao4FLbD1d93qT3lL3X3kRiejVgScboABsGi8ikN0h6\nNtEFcD+wo+3r26xbB/hfIoj2Etu31acySXqPpGsJG/hDbX+h33pggX2GCLvMXxIJwp+krfgzKTGT\nM4E3EPcdjwFXEXPUIa6XGxMzokQkhN84SPObqqKpwO2Jxhytic4BqyOZk0x+JniflLGAAaYUsLyd\nmA/b6HCfA/we+E7VTjktWl5OJAfPBXZtl6Asic6zgFcDW9n+Q036NgOOIeYSjsbviWKVy2rS0+qE\n0dV1r2r3hEwWJkiaBzw8zoDjkranV6tssJG0HnAGsAFt7H6K3/F1RMXh1nVYkEjanPjw26zb56Tl\nTpIMD5JuostNSDO2n9N5VVIXkn4LbAmsavuuDmtXBO4AzmsuaJmqTDJLlLxhnyJI+jeiy3g6aRGZ\n9BhJxwP7EAGVizqsfSUxU/cE2++pQ1+SVIWkR4nK/2UGJRFX9hnnEwnC09N2sTMlLnII8BGgncvF\nA4QF3FG2e1X4NdA07QP/ZvuFLcfGQ617Q0mLAs+lu8L0oXanGTQmep9U9T1RMjWQ9D/ATsA6tv/Z\nYe3awA3AT23vMtbaXlOK615BzMacRiRXL7F9Xc06zmNisblte69mhEwWJki6GXg2sEan6tNS1Xoz\ncJvtNerQN4gUf/ariG7CPxGtwgcBS7cGfiQdTHQKHGv7gIp1bUEMbl2sHPoH8aE35oaz6g+aJJnq\nTLQCdDSyKjTpBkk7E1ZXJ9t+Z4e13yK6nXa2/fM69CXdkTfsUwNJLyYqURclbHd+SnRR3MvIHKtX\nE7Z1DxIWS7OHwSIy6Q3lfm2m7Zldrn+AcILp2f5kMiHpJOL//8Ndrv8CsLztfapVlowXSbcDiw+S\nM4KkZ9ue3Xll0oqkJYDtgRez4Ky7Kwjr1jGdrqYaTfvA62y/oOXYuKhjbyhpXeCzRJfo4l08JQvc\nkmSIkHQnsMg4mpHuBZ60vVK1ypLxkMnCBEnfJYZynw68dSy7B0k/AN5EDOp+e00SBw5JRwAfIyxH\ndrb9ZLmRWWmUZOH6xCDVS22/rGJdvyM6TS4G9sykQ5LUw2SzPUymBpL2JyqwfwMcSczYe7I8tiiw\nOXAosB1wsO2v9ktrMjpl2Pq46VSpmdRL2UvvScwqPbAcmw/cYXu1pnUbAb8CHgVebHtuP/Qmk4/S\nXTXf9pJdrBXwEDDN9ozKxQ0go73/Oqy/EVgzu30HD0nfB3YH1rZ9S7/1JMmwIumFREftsoRLwjzg\nbjoXpqc7TdI1kmYQMeduLf2nvGvOZELSI8Tc2Zm2x5wtLGk60VH+RDf726Q+MlmYIGljYqCtgAuJ\ngOP5jaouScsD2wIfJSrA5gOb2b6qP4r7j6Q/E/ajGzX8n9slC8tj47J6XQhdDwEzyJupJKmVyWR7\nmEwNJN1QvlyJ+NyHmJtzd/l6BUZsgR4hqrZHYyjmpkm6kti/7G77hk7rk2Q8FHvnNYC1bN9ajs0H\n5thetWXtboQjxRdsH1K31mRyUj7z1wLe0KlDXNJORHfrTbbXqUPfoDGBZOFNwOpZsDV4SNqQmOn7\nE9t79ltPMn4k3cdIDCn3YJMUST8DXkeM2dkXuGgY5kom9SHpVYS984pEfLrx+mpOFjYfGwpLf0mf\nLF/ebfubLcfGhe1P90zYKEi6CtgIOMj2MR3WHgB8Gbja9iZV6krGRyYLEwAkvRv4JgsO1byfsBZo\nBCFFbPLeZ/v42kUOECUpp+bqhw7JwruIyorFWh/rsa67gEVtz6ryPEmSdIekXQlLutnAl4hhzw3b\notWArYEDgdWBd9o+qx86k8lHDxPUPb3JGtSAUCnaedz2Mv3Wkkw9Ruv6kvQEUSi2bMvaRYmur5ts\nP79epclkRdKXgA8ThR+7t5sBVUYS/JgoGDna9sH1qRwcxpMslDSNeE8+0a3Na1Ivkl4D/IBIGh5F\nOPY8XNO5e7WXGYrirNGQ9DDx/lq24+JkYJF0P7AUTQXzSdIrJD0XuJLoJDwX+DlwDBGXPpARS/9t\nieLYw4GHbH+7L4JrpOxpTNgVb9ByrOsfQw3JVUkfAI4lOo4/DXzF9oMta5YC9gc+RXQhftD2N3qo\nYVJet8scxTcxuk336bZvrE1LJguTBpJeAXwG2IZntnkb+C3wCduX1Cxt4JD0IJGUm9F0rJ0N6aLA\nw8CDtleoWNcvgVcRMzceqPJcSZKMjaSXE8nBc4FdbT/eZt2zgLOIze9Wtv9Qsa6JbJ6GNsAxqEja\nulc/q5dz0wY1ICTpeuIavXS/tSRTj1KspeZ9nqS7gVnEPOtHWtbfT+wj03In6QpJywCXA+sS92W/\nJ/YYjXnzqwFbEfbTAv6PKNoYivuB8vtpvu7cRARYNqW9fZnKc94BHAD8wfbmFcpMJsAErf57Zuvf\nRXGWGcMir+mxoeiAGQ1J1xKd90v0W0sycUpBoAZtj5+MD0m/mcDTKrf7lHQc0bH6XdvvKMdGs/Tf\nnnDo+DvwStuPValrEJB0MnE9ud32x1qOjQvbe/dUXAvFCv9MYq6pgceAq1hwv7ox0ZgkIg72xl52\nKQ9qUXU7ivXuscA7id/JaPkYAycCB9h+tHJNmSxMWpE0C9iEBTPZV9q+r3+qBgtJfwJeCKzX6JwY\nI1n4WuB/CZuGLSvWtR0xC+dztj9R5bmSJBkbSf8D7ASs02m+mKS1gRuAn9repWJdE9k8DW2AIxkf\ngxoQkvRN4D3AK6pOyCfDR7Hc2RBYqjGfQ9JFwMuB19o+t2ntukQi54EMuCXjQdIqwClEcRE8M0jU\nCC6cA+xl+466tPUbSZ8Cmi25mu3LuvoRwLtsn9RTYclCM9GgX69s/ccozlqXsE+bTnQ9juYe8hZi\nrtuBwA29LM6aTEg6CjgI2MH2Of3WM1mQ9ALgjcT+YhYj4wVGo45kzgXAywjHrMqD1Uk1jOMztVa7\nT0n/B6wDvMD238uxUV0CJL2TSJx8wvZnq9SVjB9JiwCHAB8B2hXqPgB8ATjK9kSKgsY6/0AWVY9G\ncbc4G9iOeK/NBs4Dbi1LVicaup5NvCfPJa6llSbzMlmYJBNA0pHEDMdv2P5AOfaMZKGkJYFLiMTi\nR21/qQZt7yPa9U8BPm/7+qrPmSTJM5F0J7CIu5xVKule4EnbK1Wsq9PmaSawGfAu4qb0g8DsYQ1w\nJONjUANCklYjqhpnA6+xfXeHpyRJ10j6NvA2YEvbF5djnyAski4j5szdIWkF4IdEEPkc2zv0S3My\neSlWo2PZFF3UL239QtJ/smCycKxur+Y1DwB/AU60fXIl4pKFQtJaE3lep0K9hUHSs4n32/3Aju3u\nt4ul2P8CywAvsX3baOumOpJmEr+vRYjf17V9ljTwSDqauAcbrctkNOpI5uxOJMY/aPvrVZ4rqQ5J\ne3VY0ogFvJGYe/+fhEtapXafkh4hchTN7m1PEsV1y7WsnU5cv6+1/aIqdQ0qxVEBYuRBT5NtvULS\nEsD2jL5f/VWr88owImkf4ASisGh/Yj/qljUium6PBRYD9q26uC2ThUkyAUqw5zrCuuZIIjl3DSVZ\nKGlpYAfCo3l9Ijj5/BpnKxxWzm3iQ2fOGMvTXjBJKqBseBchqi/ndVjb2PA+MSi2dJKWJyqXZhIB\njuwunwRI+jDR3VTp8PIxzj+QASFJWxHX4y8DTxAFNZcQNyxtb7DcZi5YkjTTFEA71vYB5dgs4Fri\n5vgp4rW2MjCtfP8q2xf0R3GSTG3GM7MwScaLpOOBfYjxAWMm5yW9ErgAOMH2e+rQN2hIegewEpHQ\nX4xIoHazBzulFoEDhqT3A18r3/4Z+AkRTxrzfrKO2W2SPk/YNn8aOCaD/VOXMkPwV8C9wBad4hk9\nON99ROH0ii3HlgFmtI50KY8t4iGdR1/2OfOB59i+pd96kokh6XfAFsB7bJ/YYe2+wHHABbZ71j05\n6rkyWZhIejHwJeBy2wd3WHsssBHhk3t1HfoGlVLV+1Pi4jWfkaqv+8uxxvf3EhZUl9egaXEiWLUz\n3VWgQdoLJkklFFu6jYCDbB/TYe0BRBLjatub1KGvG8rcxYtpCoAng007S+wazz+QAaGWIfDd2tP1\nbOZRMrWRtBjwGuCh5i5sSRsA/4+o0G5wK1GVf1a9KpNkeCizfOba/lC/tSS9RdJKjNKlYPvOGjXc\nTBQDzuxy/QPE63HNapUNJk17sKfnN3bzvGGNUTTdQ35tED/DJB0KHAE8TsyHvX2M5ZXboybVUeYD\nng18xvanKj7Xn4nCziVsP1mOXUaMyHrauaMcX4WYgfewh3QefbmuPNnadZlMLoq72BLEjPsnOqxd\nDHiQeN1X+nfPZGHSSADuB7zN9mkd1jYy2cfYPrAOfYOMpDWBzxFWQIu1PPwk8GPgkCptUFr0HA58\nopz7FKIr6E7GCNBC9T7MSTKMSPoAYRXwFFF9+RXbD7asWYqwG/gU0Yn1QdvfqFvrWEh6ELjb9nP6\nrSXpzAAkCwcyICTppm61NJOv+6QXSFqDmDlxP2GZlDdgSZIk46AU6h4BbNlmyfnAYXXY8Ep6FJjf\njRtIsQ97CJjWbK83TEg6j4ntwbbtvZrBp7jTLA7Msv1Av/U0KK/lrwDvJ1wSuiEL0ycx5W/+MHCL\n7fUrPtcPiLjqSxuNFmW8xcFEh+O/2p5XEibfAXYHLrS9VZW6BhVJVxPJ1aUaydWkPZJ2Ac4AzrC9\ne4e1PyecAne2/YuKdT0KPNpt8q8kF2dUvZ/IZGGCpD8RM/WWtz23w9pZwD0MWPdLv5E0A3gJsCqx\ncZoDXGb7oZp1XA+sTQ0exkmSjE3ZXJ8JvIG4QX6MmJnWmFeyGrAxcTMo4CzgjYMURC7DqR8i9gvT\n+60n6cwAJAvPIwNCSZIklSFpS+CVxD5iSdq7idj2PrUJGyDKvdlmRADm0g5rNwNmAH+s2mYtmRiS\n3kvYMk4jXu9PAY3Zw8sDDReAp4D9bB9XsZ4bgLWIebQ/77B2J8KN6Cbb61SpK5kaSLqLsFccqI4h\nSR8Cji7f/obuC9Mrt0dNqqHEMx4kih2WqPhc/w6cBBxh+5Pl2LMJS/8lCce2vwPrEZ/7ALvZ/kmV\nugYVSR8nCtLfYvtH/dbTiqTVgb3pfr9a6WgsSacBbwa2t/3rDmsb1+1Tbb+tYl2NGP76tv/RYe3z\ngL8BN1b++xqgmGTSJ4rX83zby3dczNOZ7Pm2V6hWWTJeShXaNLK6JEkGgpJsOwT4CNDOIuMB4AvA\nUYM2nFrSzpQ5GbbX6LeepDP9ThYmSZIk1SBpQ+BUoshzgYfKf91ybGg7Ooobzn8Trg5juuE0zZ/b\nx/bJNchLxoGkTYBLiXvcC4HPAOfbfqw8vjiwNeGu80oicfFS21dWqOlLwIcJC9Td2803Lt2QPwZW\nAI7uNPIlSQAk/YzoalnV9l391tNA0l+JTqZP2P5cv/Uk1dM0c/VO26tUfK6lgL0IR6MfNB3fGvgu\n8Oym5Y8Ah9r+GkNK6bC8AHgu8OZOCbA6kfRvwPHAdMZIEDY9Vvl+VdJ1wHMIu8/HOqydTiTJ/2H7\nBRXrOg7YF/gtsFO7orWi6RfEfucE2++tVFcmCxNJDwNPdTsYtngjL1p1ZUkyfiT9gwgSD+WQ3yQZ\nVCQtAWzPKDNWgF8N0nD4svFcHdgFOAxYFjje9n/0VVjSFZksnNyUbphlgWeNtc72zfUoSsaLpEWJ\nG/dZdP47jhpgTpJWJK1KuBOsCPwVOIewMX+IsIVbGXgVsC7RcXUcMcvm8L4I7jOSfgm8GniJ7as6\nrN0U+CNwtu3X1aEv6R5J3wX2BH4I7Gl7fpt104DvEzZ237P99go1LQNcTrzfDPwe+B0LuodsBWxO\nBEP/D9hskCwlk8GlJGjOA74xSDMLS2H6osAy2YU9tSl72Z2BY4A1gNOq7rDqQs/mjFj6X2T7/n7p\nGQQkfZJwRXg/0bX3J+ASIsbUtgDd9qcr1vVi4pq4KNEp+lPCbeteorNvZWJ/tieRkPsQUZhe6Wis\nkvd4xPaKHRfzdIf34lXH1iWtA1xDuI1dT3RvnwfMJpKtawLbEnv+1YB5wEa2b6hUVyYLk5Jhfy6w\nge3rOqxdn2gDr7ztdbIg6RXETcloSYDTbV9So5YvAAcCW9R53iRJJg+SxtO9KOAGYPNBqmxN2iPp\nUmCFnLU3OsVOZ1fgNcTN7wzb2zU9viRhK27bF9SkaSZwKLGX6ObvZtuLdl6W1Imk5wBHEtbTi3fx\nlPw7Jl0j6cvAAcDZwC62nygzYu+wvVrTuncDXyeSia8fJGvzOilzalcGluj0OyhJpkeA29ImcvCQ\ndAsRIFvd9u0d1q4G3EL8LSt1xJC0CnAKEfSEZ1qwNzomzgH2sn1HlXomCyUwOlbs5MZ+aRskJO1F\ndEd/G/i87Zv6qwgk3Ux05czqt5Zk4hQb5bGYDqxEfIaJKEB6Wb43B4uyB2zuzoOxR4HU4jjRVOBz\nTMPZoc1+dSNiFuWjwIs7jUTrga77iNd2N/tCEfvCJ+poxJH0euA0IunbTltjfuhbbf+sck1Dev+Q\nNCHpv4F3Az+0vUeHtY2hs//P9rvq0DeoSFqZ2Ly9pnGoZUnjzfUr4N9tz6lB09KETct8ooU5L+hJ\nkixA2ax1w01EFffnq968JVOPUnX/LhZMyq3b9PhMonvVwHfrCGhLWo8YbL4BbWxPinVww6Zka9sX\nVqxpFeAiYlZBO5uWZ2B7WlWakvEj6blENe9ylJtxYo7PmJX3mdRPukXSX4DnA5s27BVHC76U4wcD\nnwf2t/312sUOAJLmAQ+Os4J8yXTOGTzK3/LhcYxMuYf4W9Yya7tYjY6V/LqoDh2DTnFOOBZ4JyNJ\niGZc/p0IHGD70XoVDg5NyZyViM4hiK6cB8d4Wh0zv/6bsMv7F9t/qfJcSXWMIxbwGDGO5NCMKw4e\nkk5m7OTgqNjeu/dqRijFWmsAa9m+tRybD8yxvWrL2t2AHwFfsH1IxbouAzYBtu3k7CJpG2Iu659t\nv6hKXU3nXAf4OLAbMLPl4blEDONzVXcUPq0nk4WJpOcDfybmAJwKfKS1aq9Y33yRqBB4CtjE9jV1\nax0UShD0MsJ6RMDFhPXI7LJkNcJL+JXEB3jDemSsDV4vdL2DCFR9ClgCOJ34245ZhWn7lCp1Jckw\nM2idTJLW6rDkSWCu7Yer1pJMTSRtTszoWZkxZhFIugrYiChuObtiTbMIC781CJuWHwEHERXSrboO\nBo4CjrV9QMW6jieSqnOBI4CzCCuWMWcpJIOFpNOAtwC3EnY6/+OcHZ30EEkPAYsRlkgux54irtfL\nt6xdGrgPuML2S2sXOwBIupOwAl6mU9KhJDEeAB7oNiGV1Ef5Wy4LzOq0Ny176rnAfbZXqkNf0pnS\nvXs2sB2xL5xN2KzdWpasDmxDzCQzcC6wwxB3RnebzGmmjo6hVYgk+PXA66qObSXVUGYAjsWTxOfo\n320/UYOkZAoh6VFgvu0lm449QRT9LNuydlHCTv8m28+vWNfhxGzjK4iC4FH3E2UfcQHwIuAo2x+r\nUlcbDevQVHxUV4JwAQ1Dev1NWpD0AaLSy0Qy8GqgMQ9nLeBfgEWIzd2HbX+lHzoHBUlfJOw+7wLe\nYvu8Nuu2IhJ2KwBfsv3RinW1tqJ39QavemOZJMPKIHYyJUmVSFqd2EPMIgJDpxL7i2VHScr9B/AN\n4DhXPBNT0hHAx4BfAjvbfrLdfMcmy/VLbb+sYl23AqsStoKVW4ok1SBpDrHX2yq7SJIqKDPjH2vu\nlJN0P1EcON32Uy3r7wWmtQZmhgVJ5xIzXt5h+3sd1r6NsJO8wHanIGpSM5LOJgruDrN9ZIe1HyMK\nb35pe8c69CWdkbQPcALRbb8/cGJrIrAUV+5L7BkXA/a1fVLdWgeBLpI5o+LqZ35tBaxDzLF7irBJ\n7aYwPeczJwvQhRVqt1TeUZuMj+LUINsrNB27m4gNLG37kZb19wOLNicXK9K1HBFza/z3k8Re4cHy\n+NLAjsDhwPpE0d36tu+uUtegksnC5Gkk7U5c+Fdrs2Q2cKDtH9anajApF7e1gH+1/dMOa3cm2vdv\nrMEa4jwm1oq+be/VJMlwM6idTMnUoSSa9yXsrzYkNuFjzUCzK56RJulY4APA92y/vRxrl5Rbm5iJ\nebXtTSrW9Wciab+R7b+Opas8Ni7bs4XQNY+wDl/K9kQqyZMBQNIjRBXtUv3WkkxNJF1LBGlnND4r\nJF1NfPY/bU1ajs8kghzzhtVWs8xu/G+isPNV7SzzJG1IWE0tD3zQ9jfqU5l0Q5NN2XzCXveLtu9v\nWbMqcDDwQaI47022z6xbazI6kn4HbAG8x/aJHdbuCxxHJu8HjqbCdBixXO9E5fceyeRjgt2zo1F5\nR20yPopz0IbEve28cuwi4OXAa22f27R2XcKF74E6itskbQn8FFiGEevrxn5iJiMW2Q8ShbznVa1p\nUMlkYbIApQ14O+KNvHI5PAf4PfDrtFQKRmutHmOtiNbqabZndFqfJMnUYFA7mUbR+VJGmbNi+491\n6kjGR6l+OxfYlGfOfWmLK551J+nvhEX3urZvKsfGSso9Ajxe9Q1CsfBTiyXKWLruAmbaXqxiXTcA\nK9peusrzJNUi6W/AmsScrLy5SnqOpLOAnWmaFSXp68D7gNNs/1vT2q8C+xHX8k37obfflHvaSwkb\nqXnAScD/sqBzzuuAfwemE90xm6bl2mAi6dvA24nA3uOEg8Fs4m+3JrAe8CxiP/RtVzyTqUXblsTo\nkdWAJWm/J7PtferSNUiUTucliILJMd9jkhYjArUP216uDn1Jd5RZZBMpTM/5zAOMpJUYPRZwZ4Xn\n7FkhQNUdtYOOpOcABzAy8mZ6c4Je0rJEIY2Bz1e9zynX67cBW9q+uBz7BNGxdxnwBtt3SFoB+CEx\nvusc2ztUqatJ3zpE4dG/8swi6yeBM4GP2b6+gnOvWb58wmX0W9OxcWH75s6rJk4mC5NkAki6mQgi\ntg4ebbf+AWKmyIQ+CJIkmXwMaidT0/n2BD4DrN1myY2E5dP369CTjI8mO+zHCGuns4jA2byxnmf7\nnxXrepTYAC/TdKxTUm4Z24tXrOtBwuJkRtOxdsn7RYGHgQebLVQq0nU0Ycm1eSboJy+S/pOYg7F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cLbgBVs99RCW9LWwDZNhz4FPESMGmnHfCJ5/xfgvDr+rpksTJIphqTXA6cRNoLt3uAibFPe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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure object at 0x000001C4B6D9C4A8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "POS = Compiled_df['POS'].value_counts().plot(kind='bar', title = 'Parts of Speech in Chinese', figsize = (30,30), fontsize = 24)\n",
    "POS.set_xlabel(\"Part of Speech\", fontsize = 24)\n",
    "POS.set_ylabel(\"Count\", fontsize = 24)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[('死死的', 'si3si3de5', 'descriptive'), ('小小的', 'xiao3xiao3de5', 'descriptive'), ('的的', 'de5de5', 'verb'), ('细细的', 'xi4xi4de5', 'descriptive'), ('的', 'de5', 'non-predicate adj'), ('微微的', 'wei2wei2de5', 'descriptive'), ('的', 'de5', 'verb with nominal function'), ('短短的', 'duan3duan3de5', 'descriptive'), ('长长的', 'chang2chang2de5', 'descriptive'), ('高高的', 'gao1gao1de5', 'descriptive')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[('的的', 'de5de5', 'verb'), ('的士高', 'de5shi4gao1', 'common noun'), ('的', 'de5', 'non-predicate adj'), ('的', 'de5', 'verb with nominal function'), ('的确良', 'di2que4liang2', 'common noun'), ('的', 'de5', 'common noun'), ('的', 'de5', 'adv'), ('的', 'de5', 'auxiliary'), ('的确', 'di2que4', 'adv'), ('的话', 'de5hua4', 'common noun')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_final = [(w,p,pos) for (w,p,pos) in Compiled_Unique if w.endswith('的')]\n",
    "len(de_final)\n",
    "de_final[:10]\n",
    "\n",
    "de_initial = [(w,p,pos) for (w,p,pos) in Compiled_Unique if w.startswith('的')]\n",
    "len(de_initial)\n",
    "de_initial[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parts of Speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[('铃', 'ling2', 'onomatopoeia'), ('呀呀', 'ya1ya1', 'onomatopoeia'), ('喀嚓', 'ka1ca1', 'onomatopoeia'), ('喃喃', 'nan2nan2', 'onomatopoeia'), ('哇哇', 'wa1wa1', 'onomatopoeia'), ('隆隆', 'long2long2', 'onomatopoeia'), ('砰', 'peng1', 'onomatopoeia'), ('哇', 'wa1', 'onomatopoeia'), ('簌簌', 'su4su4', 'onomatopoeia'), ('啪', 'pai1', 'onomatopoeia')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[(('哈哈', 'ha1ha1', 'onomatopoeia'), 19), (('呼', 'hu1', 'onomatopoeia'), 18), (('扑', 'pu1', 'onomatopoeia'), 15), (('刷', 'shua1', 'onomatopoeia'), 14), (('哇', 'wa1', 'onomatopoeia'), 13), (('嘿嘿', 'hei1hei1', 'onomatopoeia'), 13), (('喃喃', 'nan2nan2', 'onomatopoeia'), 9), (('吧', 'ba5', 'onomatopoeia'), 9), (('砰', 'peng1', 'onomatopoeia'), 8), (('刺', 'ci4', 'onomatopoeia'), 8)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onomatopoeia = [(w,p,pos) for (w,p,pos) in Compiled_Data if pos == 'onomatopoeia']\n",
    "len(onomatopoeia)\n",
    "onomatopoeia[:10]\n",
    "onomatopoeia = Counter(onomatopoeia)\n",
    "onomatopoeia.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4766"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[('踌躇满志', 'chou2chu2man3zhi4', 'idiom'), ('晕头转向', 'yun1tou2zhuan3xiang4', 'idiom'), ('束手无策', 'shu4shou3wu2ce4', 'idiom'), ('埋头苦干', 'mai2tou2ku3gan4', 'idiom'), ('横冲直撞', 'heng2chong1zhi2zhuang4', 'idiom'), ('强词夺理', 'qiang2ci2duo2li3', 'idiom'), ('花花世界', 'hua1hua1shi4jie4', 'idiom'), ('接踵而来', 'jie1zhong3er2lai2', 'idiom'), ('可想而知', 'ke3xiang3er2zhi1', 'idiom'), ('精神抖擞', 'jing1shen2dou3sou3', 'idiom')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[(('艰苦奋斗', 'jian1ku3fen4dou4', 'idiom'), 25), (('无可奈何', 'wu2ke3nai4he2', 'idiom'), 22), (('全心全意', 'quan2xin1quan2yi4', 'idiom'), 19), (('解放思想', 'jie3fang4si1xiang3', 'idiom'), 17), (('小心翼翼', 'xiao3xin1yi4yi4', 'idiom'), 16), (('千方百计', 'qian1fang1bai3ji4', 'idiom'), 16), (('坚定不移', 'jian1ding4bu4yi2', 'idiom'), 16), (('引人注目', 'yin3ren2zhu4mu4', 'idiom'), 14), (('建功立业', 'jian4gong1li4ye4', 'idiom'), 14), (('彪形大汉', 'biao1xing2da4han4', 'idiom'), 14)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idiom = [(w,p,pos) for (w,p,pos) in Compiled_Data if pos == 'idiom']\n",
    "len(idiom)\n",
    "idiom[:10]\n",
    "idiom = Counter(idiom)\n",
    "idiom.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180211"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[(('人', 'ren2', 'common noun'), 4025), (('问题', 'wen4ti2', 'common noun'), 1149), (('经济', 'jing1ji4', 'common noun'), 1120), (('社会', 'she4hui4', 'common noun'), 1029), (('国家', 'guo2jia1', 'common noun'), 858), (('企业', 'qi4ye4', 'common noun'), 778), (('技术', 'ji4shu4', 'common noun'), 777), (('系统', 'xi4tong3', 'common noun'), 670), (('人们', 'ren2men5', 'common noun'), 669), (('孩子', 'hai2zi5', 'common noun'), 658)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nouns = [(w,p,pos) for (w,p,pos) in Compiled_Data if pos == 'common noun']\n",
    "len(nouns)\n",
    "nouns = Counter(nouns)\n",
    "nouns.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formal vs Informal Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2900"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "214"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Going to try and look at formal vs informal text\n",
    "informal_ni = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '你']\n",
    "len(informal_ni)\n",
    "formal_ni = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '您']\n",
    "len(formal_ni)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More formal vs informal\n",
    "informal_wife = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '老婆']\n",
    "len(informal_wife)\n",
    "formal_wife = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '太太']\n",
    "len(formal_wife)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "187"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More formal vs informal\n",
    "informal_parents = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '爸妈' or w == '爸爸妈妈']\n",
    "#The two terms in informal are interchangeable as one is just reduplication\n",
    "formal_parents = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '父母']\n",
    "len(informal_parents)\n",
    "len(formal_parents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at Pronouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49714"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[(('他', 'ta1', 'pronoun'), 5847), (('我', 'wo3', 'pronoun'), 5575), (('这', 'zhe4', 'pronoun'), 4142), (('你', 'ni3', 'pronoun'), 2900), (('她', 'ta1', 'pronoun'), 2825), (('我们', 'wo3men5', 'pronoun'), 1969), (('他们', 'ta1men5', 'pronoun'), 1586), (('自己', 'zi4ji3', 'pronoun'), 1578), (('那', 'na3', 'pronoun'), 1537), (('什么', 'shen2me5', 'pronoun'), 1078), (('它', 'ta1', 'pronoun'), 1077), (('这个', 'zhe4ge4', 'pronoun'), 987), (('这样', 'zhe4yang4', 'pronoun'), 852), (('其', 'qi2', 'pronoun'), 826), (('这种', 'zhe4zhong3', 'pronoun'), 798), (('这些', 'zhe4xie1', 'pronoun'), 672), (('各', 'ge4', 'pronoun'), 515), (('各种', 'ge4zhong3', 'pronoun'), 416), (('谁', 'shei2', 'pronoun'), 400), (('这里', 'zhe4li3', 'pronoun'), 385)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "352"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at pronouns now\n",
    "pronouns = [(w,p,pos) for (w,p,pos) in Compiled_Data if pos == 'pronoun' or pos == 'pronoun morpheme']\n",
    "len(pronouns)\n",
    "pronouns = Counter(pronouns)\n",
    "pronouns.most_common(20)\n",
    "\n",
    "unique_pronouns = [(w,p,pos) for (w,p,pos) in Compiled_Unique if pos == 'pronoun' or pos == 'pronoun morpheme']\n",
    "len(unique_pronouns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5898"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2825"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1077"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is the male or female version of 'ta1' used more frequently? What about the gender neutral version?\n",
    "male_ta = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '他']\n",
    "len(male_ta)\n",
    "female_ta = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '她']\n",
    "len(female_ta)\n",
    "neutral_ta = [(w,p,pos) for (w,p,pos) in Compiled_Data if w == '它']\n",
    "len(neutral_ta)"
   ]
  }
 ],
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