{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pattern.search import STRICT, search\n",
    "from pattern.en import parsetree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/clips/pattern/wiki/pattern-search\n",
    "( inspired by [videogrep](https://github.com/antiboredom/videogrep/blob/master/videogrep/searcher.py) search )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = open(\"../txt/words-for-the-future/OTHERNESS.txt\").read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Otherness | Daniel L. Everett\\n\\nWhen I was 26, I moved to the Amazon, from California, in order to st'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "generator raised StopIteration",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/__init__.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(path, encoding, comment)\u001b[0m\n\u001b[1;32m    608\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 609\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mStopIteration\u001b[0m: ",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-ac287bca8f52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsetree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/en/__init__.py\u001b[0m in \u001b[0;36mparsetree\u001b[0;34m(s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    173\u001b[0m     \"\"\" Returns a parsed Text from the given string.\n\u001b[1;32m    174\u001b[0m     \"\"\"\n\u001b[0;32m--> 175\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/en/__init__.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m     \"\"\" Returns a tagged Unicode string.\n\u001b[1;32m    168\u001b[0m     \"\"\"\n\u001b[0;32m--> 169\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/__init__.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, tokenize, tags, chunks, relations, lemmata, encoding, **kwargs)\u001b[0m\n\u001b[1;32m   1170\u001b[0m             \u001b[0;31m# Tagger (required by chunker, labeler & lemmatizer).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtags\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mchunks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mrelations\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlemmata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1172\u001b[0;31m                 \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1173\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1174\u001b[0m                 \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/en/__init__.py\u001b[0m in \u001b[0;36mfind_tags\u001b[0;34m(self, tokens, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tagset\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mUNIVERSAL\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpenntreebank2universal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_Parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/__init__.py\u001b[0m in \u001b[0;36mfind_tags\u001b[0;34m(self, tokens, **kwargs)\u001b[0m\n\u001b[1;32m   1111\u001b[0m         \u001b[0;31m# [\"The\", \"cat\", \"purs\"] => [[\"The\", \"DT\"], [\"cat\", \"NN\"], [\"purs\", \"VB\"]]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m         return find_tags(tokens,\n\u001b[0;32m-> 1113\u001b[0;31m                     \u001b[0mlexicon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"lexicon\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlexicon\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1114\u001b[0m                       \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1115\u001b[0m                  \u001b[0mmorphology\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"morphology\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmorphology\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/__init__.py\u001b[0m in \u001b[0;36m__len__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lazy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__len__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/__init__.py\u001b[0m in \u001b[0;36m_lazy\u001b[0;34m(self, method, *args)\u001b[0m\n\u001b[1;32m    366\u001b[0m         \"\"\"\n\u001b[1;32m    367\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMethodType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/__init__.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m         \u001b[0;31m# Arnold NNP x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m         \u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;31m#--- FREQUENCY -------------------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/pattern/text/__init__.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m         \u001b[0;31m# Arnold NNP x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m         \u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;31m#--- FREQUENCY -------------------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: generator raised StopIteration"
     ]
    }
   ],
   "source": [
    "tree = parsetree(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tree' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-7a1e081e78d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtree\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'tree' is not defined"
     ]
    }
   ],
   "source": [
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentence(\"This/DT/O/O encounter/RB/B-ADVP/O with/IN/B-PP/O these/DT/O/O ‘/''/O/O others/NNS/B-NP/O ,/,/O/O ’/''/O/O so/RB/B-ADVP/O unlike/IN/B-PP/B-PNP myself/PRP/B-NP/I-PNP ,/,/O/O was/VBD/B-VP/O to/TO/I-VP/O be/VB/I-VP/O the/DT/O/O defining/VBG/B-VP/O experience/NN/B-NP/O for/IN/B-PP/B-PNP the/DT/B-NP/I-PNP rest/NN/I-NP/I-PNP of/IN/B-PP/B-PNP my/PRP$/B-NP/I-PNP life/NN/I-NP/I-PNP ././O/O\")"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Match(words=[Word('unrelated/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('small/JJ')]),\n",
       " Match(words=[Word('missionary/JJ')]),\n",
       " Match(words=[Word('bumpy/JJ')]),\n",
       " Match(words=[Word('first/JJ')]),\n",
       " Match(words=[Word('weak/JJ')]),\n",
       " Match(words=[Word('taut/JJ')]),\n",
       " Match(words=[Word('unrelated/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('many/JJ')]),\n",
       " Match(words=[Word('little/JJ')]),\n",
       " Match(words=[Word('easy/JJ')]),\n",
       " Match(words=[Word('enough/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('uncomfortable/JJ')]),\n",
       " Match(words=[Word('suspicious/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('different/JJ')]),\n",
       " Match(words=[Word('simple/JJ')]),\n",
       " Match(words=[Word('binary/JJ')]),\n",
       " Match(words=[Word('old/JJ')]),\n",
       " Match(words=[Word('religious/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('different/JJ')]),\n",
       " Match(words=[Word('unintelligible/JJ')]),\n",
       " Match(words=[Word('different-looking/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('live/JJ')]),\n",
       " Match(words=[Word('unacceptable/JJ')]),\n",
       " Match(words=[Word('missionary/JJ')]),\n",
       " Match(words=[Word('eternal/JJ')]),\n",
       " Match(words=[Word('encounter/JJ')]),\n",
       " Match(words=[Word('uneasy/JJ')]),\n",
       " Match(words=[Word('dangerous/JJ')]),\n",
       " Match(words=[Word('insufficient/JJ')]),\n",
       " Match(words=[Word('ethno-centric/JJ')]),\n",
       " Match(words=[Word('own/JJ')]),\n",
       " Match(words=[Word('fortunate/JJ')]),\n",
       " Match(words=[Word('gentle/JJ')]),\n",
       " Match(words=[Word('many/JJ')]),\n",
       " Match(words=[Word('silly/JJ')]),\n",
       " Match(words=[Word('years-long/JJ')]),\n",
       " Match(words=[Word('first/JJ')]),\n",
       " Match(words=[Word('first/JJ')]),\n",
       " Match(words=[Word('young/JJ')]),\n",
       " Match(words=[Word('large/JJ')]),\n",
       " Match(words=[Word('small/JJ')]),\n",
       " Match(words=[Word('fresh/JJ')]),\n",
       " Match(words=[Word('young/JJ')]),\n",
       " Match(words=[Word('then-unintelligible/JJ')]),\n",
       " Match(words=[Word('easy/JJ')]),\n",
       " Match(words=[Word('polite/JJ')]),\n",
       " Match(words=[Word('Many/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('Western/JJ')]),\n",
       " Match(words=[Word('polite/JJ')]),\n",
       " Match(words=[Word('Western/JJ')]),\n",
       " Match(words=[Word('first/JJ')]),\n",
       " Match(words=[Word('close/JJ')]),\n",
       " Match(words=[Word('first/JJ')]),\n",
       " Match(words=[Word('small/JJ')]),\n",
       " Match(words=[Word('early/JJ')]),\n",
       " Match(words=[Word('subsequent/JJ')]),\n",
       " Match(words=[Word('individual/JJ')]),\n",
       " Match(words=[Word('normal/JJ')]),\n",
       " Match(words=[Word('comfortable/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('dissonant/JJ')]),\n",
       " Match(words=[Word('steady/JJ')]),\n",
       " Match(words=[Word('familiar/JJ')]),\n",
       " Match(words=[Word('Comfort/JJ')]),\n",
       " Match(words=[Word('acquired/JJ')]),\n",
       " Match(words=[Word('different/JJ')]),\n",
       " Match(words=[Word('different/JJ')]),\n",
       " Match(words=[Word('sexual/JJ')]),\n",
       " Match(words=[Word('different/JJ')]),\n",
       " Match(words=[Word('biological/JJ')]),\n",
       " Match(words=[Word('little/JJ')]),\n",
       " Match(words=[Word('possible/JJ')]),\n",
       " Match(words=[Word('worthwhile/JJ')]),\n",
       " Match(words=[Word('obvious/JJ')]),\n",
       " Match(words=[Word('first/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('behavioral/JJ')]),\n",
       " Match(words=[Word('earliest/JJ')]),\n",
       " Match(words=[Word('normal/JJ')]),\n",
       " Match(words=[Word('correct/JJ')]),\n",
       " Match(words=[Word('crucial/JJ')]),\n",
       " Match(words=[Word('in-group/JJ')]),\n",
       " Match(words=[Word('social/JJ')]),\n",
       " Match(words=[Word('else/JJ')]),\n",
       " Match(words=[Word('own/JJ')]),\n",
       " Match(words=[Word('own/JJ')]),\n",
       " Match(words=[Word('familiar/JJ')]),\n",
       " Match(words=[Word('fit/JJ')]),\n",
       " Match(words=[Word('several/JJ')]),\n",
       " Match(words=[Word('Pirahã/JJ')]),\n",
       " Match(words=[Word('full/JJ')]),\n",
       " Match(words=[Word('noticed/JJ')]),\n",
       " Match(words=[Word('old/JJ')]),\n",
       " Match(words=[Word('sharp/JJ')]),\n",
       " Match(words=[Word('30cm/JJ')]),\n",
       " Match(words=[Word('dangerous/JJ')]),\n",
       " Match(words=[Word('handed/JJ')]),\n",
       " Match(words=[Word('non-life-threatening/JJ')]),\n",
       " Match(words=[Word('necessary/JJ')]),\n",
       " Match(words=[Word('Dutch/JJ')]),\n",
       " Match(words=[Word('sharp/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('m)otherness/JJ')]),\n",
       " Match(words=[Word('sure/JJ')]),\n",
       " Match(words=[Word('able/JJ')]),\n",
       " Match(words=[Word('occasional/JJ')]),\n",
       " Match(words=[Word('interesting/JJ')]),\n",
       " Match(words=[Word('crooked/JJ')]),\n",
       " Match(words=[Word('straight/JJ')]),\n",
       " Match(words=[Word('bizarre/JJ')]),\n",
       " Match(words=[Word('American/JJ')]),\n",
       " Match(words=[Word('missionary/JJ')]),\n",
       " Match(words=[Word('excited/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('Pirahã/JJ')]),\n",
       " Match(words=[Word('native/JJ')]),\n",
       " Match(words=[Word('native/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('local/JJ')]),\n",
       " Match(words=[Word('comfortable/JJ')]),\n",
       " Match(words=[Word('similar/JJ')]),\n",
       " Match(words=[Word('many/JJ')]),\n",
       " Match(words=[Word('favorite/JJ')]),\n",
       " Match(words=[Word('American/JJ')]),\n",
       " Match(words=[Word('different/JJ')]),\n",
       " Match(words=[Word('irrelevant/JJ')]),\n",
       " Match(words=[Word('brilliant/JJ')]),\n",
       " Match(words=[Word('boring/JJ')]),\n",
       " Match(words=[Word('adjacent/JJ')]),\n",
       " Match(words=[Word('full/JJ')]),\n",
       " Match(words=[Word('brilliant/JJ')]),\n",
       " Match(words=[Word('good/JJ')]),\n",
       " Match(words=[Word('human/JJ')]),\n",
       " Match(words=[Word('independent/JJ')]),\n",
       " Match(words=[Word('natural/JJ')]),\n",
       " Match(words=[Word('solitary/JJ')]),\n",
       " Match(words=[Word('strange/JJ')]),\n",
       " Match(words=[Word('slow/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('original/JJ')]),\n",
       " Match(words=[Word('paradoxical/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('panoramic/JJ')]),\n",
       " Match(words=[Word('own/JJ')]),\n",
       " Match(words=[Word('unique/JJ')]),\n",
       " Match(words=[Word('important/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('individual/JJ')]),\n",
       " Match(words=[Word('s/JJ')]),\n",
       " Match(words=[Word('small/JJ')]),\n",
       " Match(words=[Word('read/JJ')]),\n",
       " Match(words=[Word('possible/JJ')]),\n",
       " Match(words=[Word('own/JJ')]),\n",
       " Match(words=[Word('i]The/JJ')]),\n",
       " Match(words=[Word('poor/JJ')]),\n",
       " Match(words=[Word('good/JJ')]),\n",
       " Match(words=[Word('measurable/JJ')]),\n",
       " Match(words=[Word('daily/JJ')]),\n",
       " Match(words=[Word('social/JJ')]),\n",
       " Match(words=[Word('little/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('conceptual/JJ')]),\n",
       " Match(words=[Word('cultural/JJ')]),\n",
       " Match(words=[Word('social/JJ')]),\n",
       " Match(words=[Word('predictable/JJ')]),\n",
       " Match(words=[Word('predictable/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('thinking/JJ')]),\n",
       " Match(words=[Word('strong/JJ')]),\n",
       " Match(words=[Word('desirable/JJ')]),\n",
       " Match(words=[Word('unexpected/JJ')]),\n",
       " Match(words=[Word('constant/JJ')]),\n",
       " Match(words=[Word('useful/JJ')]),\n",
       " Match(words=[Word('biological/JJ')]),\n",
       " Match(words=[Word('cognitive/JJ')]),\n",
       " Match(words=[Word('cultural/JJ')]),\n",
       " Match(words=[Word('unsuccessful/JJ')]),\n",
       " Match(words=[Word('strange/JJ')]),\n",
       " Match(words=[Word('successful/JJ')]),\n",
       " Match(words=[Word('own/JJ')]),\n",
       " Match(words=[Word('little/JJ')]),\n",
       " Match(words=[Word('such/JJ')]),\n",
       " Match(words=[Word('political/JJ')]),\n",
       " Match(words=[Word('important/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('unable/JJ')]),\n",
       " Match(words=[Word('little/JJ')]),\n",
       " Match(words=[Word('little/JJ')]),\n",
       " Match(words=[Word('18th/JJ')]),\n",
       " Match(words=[Word('identical/JJ')]),\n",
       " Match(words=[Word('light/JJ')]),\n",
       " Match(words=[Word('multiple/JJ')]),\n",
       " Match(words=[Word('familiar/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('same/JJ')]),\n",
       " Match(words=[Word('same/JJ')]),\n",
       " Match(words=[Word('same/JJ')]),\n",
       " Match(words=[Word('same/JJ')]),\n",
       " Match(words=[Word('same/JJ')]),\n",
       " Match(words=[Word('same/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('anti-immigration/JJ')]),\n",
       " Match(words=[Word('political/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('motivated/JJ')]),\n",
       " Match(words=[Word('ultimate/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('cognitive/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('new/JJ')]),\n",
       " Match(words=[Word('own/JJ')]),\n",
       " Match(words=[Word('only/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('otherness/JJ')]),\n",
       " Match(words=[Word('invented/JJ')]),\n",
       " Match(words=[Word('communal/JJ')]),\n",
       " Match(words=[Word('cultural/JJ')]),\n",
       " Match(words=[Word('cultural/JJ')]),\n",
       " Match(words=[Word('human/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('distinct/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('s/JJ')]),\n",
       " Match(words=[Word('other/JJ')]),\n",
       " Match(words=[Word('Amazonian/JJ')]),\n",
       " Match(words=[Word('doomed/JJ')]),\n",
       " Match(words=[Word('Greek/JJ')]),\n",
       " Match(words=[Word('repetitive/JJ')]),\n",
       " Match(words=[Word('daily/JJ')]),\n",
       " Match(words=[Word('huge/JJ')]),\n",
       " Match(words=[Word('only/JJ')]),\n",
       " Match(words=[Word('same/JJ')]),\n",
       " Match(words=[Word('next/JJ')])]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search(\"JJ\", tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Match(words=[Word('study/VB'), Word('the/DT'), Word('language/NN')]),\n",
       " Match(words=[Word('be/VB'), Word('a/DT'), Word('prostitute/NN')]),\n",
       " Match(words=[Word('seem/VB'), Word('that/DT'), Word('way/NN')]),\n",
       " Match(words=[Word('conduct/VB'), Word('a/DT'), Word('pilot/NN')]),\n",
       " Match(words=[Word('let/VB'), Word('the/DT'), Word('stick/NN')]),\n",
       " Match(words=[Word('remove/VB'), Word('the/DT'), Word('otherness/NN')]),\n",
       " Match(words=[Word('occupy/VB'), Word('a/DT'), Word('part/NN')])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search('VB DT NN', tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "study the language\n",
      "be a prostitute\n",
      "seem that way\n",
      "conduct a pilot\n",
      "let the stick\n",
      "remove the otherness\n",
      "occupy a part\n"
     ]
    }
   ],
   "source": [
    "for m in search (\"VB DT NN\", tree):\n",
    "    print (f\"{m.string}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'occupy a part'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My body\n",
      "my brain\n",
      "My task\n",
      "my life\n",
      "our species\n",
      "our child\n",
      "their differences\n",
      "my belief\n",
      "my encounter\n",
      "my own\n",
      "my silly beliefs\n",
      "my life\n",
      "my first day\n",
      "his hut\n",
      "its tongue\n",
      "our mother\n",
      "our mother\n",
      "our father\n",
      "our first experiences\n",
      "our values\n",
      "our mother and the select\n",
      "our subsequent lives\n",
      "Our earliest associations\n",
      "our narrow range\n",
      "our in-group\n",
      "my own writings.[1\n",
      "our family or our village\n",
      "our own identity\n",
      "our identity\n",
      "our family\n",
      "our norm\n",
      "our experience\n",
      "our expectations\n",
      "its occupants\n",
      "their beliefs and children\n",
      "his face\n",
      "his mother\n",
      "her toddler\n",
      "her child\n",
      "his quasi-stabbing\n",
      "her child\n",
      "her child a sharp knife\n",
      "its contribution\n",
      "our lives\n",
      "their language\n",
      "their translations\n",
      "their comments\n",
      "my request\n",
      "their language\n",
      "our behavior\n",
      "their language\n",
      "their culture\n",
      "their language\n",
      "Our sense\n",
      "our enveloping\n",
      "our childhood development\n",
      "our conversations and the structures\n",
      "our interactions\n",
      "their phrases\n",
      "my favorite book\n",
      "his year\n",
      "His year\n",
      "its institutions\n",
      "our senses\n",
      "our sense\n",
      "his lessons\n",
      "our sense\n",
      "our own unique identity\n",
      "our oneness\n",
      "my life\n",
      "his own question\n",
      "his essay\n",
      "our behavior\n",
      "His example\n",
      "his case\n",
      "our lives\n",
      "our lives\n",
      "our expectations\n",
      "our environments\n",
      "our own\n",
      "our Homo ancestors\n",
      "our environment\n",
      "their language\n",
      "their culture and language\n",
      "our familiar environment\n",
      "our world\n",
      "Our preference\n",
      "our fear then itself\n",
      "Our languages and cognitive abilities\n",
      "their relationships\n",
      "our own\n",
      "our species ability\n",
      "our human\n",
      "our greatest fears\n",
      "our greatest treasure\n",
      "his efforts\n"
     ]
    }
   ],
   "source": [
    "for m in search (\"PRP$ *\", tree):\n",
    "    print (f\"{m.string}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pattern.en import wordnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sense = wordnet.synsets(\"language\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Synset('communication.n.02')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sense.hypernym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matching neighbor\n",
      "matching friend\n",
      "matching child\n",
      "matching woman\n",
      "matching prostitute\n",
      "matching man\n",
      "matching man\n",
      "matching guest\n",
      "matching host\n",
      "matching mother\n",
      "matching mother\n",
      "matching father\n",
      "matching mother\n",
      "matching professor\n",
      "matching cowboy\n",
      "matching psychologist\n",
      "matching pilot\n",
      "matching toddler\n",
      "matching mother\n",
      "matching mother\n",
      "matching toddler\n",
      "matching woman\n",
      "matching baby\n",
      "matching child\n",
      "matching mother\n",
      "matching baby\n",
      "matching mother\n",
      "matching child\n",
      "matching mother\n",
      "matching child\n",
      "matching child\n",
      "matching child\n",
      "matching speaker\n",
      "matching speaker\n",
      "matching foreigner\n",
      "matching tourist\n",
      "matching friend\n",
      "matching man\n",
      "matching foreigner\n",
      "matching handyman\n",
      "matching stranger\n",
      "matching Homo\n",
      "matching Homo\n",
      "matching Homo\n"
     ]
    }
   ],
   "source": [
    "output = []\n",
    "search_word=\"person\"\n",
    "for search_word in search_word.split('|'):\n",
    "    synset = wordnet.synsets(search_word)[0]\n",
    "    pos = synset.pos\n",
    "    possible_words = search(pos, tree)\n",
    "    for match in possible_words:\n",
    "        # print (f\"match {match}\")\n",
    "        word = match[0].string\n",
    "        synsets = wordnet.synsets(word)\n",
    "        if len(synsets) > 0:\n",
    "            hypernyms = synsets[0].hypernyms(recursive=True)\n",
    "            if any(search_word == h.senses[0] for h in hypernyms):\n",
    "                print(f\"matching {word}\")\n",
    "                output.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['phrase']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}