{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import pymorphy2\n",
    "import cctext as ct\n",
    "\n",
    "from nltk.stem import SnowballStemmer\n",
    "stemmer = SnowballStemmer(language=\"russian\")\n",
    "# https://www.langust.ru/rus_gram/rus_gr06.shtml\n",
    "# stemmer.stem(\"обеспечение\")\n",
    "\n",
    "morpho = pymorphy2.MorphAnalyzer()\n",
    "parser = ct.RuParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "butyavka = morpho.parse('мюсли')\n",
    "butyavka[0].lexeme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct.get_all_forms('танк')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# thesaurus = open('tests/data/thesaurus.txt', mode='r', encoding='UTF-8')\n",
    "thesaurus = open('tests/data/functions.txt', mode='r', encoding='UTF-8')\n",
    "content = [term.strip() for term in thesaurus.readlines()]\n",
    "thesaurus.close()\n",
    "\n",
    "inflect_datv = [ct.inflect(term, 'datv') for term in content]\n",
    "inflect_nomn1 = [ct.inflect(term, 'nomn') for term in inflect_datv]\n",
    "inflect_gent = [ct.inflect(term, 'gent') for term in content]\n",
    "inflect_nomn2 = [ct.inflect(term, 'nomn') for term in inflect_gent]\n",
    "\n",
    "data = zip(content, inflect_datv, inflect_nomn1, inflect_gent, inflect_nomn2)\n",
    "df = pd.DataFrame(data, columns =['Term', 'Datv', 'Inverse1', 'Gent', 'Inverse2'])\n",
    "df.to_excel('output.xlsx')"
   ]
  }
 ],
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