{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example usage\n",
    "\n",
    "Here we will demonstrate how to use `bf25_pycounts` to count the words in a text file and count the top 5 results.\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bf25_pycounts.bf25_pycounts import count_words\n",
    "from bf25_pycounts.plotting import plot_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a text file\n",
    "\n",
    "We'll first create a text file to work with using a famous quote from Einstein:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "quote = \"Insanity is doing the same thing over and over and \\\n",
    "             expecting different results.\"\n",
    "with open(\"einstein.txt\", \"w\") as file:\n",
    "        file.write(quote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count words\n",
    "\n",
    "We can count the words in our text file using the `count_words()` function. Note that this function removes punctuation and makes all words lowercase before counting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'over': 2, 'and': 2, 'insanity': 1, 'is': 1, 'doing': 1, 'the': 1, 'same': 1, 'thing': 1, 'expecting': 1, 'different': 1, 'results': 1})\n"
     ]
    }
   ],
   "source": [
    "counts = count_words('einstein.txt')\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Words\n",
    "We can now plot the result using the `plot_words()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_words(counts,n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dsci524_ind_assignment",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}