{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "with open (\"errors.txt\") as file:\n",
    "    errors = file.read()\n",
    "\n",
    "matches = re.findall(r\"Citation\\s+`(.*?)'\", errors)\n",
    "\n",
    "with open (\"biblio.bib\") as file:\n",
    "    current_bib = file.read()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ge2018theoretical',\n",
       " 'barbosh2020empirical',\n",
       " 'chen2022seismic',\n",
       " 'munoz2018localization',\n",
       " 'martinez2021empirical',\n",
       " 'rilling2007one',\n",
       " 'xu2016study',\n",
       " 'huang2003confidence',\n",
       " 'zare2023end',\n",
       " 'bates2021distribution']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "found = {m : m in current_bib for m in matches}\n",
    "[k for k, v in found.items() if not v]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "snakemake",
   "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}