{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e4e4b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('Step3_output_article_response_original_3.json', encoding=\"utf8\") as f:\n",
    "    data_pos = json.load(f)\n",
    "with open('Step3_output_article_response_opposite_2.json', encoding=\"utf8\") as f:\n",
    "    data_neg = json.load(f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e7b83fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_list = []\n",
    "for art_id in range(len(data_pos)):\n",
    "    entry = {}\n",
    "    entry['article_id'] = art_id\n",
    "    entry['article_name'] = data_pos[art_id]['title']\n",
    "    entry['article_institute'] = data_pos[art_id]['universities']\n",
    "    entry['article_abstract'] = data_pos[art_id]['abstract']\n",
    "    \n",
    "    entry['measures_positive'] = [l[0] for l in data_pos[art_id]['response'][0][:]]\n",
    "    entry['responses_positive'] = [l[2] for l in data_pos[art_id]['response'][0][:]]\n",
    "    entry['scores_positive'] = [l[3] for l in data_pos[art_id]['response'][0][:]]\n",
    "    \n",
    "    entry['measures_negative'] = [l[0] for l in data_neg[art_id]['response'][0][:]]\n",
    "    entry['responses_negative'] = [l[2] for l in data_neg[art_id]['response'][0][:]]\n",
    "    entry['scores_negative'] = [l[3] for l in data_neg[art_id]['response'][0][:]]\n",
    "\n",
    "    combined_list += [entry]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24d0c9b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('output/Step4_article_response_all_combined_2.json', 'w', encoding=\"utf8\") as f:\n",
    "    json.dump(combined_list, f, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0cda7716",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "def write_csv(filename, score_lbl='scores_positive'):\n",
    "    # Open the file in append mode\n",
    "    with open(filename, mode='w', newline='', encoding=\"utf-8-sig\") as file:\n",
    "        writer = csv.writer(file)\n",
    "\n",
    "        labels = ['Value '+str(j) for j in range(64)]\n",
    "\n",
    "        writer.writerow(['Institute'] + labels)\n",
    "\n",
    "        for i in range(0, len(combined_list)):\n",
    "            article = combined_list[i]\n",
    "            uni = article['article_institute']\n",
    "            rankings = article[score_lbl]\n",
    "            writer.writerow([uni]+rankings)\n",
    "            \n",
    "write_csv(filename='output/Step4_scores_positive_2.csv', score_lbl='scores_positive')\n",
    "write_csv(filename='output/Step4_scores_negative_2.csv', score_lbl='scores_negative')"
   ]
  }
 ],
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   "file_extension": ".py",
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