aliakbar
108 lines
3.3 KiB
Plaintext
108 lines
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "0e4e4b7d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"with open('Step3_output_article_response_original_3.json', encoding=\"utf8\") as f:\n",
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" data_pos = json.load(f)\n",
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"with open('Step3_output_article_response_opposite_2.json', encoding=\"utf8\") as f:\n",
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" data_neg = json.load(f)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "e7b83fa4",
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"metadata": {},
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"outputs": [],
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"source": [
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"combined_list = []\n",
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"for art_id in range(len(data_pos)):\n",
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" entry = {}\n",
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" entry['article_id'] = art_id\n",
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" entry['article_name'] = data_pos[art_id]['title']\n",
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" entry['article_institute'] = data_pos[art_id]['universities']\n",
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" entry['article_abstract'] = data_pos[art_id]['abstract']\n",
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" \n",
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" entry['measures_positive'] = [l[0] for l in data_pos[art_id]['response'][0][:]]\n",
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" entry['responses_positive'] = [l[2] for l in data_pos[art_id]['response'][0][:]]\n",
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" entry['scores_positive'] = [l[3] for l in data_pos[art_id]['response'][0][:]]\n",
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" \n",
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" entry['measures_negative'] = [l[0] for l in data_neg[art_id]['response'][0][:]]\n",
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" entry['responses_negative'] = [l[2] for l in data_neg[art_id]['response'][0][:]]\n",
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" entry['scores_negative'] = [l[3] for l in data_neg[art_id]['response'][0][:]]\n",
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"\n",
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" combined_list += [entry]\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "24d0c9b5",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('output/Step4_article_response_all_combined_2.json', 'w', encoding=\"utf8\") as f:\n",
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" json.dump(combined_list, f, ensure_ascii=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "0cda7716",
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"metadata": {},
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"outputs": [],
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"source": [
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"import csv\n",
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"\n",
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"def write_csv(filename, score_lbl='scores_positive'):\n",
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" # Open the file in append mode\n",
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" with open(filename, mode='w', newline='', encoding=\"utf-8-sig\") as file:\n",
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" writer = csv.writer(file)\n",
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"\n",
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" labels = ['Value '+str(j) for j in range(64)]\n",
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"\n",
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" writer.writerow(['Institute'] + labels)\n",
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"\n",
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" for i in range(0, len(combined_list)):\n",
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" article = combined_list[i]\n",
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" uni = article['article_institute']\n",
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" rankings = article[score_lbl]\n",
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" writer.writerow([uni]+rankings)\n",
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" \n",
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"write_csv(filename='output/Step4_scores_positive_2.csv', score_lbl='scores_positive')\n",
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"write_csv(filename='output/Step4_scores_negative_2.csv', score_lbl='scores_negative')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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