2025-02-03 04:51:49 +00:00
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{
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"cells": [
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{
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"cell_type": "code",
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2025-02-04 01:06:13 +00:00
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"execution_count": 1,
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2025-02-03 04:51:49 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import os \n",
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"import textstat\n",
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"import csv"
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]
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},
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{
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"cell_type": "code",
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2025-02-04 01:06:13 +00:00
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"execution_count": 2,
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2025-02-03 04:51:49 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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"contributing_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/\"\n",
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"readme_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/readme/\"\n",
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"\n",
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"csv_fieldnames = [ 'filename', 'flesch_reading_ease', 'flesch_kincaid_grade', 'linsear_write_formula', 'dale_chall_readability_score', 'mcalpine_eflaw', 'reading_time', 'char_count', 'word_count']"
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]
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},
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{
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"cell_type": "code",
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2025-02-04 01:06:13 +00:00
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"execution_count": 3,
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2025-02-03 04:51:49 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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"def generate_file(output_csv, wdirectory):\n",
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" with open(output_csv, 'w') as csvfile: \n",
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" writer = csv.DictWriter(csvfile, fieldnames = csv_fieldnames) \n",
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" writer.writeheader() \n",
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" files = os.listdir(wdirectory)\n",
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" for file in files:\n",
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" file_dict = {\"filename\": file}\n",
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" full_address = wdirectory + file\n",
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" file_dict = get_readibility(full_address, file_dict)\n",
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" writer.writerow(file_dict)\n"
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]
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},
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{
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"cell_type": "code",
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2025-02-04 01:06:13 +00:00
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"execution_count": 4,
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2025-02-03 04:51:49 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_readibility(file_address, file_dict):\n",
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" file = open(file_address, \"r\", encoding='utf-8', errors=\"ignore\")\n",
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" document = file.read()\n",
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" file_dict['flesch_reading_ease'] = textstat.flesch_reading_ease(document)\n",
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" file_dict['flesch_kincaid_grade'] = textstat.flesch_kincaid_grade(document)\n",
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" file_dict['linsear_write_formula'] = textstat.linsear_write_formula(document)\n",
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" file_dict['dale_chall_readability_score'] = textstat.dale_chall_readability_score(document)\n",
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" file_dict['mcalpine_eflaw'] = textstat.mcalpine_eflaw(document)\n",
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" file_dict['reading_time'] = textstat.reading_time(document, ms_per_char=14.69)\n",
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" file_dict['char_count'] = textstat.char_count(document, ignore_spaces=True)\n",
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" file_dict['word_count'] = textstat.lexicon_count(document, removepunct=True)\n",
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" return file_dict"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"generate_file('020125_CONTRIBUTING_readability.csv', contributing_directory)"
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]
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},
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{
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"cell_type": "code",
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2025-02-04 01:06:13 +00:00
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"execution_count": 5,
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2025-02-03 04:51:49 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2025-02-04 01:06:13 +00:00
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"generate_file('020325_README_readability.csv', readme_directory)"
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2025-02-03 04:51:49 +00:00
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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": "base",
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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": "3.12.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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