{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "f4c4796f-d109-472d-8f9c-95c6ec85f757", "metadata": {}, "outputs": [], "source": [ "import os \n", "import textstat\n", "import csv" ] }, { "cell_type": "code", "execution_count": null, "id": "8f1f2fce-2335-4ee3-81f2-55822e2f63f9", "metadata": {}, "outputs": [], "source": [ "readme_wd = \"\"\n", "contributing_wd = \"\"\n", "\n", "csv_fieldnames = ['subdir', 'filename', 'flesch_reading_ease', 'flesch_kincaid_grade', 'linsear_write_formula', 'dale_chall_readability_score', 'mcalpine_eflaw', 'reading_time', 'char_count', 'word_count']" ] }, { "cell_type": "code", "execution_count": null, "id": "a0d3b5b1-ae97-4a46-95e0-92232c46c2fa", "metadata": {}, "outputs": [], "source": [ "'''\n", "gets the 3 readability scores for each individual textfile\n", "'''\n", "def get_readibility(file_address, file_dict):\n", " file = open(file_address, \"r\")\n", " document = file.read()\n", " file_dict['flesch_reading_ease'] = textstat.flesch_reading_ease(document)\n", " file_dict['flesch_kincaid_grade'] = textstat.flesch_kincaid_grade(document)\n", " file_dict['linsear_write_formula'] = textstat.linsear_write_formula(document)\n", " file_dict['dale_chall_readability_score'] = textstat.dale_chall_readability_score(document)\n", " file_dict['mcalpine_eflaw'] = textstat.mcalpine_eflaw(document)\n", " file_dict['reading_time'] = textstat.reading_time(document, ms_per_char=14.69)\n", " file_dict['char_count'] = textstat.char_count(document, ignore_spaces=True)\n", " file_dict['word_count'] = textstat.lexicon_count(document, removepunct=True)\n", " return file_dict\n" ] }, { "cell_type": "code", "execution_count": null, "id": "8b3c481e-c521-4e1d-926e-88f4b75ae7de", "metadata": {}, "outputs": [], "source": [ "'''\n", "getting readability scoring for each type of document\n", "'''\n", "def generate_file(output_csv, wdirectory, document_type):\n", " with open(output_csv, 'w') as csvfile: \n", " writer = csv.DictWriter(csvfile, fieldnames = csv_fieldnames) \n", " writer.writeheader() \n", " subdirs = os.listdir(wdirectory)\n", " print(document_type)\n", " for dir in subdirs: \n", " print(dir)\n", " files = os.listdir(wdirectory + \"/\" + dir)\n", " count = 0\n", " for file in files:\n", " file_dict = {\"subdir\": dir, \"filename\": file}\n", " print(file)\n", " full_address = wdirectory + \"/\" + dir + \"/\" + file\n", " file_dict = get_readibility(full_address, file_dict)\n", " writer.writerow(file_dict)" ] }, { "cell_type": "code", "execution_count": null, "id": "0e0a7b88-49b6-4053-84b8-f54f1c6536c0", "metadata": {}, "outputs": [], "source": [ "generate_file('dwo_readability_contributing.csv', contributing_wd, \"contributing\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.13.1" } }, "nbformat": 4, "nbformat_minor": 5 }