#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Simple script to generate metadata about corpus. """ __author__ = "Amittai Siavava" __version__ = "0.0.1" from os import mkdir from collections import Counter import csv import pandas as pd def index_pages(): """ Generate a friendly index of the pages. We create a csv and a tsv (in case one proves more convenient than the other). """ docID = 0 with open("raw2.csv", "w") as csv_file: writer = csv.writer(csv_file) writer.writerow(["id", "year", "title", "url", "text"]) while True: try: with open(f"../data/log/{docID}", "r") as meta, open(f"../data/log/{docID}.txt", "r") as data: title = meta.readline().strip() year = meta.readline().strip() # if year starts with '|', append to title and read next line if year.startswith("|"): title += " " + year year = meta.readline().strip() url = meta.readline().strip() text = data.read() meta.close() data.close() if year and 2000 <= int(year) <= 2023: print(f"Indexing: {docID}") writer.writerow([docID, year, f'"{title}"', f'"{url}"', f'"{text}"']) docID += 1 except Exception as e: print(e) print(f"{docID = }") break print("Done.") print(f"okay...") # save to file # df = pd.read_csv("raw.csv") print("Done.") def categorize(): """ Categorize the pages by year. """ docID = 0 years = Counter() years_index = { str(year) for year in range(2000, 2024)} while True: try: with open(f"../log/{docID}.txt", "r") as doc, open(f"../log/{docID}", "r") as meta: title = meta.readline().strip() year = meta.readline().strip() url = meta.readline().strip() text = doc.read() doc.close() meta.close() if year in years_index: try: mkdir(f"../categorized/{year}") except: pass id = years[year] with open(f"../categorized/{year}/{id}.txt", "w") as f: f.write(f"{title}\n{year}\n{url}\n\n{text}") f.close() years[year] += 1 docID += 1 except: break if __name__ == "__main__": index_pages() # categorize() # load_data()