Add parser, still work in progress
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@ -4,6 +4,11 @@ import argparse
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import os
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import os
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import sys
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import sys
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import time
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import time
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import pypdf
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#from PyPDF2 import PdfReader
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from pypdf import PdfReader
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import re # regular expression
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import pandas as pd
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__version__ = '1.0.0'
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__version__ = '1.0.0'
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__website__ = 'https://www.fb.gv.at/Funk/amateurfunkdienst.html'
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__website__ = 'https://www.fb.gv.at/Funk/amateurfunkdienst.html'
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@ -59,19 +64,68 @@ def call_website(url,interactive=False):
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print(element.text)
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print(element.text)
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# print(href)
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# print(href)
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if(interactive):
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if(interactive):
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time.sleep(5)
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else:
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time.sleep(300)
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time.sleep(300)
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else:
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time.sleep(5)
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driver.close()
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driver.close()
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return os.path.basename(href)
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return os.path.basename(href)
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def get_pdf_content_lines(pdf_file_path):
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with open(pdf_file_path) as f:
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pdf_reader = PdfReader(f)
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for page in pdf_reader.pages:
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for line in page.extractText().spitlines():
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yield line
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def call_analyse_pdf(file):
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# Define a regular expression to match tables
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reader = PdfReader(file)
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meta = reader.metadata
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print(' Pages:', len(reader.pages))
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# All of the following could be None!
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print(f' Author: {meta.author}')
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print(f' Creator: {meta.creator}')
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print(f'Producer: {meta.producer}')
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print(f' Subject: {meta.subject}')
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print(f' Title: {meta.title}')
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print(f' Created: {meta.creation_date}')
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print(f'Modified: {meta.modification_date}')
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for page in reader.pages:
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page_text = page.extract_text(extraction_mode="layout", layout_mode_space_vertically=False)
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#print(page_text)
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# page_text = page.extract_text(extraction_mode="layout", layout_mode_scale_weight=1.0)
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# print(page_text)
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# Find all tables in page_text
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calls = re.findall(r' +(OE[0-9][A-Z]{1,3}).*$', page_text)
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for call in calls:
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print(call)
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tables = re.findall(r'(?s)\b(?:\w+\s+){2,}\w+\b(?:\s*[,;]\s*\b(?:\w+\s+){2,}\w+\b)*', page_text)
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# Loop through each table and create a pandas DataFrame
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for table in tables:
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# Split the table into rows
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rows = table.strip().split('\n')
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# Split the rows into cells
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cells = [row.split('|') for row in rows]
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# Remove leading and trailing whitespace from cells
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cells = [[cell.strip() for cell in row] for row in cells]
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# Remove empty rows and columns
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cells = [[cell for cell in row if cell] for row in cells if row]
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# Create a pandas DataFrame from the cells
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df = pd.DataFrame(cells[1:], columns=cells[0])
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# TODO: Clean and manipulate the df as needed
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if __name__ == '__main__':
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if __name__ == '__main__':
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# call_description()
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# call_description()
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args = call_parser()
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args = call_parser()
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try:
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try:
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filename = call_website(**vars(args))
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filename = call_website(**vars(args))
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print(f'Filename: {filename}')
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print(f'Filename: {filename}')
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call_analyse_pdf(filename)
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sys.exit(0)
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sys.exit(0)
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except Exception as e:
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except Exception as e:
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print('Error: {}'.format(e), file=sys.stderr)
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print('Error: {}'.format(e), file=sys.stderr)
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