113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
import pathlib
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import os
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import argparse
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from typing import List
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import pandas as pd
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import hashlib
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from datetime import datetime
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import csv
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from pandas.core.series import Series
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from media import Media
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def find_all_files(directory: pathlib.Path) -> list:
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ps = []
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for p in directory.rglob('*'):
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if "eaDir" not in str(p):
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ps.append((p))
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return ps
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def view_unqiue_files(p: pathlib.Path, min_size):
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if p.lstat().st_size > min_size * 1024 * 1024:
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if p.lstat().st_nlink == 1:
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print("File with only one hardlink:" + str(p) + "; size: " +
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str(p.lstat().st_size/1024/1024) +
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"MB with number of links: " + str(p.lstat().st_nlink))
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else:
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print("File with more than one hardlink:" + str(p) + "; size: " +
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str(p.lstat().st_size/1024/1024) +
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"MB with number of links: " + str(p.lstat().st_nlink))
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def get_file_checksum(p: pathlib.Path):
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m = hashlib.md5()
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with open(p, 'rb') as f:
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while chunk := f.read(8192):
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m.update(chunk)
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hexvalue = m.hexdigest()
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print(hexvalue)
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return hexvalue
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='convert hardlink to symlink in download folder')
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parser.add_argument('-s','--source',
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type=str,
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help='the path to directory with source files')
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parser.add_argument('-t', '--target',
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type=str,
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help='the path to directory to move files')
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parser.add_argument('--size',
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type=int,
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help='Expected minimum file size')
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parser.add_argument('--csv',
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type=str,
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choices=['False','True'],
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help='export source folders unique files into csv')
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parser.add_argument('--order',
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type=str,
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help='order exported csv by name or inode',
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choices=['inode','name','checksum'])
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parser.add_argument('--csv_path',
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type=str,
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help='path to export csv')
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parser.add_argument('--move',
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type=str,
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help='Confirm whether to move all files with NumberOfLinks as 1 from source directory to target directory')
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args = parser.parse_args()
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min_file_size = 50
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path_library = args.source
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path_inventory = args.target
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if args.size:
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min_file_size = args.size
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paths = find_all_files(pathlib.Path(path_library))
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if args.csv == "True":
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csv_path = "result.csv"
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df_csv = pd.DataFrame(columns=['FileName', 'Path', 'inode', 'NumberOfLinks', 'CheckSum'])
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for x in paths:
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if x.lstat().st_size > min_file_size * 1024 * 1024:
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print(str(datetime.now()) + " : " + str(x))
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new_row = {'FileName': x.name,
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'Path': str(x),
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'inode': x.lstat().st_ino,
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'NumberOfLinks': x.lstat().st_nlink,
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'CheckSum': get_file_checksum(x)}
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df_csv = df_csv.append(new_row, ignore_index=True)
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if args.order == "inode":
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df_csv = df_csv.sort_values(by="inode")
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elif args.order == "name":
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df_csv = df_csv.sort_values(by="FileName")
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elif args.order == "checksum":
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df_csv = df_csv.sort_values(by="CheckSum")
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if args.csv_path:
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csv_path = args.csv_path
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df_csv.to_csv(csv_path,index_label='Index')
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else:
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for x in paths:
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print(str(x))
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view_unqiue_files(x, min_file_size)
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# dst_path = pathlib.Path(os.path.join(path_inventory,x.name))
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# print("Its new path: " + str(dst_path))
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# x.link_to(dst_path)
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