mirror of
https://github.com/moparisthebest/SickRage
synced 2024-10-31 15:35:01 -04:00
0d9fbc1ad7
This version of SickBeard uses both TVDB and TVRage to search and gather it's series data from allowing you to now have access to and download shows that you couldn't before because of being locked into only what TheTVDB had to offer. Also this edition is based off the code we used in our XEM editon so it does come with scene numbering support as well as all the other features our XEM edition has to offer. Please before using this with your existing database (sickbeard.db) please make a backup copy of it and delete any other database files such as cache.db and failed.db if present, we HIGHLY recommend starting out with no database files at all to make this a fresh start but the choice is at your own risk! Enjoy!
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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#
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# GuessIt - A library for guessing information from filenames
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# Copyright (c) 2012 Nicolas Wack <wackou@gmail.com>
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#
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# GuessIt is free software; you can redistribute it and/or modify it under
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# the terms of the Lesser GNU General Public License as published by
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# the Free Software Foundation; either version 3 of the License, or
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# (at your option) any later version.
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#
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# GuessIt is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# Lesser GNU General Public License for more details.
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#
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# You should have received a copy of the Lesser GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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#
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from __future__ import unicode_literals
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from guessit.patterns import sep
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import re
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import logging
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log = logging.getLogger(__name__)
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def process(mtree):
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for node in mtree.unidentified_leaves():
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indices = []
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didx = 0
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pattern = re.compile(sep + '-' + sep)
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match = pattern.search(node.value)
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while match:
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span = match.span()
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indices.extend([ span[0], span[1] ])
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match = pattern.search(node.value, span[1])
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if indices:
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node.partition(indices)
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