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mirror of https://github.com/moparisthebest/SickRage synced 2024-10-31 23:45:02 -04:00
SickRage/lib/guessit/transfo/__init__.py
echel0n 0d9fbc1ad7 Welcome to our SickBeard-TVRage Edition ...
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!
2014-03-09 22:39:12 -07:00

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3.7 KiB
Python

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
#
# GuessIt - A library for guessing information from filenames
# Copyright (c) 2012 Nicolas Wack <wackou@gmail.com>
#
# GuessIt is free software; you can redistribute it and/or modify it under
# the terms of the Lesser GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# GuessIt is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# Lesser GNU General Public License for more details.
#
# You should have received a copy of the Lesser GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
from __future__ import unicode_literals
from guessit import base_text_type, Guess
from guessit.patterns import canonical_form
from guessit.textutils import clean_string
import logging
log = logging.getLogger(__name__)
def found_property(node, name, confidence):
node.guess = Guess({name: node.clean_value}, confidence=confidence)
log.debug('Found with confidence %.2f: %s' % (confidence, node.guess))
def format_guess(guess):
"""Format all the found values to their natural type.
For instance, a year would be stored as an int value, etc...
Note that this modifies the dictionary given as input.
"""
for prop, value in guess.items():
if prop in ('season', 'episodeNumber', 'year', 'cdNumber',
'cdNumberTotal', 'bonusNumber', 'filmNumber'):
guess[prop] = int(guess[prop])
elif isinstance(value, base_text_type):
if prop in ('edition',):
value = clean_string(value)
guess[prop] = canonical_form(value).replace('\\', '')
return guess
def find_and_split_node(node, strategy, logger):
string = ' %s ' % node.value # add sentinels
for matcher, confidence in strategy:
if getattr(matcher, 'use_node', False):
result, span = matcher(string, node)
else:
result, span = matcher(string)
if result:
# readjust span to compensate for sentinels
span = (span[0] - 1, span[1] - 1)
if isinstance(result, Guess):
if confidence is None:
confidence = result.confidence(list(result.keys())[0])
else:
if confidence is None:
confidence = 1.0
guess = format_guess(Guess(result, confidence=confidence))
msg = 'Found with confidence %.2f: %s' % (confidence, guess)
(logger or log).debug(msg)
node.partition(span)
absolute_span = (span[0] + node.offset, span[1] + node.offset)
for child in node.children:
if child.span == absolute_span:
child.guess = guess
else:
find_and_split_node(child, strategy, logger)
return
class SingleNodeGuesser(object):
def __init__(self, guess_func, confidence, logger=None):
self.guess_func = guess_func
self.confidence = confidence
self.logger = logger
def process(self, mtree):
# strategy is a list of pairs (guesser, confidence)
# - if the guesser returns a guessit.Guess and confidence is specified,
# it will override it, otherwise it will leave the guess confidence
# - if the guesser returns a simple dict as a guess and confidence is
# specified, it will use it, or 1.0 otherwise
strategy = [ (self.guess_func, self.confidence) ]
for node in mtree.unidentified_leaves():
find_and_split_node(node, strategy, self.logger)