mirror of
https://github.com/moparisthebest/SickRage
synced 2024-11-11 03:45:01 -05:00
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!
319 lines
9.8 KiB
Python
319 lines
9.8 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) 2011 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 import UnicodeMixin, s, u, base_text_type
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from guessit.language import Language
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from guessit.country import Country
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import json
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import datetime
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import logging
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log = logging.getLogger(__name__)
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class Guess(UnicodeMixin, dict):
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"""A Guess is a dictionary which has an associated confidence for each of
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its values.
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As it is a subclass of dict, you can use it everywhere you expect a
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simple dict."""
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def __init__(self, *args, **kwargs):
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try:
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confidence = kwargs.pop('confidence')
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except KeyError:
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confidence = 0
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dict.__init__(self, *args, **kwargs)
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self._confidence = {}
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for prop in self:
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self._confidence[prop] = confidence
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def to_dict(self):
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data = dict(self)
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for prop, value in data.items():
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if isinstance(value, datetime.date):
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data[prop] = value.isoformat()
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elif isinstance(value, (Language, Country, base_text_type)):
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data[prop] = u(value)
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elif isinstance(value, list):
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data[prop] = [u(x) for x in value]
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return data
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def nice_string(self):
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data = self.to_dict()
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parts = json.dumps(data, indent=4).split('\n')
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for i, p in enumerate(parts):
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if p[:5] != ' "':
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continue
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prop = p.split('"')[1]
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parts[i] = (' [%.2f] "' % self.confidence(prop)) + p[5:]
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return '\n'.join(parts)
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def __unicode__(self):
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return u(self.to_dict())
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def confidence(self, prop):
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return self._confidence.get(prop, -1)
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def set(self, prop, value, confidence=None):
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self[prop] = value
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if confidence is not None:
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self._confidence[prop] = confidence
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def set_confidence(self, prop, value):
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self._confidence[prop] = value
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def update(self, other, confidence=None):
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dict.update(self, other)
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if isinstance(other, Guess):
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for prop in other:
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self._confidence[prop] = other.confidence(prop)
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if confidence is not None:
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for prop in other:
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self._confidence[prop] = confidence
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def update_highest_confidence(self, other):
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"""Update this guess with the values from the given one. In case
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there is property present in both, only the one with the highest one
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is kept."""
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if not isinstance(other, Guess):
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raise ValueError('Can only call this function on Guess instances')
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for prop in other:
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if prop in self and self.confidence(prop) >= other.confidence(prop):
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continue
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self[prop] = other[prop]
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self._confidence[prop] = other.confidence(prop)
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def choose_int(g1, g2):
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"""Function used by merge_similar_guesses to choose between 2 possible
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properties when they are integers."""
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v1, c1 = g1 # value, confidence
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v2, c2 = g2
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if (v1 == v2):
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return (v1, 1 - (1 - c1) * (1 - c2))
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else:
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if c1 > c2:
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return (v1, c1 - c2)
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else:
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return (v2, c2 - c1)
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def choose_string(g1, g2):
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"""Function used by merge_similar_guesses to choose between 2 possible
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properties when they are strings.
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If the 2 strings are similar, or one is contained in the other, the latter is returned
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with an increased confidence.
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If the 2 strings are dissimilar, the one with the higher confidence is returned, with
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a weaker confidence.
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Note that here, 'similar' means that 2 strings are either equal, or that they
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differ very little, such as one string being the other one with the 'the' word
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prepended to it.
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>>> s(choose_string(('Hello', 0.75), ('World', 0.5)))
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('Hello', 0.25)
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>>> s(choose_string(('Hello', 0.5), ('hello', 0.5)))
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('Hello', 0.75)
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>>> s(choose_string(('Hello', 0.4), ('Hello World', 0.4)))
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('Hello', 0.64)
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>>> s(choose_string(('simpsons', 0.5), ('The Simpsons', 0.5)))
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('The Simpsons', 0.75)
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"""
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v1, c1 = g1 # value, confidence
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v2, c2 = g2
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if not v1:
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return g2
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elif not v2:
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return g1
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v1, v2 = v1.strip(), v2.strip()
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v1l, v2l = v1.lower(), v2.lower()
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combined_prob = 1 - (1 - c1) * (1 - c2)
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if v1l == v2l:
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return (v1, combined_prob)
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# check for common patterns
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elif v1l == 'the ' + v2l:
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return (v1, combined_prob)
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elif v2l == 'the ' + v1l:
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return (v2, combined_prob)
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# if one string is contained in the other, return the shortest one
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elif v2l in v1l:
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return (v2, combined_prob)
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elif v1l in v2l:
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return (v1, combined_prob)
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# in case of conflict, return the one with highest confidence
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else:
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if c1 > c2:
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return (v1, c1 - c2)
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else:
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return (v2, c2 - c1)
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def _merge_similar_guesses_nocheck(guesses, prop, choose):
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"""Take a list of guesses and merge those which have the same properties,
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increasing or decreasing the confidence depending on whether their values
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are similar.
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This function assumes there are at least 2 valid guesses."""
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similar = [guess for guess in guesses if prop in guess]
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g1, g2 = similar[0], similar[1]
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other_props = set(g1) & set(g2) - set([prop])
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if other_props:
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log.debug('guess 1: %s' % g1)
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log.debug('guess 2: %s' % g2)
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for prop in other_props:
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if g1[prop] != g2[prop]:
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log.warning('both guesses to be merged have more than one '
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'different property in common, bailing out...')
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return
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# merge all props of s2 into s1, updating the confidence for the
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# considered property
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v1, v2 = g1[prop], g2[prop]
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c1, c2 = g1.confidence(prop), g2.confidence(prop)
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new_value, new_confidence = choose((v1, c1), (v2, c2))
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if new_confidence >= c1:
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msg = "Updating matching property '%s' with confidence %.2f"
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else:
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msg = "Updating non-matching property '%s' with confidence %.2f"
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log.debug(msg % (prop, new_confidence))
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g2[prop] = new_value
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g2.set_confidence(prop, new_confidence)
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g1.update(g2)
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guesses.remove(g2)
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def merge_similar_guesses(guesses, prop, choose):
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"""Take a list of guesses and merge those which have the same properties,
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increasing or decreasing the confidence depending on whether their values
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are similar."""
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similar = [guess for guess in guesses if prop in guess]
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if len(similar) < 2:
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# nothing to merge
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return
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if len(similar) == 2:
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_merge_similar_guesses_nocheck(guesses, prop, choose)
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if len(similar) > 2:
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log.debug('complex merge, trying our best...')
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before = len(guesses)
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_merge_similar_guesses_nocheck(guesses, prop, choose)
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after = len(guesses)
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if after < before:
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# recurse only when the previous call actually did something,
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# otherwise we end up in an infinite loop
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merge_similar_guesses(guesses, prop, choose)
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def merge_all(guesses, append=None):
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"""Merge all the guesses in a single result, remove very unlikely values,
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and return it.
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You can specify a list of properties that should be appended into a list
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instead of being merged.
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>>> s(merge_all([ Guess({'season': 2}, confidence=0.6),
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... Guess({'episodeNumber': 13}, confidence=0.8) ]))
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{'season': 2, 'episodeNumber': 13}
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>>> s(merge_all([ Guess({'episodeNumber': 27}, confidence=0.02),
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... Guess({'season': 1}, confidence=0.2) ]))
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{'season': 1}
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>>> s(merge_all([ Guess({'other': 'PROPER'}, confidence=0.8),
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... Guess({'releaseGroup': '2HD'}, confidence=0.8) ],
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... append=['other']))
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{'releaseGroup': '2HD', 'other': ['PROPER']}
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"""
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if not guesses:
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return Guess()
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result = guesses[0]
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if append is None:
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append = []
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for g in guesses[1:]:
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# first append our appendable properties
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for prop in append:
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if prop in g:
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result.set(prop, result.get(prop, []) + [g[prop]],
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# TODO: what to do with confidence here? maybe an
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# arithmetic mean...
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confidence=g.confidence(prop))
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del g[prop]
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# then merge the remaining ones
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dups = set(result) & set(g)
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if dups:
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log.warning('duplicate properties %s in merged result...' % [ (result[p], g[p]) for p in dups] )
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result.update_highest_confidence(g)
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# delete very unlikely values
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for p in list(result.keys()):
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if result.confidence(p) < 0.05:
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del result[p]
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# make sure our appendable properties contain unique values
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for prop in append:
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try:
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value = result[prop]
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if isinstance(value, list):
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result[prop] = list(set(value))
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else:
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result[prop] = [ value ]
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except KeyError:
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pass
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return result
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