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Models

ParsedData

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ParsedData()
Parsed data model for a torrent title.

Methods:

.type

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.type()
Returns the type of the torrent based on its attributes.

.to_dict

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.to_dict()
Returns a json serializable dictionary of the parsed data.

Torrent

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Torrent()
Represents a torrent with metadata parsed from its title and additional computed properties.

Attributes

  • The original title of the torrent.
  • The SHA-1 hash identifier of the torrent.
  • Metadata extracted from the torrent title.
  • Indicates whether the torrent meets the criteria for fetching based on user settings.
  • The computed ranking score of the torrent based on user-defined preferences.
  • The Levenshtein ratio comparing the parsed title and the raw title for similarity.

Methods: hash: Generates a hash based on the infohash of the torrent for set operations.

Raises

  • If the title is identified as trash and should be ignored by the scraper.

Example

>>> isinstance(torrent, Torrent)
True
>>> torrent.raw_title
'The Walking Dead S05E03 720p HDTV x264-ASAP[ettv]'
>>> torrent.infohash
'c08a9ee8ce3a5c2c08865e2b05406273cabc97e7'
>>> torrent.data.parsed_title
'The Walking Dead'
>>> torrent.fetch
True
>>> torrent.rank
500
>>> torrent.lev_ratio
0.95

Methods:

.to_dict

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.to_dict()

BaseRankingModel

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BaseRankingModel()

A base class for ranking models used in the context of media quality and attributes. The ranking values are used to determine the quality of a media item based on its attributes.

Note

  • The higher the ranking value, the better the quality of the media item.
  • The default ranking values are set to 0, which means that the attribute does not affect the overall rank.
  • Users can customize the ranking values based on their preferences and requirements by using inheritance.

DefaultRanking

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DefaultRanking()
Ranking model preset that covers the most common use cases.

BestRanking

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BestRanking()
Ranking model preset that covers the highest qualities like 4K HDR.

SettingsModel

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SettingsModel()

Represents user-defined settings for ranking torrents, including preferences for filtering torrents based on regex patterns and customizing ranks for specific torrent attributes. This model allows for advanced customization and fine-grained control over the ranking process.

Attributes

  • profile (str) : Identifier for the settings profile, allowing for multiple configurations.
  • require (List[str | Pattern]) : Patterns torrents must match to be considered.
  • exclude (List[str | Pattern]) : Patterns that, if matched, result in torrent exclusion.
  • preferred (List[str | Pattern]) : Patterns indicating preferred attributes in torrents. Given +5000 points by default.
  • custom_ranks (Dict[str, Dict[str, CustomRank]]) : Custom ranking configurations for specific attributes, allowing users to define how different torrent qualities and features affect the overall rank.

Methods: getitem(item: str) -> CustomRank: Access custom rank settings via attribute keys.

Note

  • The profile attribute allows users to define multiple settings profiles for different use cases.
  • The require, exclude, and preferred attributes are optional!
  • The custom_ranks attribute contains default values for common torrent attributes, which can be customized by users.
  • Patterns enclosed in '/' without a trailing 'i' are compiled as case-sensitive.
  • Patterns not enclosed are compiled as case-insensitive by default.

This model supports advanced regex features, enabling powerful and precise filtering and ranking based on torrent titles and attributes.

Example

>>> print([pattern.pattern for pattern in settings.require])
['\b4K|1080p\b', '720p']
>>> print([pattern.pattern for pattern in settings.preferred])
['BluRay', '\bS\d+', 'HDR|HDR10']
>>> print(settings.custom_ranks["uhd"].rank)
150

CustomRank

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CustomRank()

Custom Ranks used in SettingsModel.