Change venv
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Ambulance Clerc
@@ -26,70 +26,77 @@
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# 02110-1301 USA
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######################### END LICENSE BLOCK #########################
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from collections import namedtuple
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from typing import Dict, List, NamedTuple, Optional, Union
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from .charsetprober import CharSetProber
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from .enums import CharacterCategory, ProbingState, SequenceLikelihood
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SingleByteCharSetModel = namedtuple('SingleByteCharSetModel',
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['charset_name',
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'language',
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'char_to_order_map',
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'language_model',
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'typical_positive_ratio',
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'keep_ascii_letters',
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'alphabet'])
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class SingleByteCharSetModel(NamedTuple):
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charset_name: str
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language: str
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char_to_order_map: Dict[int, int]
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language_model: Dict[int, Dict[int, int]]
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typical_positive_ratio: float
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keep_ascii_letters: bool
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alphabet: str
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class SingleByteCharSetProber(CharSetProber):
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SAMPLE_SIZE = 64
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SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2
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SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2
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POSITIVE_SHORTCUT_THRESHOLD = 0.95
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NEGATIVE_SHORTCUT_THRESHOLD = 0.05
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def __init__(self, model, reversed=False, name_prober=None):
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super(SingleByteCharSetProber, self).__init__()
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def __init__(
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self,
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model: SingleByteCharSetModel,
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is_reversed: bool = False,
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name_prober: Optional[CharSetProber] = None,
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) -> None:
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super().__init__()
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self._model = model
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# TRUE if we need to reverse every pair in the model lookup
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self._reversed = reversed
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self._reversed = is_reversed
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# Optional auxiliary prober for name decision
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self._name_prober = name_prober
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self._last_order = None
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self._seq_counters = None
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self._total_seqs = None
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self._total_char = None
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self._freq_char = None
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self._last_order = 255
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self._seq_counters: List[int] = []
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self._total_seqs = 0
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self._total_char = 0
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self._control_char = 0
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self._freq_char = 0
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self.reset()
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def reset(self):
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super(SingleByteCharSetProber, self).reset()
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def reset(self) -> None:
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super().reset()
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# char order of last character
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self._last_order = 255
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self._seq_counters = [0] * SequenceLikelihood.get_num_categories()
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self._total_seqs = 0
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self._total_char = 0
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self._control_char = 0
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# characters that fall in our sampling range
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self._freq_char = 0
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@property
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def charset_name(self):
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def charset_name(self) -> Optional[str]:
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if self._name_prober:
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return self._name_prober.charset_name
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else:
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return self._model.charset_name
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return self._model.charset_name
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@property
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def language(self):
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def language(self) -> Optional[str]:
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if self._name_prober:
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return self._name_prober.language
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else:
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return self._model.language
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return self._model.language
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def feed(self, byte_str):
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def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
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# TODO: Make filter_international_words keep things in self.alphabet
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if not self._model.keep_ascii_letters:
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byte_str = self.filter_international_words(byte_str)
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else:
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byte_str = self.remove_xml_tags(byte_str)
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if not byte_str:
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return self.state
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char_to_order_map = self._model.char_to_order_map
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@@ -103,9 +110,6 @@ class SingleByteCharSetProber(CharSetProber):
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# _total_char purposes.
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if order < CharacterCategory.CONTROL:
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self._total_char += 1
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# TODO: Follow uchardet's lead and discount confidence for frequent
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# control characters.
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# See https://github.com/BYVoid/uchardet/commit/55b4f23971db61
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if order < self.SAMPLE_SIZE:
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self._freq_char += 1
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if self._last_order < self.SAMPLE_SIZE:
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@@ -122,23 +126,36 @@ class SingleByteCharSetProber(CharSetProber):
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if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD:
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confidence = self.get_confidence()
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if confidence > self.POSITIVE_SHORTCUT_THRESHOLD:
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self.logger.debug('%s confidence = %s, we have a winner',
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charset_name, confidence)
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self.logger.debug(
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"%s confidence = %s, we have a winner", charset_name, confidence
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)
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self._state = ProbingState.FOUND_IT
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elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD:
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self.logger.debug('%s confidence = %s, below negative '
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'shortcut threshhold %s', charset_name,
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confidence,
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self.NEGATIVE_SHORTCUT_THRESHOLD)
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self.logger.debug(
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"%s confidence = %s, below negative shortcut threshold %s",
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charset_name,
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confidence,
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self.NEGATIVE_SHORTCUT_THRESHOLD,
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)
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self._state = ProbingState.NOT_ME
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return self.state
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def get_confidence(self):
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def get_confidence(self) -> float:
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r = 0.01
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if self._total_seqs > 0:
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r = ((1.0 * self._seq_counters[SequenceLikelihood.POSITIVE]) /
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self._total_seqs / self._model.typical_positive_ratio)
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r = (
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(
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self._seq_counters[SequenceLikelihood.POSITIVE]
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+ 0.25 * self._seq_counters[SequenceLikelihood.LIKELY]
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)
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/ self._total_seqs
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/ self._model.typical_positive_ratio
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)
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# The more control characters (proportionnaly to the size
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# of the text), the less confident we become in the current
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# charset.
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r = r * (self._total_char - self._control_char) / self._total_char
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r = r * self._freq_char / self._total_char
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if r >= 1.0:
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r = 0.99
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