######################## BEGIN LICENSE BLOCK ########################
# The Original Code is Mozilla Universal charset detector code.
#
# The Initial Developer of the Original Code is
# Netscape Communications Corporation.
# Portions created by the Initial Developer are Copyright (C) 2001
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# Mark Pilgrim - port to Python
# Shy Shalom - original C code
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library 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 GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, see
# <https://www.gnu.org/licenses/>.
######################### END LICENSE BLOCK #########################
import logging
from collections.abc import Mapping
from typing import NamedTuple, Optional, Union
from .charsetprober import CharSetProber
from .enums import CharacterCategory, ProbingState, SequenceLikelihood
[docs]
class SingleByteCharSetModel(NamedTuple):
charset_name: str
language: str
char_to_order_map: Mapping[int, Union[CharacterCategory, int]]
language_model: Mapping[int, Mapping[int, Union[SequenceLikelihood, int]]]
typical_positive_ratio: float
keep_ascii_letters: bool
alphabet: str
[docs]
class SingleByteCharSetProber(CharSetProber):
SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2 (SAMPLE_SIZE was 64)
POSITIVE_SHORTCUT_THRESHOLD = 0.95
NEGATIVE_SHORTCUT_THRESHOLD = 0.05
def __init__(
self,
model: SingleByteCharSetModel,
is_reversed: bool = False,
name_prober: Optional[CharSetProber] = None,
) -> None:
super().__init__()
self._model = model
# TRUE if we need to reverse every pair in the model lookup
self._reversed = is_reversed
# Optional auxiliary prober for name decision
self._name_prober = name_prober
self._last_order = CharacterCategory.UNDEFINED
self._seq_counters: list[int] = []
self._total_seqs = 0
self._total_char = 0
self._control_char = 0
self._freq_char = 0
self.logger = logging.getLogger(__name__)
self.reset()
[docs]
def reset(self) -> None:
super().reset()
# char order of last character
self._last_order = CharacterCategory.UNDEFINED
self._seq_counters = [0] * len(SequenceLikelihood)
self._total_seqs = 0
self._total_char = 0
self._control_char = 0
# characters that fall in our sampling range
self._freq_char = 0
@property
def charset_name(self) -> Optional[str]:
if self._name_prober:
return self._name_prober.charset_name
return self._model.charset_name
@property
def language(self) -> Optional[str]:
if self._name_prober:
return self._name_prober.language
return self._model.language
[docs]
def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
if self._model.keep_ascii_letters:
byte_str = self.remove_xml_tags(byte_str)
else:
byte_str = self.filter_international_words(byte_str)
if not byte_str:
return self.state
char_to_order_map = self._model.char_to_order_map
language_model = self._model.language_model
for char in byte_str:
order = char_to_order_map[char]
if order < CharacterCategory.DIGIT:
self._total_char += 1
elif order == CharacterCategory.UNDEFINED:
# If we find a character that is undefined in the mapping,
# this cannot be the right charset
self._state = ProbingState.NOT_ME
self._last_order = order
break
# TODO: Follow uchardet's lead and discount confidence for frequent
# control characters.
# See https://github.com/BYVoid/uchardet/commit/55b4f23971db61
elif order == CharacterCategory.CONTROL:
self._control_char += 1
if 0 < order < CharacterCategory.DIGIT:
self._freq_char += 1
if 0 < self._last_order < CharacterCategory.DIGIT:
self._total_seqs += 1
if not self._reversed:
lm_cat = language_model[self._last_order][order]
else:
lm_cat = language_model[order][self._last_order]
self._seq_counters[lm_cat] += 1
self._last_order = order
charset_name = self._model.charset_name
if self.state == ProbingState.DETECTING:
if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD:
confidence = self.get_confidence()
if confidence > self.POSITIVE_SHORTCUT_THRESHOLD:
self.logger.debug(
"%s confidence = %s, we have a winner", charset_name, confidence
)
self._state = ProbingState.FOUND_IT
elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD:
self.logger.debug(
"%s confidence = %s, below negative shortcut threshold %s",
charset_name,
confidence,
self.NEGATIVE_SHORTCUT_THRESHOLD,
)
self._state = ProbingState.NOT_ME
# Early termination: if we have enough data and very low confidence, give up
elif self._total_seqs > 512 and self._total_char > 1000:
confidence = self.get_confidence()
if confidence < 0.01:
self.logger.debug(
"%s confidence = %s, giving up early", charset_name, confidence
)
self._state = ProbingState.NOT_ME
return self.state
[docs]
def get_confidence(self) -> float:
r = 0.01
if self._total_seqs > 0 and self._total_char > 0:
r = (
self._seq_counters[SequenceLikelihood.POSITIVE]
/ self._total_seqs
/ self._model.typical_positive_ratio
)
# Multiply by ratio of positive sequences per character.
# This helps distinguish close winners by penalizing models
# that have very few positive sequences relative to the
# number of characters. If you add a letter, you'd expect
# the positive sequence count to increase proportionally.
# If it doesn't, this new character may not have been a letter
# but a symbol, making the model less confident.
r *= (
self._seq_counters[SequenceLikelihood.POSITIVE]
+ self._seq_counters[SequenceLikelihood.LIKELY] / 4
) / self._total_char
# The more control characters (proportionnaly to the size
# of the text), the less confident we become in the current
# charset.
r *= (self._total_char - self._control_char) / self._total_char
# The more frequent characters (proportionnaly to the size
# of the text), the more confident we become in the current
# charset.
r *= self._freq_char / self._total_char
if r >= 1.0:
r = 0.99
return r