name : sbcharsetprober.py ######################## 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, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA # 02110-1301 USA ######################### END LICENSE BLOCK ######################### from typing import Dict, List, NamedTuple, Optional, Union from .charsetprober import CharSetProber from .enums import CharacterCategory, ProbingState, SequenceLikelihood class SingleByteCharSetModel(NamedTuple): charset_name: str language: str char_to_order_map: Dict[int, int] language_model: Dict[int, Dict[int, int]] typical_positive_ratio: float keep_ascii_letters: bool alphabet: str class SingleByteCharSetProber(CharSetProber): SAMPLE_SIZE = 64 SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2 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 = 255 self._seq_counters: List[int] = [] self._total_seqs = 0 self._total_char = 0 self._control_char = 0 self._freq_char = 0 self.reset() def reset(self) -> None: super().reset() # char order of last character self._last_order = 255 self._seq_counters = [0] * SequenceLikelihood.get_num_categories() 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 def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: # TODO: Make filter_international_words keep things in self.alphabet if not self._model.keep_ascii_letters: byte_str = self.filter_international_words(byte_str) else: byte_str = self.remove_xml_tags(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.get(char, CharacterCategory.UNDEFINED) # XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but # CharacterCategory.SYMBOL is actually 253, so we use CONTROL # to make it closer to the original intent. The only difference # is whether or not we count digits and control characters for # _total_char purposes. if order < CharacterCategory.CONTROL: self._total_char += 1 if order < self.SAMPLE_SIZE: self._freq_char += 1 if self._last_order < self.SAMPLE_SIZE: 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 return self.state def get_confidence(self) -> float: r = 0.01 if self._total_seqs > 0: r = ( ( self._seq_counters[SequenceLikelihood.POSITIVE] + 0.25 * self._seq_counters[SequenceLikelihood.LIKELY] ) / self._total_seqs / self._model.typical_positive_ratio ) # The more control characters (proportionnaly to the size # of the text), the less confident we become in the current # charset. r = r * (self._total_char - self._control_char) / self._total_char r = r * self._freq_char / self._total_char if r >= 1.0: r = 0.99 return r