Filename: //lib/python3//dist-packages//chardet/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 collections import namedtuple from .charsetprober import CharSetProber from .enums import CharacterCategory, ProbingState, SequenceLikelihood SingleByteCharSetModel = namedtuple('SingleByteCharSetModel', ['charset_name', 'language', 'char_to_order_map', 'language_model', 'typical_positive_ratio', 'keep_ascii_letters', 'alphabet']) 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, reversed=False, name_prober=None): super(SingleByteCharSetProber, self).__init__() self._model = model # TRUE if we need to reverse every pair in the model lookup self._reversed = reversed # Optional auxiliary prober for name decision self._name_prober = name_prober self._last_order = None self._seq_counters = None self._total_seqs = None self._total_char = None self._freq_char = None self.reset() def reset(self): super(SingleByteCharSetProber, self).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 # characters that fall in our sampling range self._freq_char = 0 @property def charset_name(self): if self._name_prober: return self._name_prober.charset_name else: return self._model.charset_name @property def language(self): if self._name_prober: return self._name_prober.language else: return self._model.language def feed(self, byte_str): # 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) 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 # TODO: Follow uchardet's lead and discount confidence for frequent # control characters. # See https://github.com/BYVoid/uchardet/commit/55b4f23971db61 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 threshhold %s', charset_name, confidence, self.NEGATIVE_SHORTCUT_THRESHOLD) self._state = ProbingState.NOT_ME return self.state def get_confidence(self): r = 0.01 if self._total_seqs > 0: r = ((1.0 * self._seq_counters[SequenceLikelihood.POSITIVE]) / self._total_seqs / self._model.typical_positive_ratio) r = r * self._freq_char / self._total_char if r >= 1.0: r = 0.99 return r

./Ninja\.