--- a/eric6/ThirdParty/CharDet/chardet/sbcharsetprober.py Tue Apr 20 19:47:39 2021 +0200
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,145 +0,0 @@
-######################## 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
