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1######################## BEGIN LICENSE BLOCK ########################
2# The Original Code is Mozilla Universal charset detector code.
3#
4# The Initial Developer of the Original Code is
5# Netscape Communications Corporation.
6# Portions created by the Initial Developer are Copyright (C) 2001
7# the Initial Developer. All Rights Reserved.
8#
9# Contributor(s):
10# Mark Pilgrim - port to Python
11# Shy Shalom - original C code
12#
13# This library is free software; you can redistribute it and/or
14# modify it under the terms of the GNU Lesser General Public
15# License as published by the Free Software Foundation; either
16# version 2.1 of the License, or (at your option) any later version.
17#
18# This library is distributed in the hope that it will be useful,
19# but WITHOUT ANY WARRANTY; without even the implied warranty of
20# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
21# Lesser General Public License for more details.
22#
23# You should have received a copy of the GNU Lesser General Public
24# License along with this library; if not, write to the Free Software
25# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
26# 02110-1301 USA
27######################### END LICENSE BLOCK #########################
29from typing import Dict, List, NamedTuple, Optional, Union
31from .charsetprober import CharSetProber
32from .enums import CharacterCategory, ProbingState, SequenceLikelihood
35class SingleByteCharSetModel(NamedTuple):
36 charset_name: str
37 language: str
38 char_to_order_map: Dict[int, int]
39 language_model: Dict[int, Dict[int, int]]
40 typical_positive_ratio: float
41 keep_ascii_letters: bool
42 alphabet: str
45class SingleByteCharSetProber(CharSetProber):
46 SAMPLE_SIZE = 64
47 SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2
48 POSITIVE_SHORTCUT_THRESHOLD = 0.95
49 NEGATIVE_SHORTCUT_THRESHOLD = 0.05
51 def __init__(
52 self,
53 model: SingleByteCharSetModel,
54 is_reversed: bool = False,
55 name_prober: Optional[CharSetProber] = None,
56 ) -> None:
57 super().__init__()
58 self._model = model
59 # TRUE if we need to reverse every pair in the model lookup
60 self._reversed = is_reversed
61 # Optional auxiliary prober for name decision
62 self._name_prober = name_prober
63 self._last_order = 255
64 self._seq_counters: List[int] = []
65 self._total_seqs = 0
66 self._total_char = 0
67 self._control_char = 0
68 self._freq_char = 0
69 self.reset()
71 def reset(self) -> None:
72 super().reset()
73 # char order of last character
74 self._last_order = 255
75 self._seq_counters = [0] * SequenceLikelihood.get_num_categories()
76 self._total_seqs = 0
77 self._total_char = 0
78 self._control_char = 0
79 # characters that fall in our sampling range
80 self._freq_char = 0
82 @property
83 def charset_name(self) -> Optional[str]:
84 if self._name_prober:
85 return self._name_prober.charset_name
86 return self._model.charset_name
88 @property
89 def language(self) -> Optional[str]:
90 if self._name_prober:
91 return self._name_prober.language
92 return self._model.language
94 def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
95 # TODO: Make filter_international_words keep things in self.alphabet
96 if not self._model.keep_ascii_letters:
97 byte_str = self.filter_international_words(byte_str)
98 else:
99 byte_str = self.remove_xml_tags(byte_str)
100 if not byte_str:
101 return self.state
102 char_to_order_map = self._model.char_to_order_map
103 language_model = self._model.language_model
104 for char in byte_str:
105 order = char_to_order_map.get(char, CharacterCategory.UNDEFINED)
106 # XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but
107 # CharacterCategory.SYMBOL is actually 253, so we use CONTROL
108 # to make it closer to the original intent. The only difference
109 # is whether or not we count digits and control characters for
110 # _total_char purposes.
111 if order < CharacterCategory.CONTROL:
112 self._total_char += 1
113 if order < self.SAMPLE_SIZE:
114 self._freq_char += 1
115 if self._last_order < self.SAMPLE_SIZE:
116 self._total_seqs += 1
117 if not self._reversed:
118 lm_cat = language_model[self._last_order][order]
119 else:
120 lm_cat = language_model[order][self._last_order]
121 self._seq_counters[lm_cat] += 1
122 self._last_order = order
124 charset_name = self._model.charset_name
125 if self.state == ProbingState.DETECTING:
126 if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD:
127 confidence = self.get_confidence()
128 if confidence > self.POSITIVE_SHORTCUT_THRESHOLD:
129 self.logger.debug(
130 "%s confidence = %s, we have a winner", charset_name, confidence
131 )
132 self._state = ProbingState.FOUND_IT
133 elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD:
134 self.logger.debug(
135 "%s confidence = %s, below negative shortcut threshold %s",
136 charset_name,
137 confidence,
138 self.NEGATIVE_SHORTCUT_THRESHOLD,
139 )
140 self._state = ProbingState.NOT_ME
142 return self.state
144 def get_confidence(self) -> float:
145 r = 0.01
146 if self._total_seqs > 0:
147 r = (
148 (
149 self._seq_counters[SequenceLikelihood.POSITIVE]
150 + 0.25 * self._seq_counters[SequenceLikelihood.LIKELY]
151 )
152 / self._total_seqs
153 / self._model.typical_positive_ratio
154 )
155 # The more control characters (proportionnaly to the size
156 # of the text), the less confident we become in the current
157 # charset.
158 r = r * (self._total_char - self._control_char) / self._total_char
159 r = r * self._freq_char / self._total_char
160 if r >= 1.0:
161 r = 0.99
162 return r