163 lines
5.9 KiB
Python
163 lines
5.9 KiB
Python
######################## BEGIN LICENSE BLOCK ########################
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# This code was modified from latin1prober.py by Rob Speer <rob@lumino.so>.
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# The Original Code is Mozilla Universal charset detector code.
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#
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# The Initial Developer of the Original Code is
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# Netscape Communications Corporation.
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# Portions created by the Initial Developer are Copyright (C) 2001
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# the Initial Developer. All Rights Reserved.
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#
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# Contributor(s):
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# Rob Speer - adapt to MacRoman encoding
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# Mark Pilgrim - port to Python
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# Shy Shalom - original C code
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#
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# This library is free software; you can redistribute it and/or
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# modify it under the terms of the GNU Lesser General Public
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# License as published by the Free Software Foundation; either
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# version 2.1 of the License, or (at your option) any later version.
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#
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# This library is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with this library; if not, write to the Free Software
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# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
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# 02110-1301 USA
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######################### END LICENSE BLOCK #########################
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from typing import List, Union
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from .charsetprober import CharSetProber
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from .enums import ProbingState
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FREQ_CAT_NUM = 4
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UDF = 0 # undefined
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OTH = 1 # other
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ASC = 2 # ascii capital letter
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ASS = 3 # ascii small letter
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ACV = 4 # accent capital vowel
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ACO = 5 # accent capital other
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ASV = 6 # accent small vowel
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ASO = 7 # accent small other
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ODD = 8 # character that is unlikely to appear
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CLASS_NUM = 9 # total classes
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# The change from Latin1 is that we explicitly look for extended characters
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# that are infrequently-occurring symbols, and consider them to always be
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# improbable. This should let MacRoman get out of the way of more likely
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# encodings in most situations.
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# fmt: off
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MacRoman_CharToClass = (
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 00 - 07
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 08 - 0F
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 10 - 17
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 18 - 1F
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 20 - 27
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 28 - 2F
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 30 - 37
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 38 - 3F
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OTH, ASC, ASC, ASC, ASC, ASC, ASC, ASC, # 40 - 47
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ASC, ASC, ASC, ASC, ASC, ASC, ASC, ASC, # 48 - 4F
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ASC, ASC, ASC, ASC, ASC, ASC, ASC, ASC, # 50 - 57
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ASC, ASC, ASC, OTH, OTH, OTH, OTH, OTH, # 58 - 5F
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OTH, ASS, ASS, ASS, ASS, ASS, ASS, ASS, # 60 - 67
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ASS, ASS, ASS, ASS, ASS, ASS, ASS, ASS, # 68 - 6F
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ASS, ASS, ASS, ASS, ASS, ASS, ASS, ASS, # 70 - 77
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ASS, ASS, ASS, OTH, OTH, OTH, OTH, OTH, # 78 - 7F
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ACV, ACV, ACO, ACV, ACO, ACV, ACV, ASV, # 80 - 87
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ASV, ASV, ASV, ASV, ASV, ASO, ASV, ASV, # 88 - 8F
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ASV, ASV, ASV, ASV, ASV, ASV, ASO, ASV, # 90 - 97
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ASV, ASV, ASV, ASV, ASV, ASV, ASV, ASV, # 98 - 9F
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, ASO, # A0 - A7
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OTH, OTH, ODD, ODD, OTH, OTH, ACV, ACV, # A8 - AF
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # B0 - B7
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OTH, OTH, OTH, OTH, OTH, OTH, ASV, ASV, # B8 - BF
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OTH, OTH, ODD, OTH, ODD, OTH, OTH, OTH, # C0 - C7
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OTH, OTH, OTH, ACV, ACV, ACV, ACV, ASV, # C8 - CF
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OTH, OTH, OTH, OTH, OTH, OTH, OTH, ODD, # D0 - D7
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ASV, ACV, ODD, OTH, OTH, OTH, OTH, OTH, # D8 - DF
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OTH, OTH, OTH, OTH, OTH, ACV, ACV, ACV, # E0 - E7
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ACV, ACV, ACV, ACV, ACV, ACV, ACV, ACV, # E8 - EF
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ODD, ACV, ACV, ACV, ACV, ASV, ODD, ODD, # F0 - F7
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ODD, ODD, ODD, ODD, ODD, ODD, ODD, ODD, # F8 - FF
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)
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# 0 : illegal
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# 1 : very unlikely
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# 2 : normal
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# 3 : very likely
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MacRomanClassModel = (
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# UDF OTH ASC ASS ACV ACO ASV ASO ODD
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0, 0, 0, 0, 0, 0, 0, 0, 0, # UDF
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0, 3, 3, 3, 3, 3, 3, 3, 1, # OTH
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0, 3, 3, 3, 3, 3, 3, 3, 1, # ASC
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0, 3, 3, 3, 1, 1, 3, 3, 1, # ASS
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0, 3, 3, 3, 1, 2, 1, 2, 1, # ACV
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0, 3, 3, 3, 3, 3, 3, 3, 1, # ACO
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0, 3, 1, 3, 1, 1, 1, 3, 1, # ASV
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0, 3, 1, 3, 1, 1, 3, 3, 1, # ASO
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0, 1, 1, 1, 1, 1, 1, 1, 1, # ODD
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)
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# fmt: on
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class MacRomanProber(CharSetProber):
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def __init__(self) -> None:
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super().__init__()
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self._last_char_class = OTH
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self._freq_counter: List[int] = []
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self.reset()
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def reset(self) -> None:
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self._last_char_class = OTH
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self._freq_counter = [0] * FREQ_CAT_NUM
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# express the prior that MacRoman is a somewhat rare encoding;
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# this can be done by starting out in a slightly improbable state
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# that must be overcome
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self._freq_counter[2] = 10
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super().reset()
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@property
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def charset_name(self) -> str:
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return "MacRoman"
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@property
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def language(self) -> str:
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return ""
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def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
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byte_str = self.remove_xml_tags(byte_str)
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for c in byte_str:
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char_class = MacRoman_CharToClass[c]
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freq = MacRomanClassModel[(self._last_char_class * CLASS_NUM) + char_class]
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if freq == 0:
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self._state = ProbingState.NOT_ME
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break
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self._freq_counter[freq] += 1
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self._last_char_class = char_class
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return self.state
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def get_confidence(self) -> float:
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if self.state == ProbingState.NOT_ME:
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return 0.01
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total = sum(self._freq_counter)
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confidence = (
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0.0
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if total < 0.01
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else (self._freq_counter[3] - self._freq_counter[1] * 20.0) / total
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)
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confidence = max(confidence, 0.0)
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# lower the confidence of MacRoman so that other more accurate
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# detector can take priority.
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confidence *= 0.73
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return confidence
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