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annotate venv/lib/python2.7/site-packages/requests/packages/chardet/hebrewprober.py @ 0:d67268158946 draft

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planemo upload commit a3f181f5f126803c654b3a66dd4e83a48f7e203b
author bcclaywell
date Mon, 12 Oct 2015 17:43:33 -0400
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1 ######################## BEGIN LICENSE BLOCK ########################
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2 # The Original Code is Mozilla Universal charset detector code.
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3 #
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4 # The Initial Developer of the Original Code is
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5 # Shy Shalom
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6 # Portions created by the Initial Developer are Copyright (C) 2005
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7 # the Initial Developer. All Rights Reserved.
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8 #
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9 # Contributor(s):
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10 # Mark Pilgrim - port to Python
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11 #
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12 # This library is free software; you can redistribute it and/or
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13 # modify it under the terms of the GNU Lesser General Public
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14 # License as published by the Free Software Foundation; either
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15 # version 2.1 of the License, or (at your option) any later version.
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16 #
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17 # This library is distributed in the hope that it will be useful,
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18 # but WITHOUT ANY WARRANTY; without even the implied warranty of
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19 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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20 # Lesser General Public License for more details.
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21 #
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22 # You should have received a copy of the GNU Lesser General Public
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23 # License along with this library; if not, write to the Free Software
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24 # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
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25 # 02110-1301 USA
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26 ######################### END LICENSE BLOCK #########################
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27
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28 from .charsetprober import CharSetProber
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29 from .constants import eNotMe, eDetecting
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30 from .compat import wrap_ord
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31
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32 # This prober doesn't actually recognize a language or a charset.
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33 # It is a helper prober for the use of the Hebrew model probers
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34
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35 ### General ideas of the Hebrew charset recognition ###
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36 #
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37 # Four main charsets exist in Hebrew:
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38 # "ISO-8859-8" - Visual Hebrew
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39 # "windows-1255" - Logical Hebrew
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40 # "ISO-8859-8-I" - Logical Hebrew
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41 # "x-mac-hebrew" - ?? Logical Hebrew ??
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42 #
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43 # Both "ISO" charsets use a completely identical set of code points, whereas
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44 # "windows-1255" and "x-mac-hebrew" are two different proper supersets of
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45 # these code points. windows-1255 defines additional characters in the range
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46 # 0x80-0x9F as some misc punctuation marks as well as some Hebrew-specific
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47 # diacritics and additional 'Yiddish' ligature letters in the range 0xc0-0xd6.
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48 # x-mac-hebrew defines similar additional code points but with a different
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49 # mapping.
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50 #
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51 # As far as an average Hebrew text with no diacritics is concerned, all four
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52 # charsets are identical with respect to code points. Meaning that for the
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53 # main Hebrew alphabet, all four map the same values to all 27 Hebrew letters
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54 # (including final letters).
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55 #
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56 # The dominant difference between these charsets is their directionality.
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57 # "Visual" directionality means that the text is ordered as if the renderer is
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58 # not aware of a BIDI rendering algorithm. The renderer sees the text and
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59 # draws it from left to right. The text itself when ordered naturally is read
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60 # backwards. A buffer of Visual Hebrew generally looks like so:
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61 # "[last word of first line spelled backwards] [whole line ordered backwards
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62 # and spelled backwards] [first word of first line spelled backwards]
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63 # [end of line] [last word of second line] ... etc' "
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64 # adding punctuation marks, numbers and English text to visual text is
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65 # naturally also "visual" and from left to right.
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66 #
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67 # "Logical" directionality means the text is ordered "naturally" according to
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68 # the order it is read. It is the responsibility of the renderer to display
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69 # the text from right to left. A BIDI algorithm is used to place general
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70 # punctuation marks, numbers and English text in the text.
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71 #
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72 # Texts in x-mac-hebrew are almost impossible to find on the Internet. From
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73 # what little evidence I could find, it seems that its general directionality
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74 # is Logical.
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75 #
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76 # To sum up all of the above, the Hebrew probing mechanism knows about two
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77 # charsets:
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78 # Visual Hebrew - "ISO-8859-8" - backwards text - Words and sentences are
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79 # backwards while line order is natural. For charset recognition purposes
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80 # the line order is unimportant (In fact, for this implementation, even
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81 # word order is unimportant).
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82 # Logical Hebrew - "windows-1255" - normal, naturally ordered text.
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83 #
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84 # "ISO-8859-8-I" is a subset of windows-1255 and doesn't need to be
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85 # specifically identified.
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86 # "x-mac-hebrew" is also identified as windows-1255. A text in x-mac-hebrew
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87 # that contain special punctuation marks or diacritics is displayed with
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88 # some unconverted characters showing as question marks. This problem might
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89 # be corrected using another model prober for x-mac-hebrew. Due to the fact
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90 # that x-mac-hebrew texts are so rare, writing another model prober isn't
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91 # worth the effort and performance hit.
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92 #
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93 #### The Prober ####
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94 #
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95 # The prober is divided between two SBCharSetProbers and a HebrewProber,
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96 # all of which are managed, created, fed data, inquired and deleted by the
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97 # SBCSGroupProber. The two SBCharSetProbers identify that the text is in
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98 # fact some kind of Hebrew, Logical or Visual. The final decision about which
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99 # one is it is made by the HebrewProber by combining final-letter scores
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100 # with the scores of the two SBCharSetProbers to produce a final answer.
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101 #
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102 # The SBCSGroupProber is responsible for stripping the original text of HTML
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103 # tags, English characters, numbers, low-ASCII punctuation characters, spaces
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104 # and new lines. It reduces any sequence of such characters to a single space.
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105 # The buffer fed to each prober in the SBCS group prober is pure text in
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106 # high-ASCII.
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107 # The two SBCharSetProbers (model probers) share the same language model:
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108 # Win1255Model.
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109 # The first SBCharSetProber uses the model normally as any other
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110 # SBCharSetProber does, to recognize windows-1255, upon which this model was
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111 # built. The second SBCharSetProber is told to make the pair-of-letter
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112 # lookup in the language model backwards. This in practice exactly simulates
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113 # a visual Hebrew model using the windows-1255 logical Hebrew model.
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114 #
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115 # The HebrewProber is not using any language model. All it does is look for
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116 # final-letter evidence suggesting the text is either logical Hebrew or visual
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117 # Hebrew. Disjointed from the model probers, the results of the HebrewProber
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118 # alone are meaningless. HebrewProber always returns 0.00 as confidence
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119 # since it never identifies a charset by itself. Instead, the pointer to the
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120 # HebrewProber is passed to the model probers as a helper "Name Prober".
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121 # When the Group prober receives a positive identification from any prober,
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122 # it asks for the name of the charset identified. If the prober queried is a
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123 # Hebrew model prober, the model prober forwards the call to the
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124 # HebrewProber to make the final decision. In the HebrewProber, the
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125 # decision is made according to the final-letters scores maintained and Both
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126 # model probers scores. The answer is returned in the form of the name of the
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127 # charset identified, either "windows-1255" or "ISO-8859-8".
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128
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129 # windows-1255 / ISO-8859-8 code points of interest
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130 FINAL_KAF = 0xea
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131 NORMAL_KAF = 0xeb
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132 FINAL_MEM = 0xed
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133 NORMAL_MEM = 0xee
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134 FINAL_NUN = 0xef
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135 NORMAL_NUN = 0xf0
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136 FINAL_PE = 0xf3
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137 NORMAL_PE = 0xf4
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138 FINAL_TSADI = 0xf5
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139 NORMAL_TSADI = 0xf6
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140
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141 # Minimum Visual vs Logical final letter score difference.
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142 # If the difference is below this, don't rely solely on the final letter score
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143 # distance.
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144 MIN_FINAL_CHAR_DISTANCE = 5
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145
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146 # Minimum Visual vs Logical model score difference.
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147 # If the difference is below this, don't rely at all on the model score
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148 # distance.
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149 MIN_MODEL_DISTANCE = 0.01
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150
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151 VISUAL_HEBREW_NAME = "ISO-8859-8"
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152 LOGICAL_HEBREW_NAME = "windows-1255"
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153
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154
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155 class HebrewProber(CharSetProber):
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156 def __init__(self):
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157 CharSetProber.__init__(self)
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158 self._mLogicalProber = None
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159 self._mVisualProber = None
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160 self.reset()
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161
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162 def reset(self):
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163 self._mFinalCharLogicalScore = 0
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164 self._mFinalCharVisualScore = 0
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165 # The two last characters seen in the previous buffer,
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166 # mPrev and mBeforePrev are initialized to space in order to simulate
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167 # a word delimiter at the beginning of the data
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168 self._mPrev = ' '
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169 self._mBeforePrev = ' '
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170 # These probers are owned by the group prober.
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171
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172 def set_model_probers(self, logicalProber, visualProber):
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173 self._mLogicalProber = logicalProber
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174 self._mVisualProber = visualProber
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175
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176 def is_final(self, c):
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177 return wrap_ord(c) in [FINAL_KAF, FINAL_MEM, FINAL_NUN, FINAL_PE,
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178 FINAL_TSADI]
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179
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180 def is_non_final(self, c):
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181 # The normal Tsadi is not a good Non-Final letter due to words like
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182 # 'lechotet' (to chat) containing an apostrophe after the tsadi. This
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183 # apostrophe is converted to a space in FilterWithoutEnglishLetters
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184 # causing the Non-Final tsadi to appear at an end of a word even
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185 # though this is not the case in the original text.
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186 # The letters Pe and Kaf rarely display a related behavior of not being
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187 # a good Non-Final letter. Words like 'Pop', 'Winamp' and 'Mubarak'
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188 # for example legally end with a Non-Final Pe or Kaf. However, the
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189 # benefit of these letters as Non-Final letters outweighs the damage
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190 # since these words are quite rare.
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191 return wrap_ord(c) in [NORMAL_KAF, NORMAL_MEM, NORMAL_NUN, NORMAL_PE]
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192
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193 def feed(self, aBuf):
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194 # Final letter analysis for logical-visual decision.
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195 # Look for evidence that the received buffer is either logical Hebrew
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196 # or visual Hebrew.
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197 # The following cases are checked:
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198 # 1) A word longer than 1 letter, ending with a final letter. This is
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199 # an indication that the text is laid out "naturally" since the
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200 # final letter really appears at the end. +1 for logical score.
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201 # 2) A word longer than 1 letter, ending with a Non-Final letter. In
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202 # normal Hebrew, words ending with Kaf, Mem, Nun, Pe or Tsadi,
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203 # should not end with the Non-Final form of that letter. Exceptions
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204 # to this rule are mentioned above in isNonFinal(). This is an
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205 # indication that the text is laid out backwards. +1 for visual
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206 # score
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207 # 3) A word longer than 1 letter, starting with a final letter. Final
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208 # letters should not appear at the beginning of a word. This is an
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209 # indication that the text is laid out backwards. +1 for visual
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210 # score.
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211 #
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212 # The visual score and logical score are accumulated throughout the
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213 # text and are finally checked against each other in GetCharSetName().
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214 # No checking for final letters in the middle of words is done since
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215 # that case is not an indication for either Logical or Visual text.
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216 #
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217 # We automatically filter out all 7-bit characters (replace them with
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218 # spaces) so the word boundary detection works properly. [MAP]
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219
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220 if self.get_state() == eNotMe:
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221 # Both model probers say it's not them. No reason to continue.
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222 return eNotMe
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223
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224 aBuf = self.filter_high_bit_only(aBuf)
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225
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226 for cur in aBuf:
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227 if cur == ' ':
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228 # We stand on a space - a word just ended
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229 if self._mBeforePrev != ' ':
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230 # next-to-last char was not a space so self._mPrev is not a
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231 # 1 letter word
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232 if self.is_final(self._mPrev):
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233 # case (1) [-2:not space][-1:final letter][cur:space]
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234 self._mFinalCharLogicalScore += 1
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235 elif self.is_non_final(self._mPrev):
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236 # case (2) [-2:not space][-1:Non-Final letter][
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237 # cur:space]
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238 self._mFinalCharVisualScore += 1
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239 else:
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240 # Not standing on a space
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241 if ((self._mBeforePrev == ' ') and
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242 (self.is_final(self._mPrev)) and (cur != ' ')):
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243 # case (3) [-2:space][-1:final letter][cur:not space]
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244 self._mFinalCharVisualScore += 1
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245 self._mBeforePrev = self._mPrev
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246 self._mPrev = cur
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247
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248 # Forever detecting, till the end or until both model probers return
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249 # eNotMe (handled above)
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250 return eDetecting
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251
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252 def get_charset_name(self):
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253 # Make the decision: is it Logical or Visual?
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254 # If the final letter score distance is dominant enough, rely on it.
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255 finalsub = self._mFinalCharLogicalScore - self._mFinalCharVisualScore
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256 if finalsub >= MIN_FINAL_CHAR_DISTANCE:
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257 return LOGICAL_HEBREW_NAME
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258 if finalsub <= -MIN_FINAL_CHAR_DISTANCE:
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259 return VISUAL_HEBREW_NAME
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260
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261 # It's not dominant enough, try to rely on the model scores instead.
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262 modelsub = (self._mLogicalProber.get_confidence()
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263 - self._mVisualProber.get_confidence())
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264 if modelsub > MIN_MODEL_DISTANCE:
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265 return LOGICAL_HEBREW_NAME
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266 if modelsub < -MIN_MODEL_DISTANCE:
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267 return VISUAL_HEBREW_NAME
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268
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269 # Still no good, back to final letter distance, maybe it'll save the
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270 # day.
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271 if finalsub < 0.0:
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272 return VISUAL_HEBREW_NAME
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273
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274 # (finalsub > 0 - Logical) or (don't know what to do) default to
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275 # Logical.
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276 return LOGICAL_HEBREW_NAME
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277
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278 def get_state(self):
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279 # Remain active as long as any of the model probers are active.
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280 if (self._mLogicalProber.get_state() == eNotMe) and \
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281 (self._mVisualProber.get_state() == eNotMe):
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282 return eNotMe
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283 return eDetecting