Mercurial > repos > shellac > guppy_basecaller

annotate env/lib/python3.7/site-packages/chardet/sbcharsetprober.py @ 4:79f47841a781 draft

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"planemo upload commit 2a0fe2cc28b09e101d37293e53e82f61762262ec"
author shellac
date Thu, 14 May 2020 16:47:39 -0400
parents 26e78fe6e8c4
children
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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 # Netscape Communications Corporation.
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6 # Portions created by the Initial Developer are Copyright (C) 2001
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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 # Shy Shalom - original C code
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12 #
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13 # This library is free software; you can redistribute it and/or
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14 # modify it under the terms of the GNU Lesser General Public
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15 # License as published by the Free Software Foundation; either
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16 # version 2.1 of the License, or (at your option) any later version.
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17 #
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18 # This library is distributed in the hope that it will be useful,
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19 # but WITHOUT ANY WARRANTY; without even the implied warranty of
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20 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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21 # Lesser General Public License for more details.
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22 #
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23 # You should have received a copy of the GNU Lesser General Public
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24 # License along with this library; if not, write to the Free Software
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25 # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
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26 # 02110-1301 USA
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27 ######################### END LICENSE BLOCK #########################
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28
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29 from .charsetprober import CharSetProber
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30 from .enums import CharacterCategory, ProbingState, SequenceLikelihood
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31
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32
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33 class SingleByteCharSetProber(CharSetProber):
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34 SAMPLE_SIZE = 64
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35 SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2
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36 POSITIVE_SHORTCUT_THRESHOLD = 0.95
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37 NEGATIVE_SHORTCUT_THRESHOLD = 0.05
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38
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39 def __init__(self, model, reversed=False, name_prober=None):
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40 super(SingleByteCharSetProber, self).__init__()
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41 self._model = model
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42 # TRUE if we need to reverse every pair in the model lookup
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43 self._reversed = reversed
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44 # Optional auxiliary prober for name decision
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45 self._name_prober = name_prober
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46 self._last_order = None
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47 self._seq_counters = None
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48 self._total_seqs = None
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49 self._total_char = None
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50 self._freq_char = None
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51 self.reset()
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52
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53 def reset(self):
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54 super(SingleByteCharSetProber, self).reset()
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55 # char order of last character
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56 self._last_order = 255
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57 self._seq_counters = [0] * SequenceLikelihood.get_num_categories()
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58 self._total_seqs = 0
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59 self._total_char = 0
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60 # characters that fall in our sampling range
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61 self._freq_char = 0
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62
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63 @property
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64 def charset_name(self):
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65 if self._name_prober:
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66 return self._name_prober.charset_name
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67 else:
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68 return self._model['charset_name']
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69
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70 @property
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71 def language(self):
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72 if self._name_prober:
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73 return self._name_prober.language
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74 else:
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75 return self._model.get('language')
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76
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77 def feed(self, byte_str):
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78 if not self._model['keep_english_letter']:
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79 byte_str = self.filter_international_words(byte_str)
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80 if not byte_str:
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81 return self.state
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82 char_to_order_map = self._model['char_to_order_map']
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83 for i, c in enumerate(byte_str):
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84 # XXX: Order is in range 1-64, so one would think we want 0-63 here,
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85 # but that leads to 27 more test failures than before.
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86 order = char_to_order_map[c]
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87 # XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but
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88 # CharacterCategory.SYMBOL is actually 253, so we use CONTROL
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89 # to make it closer to the original intent. The only difference
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90 # is whether or not we count digits and control characters for
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91 # _total_char purposes.
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92 if order < CharacterCategory.CONTROL:
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93 self._total_char += 1
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94 if order < self.SAMPLE_SIZE:
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95 self._freq_char += 1
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96 if self._last_order < self.SAMPLE_SIZE:
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97 self._total_seqs += 1
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98 if not self._reversed:
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99 i = (self._last_order * self.SAMPLE_SIZE) + order
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100 model = self._model['precedence_matrix'][i]
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101 else: # reverse the order of the letters in the lookup
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102 i = (order * self.SAMPLE_SIZE) + self._last_order
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103 model = self._model['precedence_matrix'][i]
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104 self._seq_counters[model] += 1
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105 self._last_order = order
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106
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107 charset_name = self._model['charset_name']
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108 if self.state == ProbingState.DETECTING:
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109 if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD:
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110 confidence = self.get_confidence()
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111 if confidence > self.POSITIVE_SHORTCUT_THRESHOLD:
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112 self.logger.debug('%s confidence = %s, we have a winner',
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113 charset_name, confidence)
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114 self._state = ProbingState.FOUND_IT
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115 elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD:
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116 self.logger.debug('%s confidence = %s, below negative '
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117 'shortcut threshhold %s', charset_name,
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118 confidence,
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119 self.NEGATIVE_SHORTCUT_THRESHOLD)
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120 self._state = ProbingState.NOT_ME
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121
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122 return self.state
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123
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124 def get_confidence(self):
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125 r = 0.01
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126 if self._total_seqs > 0:
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127 r = ((1.0 * self._seq_counters[SequenceLikelihood.POSITIVE]) /
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128 self._total_seqs / self._model['typical_positive_ratio'])
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129 r = r * self._freq_char / self._total_char
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130 if r >= 1.0:
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131 r = 0.99
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132 return r