Coverage for chardet/sbcharsetprober.py: 18%

# Coverage for chardet/sbcharsetprober.py : 18%

## 73 statements 13 run 60 missing 0 excluded

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | ######################## 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 .charsetprober import CharSetProber from .enums import CharacterCategory, ProbingState, SequenceLikelihood 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.get('language') def feed(self, byte_str): if not self._model['keep_english_letter']: 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'] for i, c in enumerate(byte_str): # XXX: Order is in range 1-64, so one would think we want 0-63 here, # but that leads to 27 more test failures than before. order = char_to_order_map[c] # 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 if order < self.SAMPLE_SIZE: self._freq_char += 1 if self._last_order < self.SAMPLE_SIZE: self._total_seqs += 1 if not self._reversed: i = (self._last_order * self.SAMPLE_SIZE) + order model = self._model['precedence_matrix'][i] else: # reverse the order of the letters in the lookup i = (order * self.SAMPLE_SIZE) + self._last_order model = self._model['precedence_matrix'][i] self._seq_counters[model] += 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