Mercurial > repos > shellac > guppy_basecaller

comparison env/lib/python3.7/site-packages/chardet/sbcharsetprober.py @ 0:26e78fe6e8c4 draft

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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
author shellac
date Sat, 02 May 2020 07:14:21 -0400
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-1:000000000000 0:26e78fe6e8c4
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 #########################
28
29 from .charsetprober import CharSetProber
30 from .enums import CharacterCategory, ProbingState, SequenceLikelihood
31
32
33 class SingleByteCharSetProber(CharSetProber):
34 SAMPLE_SIZE = 64
35 SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2
36 POSITIVE_SHORTCUT_THRESHOLD = 0.95
37 NEGATIVE_SHORTCUT_THRESHOLD = 0.05
38
39 def __init__(self, model, reversed=False, name_prober=None):
40 super(SingleByteCharSetProber, self).__init__()
41 self._model = model
42 # TRUE if we need to reverse every pair in the model lookup
43 self._reversed = reversed
44 # Optional auxiliary prober for name decision
45 self._name_prober = name_prober
46 self._last_order = None
47 self._seq_counters = None
48 self._total_seqs = None
49 self._total_char = None
50 self._freq_char = None
51 self.reset()
52
53 def reset(self):
54 super(SingleByteCharSetProber, self).reset()
55 # char order of last character
56 self._last_order = 255
57 self._seq_counters = [0] * SequenceLikelihood.get_num_categories()
58 self._total_seqs = 0
59 self._total_char = 0
60 # characters that fall in our sampling range
61 self._freq_char = 0
62
63 @property
64 def charset_name(self):
65 if self._name_prober:
66 return self._name_prober.charset_name
67 else:
68 return self._model['charset_name']
69
70 @property
71 def language(self):
72 if self._name_prober:
73 return self._name_prober.language
74 else:
75 return self._model.get('language')
76
77 def feed(self, byte_str):
78 if not self._model['keep_english_letter']:
79 byte_str = self.filter_international_words(byte_str)
80 if not byte_str:
81 return self.state
82 char_to_order_map = self._model['char_to_order_map']
83 for i, c in enumerate(byte_str):
84 # XXX: Order is in range 1-64, so one would think we want 0-63 here,
85 # but that leads to 27 more test failures than before.
86 order = char_to_order_map[c]
87 # XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but
88 # CharacterCategory.SYMBOL is actually 253, so we use CONTROL
89 # to make it closer to the original intent. The only difference
90 # is whether or not we count digits and control characters for
91 # _total_char purposes.
92 if order < CharacterCategory.CONTROL:
93 self._total_char += 1
94 if order < self.SAMPLE_SIZE:
95 self._freq_char += 1
96 if self._last_order < self.SAMPLE_SIZE:
97 self._total_seqs += 1
98 if not self._reversed:
99 i = (self._last_order * self.SAMPLE_SIZE) + order
100 model = self._model['precedence_matrix'][i]
101 else: # reverse the order of the letters in the lookup
102 i = (order * self.SAMPLE_SIZE) + self._last_order
103 model = self._model['precedence_matrix'][i]
104 self._seq_counters[model] += 1
105 self._last_order = order
106
107 charset_name = self._model['charset_name']
108 if self.state == ProbingState.DETECTING:
109 if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD:
110 confidence = self.get_confidence()
111 if confidence > self.POSITIVE_SHORTCUT_THRESHOLD:
112 self.logger.debug('%s confidence = %s, we have a winner',
113 charset_name, confidence)
114 self._state = ProbingState.FOUND_IT
115 elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD:
116 self.logger.debug('%s confidence = %s, below negative '
117 'shortcut threshhold %s', charset_name,
118 confidence,
119 self.NEGATIVE_SHORTCUT_THRESHOLD)
120 self._state = ProbingState.NOT_ME
121
122 return self.state
123
124 def get_confidence(self):
125 r = 0.01
126 if self._total_seqs > 0:
127 r = ((1.0 * self._seq_counters[SequenceLikelihood.POSITIVE]) /
128 self._total_seqs / self._model['typical_positive_ratio'])
129 r = r * self._freq_char / self._total_char
130 if r >= 1.0:
131 r = 0.99
132 return r