Mercurial > repos > bcclaywell > argo_navis

comparison venv/lib/python2.7/site-packages/requests/packages/chardet/sbcharsetprober.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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-1:000000000000 0:d67268158946
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 import sys
30 from . import constants
31 from .charsetprober import CharSetProber
32 from .compat import wrap_ord
33
34 SAMPLE_SIZE = 64
35 SB_ENOUGH_REL_THRESHOLD = 1024
36 POSITIVE_SHORTCUT_THRESHOLD = 0.95
37 NEGATIVE_SHORTCUT_THRESHOLD = 0.05
38 SYMBOL_CAT_ORDER = 250
39 NUMBER_OF_SEQ_CAT = 4
40 POSITIVE_CAT = NUMBER_OF_SEQ_CAT - 1
41 #NEGATIVE_CAT = 0
42
43
44 class SingleByteCharSetProber(CharSetProber):
45 def __init__(self, model, reversed=False, nameProber=None):
46 CharSetProber.__init__(self)
47 self._mModel = model
48 # TRUE if we need to reverse every pair in the model lookup
49 self._mReversed = reversed
50 # Optional auxiliary prober for name decision
51 self._mNameProber = nameProber
52 self.reset()
53
54 def reset(self):
55 CharSetProber.reset(self)
56 # char order of last character
57 self._mLastOrder = 255
58 self._mSeqCounters = [0] * NUMBER_OF_SEQ_CAT
59 self._mTotalSeqs = 0
60 self._mTotalChar = 0
61 # characters that fall in our sampling range
62 self._mFreqChar = 0
63
64 def get_charset_name(self):
65 if self._mNameProber:
66 return self._mNameProber.get_charset_name()
67 else:
68 return self._mModel['charsetName']
69
70 def feed(self, aBuf):
71 if not self._mModel['keepEnglishLetter']:
72 aBuf = self.filter_without_english_letters(aBuf)
73 aLen = len(aBuf)
74 if not aLen:
75 return self.get_state()
76 for c in aBuf:
77 order = self._mModel['charToOrderMap'][wrap_ord(c)]
78 if order < SYMBOL_CAT_ORDER:
79 self._mTotalChar += 1
80 if order < SAMPLE_SIZE:
81 self._mFreqChar += 1
82 if self._mLastOrder < SAMPLE_SIZE:
83 self._mTotalSeqs += 1
84 if not self._mReversed:
85 i = (self._mLastOrder * SAMPLE_SIZE) + order
86 model = self._mModel['precedenceMatrix'][i]
87 else: # reverse the order of the letters in the lookup
88 i = (order * SAMPLE_SIZE) + self._mLastOrder
89 model = self._mModel['precedenceMatrix'][i]
90 self._mSeqCounters[model] += 1
91 self._mLastOrder = order
92
93 if self.get_state() == constants.eDetecting:
94 if self._mTotalSeqs > SB_ENOUGH_REL_THRESHOLD:
95 cf = self.get_confidence()
96 if cf > POSITIVE_SHORTCUT_THRESHOLD:
97 if constants._debug:
98 sys.stderr.write('%s confidence = %s, we have a'
99 'winner\n' %
100 (self._mModel['charsetName'], cf))
101 self._mState = constants.eFoundIt
102 elif cf < NEGATIVE_SHORTCUT_THRESHOLD:
103 if constants._debug:
104 sys.stderr.write('%s confidence = %s, below negative'
105 'shortcut threshhold %s\n' %
106 (self._mModel['charsetName'], cf,
107 NEGATIVE_SHORTCUT_THRESHOLD))
108 self._mState = constants.eNotMe
109
110 return self.get_state()
111
112 def get_confidence(self):
113 r = 0.01
114 if self._mTotalSeqs > 0:
115 r = ((1.0 * self._mSeqCounters[POSITIVE_CAT]) / self._mTotalSeqs
116 / self._mModel['mTypicalPositiveRatio'])
117 r = r * self._mFreqChar / self._mTotalChar
118 if r >= 1.0:
119 r = 0.99
120 return r