annotate env/lib/python3.9/site-packages/chardet/sbcharsetprober.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

######################## 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 collections import namedtuple

from .charsetprober import CharSetProber
from .enums import CharacterCategory, ProbingState, SequenceLikelihood


SingleByteCharSetModel = namedtuple('SingleByteCharSetModel',
                                    ['charset_name',
                                     'language',
                                     'char_to_order_map',
                                     'language_model',
                                     'typical_positive_ratio',
                                     'keep_ascii_letters',
                                     'alphabet'])


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.language

    def feed(self, byte_str):
        # TODO: Make filter_international_words keep things in self.alphabet
        if not self._model.keep_ascii_letters:
            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
        language_model = self._model.language_model
        for char in byte_str:
            order = char_to_order_map.get(char, CharacterCategory.UNDEFINED)
            # 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
            # TODO: Follow uchardet's lead and discount confidence for frequent
            #       control characters.
            #       See https://github.com/BYVoid/uchardet/commit/55b4f23971db61
            if order < self.SAMPLE_SIZE:
                self._freq_char += 1
                if self._last_order < self.SAMPLE_SIZE:
                    self._total_seqs += 1
                    if not self._reversed:
                        lm_cat = language_model[self._last_order][order]
                    else:
                        lm_cat = language_model[order][self._last_order]
                    self._seq_counters[lm_cat] += 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