v1.0

text/custom_pypinyin_dict/genshin.py

+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+phrases_dict = {
+    '㐖毒': [['xié'], ['dú']],
+    '若陀': [['rě'], ['tuó']], 
+    '平藏': [['píng'], ['zàng']],
+    '派蒙': [['pài'], ['méng']], 
+    '安柏': [['ān'], ['bó']],
+    '一斗': [['yī'], ['dǒu']]
+}
\ No newline at end of file

text/custom_pypinyin_dict/phrase_pinyin_data.py

+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+from pypinyin import load_phrases_dict
+
+from text.custom_pypinyin_dict import cc_cedict_0
+from text.custom_pypinyin_dict import cc_cedict_1
+from text.custom_pypinyin_dict import cc_cedict_2
+from text.custom_pypinyin_dict import cc_cedict_3
+from text.custom_pypinyin_dict import genshin
+
+phrases_dict = {}
+phrases_dict.update(cc_cedict_0.phrases_dict)
+phrases_dict.update(cc_cedict_1.phrases_dict)
+phrases_dict.update(cc_cedict_2.phrases_dict)
+phrases_dict.update(cc_cedict_3.phrases_dict)
+phrases_dict.update(genshin.phrases_dict)
+
+def load():
+    load_phrases_dict(phrases_dict)
+    print("加载自定义词典成功")
+
+if __name__ == '__main__':
+    print(phrases_dict)
\ No newline at end of file

text/english.py

+""" from https://github.com/keithito/tacotron """
+
+'''
+Cleaners are transformations that run over the input text at both training and eval time.
+
+Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
+hyperparameter. Some cleaners are English-specific. You'll typically want to use:
+  1. "english_cleaners" for English text
+  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
+     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
+  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
+     the symbols in symbols.py to match your data).
+'''
+
+
+# Regular expression matching whitespace:
+
+
+import re
+import inflect
+from unidecode import unidecode
+import eng_to_ipa as ipa
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
+_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
+_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
+_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
+_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
+_number_re = re.compile(r'[0-9]+')
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
+    ('mrs', 'misess'),
+    ('mr', 'mister'),
+    ('dr', 'doctor'),
+    ('st', 'saint'),
+    ('co', 'company'),
+    ('jr', 'junior'),
+    ('maj', 'major'),
+    ('gen', 'general'),
+    ('drs', 'doctors'),
+    ('rev', 'reverend'),
+    ('lt', 'lieutenant'),
+    ('hon', 'honorable'),
+    ('sgt', 'sergeant'),
+    ('capt', 'captain'),
+    ('esq', 'esquire'),
+    ('ltd', 'limited'),
+    ('col', 'colonel'),
+    ('ft', 'fort'),
+]]
+
+
+# List of (ipa, lazy ipa) pairs:
+_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
+    ('r', 'ɹ'),
+    ('æ', 'e'),
+    ('ɑ', 'a'),
+    ('ɔ', 'o'),
+    ('ð', 'z'),
+    ('θ', 's'),
+    ('ɛ', 'e'),
+    ('ɪ', 'i'),
+    ('ʊ', 'u'),
+    ('ʒ', 'ʥ'),
+    ('ʤ', 'ʥ'),
+    ('ˈ', '↓'),
+]]
+
+# List of (ipa, lazy ipa2) pairs:
+_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
+    ('r', 'ɹ'),
+    ('ð', 'z'),
+    ('θ', 's'),
+    ('ʒ', 'ʑ'),
+    ('ʤ', 'dʑ'),
+    ('ˈ', '↓'),
+]]
+
+# List of (ipa, ipa2) pairs
+_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
+    ('r', 'ɹ'),
+    ('ʤ', 'dʒ'),
+    ('ʧ', 'tʃ')
+]]
+
+
+def expand_abbreviations(text):
+    for regex, replacement in _abbreviations:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def collapse_whitespace(text):
+    return re.sub(r'\s+', ' ', text)
+
+
+def _remove_commas(m):
+    return m.group(1).replace(',', '')
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace('.', ' point ')
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split('.')
+    if len(parts) > 2:
+        return match + ' dollars'  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+        cent_unit = 'cent' if cents == 1 else 'cents'
+        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+        return '%s %s' % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = 'cent' if cents == 1 else 'cents'
+        return '%s %s' % (cents, cent_unit)
+    else:
+        return 'zero dollars'
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return 'two thousand'
+        elif num > 2000 and num < 2010:
+            return 'two thousand ' + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + ' hundred'
+        else:
+            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+    else:
+        return _inflect.number_to_words(num, andword='')
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r'\1 pounds', text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+def mark_dark_l(text):
+    return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
+
+
+def english_to_ipa(text):
+    text = unidecode(text).lower()
+    text = expand_abbreviations(text)
+    text = normalize_numbers(text)
+    phonemes = ipa.convert(text)
+    phonemes = collapse_whitespace(phonemes)
+    return phonemes
+
+
+def english_to_ipa2(text):
+    text = english_to_ipa(text)
+    text = mark_dark_l(text)
+    for regex, replacement in _ipa_to_ipa2:
+        text = re.sub(regex, replacement, text)
+    return list(text.replace('...', '…'))
+

text/japanese.py

+import re
+from unidecode import unidecode
+import pyopenjtalk
+
+
+# Regular expression matching Japanese without punctuation marks:
+_japanese_characters = re.compile(
+    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
+
+# Regular expression matching non-Japanese characters or punctuation marks:
+_japanese_marks = re.compile(
+    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
+
+# List of (symbol, Japanese) pairs for marks:
+_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
+    ('％', 'パーセント')
+]]
+
+# List of (romaji, ipa) pairs for marks:
+_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
+    ('ts', 'ʦ'),
+    ('u', 'ɯ'),
+    ('j', 'ʥ'),
+    ('y', 'j'),
+    ('ni', 'n^i'),
+    ('nj', 'n^'),
+    ('hi', 'çi'),
+    ('hj', 'ç'),
+    ('f', 'ɸ'),
+    ('I', 'i*'),
+    ('U', 'ɯ*'),
+    ('r', 'ɾ')
+]]
+
+# List of (romaji, ipa2) pairs for marks:
+_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
+    ('u', 'ɯ'),
+    ('ʧ', 'tʃ'),
+    ('j', 'dʑ'),
+    ('y', 'j'),
+    ('ni', 'n^i'),
+    ('nj', 'n^'),
+    ('hi', 'çi'),
+    ('hj', 'ç'),
+    ('f', 'ɸ'),
+    ('I', 'i*'),
+    ('U', 'ɯ*'),
+    ('r', 'ɾ')
+]]
+
+# List of (consonant, sokuon) pairs:
+_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
+    (r'Q([↑↓]*[kg])', r'k#\1'),
+    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
+    (r'Q([↑↓]*[sʃ])', r's\1'),
+    (r'Q([↑↓]*[pb])', r'p#\1')
+]]
+
+# List of (consonant, hatsuon) pairs:
+_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
+    (r'N([↑↓]*[pbm])', r'm\1'),
+    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
+    (r'N([↑↓]*[tdn])', r'n\1'),
+    (r'N([↑↓]*[kg])', r'ŋ\1')
+]]
+
+
+def symbols_to_japanese(text):
+    for regex, replacement in _symbols_to_japanese:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def japanese_to_romaji_with_accent(text):
+    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
+    text = symbols_to_japanese(text)
+    sentences = re.split(_japanese_marks, text)
+    marks = re.findall(_japanese_marks, text)
+    text = ''
+    for i, sentence in enumerate(sentences):
+        if re.match(_japanese_characters, sentence):
+            if text != '':
+                text += ' '
+            labels = pyopenjtalk.extract_fullcontext(sentence)
+            for n, label in enumerate(labels):
+                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
+                if phoneme not in ['sil', 'pau']:
+                    text += phoneme.replace('ch', 'ʧ').replace('sh',
+                                                               'ʃ').replace('cl', 'Q')
+                else:
+                    continue
+                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
+                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
+                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
+                a3 = int(re.search(r"\+(\d+)/", label).group(1))
+                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
+                    a2_next = -1
+                else:
+                    a2_next = int(
+                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
+                # Accent phrase boundary
+                if a3 == 1 and a2_next == 1:
+                    text += ' '
+                # Falling
+                elif a1 == 0 and a2_next == a2 + 1:
+                    text += '↓'
+                # Rising
+                elif a2 == 1 and a2_next == 2:
+                    text += '↑'
+        if i < len(marks):
+            text += unidecode(marks[i]).replace(' ', '')
+    return text
+
+
+def get_real_sokuon(text):
+    for regex, replacement in _real_sokuon:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def get_real_hatsuon(text):
+    for regex, replacement in _real_hatsuon:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def japanese_to_ipa(text):
+    text = japanese_to_romaji_with_accent(text).replace('...', '…')
+    text = re.sub(
+        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
+    text = get_real_sokuon(text)
+    text = get_real_hatsuon(text)
+    for regex, replacement in _romaji_to_ipa:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def japanese_to_ipa2(text):
+    text = japanese_to_romaji_with_accent(text).replace('...', '…')
+    text = get_real_sokuon(text)
+    text = get_real_hatsuon(text)
+    for regex, replacement in _romaji_to_ipa2:
+        text = re.sub(regex, replacement, text)
+    return list(text)
+
+
+def japanese_to_ipa3(text):
+    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
+        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
+    text = re.sub(
+        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
+    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
+    return text
+
+if __name__ == '__main__':
+    a = japanese_to_romaji_with_accent('こんにちは！はい、元気です。あなたは？')
+    print(a)

text/langdetect/__init__.py

+from .detector_factory import DetectorFactory, PROFILES_DIRECTORY, detect, detect_langs
+from .lang_detect_exception import LangDetectException

text/langdetect/detector.py

+import random
+import re
+
+import six
+from six.moves import zip, xrange
+
+from .lang_detect_exception import ErrorCode, LangDetectException
+from .language import Language
+from .utils.ngram import NGram
+from .utils.unicode_block import unicode_block
+
+
+class Detector(object):
+    '''
+    Detector class is to detect language from specified text.
+    Its instance is able to be constructed via the factory class DetectorFactory.
+
+    After appending a target text to the Detector instance with .append(string),
+    the detector provides the language detection results for target text via .detect() or .get_probabilities().
+
+    .detect() method returns a single language name which has the highest probability.
+    .get_probabilities() methods returns a list of multiple languages and their probabilities.
+
+    The detector has some parameters for language detection.
+    See set_alpha(double), .set_max_text_length(int) .set_prior_map(dict).
+
+    Example:
+
+        from langdetect.detector_factory import DetectorFactory
+        factory = DetectorFactory()
+        factory.load_profile('/path/to/profile/directory')
+
+        def detect(text):
+            detector = factory.create()
+            detector.append(text)
+            return detector.detect()
+
+        def detect_langs(text):
+            detector = factory.create()
+            detector.append(text)
+            return detector.get_probabilities()
+    '''
+
+    ALPHA_DEFAULT = 0.5
+    ALPHA_WIDTH = 0.05
+
+    ITERATION_LIMIT = 1000
+    PROB_THRESHOLD = 0.1
+    CONV_THRESHOLD = 0.99999
+    BASE_FREQ = 10000
+    UNKNOWN_LANG = 'unknown'
+
+    URL_RE = re.compile(r'https?://[-_.?&~;+=/#0-9A-Za-z]{1,2076}')
+    MAIL_RE = re.compile(r'[-_.0-9A-Za-z]{1,64}@[-_0-9A-Za-z]{1,255}[-_.0-9A-Za-z]{1,255}')
+
+    def __init__(self, factory):
+        self.word_lang_prob_map = factory.word_lang_prob_map
+        self.langlist = factory.langlist
+        self.seed = factory.seed
+        self.random = random.Random()
+        self.text = ''
+        self.langprob = None
+
+        self.alpha = self.ALPHA_DEFAULT
+        self.n_trial = 7
+        self.max_text_length = 10000
+        self.prior_map = None
+        self.verbose = False
+
+    def set_verbose(self):
+        self.verbose = True
+
+    def set_alpha(self, alpha):
+        self.alpha = alpha
+
+    def set_prior_map(self, prior_map):
+        '''Set prior information about language probabilities.'''
+        self.prior_map = [0.0] * len(self.langlist)
+        sump = 0.0
+        for i in xrange(len(self.prior_map)):
+            lang = self.langlist[i]
+            if lang in prior_map:
+                p = prior_map[lang]
+                if p < 0:
+                    raise LangDetectException(ErrorCode.InitParamError, 'Prior probability must be non-negative.')
+                self.prior_map[i] = p
+                sump += p
+        if sump <= 0.0:
+            raise LangDetectException(ErrorCode.InitParamError, 'More one of prior probability must be non-zero.')
+        for i in xrange(len(self.prior_map)):
+            self.prior_map[i] /= sump
+
+    def set_max_text_length(self, max_text_length):
+        '''Specify max size of target text to use for language detection.
+        The default value is 10000(10KB).
+        '''
+        self.max_text_length = max_text_length
+
+    def append(self, text):
+        '''Append the target text for language detection.
+        If the total size of target text exceeds the limit size specified by
+        Detector.set_max_text_length(int), the rest is cut down.
+        '''
+        text = self.URL_RE.sub(' ', text)
+        text = self.MAIL_RE.sub(' ', text)
+        text = NGram.normalize_vi(text)
+        pre = 0
+        for i in xrange(min(len(text), self.max_text_length)):
+            ch = text[i]
+            if ch != ' ' or pre != ' ':
+                self.text += ch
+            pre = ch
+
+    def cleaning_text(self):
+        '''Cleaning text to detect
+        (eliminate URL, e-mail address and Latin sentence if it is not written in Latin alphabet).
+        '''
+        latin_count, non_latin_count = 0, 0
+        for ch in self.text:
+            if 'A' <= ch <= 'z':
+                latin_count += 1
+            elif ch >= six.u('\u0300') and unicode_block(ch) != 'Latin Extended Additional':
+                non_latin_count += 1
+
+        if latin_count * 2 < non_latin_count:
+            text_without_latin = ''
+            for ch in self.text:
+                if ch < 'A' or 'z' < ch:
+                    text_without_latin += ch
+            self.text = text_without_latin
+
+    def detect(self):
+        '''Detect language of the target text and return the language name
+        which has the highest probability.
+        '''
+        probabilities = self.get_probabilities()
+        if probabilities:
+            return probabilities[0].lang
+        return self.UNKNOWN_LANG
+
+    def get_probabilities(self):
+        if self.langprob is None:
+            self._detect_block()
+        return self._sort_probability(self.langprob)
+
+    def _detect_block(self):
+        self.cleaning_text()
+        ngrams = self._extract_ngrams()
+        if not ngrams:
+            raise LangDetectException(ErrorCode.CantDetectError, 'No features in text.')
+
+        self.langprob = [0.0] * len(self.langlist)
+
+        self.random.seed(self.seed)
+        for t in xrange(self.n_trial):
+            prob = self._init_probability()
+            alpha = self.alpha + self.random.gauss(0.0, 1.0) * self.ALPHA_WIDTH
+
+            i = 0
+            while True:
+                self._update_lang_prob(prob, self.random.choice(ngrams), alpha)
+                if i % 5 == 0:
+                    if self._normalize_prob(prob) > self.CONV_THRESHOLD or i >= self.ITERATION_LIMIT:
+                        break
+                    if self.verbose:
+                        six.print_('>', self._sort_probability(prob))
+                i += 1
+            for j in xrange(len(self.langprob)):
+                self.langprob[j] += prob[j] / self.n_trial
+            if self.verbose:
+                six.print_('==>', self._sort_probability(prob))
+
+    def _init_probability(self):
+        '''Initialize the map of language probabilities.
+        If there is the specified prior map, use it as initial map.
+        '''
+        if self.prior_map is not None:
+            return list(self.prior_map)
+        else:
+            return [1.0 / len(self.langlist)] * len(self.langlist)
+
+    def _extract_ngrams(self):
+        '''Extract n-grams from target text.'''
+        RANGE = list(xrange(1, NGram.N_GRAM + 1))
+
+        result = []
+        ngram = NGram()
+        for ch in self.text:
+            ngram.add_char(ch)
+            if ngram.capitalword:
+                continue
+            for n in RANGE:
+                # optimized w = ngram.get(n)
+                if len(ngram.grams) < n:
+                    break
+                w = ngram.grams[-n:]
+                if w and w != ' ' and w in self.word_lang_prob_map:
+                    result.append(w)
+        return result
+
+    def _update_lang_prob(self, prob, word, alpha):
+        '''Update language probabilities with N-gram string(N=1,2,3).'''
+        if word is None or word not in self.word_lang_prob_map:
+            return False
+
+        lang_prob_map = self.word_lang_prob_map[word]
+        if self.verbose:
+            six.print_('%s(%s): %s' % (word, self._unicode_encode(word), self._word_prob_to_string(lang_prob_map)))
+
+        weight = alpha / self.BASE_FREQ
+        for i in xrange(len(prob)):
+            prob[i] *= weight + lang_prob_map[i]
+        return True
+
+    def _word_prob_to_string(self, prob):
+        result = ''
+        for j in xrange(len(prob)):
+            p = prob[j]
+            if p >= 0.00001:
+                result += ' %s:%.5f' % (self.langlist[j], p)
+        return result
+
+    def _normalize_prob(self, prob):
+        '''Normalize probabilities and check convergence by the maximun probability.
+        '''
+        maxp, sump = 0.0, sum(prob)
+        for i in xrange(len(prob)):
+            p = prob[i] / sump
+            if maxp < p:
+                maxp = p
+            prob[i] = p
+        return maxp
+
+    def _sort_probability(self, prob):
+        result = [Language(lang, p) for (lang, p) in zip(self.langlist, prob) if p > self.PROB_THRESHOLD]
+        result.sort(reverse=True)
+        return result
+
+    def _unicode_encode(self, word):
+        buf = ''
+        for ch in word:
+            if ch >= six.u('\u0080'):
+                st = hex(0x10000 + ord(ch))[2:]
+                while len(st) < 4:
+                    st = '0' + st
+                buf += r'\u' + st[1:5]
+            else:
+                buf += ch
+        return buf

text/langdetect/detector_factory.py

+import os
+from os import path
+import sys
+
+import json
+
+from .detector import Detector
+from .lang_detect_exception import ErrorCode, LangDetectException
+from .utils.lang_profile import LangProfile
+
+
+class DetectorFactory(object):
+    '''
+    Language Detector Factory Class.
+
+    This class manages an initialization and constructions of Detector.
+
+    Before using language detection library,
+    load profiles with DetectorFactory.load_profile(str)
+    and set initialization parameters.
+
+    When the language detection,
+    construct Detector instance via DetectorFactory.create().
+    See also Detector's sample code.
+    '''
+    seed = None
+
+    def __init__(self):
+        self.word_lang_prob_map = {}
+        self.langlist = []
+
+    def load_profile(self, profile_directory):
+        list_files = os.listdir(profile_directory)
+        if not list_files:
+            raise LangDetectException(ErrorCode.NeedLoadProfileError, 'Not found profile: ' + profile_directory)
+
+        langsize, index = len(list_files), 0
+        for filename in list_files:
+            if filename.startswith('.'):
+                continue
+            filename = path.join(profile_directory, filename)
+            if not path.isfile(filename):
+                continue
+
+            f = None
+            try:
+                if sys.version_info[0] < 3:
+                    f = open(filename, 'r')
+                else:
+                    f = open(filename, 'r', encoding='utf-8')
+                json_data = json.load(f)
+                profile = LangProfile(**json_data)
+                self.add_profile(profile, index, langsize)
+                index += 1
+            except IOError:
+                raise LangDetectException(ErrorCode.FileLoadError, 'Cannot open "%s"' % filename)
+            except:
+                raise LangDetectException(ErrorCode.FormatError, 'Profile format error in "%s"' % filename)
+            finally:
+                if f:
+                    f.close()
+
+    def load_json_profile(self, json_profiles):
+        langsize, index = len(json_profiles), 0
+        if langsize < 2:
+            raise LangDetectException(ErrorCode.NeedLoadProfileError, 'Need more than 2 profiles.')
+
+        for json_profile in json_profiles:
+            try:
+                json_data = json.loads(json_profile)
+                profile = LangProfile(**json_data)
+                self.add_profile(profile, index, langsize)
+                index += 1
+            except:
+                raise LangDetectException(ErrorCode.FormatError, 'Profile format error.')
+
+    def add_profile(self, profile, index, langsize):
+        lang = profile.name
+        if lang in self.langlist:
+            raise LangDetectException(ErrorCode.DuplicateLangError, 'Duplicate the same language profile.')
+        self.langlist.append(lang)
+
+        for word in profile.freq:
+            if word not in self.word_lang_prob_map:
+                self.word_lang_prob_map[word] = [0.0] * langsize
+            length = len(word)
+            if 1 <= length <= 3:
+                prob = 1.0 * profile.freq.get(word) / profile.n_words[length - 1]
+                self.word_lang_prob_map[word][index] = prob
+
+    def clear(self):
+        self.langlist = []
+        self.word_lang_prob_map = {}
+
+    def create(self, alpha=None):
+        '''Construct Detector instance with smoothing parameter.'''
+        detector = self._create_detector()
+        if alpha is not None:
+            detector.set_alpha(alpha)
+        return detector
+
+    def _create_detector(self):
+        if not self.langlist:
+            raise LangDetectException(ErrorCode.NeedLoadProfileError, 'Need to load profiles.')
+        return Detector(self)
+
+    def set_seed(self, seed):
+        self.seed = seed
+
+    def get_lang_list(self):
+        return list(self.langlist)
+
+
+PROFILES_DIRECTORY = path.join(path.dirname(__file__), 'profiles')
+_factory = None
+
+def init_factory():
+    global _factory
+    if _factory is None:
+        _factory = DetectorFactory()
+        _factory.load_profile(PROFILES_DIRECTORY)
+
+def detect(text):
+    init_factory()
+    detector = _factory.create()
+    detector.append(text)
+    return detector.detect()
+
+
+def detect_langs(text):
+    init_factory()
+    detector = _factory.create()
+    detector.append(text)
+    return detector.get_probabilities()

text/langdetect/lang_detect_exception.py

+_error_codes = {
+    'NoTextError': 0,
+    'FormatError': 1,
+    'FileLoadError': 2,
+    'DuplicateLangError': 3,
+    'NeedLoadProfileError': 4,
+    'CantDetectError': 5,
+    'CantOpenTrainData': 6,
+    'TrainDataFormatError': 7,
+    'InitParamError': 8,
+}
+
+ErrorCode = type('ErrorCode', (), _error_codes)
+
+
+class LangDetectException(Exception):
+    def __init__(self, code, message):
+        super(LangDetectException, self).__init__(message)
+        self.code = code
+
+    def get_code(self):
+        return self.code

text/langdetect/language.py

+class Language(object):
+    '''
+    Language is to store the detected language.
+    Detector.get_probabilities() returns a list of Languages.
+    '''
+
+    def __init__(self, lang, prob):
+        self.lang = lang
+        self.prob = prob
+
+    def __repr__(self):
+        if self.lang is None:
+            return ''
+        return '%s:%s' % (self.lang, self.prob)
+
+    def __lt__(self, other):
+        return self.prob < other.prob

text/langdetect/profiles/af

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