(lang, expression=default_pattern, gaps=False,
                     cachesize=50000)
return chain
Configures a simple analyzer for the given language, with a
    LowercaseFilter, StopFilter, and StemFilter.

    >>> ana = LanguageAnalyzer("es")
    >>> [token.text for token in ana("Por el mar corren las liebres")]
    ['mar', 'corr', 'liebr']

    The list of available languages is in `whoosh.lang.languages`.
    You can use :func:`whoosh.lang.has_stemmer` and
    :func:`whoosh.lang.has_stopwords` to check if a given language has a
    stemming function and/or stop word list available.

    :param expression: The regular expression pattern to use to extract tokens.
    :param gaps: If True, the tokenizer *splits* on the expression, rather
        than matching on the expression.
    :param cachesize: the maximum number of stemmed words to cache. The larger
        this number, the faster stemming will be but the more memory it will
        use.
Configures a simple analyzer for the given language, with a
    LowercaseFilter, StopFilter, and StemFilter.
Configures a simple analyzer for the given language with a LowercaseFilter StopFilter and StemFilter .
def LanguageAnalyzer(lang, expression=default_pattern, gaps=False,
                     cachesize=50000):
    """Configures a simple analyzer for the given language, with a
    LowercaseFilter, StopFilter, and StemFilter.

    >>> ana = LanguageAnalyzer("es")
    >>> [token.text for token in ana("Por el mar corren las liebres")]
    ['mar', 'corr', 'liebr']

    The list of available languages is in `whoosh.lang.languages`.
    You can use :func:`whoosh.lang.has_stemmer` and
    :func:`whoosh.lang.has_stopwords` to check if a given language has a
    stemming function and/or stop word list available.

    :param expression: The regular expression pattern to use to extract tokens.
    :param gaps: If True, the tokenizer *splits* on the expression, rather
        than matching on the expression.
    :param cachesize: the maximum number of stemmed words to cache. The larger
        this number, the faster stemming will be but the more memory it will
        use.
    """

    from whoosh.lang import NoStemmer, NoStopWords

    # Make the start of the chain
    chain = (RegexTokenizer(expression=expression, gaps=gaps)
             | LowercaseFilter())

    # Add a stop word filter
    try:
        chain = chain | StopFilter(lang=lang)
    except NoStopWords:
        pass

    # Add a stemming filter
    try:
        chain = chain | StemFilter(lang=lang, cachesize=cachesize)
    except NoStemmer:
        pass

    return chain
def LanguageAnalyzer ( lang , expression = default_pattern , gaps = False , cachesize = 50000 ) : from whoosh . lang import NoStemmer , NoStopWords # Make the start of the chain chain = ( RegexTokenizer ( expression = expression , gaps = gaps ) | LowercaseFilter ( ) ) # Add a stop word filter try : chain = chain | StopFilter ( lang = lang ) except NoStopWords : pass # Add a stemming filter try : chain = chain | StemFilter ( lang = lang , cachesize = cachesize ) except NoStemmer : pass return chain