SPPAS 4.20

def fix_options(self, options):
    """Fix all options.

        Available options are:

            - phonunk
            - usesstdtokens

        :param options: (sppasOption)

        """
    for opt in options:
        key = opt.get_key()
        if key == 'phonunk':
            self.set_unk(opt.get_value())
        elif key == 'usestdtokens':
            self.set_usestdtokens(opt.get_value())
        elif 'pattern' in key:
            self._options[key] = opt.get_value()
        else:
            raise AnnotationOptionError(key)

def set_unk(self, unk):
    """Fix the unk option value.

        :param unk: (bool) If unk is set to True, the system attempts
        to phonetize unknown entries (i.e. tokens missing in the dictionary).
        Otherwise, the phonetization of an unknown entry unit is set to the
        default stamp.

        """
    self._options['phonunk'] = unk

def set_usestdtokens(self, stdtokens):
    """Fix the stdtokens option.

        :param stdtokens: (bool) If it is set to True, the phonetization
        uses the standard transcription as input, instead of the faked
        transcription. This option does make sense only for an Enriched
        Orthographic Transcription.

        """
    self._options['usestdtokens'] = stdtokens

def load_resources(self, dict_filename=None, map_filename=None, lang='und', **kwargs):
    """Set the pronunciation dictionary and the mapping table.

        :param dict_filename: (str) The pronunciation dictionary in HTK-ASCII
        format with UTF-8 encoding.

        :param map_filename: (str) is the filename of a mapping table. It is         used to generate new pronunciations by mapping phonemes of the dict.

        :param lang: (str) Iso639-3 of the language or "und" if unknown.

        """
    self.__lang = lang
    if map_filename is not None:
        self.maptable = sppasMapping(map_filename)
        self.logfile.print_message(info(1160, 'annotations').format(len(self.maptable)), indent=0)
    else:
        self.maptable = sppasMapping()
    pdict = sppasDictPron(dict_filename, nodump=False)
    if dict_filename is not None:
        self.__phonetizer = sppasDictPhonetizer(pdict, self.maptable)
        self.logfile.print_message(info(1162, 'annotations').format(len(pdict)), indent=0)
    else:
        self.__phonetizer = sppasDictPhonetizer(pdict)

def convert(self, tier):
    """Phonetize annotations of a tokenized tier.

        :param tier: (Tier) the ortho transcription previously tokenized.
        :returns: (Tier) phonetized tier with name "Phones"

        """
    if tier is None:
        raise IOError('No given tier.')
    if tier.is_empty() is True:
        raise EmptyInputError(name=tier.get_name())
    phones_tier = sppasTier('Phones')
    phones_tier.set_meta('linguistic_resource_dict', self.__phonetizer.get_dict_filename())
    tier.set_meta('language', '0')
    for i, ann in enumerate(tier):
        logging.info(info(1220, 'annotations').format(number=i + 1))
        location = ann.get_location().copy()
        labels = list()
        normalized = list()
        for label in ann.get_labels():
            if ' ' in label:
                normalized.extend(label.split())
            else:
                normalized.append(label)
        for label in normalized:
            phonetizations = list()
            for text, score in label:
                if text.is_pause() or text.is_silence():
                    phonetizations.append(SIL)
                elif text.is_empty() is False:
                    phones = self._phonetize(text.get_content(), track_nb=i + 1)
                    for p in phones:
                        phonetizations.extend(p.split(separators.variants))
            tags = [sppasTag(p) for p in set(phonetizations)]
            labels.append(sppasLabel(tags))
        phones_tier.create_annotation(location, labels)
    return phones_tier

def get_inputs(self, input_files):
    """Return the the tier with aligned tokens.

        :param input_files: (list)
        :raise: NoTierInputError
        :return: (sppasTier)

        """
    tier = None
    annot_ext = self.get_input_extensions()
    tier_pattern = ''
    if self._options['usestdtokens'] is True:
        tier_pattern = 'std'
    for filename in input_files:
        if filename is None:
            continue
        fn, fe = os.path.splitext(filename)
        if tier is None and fe in annot_ext[0]:
            parser = sppasTrsRW(filename)
            trs_input = parser.read()
            tier = sppasFindTier.tokenization(trs_input, tier_pattern)
            if tier is not None:
                if self.logfile:
                    self.logfile.print_message('Input tier to be phonetized: {}'.format(tier.get_name()), indent=1)
                return tier
    logging.error('A tier with a normalized text was not found.')
    raise NoTierInputError

def run(self, input_files, output=None):
    """Run the automatic annotation process on an input.

        :param input_files: (list of str) Normalized text
        :param output: (str) the output name
        :returns: (sppasTranscription)

        """
    tier_input = self.get_inputs(input_files)
    tier_phon = self.convert(tier_input)
    trs_output = sppasTranscription(self.name)
    trs_output.set_meta('annotation_result_of', input_files[0])
    trs_output.set_meta('language_iso', 'iso639-3')
    trs_output.set_meta('language_name_0', 'Undetermined')
    if len(self.__lang) == 3:
        trs_output.set_meta('language_code_0', self.__lang)
        trs_output.set_meta('language_url_0', 'https://iso639-3.sil.org/code/' + self.__lang)
    else:
        trs_output.set_meta('language_code_0', 'und')
        trs_output.set_meta('language_url_0', 'https://iso639-3.sil.org/code/und')
    if tier_phon is not None:
        trs_output.append(tier_phon)
    if output is not None:
        if len(trs_output) > 0:
            output_file = self.fix_out_file_ext(output)
            parser = sppasTrsRW(output_file)
            parser.write(trs_output)
            return [output_file]
        else:
            raise EmptyOutputError
    return trs_output

def get_output_pattern(self):
    """Pattern this annotation uses in an output filename."""
    return self._options.get('outputpattern', '-phon')

def get_input_patterns(self):
    """Pattern this annotation expects for its input filename."""
    return [self._options.get('inputpattern', '-token')]

def _phonetize(self, entry, track_nb=0):
    """Phonetize a text.

        Because we absolutely need to match with the number of tokens, this
        method will always return a string: either the automatic phonetization
        (from dict or from phonunk) or the unk stamp.

        :param entry: (str) The string to be phonetized.
        :returns: phonetization of the given entry

        """
    unk = symbols.unk
    tab = self.__phonetizer.get_phon_tokens(entry.split(), phonunk=self._options['phonunk'])
    tab_phones = list()
    for tex, p, s in tab:
        message = None
        if s == annots.error:
            message = info(1110, 'annotations').format(tex) + info(1114, 'annotations')
            self.logfile.print_message(message, indent=2, status=s)
            return [unk]
        else:
            if s == annots.warning:
                message = info(1110, 'annotations').format(tex)
                if len(p) > 0:
                    message = message + info(1112, 'annotations').format(p)
                else:
                    message = message + info(1114, 'annotations')
                    p = unk
            tab_phones.append(p)
        if message:
            self.logfile.print_message(MSG_TRACK.format(number=track_nb), indent=1)
            self.logfile.print_message(message, indent=2, status=s)
    return tab_phones