def __str__(self):
"""
Return the reference representation of the sound.
Note
----
We first try to return the non-alias value in our data. If this fails,
we create the sound based on it's feature representation.
"""
# generated sounds need to be re-produced for double-checking
if not self.generated:
if not self.alias and self.grapheme in self.ts.sounds:
return self.grapheme
elif self.alias and self.featureset in self.ts.features:
return str(self.ts.features[self.featureset])
# this can usually not happen, as we catch these errors when loading a ts!
raise ValueError('Orphaned alias {0}'.format(
self.grapheme)) # pragma: no cover
# search for best base-string
elements = [f for f in self._features() if f not in EXCLUDE_FEATURES
] + [self.type]
base_str = self.base or '<?>'
base_graphemes = []
while elements:
base = self.ts.features.get(frozenset(elements))
if base:
base_graphemes.append(base.grapheme)
elements.pop(0)
base_str = base_graphemes[-1] if base_graphemes else base_str or '<?>'
base_vals = {
self.ts._feature_values[elm] for elm in
self.ts.sounds[base_str].name.split(' ')[:-1]} if \
base_str != '<?>' else {}
out = []
for p in self._write_order['pre']:
if p not in base_vals and getattr(self, p, '') in self._features():
out.append(
norm(self.ts.features[self.type].get(
getattr(self, p, ''), '<!>')))
out.append(base_str)
for p in self._write_order['post']:
if p not in base_vals and getattr(self, p, '') in self._features():
out.append(
norm(self.ts.features[self.type].get(
getattr(self, p, ''), '<!>')))
return ''.join(out)
def _norm(self, string):
"""Extended normalization: normalize by list of norm-characers, split
by character "/"."""
nstring = norm(string)
if "/" in string:
s, t = string.split('/')
nstring = t
return self.normalize(nstring)
def __init__(self, id_):
"""
:param system: The name of a transcription system or a directory containing one.
"""
if hasattr(self, 'features'):
# Only initialize, if this is really a new instance!
return
assert id_
system = pkg_path('transcriptionsystems', id_)
if not (system.exists() and system.is_dir()):
raise ValueError('unknown system: {0}'.format(id_))
self.system = TableGroup.from_file(
pkg_path('transcriptionsystems',
'transcription-system-metadata.json'))
self.system._fname = system / 'metadata.json'
self.features = {'consonant': {}, 'vowel': {}, 'tone': {}}
# dictionary for feature values, checks when writing elements from
# write_order to make sure no output is doubled
self._feature_values = {}
# load the general features
features = jsonlib.load(
pkg_path('transcriptionsystems', 'features.json'))
self.diacritics = dict(consonant={},
vowel={},
click={},
diphthong={},
tone={},
cluster={})
for dia in itertable(self.system.tabledict['diacritics.tsv']):
if not dia['alias'] and not dia['typography']:
self.features[dia['type']][dia['value']] = dia['grapheme']
# assign feature values to the dictionary
self._feature_values[dia['value']] = dia['feature']
self.diacritics[dia['type']][dia['grapheme']] = dia['value']
self.sound_classes = {}
self.columns = {} # the basic column structure, to allow for rendering
self.sounds = {} # Sounds by grapheme
self._covered = {}
# check for unresolved aliased sounds
aliases = []
for cls in [Consonant, Vowel, Tone, Marker]: # noqa: F405
type_ = cls.__name__.lower()
self.sound_classes[type_] = cls
# store information on column structure to allow for rendering of a
# sound in this form, which will make it easier to insert it when
# finding generated sounds
self.columns[type_] = [
c['name'].lower()
for c in self.system.tabledict['{0}s.tsv'.format(
type_)].asdict()['tableSchema']['columns']
]
for l, item in enumerate(
itertable(
self.system.tabledict['{0}s.tsv'.format(type_)])):
if item['grapheme'] in self.sounds:
raise ValueError(
'duplicate grapheme in {0}:{1}: {2}'.format(
type_ + 's.tsv', l + 2, item['grapheme']))
sound = cls(ts=self, **item)
# make sure this does not take too long
for key, value in item.items():
if key not in {'grapheme', 'note', 'alias'} and \
value and value not in self._feature_values:
self._feature_values[value] = key
if type_ != 'marker' and value not in features[type_][
key]:
raise ValueError(
"Unrecognized features ({0}: {1}, line {2}))".
format(key, value, l + 2))
self.sounds[item['grapheme']] = sound
if not sound.alias:
if sound.featureset in self.features:
raise ValueError(
'duplicate features in {0}:{1}: {2}'.format(
type_ + 's.tsv', l + 2, sound.name))
self.features[sound.featureset] = sound
else:
aliases += [(l, sound.type, sound.featureset)]
# check for consistency of aliases: if an alias has no counterpart, it
# is orphaned and needs to be deleted or given an accepted non-aliased
# sound
if [x for x in aliases
if x[2] not in self.features]: # pragma: no cover
error = ', '.join(
text_type(x[0] + 2) + '/' + text_type(x[1]) for x in aliases
if x[2] not in self.features)
raise ValueError('Orphaned aliases in line(s) {0}'.format(error))
# basic regular expression, used to match the basic sounds in the system.
self._regex = None
self._update_regex()
# normalization data
self._normalize = {
norm(r['source']): norm(r['target'])
for r in itertable(self.system.tabledict['normalize.tsv'])
}
