def _digestTokens(tokens, db_con):
(pastalie, pastalie_prefix_only, words, prefixes, suffixes, slots) = _sanitizePastalie(tokens)
pastalie_prefix_valid = False #Enforces Pastalie when a Pastalie prefix is present.
word_list = lookup.readWords(words, db_con)
decorated_words = []
words_details = []
for (w, p, s, l) in zip(words, prefixes, suffixes, slots):
lexicon_entry = word_list.get(w.lower())
if lexicon_entry is None:
if w.isdigit(): #It's a number.
lexicon_entry = ([w, w, w, 8, 1, None, ''],)
if p: #Reattach the prefix, since it may be a song or a mistakenly capitalized word.
song_check = p.lower() + w.lower()
p = None
lexicon_entry = lookup.readWords((song_check,), db_con).get(song_check)
if lexicon_entry is None:
raise ContentError("unknown word in input: %(word)s" % {
'word': w,
})
elif pastalie and p:
pastalie_prefix_valid = True
pastalie_entry = 0
for (i, l_e) in enumerate(lexicon_entry):
if l_e[4] % _DIALECT_SHIFT == _DLCT_PASTALIE: #Favour Pastalie forms.
pastalie_entry = i
#Duplicate the best candidate, mark it as a noun, and use it to replace the list.
new_entry = lexicon_entry[i][:]
new_entry[3] = 4
lexicon_entry = (new_entry,)
else:
if not s and w in _COLLIDING_EMOTION_VERBS: #Handle exceptions where Emotion Verbs match basic words.
basic_form = w.replace('.', '')
l_e = lookup.readWords((basic_form,), db_con).get(basic_form)
if l_e: #Just in case this fails somehow.
lexicon_entry = tuple([l_e[0]] + list(lexicon_entry))
decorated_words.append(_decorateWord(lexicon_entry[0][0], p, s, l, False))
words_details.append((lexicon_entry, p, s, l))
return (words_details, ' '.join(decorated_words), pastalie_prefix_valid or (pastalie and not pastalie_prefix_only))
def _divideAndCapitaliseLine(words, db_con):
lines = []
buffer = []
unknown = set()
es_i_values = lookup.SYNTAX_CLASS_REV['ES(I)']
plain_words = lookup.readWords(tuple(set(words)), db_con)
for word in words:
(word, syntax_class, dialect, syllables) = _readWord(word.lower(), plain_words, db_con)
if syntax_class > 0:
if syntax_class in es_i_values and buffer: #Trailing ES(I)
lines.append(' '.join(buffer))
buffer = []
buffer.append(word)
else:
word = word.lower()
buffer.append(word)
unknown.add(word)
return (lines + [' '.join(buffer)], unknown)
def _dissectSyllables(words, db_con):
lines = []
buffer = []
line_syllables = []
unknown = set()
es_i_values = lookup.SYNTAX_CLASS_REV['ES(I)']
plain_words = lookup.readWords(tuple(set(words)), db_con)
for word in words:
(word, syntax_class, dialect, syllables) = _readWord(word, plain_words, db_con)
if syntax_class > 0:
if syntax_class in es_i_values and buffer: #Trailing ES(I)
lines.append(' '.join(buffer))
buffer = []
buffer.append(word)
line_syllables += [syllable.upper() + 'x' for syllable in syllables[:-1]] + [syllables[-1].upper()]
else:
buffer.append(word.lower())
line_syllables.append(word.upper())
unknown.add(word)
return (lines + [' '.join(buffer)], line_syllables, unknown)
def _applyPersistentEmotionSounds(es_i, es_ii, es_iii, words, db_con):
lines = []
buffer = []
line = []
unknown = set()
es_i_values = lookup.SYNTAX_CLASS_REV['ES(I)']
plain_words = lookup.readWords(tuple(set(words)), db_con)
for word in words:
(word, syntax_class, dialect, syllables) = _readWord(word, plain_words, db_con)
if syntax_class > 0:
if syntax_class in es_i_values and buffer: #Trailing ES(I)
lines.append(' '.join(buffer))
buffer = []
if not buffer and not lines and not syntax_class in lookup.SYNTAX_CLASS_REV['ES(I)']:
buffer = [es_i, es_ii, es_iii]
buffer.append(word)
line.append(word)
else:
word = word.lower()
buffer.append(word)
line.append(word)
unknown.add(word)
return (' '.join(line), lines + [' '.join(buffer)], unknown)
