def processSyntax(line, db_con): lookup.initialiseEmotionVerbRegexps(db_con) tree = _SyntaxTree() (display_string, result) = _processInput(tree, line.split(), db_con) return (tree, display_string, result)
def encodeBinasphere(lines, db_con):
lookup.initialiseEmotionVerbRegexps(db_con)
new_lines = []
syllable_lines = []
unknown_set = set()
for line in lines:
if not _GENERAL_CONTENT_REGEXP.match(line):
raise ContentError("Non-Hymmnos content provided for Binasphere encoding")
(lines, syllable_line, unknown) = _dissectSyllables(line.split(), db_con)
new_lines.append(lines)
syllable_lines.append(syllable_line)
unknown_set.update(unknown)
return (_multiplexBinasphere(syllable_lines, ''.join(lines).lower()), new_lines, sorted(unknown))
def decodeBinasphere(line, db_con):
match = _BINASPHERE_REGEXP.match(line)
if not match:
raise FormatError("Non-Binasphere input provided")
if not _BINASPHERE_CONTENT_REGEXP.match(match.group(1)):
raise ContentError("Invalid Binasphere content provided")
tokens = match.group(1).strip().split()
size = int(match.group(2))
sequence = match.group(3).strip().split()
if len(sequence) == 1:
if size > 9:
raise ContentError("For line-counts greater than 9, sequence entries must be space-delimited")
sequence = sequence[0]
sequence = [int(i) for i in sequence]
lookup.initialiseEmotionVerbRegexps(db_con)
return _divideAndCapitalise(_reconstructBinasphere(tokens, sequence, size), db_con)
def applyPersistentEmotionSounds(lines, db_con):
m = _PERSISTANT_START_REGEXP.match(lines[0])
if not _PERSISTANT_END_REGEXP.match(lines[-1]):
if not m:
raise FormatError("Persistant syntax not detected")
else:
raise ContentError("Persistent Emotion Sounds terminator not found")
elif not m:
raise ContentError("Persistent Emotion Sounds initiator not found")
lookup.initialiseEmotionVerbRegexps(db_con)
new_lines = []
processed = []
unknown_set = set()
es_i = None
es_ii = None
es_iii = None
es_i_values = lookup.SYNTAX_CLASS_REV['ES(I)']
words = lookup.readWord(m.group(1).lower(), None, db_con)
for (word, meaning, kana, syntax_class, dialect, decorations, syllables) in words:
if syntax_class in es_i_values:
es_i = word
break
if not es_i:
es_i = m.group(1).title()
unknown.add(es_i)
es_ii_values = lookup.SYNTAX_CLASS_REV['ES(II)']
words = lookup.readWord(m.group(2).lower(), None, db_con)
for (word, meaning, kana, syntax_class, dialect, decorations, syllables) in words:
if syntax_class in es_ii_values:
es_ii = word
break
if not es_ii:
es_ii = m.group(2).lower()
unknown.add(es_ii)
es_iii_values = lookup.SYNTAX_CLASS_REV['ES(III)']
words = lookup.readWord(m.group(3).lower(), None, db_con)
for (word, meaning, kana, syntax_class, dialect, decorations, syllables) in words:
if syntax_class in es_iii_values:
es_iii = word
break
if not es_iii:
es_iii = m.group(3).title()
unknown.add(es_iii)
for line in lines[1:-1]:
if not _GENERAL_CONTENT_REGEXP.match(line):
raise ContentError("Non-Hymmnos content provided")
(line, processed_lines, unknown) = _applyPersistentEmotionSounds(es_i, es_ii, es_iii, line.split(), db_con)
new_lines.append(line)
processed.append(processed_lines)
unknown_set.update(unknown)
return (
["%(es_i)s %(es_ii)s %(es_iii)s 0x vvi." % {
'es_i': es_i,
'es_ii': es_ii,
'es_iii': es_iii,
}] + new_lines + lines[-1:],
processed,
sorted(unknown_set)
)
