def top_words(target, source, env):
args = source[-1].read()
with meta_open(source[0].rstr()) as words_ifd, meta_open(source[1].rstr()) as pron_ifd:
top = ProbabilityList(words_ifd).get_top_n(args["COUNT"])
prons = Pronunciations(pron_ifd)
prons.filter_by(top)
with meta_open(target[0].rstr(), "w") as words_ofd, meta_open(target[1].rstr(), "w") as pron_ofd:
words_ofd.write(top.format())
pron_ofd.write(prons.format())
return None
def filter_babel_gum(target, source, env):
with meta_open(source[0].rstr()) as pron_ifd, meta_open(source[1].rstr()) as prob_ifd, meta_open(source[2].rstr()) as lim_ifd:
pron = Pronunciations(pron_ifd)
logging.info("Old pronunciations: %s", pron)
prob = ProbabilityList(prob_ifd)
logging.info("Old probabilities: %s", prob)
filt = Vocabulary(lim_ifd)
logging.info("Correct words: %s", filt)
pron.filter_by(filt)
logging.info("New pronunciations: %s", pron)
prob.filter_by(filt)
logging.info("New probabilities: %s", prob)
with meta_open(target[0].rstr(), "w") as pron_ofd, meta_open(target[1].rstr(), "w") as prob_ofd:
pron_ofd.write(pron.format())
prob_ofd.write(prob.format())
return None
