def t_stop_ds(ds: Dataset, t: float):
"Dummy-event for the end of the last step"
t_stop = ds.info['tstop'] + t
out = {}
for k, v in ds.items():
if k == 'time':
out['time'] = Var([t_stop])
elif isinstance(v, Var):
out[k] = Var(numpy.asarray([0], v.x.dtype))
elif isinstance(v, Factor):
out[k] = Factor([''])
else:
raise ValueError(f"{k!r} in predictor: {v!r}")
return Dataset(out)
def align_word_dataset(
self,
ds: Dataset,
words: FactorArg = 'word',
) -> Dataset:
"""Align ``ds`` to the TextGrid
Parameters
----------
ds
Dataset with data to align.
words
Words in ``ds`` to use to align to the TextGrid words.
Returns
-------
aligned_ds
Dataset with the variables in ``ds`` aligned to the TextGrid,
including time stamps and TextGrid words.
"""
words_ = asfactor(words, ds=ds)
index = self._align_index(words_, silence=-1, missing=-2)
out = Dataset(
{
'time': Var([r.times[0] for r in self.realizations]),
'grid_word': Factor([r.graphs for r in self.realizations]),
},
info={'tstop': self.realizations[-1].tstop})
for key, variable in ds.items():
if isinstance(variable, (Var, Factor)):
values = dict(enumerate(variable))
if isinstance(variable, Var):
values[-1] = values[
-2] = False # coerced to 0 unless all values are boolean
out[key] = Var([values[i] for i in index])
else:
values[-1] = values[-2] = ''
out[key] = Factor([values[i] for i in index],
random=variable.random)
return out
