def collate(cls, batch: Sequence['TimeSeriesDataset']) -> 'TimeSeriesDataset':
to_concat = {
'tensors': [batch[0].tensors],
'group_names': [batch[0].group_names],
'start_times': [batch[0].start_times]
}
fixed = {'dt_unit': batch[0].dt_unit, 'measures': batch[0].measures}
for i, ts_dataset in enumerate(batch[1:], 1):
for attr, appendlist in to_concat.items():
to_concat[attr].append(getattr(ts_dataset, attr))
for attr, required_val in fixed.items():
new_val = getattr(ts_dataset, attr)
if new_val != required_val:
raise ValueError(f"Element {i} has `{attr}` = {new_val}, but for element 0 it's {required_val}.")
tensors = tuple(ragged_cat(t, ragged_dim=1) for t in zip(*to_concat['tensors']))
return cls(
*tensors,
group_names=np.concatenate(to_concat['group_names']),
start_times=np.concatenate(to_concat['start_times']),
measures=fixed['measures'],
dt_unit=fixed['dt_unit']
)
def with_new_start_times(self, start_times: Union[np.ndarray, Sequence]) -> 'TimeSeriesDataset':
"""
Subset a TimeSeriesDataset so that some/all of the groups have later start times.
:param start_times: An array/list of new datetimes.
:return: A new TimeSeriesDataset.
"""
new_tensors = []
for i, tens in enumerate(self.tensors):
times = self.times(i)
new_tens = []
for g, (new_time, old_times) in enumerate(zip(start_times, times)):
if (old_times <= new_time).all():
raise ValueError(f"{new_time} is later than all the times for group {self.group_names[g]}")
elif (old_times > new_time).all():
raise ValueError(f"{new_time} is earlier than all the times for group {self.group_names[g]}")
new_tens.append(tens[g, true1d_idx(old_times >= new_time), :].unsqueeze(0))
new_tens = ragged_cat(new_tens, ragged_dim=1, cat_dim=0)
new_tensors.append(new_tens)
return type(self)(
*new_tensors,
group_names=self.group_names,
start_times=start_times,
measures=self.measures,
dt_unit=self.dt_unit
)
def to_dataframe(self,
group_colname: str = 'group',
time_colname: str = 'time') -> 'DataFrame':
return self.tensor_to_dataframe(tensor=ragged_cat(self.tensors,
ragged_dim=1,
cat_dim=2),
times=self.times(),
group_names=self.group_names,
group_colname=group_colname,
time_colname=time_colname,
measures=self.all_measures)
def with_new_start_times(
self, start_times: Union[np.ndarray,
Sequence]) -> 'TimeSeriesDataset':
"""
Subset a TimeSeriesDataset so that some/all of the groups have later start times.
:param start_times: An array/list of new datetimes.
:return: A new TimeSeriesDataset.
"""
new_tensors = []
for i, tens in enumerate(self.tensors):
times = self.times(i)
new_tens = []
for g, (new_time, old_times) in enumerate(zip(start_times, times)):
if (old_times <= new_time).all():
warn(
f"{new_time} is later than all the times for group {self.group_names[g]}"
)
new_tens.append(tens[[g], 0:0])
continue
elif (old_times > new_time).all():
warn(
f"{new_time} is earlier than all the times for group {self.group_names[g]}"
)
new_tens.append(tens[[g], 0:0])
continue
# drop if before new_time:
g_tens = tens[g, true1d_idx(old_times >= new_time)]
# drop if after last nan:
all_nan, _ = torch.min(torch.isnan(g_tens), 1)
if all_nan.all():
warn(
f"Group '{self.group_names[g]}' (tensor {i}) has only `nans` after {new_time}"
)
end_idx = 0
else:
end_idx = true1d_idx(~all_nan).max() + 1
new_tens.append(g_tens[:end_idx].unsqueeze(0))
new_tens = ragged_cat(new_tens, ragged_dim=1, cat_dim=0)
new_tensors.append(new_tens)
return type(self)(*new_tensors,
group_names=self.group_names,
start_times=start_times,
measures=self.measures,
dt_unit=self.dt_unit)