def subset_time(self, by, t_from, t_to):
""" Returns a subsetted TemporalDataset with time between t_from to t_to
Args:
by(String): the descriptor by which the subset selection is
made from channel dimension
t_from: time-point from which onwards data should be subsetted
t_to: time-point until which data should be subsetted
Returns:
TemporalDataset, with subset defined by the selected time_descriptor
"""
time = get_unique_unsorted(self.time_descriptors[by])
sel_time = [t for t in time if t <= t_to and t>=t_from]
selection = bool_index(self.time_descriptors[by], sel_time)
measurements = self.measurements[:, :, selection]
descriptors = self.descriptors
obs_descriptors = self.obs_descriptors
channel_descriptors = self.channel_descriptors
time_descriptors = subset_descriptor(
self.time_descriptors, selection)
dataset = TemporalDataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors)
return dataset
def split_time(self, by):
""" Returns a list TemporalDataset splited by time
Args:
by(String): the descriptor by which the splitting is made
Returns:
list of TemporalDataset, splitted by the selected time_descriptor
"""
time = get_unique_unsorted(self.time_descriptors[by])
dataset_list = []
for v in time:
selection = (self.time_descriptors[by] == v)
measurements = self.measurements[:, :, selection]
descriptors = self.descriptors
obs_descriptors = self.obs_descriptors
channel_descriptors = self.channel_descriptors
time_descriptors = subset_descriptor(
self.time_descriptors, selection)
dataset = TemporalDataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors)
dataset_list.append(dataset)
return dataset_list
def split_channel(self, by):
""" Returns a list TemporalDataset splited by channels
Args:
by(String): the descriptor by which the splitting is made
Returns:
list of TemporalDataset, splitted by the selected channel_descriptor
"""
unique_values = get_unique_unsorted(self.channel_descriptors[by])
dataset_list = []
for v in unique_values:
selection = (self.channel_descriptors[by] == v)
measurements = self.measurements[:, selection, :]
descriptors = self.descriptors.copy()
descriptors[by] = v
obs_descriptors = self.obs_descriptors
channel_descriptors = subset_descriptor(
self.channel_descriptors, selection)
time_descriptors = self.time_descriptors
dataset = TemporalDataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors,
time_descriptors=time_descriptors)
dataset_list.append(dataset)
return dataset_list
def convert_to_dataset(self, by):
""" converts to Dataset long format.
time dimension is absorbed into observation dimension
Args:
by(String): the descriptor which indicates the time dimension in
the time_descriptor
Returns:
Dataset
"""
time = get_unique_unsorted(self.time_descriptors[by])
descriptors = self.descriptors
channel_descriptors = self.channel_descriptors.copy()
measurements = np.empty([0, self.n_channel])
obs_descriptors = dict.fromkeys(self.obs_descriptors, [])
for key in self.time_descriptors:
obs_descriptors[key] = np.array([])
for v in time:
selection = (self.time_descriptors[by] == v)
measurements = np.concatenate((measurements,
self.measurements[:, :, selection].squeeze()),
axis=0)
for key in self.obs_descriptors:
obs_descriptors[key] = np.concatenate((obs_descriptors[key],
self.obs_descriptors[key].copy()),
axis=0)
for key in self.time_descriptors:
obs_descriptors[key] = np.concatenate((obs_descriptors[key],
np.repeat(self.time_descriptors[key][selection],
self.n_obs)), axis=0)
dataset = Dataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors)
return dataset
def split_obs(self, by):
""" Returns a list Datasets splited by obs
Args:
by(String): the descriptor by which the splitting is made
Returns:
list of Datasets, splitted by the selected obs_descriptor
"""
unique_values = get_unique_unsorted(self.obs_descriptors[by])
dataset_list = []
for v in unique_values:
selection = (self.obs_descriptors[by] == v)
measurements = self.measurements[selection, :]
descriptors = self.descriptors
obs_descriptors = extract_dict(self.obs_descriptors, selection)
channel_descriptors = self.channel_descriptors
dataset = Dataset(measurements=measurements,
descriptors=descriptors,
obs_descriptors=obs_descriptors,
channel_descriptors=channel_descriptors)
dataset_list.append(dataset)
return dataset_list
def test_get_unique_unsorted_ds(self):
unique_values = du.get_unique_unsorted(
self.data.obs_descriptors['conds'])
assert np.all(np.array(['cond_foo', 'cond_bar']) == unique_values)
def test_get_unique_unsorted_ints(self):
self.array = np.array([self.full_ints])
self.unique_unsorted = du.get_unique_unsorted(self.array)
assert np.all(self.unique_unsorted == self.unique_ints)
def test_get_unique_unsorted_str(self):
self.array = np.array([self.full_str.split(' ')])
self.unique_unsorted = du.get_unique_unsorted(self.array)
assert np.all(self.unique_unsorted == self.unique_str.split(' '))
