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 subset(self, by, value):
""" Returns a set of fewer RDMs matching descriptor values
Args:
by(String): the descriptor by which the subset selection
is made from descriptors
value: the value by which the subset selection is made
from descriptors
Returns:
RDMs object, with fewer RDMs
"""
if by is None:
by = 'index'
selection = bool_index(self.rdm_descriptors[by], value)
dissimilarities = self.dissimilarities[selection, :]
descriptors = self.descriptors
pattern_descriptors = self.pattern_descriptors
rdm_descriptors = extract_dict(self.rdm_descriptors, selection)
dissimilarity_measure = self.dissimilarity_measure
rdms = RDMs(dissimilarities=dissimilarities,
descriptors=descriptors,
rdm_descriptors=rdm_descriptors,
pattern_descriptors=pattern_descriptors,
dissimilarity_measure=dissimilarity_measure)
return rdms
def subset_channel(self, by, value):
""" Returns a subsetted TemporalDataset defined by certain channel value
Args:
by(String): the descriptor by which the subset selection is
made from channel dimension
value: the value by which the subset selection is made
from channel dimension
Returns:
TemporalDataset, with subset defined by the selected channel_descriptor
"""
selection = bool_index(self.channel_descriptors[by], value)
measurements = self.measurements[:, selection]
descriptors = self.descriptors
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)
return dataset
def subset_pattern(self, by, value):
""" Returns a smaller RDMs with patterns with certain descriptor values
Args:
by(String): the descriptor by which the subset selection
is made from pattern_descriptors
value: the value by which the subset selection is made
from pattern_descriptors
Returns:
RDMs object, with fewer patterns
"""
if by is None:
by = 'index'
selection = bool_index(self.pattern_descriptors[by], value)
dissimilarities = self.get_matrices()[:, selection][:, :, selection]
descriptors = self.descriptors
pattern_descriptors = extract_dict(
self.pattern_descriptors, selection)
rdm_descriptors = self.rdm_descriptors
dissimilarity_measure = self.dissimilarity_measure
rdms = RDMs(dissimilarities=dissimilarities,
descriptors=descriptors,
rdm_descriptors=rdm_descriptors,
pattern_descriptors=pattern_descriptors,
dissimilarity_measure=dissimilarity_measure)
return rdms
def subset_obs(self, by, value):
""" Returns a subsetted Dataset defined by certain obs value
Args:
by(String): the descriptor by which the subset selection
is made from obs dimension
value: the value by which the subset selection is made
from obs dimension
Returns:
Dataset, with subset defined by the selected obs_descriptor
"""
selection = bool_index(self.obs_descriptors[by], value)
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)
return dataset