def setup(self, inputs=None, drop_na=False, **kwargs):
"""Set up the Step and construct the design matrix.
Parameters
----------
inputs : list
Optional list of BIDSVariableCollections produced as output by the
preceding Step in the analysis. If None, uses inputs passed at
initialization (if any).
drop_na : bool
Boolean indicating whether or not to automatically drop events that
have a n/a amplitude when reading in data from event files.
kwargs : dict
Optional keyword arguments to pass onto load_variables.
"""
self._collections = []
# Convert input contrasts to a list of BIDSVariableCollections
inputs = inputs or self.inputs or []
input_grps = self._merge_contrast_inputs(inputs) if inputs else {}
# TODO: remove the scan_length argument entirely once we switch tests
# to use the synthetic dataset with image headers.
if self.level != 'run':
kwargs = kwargs.copy()
kwargs.pop('scan_length', None)
# Now handle variables read from the BIDS dataset: read them in, filter
# on passed selectors, and group by unit of current level
collections = self.layout.get_collections(self.level,
drop_na=drop_na,
**kwargs)
collections, _ = self._filter_collections(collections, kwargs)
groups = self._group_objects_by_entities(collections)
# Merge in the inputs
for key, input_ in input_grps.items():
if key not in groups:
groups[key] = []
groups[key].append(input_)
# Set up and validate variable lists
model = self.model or {}
X = model.get('x', [])
for grp, colls in groups.items():
coll = merge_collections(colls)
colls = tm.TransformerManager().transform(coll,
self.transformations)
if X:
tm.Select(coll, X)
self._collections.append(coll)
def setup(self, inputs=None, **kwargs):
"""Set up the Step.
Processes inputs from previous step, combines it with currently loaded
data, and applies transformations to produce a design matrix-ready set
of variable collections.
Parameters
----------
inputs : list
Optional list of BIDSVariableCollections produced as output by the
preceding Step in the analysis. If None, uses inputs passed at
initialization (if any).
kwargs : dict
Optional keyword arguments constraining the collections to include.
"""
inputs = inputs or self.inputs or []
input_grps = self._merge_contrast_inputs(inputs) if inputs else {}
# filter on passed selectors and group by unit of current level
collections, _ = self._filter_collections(self._raw_collections,
kwargs)
groups = self._group_objects_by_entities(collections)
# Merge in the inputs
for key, input_ in input_grps.items():
if key not in groups:
groups[key] = []
groups[key].append(input_)
# Set up and validate variable lists
model = self.model or {}
X = model.get('x', [])
for grp, colls in groups.items():
coll = merge_collections(colls)
colls = tm.TransformerManager().transform(coll,
self.transformations)
if X:
tm.Select(coll, X)
self._collections.append(coll)
def test_select(collection):
coll = collection.clone()
keep = ['RT', 'parametric gain', 'respcat']
transform.Select(coll, keep)
assert set(coll.variables.keys()) == set(keep)