def test_orthogonalize_dense(collection):
transform.factor(collection, 'trial_type', sep=sep)
# Store pre-orth variables needed for tests
pg_pre = collection['trial_type/parametric gain'].to_dense(10)
rt = collection['RT'].to_dense(10)
# Orthogonalize and store result
transform.orthogonalize(collection,
variables='trial_type/parametric gain',
other='RT',
dense=True,
groupby=['run', 'subject'])
pg_post = collection['trial_type/parametric gain']
# Verify that the to_dense() calls result in identical indexing
ent_cols = ['subject', 'run']
assert pg_pre.to_df()[ent_cols].equals(rt.to_df()[ent_cols])
assert pg_post.to_df()[ent_cols].equals(rt.to_df()[ent_cols])
vals = np.c_[rt.values, pg_pre.values, pg_post.values]
df = pd.DataFrame(vals, columns=['rt', 'pre', 'post'])
groupby = rt.get_grouper(['run', 'subject'])
pre_r = df.groupby(groupby).apply(lambda x: x.corr().iloc[0, 1])
post_r = df.groupby(groupby).apply(lambda x: x.corr().iloc[0, 2])
assert (pre_r > 0.2).any()
assert (post_r < 0.0001).all()
def test_orthogonalize_sparse(collection):
pg_pre = collection['parametric gain'].values
rt = collection['RT'].values
transform.orthogonalize(collection, variables='parametric gain',
other='RT')
pg_post = collection['parametric gain'].values
vals = np.c_[rt.values, pg_pre.values, pg_post.values]
df = pd.DataFrame(vals, columns=['rt', 'pre', 'post'])
ents = collection['RT'].index
groupby = pd.core.groupby._get_grouper(ents, ['run', 'subject'])[0]
pre_r = df.groupby(groupby).apply(lambda x: x.corr().iloc[0, 1])
post_r = df.groupby(groupby).apply(lambda x: x.corr().iloc[0, 2])
assert (pre_r > 0.2).any()
assert (post_r < 0.0001).all()