lose obsolete test
@@ -230,45 +230,6 @@ def test_run_variable_collection_to_df_mixed_vars(run_coll):
assert not {'RT', 'respcat'} - set(df.columns)
-def test_run(analysis):
- kwargs = dict(run=1, subject='01')
- collections = analysis['run'].get_collections(**kwargs)
- assert len(collections) == 1
- sparse_coll = collections[0]
-
- # Long format, all variables sparse
- result = sparse_coll.to_df(format='long')
- assert result.shape == (172, 9)
-
- # Long format, include only dense, but there are none, so it fails
- result = collections[0].to_df(include_sparse=False, format='long')
- assert result is None
-
- # Check that mixed collections are handled properly
-
- result = collections[0].to_df(sparse=False, include_sparse=True,
- sampling_rate='highest', format='wide',
- timing=False)
- assert result.shape == (4800, 10)
-
- result = collections[0].get_design_matrix(mode='dense', force=True,
- sampling_rate='TR')
- assert result.shape == (240, 10)
-
- result = collections[0].get_design_matrix(mode='dense', force=True,
- sampling_rate=0.5)
- assert result.shape == (240, 10)
-
- # format='long' should be ignored for dense output
- result = collections[0].get_design_matrix(mode='dense', force=True, format='long',
- entities=False)
- assert result.shape == (240, 1)
-
- result = collections[0].get_design_matrix(mode='sparse', format='wide',
- entities=False)
- assert result.shape == (86, 4)
-
-
def test_merge_collections(run_coll, run_coll_list):
df1 = run_coll.to_df().sort_values(['subject', 'run', 'onset'])
rcl = [c.clone() for c in run_coll_list]
