def test_merge_wrapper():
dev = generate_DEV()
data = pd.DataFrame({'amplitude': [4, 3, 2, 5]})
sev = SimpleVariable('simple', data, 'dummy')
# Should break if asked to merge different classes
with pytest.raises(ValueError, match='Variables of different classes'):
merge_variables([dev, sev])
def test_merge_wrapper():
dev = generate_DEV()
data = pd.DataFrame({'amplitude': [4, 3, 2, 5]})
sev = SimpleVariable('simple', data, 'dummy')
# Should break if asked to merge different classes
with pytest.raises(ValueError) as e:
merge_variables([dev, sev])
assert "Variables of different classes" in str(e)
def test_merge_dense_run_variables(layout2):
variables = [generate_DEV() for i in range(20)]
variables += [generate_DEV(duration=400) for i in range(8)]
n_rows = sum([len(c.values) for c in variables])
merged = merge_variables(variables)
assert len(merged.values) == n_rows
assert set(merged.index.columns) == set(variables[0].index.columns)
def test_merge_dense_run_variables(layout2):
variables = [generate_DEV() for i in range(20)]
variables += [generate_DEV(duration=400) for i in range(8)]
n_rows = sum([len(c.values) for c in variables])
merged = merge_variables(variables)
assert len(merged.values) == n_rows
assert set(merged.index.columns) == set(variables[0].index.columns)
def test_merge_sparse_run_variables(layout1):
dataset = load_variables(layout1, types='events', scan_length=480)
runs = dataset.get_nodes('run')
variables = [r.variables['RT'] for r in runs]
n_rows = sum([len(c.values) for c in variables])
merged = merge_variables(variables)
assert len(merged.values) == n_rows
assert set(merged.index.columns) == set(variables[0].index.columns)
def test_merge_sparse_run_variables(layout1):
dataset = load_variables(layout1, types='events', scan_length=480)
runs = dataset.get_nodes('run')
variables = [r.variables['RT'] for r in runs]
n_rows = sum([len(c.values) for c in variables])
merged = merge_variables(variables)
assert len(merged.values) == n_rows
assert set(merged.index.columns) == set(variables[0].index.columns)
def test_merge_simple_variables(layout2):
index = load_variables(layout2, types='sessions')
subjects = index.get_nodes('subject')
variables = [s.variables['panas_sad'] for s in subjects]
n_rows = sum([len(c.values) for c in variables])
merged = merge_variables(variables)
assert len(merged.values) == n_rows
assert set(merged.index.columns) == set(variables[0].index.columns)
assert variables[3].values.iloc[1] == merged.values.iloc[7]
def test_merge_simple_variables(layout2):
index = load_variables(layout2, types='sessions')
subjects = index.get_nodes('subject')
variables = [s.variables['panas_sad'] for s in subjects]
n_rows = sum([len(c.values) for c in variables])
merged = merge_variables(variables)
assert len(merged.values) == n_rows
assert set(merged.index.columns) == set(variables[0].index.columns)
assert variables[3].values.iloc[1] == merged.values.iloc[7]
def test_merge_densified_variables(layout1):
SR = 10
dataset = load_variables(layout1, types='events', scan_length=480)
runs = dataset.get_nodes('run')
vars_ = [r.variables['RT'].to_dense(SR) for r in runs]
dense = merge_variables(vars_)
assert isinstance(dense, DenseRunVariable)
n_rows = 480 * SR
assert dense.values.shape == (len(runs) * n_rows, 1)
for i in range(len(runs)):
onset = i * n_rows
offset = onset + n_rows
run_vals = vars_[i].values
dense_vals = dense.values.iloc[onset:offset].reset_index(drop=True)
assert dense_vals.equals(run_vals)
def test_merge_densified_variables(layout1):
SR = 10
dataset = load_variables(layout1, types='events', scan_length=480)
runs = dataset.get_nodes('run')
vars_ = [r.variables['RT'].to_dense(SR) for r in runs]
dense = merge_variables(vars_)
assert isinstance(dense, DenseRunVariable)
n_rows = 480 * SR
assert dense.values.shape == (len(runs) * n_rows, 1)
for i in range(len(runs)):
onset = i * n_rows
offset = onset + n_rows
run_vals = vars_[i].values
dense_vals = dense.values.iloc[onset:offset].reset_index(drop=True)
assert dense_vals.equals(run_vals)
def test_filter_simple_variable(layout2):
dataset = load_variables(layout2, types=['scans'])
sessions = dataset.get_nodes('session')
variables = [s.variables['surroundings'] for s in sessions]
merged = merge_variables(variables)
assert merged.to_df().shape == (60, 9)
filt = merged.filter({'acq': 'fullbrain'})
assert filt.to_df().shape == (40, 9)
flt1 = merged.filter({'acq': 'fullbrain', 'subject': ['01', '02']}).to_df()
assert flt1.shape == (8, 9)
flt2 = merged.filter(query='acq=="fullbrain" and subject in ["01", "02"]')
flt2 = flt2.to_df()
assert flt1.equals(flt2)
assert merged.filter({'nonexistent': 2}, strict=True) is None
merged.filter({'acq': 'fullbrain'}, inplace=True)
assert merged.to_df().shape == (40, 9)
def test_filter_simple_variable(layout2):
dataset = load_variables(layout2, types=['scans'])
sessions = dataset.get_nodes('session')
variables = [s.variables['surroundings'] for s in sessions]
merged = merge_variables(variables)
assert merged.to_df().shape == (60, 9)
filt = merged.filter({'acquisition': 'fullbrain'})
assert filt.to_df().shape == (40, 9)
flt1 = merged.filter({'acquisition': 'fullbrain',
'subject': ['01', '02']}).to_df()
assert flt1.shape == (8, 9)
query = 'acquisition=="fullbrain" and subject in ["01", "02"]'
flt2 = merged.filter(query=query)
flt2 = flt2.to_df()
assert flt1.equals(flt2)
assert merged.filter({'nonexistent': 2}, strict=True) is None
merged.filter({'acquisition': 'fullbrain'}, inplace=True)
assert merged.to_df().shape == (40, 9)
def test_densify_merged_variables(layout1):
SR = 10
dataset = load_variables(layout1, types='events', scan_length=480)
runs = dataset.get_nodes('run')
vars_ = [r.variables['RT'] for r in runs]
var = merge_variables(vars_)
dense = var.to_dense(SR)
assert isinstance(dense, DenseRunVariable)
n_rows = 480 * SR
assert dense.values.shape == (len(runs) * n_rows, 1)
for i in range(len(runs)):
onset = i * n_rows
offset = onset + n_rows
# resampled values from split vs. merged will be trivially different
# at boundaries due to interpolation, so leave a buffer
run_vals = vars_[i].to_dense(SR).values.values.ravel()
dense_vals = dense.values.values.ravel()[onset:offset]
assert run_vals.shape == dense_vals.shape
assert np.array_equal(run_vals, dense_vals)