def test_t_contrast():
ds = datasets.get_uts()
# simple contrast
res = testnd.t_contrast_rel('uts', 'A', 'a1>a0', 'rm', ds=ds, samples=10, pmin=0.05)
assert repr(res) == "<t_contrast_rel 'uts', 'A', 'a1>a0', match='rm', samples=10, pmin=0.05, 7 clusters, p < .001>"
res_ = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_array_equal(res.t.x, res_.t.x)
# complex contrast
res = testnd.t_contrast_rel('uts', 'A%B', 'min(a0|b0>a1|b0, a0|b1>a1|b1)', 'rm', ds=ds, samples=10, pmin=0.05)
res_b0 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b0'), ('a1', 'b0'), 'rm', ds=ds)
res_b1 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b1'), ('a1', 'b1'), 'rm', ds=ds)
assert_array_equal(res.t.x, np.min([res_b0.t.x, res_b1.t.x], axis=0))
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == repr(res)
assert_dataobj_equal(res.p, res_.p)
# contrast with "*"
res = testnd.t_contrast_rel('uts', 'A%B', 'min(a1|b0>a0|b0, a1|b1>a0|b1)',
'rm', ds=ds, tail=1)
# zero variance
ds['uts'].x[:, 10] = 0.
with pytest.raises(ZeroVariance):
testnd.t_contrast_rel('uts', 'A%B', 'min(a1|b0>a0|b0, a1|b1>a0|b1)', 'rm', tail=1, ds=ds)
def test_t_contrast():
ds = datasets.get_uts()
# simple contrast
res = testnd.t_contrast_rel('uts', 'A', 'a1>a0', 'rm', ds=ds, samples=10,
pmin=0.05)
repr(res)
res_ = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_array_equal(res.t.x, res_.t.x)
assert_in('samples', repr(res))
# complex contrast
res = testnd.t_contrast_rel('uts', 'A%B', 'min(a0|b0>a1|b0, a0|b1>a1|b1)',
'rm', ds=ds, samples=10, pmin=0.05)
res_b0 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b0'), ('a1', 'b0'), 'rm',
ds=ds)
res_b1 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b1'), ('a1', 'b1'), 'rm',
ds=ds)
assert_array_equal(res.t.x, np.min([res_b0.t.x, res_b1.t.x], axis=0))
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p, res_.p)
# contrast with "*"
res = testnd.t_contrast_rel('uts', 'A%B', 'min(a1|b0>a0|b0, a1|b1>a0|b1)',
'rm', ds=ds, tail=1)
def test_t_contrast():
ds = datasets.get_uts()
# simple contrast
res = testnd.t_contrast_rel('uts',
'A',
'a1>a0',
'rm',
ds=ds,
samples=10,
pmin=0.05)
assert repr(
res
) == "<t_contrast_rel 'uts', 'A', 'a1>a0', match='rm', samples=10, pmin=0.05, 7 clusters, p < .001>"
res_ = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_array_equal(res.t.x, res_.t.x)
# complex contrast
res = testnd.t_contrast_rel('uts',
'A%B',
'min(a0|b0>a1|b0, a0|b1>a1|b1)',
'rm',
ds=ds,
samples=10,
pmin=0.05)
res_b0 = testnd.ttest_rel('uts',
'A%B', ('a0', 'b0'), ('a1', 'b0'),
'rm',
ds=ds)
res_b1 = testnd.ttest_rel('uts',
'A%B', ('a0', 'b1'), ('a1', 'b1'),
'rm',
ds=ds)
assert_array_equal(res.t.x, np.min([res_b0.t.x, res_b1.t.x], axis=0))
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == repr(res)
assert_dataobj_equal(res.p, res_.p)
# contrast with "*"
res = testnd.t_contrast_rel('uts',
'A%B',
'min(a1|b0>a0|b0, a1|b1>a0|b1)',
'rm',
ds=ds,
tail=1,
samples=0)
# zero variance
ds['uts'].x[:, 10] = 0.
with pytest.raises(ZeroVariance):
testnd.t_contrast_rel('uts',
'A%B',
'min(a1|b0>a0|b0, a1|b1>a0|b1)',
'rm',
tail=1,
ds=ds,
samples=0)
def test_t_contrast():
ds = datasets.get_uts()
# simple contrast
res = testnd.t_contrast_rel('uts', 'A', 'a1>a0', 'rm', ds=ds, samples=10,
pmin=0.05)
repr(res)
res_ = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_array_equal(res.t.x, res_.t.x)
assert_in('samples', repr(res))
# complex contrast
res = testnd.t_contrast_rel('uts', 'A%B', 'min(a0|b0>a1|b0, a0|b1>a1|b1)',
'rm', ds=ds, samples=10, pmin=0.05)
res_b0 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b0'), ('a1', 'b0'), 'rm',
ds=ds)
res_b1 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b1'), ('a1', 'b1'), 'rm',
ds=ds)
assert_array_equal(res.t.x, np.min([res_b0.t.x, res_b1.t.x], axis=0))
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p, res_.p)
# contrast with "*"
res = testnd.t_contrast_rel('uts', 'A%B', 'min(a1|b0>a0|b0, a1|b1>a0|b1)',
'rm', ds=ds, tail=1)
def test_t_contrast():
ds = datasets.get_uts()
# test aux functions
y = np.arange(9.).reshape((3, 3))
indexes = {'a': 0, 'b': 1, 'c': 2}
contrast = "+sum(a>c, b>c)"
contrast_ = _testnd._parse_t_contrast(contrast)
assert_equal(contrast_, ('func', '+', np.sum, [('comp', None, 'a', 'c'),
('comp', None, 'b', 'c')]))
contrast = "+sum(a>*, b>*)"
contrast_ = _testnd._parse_t_contrast(contrast)
assert_equal(contrast_, ('func', '+', np.sum, [('comp', None, 'a', '*'),
('comp', None, 'b', '*')]))
_, cells = _testnd._t_contrast_rel_properties(contrast_)
pc, mc = _testnd._t_contrast_rel_expand_cells(cells, ('a', 'b', 'c'))
data = _testnd._t_contrast_rel_data(y, indexes, pc, mc)
assert_array_equal(data['a'], np.arange(3.))
assert_array_equal(data['*'], y.mean(0))
assert_raises(ValueError, _testnd._t_contrast_rel_expand_cells, cells,
('a|c', 'b|c', 'c|c'))
# simple contrast
res = testnd.t_contrast_rel('uts', 'A', 'a1>a0', 'rm', ds=ds, samples=10,
pmin=0.05)
repr(res)
res_ = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_array_equal(res.t.x, res_.t.x)
assert_in('samples', repr(res))
# complex contrast
res = testnd.t_contrast_rel('uts', 'A%B', 'min(a0|b0>a1|b0, a0|b1>a1|b1)',
'rm', ds=ds, samples=10, pmin=0.05)
res_b0 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b0'), ('a1', 'b0'), 'rm',
ds=ds)
res_b1 = testnd.ttest_rel('uts', 'A%B', ('a0', 'b1'), ('a1', 'b1'), 'rm',
ds=ds)
assert_array_equal(res.t.x, np.min([res_b0.t.x, res_b1.t.x], axis=0))
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p, res_.p)
# contrast with "*"
contrast_star = '+min(a1|b0>a0|*, a1|b1>a0|*)'
res = testnd.t_contrast_rel('uts', 'A%B', contrast_star, 'rm', ds=ds)
def test_merged_temporal_cluster_dist():
"Test use of _MergedTemporalClusterDist with testnd test results"
ds1 = datasets.get_uts()
ds2 = datasets.get_uts(seed=42)
anova_kw = dict(Y='uts', X='A*B*rm', pmin=0.05, samples=10)
ttest_kw = dict(Y='uts', X='A', c1='a1', c0='a0', pmin=0.05, samples=10)
contrast_kw = dict(Y='uts', X='A', contrast='a1>a0', pmin=0.05, samples=10)
def test_merged(res1, res2):
merged_dist = _MergedTemporalClusterDist([res1._cdist, res2._cdist])
if isinstance(res1, testnd.anova):
assert_equal(len(merged_dist.dist), len(res1.effects))
for effect, dist in merged_dist.dist.iteritems():
assert_in(effect, res1.effects)
assert_equal(len(dist), res1.samples)
else:
assert_equal(len(merged_dist.dist), res1.samples)
res1_clusters = merged_dist.correct_cluster_p(res1)
res2_clusters = merged_dist.correct_cluster_p(res2)
for clusters in [res1_clusters, res2_clusters]:
assert_in('p_parc', clusters)
for cl in clusters.itercases():
assert_greater_equal(cl['p_parc'], cl['p'])
# multi-effect
res1 = testnd.anova(ds=ds1, **anova_kw)
res2 = testnd.anova(ds=ds2, **anova_kw)
test_merged(res1, res2)
# ttest_rel
res1 = testnd.ttest_rel(ds=ds1, match='rm', **ttest_kw)
res2 = testnd.ttest_rel(ds=ds2, match='rm', **ttest_kw)
test_merged(res1, res2)
# ttest_ind
res1 = testnd.ttest_ind(ds=ds1, **ttest_kw)
res2 = testnd.ttest_ind(ds=ds2, **ttest_kw)
test_merged(res1, res2)
# ttest_1samp
res1 = testnd.ttest_1samp('uts', ds=ds1, pmin=0.05, samples=10)
res2 = testnd.ttest_1samp('uts', ds=ds2, pmin=0.05, samples=10)
test_merged(res1, res2)
# t_contrast_rel
res1 = testnd.t_contrast_rel(ds=ds1, match='rm', **contrast_kw)
res2 = testnd.t_contrast_rel(ds=ds2, match='rm', **contrast_kw)
test_merged(res1, res2)
def test_merged_temporal_cluster_dist():
"Test use of _MergedTemporalClusterDist with testnd test results"
ds1 = datasets.get_uts()
ds2 = datasets.get_uts(seed=42)
anova_kw = dict(Y='uts', X='A*B*rm', pmin=0.05, samples=10)
ttest_kw = dict(Y='uts', X='A', c1='a1', c0='a0', pmin=0.05, samples=10)
contrast_kw = dict(Y='uts', X='A', contrast='a1>a0', pmin=0.05, samples=10)
def test_merged(res1, res2):
merged_dist = _MergedTemporalClusterDist([res1._cdist, res2._cdist])
if isinstance(res1, testnd.anova):
assert_equal(len(merged_dist.dist), len(res1.effects))
for effect, dist in merged_dist.dist.iteritems():
assert_in(effect, res1.effects)
assert_equal(len(dist), res1.samples)
else:
assert_equal(len(merged_dist.dist), res1.samples)
res1_clusters = merged_dist.correct_cluster_p(res1)
res2_clusters = merged_dist.correct_cluster_p(res2)
for clusters in [res1_clusters, res2_clusters]:
assert_in('p_parc', clusters)
for cl in clusters.itercases():
assert_greater_equal(cl['p_parc'], cl['p'])
# multi-effect
res1 = testnd.anova(ds=ds1, **anova_kw)
res2 = testnd.anova(ds=ds2, **anova_kw)
test_merged(res1, res2)
# ttest_rel
res1 = testnd.ttest_rel(ds=ds1, match='rm', **ttest_kw)
res2 = testnd.ttest_rel(ds=ds2, match='rm', **ttest_kw)
test_merged(res1, res2)
# ttest_ind
res1 = testnd.ttest_ind(ds=ds1, **ttest_kw)
res2 = testnd.ttest_ind(ds=ds2, **ttest_kw)
test_merged(res1, res2)
# ttest_1samp
res1 = testnd.ttest_1samp('uts', ds=ds1, pmin=0.05, samples=10)
res2 = testnd.ttest_1samp('uts', ds=ds2, pmin=0.05, samples=10)
test_merged(res1, res2)
# t_contrast_rel
res1 = testnd.t_contrast_rel(ds=ds1, match='rm', **contrast_kw)
res2 = testnd.t_contrast_rel(ds=ds2, match='rm', **contrast_kw)
test_merged(res1, res2)
def test_t_contrast_testnd():
ds = datasets.get_uts()
# binary function
res = testnd.t_contrast_rel('uts',
'A',
"a1>a0 - a0>a1",
'rm',
ds=ds,
tmin=4,
samples=10)
assert_equal(res.find_clusters()['p'],
np.array([1, 1, 0.9, 0, 0.2, 1, 1, 0]))
res_t = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
match='rm',
ds=ds,
tmin=2,
samples=10)
assert_array_equal(res.t.x, res_t.t.x * 2)
assert_array_equal(res.clusters['tstart'], res_t.clusters['tstart'])
assert_array_equal(res.clusters['tstop'], res_t.clusters['tstop'])
assert_array_equal(res.clusters['v'], res_t.clusters['v'] * 2)
def test_t_contrast_testnd():
ds = datasets.get_uts()
# binary function
res = testnd.t_contrast_rel('uts', 'A', "a1>a0 - a0>a1", 'rm', ds=ds, tmin=4, samples=10)
assert_equal(res.find_clusters()['p'], np.array([1, 1, 0.9, 0, 0.2, 1, 1, 0]))
res_t = testnd.ttest_rel('uts', 'A', 'a1', 'a0', match='rm', ds=ds, tmin=2, samples=10)
assert_array_equal(res.t.x, res_t.t.x * 2)
assert_array_equal(res.clusters['tstart'], res_t.clusters['tstart'])
assert_array_equal(res.clusters['tstop'], res_t.clusters['tstop'])
assert_array_equal(res.clusters['v'], res_t.clusters['v'] * 2)
def test_t_contrast_testnd():
ds = datasets.get_uts()
# binary function
res = testnd.t_contrast_rel("uts", "A", "a1>a0 - a0>a1", "rm", ds=ds, tmin=4, samples=10)
assert_equal(res.find_clusters()["p"], np.array([1, 1, 0.9, 0, 0.2, 1, 1, 0]))
res_t = testnd.ttest_rel("uts", "A", "a1", "a0", match="rm", ds=ds, tmin=2, samples=10)
assert_array_equal(res.t.x, res_t.t.x * 2)
assert_array_equal(res.clusters["tstart"], res_t.clusters["tstart"])
assert_array_equal(res.clusters["tstop"], res_t.clusters["tstop"])
assert_array_equal(res.clusters["v"], res_t.clusters["v"] * 2)
def test_vector():
"""Test vector tests"""
# single vector
ds = datasets.get_uv(vector=True)
res = testnd.Vector('v[:40]', ds=ds, samples=10)
assert res.p == 0.0
res = testnd.Vector('v[40:]', ds=ds, samples=10)
assert res.p == 1.0
# single vector with norm stat
res_t = testnd.Vector('v[:40]', ds=ds, samples=10, norm=True)
assert res_t.p == 0.0
res_t = testnd.Vector('v[40:]', ds=ds, samples=10, norm=True)
assert res_t.p == 1.0
# non-space tests should raise error
with pytest.raises(WrongDimension):
testnd.ttest_1samp('v', ds=ds)
with pytest.raises(WrongDimension):
testnd.ttest_rel('v', 'A', match='rm', ds=ds)
with pytest.raises(WrongDimension):
testnd.ttest_ind('v', 'A', ds=ds)
with pytest.raises(WrongDimension):
testnd.t_contrast_rel('v', 'A', 'a0 > a1', 'rm', ds=ds)
with pytest.raises(WrongDimension):
testnd.corr('v', 'fltvar', ds=ds)
with pytest.raises(WrongDimension):
testnd.anova('v', 'A * B', ds=ds)
# vector in time
ds = datasets.get_uts(vector3d=True)
v1 = ds[30:, 'v3d']
v2 = ds[:30, 'v3d']
vd = v1 - v2
res = testnd.Vector(vd, samples=10)
assert res.p.min() == 0.2
difference = res.masked_difference(0.5)
assert difference.x.mask.sum() == 288
# diff related
resd = testnd.VectorDifferenceRelated(v1, v2, samples=10)
assert_dataobj_equal(resd.p, res.p, name=False)
assert_dataobj_equal(resd.t2, res.t2, name=False)
# diff independent
res = VectorDifferenceIndependent(v1, v2, samples=10, norm=True)
assert_dataobj_equal(res.difference, v1.mean('case') - v2.mean('case'), name=False)
assert res.p.max() == 1
assert res.p.min() == 0
# with mp
res = testnd.Vector(v1, samples=10)
assert res.p.min() == 0.4
# without mp
configure(n_workers=0)
res0 = testnd.Vector(v1, samples=10)
assert_array_equal(np.sort(res0._cdist.dist), np.sort(res._cdist.dist))
configure(n_workers=True)
# time window
res = testnd.Vector(v2, samples=10, tstart=0.1, tstop=0.4)
assert res.p.min() == 0.3
difference = res.masked_difference(0.5)
assert difference.x.mask.sum() == 294
# vector in time with norm stat
res = testnd.Vector(vd, samples=10, norm=True)
assert res.p.min() == 0
difference = res.masked_difference()
assert difference.x.mask.sum() == 297
resd = testnd.VectorDifferenceRelated(v1, v2, samples=10, norm=True)
assert_dataobj_equal(resd.p, res.p, name=False)
assert_dataobj_equal(resd.difference, res.difference, name=False)
v_small = v2 / 100
res = testnd.Vector(v_small, tfce=True, samples=10, norm=True)
assert 'WARNING' in repr(res)
res = testnd.Vector(v_small, tfce=0.1, samples=10)
assert res.p.min() == 0.0
def test_vector():
"""Test vector tests"""
# single vector
ds = datasets.get_uv(vector=True)
res = testnd.Vector('v[:40]', ds=ds, samples=10)
assert res.p == 0.0
res = testnd.Vector('v[40:]', ds=ds, samples=10)
assert res.p == 1.0
# single vector with norm stat
res_t = testnd.Vector('v[:40]', ds=ds, samples=10, norm=True)
assert res_t.p == 0.0
res_t = testnd.Vector('v[40:]', ds=ds, samples=10, norm=True)
assert res_t.p == 1.0
# non-space tests should raise error
with pytest.raises(WrongDimension):
testnd.ttest_1samp('v', ds=ds)
with pytest.raises(WrongDimension):
testnd.ttest_rel('v', 'A', match='rm', ds=ds)
with pytest.raises(WrongDimension):
testnd.ttest_ind('v', 'A', ds=ds)
with pytest.raises(WrongDimension):
testnd.t_contrast_rel('v', 'A', 'a0 > a1', 'rm', ds=ds)
with pytest.raises(WrongDimension):
testnd.corr('v', 'fltvar', ds=ds)
with pytest.raises(WrongDimension):
testnd.anova('v', 'A * B', ds=ds)
# vector in time
ds = datasets.get_uts(vector3d=True)
v1 = ds[30:, 'v3d']
v2 = ds[:30, 'v3d']
vd = v1 - v2
res = testnd.Vector(vd, samples=10)
assert res.p.min() == 0.2
difference = res.masked_difference(0.5)
assert difference.x.mask.sum() == 288
# diff related
resd = testnd.VectorDifferenceRelated(v1, v2, samples=10)
assert_dataobj_equal(resd.p, res.p, name=False)
assert_dataobj_equal(resd.t2, res.t2, name=False)
# diff independent
res = VectorDifferenceIndependent(v1, v2, samples=10, norm=True)
assert_dataobj_equal(res.difference,
v1.mean('case') - v2.mean('case'),
name=False)
assert res.p.max() == 1
assert res.p.min() == 0
# with mp
res = testnd.Vector(v1, samples=10)
assert res.p.min() == 0.4
# without mp
configure(n_workers=0)
res0 = testnd.Vector(v1, samples=10)
assert_array_equal(np.sort(res0._cdist.dist), np.sort(res._cdist.dist))
configure(n_workers=True)
# time window
res = testnd.Vector(v2, samples=10, tstart=0.1, tstop=0.4)
assert res.p.min() == 0.3
difference = res.masked_difference(0.5)
assert difference.x.mask.sum() == 294
# vector in time with norm stat
res = testnd.Vector(vd, samples=10, norm=True)
assert res.p.min() == 0
difference = res.masked_difference()
assert difference.x.mask.sum() == 297
resd = testnd.VectorDifferenceRelated(v1, v2, samples=10, norm=True)
assert_dataobj_equal(resd.p, res.p, name=False)
assert_dataobj_equal(resd.difference, res.difference, name=False)
v_small = v2 / 100
res = testnd.Vector(v_small, tfce=True, samples=10, norm=True)
assert 'WARNING' in repr(res)
res = testnd.Vector(v_small, tfce=0.1, samples=10)
assert res.p.min() == 0.0
def test_t_contrast():
ds = datasets.get_rand()
# test aux functions
y = np.arange(9.).reshape((3, 3))
indexes = {'a': 0, 'b': 1, 'c': 2}
contrast = "+sum(a>c, b>c)"
contrast_ = _testnd._parse_t_contrast(contrast)
assert_equal(contrast_, ('func', '+', np.sum, [('comp', None, 'a', 'c'),
('comp', None, 'b', 'c')]))
contrast = "+sum(a>*, b>*)"
contrast_ = _testnd._parse_t_contrast(contrast)
assert_equal(contrast_, ('func', '+', np.sum, [('comp', None, 'a', '*'),
('comp', None, 'b', '*')]))
_, cells = _testnd._t_contrast_rel_properties(contrast_)
pc, mc = _testnd._t_contrast_rel_expand_cells(cells, ('a', 'b', 'c'))
data = _testnd._t_contrast_rel_data(y, indexes, pc, mc)
assert_array_equal(data['a'], np.arange(3.))
assert_array_equal(data['*'], y.mean(0))
assert_raises(ValueError, _testnd._t_contrast_rel_expand_cells, cells,
('a|c', 'b|c', 'c|c'))
# simple contrast
res = testnd.t_contrast_rel('uts',
'A',
'a1>a0',
'rm',
ds=ds,
samples=10,
pmin=0.05)
repr(res)
res_ = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_array_equal(res.t.x, res_.t.x)
assert_in('samples', repr(res))
# complex contrast
res = testnd.t_contrast_rel('uts',
'A%B',
'min(a0|b0>a1|b0, a0|b1>a1|b1)',
'rm',
ds=ds,
samples=10,
pmin=0.05)
res_b0 = testnd.ttest_rel('uts',
'A%B', ('a0', 'b0'), ('a1', 'b0'),
'rm',
ds=ds)
res_b1 = testnd.ttest_rel('uts',
'A%B', ('a0', 'b1'), ('a1', 'b1'),
'rm',
ds=ds)
assert_array_equal(res.t.x, np.min([res_b0.t.x, res_b1.t.x], axis=0))
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p, res_.p)
# contrast with "*"
contrast_star = '+min(a1|b0>a0|*, a1|b1>a0|*)'
res = testnd.t_contrast_rel('uts', 'A%B', contrast_star, 'rm', ds=ds)
