def test_ttest_ind():
"Test testnd.ttest_ind()"
ds = datasets.get_uts(True)
# basic
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds)
repr(res)
assert_less(res.p_uncorrected.min(), 0.05)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
repr(res_)
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# cluster
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds, tail=1, samples=1)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# nd
res = testnd.ttest_ind('utsnd',
'A',
'a1',
'a0',
ds=ds,
pmin=0.05,
samples=2)
eq_(res._cdist.n_clusters, 10)
def test_ttest_ind():
"Test testnd.ttest_ind()"
ds = datasets.get_uts(True)
# basic
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds)
eq_(repr(res), "<ttest_ind 'uts', 'A', 'a1' (n=30), 'a0' (n=30)>")
assert_less(res.p_uncorrected.min(), 0.05)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
eq_(repr(res_), "<ttest_ind 'uts', 'A', 'a1' (n=30), 'a0' (n=30)>")
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# cluster
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds, tail=1, samples=1)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# nd
res = testnd.ttest_ind('utsnd',
'A',
'a1',
'a0',
ds=ds,
pmin=0.05,
samples=2)
eq_(res._cdist.n_clusters, 10)
# zero variance
ds['utsnd'].x[:, 1, 10] = 0.
assert_raises(ZeroVariance, testnd.ttest_ind, 'utsnd', 'A', ds=ds)
def test_ttest_ind():
"Test testnd.ttest_ind()"
ds = datasets.get_uts(True)
# basic
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds)
repr(res)
assert_less(res.p_uncorrected.min(), 0.05)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
repr(res_)
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# cluster
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds, tail=1, samples=1)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# nd
res = testnd.ttest_ind('utsnd', 'A', 'a1', 'a0', ds=ds, pmin=0.05, samples=2)
eq_(res._cdist.n_clusters, 10)
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_plot_array():
"Test plot.TopoArray"
ds = datasets.get_uts(utsnd=True)
p = plot.TopoArray('utsnd', ds=ds)
assert repr(p) == "<TopoArray: utsnd>"
p.set_topo_t(0, 0.2)
p.close()
p = plot.TopoArray('utsnd', ds=ds, vmax=0.2, w=2)
p.close()
p = plot.TopoArray('utsnd', 'A%B', ds=ds, axw=4)
assert repr(p) == "<TopoArray: utsnd ~ A x B>"
p.close()
# results
res = testnd.ttest_ind('utsnd',
'A',
ds=ds,
pmin=0.05,
tstart=0.1,
tstop=0.3,
samples=2)
p = plot.TopoArray(res)
assert repr(p) == "<TopoArray: a0, a1, a0 - a1>"
p.set_topo_t(0, 0.)
p.close()
def test_plot_array():
"Test plot.TopoArray"
ds = datasets.get_uts(utsnd=True)
p = plot.TopoArray("utsnd", ds=ds, show=False)
p.set_topo_t(0, 0.2)
p.close()
p = plot.TopoArray("utsnd", ds=ds, vmax=0.2, w=2, show=False)
p.close()
p = plot.TopoArray("utsnd", "A%B", ds=ds, axw=4, show=False)
p.close()
# results
res = testnd.ttest_ind("utsnd", "A", ds=ds, pmin=0.05, tstart=0.1, tstop=0.3, samples=2)
p = plot.TopoArray(res, show=False)
p.set_topo_t(0, 0.0)
p.close()
def test_ttest_ind():
"Test testnd.ttest_ind()"
ds = datasets.get_uts(True)
# basic
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds, samples=0)
assert repr(
res) == "<ttest_ind 'uts', 'A', 'a1' (n=30), 'a0' (n=30), samples=0>"
assert res.p_uncorrected.min() < 0.05
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(
res_) == "<ttest_ind 'uts', 'A', 'a1' (n=30), 'a0' (n=30), samples=0>"
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# alternate argspec
res_ = testnd.ttest_ind("uts[A == 'a1']",
"uts[A == 'a0']",
ds=ds,
samples=0)
assert repr(res_) == "<ttest_ind 'uts' (n=30), 'uts' (n=30), samples=0>"
assert_dataobj_equal(res_.t, res.t)
# cluster
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds, tail=1, samples=1)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == repr(res)
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# nd
res = testnd.ttest_ind('utsnd',
'A',
'a1',
'a0',
ds=ds,
pmin=0.05,
samples=2)
assert res._cdist.n_clusters == 10
# zero variance
ds['utsnd'].x[:, 1, 10] = 0.
res_zv = testnd.ttest_ind('utsnd', 'A', 'a1', 'a0', ds=ds, samples=0)
assert_array_equal(res_zv.t.x[0], res.t.x[0])
assert res_zv.t.x[1, 10] == 0.
# argument mismatch
with pytest.raises(ValueError):
testnd.ttest_ind(ds['utsnd'], ds[:-1, 'A'], samples=0)
def test_plot_array():
"Test plot.TopoArray"
ds = datasets.get_uts(utsnd=True)
p = plot.TopoArray('utsnd', ds=ds, show=False)
p.set_topo_t(0, 0.2)
p.close()
p = plot.TopoArray('utsnd', ds=ds, vmax=0.2, w=2, show=False)
p.close()
p = plot.TopoArray('utsnd', 'A%B', ds=ds, axw=4, show=False)
p.close()
# results
res = testnd.ttest_ind('utsnd', 'A', ds=ds, pmin=0.05, tstart=0.1,
tstop=0.3, samples=2)
p = plot.TopoArray(res, show=False)
p.set_topo_t(0, 0.)
p.close()
def test_plot_array():
"Test plot.TopoArray"
ds = datasets.get_uts(utsnd=True)
p = plot.TopoArray('utsnd', ds=ds)
assert repr(p) == "<TopoArray: utsnd>"
p.set_topo_t(0, 0.2)
p.close()
p = plot.TopoArray('utsnd', ds=ds, vmax=0.2, w=2)
p.close()
p = plot.TopoArray('utsnd', 'A%B', ds=ds, axw=4)
assert repr(p) == "<TopoArray: utsnd ~ A x B>"
p.close()
# results
res = testnd.ttest_ind('utsnd', 'A', ds=ds, pmin=0.05, tstart=0.1, tstop=0.3, samples=2)
p = plot.TopoArray(res)
assert repr(p) == "<TopoArray: a0, a1, difference>"
p.set_topo_t(0, 0.)
p.close()
def test_result_report():
"Test result_report function for different Results"
ds = datasets.get_uts(True)
sds = ds.sub("B == 'b0'")
for y in (
'uts', # time
"utsnd.summary(time=(0.25, 0.35))", # sensor
'utsnd', # sensor x time
):
dims = tuple(dim.name for dim in ds.eval(y).dims[1:])
logging.info("y=%s %s", y, dims)
kwargs = dict(pmin=0.1, samples=100)
if 'time' in dims:
kwargs['tstart'] = 0.2
kwargs['tstop'] = 0.4
for match in (None, 'rm'):
logging.info(" match=%s", match)
res = testnd.ttest_1samp(y, match=match, ds=sds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.ttest_ind(y, 'A', ds=sds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.ttest_rel(y, 'A', ds=sds, match='rm', **kwargs)
rep = result_report(res, sds)
html(rep)
res = testnd.anova(y, 'A * B', ds=ds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.anova(y, 'A * rm', ds=sds, match='rm', **kwargs)
rep = result_report(res, ds)
html(rep)
def test_result_report():
"Test result_report function for different Results"
ds = datasets.get_uts(True)
sds = ds.sub("B == 'b0'")
ys = [
'uts', # time
"utsnd.summary(time=(0.25, 0.35))", # sensor
'utsnd', # sensor x time
]
for y in ys:
y_obj = sds.eval(y)
kwargs = dict(pmin=0.1, samples=100)
if y_obj.has_dim('time'):
kwargs['tstart'] = 0.2
kwargs['tstop'] = 0.4
for match in (None, 'rm'):
logging.info(" match=%s", match)
res = testnd.ttest_1samp(y, match=match, ds=sds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.ttest_ind(y, 'A', ds=sds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.ttest_rel(y, 'A', ds=sds, match='rm', **kwargs)
rep = result_report(res, sds)
html(rep)
res = testnd.anova(y, 'A * B', ds=ds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.anova(y, 'A * rm', ds=sds, match='rm', **kwargs)
rep = result_report(res, ds)
html(rep)
def test_result_report():
"Test result_report function for different Results"
ds = datasets.get_uts(True)
sds = ds.sub("B == 'b0'")
for y in ('uts', # time
"utsnd.summary(time=(0.25, 0.35))", # sensor
'utsnd', # sensor x time
):
dims = tuple(dim.name for dim in ds.eval(y).dims[1:])
logging.info("y=%s %s", y, dims)
kwargs = dict(pmin=0.1, samples=100)
if 'time' in dims:
kwargs['tstart'] = 0.2
kwargs['tstop'] = 0.4
for match in (None, 'rm'):
logging.info(" match=%s", match)
res = testnd.ttest_1samp(y, match=match, ds=sds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.ttest_ind(y, 'A', ds=sds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.ttest_rel(y, 'A', ds=sds, match='rm', **kwargs)
rep = result_report(res, sds)
html(rep)
res = testnd.anova(y, 'A * B', ds=ds, **kwargs)
rep = result_report(res, ds)
html(rep)
res = testnd.anova(y, 'A * rm', ds=sds, match='rm', **kwargs)
rep = result_report(res, ds)
html(rep)
def test_ttest_ind():
"Test testnd.ttest_ind()"
ds = datasets.get_uts(True)
# basic
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds)
assert repr(res) == "<ttest_ind 'uts', 'A', 'a1' (n=30), 'a0' (n=30)>"
assert res.p_uncorrected.min() < 0.05
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == "<ttest_ind 'uts', 'A', 'a1' (n=30), 'a0' (n=30)>"
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# alternate argspec
res_ = testnd.ttest_ind("uts[A == 'a1']", "uts[A == 'a0']", ds=ds)
assert repr(res_) == "<ttest_ind 'uts' (n=30), 'uts' (n=30)>"
assert_dataobj_equal(res_.t, res.t)
# cluster
res = testnd.ttest_ind('uts', 'A', 'a1', 'a0', ds=ds, tail=1, samples=1)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == repr(res)
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# nd
res = testnd.ttest_ind('utsnd', 'A', 'a1', 'a0', ds=ds, pmin=0.05, samples=2)
assert res._cdist.n_clusters == 10
# zero variance
ds['utsnd'].x[:, 1, 10] = 0.
res_zv = testnd.ttest_ind('utsnd', 'A', 'a1', 'a0', ds=ds)
assert_array_equal(res_zv.t.x[0], res.t.x[0])
assert res_zv.t.x[1, 10] == 0.
# argument mismatch
with pytest.raises(ValueError):
testnd.ttest_ind(ds['utsnd'], ds[:-1, 'A'])
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_source_estimate():
"Test SourceSpace dimension"
mne.set_log_level('warning')
ds = datasets.get_mne_sample(src='ico')
dsa = ds.aggregate('side')
# test auto-conversion
asndvar('epochs', ds=ds)
asndvar('epochs', ds=dsa)
asndvar(dsa['epochs'][0])
# source space clustering
res = testnd.ttest_ind('src', 'side', ds=ds, samples=0, pmin=0.05,
tstart=0.05, mintime=0.02, minsource=10)
assert_equal(res.clusters.n_cases, 52)
# test disconnecting parc
src = ds['src']
source = src.source
parc = source.parc
orig_conn = set(map(tuple, source.connectivity()))
disc_conn = set(map(tuple, source.connectivity(True)))
assert_true(len(disc_conn) < len(orig_conn))
for pair in orig_conn:
s, d = pair
if pair in disc_conn:
assert_equal(parc[s], parc[d])
else:
assert_not_equal(parc[s], parc[d])
# threshold-based test with parc
srcl = src.sub(source='lh')
res = testnd.ttest_ind(srcl, 'side', ds=ds, samples=10, pmin=0.05,
tstart=0.05, mintime=0.02, minsource=10,
parc='source')
assert_equal(res._cdist.dist.shape[1], len(srcl.source.parc.cells))
label = 'superiortemporal-lh'
c_all = res._clusters(maps=True)
c_label = res._clusters(maps=True, source=label)
assert_array_equal(c_label['location'], label)
for case in c_label.itercases():
id_ = case['id']
idx = c_all['id'].index(id_)[0]
assert_equal(case['v'], c_all[idx, 'v'])
assert_equal(case['tstart'], c_all[idx, 'tstart'])
assert_equal(case['tstop'], c_all[idx, 'tstop'])
assert_less_equal(case['p'], c_all[idx, 'p'])
assert_dataobj_equal(case['cluster'],
c_all[idx, 'cluster'].sub(source=label))
# threshold-free test with parc
res = testnd.ttest_ind(srcl, 'side', ds=ds, samples=10, tstart=0.05,
parc='source')
cl = res._clusters(0.05)
assert_equal(cl.eval("p.min()"), res.p.min())
mp = res.masked_parameter_map()
assert_in(mp.min(), (0, res.t.min()))
assert_in(mp.max(), (0, res.t.max()))
# indexing source space
s_sub = src.sub(source='fusiform-lh')
idx = source.index_for_label('fusiform-lh')
s_idx = src[idx]
assert_dataobj_equal(s_sub, s_idx)
def test_source_estimate():
"Test SourceSpace dimension"
mne.set_log_level('warning')
ds = datasets.get_mne_sample(src='ico')
dsa = ds.aggregate('side')
# test auto-conversion
asndvar('epochs', ds=ds)
asndvar('epochs', ds=dsa)
asndvar(dsa['epochs'][0])
# source space clustering
res = testnd.ttest_ind('src',
'side',
ds=ds,
samples=0,
pmin=0.05,
tstart=0.05,
mintime=0.02,
minsource=10)
assert res.clusters.n_cases == 52
# test disconnecting parc
src = ds['src']
source = src.source
parc = source.parc
orig_conn = set(map(tuple, source.connectivity()))
disc_conn = set(map(tuple, source.connectivity(True)))
assert len(disc_conn) < len(orig_conn)
for pair in orig_conn:
s, d = pair
if pair in disc_conn:
assert parc[s] == parc[d]
else:
assert parc[s] != parc[d]
# threshold-based test with parc
srcl = src.sub(source='lh')
res = testnd.ttest_ind(srcl,
'side',
ds=ds,
samples=10,
pmin=0.05,
tstart=0.05,
mintime=0.02,
minsource=10,
parc='source')
assert res._cdist.dist.shape[1] == len(srcl.source.parc.cells)
label = 'superiortemporal-lh'
c_all = res.find_clusters(maps=True)
c_label = res.find_clusters(maps=True, source=label)
assert_array_equal(c_label['location'], label)
for case in c_label.itercases():
id_ = case['id']
idx = c_all['id'].index(id_)[0]
assert case['v'] == c_all[idx, 'v']
assert case['tstart'] == c_all[idx, 'tstart']
assert case['tstop'] == c_all[idx, 'tstop']
assert case['p'] <= c_all[idx, 'p']
assert_dataobj_equal(case['cluster'],
c_all[idx, 'cluster'].sub(source=label))
# threshold-free test with parc
res = testnd.ttest_ind(srcl,
'side',
ds=ds,
samples=10,
tstart=0.05,
parc='source')
cl = res.find_clusters(0.05)
assert cl.eval("p.min()") == res.p.min()
mp = res.masked_parameter_map()
assert mp.min() == res.t.min()
assert mp.max() == res.t.max(res.p <= 0.05)
assert mp.max() == pytest.approx(-4.95817732)
# indexing source space
s_sub = src.sub(source='fusiform-lh')
idx = source.index_for_label('fusiform-lh')
s_idx = src[idx]
assert_dataobj_equal(s_sub, s_idx)
# concatenate
src_reconc = concatenate((src.sub(source='lh'), src.sub(source='rh')),
'source')
assert_dataobj_equal(src_reconc, src)
