def test_corr():
"Test testnd.corr()"
plot.configure_backend(False, False)
ds = datasets.get_uts(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x.shape
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds, samples=10, pmin=0.05)
p = plot.Array(res)
p.close()
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
# NaN
r = _testnd._corr(np.arange(10), np.zeros(10))
assert_equal(r, 0)
def test_corr():
"Test testnd.corr()"
ds = datasets.get_uts(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', ds=ds)
assert repr(res) == "<corr 'utsnd', 'Y'>"
for s, t in product('01234', (0.1, 0.2, 0.35)):
target = test.Correlation(utsnd.sub(sensor=s, time=t), Y).r
assert res.r.sub(sensor=s, time=t) == pytest.approx(target)
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
res = testnd.corr('utsnd', 'Y', ds=ds, samples=10, pmin=0.05)
repr(res)
res = testnd.corr('utsnd', 'Y', ds=ds, samples=10, tfce=True)
repr(res)
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == repr(res)
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
def test_corr():
"Test testnd.corr()"
ds = datasets.get_uts(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', ds=ds)
repr(res)
for s, t in product('01234', (0.1, 0.2, 0.35)):
target = test.Correlation(utsnd.sub(sensor=s, time=t), Y).r
assert_almost_equal(res.r.sub(sensor=s, time=t), target, 10)
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
res = testnd.corr('utsnd', 'Y', ds=ds, samples=10, pmin=0.05)
repr(res)
res = testnd.corr('utsnd', 'Y', ds=ds, samples=10, tfce=True)
repr(res)
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
def test_plot_results():
"Test plotting test results"
ds = datasets.get_uts(True)
# ANOVA
res = testnd.anova('utsnd',
'A*B*rm',
match='rm',
ds=ds,
samples=0,
pmin=0.05)
p = plot.Array(res, show=False)
p.close()
res = testnd.anova('utsnd',
'A*B*rm',
match='rm',
ds=ds,
samples=2,
pmin=0.05)
p = plot.Array(res, show=False)
p.close()
# Correlation
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
p = plot.Array(res, show=False)
p.close()
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds, samples=10, pmin=0.05)
p = plot.Array(res, show=False)
p.close()
def test_corr():
"Test testnd.corr()"
plot.configure_backend(False, False)
ds = datasets.get_rand(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x.shape
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds, samples=10, pmin=0.05)
p = plot.Array(res)
p.close()
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
# NaN
r = _testnd._corr(np.arange(10), np.zeros(10))
assert_equal(r, 0)
def test_plot_results():
"Test plotting test results"
ds = datasets.get_uts(True)
# ANOVA
res = testnd.anova("utsnd", "A*B*rm", ds=ds, samples=0, pmin=0.05)
p = plot.Array(res, show=False)
p.close()
res = testnd.anova("utsnd", "A*B*rm", ds=ds, samples=2, pmin=0.05)
p = plot.Array(res, show=False)
p.close()
# Correlation
res = testnd.corr("utsnd", "Y", "rm", ds=ds)
p = plot.Array(res, show=False)
p.close()
res = testnd.corr("utsnd", "Y", "rm", ds=ds, samples=10, pmin=0.05)
p = plot.Array(res, show=False)
p.close()
def test_corr():
"Test testnd.corr()"
ds = datasets.get_uts(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds, samples=10, pmin=0.05)
repr(res)
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
def test_corr():
"Test testnd.corr()"
ds = datasets.get_uts(True)
# add correlation
Y = ds['Y']
utsnd = ds['utsnd']
utsnd.x[:, 3:5, 50:65] += Y.x[:, None, None]
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds)
repr(res)
res = testnd.corr('utsnd', 'Y', 'rm', ds=ds, samples=10, pmin=0.05)
repr(res)
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
assert_dataobj_equal(res.p, res_.p)
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
