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_clusters():
"test plot.uts cluster plotting functions"
ds = datasets.get_uts()
A = ds['A']
B = ds['B']
Y = ds['uts']
# fixed effects model
res = testnd.anova(Y, A * B)
p = plot.UTSClusters(res, title="Fixed Effects Model", show=False)
p.close()
# random effects model:
subject = Factor(range(15), tile=4, random=True, name='subject')
res = testnd.anova(Y, A * B * subject, samples=2)
p = plot.UTSClusters(res, title="Random Effects Model", show=False)
p.close()
# plot UTSStat
p = plot.UTSStat(Y, A % B, match=subject, show=False)
p.set_clusters(res.clusters)
p.close()
p = plot.UTSStat(Y, A, Xax=B, match=subject, show=False)
p.close()
def test_clusters():
"test plot.uts cluster plotting functions"
plot.configure_backend(False, False)
ds = datasets.get_rand()
A = ds['A']
B = ds['B']
Y = ds['uts']
# fixed effects model
res = testnd.anova(Y, A * B)
p = plot.UTSClusters(res, title="Fixed Effects Model")
p.close()
# random effects model:
subject = Factor(range(15), tile=4, random=True, name='subject')
res = testnd.anova(Y, A * B * subject, samples=2)
p = plot.UTSClusters(res, title="Random Effects Model")
p.close()
# plot UTSStat
p = plot.UTSStat(Y, A % B, match=subject)
p.set_clusters(res.clusters)
p.close()
p = plot.UTSStat(Y, A, Xax=B, match=subject)
p.close()
def test_ndanova():
ds = datasets.get_uts(nrm=True)
ds['An'] = ds['A'].as_var({'a0': 0, 'a1': 1})
with pytest.raises(NotImplementedError):
testnd.anova('uts', 'An*B*rm', ds=ds)
# nested random effect
res = testnd.anova('uts', 'A + A%B + B * nrm(A)', ds=ds, match='nrm', samples=100, pmin=0.05)
assert len(res.find_clusters(0.05)) == 8
def test_ndanova():
ds = datasets.get_uts(nrm=True)
ds['An'] = ds['A'].as_var({'a0': 0, 'a1': 1})
with pytest.raises(NotImplementedError):
testnd.anova('uts', 'An*B*rm', ds=ds)
# nested random effect
res = testnd.anova('uts', 'A + A%B + B * nrm(A)', ds=ds, match='nrm',
samples=100, pmin=0.05)
assert len(res.find_clusters(0.05)) == 8
def test_anova_parc():
"Test ANOVA with parc argument and source space data"
set_log_level('warning', 'mne')
ds = datasets.get_mne_sample(src='ico', sub="side.isin(('L', 'R'))")
y = ds['src'].sub(source=('lateraloccipital-lh', 'cuneus-lh'))
y1 = y.sub(source='lateraloccipital-lh')
y2 = y.sub(source='cuneus-lh')
kwa = dict(ds=ds, tstart=0.2, tstop=0.3, samples=100)
resp = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1p = resp.find_clusters(source='lateraloccipital-lh')
c2p = resp.find_clusters(source='cuneus-lh')
del c1p['p_parc', 'id']
del c2p['p_parc', 'id']
res1 = testnd.anova(y1, "side*modality", pmin=0.05, **kwa)
c1 = res1.find_clusters()
del c1['id']
res2 = testnd.anova(y2, "side*modality", pmin=0.05, **kwa)
c2 = res2.find_clusters()
del c2['id']
assert_dataset_equal(c1p, c1)
assert_dataset_equal(c2p, c2)
assert_array_equal(c2['p'], [
0.85, 0.88, 0.97, 0.75, 0.99, 0.99, 0.98, 0.0, 0.12, 0.88, 0.25, 0.97,
0.34, 0.96
])
# without multiprocessing
configure(n_workers=0)
ress = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1s = ress.find_clusters(source='lateraloccipital-lh')
c2s = ress.find_clusters(source='cuneus-lh')
del c1s['p_parc', 'id']
del c2s['p_parc', 'id']
assert_dataset_equal(c1s, c1)
assert_dataset_equal(c2s, c2)
configure(n_workers=True)
# parc but single label
resp2 = testnd.anova(y2, "side*modality", pmin=0.05, parc='source', **kwa)
c2sp = resp2.find_clusters(source='cuneus-lh')
del c2sp['p_parc', 'id']
assert_dataset_equal(c2sp, c2)
# not defined
assert_raises(NotImplementedError,
testnd.anova,
y,
"side*modality",
tfce=True,
parc='source',
**kwa)
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_anova_parc():
"Test ANOVA with parc argument and source space data"
set_log_level('warning', 'mne')
ds = datasets.get_mne_sample(src='ico', sub="side.isin(('L', 'R'))")
y = ds['src'].sub(source=('lateraloccipital-lh', 'cuneus-lh'))
y1 = y.sub(source='lateraloccipital-lh')
y2 = y.sub(source='cuneus-lh')
kwa = dict(ds=ds, tstart=0.2, tstop=0.3, samples=100)
resp = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1p = resp.find_clusters(source='lateraloccipital-lh')
c2p = resp.find_clusters(source='cuneus-lh')
del c1p['p_parc', 'id']
del c2p['p_parc', 'id']
res1 = testnd.anova(y1, "side*modality", pmin=0.05, **kwa)
c1 = res1.find_clusters()
del c1['id']
res2 = testnd.anova(y2, "side*modality", pmin=0.05, **kwa)
c2 = res2.find_clusters()
del c2['id']
assert_dataset_equal(c1p, c1)
assert_dataset_equal(c2p, c2)
assert_array_equal(c2['p'], [0.85, 0.88, 0.97, 0.75, 0.99, 0.99, 0.98, 0.0,
0.12, 0.88, 0.25, 0.97, 0.34, 0.96])
# without multiprocessing
testnd.configure(0)
ress = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1s = ress.find_clusters(source='lateraloccipital-lh')
c2s = ress.find_clusters(source='cuneus-lh')
del c1s['p_parc', 'id']
del c2s['p_parc', 'id']
assert_dataset_equal(c1s, c1)
assert_dataset_equal(c2s, c2)
testnd.configure(-1)
# parc but single label
resp2 = testnd.anova(y2, "side*modality", pmin=0.05, parc='source', **kwa)
c2sp = resp2.find_clusters(source='cuneus-lh')
del c2sp['p_parc', 'id']
assert_dataset_equal(c2sp, c2)
# not defined
assert_raises(NotImplementedError, testnd.anova, y, "side*modality",
tfce=True, parc='source', **kwa)
def run_as_ndanova(y, x, ds):
yt = ds.eval(y).x[:, None]
y2 = np.concatenate((yt, yt * 2), 1)
ndvar = NDVar(y2, ('case', UTS(0, 0.1, 2)))
res = testnd.anova(ndvar, x, ds=ds)
f1 = [fmap.x[0] for fmap in res.f]
f2 = [fmap.x[1] for fmap in res.f]
for f1_, f2_ in izip(f1, f2):
eq_(f1_, f2_)
return f1
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 run_as_ndanova(y, x, ds):
yt = ds.eval(y).x[:, None]
y2 = np.concatenate((yt, yt * 2), 1)
ndvar = NDVar(y2, ('case', UTS(0, 0.1, 2)))
res = testnd.anova(ndvar, x, ds=ds)
f1 = [fmap.x[0] for fmap in res.f]
f2 = [fmap.x[1] for fmap in res.f]
for f1_, f2_ in zip(f1, f2):
assert f1_ == f2_
return f1
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_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_anova_incremental():
"Test testnd.anova() with incremental f-tests"
ds = datasets.get_uts()
testnd.anova('uts', 'A*B', ds=ds[3:], pmin=0.05, samples=10)
def test_anova():
"Test testnd.anova()"
ds = datasets.get_uts(True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logging.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=0, pmin=0.05)
repr(res)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=2, pmin=0.05)
repr(res)
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
repr(res)
assert_in('A clusters', res.clusters.info)
assert_in('B clusters', res.clusters.info)
assert_in('A x B clusters', res.clusters.info)
# no clusters
res = testnd.anova('uts', 'B', sub="A=='a1'", ds=ds, samples=5, pmin=0.05,
mintime=0.02)
repr(res)
assert_in('v', res.clusters)
assert_in('p', res.clusters)
# all effects with clusters
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5, pmin=0.05,
tstart=0.1, mintime=0.02)
assert_equal(set(res.clusters['effect'].cells), set(res.effects))
# some effects with clusters, some without
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5, pmin=0.05,
tstart=0.37, mintime=0.02)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
resr = pickle.loads(string)
tf_clusters = resr.find_clusters(pmin=0.05)
peaks = resr.find_peaks()
assert_dataobj_equal(tf_clusters, res.find_clusters(pmin=0.05))
assert_dataobj_equal(peaks, res.find_peaks())
assert_equal(tf_clusters.eval("p.min()"), peaks.eval("p.min()"))
unmasked = resr.f[0]
masked = resr.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
# reproducibility
res0 = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters)
testnd.configure(0)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters)
testnd.configure(-1)
# permutation
eelbrain._stats.permutation._YIELD_ORIGINAL = 1
samples = 4
# raw
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=samples)
for dist in res._cdist:
eq_(len(dist.dist), samples)
assert_array_equal(dist.dist, dist.parameter_map.abs().max())
# TFCE
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, tfce=True, samples=samples)
for dist in res._cdist:
eq_(len(dist.dist), samples)
assert_array_equal(dist.dist, dist.tfce_map.abs().max())
# thresholded
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=samples)
clusters = res.find_clusters()
for dist, effect in izip(res._cdist, res.effects):
effect_idx = clusters.eval("effect == %r" % effect)
vmax = clusters[effect_idx, 'v'].abs().max()
eq_(len(dist.dist), samples)
assert_array_equal(dist.dist, vmax)
eelbrain._stats.permutation._YIELD_ORIGINAL = 0
# 1d TFCE
testnd.configure(0)
res = testnd.anova('utsnd.rms(time=(0.1, 0.3))', 'A*B*rm', ds=ds, tfce=True, samples=samples)
testnd.configure(-1)
def test_anova_incremental():
"Test testnd.anova() with incremental f-tests"
ds = datasets.get_uts()
testnd.anova('uts', 'A*B', ds=ds[3:], pmin=0.05, samples=10)
def test_anova():
"Test testnd.anova()"
plot.configure_backend(False, False)
ds = datasets.get_uts(True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logger.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=0, pmin=0.05)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=2, pmin=0.05)
repr(res)
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))
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
repr(res)
assert_in('A clusters', res.clusters.info)
assert_in('B clusters', res.clusters.info)
assert_in('A x B clusters', res.clusters.info)
# no clusters
res = testnd.anova('uts', 'B', sub="A=='a1'", ds=ds, samples=5, pmin=0.05,
mintime=0.02)
repr(res)
assert_in('v', res.clusters)
assert_in('p', res.clusters)
# all effects with clusters
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5, pmin=0.05,
tstart=0.1, mintime=0.02)
assert_equal(set(res.clusters['effect'].cells), set(res.effects))
# some effects with clusters, some without
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5, pmin=0.05,
tstart=0.37, mintime=0.02)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res = pickle.loads(string)
tfce_clusters = res.find_clusters(pmin=0.05)
peaks = res.find_peaks()
assert_equal(tfce_clusters.eval("p.min()"), peaks.eval("p.min()"))
unmasked = res.f[0]
masked = res.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
def test_anova():
"Test testnd.anova()"
plot.configure_backend(False, False)
ds = datasets.get_rand(True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logger.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=0, pmin=0.05)
repr(res)
p = plot.Array(res)
p.close()
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=2, pmin=0.05)
repr(res)
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))
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
repr(res)
assert_in('A clusters', res.clusters.info)
assert_in('B clusters', res.clusters.info)
assert_in('A x B clusters', res.clusters.info)
# no clusters
res = testnd.anova('uts',
'B',
sub="A=='a1'",
ds=ds,
samples=5,
pmin=0.05,
mintime=0.02)
repr(res)
assert_in('v', res.clusters)
assert_in('p', res.clusters)
# all effects with clusters
res = testnd.anova('uts',
'A*B*rm',
ds=ds,
samples=5,
pmin=0.05,
tstart=0.1,
mintime=0.02)
assert_equal(set(res.clusters['effect'].cells), set(res.effects))
# some effects with clusters, some without
res = testnd.anova('uts',
'A*B*rm',
ds=ds,
samples=5,
pmin=0.05,
tstart=0.37,
mintime=0.02)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res = pickle.loads(string)
tfce_clusters = res._clusters(pmin=0.05)
peaks = res.find_peaks()
assert_equal(tfce_clusters.eval("p.min()"), peaks.eval("p.min()"))
unmasked = res.f[0]
masked = res.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
def test_anova():
"Test testnd.anova()"
ds = datasets.get_uts(True, nrm=True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logging.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
res = testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
assert res._plot_model() == 'A%B'
asfmtext(res)
res = testnd.anova('utsnd', 'A*B*rm', match=False, ds=ds, samples=0, pmin=0.05)
assert repr(res) == "<anova 'utsnd', 'A*B*rm', match=False, samples=0, pmin=0.05, 'A': 17 clusters, 'B': 20 clusters, 'A x B': 22 clusters>"
assert res._plot_model() == 'A%B'
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=2, pmin=0.05)
assert res.match == 'rm'
assert repr(res) == "<anova 'utsnd', 'A*B*rm', match='rm', samples=2, pmin=0.05, 'A': 17 clusters, p < .001, 'B': 20 clusters, p < .001, 'A x B': 22 clusters, p < .001>"
assert res._plot_model() == 'A%B'
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == repr(res)
assert res_._plot_model() == 'A%B'
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
assert res.match == 'rm'
assert repr(res) == "<anova 'utsnd', 'A*B*rm', match='rm', samples=10, 'A': p < .001, 'B': p < .001, 'A x B': p < .001>"
assert 'A clusters' in res.clusters.info
assert 'B clusters' in res.clusters.info
assert 'A x B clusters' in res.clusters.info
# no clusters
res = testnd.anova('uts', 'B', sub="A=='a1'", ds=ds, samples=5, pmin=0.05, mintime=0.02)
repr(res)
assert 'v' in res.clusters
assert 'p' in res.clusters
assert res._plot_model() == 'B'
# all effects with clusters
res = testnd.anova('uts', 'A*B*rm', match=False, ds=ds, samples=5, pmin=0.05, tstart=0.1, mintime=0.02)
assert set(res.clusters['effect'].cells) == set(res.effects)
# some effects with clusters, some without
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5, pmin=0.05, tstart=0.37, mintime=0.02)
assert res.match == 'rm'
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
assert res.match == 'rm'
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
resr = pickle.loads(string)
tf_clusters = resr.find_clusters(pmin=0.05)
peaks = resr.find_peaks()
assert_dataobj_equal(tf_clusters, res.find_clusters(pmin=0.05))
assert_dataobj_equal(peaks, res.find_peaks())
assert tf_clusters.eval("p.min()") == peaks.eval("p.min()")
unmasked = resr.f[0]
masked = resr.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
# reproducibility
decimal = 12 if IS_WINDOWS else None # FIXME: why is Windows sometimes different???
res0 = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters, decimal=decimal)
configure(n_workers=0)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters, decimal=decimal)
configure(n_workers=True)
# permutation
eelbrain._stats.permutation._YIELD_ORIGINAL = 1
samples = 4
# raw
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=samples)
for dist in res._cdist:
assert len(dist.dist) == samples
assert_array_equal(dist.dist, dist.parameter_map.abs().max())
# TFCE
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, tfce=True, samples=samples)
for dist in res._cdist:
assert len(dist.dist) == samples
assert_array_equal(dist.dist, dist.tfce_map.abs().max())
# thresholded
res1 = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=samples)
clusters = res1.find_clusters()
for dist, effect in zip(res1._cdist, res1.effects):
effect_idx = clusters.eval("effect == %r" % effect)
vmax = clusters[effect_idx, 'v'].abs().max()
assert len(dist.dist) == samples
assert_array_equal(dist.dist, vmax)
eelbrain._stats.permutation._YIELD_ORIGINAL = 0
# 1d TFCE
configure(n_workers=0)
res = testnd.anova('utsnd.rms(time=(0.1, 0.3))', 'A*B*rm', ds=ds, tfce=True, samples=samples)
configure(n_workers=True)
# zero variance
res2 = testnd.anova('utsnd', 'A', ds=ds)
ds['utsnd'].x[:, 1, 10] = 0.
zero_var = ds['utsnd'].var('case') == 0
zv_index = tuple(i[0] for i in zero_var.nonzero())
res1_zv = testnd.anova('utsnd', 'A*B*rm', ds=ds)
res2_zv = testnd.anova('utsnd', 'A', ds=ds)
for res, res_zv in ((res1, res1_zv), (res2, res2_zv)):
for f, f_zv in zip(res.f, res_zv.f):
assert_array_equal((f_zv == 0).x, zero_var.x)
assert f_zv[zv_index] == 0
f_zv[zv_index] = f[zv_index]
assert_dataobj_equal(f_zv, f, decimal=decimal)
# nested random effect
res = testnd.anova('uts', 'A * B * nrm(A)', ds=ds, samples=10, tstart=.4)
assert res.match == 'nrm(A)'
assert [p.min() for p in res.p] == [0.0, 0.6, 0.9]
# unequal argument length
with pytest.raises(ValueError):
testnd.anova('uts', 'A[:-1]', ds=ds)
with pytest.raises(ValueError):
testnd.anova('uts[:-1]', 'A * B * nrm(A)', ds=ds)
def test_anova():
"Test testnd.anova()"
ds = datasets.get_uts(True, nrm=True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logging.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
res = testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
assert res._plot_model() == 'A%B'
asfmtext(res)
res = testnd.anova('utsnd',
'A*B*rm',
match=False,
ds=ds,
samples=0,
pmin=0.05)
assert repr(
res
) == "<anova 'utsnd', 'A*B*rm', match=False, samples=0, pmin=0.05, 'A': 17 clusters, 'B': 20 clusters, 'A x B': 22 clusters>"
assert res._plot_model() == 'A%B'
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=2, pmin=0.05)
assert res.match == 'rm'
assert repr(
res
) == "<anova 'utsnd', 'A*B*rm', match='rm', samples=2, pmin=0.05, 'A': 17 clusters, p < .001, 'B': 20 clusters, p < .001, 'A x B': 22 clusters, p < .001>"
assert res._plot_model() == 'A%B'
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert repr(res_) == repr(res)
assert res_._plot_model() == 'A%B'
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
assert res.match == 'rm'
assert repr(
res
) == "<anova 'utsnd', 'A*B*rm', match='rm', samples=10, 'A': p < .001, 'B': p < .001, 'A x B': p < .001>"
assert 'A clusters' in res.clusters.info
assert 'B clusters' in res.clusters.info
assert 'A x B clusters' in res.clusters.info
# no clusters
res = testnd.anova('uts',
'B',
sub="A=='a1'",
ds=ds,
samples=5,
pmin=0.05,
mintime=0.02)
repr(res)
assert 'v' in res.clusters
assert 'p' in res.clusters
assert res._plot_model() == 'B'
# all effects with clusters
res = testnd.anova('uts',
'A*B*rm',
match=False,
ds=ds,
samples=5,
pmin=0.05,
tstart=0.1,
mintime=0.02)
assert set(res.clusters['effect'].cells) == set(res.effects)
# some effects with clusters, some without
res = testnd.anova('uts',
'A*B*rm',
ds=ds,
samples=5,
pmin=0.05,
tstart=0.37,
mintime=0.02)
assert res.match == 'rm'
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
assert res.match == 'rm'
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
resr = pickle.loads(string)
tf_clusters = resr.find_clusters(pmin=0.05)
peaks = resr.find_peaks()
assert_dataobj_equal(tf_clusters, res.find_clusters(pmin=0.05))
assert_dataobj_equal(peaks, res.find_peaks())
assert tf_clusters.eval("p.min()") == peaks.eval("p.min()")
unmasked = resr.f[0]
masked = resr.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
# reproducibility
decimal = 12 if IS_WINDOWS else None # FIXME: why is Windows sometimes different???
res0 = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters, decimal=decimal)
configure(n_workers=0)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters, decimal=decimal)
configure(n_workers=True)
# permutation
eelbrain._stats.permutation._YIELD_ORIGINAL = 1
samples = 4
# raw
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=samples)
for dist in res._cdist:
assert len(dist.dist) == samples
assert_array_equal(dist.dist, dist.parameter_map.abs().max())
# TFCE
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, tfce=True, samples=samples)
for dist in res._cdist:
assert len(dist.dist) == samples
assert_array_equal(dist.dist, dist.tfce_map.abs().max())
# thresholded
res1 = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=samples)
clusters = res1.find_clusters()
for dist, effect in zip(res1._cdist, res1.effects):
effect_idx = clusters.eval("effect == %r" % effect)
vmax = clusters[effect_idx, 'v'].abs().max()
assert len(dist.dist) == samples
assert_array_equal(dist.dist, vmax)
eelbrain._stats.permutation._YIELD_ORIGINAL = 0
# 1d TFCE
configure(n_workers=0)
res = testnd.anova('utsnd.rms(time=(0.1, 0.3))',
'A*B*rm',
ds=ds,
tfce=True,
samples=samples)
configure(n_workers=True)
# zero variance
res2 = testnd.anova('utsnd', 'A', ds=ds)
ds['utsnd'].x[:, 1, 10] = 0.
zero_var = ds['utsnd'].var('case') == 0
zv_index = tuple(i[0] for i in zero_var.nonzero())
res1_zv = testnd.anova('utsnd', 'A*B*rm', ds=ds)
res2_zv = testnd.anova('utsnd', 'A', ds=ds)
for res, res_zv in ((res1, res1_zv), (res2, res2_zv)):
for f, f_zv in zip(res.f, res_zv.f):
assert_array_equal((f_zv == 0).x, zero_var.x)
assert f_zv[zv_index] == 0
f_zv[zv_index] = f[zv_index]
assert_dataobj_equal(f_zv, f, decimal=decimal)
# nested random effect
res = testnd.anova('uts', 'A * B * nrm(A)', ds=ds, samples=10, tstart=.4)
assert res.match == 'nrm(A)'
assert [p.min() for p in res.p] == [0.0, 0.6, 0.9]
# unequal argument length
with pytest.raises(ValueError):
testnd.anova('uts', 'A[:-1]', ds=ds)
with pytest.raises(ValueError):
testnd.anova('uts[:-1]', 'A * B * nrm(A)', ds=ds)
def test_anova():
"Test testnd.anova()"
ds = datasets.get_uts(True)
testnd.anova('utsnd', 'A*B', ds=ds)
for samples in (0, 2):
logging.info("TEST: samples=%r" % samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, pmin=0.05)
testnd.anova('utsnd', 'A*B', ds=ds, samples=samples, tfce=True)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=0, pmin=0.05)
eq_(
repr(res), "<anova 'utsnd', 'A*B*rm', samples=0, pmin=0.05, "
"'A': 17 clusters, 'B': 20 clusters, 'A x B': 22 clusters>")
res = testnd.anova('utsnd',
'A*B*rm',
match='rm',
ds=ds,
samples=2,
pmin=0.05)
eq_(
repr(res),
"<anova 'utsnd', 'A*B*rm', match='rm', samples=2, pmin=0.05, "
"'A': 17 clusters, p >= 0.000, 'B': 20 clusters, p >= 0.000, "
"'A x B': 22 clusters, p >= 0.000>")
# persistence
string = pickle.dumps(res, protocol=pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_equal(repr(res_), repr(res))
# threshold-free
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=10)
repr(res)
assert_in('A clusters', res.clusters.info)
assert_in('B clusters', res.clusters.info)
assert_in('A x B clusters', res.clusters.info)
# no clusters
res = testnd.anova('uts',
'B',
sub="A=='a1'",
ds=ds,
samples=5,
pmin=0.05,
mintime=0.02)
repr(res)
assert_in('v', res.clusters)
assert_in('p', res.clusters)
# all effects with clusters
res = testnd.anova('uts',
'A*B*rm',
ds=ds,
samples=5,
pmin=0.05,
tstart=0.1,
mintime=0.02)
assert_equal(set(res.clusters['effect'].cells), set(res.effects))
# some effects with clusters, some without
res = testnd.anova('uts',
'A*B*rm',
ds=ds,
samples=5,
pmin=0.05,
tstart=0.37,
mintime=0.02)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
assert_dataobj_equal(res.clusters, res_.clusters)
# test multi-effect results (with persistence)
# UTS
res = testnd.anova('uts', 'A*B*rm', ds=ds, samples=5)
repr(res)
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
resr = pickle.loads(string)
tf_clusters = resr.find_clusters(pmin=0.05)
peaks = resr.find_peaks()
assert_dataobj_equal(tf_clusters, res.find_clusters(pmin=0.05))
assert_dataobj_equal(peaks, res.find_peaks())
assert_equal(tf_clusters.eval("p.min()"), peaks.eval("p.min()"))
unmasked = resr.f[0]
masked = resr.masked_parameter_map(effect=0, pmin=0.05)
assert_array_equal(masked.x <= unmasked.x, True)
# reproducibility
res0 = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters)
configure(n_workers=0)
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters)
configure(n_workers=True)
# permutation
eelbrain._stats.permutation._YIELD_ORIGINAL = 1
samples = 4
# raw
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, samples=samples)
for dist in res._cdist:
eq_(len(dist.dist), samples)
assert_array_equal(dist.dist, dist.parameter_map.abs().max())
# TFCE
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, tfce=True, samples=samples)
for dist in res._cdist:
eq_(len(dist.dist), samples)
assert_array_equal(dist.dist, dist.tfce_map.abs().max())
# thresholded
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=samples)
clusters = res.find_clusters()
for dist, effect in izip(res._cdist, res.effects):
effect_idx = clusters.eval("effect == %r" % effect)
vmax = clusters[effect_idx, 'v'].abs().max()
eq_(len(dist.dist), samples)
assert_array_equal(dist.dist, vmax)
eelbrain._stats.permutation._YIELD_ORIGINAL = 0
# 1d TFCE
configure(n_workers=0)
res = testnd.anova('utsnd.rms(time=(0.1, 0.3))',
'A*B*rm',
ds=ds,
tfce=True,
samples=samples)
configure(n_workers=True)
# zero variance
ds['utsnd'].x[:, 1, 10] = 0.
assert_raises(ZeroVariance, testnd.anova, 'utsnd', 'A', ds=ds)
assert_raises(ZeroVariance, testnd.anova, 'utsnd', 'A*B*rm', ds=ds)
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