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 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 test_configure():
"""Test testnd.configure to change multiprocessing settings"""
testnd.configure(0)
eq_(eelbrain._stats.testnd.MULTIPROCESSING, 0)
testnd.configure(2)
eq_(eelbrain._stats.testnd.MULTIPROCESSING, 1)
eq_(eelbrain._stats.testnd.N_WORKERS, 2)
testnd.configure(-1)
eq_(eelbrain._stats.testnd.MULTIPROCESSING, 1)
def test_configure():
"""Test testnd.configure to change multiprocessing settings"""
testnd.configure(0)
eq_(eelbrain._stats.testnd.MULTIPROCESSING, 0)
testnd.configure(2)
eq_(eelbrain._stats.testnd.MULTIPROCESSING, 1)
eq_(eelbrain._stats.testnd.N_WORKERS, 2)
testnd.configure(-1)
eq_(eelbrain._stats.testnd.MULTIPROCESSING, 1)
def test_ttest_rel():
"Test testnd.ttest_rel()"
ds = datasets.get_uts(True)
# basic
res = testnd.ttest_rel('uts', 'A%B', ('a1', 'b1'), ('a0', 'b0'), 'rm',
ds=ds, samples=100)
repr(res)
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
repr(res_)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# collapsing cells
res2 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_less(res2.p_uncorrected.min(), 0.05)
assert_equal(res2.n, res.n)
# reproducibility
res3 = testnd.ttest_rel('uts', 'A%B', ('a1', 'b1'), ('a0', 'b0'), 'rm',
ds=ds, samples=100)
assert_dataset_equal(res3.find_clusters(maps=True), res.clusters)
testnd.configure(0)
res4 = testnd.ttest_rel('uts', 'A%B', ('a1', 'b1'), ('a0', 'b0'), 'rm',
ds=ds, samples=100)
assert_dataset_equal(res4.find_clusters(maps=True), res.clusters)
testnd.configure(-1)
sds = ds.sub("B=='b0'")
# thresholded, UTS
testnd.configure(0)
res0 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
tgt = res0.find_clusters()
testnd.configure(-1)
res1 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
assert_dataset_equal(res1.find_clusters(), tgt)
# thresholded, UTSND
testnd.configure(0)
res0 = testnd.ttest_rel('utsnd', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
tgt = res0.find_clusters()
testnd.configure(-1)
res1 = testnd.ttest_rel('utsnd', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
assert_dataset_equal(res1.find_clusters(), tgt)
# TFCE, UTS
testnd.configure(0)
res0 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, tfce=True,
samples=10)
tgt = res0.compute_probability_map()
testnd.configure(-1)
res1 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, tfce=True,
samples=10)
assert_dataobj_equal(res1.compute_probability_map(), tgt)
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_ttest_rel():
"Test testnd.ttest_rel()"
ds = datasets.get_uts(True)
# basic
res = testnd.ttest_rel('uts', 'A%B', ('a1', 'b1'), ('a0', 'b0'), 'rm',
ds=ds, samples=100)
eq_(repr(res), "<ttest_rel 'uts', 'A x B', ('a1', 'b1'), ('a0', 'b0'), "
"'rm' (n=15), samples=100, p >= 0.000>")
# persistence
string = pickle.dumps(res, pickle.HIGHEST_PROTOCOL)
res_ = pickle.loads(string)
repr(res_)
assert_equal(repr(res_), repr(res))
assert_dataobj_equal(res.p_uncorrected, res_.p_uncorrected)
# collapsing cells
res2 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=ds)
assert_less(res2.p_uncorrected.min(), 0.05)
assert_equal(res2.n, res.n)
# reproducibility
res3 = testnd.ttest_rel('uts', 'A%B', ('a1', 'b1'), ('a0', 'b0'), 'rm',
ds=ds, samples=100)
assert_dataset_equal(res3.find_clusters(maps=True), res.clusters)
testnd.configure(0)
res4 = testnd.ttest_rel('uts', 'A%B', ('a1', 'b1'), ('a0', 'b0'), 'rm',
ds=ds, samples=100)
assert_dataset_equal(res4.find_clusters(maps=True), res.clusters)
testnd.configure(-1)
sds = ds.sub("B=='b0'")
# thresholded, UTS
testnd.configure(0)
res0 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
tgt = res0.find_clusters()
testnd.configure(-1)
res1 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
assert_dataset_equal(res1.find_clusters(), tgt)
# thresholded, UTSND
testnd.configure(0)
res0 = testnd.ttest_rel('utsnd', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
tgt = res0.find_clusters()
testnd.configure(-1)
res1 = testnd.ttest_rel('utsnd', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
assert_dataset_equal(res1.find_clusters(), tgt)
# TFCE, UTS
testnd.configure(0)
res0 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, tfce=True,
samples=10)
tgt = res0.compute_probability_map()
testnd.configure(-1)
res1 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, tfce=True,
samples=10)
assert_dataobj_equal(res1.compute_probability_map(), tgt)
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)
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 zip(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)
