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)
eelbrain._stats.testnd.MULTIPROCESSING = 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)
eelbrain._stats.testnd.MULTIPROCESSING = 1
sds = ds.sub("B=='b0'")
# thresholded, UTS
eelbrain._stats.testnd.MULTIPROCESSING = 0
res0 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
tgt = res0.find_clusters()
eelbrain._stats.testnd.MULTIPROCESSING = 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
eelbrain._stats.testnd.MULTIPROCESSING = 0
res0 = testnd.ttest_rel('utsnd', 'A', 'a1', 'a0', 'rm', ds=sds, pmin=0.1,
samples=100)
tgt = res0.find_clusters()
eelbrain._stats.testnd.MULTIPROCESSING = 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
eelbrain._stats.testnd.MULTIPROCESSING = 0
res0 = testnd.ttest_rel('uts', 'A', 'a1', 'a0', 'rm', ds=sds, tfce=True,
samples=10)
tgt = res0.compute_probability_map()
eelbrain._stats.testnd.MULTIPROCESSING = 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_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)
eelbrain._stats.testnd.MULTIPROCESSING = 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)
eelbrain._stats.testnd.MULTIPROCESSING = 1
sds = ds.sub("B=='b0'")
# thresholded, UTS
eelbrain._stats.testnd.MULTIPROCESSING = 0
res0 = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
'rm',
ds=sds,
pmin=0.1,
samples=100)
tgt = res0.find_clusters()
eelbrain._stats.testnd.MULTIPROCESSING = 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
eelbrain._stats.testnd.MULTIPROCESSING = 0
res0 = testnd.ttest_rel('utsnd',
'A',
'a1',
'a0',
'rm',
ds=sds,
pmin=0.1,
samples=100)
tgt = res0.find_clusters()
eelbrain._stats.testnd.MULTIPROCESSING = 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
eelbrain._stats.testnd.MULTIPROCESSING = 0
res0 = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
'rm',
ds=sds,
tfce=True,
samples=10)
tgt = res0.compute_probability_map()
eelbrain._stats.testnd.MULTIPROCESSING = 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):
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)
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)
eelbrain._stats.testnd.MULTIPROCESSING = 0
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters)
eelbrain._stats.testnd.MULTIPROCESSING = 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
def test_anova():
"Test testnd.anova()"
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)
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)
eelbrain._stats.testnd.MULTIPROCESSING = 0
res = testnd.anova('utsnd', 'A*B*rm', ds=ds, pmin=0.05, samples=5)
assert_dataset_equal(res.clusters, res0.clusters)
eelbrain._stats.testnd.MULTIPROCESSING = 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
