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_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_io_pickle():
"Test io by pickling"
ds = datasets.get_uts()
ds.info['info'] = "Some very useful information about the Dataset"
tempdir = tempfile.mkdtemp()
try:
dest = os.path.join(tempdir, 'test.pickled')
with open(dest, 'wb') as fid:
pickle.dump(ds, fid, protocol=pickle.HIGHEST_PROTOCOL)
with open(dest, 'rb') as fid:
ds2 = pickle.load(fid)
finally:
shutil.rmtree(tempdir)
assert_dataset_equal(ds, ds2)
def test_io_pickle():
"Test io by pickling"
ds = datasets.get_uts()
ds.info['info'] = "Some very useful information about the Dataset"
tempdir = tempfile.mkdtemp()
try:
dest = os.path.join(tempdir, 'test.pickled')
with open(dest, 'wb') as fid:
pickle.dump(ds, fid, protocol=pickle.HIGHEST_PROTOCOL)
with open(dest, 'rb') as fid:
ds2 = pickle.load(fid)
finally:
shutil.rmtree(tempdir)
assert_dataset_equal(ds, ds2)
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_r():
"Test interaction with R thorugh rpy2"
from rpy2.robjects import r
r("data(sleep)")
ds = Dataset.from_r("sleep")
eq_(ds.name, 'sleep')
extra = (0.7, -1.6, -0.2, -1.2, -0.1, 3.4, 3.7, 0.8, 0.0, 2.0, 1.9, 0.8,
1.1, 0.1, -0.1, 4.4, 5.5, 1.6, 4.6, 3.4)
assert_array_equal(ds.eval('extra'), extra)
assert_array_equal(ds.eval('ID'), map(str, xrange(1, 11)) * 2)
assert_array_equal(ds.eval('group'), ['1'] * 10 + ['2'] * 10)
# test putting
ds.to_r('sleep_copy')
ds_copy = Dataset.from_r('sleep_copy')
assert_dataset_equal(ds_copy, ds)
def test_r():
"Test interaction with R through rpy2"
from rpy2.robjects import r
r("data(sleep)")
ds = Dataset.from_r("sleep")
eq_(ds.name, 'sleep')
extra = (0.7, -1.6, -0.2, -1.2, -0.1, 3.4, 3.7, 0.8, 0.0, 2.0, 1.9, 0.8,
1.1, 0.1, -0.1, 4.4, 5.5, 1.6, 4.6, 3.4)
assert_array_equal(ds.eval('extra'), extra)
assert_array_equal(ds.eval('ID'), list(map(str, range(1, 11))) * 2)
assert_array_equal(ds.eval('group'), ['1'] * 10 + ['2'] * 10)
# test putting
ds.to_r('sleep_copy')
ds_copy = Dataset.from_r('sleep_copy')
assert_dataset_equal(ds_copy, ds)
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_io_txt():
"Test Dataset io as text"
ds = datasets.get_uv()
# Var that has integer values as float
ds['intflt'] = ds.eval('intvar * 1.')
ds['intflt'].name = 'intflt'
# io test
tempdir = tempfile.mkdtemp()
try:
dest = os.path.join(tempdir, 'test.txt')
ds.save_txt(dest)
ds2 = load.tsv(dest)
finally:
shutil.rmtree(tempdir)
assert_dataset_equal(ds, ds2, decimal=6)
def test_io_txt():
"Test Dataset io as text"
ds = datasets.get_uv()
# Var that has integer values as float
ds['intflt'] = ds.eval('intvar * 1.')
ds['intflt'].name = 'intflt'
# io test
tempdir = tempfile.mkdtemp()
try:
dest = os.path.join(tempdir, 'test.txt')
ds.save_txt(dest)
ds2 = load.tsv(dest)
finally:
shutil.rmtree(tempdir)
assert_dataset_equal(ds, ds2, decimal=6)
def test_dataset_indexing():
"""Test Dataset indexing"""
ds = datasets.get_uv()
# indexing values
eq_(ds['A', 1], ds['A'][1])
eq_(ds[1, 'A'], ds['A'][1])
# indexing variables
assert_dataobj_equal(ds[:, 'A'], ds['A'])
assert_dataobj_equal(ds['A', :], ds['A'])
assert_dataobj_equal(ds[:10, 'A'], ds['A'][:10])
assert_dataobj_equal(ds['A', :10], ds['A'][:10])
# new Dataset through indexing
ds2 = Dataset()
ds2['A'] = ds['A']
assert_dataset_equal(ds[('A',)], ds2)
ds2['B'] = ds['B']
assert_dataset_equal(ds['A', 'B'], ds2)
assert_dataset_equal(ds[('A', 'B'), :10], ds2[:10])
assert_dataset_equal(ds[:10, ('A', 'B')], ds2[:10])
# assigning value
ds[2, 'A'] = 'hello'
eq_(ds[2, 'A'], 'hello')
ds['A', 2] = 'not_hello'
eq_(ds[2, 'A'], 'not_hello')
# assigning new factor
ds['C', :] = 'c'
ok_(np.all(ds.eval("C == 'c'")))
# assigning new Var
ds['D1', :] = 5.
ds[:, 'D2'] = 5.
assert_array_equal(ds['D1'], 5)
assert_array_equal(ds['D2'], 5)
# test illegal names
f = Factor('aaabbb')
assert_raises(ValueError, ds.__setitem__, '%dsa', f)
assert_raises(ValueError, ds.__setitem__, '432', f)
assert_raises(ValueError, ds.__setitem__, ('%dsa', slice(None)), 'value')
assert_raises(ValueError, ds.__setitem__, (slice(None), '%dsa'), 'value')
assert_raises(ValueError, ds.__setitem__, ('432', slice(None)), 4.)
assert_raises(ValueError, ds.__setitem__, (slice(None), '432'), 4.)
# deleting items
del ds['A']
ok_('A' not in ds)
assert_raises(KeyError, ds.__getitem__, 'A')
del ds['B', 'rm']
ok_('B' not in ds and 'rm' not in ds)
def test_dataset_indexing():
"""Test Dataset indexing"""
ds = datasets.get_uv()
# indexing values
eq_(ds['A', 1], ds['A'][1])
eq_(ds[1, 'A'], ds['A'][1])
# indexing variables
assert_dataobj_equal(ds[:, 'A'], ds['A'])
assert_dataobj_equal(ds['A', :], ds['A'])
assert_dataobj_equal(ds[:10, 'A'], ds['A'][:10])
assert_dataobj_equal(ds['A', :10], ds['A'][:10])
# new Dataset through indexing
ds2 = Dataset()
ds2['A'] = ds['A']
assert_dataset_equal(ds[('A',)], ds2)
ds2['B'] = ds['B']
assert_dataset_equal(ds['A', 'B'], ds2)
assert_dataset_equal(ds[('A', 'B'), :10], ds2[:10])
assert_dataset_equal(ds[:10, ('A', 'B')], ds2[:10])
# assigning value
ds[2, 'A'] = 'hello'
eq_(ds[2, 'A'], 'hello')
ds['A', 2] = 'not_hello'
eq_(ds[2, 'A'], 'not_hello')
# assigning new factor
ds['C', :] = 'c'
ok_(np.all(ds.eval("C == 'c'")))
# assigning new Var
ds['D1', :] = 5.
ds[:, 'D2'] = 5.
assert_array_equal(ds['D1'], 5)
assert_array_equal(ds['D2'], 5)
# test illegal names
f = Factor('aaabbb')
assert_raises(ValueError, ds.__setitem__, '%dsa', f)
assert_raises(ValueError, ds.__setitem__, '432', f)
assert_raises(ValueError, ds.__setitem__, ('%dsa', slice(None)), 'value')
assert_raises(ValueError, ds.__setitem__, (slice(None), '%dsa'), 'value')
assert_raises(ValueError, ds.__setitem__, ('432', slice(None)), 4.)
assert_raises(ValueError, ds.__setitem__, (slice(None), '432'), 4.)
# deleting items
del ds['A']
ok_('A' not in ds)
assert_raises(KeyError, ds.__getitem__, 'A')
del ds['B', 'rm']
ok_('B' not in ds and 'rm' not in ds)
def test_dataset_sorting():
"Test Dataset sorting methods"
test_array = np.arange(10)
ds = Dataset()
ds['v'] = Var(test_array)
ds['f'] = Factor(test_array)
# shuffle the Dataset
rand_idx = test_array.copy()
np.random.shuffle(rand_idx)
ds_shuffled = ds[rand_idx]
# ascending, Var, copy
dsa = ds_shuffled.sorted('v')
assert_dataset_equal(dsa, ds, "Copy sorted by Var, ascending")
# descending, Factor, in-place
ds_shuffled.sort('f', descending=True)
assert_dataset_equal(ds_shuffled, ds[::-1], "In-place sorted by Factor, "
"descending")
def test_dataset_sorting():
"Test Dataset sorting methods"
test_array = np.arange(10)
ds = Dataset()
ds['v'] = Var(test_array)
ds['f'] = Factor(test_array)
# shuffle the Dataset
rand_idx = test_array.copy()
np.random.shuffle(rand_idx)
ds_shuffled = ds[rand_idx]
# ascending, Var, copy
dsa = ds_shuffled.sorted('v')
assert_dataset_equal(dsa, ds, "Copy sorted by Var, ascending")
# descending, Factor, in-place
ds_shuffled.sort('f', descending=True)
assert_dataset_equal(ds_shuffled, ds[::-1], "In-place sorted by Factor, "
"descending")
def test_align():
"Testing align() and align1() functions"
ds = datasets.get_uv()
# index the dataset
ds.index()
ds['aindex'] = ds.eval("A.enumerate_cells()")
# subset
idx4 = np.arange(0, ds.n_cases, 4)
idx4i = idx4[::-1]
ds2 = ds.sub(np.arange(0, ds.n_cases, 2))
# shuffle the whole dataset
shuffle_index = np.arange(ds.n_cases)
np.random.shuffle(shuffle_index)
ds_shuffled = ds[shuffle_index]
# align1: align Dataset to index
dsa = align1(ds2, idx4)
assert_array_equal(dsa['index'], idx4, "align1() failure")
dsa = align1(ds2, idx4i)
assert_array_equal(dsa['index'], idx4i, "align1() failure")
# d_idx as Var
dsa = align1(ds2[::2], idx4, idx4i)
assert_array_equal(dsa['index'], idx4i, "align1() failure")
assert_raises(ValueError, align1, ds2, idx4, idx4i)
# Factor index
assert_raises(ValueError, align1, ds, ds['rm', ::-1], 'rm')
fds = ds[:20]
dsa = align1(fds, fds['rm', ::-1], 'rm')
assert_array_equal(dsa['index'], np.arange(19, -1, -1), "align1 Factor")
# align two datasets
dsa1, dsa2 = align(ds, ds2)
assert_array_equal(dsa1['index'], dsa2['index'], "align() failure")
dsa1, dsa2 = align(ds, ds2[::-1])
assert_array_equal(dsa1['index'], dsa2['index'], "align() failure")
dsa1, dsa2 = align(ds, ds_shuffled)
assert_dataset_equal(dsa1, dsa2)
# align using categorial
dsa1, dsa2 = align(ds, ds_shuffled, 'A % aindex')
assert_dataset_equal(dsa1, dsa2)
dsa1, dsa2 = align(ds, ds_shuffled, 'aindex % A')
assert_dataset_equal(dsa1, dsa2)
def test_align():
"Testing align() and align1() functions"
ds = datasets.get_uv()
# index the dataset
ds.index()
ds['aindex'] = ds.eval("A.enumerate_cells()")
# subset
idx4 = np.arange(0, ds.n_cases, 4)
idx4i = idx4[::-1]
ds2 = ds.sub(np.arange(0, ds.n_cases, 2))
# shuffle the whole dataset
shuffle_index = np.arange(ds.n_cases)
np.random.shuffle(shuffle_index)
ds_shuffled = ds[shuffle_index]
# align1: align Dataset to index
dsa = align1(ds2, idx4)
assert_array_equal(dsa['index'], idx4, "align1() failure")
dsa = align1(ds2, idx4i)
assert_array_equal(dsa['index'], idx4i, "align1() failure")
# d_idx as Var
dsa = align1(ds2[::2], idx4, idx4i)
assert_array_equal(dsa['index'], idx4i, "align1() failure")
assert_raises(ValueError, align1, ds2, idx4, idx4i)
# Factor index
assert_raises(ValueError, align1, ds, ds['rm', ::-1], 'rm')
fds = ds[:20]
dsa = align1(fds, fds['rm', ::-1], 'rm')
assert_array_equal(dsa['index'], np.arange(19, -1, -1), "align1 Factor")
# align two datasets
dsa1, dsa2 = align(ds, ds2)
assert_array_equal(dsa1['index'], dsa2['index'], "align() failure")
dsa1, dsa2 = align(ds, ds2[::-1])
assert_array_equal(dsa1['index'], dsa2['index'], "align() failure")
dsa1, dsa2 = align(ds, ds_shuffled)
assert_dataset_equal(dsa1, dsa2)
# align using categorial
dsa1, dsa2 = align(ds, ds_shuffled, 'A % aindex')
assert_dataset_equal(dsa1, dsa2)
dsa1, dsa2 = align(ds, ds_shuffled, 'aindex % A')
assert_dataset_equal(dsa1, dsa2)
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_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=.000>")
# alternate argspec
ds1 = Dataset()
ds1['a1b1'] = ds.eval("uts[A%B == ('a1', 'b1')]")
ds1['a0b0'] = ds.eval("uts[A%B == ('a0', 'b0')]")
res1 = testnd.ttest_rel('a1b1', 'a0b0', ds=ds1, samples=100)
assert_dataobj_equal(res1.t, res.t)
eq_(repr(res1), "<ttest_rel 'a1b1', 'a0b0' (n=15), samples=100, p=.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)
configure(n_workers=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)
configure(n_workers=True)
sds = ds.sub("B=='b0'")
# thresholded, UTS
configure(n_workers=0)
res0 = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
'rm',
ds=sds,
pmin=0.1,
samples=100)
tgt = res0.find_clusters()
configure(n_workers=True)
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
configure(n_workers=0)
res0 = testnd.ttest_rel('utsnd',
'A',
'a1',
'a0',
'rm',
ds=sds,
pmin=0.1,
samples=100)
tgt = res0.find_clusters()
configure(n_workers=True)
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
configure(n_workers=0)
res0 = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
'rm',
ds=sds,
tfce=True,
samples=10)
tgt = res0.compute_probability_map()
configure(n_workers=True)
res1 = testnd.ttest_rel('uts',
'A',
'a1',
'a0',
'rm',
ds=sds,
tfce=True,
samples=10)
assert_dataobj_equal(res1.compute_probability_map(), tgt)
# zero variance
ds['utsnd'].x[:, 1, 10] = 0.
res = testnd.ttest_rel('utsnd', 'A', match='rm', ds=ds)
eq_(res.t.x[1, 10], 0)
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)
