def test_time_slicer():
"Test linked time axes"
ds = datasets.get_uts(True)
p1 = plot.Butterfly(ds['utsnd'], show=False)
p2 = plot.Array('utsnd', 'A', ds=ds, show=False)
p1.link_time_axis(p2)
p1._set_time(.1, True)
eq_(p2._current_time, .1)
eq_(p2._time_fixed, True)
p2._set_time(.2)
eq_(p1._current_time, .2)
eq_(p1._time_fixed, False)
p1 = plot.TopoButterfly(ds['utsnd'], show=False)
p2 = plot.Array('utsnd', 'A', ds=ds, show=False)
p2.link_time_axis(p1)
p1._set_time(.1, True)
eq_(p2._current_time, .1)
eq_(p2._time_fixed, True)
p2._set_time(.2)
eq_(p1._current_time, .2)
eq_(p1._time_fixed, False)
def test_time_slicer():
"Test linked time axes"
ds = datasets.get_uts(True)
p1 = plot.Butterfly(ds['utsnd'])
p2 = plot.Array('utsnd', 'A', ds=ds)
p1.link_time_axis(p2)
p1._set_time(.1, True)
assert p2._current_time == .1
assert p2._time_fixed == True
p2._set_time(.2)
assert p1._current_time == .2
assert p1._time_fixed == False
p1 = plot.TopoButterfly(ds['utsnd'])
p2 = plot.Array('utsnd', 'A', ds=ds)
p2.link_time_axis(p1)
p1._set_time(.1, True)
assert p2._current_time == .1
assert p2._time_fixed == True
# merge another
p3 = plot.TopoButterfly(ds[0, 'utsnd'])
p3.link_time_axis(p2)
p2._set_time(.2)
assert p1._current_time == .2
assert p1._time_fixed == False
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_array():
"Test plot.Array"
ds = datasets.get_uts(utsnd=True)
p = plot.Array('utsnd', 'A%B', ds=ds, show=False)
p.close()
p = plot.Array('utsnd', ds=ds, show=False)
p.close()
def test_plot_array():
"Test plot.Array"
plot.configure_backend(False, False)
ds = datasets.get_rand(utsnd=True)
p = plot.Array('utsnd', 'A%B', ds=ds)
p.close()
p = plot.Array('utsnd', ds=ds)
p.close()
def test_plot_mne_epochs():
"Test plotting epochs from the mne sample dataset"
epochs = datasets.get_mne_epochs()
# grand average
p = plot.Array(epochs)
p.close()
# with model
p = plot.Array(epochs, np.arange(2).repeat(8))
p.close()
def test_plot_array():
"Test plot.Array"
ds = datasets.get_uts(utsnd=True)
p = plot.Array('utsnd', ds=ds)
p.close()
p = plot.Array('utsnd', 'A%B', ds=ds)
assert p._layout.nax == 4
p.close()
p = plot.Array('utsnd', 'A', sub='B=="b1"', ds=ds)
assert p._layout.nax == 2
p.close()
# Categorial dimension
ds = datasets._get_continuous()
p = plot.Array(ds['x2'], interpolation='none')
assert len(p.figure.axes[0].get_yticks()) == 2
def test_plot_array():
"Test plot.Array"
ds = datasets.get_uts(utsnd=True)
p = plot.Array('utsnd', ds=ds, show=False)
p.close()
p = plot.Array('utsnd', 'A%B', ds=ds, show=False)
eq_(p._layout.nax, 4)
p.close()
p = plot.Array('utsnd', 'A', sub='B=="b1"', ds=ds, show=False)
eq_(p._layout.nax, 2)
p.close()
# Categorial dimension
ds = datasets._get_continuous()
p = plot.Array(ds['x2'], interpolation='none', show=False)
eq_(len(p.figure.axes[0].get_yticks()), 2)
def test_plot_mne_evoked():
"Test plotting evoked from the mne sample dataset"
data_path = mne.datasets.sample.data_path()
evoked_path = os.path.join(data_path, 'MEG', 'sample',
'sample_audvis-ave.fif')
evoked = Evoked(evoked_path, setno="Left Auditory")
p = plot.Array(evoked)
p.close()
def test_plot_mne_epochs():
"Test plotting epochs from the mne sample dataset"
# find paths
data_path = mne.datasets.sample.data_path()
raw_path = os.path.join(data_path, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
events_path = os.path.join(data_path, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw-eve.fif')
# read epochs
raw = Raw(raw_path)
events = mne.read_events(events_path)
idx = np.logical_or(events[:, 2] == 5, events[:, 2] == 32)
events = events[idx]
epochs = mne.Epochs(raw, events, None, -0.1, 0.3)
# grand average
p = plot.Array(epochs)
p.close()
# with model
p = plot.Array(epochs, events[:, 2])
p.close()
def test_plot_mne_evoked():
"Test plotting evoked from the mne sample dataset"
evoked = datasets.get_mne_evoked()
p = plot.Array(evoked)
p.close()
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)
