def test_select_components():
"Test Select-Epochs GUI Document"
tempdir = TempDir()
PATH = join(tempdir, 'test-ica.fif')
data_path = mne.datasets.testing.data_path()
raw_path = join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif')
raw = mne.io.Raw(raw_path, preload=True).pick_types('mag', stim=True)
ds = load.fiff.events(raw)
ds['epochs'] = load.fiff.mne_epochs(ds, tmax=0.1)
ica = mne.preprocessing.ICA(0.95, max_iter=1)
with catch_warnings():
filterwarnings('ignore', 'FastICA did not converge')
ica.fit(raw)
ica.save(PATH)
frame = gui.select_components(PATH, ds)
frame.model.toggle(1)
frame.OnSave(None)
ica = mne.preprocessing.read_ica(PATH)
assert ica.exclude == [1]
frame.OnUndo(None)
frame.OnSave(None)
ica = mne.preprocessing.read_ica(PATH)
assert ica.exclude == []
# plotting
for i in [ID.BASELINE_NONE, ID.BASELINE_GLOABL_MEAN, ID.BASELINE_CUSTOM]:
frame.butterfly_baseline = i
frame.OnPlotGrandAverage(None)
frame.Close()
def test_mne_experiment_visit():
set_log_level('warning', 'mne')
tempdir = TempDir()
datasets.setup_samples_experiment(tempdir, 3, 1, n_visits=2)
root = join(tempdir, 'SampleExperiment')
e = Experiment(root)
ds0 = e.load_events(epoch='target', visit='')
ds1 = e.load_events(epoch='target', visit='1')
assert ds0[1, 'trigger'] != ds1[1, 'trigger']
for _ in e:
e.make_epoch_selection(auto=2e-12)
e.set(visit='')
for _ in e:
e.make_epoch_selection(auto=2e-12)
res0 = e.load_test('side', 0.05, 0.15, 0.05, data='sensor', make=True)
e.set(visit='1')
with pytest.raises(IOError):
e.load_test('side', 0.05, 0.15, 0.05, data='sensor')
res1 = e.load_test('side', 0.05, 0.15, 0.05, data='sensor', make=True)
assert res1.p.min() != res0.p.min()
# ica
e.set(raw='ica')
with catch_warnings():
filterwarnings('ignore', "FastICA did not converge", UserWarning)
assert e.make_ica(visit='') == join(root, 'meg', 'R0000',
'R0000 ica-ica.fif')
assert e.make_ica(visit='1') == join(root, 'meg', 'R0000',
'R0000 1 ica-ica.fif')
def test_sample_sessions():
set_log_level('warning', 'mne')
SampleExperiment = import_attr(sample_path / 'sample_experiment_sessions.py', 'SampleExperiment')
tempdir = TempDir()
datasets.setup_samples_experiment(tempdir, 2, 1, 2)
class Experiment(SampleExperiment):
raw = {
'ica': RawICA('raw', ('sample1', 'sample2'), 'fastica', max_iter=1),
**SampleExperiment.raw,
}
root = join(tempdir, 'SampleExperiment')
e = Experiment(root)
# bad channels
e.make_bad_channels('0111')
assert e.load_bad_channels() == ['MEG 0111']
assert e.load_bad_channels(session='sample2') == []
e.show_bad_channels()
e.merge_bad_channels()
assert e.load_bad_channels(session='sample2') == ['MEG 0111']
e.show_bad_channels()
# rejection
for _ in e:
for epoch in ('target1', 'target2'):
e.set(epoch=epoch)
e.make_epoch_selection(auto=2e-12)
ds = e.load_evoked('R0000', epoch='target2')
e.set(session='sample1')
ds2 = e.load_evoked('R0000')
assert_dataobj_equal(ds2, ds, decimal=19)
# super-epoch
ds1 = e.load_epochs(epoch='target1')
ds2 = e.load_epochs(epoch='target2')
ds_super = e.load_epochs(epoch='super')
assert_dataobj_equal(ds_super['meg'], combine((ds1['meg'], ds2['meg'])))
# evoked
dse_super = e.load_evoked(epoch='super', model='modality%side')
target = ds_super.aggregate('modality%side', drop=('i_start', 't_edf', 'T', 'index', 'trigger', 'session', 'interpolate_channels', 'epoch'))
assert_dataobj_equal(dse_super, target)
# conflicting session and epoch settings
rej_path = join(root, 'meg', 'R0000', 'epoch selection', 'sample2_1-40_target2-man.pickled')
e.set(epoch='target2', raw='1-40')
assert not exists(rej_path)
e.set(session='sample1')
e.make_epoch_selection(auto=2e-12)
assert exists(rej_path)
# ica
e.set('R0000', raw='ica')
with catch_warnings():
filterwarnings('ignore', "FastICA did not converge", UserWarning)
assert e.make_ica() == join(root, 'meg', 'R0000', 'R0000 ica-ica.fif')
def test_failed_load():
tmp = TempDir()
frame = gui.load_stcs()
frame.dir_ctl.SetPath(tmp)
evt = wx.FileDirPickerEvent()
evt.SetPath(tmp)
frame.OnDirChange(evt)
assert "No .stc" in frame.status.GetStatusText()
frame.Close()
def test_visit():
tempdir = TempDir()
for subject in SUBJECTS:
sdir = os.path.join(tempdir, 'meg', subject)
os.makedirs(sdir)
e = VisitExperiment(tempdir, raw='ica')
assert e.get('ica-file') == os.path.join(tempdir, 'meg', 'R0001',
'R0001 ica-ica.fif')
e.set(visit='1')
assert e.get('ica-file') == os.path.join(tempdir, 'meg', 'R0001',
'R0001 1 ica-ica.fif')
def test_load_stcs():
tmp = TempDir()
_create_fake_files(tmp)
frame = gui.load_stcs()
assert frame.dir_ctl.GetPath() == ""
frame.dir_ctl.SetPath(tmp)
evt = wx.FileDirPickerEvent()
evt.SetPath(tmp)
frame.OnDirChange(evt)
assert frame.loader is not None
assert len(frame.factor_name_ctrls) == 2
assert "verb" in frame.loader.levels[1]
frame.Close()
def test_sample_source():
set_log_level('warning', 'mne')
SampleExperiment = import_attr(sample_path / 'sample_experiment.py',
'SampleExperiment')
tempdir = TempDir()
datasets.setup_samples_experiment(
tempdir, 3, 2,
mris=True) # TODO: use sample MRI which already has forward solution
root = join(tempdir, 'SampleExperiment')
e = SampleExperiment(root)
# source space tests
e.set(src='ico-4', rej='', epoch='auditory')
# These two tests are only identical if the evoked has been cached before the first test is loaded
ds = e.load_evoked(-1, model='side')
resp = e.load_test('left=right',
0.05,
0.2,
0.05,
samples=100,
parc='ac',
make=True)
resm = e.load_test('left=right',
0.05,
0.2,
0.05,
samples=100,
mask='ac',
make=True)
assert_dataobj_equal(resp.t, resm.t)
# ROI tests
e.set(epoch='target')
ress = e.load_test('left=right',
0.05,
0.2,
0.05,
samples=100,
data='source.rms',
parc='ac',
make=True)
res = ress.res['ac-lh']
assert res.p.min() == pytest.approx(0.429, abs=.001)
ress = e.load_test('twostage',
0.05,
0.2,
0.05,
samples=100,
data='source.rms',
parc='ac',
make=True)
res = ress.res['ac-lh']
def test_tsv_io():
"""Test tsv I/O"""
tempdir = TempDir()
names = ['A', 'B', 'rm', 'intvar', 'fltvar', 'fltvar2', 'index']
ds = datasets.get_uv()
ds['fltvar'][5:10] = np.nan
ds[:4, 'rm'] = ''
# save and load
dst = Path(tempdir) / 'ds.txt'
ds.save_txt(dst)
ds1 = load.tsv(dst, random='rm')
assert_dataset_equal(ds1, ds, decimal=10)
ds1 = load.tsv(dst, skiprows=1, names=names, random='rm')
assert_dataset_equal(ds1, ds, decimal=10)
# delimiter
for delimiter in [' ', ',']:
ds.save_txt(dst, delimiter=delimiter)
ds1 = load.tsv(dst, delimiter=delimiter, random='rm')
assert_dataset_equal(ds1, ds, decimal=10)
# guess data types with missing
intvar2 = ds['intvar'].as_factor()
intvar2[10:] = ''
ds_intvar = Dataset((intvar2, ))
ds_intvar.save_txt(dst)
ds_intvar1 = load.tsv(dst, empty='nan')
assert_dataobj_equal(ds_intvar1['intvar', :10], ds['intvar', :10])
assert_array_equal(ds_intvar1['intvar', 10:], np.nan)
# str with space
ds[:5, 'A'] = 'a 1'
ds.save_txt(dst)
ds1 = load.tsv(dst, random='rm')
assert_dataset_equal(ds1, ds, decimal=10)
ds.save_txt(dst, delimiter=' ')
ds1 = load.tsv(dst, delimiter=' ', random='rm')
assert_dataset_equal(ds1, ds, decimal=10)
# Fixed column width
path = file_path('fox-prestige')
ds = load.tsv(path, delimiter=' ', skipinitialspace=True)
assert ds[1] == {
'id': 'GENERAL.MANAGERS',
'education': 12.26,
'income': 25879,
'women': 4.02,
'prestige': 69.1,
'census': 1130,
'type': 'prof'
}
def test_image():
"Test FMText Image"
tempdir = TempDir()
filename = os.path.join(tempdir, 'rgb.png')
rgba = np.random.uniform(0, 1, (100, 100, 4))
rgb = rgba[:, :, :3]
for array in (rgb, rgba):
im = fmtxt.Image.from_array(array, alt='array')
im.save_image(filename)
assert im.get_html().startswith('<img src=')
im2 = fmtxt.Image.from_file(filename, alt='array')
assert im.get_html() == im2.get_html()
def test_file_tree():
"Test file management tree"
class Tree(FileTree):
_templates = {
'a-folder': '{root}/{folder}',
'a-file': '{a-folder}/{name}.txt',
'folder': ('f1', 'f2'),
'name': ('a1', 'a2', 'a3')
}
def __init__(self, *args, **kwargs):
FileTree.__init__(self, *args, **kwargs)
self._bind_make('a-file', self._make_a)
def load_a(self):
with open(self.get('a-file', make=True)) as fid:
return fid.read()
def _make_a(self):
with open(self.get('a-file', mkdir=True), 'w') as fid:
fid.write(self.format("{folder} {name}"))
root = TempDir()
tree = Tree(root=root)
for _ in tree.iter_temp('a-file'):
assert tree.load_a() == tree.format("{folder} {name}")
for i, fname in enumerate(tree.iter_temp('a-file')):
assert os.path.exists(fname)
assert i == 5
assert tree._glob_pattern('a-file', True,
folder='f1') == f'{root}/f1/*.txt'
tree.rm('a-file', name='*', folder='f1', confirm=True)
for fname in tree.iter_temp('a-file', folder='f1'):
assert fname[-6:-4] == tree.get('name')
assert not os.path.exists(fname)
for fname in tree.iter_temp('a-file', folder='f2'):
assert fname[-6:-4] == tree.get('name')
assert os.path.exists(fname)
def test_loader():
tmp = TempDir()
_create_fake_files(tmp)
loader = DatasetSTCLoader(tmp)
levels = loader.levels
# check detected level names
assert "level-a" in levels[0]
assert "level-b" in levels[0]
assert "noun" in levels[1]
assert "verb" in levels[1]
assert "R0000" in loader.subjects
with pytest.raises(ValueError):
loader.set_factor_names(["only-one-factor"])
loader.set_factor_names(["factor1", "factor2"])
assert loader.design_shape == "2 x 2"
ds = loader.make_dataset(load_stcs=False)
assert "factor1" in ds
assert "factor2" in ds
assert "subject" in ds
assert ds["subject"].random
def test_sample():
set_log_level('warning', 'mne')
# import from file: http://stackoverflow.com/a/67692/166700
SampleExperiment = import_attr(sample_path / 'sample_experiment.py',
'SampleExperiment')
tempdir = TempDir()
datasets.setup_samples_experiment(tempdir, 3, 2)
root = join(tempdir, 'SampleExperiment')
e = SampleExperiment(root)
assert e.get('raw') == '1-40'
assert e.get('subject') == 'R0000'
assert e.get('subject', subject='R0002') == 'R0002'
# events
e.set('R0001', rej='')
ds = e.load_selected_events(epoch='target')
assert ds.n_cases == 39
ds = e.load_selected_events(epoch='auditory')
assert ds.n_cases == 20
ds = e.load_selected_events(epoch='av')
assert ds.n_cases == 39
# evoked cache invalidated by change in bads
e.set('R0001', rej='', epoch='target')
ds = e.load_evoked()
assert ds[0, 'evoked'].info['bads'] == []
e.make_bad_channels(['MEG 0331'])
ds = e.load_evoked()
assert ds[0, 'evoked'].info['bads'] == ['MEG 0331']
e.set(rej='man', model='modality')
sds = []
for _ in e:
e.make_epoch_selection(auto=2.5e-12)
sds.append(e.load_evoked())
ds = e.load_evoked('all')
assert_dataobj_equal(combine(sds), ds)
# sensor space tests
megs = [e.load_evoked(cat='auditory')['meg'] for _ in e]
res = e.load_test('a>v',
0.05,
0.2,
0.05,
samples=100,
data='sensor.rms',
baseline=False,
make=True)
meg_rms = combine(meg.rms('sensor') for meg in megs).mean('case',
name='auditory')
assert_dataobj_equal(res.c1_mean, meg_rms, decimal=21)
res = e.load_test('a>v',
0.05,
0.2,
0.05,
samples=100,
data='sensor.mean',
baseline=False,
make=True)
meg_mean = combine(meg.mean('sensor')
for meg in megs).mean('case', name='auditory')
assert_dataobj_equal(res.c1_mean, meg_mean, decimal=21)
with pytest.raises(IOError):
res = e.load_test('a>v',
0.05,
0.2,
0.05,
samples=20,
data='sensor',
baseline=False)
res = e.load_test('a>v',
0.05,
0.2,
0.05,
samples=20,
data='sensor',
baseline=False,
make=True)
assert res.p.min() == pytest.approx(.143, abs=.001)
assert res.difference.max() == pytest.approx(4.47e-13, 1e-15)
# plot
e.plot_evoked(1, epoch='target', model='')
# e._report_subject_info() broke with non-alphabetic subject order
subjects = e.get_field_values('subject')
ds = Dataset()
ds['subject'] = Factor(reversed(subjects))
ds['n'] = Var(range(3))
s_table = e._report_subject_info(ds, '')
# post_baseline_trigger_shift
# use multiple of tstep to shift by even number of samples
tstep = 0.008324800548266162
shift = -7 * tstep
class Experiment(SampleExperiment):
epochs = {
**SampleExperiment.epochs,
'visual-s':
SecondaryEpoch('target',
"modality == 'visual'",
post_baseline_trigger_shift='shift',
post_baseline_trigger_shift_max=0,
post_baseline_trigger_shift_min=shift),
}
variables = {
**SampleExperiment.variables, 'shift':
LabelVar('side', {
'left': 0,
'right': shift
}),
'shift_t':
LabelVar('trigger', {
(1, 3): 0,
(2, 4): shift
})
}
e = Experiment(root)
# test shift in events
ds = e.load_events()
assert_dataobj_equal(ds['shift_t'], ds['shift'], name=False)
# compare against epochs (baseline correction on epoch level rather than evoked for smaller numerical error)
ep = e.load_epochs(baseline=True, epoch='visual', rej='').aggregate('side')
evs = e.load_evoked(baseline=True, epoch='visual-s', rej='', model='side')
tstart = ep['meg'].time.tmin - shift
assert_dataobj_equal(evs[0, 'meg'],
ep[0, 'meg'].sub(time=(tstart, None)),
decimal=20)
tstop = ep['meg'].time.tstop + shift
assert_almost_equal(evs[1, 'meg'].x,
ep[1, 'meg'].sub(time=(None, tstop)).x,
decimal=20)
# post_baseline_trigger_shift & multiple epochs with same time stamp
class Experiment(SampleExperiment):
epochs = {
**SampleExperiment.epochs,
'v1': {
'base': 'visual',
'vars': {
'shift': 'Var([0.0], repeat=len(side))'
}
},
'v2': {
'base': 'visual',
'vars': {
'shift': 'Var([0.1], repeat=len(side))'
}
},
'vc': {
'sub_epochs': ('v1', 'v2'),
'post_baseline_trigger_shift': 'shift',
'post_baseline_trigger_shift_max': 0.1,
'post_baseline_trigger_shift_min': 0.0
},
}
groups = {
'group0': Group(['R0000']),
'group1': SubGroup('all', ['R0000']),
}
variables = {
'group': GroupVar(['group0', 'group1']),
**SampleExperiment.variables,
}
e = Experiment(root)
events = e.load_selected_events(epoch='vc')
ds = e.load_epochs(baseline=True, epoch='vc')
v1 = ds.sub("epoch=='v1'", 'meg').sub(time=(0, 0.199))
v2 = ds.sub("epoch=='v2'", 'meg').sub(time=(-0.1, 0.099))
assert_almost_equal(v1.x, v2.x, decimal=20)
# duplicate subject
class BadExperiment(SampleExperiment):
groups = {'group': ('R0001', 'R0002', 'R0002')}
with pytest.raises(DefinitionError):
BadExperiment(root)
# non-existing subject
class BadExperiment(SampleExperiment):
groups = {'group': ('R0001', 'R0003', 'R0002')}
with pytest.raises(DefinitionError):
BadExperiment(root)
# unsorted subjects
class Experiment(SampleExperiment):
groups = {'group': ('R0002', 'R0000', 'R0001')}
e = Experiment(root)
assert [s for s in e] == ['R0000', 'R0001', 'R0002']
# changes
class Changed(SampleExperiment):
variables = {
'event': {
(1, 2, 3, 4): 'target',
5: 'smiley',
32: 'button'
},
'side': {
(1, 3): 'left',
(2, 4): 'right_changed'
},
'modality': {
(1, 2): 'auditory',
(3, 4): 'visual'
}
}
tests = {
'twostage':
TwoStageTest(
'side_left + modality_a', {
'side_left': "side == 'left'",
'modality_a': "modality == 'auditory'"
}),
'novars':
TwoStageTest('side + modality'),
}
e = Changed(root)
# changed variable, while a test with model=None is not changed
class Changed(Changed):
variables = {
'side': {
(1, 3): 'left',
(2, 4): 'right_changed'
},
'modality': {
(1, 2): 'auditory',
(3, 4): 'visual_changed'
}
}
e = Changed(root)
# changed variable, unchanged test with vardef=None
class Changed(Changed):
variables = {
'side': {
(1, 3): 'left',
(2, 4): 'right_changed'
},
'modality': {
(1, 2): 'auditory',
(3, 4): 'visual_changed'
}
}
e = Changed(root)
# ICA
# ---
class Experiment(SampleExperiment):
raw = {
'apply-ica': RawApplyICA('tsss', 'ica'),
**SampleExperiment.raw,
}
e = Experiment(root)
ica_path = e.make_ica(raw='ica')
e.set(raw='ica1-40', model='')
e.make_epoch_selection(auto=2e-12, overwrite=True)
ds1 = e.load_evoked(raw='ica1-40')
ica = e.load_ica(raw='ica')
ica.exclude = [0, 1, 2]
ica.save(ica_path)
ds2 = e.load_evoked(raw='ica1-40')
assert not np.allclose(ds1['meg'].x, ds2['meg'].x,
atol=1e-20), "ICA change ignored"
# apply-ICA
with catch_warnings():
filterwarnings(
'ignore',
"The measurement information indicates a low-pass frequency",
RuntimeWarning)
ds1 = e.load_evoked(raw='ica', rej='')
ds2 = e.load_evoked(raw='apply-ica', rej='')
assert_dataobj_equal(ds2, ds1)
# rename subject
# --------------
src = Path(e.get('raw-dir', subject='R0001'))
dst = Path(e.get('raw-dir', subject='R0003', match=False))
shutil.move(src, dst)
for path in dst.glob('*.fif'):
shutil.move(path,
dst / path.parent / path.name.replace('R0001', 'R0003'))
# check subject list
e = SampleExperiment(root)
assert list(e) == ['R0000', 'R0002', 'R0003']
# check that cached test got deleted
assert e.get('raw') == '1-40'
with pytest.raises(IOError):
e.load_test('a>v',
0.05,
0.2,
0.05,
samples=20,
data='sensor',
baseline=False)
res = e.load_test('a>v',
0.05,
0.2,
0.05,
samples=20,
data='sensor',
baseline=False,
make=True)
assert res.df == 2
assert res.p.min() == pytest.approx(.143, abs=.001)
assert res.difference.max() == pytest.approx(4.47e-13, 1e-15)
# remove subject
# --------------
shutil.rmtree(dst)
# check cache
e = SampleExperiment(root)
assert list(e) == ['R0000', 'R0002']
# check that cached test got deleted
assert e.get('raw') == '1-40'
with pytest.raises(IOError):
e.load_test('a>v',
0.05,
0.2,
0.05,
samples=20,
data='sensor',
baseline=False)
# label_events
# ------------
class Experiment(SampleExperiment):
def label_events(self, ds):
SampleExperiment.label_events(self, ds)
ds = ds.sub("event == 'smiley'")
ds['new_var'] = Var([i + 1 for i in ds['i_start']])
return ds
e = Experiment(root)
events = e.load_events()
assert_array_equal(events['new_var'], [67402, 75306])
def root_dir():
tempdir = TempDir()
for subject in SUBJECTS:
sdir = os.path.join(tempdir, 'meg', subject)
os.makedirs(sdir)
return tempdir
def test_mne_experiment_templates():
"Test MneExperiment template formatting"
tempdir = TempDir()
e = BaseExperiment(tempdir, False)
# Don't create dirs without root
assert e.get('raw-file', mkdir=True).endswith('-raw.fif')
# model
assert e.get('model', model='a % b') == 'a%b'
assert e.get('model', model='b % a') == 'a%b'
with pytest.raises(ValueError):
e.set(model='a*b')
with pytest.raises(ValueError):
e.set(model='log(a)')
# compounds
assert e.get('src_kind') == '0-40 bestreg free-3-dSPM'
e.set_inv('fixed')
assert e.get('src_kind') == '0-40 bestreg fixed-3-dSPM'
e.set(cov='noreg')
assert e.get('src_kind') == '0-40 noreg fixed-3-dSPM'
e.set(raw='1-40')
assert e.get('src_kind') == '1-40 noreg fixed-3-dSPM'
e.set(src='ico-5')
assert e.get('src_kind') == '1-40 noreg ico-5 fixed-3-dSPM'
e.set(src='ico-4')
assert e.get('src_kind') == '1-40 noreg fixed-3-dSPM'
# find terminal field names
assert e.find_keys('raw-file') == ['root', 'subject', 'session', 'visit']
assert e.find_keys('evoked-file', False) == [
'subject', 'raw', 'epoch', 'visit', 'model', 'rej',
'equalize_evoked_count'
]
assert_inv_works(e, 'free-3-MNE', ('free', 3, 'MNE'), {
'loose': 1,
'depth': 0.8
}, {
'method': 'MNE',
'lambda2': 1 / 9
})
assert_inv_works(e, 'free-3-dSPM-0.2-pick_normal',
('free', 3, 'dSPM', .2, True), {
'loose': 1,
'depth': 0.2
}, {
'method': 'dSPM',
'lambda2': 1 / 9,
'pick_ori': 'normal'
})
assert_inv_works(e, 'fixed-2-MNE-0.2', ('fixed', 2, 'MNE', .2), {
'fixed': True,
'depth': 0.2
}, {
'method': 'MNE',
'lambda2': 1 / 4
})
assert_inv_works(e, 'loose.2-2-MNE-pick_normal',
(0.2, 2, 'MNE', None, True), {
'loose': 0.2,
'depth': 0.8
}, {
'method': 'MNE',
'lambda2': 1 / 4,
'pick_ori': 'normal'
})
assert_inv_works(e, 'loose.5-3-sLORETA', (0.5, 3, 'sLORETA'), {
'loose': 0.5,
'depth': 0.8
}, {
'method': 'sLORETA',
'lambda2': 1 / 9
})
assert_inv_works(e, 'fixed-1-MNE-0', ('fixed', 1, 'MNE', 0), {
'fixed': True,
'depth': None
}, {
'method': 'MNE',
'lambda2': 1
})
# should remove this
assert_inv_works(e, 'fixed-1-MNE-0.8', ('fixed', 1, 'MNE', 0.8), {
'fixed': True,
'depth': 0.8
}, {
'method': 'MNE',
'lambda2': 1
})
with pytest.raises(ValueError):
e.set_inv('free', -3, 'dSPM')
with pytest.raises(ValueError):
e.set(inv='free-3-mne')
with pytest.raises(ValueError):
e.set(inv='free-3-MNE-2')
assert e.find_keys('test-file', False) == [
'analysis', 'group', 'epoch', 'visit', 'test', 'test_options',
'test_dims'
]
assert e._glob_pattern('test-file', True, group='all') == os.path.join(
tempdir, 'eelbrain-cache', 'test', '* all', '* *.pickled')
def test_table():
table = fmtxt.Table('ll')
table.cells('A', 'B')
table.midrule()
table.cells('a1', 'b1', 'a2', 'b2')
assert str(table) == 'A B \n-------\na1 b1\na2 b2'
assert html(table) == (
'<figure><table border="1" cellpadding="2" frame="hsides" rules="none"><tr>\n'
' <td>A</td>\n <td>B</td>\n</tr>\n<tr>\n'
' <td>a1</td>\n <td>b1</td>\n</tr>\n<tr>\n'
' <td>a2</td>\n <td>b2</td>\n</tr></table></figure>')
assert table.get_rtf() == ('\\trowd\n\\cellx0000\n\\cellx1000\n\\row\n'
'A\\intbl\\cell\nB\\intbl\\cell\n\\row\n'
'a1\\intbl\\cell\nb1\\intbl\\cell\n\\row\n'
'a2\\intbl\\cell\nb2\\intbl\\cell\n\\row')
assert table.get_tex() == (
'\\begin{center}\n\\begin{tabular}{ll}\n\\toprule\n'
'A & B \\\\\n\\midrule\n'
'a1 & b1 \\\\\na2 & b2 \\\\\n'
'\\bottomrule\n\\end{tabular}\n\\end{center}')
# empty table
str(fmtxt.Table(''))
# saving
tempdir = TempDir()
# HTML
path = os.path.join(tempdir, 'test.html')
table.save_html(path)
assert open(path).read() == (
'<!DOCTYPE html>\n<html>\n<head>\n'
' <title>Untitled</title>\n'
'<style>\n\n.float {\n float:left\n}\n\n'
'</style>\n</head>\n\n'
'<body>\n\n<figure>'
'<table border="1" cellpadding="2" frame="hsides" rules="none"><tr>\n'
' <td>A</td>\n <td>B</td>\n</tr>\n<tr>\n'
' <td>a1</td>\n <td>b1</td>\n</tr>\n<tr>\n'
' <td>a2</td>\n <td>b2</td>\n</tr>'
'</table></figure>\n\n</body>\n</html>\n')
# rtf
path = os.path.join(tempdir, 'test.rtf')
table.save_rtf(path)
assert open(path).read() == ('{\\rtf1\\ansi\\deff0\n\n'
'\\trowd\n\\cellx0000\n\\cellx1000\n\\row\n'
'A\\intbl\\cell\nB\\intbl\\cell\n\\row\n'
'a1\\intbl\\cell\nb1\\intbl\\cell\n\\row\n'
'a2\\intbl\\cell\nb2\\intbl\\cell\n\\row\n}')
# TeX
path = os.path.join(tempdir, 'test.tex')
table.save_tex(path)
assert open(path).read() == (
'\\begin{center}\n\\begin{tabular}{ll}\n\\toprule\n'
'A & B \\\\\n\\midrule\n'
'a1 & b1 \\\\\na2 & b2 \\\\\n'
'\\bottomrule\n\\end{tabular}\n\\end{center}')
# txt
path = os.path.join(tempdir, 'test.txt')
table.save_txt(path)
assert open(path).read() == 'A B \n-------\na1 b1\na2 b2'
# editing
table[0, 0] = 'X'
assert str(table) == 'X B \n-------\na1 b1\na2 b2'
table[0] = ['C', 'D']
assert str(table) == 'C D \n-------\na1 b1\na2 b2'
table[2, 0] = 'cd'
assert str(table) == 'C D \n-------\ncd b1\na2 b2'
table[2:4, 1] = ['x', 'y']
assert str(table) == 'C D\n------\ncd x\na2 y'
def test_select_epochs():
"Test Select-Epochs GUI Document"
set_log_level('warning', 'mne')
data_path = mne.datasets.testing.data_path()
raw_path = join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif')
raw = mne.io.Raw(raw_path, preload=True).pick_types('mag', stim=True)
ds = load.fiff.events(raw)
ds['meg'] = load.fiff.epochs(ds, tmax=0.1)
tempdir = TempDir()
path = join(tempdir, 'rej.pickled')
# Test Document
# =============
# create a file
doc = Document(ds, 'meg')
doc.set_path(path)
doc.set_case(1, False, 'tag', None)
doc.set_case(2, None, None, ['2'])
doc.set_bad_channels([1])
# check modifications
assert doc.accept[1] == False
assert doc.tag[1] == 'tag'
assert doc.interpolate[1] == []
assert doc.interpolate[2] == ['2']
assert doc.bad_channels == [1]
assert_array_equal(doc.accept[2:], True)
# save
doc.save()
# check the file
ds_ = load.unpickle(path)
assert doc.epochs.sensor._array_index(ds_.info['bad_channels']) == [1]
# load the file
doc = Document(ds, 'meg', path=path)
# modification checks
assert doc.accept[1] == False
assert doc.tag[1] == 'tag'
assert doc.interpolate[1] == []
assert doc.interpolate[2] == ['2']
assert doc.bad_channels == [1]
assert_array_equal(doc.accept[2:], True)
# Test Model
# ==========
doc = Document(ds, 'meg')
model = Model(doc)
# accept
model.set_case(0, False, None, None)
assert doc.accept[0] == False
model.history.undo()
assert doc.accept[0] == True
model.history.redo()
assert doc.accept[0] == False
# interpolate
model.toggle_interpolation(2, '3')
assert doc.interpolate[2] == ['3']
model.toggle_interpolation(2, '4')
assert doc.interpolate[2] == ['3', '4']
model.toggle_interpolation(2, '3')
assert doc.interpolate[2] == ['4']
model.toggle_interpolation(3, '3')
assert doc.interpolate[2] == ['4']
assert doc.interpolate[3] == ['3']
model.history.undo()
model.history.undo()
assert doc.interpolate[2] == ['3', '4']
assert doc.interpolate[3] == []
model.history.redo()
assert doc.interpolate[2] == ['4']
# bad channels
model.set_bad_channels([1])
model.set_bad_channels([1, 10])
assert doc.bad_channels == [1, 10]
model.history.undo()
assert doc.bad_channels == [1]
model.history.redo()
assert doc.bad_channels == [1, 10]
# reload to reset
model.load(path)
# tests
assert doc.accept[1] == False
assert doc.tag[1] == 'tag'
assert doc.interpolate[1] == []
assert doc.interpolate[2] == ['2']
assert doc.bad_channels == [1]
assert_array_equal(doc.accept[2:], True)
# Test GUI
# ========
frame = gui.select_epochs(ds, nplots=9)
assert not frame.CanBackward()
assert frame.CanForward()
frame.OnForward(None)
frame.SetVLim(1e-12)
