def test_array_epochs():
"""Test creating epochs from array
"""
# creating
rng = np.random.RandomState(42)
data = rng.random_sample((10, 20, 50))
sfreq = 1e3
ch_names = ['EEG %03d' % (i + 1) for i in range(20)]
types = ['eeg'] * 20
info = create_info(ch_names, sfreq, types)
events = np.c_[np.arange(1, 600, 60),
np.zeros(10),
[1, 2] * 5]
event_id = {'a': 1, 'b': 2}
epochs = EpochsArray(data, info, events=events, event_id=event_id,
tmin=-.2)
# saving
temp_fname = op.join(tempdir, 'epo.fif')
epochs.save(temp_fname)
epochs2 = read_epochs(temp_fname)
data2 = epochs2.get_data()
assert_allclose(data, data2)
assert_allclose(epochs.times, epochs2.times)
assert_equal(epochs.event_id, epochs2.event_id)
assert_array_equal(epochs.events, epochs2.events)
# plotting
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
epochs[0].plot()
# indexing
assert_array_equal(np.unique(epochs['a'].events[:, 2]), np.array([1]))
assert_equal(len(epochs[:2]), 2)
def test_array():
"""Test creating raw from array
"""
# creating
raw = Raw(fif_fname).crop(2, 5, copy=False)
data, times = raw[:, :]
sfreq = raw.info['sfreq']
ch_names = [(ch[4:] if 'STI' not in ch else ch)
for ch in raw.info['ch_names']] # change them, why not
#del raw
types = list()
for ci in range(102):
types.extend(('grad', 'grad', 'mag'))
types.extend(['stim'] * 9)
types.extend(['eeg'] * 60)
# wrong length
assert_raises(ValueError, create_info, ch_names, sfreq, types)
# bad entry
types.append('foo')
assert_raises(KeyError, create_info, ch_names, sfreq, types)
types[-1] = 'eog'
info = create_info(ch_names, sfreq, types)
raw2 = RawArray(data, info)
data2, times2 = raw2[:, :]
assert_allclose(data, data2)
assert_allclose(times, times2)
# saving
temp_fname = op.join(tempdir, 'raw.fif')
raw2.save(temp_fname)
raw3 = Raw(temp_fname)
data3, times3 = raw3[:, :]
assert_allclose(data, data3)
assert_allclose(times, times3)
# filtering
picks = pick_types(raw2.info, misc=True, exclude='bads')[:4]
assert_equal(len(picks), 4)
raw_lp = raw2.copy()
with warnings.catch_warnings(record=True):
raw_lp.filter(0., 4.0 - 0.25, picks=picks, n_jobs=2)
raw_hp = raw2.copy()
with warnings.catch_warnings(record=True):
raw_hp.filter(8.0 + 0.25, None, picks=picks, n_jobs=2)
raw_bp = raw2.copy()
with warnings.catch_warnings(record=True):
raw_bp.filter(4.0 + 0.25, 8.0 - 0.25, picks=picks)
raw_bs = raw2.copy()
with warnings.catch_warnings(record=True):
raw_bs.filter(8.0 + 0.25, 4.0 - 0.25, picks=picks, n_jobs=2)
data, _ = raw2[picks, :]
lp_data, _ = raw_lp[picks, :]
hp_data, _ = raw_hp[picks, :]
bp_data, _ = raw_bp[picks, :]
bs_data, _ = raw_bs[picks, :]
sig_dec = 11
assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec)
assert_array_almost_equal(data, bp_data + bs_data, sig_dec)
# plotting
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
raw2.plot()
raw2.plot_psds()
# epoching
events = find_events(raw2, stim_channel='STI 014')
events[:, 2] = 1
assert_true(len(events) > 2)
epochs = Epochs(raw2, events, 1, -0.2, 0.4, preload=True)
epochs.plot_drop_log()
epochs.plot()
evoked = epochs.average()
evoked.plot()
def test_array_raw():
"""Test creating raw from array
"""
# creating
raw = Raw(fif_fname).crop(2, 5, copy=False)
data, times = raw[:, :]
sfreq = raw.info['sfreq']
ch_names = [(ch[4:] if 'STI' not in ch else ch)
for ch in raw.info['ch_names']] # change them, why not
#del raw
types = list()
for ci in range(102):
types.extend(('grad', 'grad', 'mag'))
types.extend(['stim'] * 9)
types.extend(['eeg'] * 60)
# wrong length
assert_raises(ValueError, create_info, ch_names, sfreq, types)
# bad entry
types.append('foo')
assert_raises(KeyError, create_info, ch_names, sfreq, types)
types[-1] = 'eog'
# default type
info = create_info(ch_names, sfreq)
assert_equal(info['chs'][0]['kind'], _kind_dict['misc'][0])
# use real types
info = create_info(ch_names, sfreq, types)
raw2 = RawArray(data, info)
data2, times2 = raw2[:, :]
assert_allclose(data, data2)
assert_allclose(times, times2)
# saving
temp_fname = op.join(tempdir, 'raw.fif')
raw2.save(temp_fname)
raw3 = Raw(temp_fname)
data3, times3 = raw3[:, :]
assert_allclose(data, data3)
assert_allclose(times, times3)
# filtering
picks = pick_types(raw2.info, misc=True, exclude='bads')[:4]
assert_equal(len(picks), 4)
raw_lp = raw2.copy()
with warnings.catch_warnings(record=True):
raw_lp.filter(0., 4.0 - 0.25, picks=picks, n_jobs=2)
raw_hp = raw2.copy()
with warnings.catch_warnings(record=True):
raw_hp.filter(8.0 + 0.25, None, picks=picks, n_jobs=2)
raw_bp = raw2.copy()
with warnings.catch_warnings(record=True):
raw_bp.filter(4.0 + 0.25, 8.0 - 0.25, picks=picks)
raw_bs = raw2.copy()
with warnings.catch_warnings(record=True):
raw_bs.filter(8.0 + 0.25, 4.0 - 0.25, picks=picks, n_jobs=2)
data, _ = raw2[picks, :]
lp_data, _ = raw_lp[picks, :]
hp_data, _ = raw_hp[picks, :]
bp_data, _ = raw_bp[picks, :]
bs_data, _ = raw_bs[picks, :]
sig_dec = 11
assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec)
assert_array_almost_equal(data, bp_data + bs_data, sig_dec)
# plotting
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
raw2.plot()
raw2.plot_psds()
# epoching
events = find_events(raw2, stim_channel='STI 014')
events[:, 2] = 1
assert_true(len(events) > 2)
epochs = Epochs(raw2, events, 1, -0.2, 0.4, preload=True)
epochs.plot_drop_log()
epochs.plot()
evoked = epochs.average()
evoked.plot()