def test_array_raw():
"""Test creating raw from array."""
import matplotlib.pyplot as plt
# creating
raw = read_raw_fif(fif_fname).crop(2, 5)
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(101):
types.extend(('grad', 'grad', 'mag'))
types.extend(['ecog', 'seeg', 'hbo']) # really 3 meg channels
types.extend(['stim'] * 9)
types.extend(['eeg'] * 60)
# wrong length
pytest.raises(ValueError, create_info, ch_names, sfreq, types)
# bad entry
types.append('foo')
pytest.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 = _test_raw_reader(RawArray,
test_preloading=False,
data=data,
info=info,
first_samp=2 * data.shape[1])
data2, times2 = raw2[:, :]
assert_allclose(data, data2)
assert_allclose(times, times2)
assert ('RawArray' in repr(raw2))
pytest.raises(TypeError, RawArray, info, data)
# filtering
picks = pick_types(raw2.info, misc=True, exclude='bads')[:4]
assert_equal(len(picks), 4)
raw_lp = raw2.copy()
kwargs = dict(fir_design='firwin', picks=picks)
raw_lp.filter(None, 4.0, h_trans_bandwidth=4., n_jobs=2, **kwargs)
raw_hp = raw2.copy()
raw_hp.filter(16.0, None, l_trans_bandwidth=4., n_jobs=2, **kwargs)
raw_bp = raw2.copy()
raw_bp.filter(8.0,
12.0,
l_trans_bandwidth=4.,
h_trans_bandwidth=4.,
**kwargs)
raw_bs = raw2.copy()
raw_bs.filter(16.0,
4.0,
l_trans_bandwidth=4.,
h_trans_bandwidth=4.,
n_jobs=2,
**kwargs)
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 = 15
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
raw2.plot()
raw2.plot_psd(tmax=np.inf, average=True, n_fft=1024, spatial_colors=False)
plt.close('all')
# epoching
events = find_events(raw2, stim_channel='STI 014')
events[:, 2] = 1
assert (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(time_unit='s')
assert_equal(evoked.nave, len(events) - 1)
plt.close('all')
# complex data
rng = np.random.RandomState(0)
data = rng.randn(1, 100) + 1j * rng.randn(1, 100)
raw = RawArray(data, create_info(1, 1000., 'eeg'))
assert_allclose(raw._data, data)
# Using digital montage to give MNI electrode coordinates
n_elec = 10
ts_size = 10000
Fs = 512.
elec_labels = [str(i) for i in range(n_elec)]
elec_coords = np.random.randint(60, size=(n_elec, 3)).tolist()
electrode = np.random.rand(n_elec, ts_size)
dig_ch_pos = dict(zip(elec_labels, elec_coords))
mon = channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = create_info(elec_labels, Fs, 'ecog', montage=mon)
raw = RawArray(electrode, info)
raw.plot_psd(average=False) # looking for inexistent layout
raw.plot_psd_topo()
def test_array_raw():
"""Test creating raw from array
"""
import matplotlib.pyplot as plt
tempdir = _TempDir()
# 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)
# Make sure concatenation works
raw_concat = concatenate_raws([raw2.copy(), raw2])
assert_equal(raw_concat.n_times, 2 * raw2.n_times)
assert_true('RawArray' in repr(raw2))
# 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
raw2.plot()
raw2.plot_psd()
plt.close('all')
# 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()
with warnings.catch_warnings(record=True): # deprecation
warnings.simplefilter('always')
epochs.plot()
evoked = epochs.average()
evoked.plot()
plt.close('all')
def test_array_raw():
"""Test creating raw from array
"""
tempdir = _TempDir()
# 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)
# Make sure concatenation works
raw_concat = concatenate_raws([raw2.copy(), raw2])
assert_equal(raw_concat.n_times, 2 * raw2.n_times)
assert_true('RawArray' in repr(raw2))
# 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
raw2.plot()
raw2.plot_psd()
# 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."""
import matplotlib.pyplot as plt
# creating
raw = read_raw_fif(fif_fname).crop(2, 5)
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(101):
types.extend(('grad', 'grad', 'mag'))
types.extend(['ecog', 'seeg', 'hbo']) # really 3 meg channels
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 = _test_raw_reader(RawArray, test_preloading=False,
data=data, info=info, first_samp=2 * data.shape[1])
data2, times2 = raw2[:, :]
assert_allclose(data, data2)
assert_allclose(times, times2)
assert_true('RawArray' in repr(raw2))
assert_raises(TypeError, RawArray, info, data)
# filtering
picks = pick_types(raw2.info, misc=True, exclude='bads')[:4]
assert_equal(len(picks), 4)
raw_lp = raw2.copy()
kwargs = dict(fir_design='firwin', picks=picks)
raw_lp.filter(None, 4.0, h_trans_bandwidth=4., n_jobs=2, **kwargs)
raw_hp = raw2.copy()
raw_hp.filter(16.0, None, l_trans_bandwidth=4., n_jobs=2, **kwargs)
raw_bp = raw2.copy()
raw_bp.filter(8.0, 12.0, l_trans_bandwidth=4., h_trans_bandwidth=4.,
**kwargs)
raw_bs = raw2.copy()
raw_bs.filter(16.0, 4.0, l_trans_bandwidth=4., h_trans_bandwidth=4.,
n_jobs=2, **kwargs)
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 = 15
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
raw2.plot()
raw2.plot_psd(tmax=np.inf, average=True, n_fft=1024, spatial_colors=False)
plt.close('all')
# 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(time_unit='s')
assert_equal(evoked.nave, len(events) - 1)
plt.close('all')
# complex data
rng = np.random.RandomState(0)
data = rng.randn(1, 100) + 1j * rng.randn(1, 100)
raw = RawArray(data, create_info(1, 1000., 'eeg'))
assert_allclose(raw._data, data)
# Using digital montage to give MNI electrode coordinates
n_elec = 10
ts_size = 10000
Fs = 512.
elec_labels = [str(i) for i in range(n_elec)]
elec_coords = np.random.randint(60, size=(n_elec, 3)).tolist()
electrode = np.random.rand(n_elec, ts_size)
dig_ch_pos = dict(zip(elec_labels, elec_coords))
mon = channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = create_info(elec_labels, Fs, 'ecog', montage=mon)
raw = RawArray(electrode, info)
raw.plot_psd(average=False) # looking for inexistent layout
raw.plot_psd_topo()
