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()