Python Raw.append方法代码示例

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import append [as 别名]
def test_copy_append():
    """Test raw copying and appending combinations
    """
    raw = Raw(fif_fname, preload=True).copy()
    raw_full = Raw(fif_fname)
    raw_full.append(raw)
    data = raw_full[:, :][0]
    assert_true(data.shape[1] == 2 * raw._data.shape[1])

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import append [as 别名]
def test_multiple_files():
    """Test loading multiple files simultaneously
    """
    # split file
    raw = Raw(fif_fname, preload=True).crop(0, 10)
    split_size = 3.  # in seconds
    sfreq = raw.info['sfreq']
    nsamp = (raw.last_samp - raw.first_samp)
    tmins = np.round(np.arange(0., nsamp, split_size * sfreq))
    tmaxs = np.concatenate((tmins[1:] - 1, [nsamp]))
    tmaxs /= sfreq
    tmins /= sfreq
    assert_equal(raw.n_times, len(raw._times))

    # going in reverse order so the last fname is the first file (need later)
    raws = [None] * len(tmins)
    for ri in range(len(tmins) - 1, -1, -1):
        fname = op.join(tempdir, 'test_raw_split-%d_raw.fif' % ri)
        raw.save(fname, tmin=tmins[ri], tmax=tmaxs[ri])
        raws[ri] = Raw(fname)
    events = [find_events(r, stim_channel='STI 014') for r in raws]
    last_samps = [r.last_samp for r in raws]
    first_samps = [r.first_samp for r in raws]

    # test concatenation of split file
    all_raw_1 = concatenate_raws(raws, preload=False)
    assert_true(raw.first_samp == all_raw_1.first_samp)
    assert_true(raw.last_samp == all_raw_1.last_samp)
    assert_allclose(raw[:, :][0], all_raw_1[:, :][0])
    raws[0] = Raw(fname)
    all_raw_2 = concatenate_raws(raws, preload=True)
    assert_allclose(raw[:, :][0], all_raw_2[:, :][0])

    # test proper event treatment for split files
    events = concatenate_events(events, first_samps, last_samps)
    events2 = find_events(all_raw_2, stim_channel='STI 014')
    assert_array_equal(events, events2)

    # test various methods of combining files
    raw = Raw(fif_fname, preload=True)
    n_times = len(raw._times)
    # make sure that all our data match
    times = list(range(0, 2 * n_times, 999))
    # add potentially problematic points
    times.extend([n_times - 1, n_times, 2 * n_times - 1])

    raw_combo0 = Raw([fif_fname, fif_fname], preload=True)
    _compare_combo(raw, raw_combo0, times, n_times)
    raw_combo = Raw([fif_fname, fif_fname], preload=False)
    _compare_combo(raw, raw_combo, times, n_times)
    raw_combo = Raw([fif_fname, fif_fname], preload='memmap8.dat')
    _compare_combo(raw, raw_combo, times, n_times)
    assert_raises(ValueError, Raw, [fif_fname, ctf_fname])
    assert_raises(ValueError, Raw, [fif_fname, fif_bad_marked_fname])
    assert_true(raw[:, :][0].shape[1] * 2 == raw_combo0[:, :][0].shape[1])
    assert_true(raw_combo0[:, :][0].shape[1] == len(raw_combo0._times))

    # with all data preloaded, result should be preloaded
    raw_combo = Raw(fif_fname, preload=True)
    raw_combo.append(Raw(fif_fname, preload=True))
    assert_true(raw_combo._preloaded is True)
    assert_true(len(raw_combo._times) == raw_combo._data.shape[1])
    _compare_combo(raw, raw_combo, times, n_times)

    # with any data not preloaded, don't set result as preloaded
    raw_combo = concatenate_raws([Raw(fif_fname, preload=True),
                                  Raw(fif_fname, preload=False)])
    assert_true(raw_combo._preloaded is False)
    assert_array_equal(find_events(raw_combo, stim_channel='STI 014'),
                       find_events(raw_combo0, stim_channel='STI 014'))
    _compare_combo(raw, raw_combo, times, n_times)

    # user should be able to force data to be preloaded upon concat
    raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
                                  Raw(fif_fname, preload=True)],
                                 preload=True)
    assert_true(raw_combo._preloaded is True)
    _compare_combo(raw, raw_combo, times, n_times)

    raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
                                  Raw(fif_fname, preload=True)],
                                 preload='memmap3.dat')
    _compare_combo(raw, raw_combo, times, n_times)

    raw_combo = concatenate_raws([Raw(fif_fname, preload=True),
                                  Raw(fif_fname, preload=True)],
                                 preload='memmap4.dat')
    _compare_combo(raw, raw_combo, times, n_times)

    raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
                                  Raw(fif_fname, preload=False)],
                                 preload='memmap5.dat')
    _compare_combo(raw, raw_combo, times, n_times)

    # verify that combining raws with different projectors throws an exception
    raw.add_proj([], remove_existing=True)
    assert_raises(ValueError, raw.append, Raw(fif_fname, preload=True))

    # now test event treatment for concatenated raw files
    events = [find_events(raw, stim_channel='STI 014'),
#.........这里部分代码省略.........

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import append [as 别名]
import mne
from mne import fiff
from mne.fiff import Raw
from mne.datasets import spm_face
from mne.decoding import time_generalization

data_path = spm_face.data_path()

###############################################################################
# Load and filter data, set up epochs

raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D_raw.fif'

raw = Raw(raw_fname % 1, preload=True)  # Take first run
raw.append(Raw(raw_fname % 2, preload=True))  # Take second run too

picks = mne.fiff.pick_types(raw.info, meg=True, exclude='bads')
raw.filter(1, 45, method='iir')

events = mne.find_events(raw, stim_channel='UPPT001')
event_id = {"faces": 1, "scrambled": 2}
tmin, tmax = -0.1, 0.5

# Set up pick list
picks = fiff.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,
                        ref_meg=False, exclude='bads')

# Read epochs
decim = 4  # decimate to make the example faster to run
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,