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])
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])
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'),
find_events(raw, stim_channel='STI 014')]
last_samps = [raw.last_samp, raw.last_samp]
first_samps = [raw.first_samp, raw.first_samp]
events = concatenate_events(events, first_samps, last_samps)
events2 = find_events(raw_combo0, stim_channel='STI 014')
assert_array_equal(events, events2)
# check out the len method
assert_true(len(raw) == raw.n_times)
assert_true(len(raw) == raw.last_samp - raw.first_samp + 1)
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,
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'),
find_events(raw, stim_channel='STI 014')
]
last_samps = [raw.last_samp, raw.last_samp]
first_samps = [raw.first_samp, raw.first_samp]
events = concatenate_events(events, first_samps, last_samps)
events2 = find_events(raw_combo0, stim_channel='STI 014')
assert_array_equal(events, events2)
# check out the len method
assert_true(len(raw) == raw.n_times)
assert_true(len(raw) == raw.last_samp - raw.first_samp + 1)
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,
