def test_resample():
"""Test resample (with I/O and multiple files)
"""
raw = Raw(fif_fname, preload=True).crop(0, 3, False)
raw_resamp = raw.copy()
sfreq = raw.info['sfreq']
# test parallel on upsample
raw_resamp.resample(sfreq * 2, n_jobs=2)
assert_true(raw_resamp.n_times == len(raw_resamp._times))
raw_resamp.save(op.join(tempdir, 'raw_resamp.fif'))
raw_resamp = Raw(op.join(tempdir, 'raw_resamp.fif'), preload=True)
assert_true(sfreq == raw_resamp.info['sfreq'] / 2)
assert_true(raw.n_times == raw_resamp.n_times / 2)
assert_true(raw_resamp._data.shape[1] == raw_resamp.n_times)
assert_true(raw._data.shape[0] == raw_resamp._data.shape[0])
# test non-parallel on downsample
raw_resamp.resample(sfreq, n_jobs=1)
assert_true(raw_resamp.info['sfreq'] == sfreq)
assert_true(raw._data.shape == raw_resamp._data.shape)
assert_true(raw.first_samp == raw_resamp.first_samp)
assert_true(raw.last_samp == raw.last_samp)
# upsampling then downsampling doubles resampling error, but this still
# works (hooray). Note that the stim channels had to be sub-sampled
# without filtering to be accurately preserved
# note we have to treat MEG and EEG+STIM channels differently (tols)
assert_allclose(raw._data[:306, 200:-200],
raw_resamp._data[:306, 200:-200],
rtol=1e-2,
atol=1e-12)
assert_allclose(raw._data[306:, 200:-200],
raw_resamp._data[306:, 200:-200],
rtol=1e-2,
atol=1e-7)
# now check multiple file support w/resampling, as order of operations
# (concat, resample) should not affect our data
raw1 = raw.copy()
raw2 = raw.copy()
raw3 = raw.copy()
raw4 = raw.copy()
raw1 = concatenate_raws([raw1, raw2])
raw1.resample(10)
raw3.resample(10)
raw4.resample(10)
raw3 = concatenate_raws([raw3, raw4])
assert_array_equal(raw1._data, raw3._data)
assert_array_equal(raw1._first_samps, raw3._first_samps)
assert_array_equal(raw1._last_samps, raw3._last_samps)
assert_array_equal(raw1._raw_lengths, raw3._raw_lengths)
assert_equal(raw1.first_samp, raw3.first_samp)
assert_equal(raw1.last_samp, raw3.last_samp)
assert_equal(raw1.info['sfreq'], raw3.info['sfreq'])
def test_resample():
"""Test resample (with I/O and multiple files)
"""
raw = Raw(fif_fname, preload=True).crop(0, 3, False)
raw_resamp = raw.copy()
sfreq = raw.info['sfreq']
# test parallel on upsample
raw_resamp.resample(sfreq * 2, n_jobs=2)
assert_true(raw_resamp.n_times == len(raw_resamp._times))
raw_resamp.save(op.join(tempdir, 'raw_resamp.fif'))
raw_resamp = Raw(op.join(tempdir, 'raw_resamp.fif'), preload=True)
assert_true(sfreq == raw_resamp.info['sfreq'] / 2)
assert_true(raw.n_times == raw_resamp.n_times / 2)
assert_true(raw_resamp._data.shape[1] == raw_resamp.n_times)
assert_true(raw._data.shape[0] == raw_resamp._data.shape[0])
# test non-parallel on downsample
raw_resamp.resample(sfreq, n_jobs=1)
assert_true(raw_resamp.info['sfreq'] == sfreq)
assert_true(raw._data.shape == raw_resamp._data.shape)
assert_true(raw.first_samp == raw_resamp.first_samp)
assert_true(raw.last_samp == raw.last_samp)
# upsampling then downsampling doubles resampling error, but this still
# works (hooray). Note that the stim channels had to be sub-sampled
# without filtering to be accurately preserved
# note we have to treat MEG and EEG+STIM channels differently (tols)
assert_allclose(raw._data[:306, 200:-200],
raw_resamp._data[:306, 200:-200],
rtol=1e-2, atol=1e-12)
assert_allclose(raw._data[306:, 200:-200],
raw_resamp._data[306:, 200:-200],
rtol=1e-2, atol=1e-7)
# now check multiple file support w/resampling, as order of operations
# (concat, resample) should not affect our data
raw1 = raw.copy()
raw2 = raw.copy()
raw3 = raw.copy()
raw4 = raw.copy()
raw1 = concatenate_raws([raw1, raw2])
raw1.resample(10)
raw3.resample(10)
raw4.resample(10)
raw3 = concatenate_raws([raw3, raw4])
assert_array_equal(raw1._data, raw3._data)
assert_array_equal(raw1._first_samps, raw3._first_samps)
assert_array_equal(raw1._last_samps, raw3._last_samps)
assert_array_equal(raw1._raw_lengths, raw3._raw_lengths)
assert_equal(raw1.first_samp, raw3.first_samp)
assert_equal(raw1.last_samp, raw3.last_samp)
assert_equal(raw1.info['sfreq'], raw3.info['sfreq'])
def test_crop():
"""Test cropping raw files
"""
# split a concatenated file to test a difficult case
raw = Raw([fif_fname, fif_fname], preload=True)
split_size = 10. # in seconds
sfreq = raw.info['sfreq']
nsamp = (raw.last_samp - raw.first_samp + 1)
# do an annoying case (off-by-one splitting)
tmins = np.r_[1., np.round(np.arange(0., nsamp - 1, split_size * sfreq))]
tmins = np.sort(tmins)
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.crop(tmin, tmax, True)
all_raw_2 = concatenate_raws(raws, preload=True)
assert_true(raw.first_samp == all_raw_2.first_samp)
assert_true(raw.last_samp == all_raw_2.last_samp)
assert_array_equal(raw[:, :][0], all_raw_2[:, :][0])
tmins = np.round(np.arange(0., nsamp - 1, split_size * sfreq))
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
# going in revere order so the last fname is the first file (need it later)
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.copy()
raws[ri].crop(tmin, tmax, False)
# test concatenation of split file
all_raw_1 = concatenate_raws(raws, preload=True)
all_raw_2 = raw.crop(0, None, True)
for ar in [all_raw_1, all_raw_2]:
assert_true(raw.first_samp == ar.first_samp)
assert_true(raw.last_samp == ar.last_samp)
assert_array_equal(raw[:, :][0], ar[:, :][0])
def test_crop():
"""Test cropping raw files
"""
# split a concatenated file to test a difficult case
raw = Raw([fif_fname, fif_fname], preload=True)
split_size = 10. # in seconds
sfreq = raw.info['sfreq']
nsamp = (raw.last_samp - raw.first_samp + 1)
# do an annoying case (off-by-one splitting)
tmins = np.r_[1., np.round(np.arange(0., nsamp - 1, split_size * sfreq))]
tmins = np.sort(tmins)
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.crop(tmin, tmax, True)
all_raw_2 = concatenate_raws(raws, preload=True)
assert_true(raw.first_samp == all_raw_2.first_samp)
assert_true(raw.last_samp == all_raw_2.last_samp)
assert_array_equal(raw[:, :][0], all_raw_2[:, :][0])
tmins = np.round(np.arange(0., nsamp - 1, split_size * sfreq))
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
# going in revere order so the last fname is the first file (need it later)
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.copy()
raws[ri].crop(tmin, tmax, False)
# test concatenation of split file
all_raw_1 = concatenate_raws(raws, preload=True)
all_raw_2 = raw.crop(0, None, True)
for ar in [all_raw_1, all_raw_2]:
assert_true(raw.first_samp == ar.first_samp)
assert_true(raw.last_samp == ar.last_samp)
assert_array_equal(raw[:, :][0], ar[:, :][0])
def test_multiple_files():
"""Test loading multiple files simultaneously
"""
# split file
raw = Raw(fif_fname, preload=True)
split_size = 10. # 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
# 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) 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_array_almost_equal(raw[:, :][0], all_raw_1[:, :][0])
raws[0] = Raw(fname)
all_raw_2 = concatenate_raws(raws, preload=True)
assert_array_almost_equal(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)
assert_array_equal(events, events2)
# test various methods of combining files
n_combos = 9
raw_combos = [None] * n_combos
raw = Raw(fif_fname, preload=True)
raw_combos[0] = Raw([fif_fname, fif_fname], preload=True)
raw_combos[1] = Raw([fif_fname, fif_fname], preload=False)
raw_combos[2] = Raw([fif_fname, fif_fname], preload='memmap8.dat')
assert_raises(ValueError, Raw, [fif_fname, ctf_fname])
assert_raises(ValueError, Raw, [fif_fname, fif_bad_marked_fname])
n_times = len(raw._times)
assert_true(raw[:, :][0].shape[1] * 2 == raw_combos[0][:, :][0].shape[1])
assert_true(raw_combos[0][:, :][0].shape[1] == len(raw_combos[0]._times))
# with all data preloaded, result should be preloaded
raw_combos[3] = Raw(fif_fname, preload=True)
raw_combos[3].append(Raw(fif_fname, preload=True))
assert_true(raw_combos[0]._preloaded == True)
# with any data not preloaded, don't set result as preloaded
raw_combos[4] = concatenate_raws([Raw(fif_fname, preload=True),
Raw(fif_fname, preload=False)])
assert_true(raw_combos[1]._preloaded == False)
assert_array_equal(find_events(raw_combos[4]), find_events(raw_combos[0]))
# user should be able to force data to be preloaded upon concat
raw_combos[5] = concatenate_raws([Raw(fif_fname, preload=False),
Raw(fif_fname, preload=True)],
preload=True)
assert_true(raw_combos[2]._preloaded == True)
raw_combos[6] = concatenate_raws([Raw(fif_fname, preload=False),
Raw(fif_fname, preload=True)],
preload='memmap3.dat')
raw_combos[7] = concatenate_raws([Raw(fif_fname, preload=True),
Raw(fif_fname, preload=True)],
preload='memmap4.dat')
raw_combos[8] = concatenate_raws([Raw(fif_fname, preload=False),
Raw(fif_fname, preload=False)],
preload='memmap5.dat')
# make sure that all our data match
times = range(0, 2 * n_times, 999)
# add potentially problematic points
times.extend([n_times - 1, n_times, 2 * n_times - 1])
for ti in times: # let's do a subset of points for speed
orig = raw[:, ti % n_times][0]
for raw_combo in raw_combos:
# these are almost_equals because of possible dtype differences
assert_array_almost_equal(orig, raw_combo[:, ti][0])
# 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), find_events(raw)]
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_combos[0])
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)
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)
def test_multiple_files():
"""Test loading multiple files simultaneously
"""
# split file
raw = Raw(fif_fname, preload=True)
split_size = 10. # 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
# 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
n_combos = 9
raw_combos = [None] * n_combos
raw = Raw(fif_fname, preload=True)
raw_combos[0] = Raw([fif_fname, fif_fname], preload=True)
raw_combos[1] = Raw([fif_fname, fif_fname], preload=False)
raw_combos[2] = Raw([fif_fname, fif_fname], preload='memmap8.dat')
assert_raises(ValueError, Raw, [fif_fname, ctf_fname])
assert_raises(ValueError, Raw, [fif_fname, fif_bad_marked_fname])
n_times = len(raw._times)
assert_true(raw[:, :][0].shape[1] * 2 == raw_combos[0][:, :][0].shape[1])
assert_true(raw_combos[0][:, :][0].shape[1] == len(raw_combos[0]._times))
# with all data preloaded, result should be preloaded
raw_combos[3] = Raw(fif_fname, preload=True)
raw_combos[3].append(Raw(fif_fname, preload=True))
assert_true(raw_combos[0]._preloaded == True)
assert_true(len(raw_combos[3]._times) == raw_combos[3]._data.shape[1])
# with any data not preloaded, don't set result as preloaded
raw_combos[4] = concatenate_raws(
[Raw(fif_fname, preload=True),
Raw(fif_fname, preload=False)])
assert_true(raw_combos[1]._preloaded == False)
assert_array_equal(find_events(raw_combos[4], stim_channel='STI 014'),
find_events(raw_combos[0], stim_channel='STI 014'))
# user should be able to force data to be preloaded upon concat
raw_combos[5] = concatenate_raws(
[Raw(fif_fname, preload=False),
Raw(fif_fname, preload=True)],
preload=True)
assert_true(raw_combos[2]._preloaded == True)
raw_combos[6] = concatenate_raws(
[Raw(fif_fname, preload=False),
Raw(fif_fname, preload=True)],
preload='memmap3.dat')
raw_combos[7] = concatenate_raws(
[Raw(fif_fname, preload=True),
Raw(fif_fname, preload=True)],
preload='memmap4.dat')
raw_combos[8] = concatenate_raws(
[Raw(fif_fname, preload=False),
Raw(fif_fname, preload=False)],
preload='memmap5.dat')
# make sure that all our data match
times = range(0, 2 * n_times, 999)
# add potentially problematic points
times.extend([n_times - 1, n_times, 2 * n_times - 1])
for ti in times: # let's do a subset of points for speed
orig = raw[:, ti % n_times][0]
for raw_combo in raw_combos:
# these are almost_equals because of possible dtype differences
assert_allclose(orig, raw_combo[:, ti][0])
# 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_combos[0], 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)
