def test_egi_dig_montage():
"""Test EGI MFF XML dig montage support."""
dig_montage = read_dig_montage(egi=egi_dig_montage_fname, unit='m')
# # test round-trip IO
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, 'egi_test.fif')
_check_roundtrip(dig_montage, fname_temp)
# Test coordinate transform
dig_montage.transform_to_head()
# nasion
assert_almost_equal(dig_montage.nasion[0], 0)
assert_almost_equal(dig_montage.nasion[2], 0)
# lpa and rpa
assert_allclose(dig_montage.lpa[1:], 0, atol=1e-16)
assert_allclose(dig_montage.rpa[1:], 0, atol=1e-16)
# Test accuracy and embedding within raw object
raw_egi = read_raw_egi(egi_raw_fname, channel_naming='EEG %03d')
raw_egi.set_montage(dig_montage)
test_raw_egi = read_raw_fif(egi_fif_fname)
assert_equal(len(raw_egi.ch_names), len(test_raw_egi.ch_names))
for ch_raw, ch_test_raw in zip(raw_egi.info['chs'],
test_raw_egi.info['chs']):
assert_equal(ch_raw['ch_name'], ch_test_raw['ch_name'])
assert_equal(ch_raw['coord_frame'], FIFF.FIFFV_COORD_HEAD)
assert_allclose(ch_raw['loc'], ch_test_raw['loc'], atol=1e-7)
assert_dig_allclose(raw_egi.info, test_raw_egi.info)
def test_ch_loc():
"""Test raw kit loc."""
raw_py = read_raw_kit(sqd_path, mrk_path, elp_txt_path, hsp_txt_path,
stim='<')
raw_bin = read_raw_fif(op.join(data_dir, 'test_bin_raw.fif'))
ch_py = np.array([ch['loc'] for ch in
raw_py._raw_extras[0]['channels'][:160]])
# ch locs stored as m, not mm
ch_py[:, :3] *= 1e3
ch_sns = read_sns(op.join(data_dir, 'sns.txt'))
assert_array_almost_equal(ch_py, ch_sns, 2)
assert_array_almost_equal(raw_py.info['dev_head_t']['trans'],
raw_bin.info['dev_head_t']['trans'], 4)
for py_ch, bin_ch in zip(raw_py.info['chs'], raw_bin.info['chs']):
if bin_ch['ch_name'].startswith('MEG'):
# the stored ch locs have more precision than the sns.txt
assert_array_almost_equal(py_ch['loc'], bin_ch['loc'], decimal=2)
# test when more than one marker file provided
mrks = [mrk_path, mrk2_path, mrk3_path]
read_raw_kit(sqd_path, mrks, elp_txt_path, hsp_txt_path, preload=False)
# this dataset does not have the equivalent set of points :(
raw_bin.info['dig'] = raw_bin.info['dig'][:8]
raw_py.info['dig'] = raw_py.info['dig'][:8]
assert_dig_allclose(raw_py.info, raw_bin.info)
def test_raw():
"""Test bti conversion to Raw object."""
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
pytest.raises(ValueError, read_raw_bti, pdf, 'eggs', preload=False)
pytest.raises(ValueError,
read_raw_bti,
pdf,
config,
'spam',
preload=False)
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
ex = read_raw_fif(exported, preload=True)
ra = read_raw_bti(pdf, config, hs, preload=False)
assert ('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
assert len(ex.info['dig']) in (3563, 5154)
assert_dig_allclose(ex.info, ra.info, limit=100)
coil1, coil2 = [
np.concatenate([d['loc'].flatten() for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)
]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [
np.concatenate([d['loc'].flatten() for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)
]
assert_allclose(loc1, loc2)
assert_allclose(ra[:NCH][0], ex[:NCH][0])
assert_array_equal([c['range'] for c in ra.info['chs'][:NCH]],
[c['range'] for c in ex.info['chs'][:NCH]])
assert_array_equal([c['cal'] for c in ra.info['chs'][:NCH]],
[c['cal'] for c in ex.info['chs'][:NCH]])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# check our transforms
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
if ex.info[key] is None:
pass
else:
assert (ra.info[key] is not None)
for ent in ('to', 'from', 'trans'):
assert_allclose(ex.info[key][ent], ra.info[key][ent])
ra.save(tmp_raw_fname)
re = read_raw_fif(tmp_raw_fname)
print(re)
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert (isinstance(re.info[key], dict))
this_t = re.info[key]['trans']
assert_equal(this_t.shape, (4, 4))
# check that matrix by is not identity
assert (not np.allclose(this_t, np.eye(4)))
os.remove(tmp_raw_fname)
def test_fif_dig_montage():
"""Test FIF dig montage support"""
dig_montage = read_dig_montage(fif=fif_dig_montage_fname)
# Make a BrainVision file like the one the user would have had
with warnings.catch_warnings(record=True) as w:
raw_bv = read_raw_brainvision(bv_fname, preload=True)
assert_true(any('will be dropped' in str(ww.message) for ww in w))
raw_bv_2 = raw_bv.copy()
mapping = dict()
for ii, ch_name in enumerate(raw_bv.ch_names[:-1]):
mapping[ch_name] = 'EEG%03d' % (ii + 1,)
raw_bv.rename_channels(mapping)
for ii, ch_name in enumerate(raw_bv_2.ch_names[:-1]):
mapping[ch_name] = 'EEG%03d' % (ii + 33,)
raw_bv_2.rename_channels(mapping)
raw_bv.drop_channels(['STI 014'])
raw_bv.add_channels([raw_bv_2])
# Set the montage
raw_bv.set_montage(dig_montage)
# Check the result
evoked = read_evokeds(evoked_fname)[0]
assert_equal(len(raw_bv.ch_names), len(evoked.ch_names))
for ch_py, ch_c in zip(raw_bv.info['chs'], evoked.info['chs']):
assert_equal(ch_py['ch_name'], ch_c['ch_name'].replace('EEG ', 'EEG'))
# C actually says it's unknown, but it's not (?):
# assert_equal(ch_py['coord_frame'], ch_c['coord_frame'])
assert_equal(ch_py['coord_frame'], FIFF.FIFFV_COORD_HEAD)
assert_allclose(ch_py['loc'], ch_c['loc'])
assert_dig_allclose(raw_bv.info, evoked.info)
def test_ch_loc():
"""Test raw kit loc
"""
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path, stim='<')
raw_bin = Raw(op.join(data_dir, 'test_bin_raw.fif'))
ch_py = raw_py._raw_extras[0]['sensor_locs'][:, :5]
# ch locs stored as m, not mm
ch_py[:, :3] *= 1e3
ch_sns = read_sns(op.join(data_dir, 'sns.txt'))
assert_array_almost_equal(ch_py, ch_sns, 2)
assert_array_almost_equal(raw_py.info['dev_head_t']['trans'],
raw_bin.info['dev_head_t']['trans'], 4)
for py_ch, bin_ch in zip(raw_py.info['chs'], raw_bin.info['chs']):
if bin_ch['ch_name'].startswith('MEG'):
# the stored ch locs have more precision than the sns.txt
assert_array_almost_equal(py_ch['loc'], bin_ch['loc'], decimal=2)
# test when more than one marker file provided
mrks = [mrk_path, mrk2_path, mrk3_path]
read_raw_kit(sqd_path, mrks, elp_path, hsp_path, preload=False)
# this dataset does not have the equivalent set of points :(
raw_bin.info['dig'] = raw_bin.info['dig'][:8]
raw_py.info['dig'] = raw_py.info['dig'][:8]
assert_dig_allclose(raw_py.info, raw_bin.info)
def test_egi_dig_montage():
"""Test EGI MFF XML dig montage support."""
dig_montage = read_dig_montage(egi=egi_dig_montage_fname, unit='m')
# # test round-trip IO
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, 'egi_test.fif')
_check_roundtrip(dig_montage, fname_temp)
# Test coordinate transform
dig_montage.transform_to_head()
# nasion
assert_almost_equal(dig_montage.nasion[0], 0)
assert_almost_equal(dig_montage.nasion[2], 0)
# lpa and rpa
assert_allclose(dig_montage.lpa[1:], 0, atol=1e-16)
assert_allclose(dig_montage.rpa[1:], 0, atol=1e-16)
# Test accuracy and embedding within raw object
raw_egi = read_raw_egi(egi_raw_fname, channel_naming='EEG %03d')
raw_egi.set_montage(dig_montage)
test_raw_egi = read_raw_fif(egi_fif_fname)
assert_equal(len(raw_egi.ch_names), len(test_raw_egi.ch_names))
for ch_raw, ch_test_raw in zip(raw_egi.info['chs'],
test_raw_egi.info['chs']):
assert_equal(ch_raw['ch_name'], ch_test_raw['ch_name'])
assert_equal(ch_raw['coord_frame'], FIFF.FIFFV_COORD_HEAD)
assert_allclose(ch_raw['loc'], ch_test_raw['loc'], atol=1e-7)
assert_dig_allclose(raw_egi.info, test_raw_egi.info)
def test_raw():
""" Test bti conversion to Raw object """
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
assert_raises(ValueError, read_raw_bti, pdf, 'eggs', preload=False)
assert_raises(ValueError, read_raw_bti, pdf, config, 'spam',
preload=False)
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
ex = Raw(exported, preload=True)
with warnings.catch_warnings(record=True): # weight tables
ra = read_raw_bti(pdf, config, hs, preload=False)
assert_true('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
with warnings.catch_warnings(record=True): # headshape
assert_dig_allclose(ex.info, ra.info)
coil1, coil2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_allclose(loc1, loc2)
assert_allclose(ra[:NCH][0], ex[:NCH][0])
assert_array_equal([c['range'] for c in ra.info['chs'][:NCH]],
[c['range'] for c in ex.info['chs'][:NCH]])
assert_array_equal([c['cal'] for c in ra.info['chs'][:NCH]],
[c['cal'] for c in ex.info['chs'][:NCH]])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# check our transforms
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
if ex.info[key] is None:
pass
else:
assert_true(ra.info[key] is not None)
for ent in ('to', 'from', 'trans'):
assert_allclose(ex.info[key][ent],
ra.info[key][ent])
ra.save(tmp_raw_fname)
re = Raw(tmp_raw_fname)
print(re)
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_true(isinstance(re.info[key], dict))
this_t = re.info[key]['trans']
assert_equal(this_t.shape, (4, 4))
# cehck that matrix by is not identity
assert_true(not np.allclose(this_t, np.eye(4)))
os.remove(tmp_raw_fname)
def test_fif_dig_montage():
"""Test FIF dig montage support."""
dig_montage = read_dig_montage(fif=fif_dig_montage_fname)
# test round-trip IO
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, 'test.fif')
_check_roundtrip(dig_montage, fname_temp)
# Make a BrainVision file like the one the user would have had
with warnings.catch_warnings(record=True) as w:
raw_bv = read_raw_brainvision(bv_fname, preload=True)
assert_true(any('will be dropped' in str(ww.message) for ww in w))
raw_bv_2 = raw_bv.copy()
mapping = dict()
for ii, ch_name in enumerate(raw_bv.ch_names[:-1]):
mapping[ch_name] = 'EEG%03d' % (ii + 1,)
raw_bv.rename_channels(mapping)
for ii, ch_name in enumerate(raw_bv_2.ch_names[:-1]):
mapping[ch_name] = 'EEG%03d' % (ii + 33,)
raw_bv_2.rename_channels(mapping)
raw_bv.drop_channels(['STI 014'])
raw_bv.add_channels([raw_bv_2])
for ii in range(2):
if ii == 1:
dig_montage.transform_to_head() # should have no meaningful effect
# Set the montage
raw_bv.set_montage(dig_montage)
# Check the result
evoked = read_evokeds(evoked_fname)[0]
assert_equal(len(raw_bv.ch_names), len(evoked.ch_names))
for ch_py, ch_c in zip(raw_bv.info['chs'], evoked.info['chs']):
assert_equal(ch_py['ch_name'],
ch_c['ch_name'].replace('EEG ', 'EEG'))
# C actually says it's unknown, but it's not (?):
# assert_equal(ch_py['coord_frame'], ch_c['coord_frame'])
assert_equal(ch_py['coord_frame'], FIFF.FIFFV_COORD_HEAD)
c_loc = ch_c['loc'].copy()
c_loc[c_loc == 0] = np.nan
assert_allclose(ch_py['loc'], c_loc, atol=1e-7)
assert_dig_allclose(raw_bv.info, evoked.info)
# Roundtrip of non-FIF start
names = ['nasion', 'lpa', 'rpa', '1', '2', '3', '4', '5']
montage = read_dig_montage(hsp, hpi, elp, names, transform=False)
assert_raises(RuntimeError, montage.save, fname_temp) # must be head coord
montage = read_dig_montage(hsp, hpi, elp, names)
_check_roundtrip(montage, fname_temp)
def test_fif_dig_montage():
"""Test FIF dig montage support."""
dig_montage = read_dig_montage(fif=fif_dig_montage_fname)
# test round-trip IO
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, 'test.fif')
_check_roundtrip(dig_montage, fname_temp)
# Make a BrainVision file like the one the user would have had
with warnings.catch_warnings(record=True) as w:
raw_bv = read_raw_brainvision(bv_fname, preload=True)
assert_true(any('will be dropped' in str(ww.message) for ww in w))
raw_bv_2 = raw_bv.copy()
mapping = dict()
for ii, ch_name in enumerate(raw_bv.ch_names[:-1]):
mapping[ch_name] = 'EEG%03d' % (ii + 1,)
raw_bv.rename_channels(mapping)
for ii, ch_name in enumerate(raw_bv_2.ch_names[:-1]):
mapping[ch_name] = 'EEG%03d' % (ii + 33,)
raw_bv_2.rename_channels(mapping)
raw_bv.drop_channels(['STI 014'])
raw_bv.add_channels([raw_bv_2])
for ii in range(2):
if ii == 1:
dig_montage.transform_to_head() # should have no meaningful effect
# Set the montage
raw_bv.set_montage(dig_montage)
# Check the result
evoked = read_evokeds(evoked_fname)[0]
assert_equal(len(raw_bv.ch_names), len(evoked.ch_names))
for ch_py, ch_c in zip(raw_bv.info['chs'], evoked.info['chs']):
assert_equal(ch_py['ch_name'],
ch_c['ch_name'].replace('EEG ', 'EEG'))
# C actually says it's unknown, but it's not (?):
# assert_equal(ch_py['coord_frame'], ch_c['coord_frame'])
assert_equal(ch_py['coord_frame'], FIFF.FIFFV_COORD_HEAD)
c_loc = ch_c['loc'].copy()
c_loc[c_loc == 0] = np.nan
assert_allclose(ch_py['loc'], c_loc, atol=1e-7)
assert_dig_allclose(raw_bv.info, evoked.info)
# Roundtrip of non-FIF start
names = ['nasion', 'lpa', 'rpa', '1', '2', '3', '4', '5']
montage = read_dig_montage(hsp, hpi, elp, names, transform=False)
assert_raises(RuntimeError, montage.save, fname_temp) # must be head coord
montage = read_dig_montage(hsp, hpi, elp, names)
_check_roundtrip(montage, fname_temp)
def test_read_ctf():
"""Test CTF reader"""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
assert_raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (
str(ch_num + 1),
raw.ch_names[ch_num],
) + tuple('%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with warnings.catch_warnings(record=True) as w: # reclassified channel
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
assert_true(all('MISC channel' in str(ww.message) for ww in w))
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos),
pos_read,
rtol=1e-5,
atol=1e-5)
assert_true((pos_read == pos_read_old).mean() < 0.1)
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with warnings.catch_warnings(record=True): # no coord tr
assert_raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = Raw(fname + '_raw.fif', add_eeg_ref=False, preload=True)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
for key in ('version', 'usecs'):
assert_equal(raw.info['meas_id'][key], raw_c.info['meas_id'][key])
py_time = raw.info['meas_id']['secs']
c_time = raw_c.info['meas_id']['secs']
max_offset = 24 * 60 * 60 # probably overkill but covers timezone
assert_true(c_time - max_offset <= py_time <= c_time)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'],
raw_c.info[t]['trans'],
rtol=1e-4,
atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads', 'ch_names',
'custom_ref_applied', 'description', 'events',
'experimenter', 'highpass', 'line_freq', 'lowpass',
'nchan', 'proj_id', 'proj_name', 'projs', 'sfreq',
'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert_equal(raw.info['buffer_size_sec'],
raw_c.info['buffer_size_sec'])
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'],
c2['data']['data'],
atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5,
atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal', ):
assert_allclose(c1[key],
c2[key],
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc', ):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
assert_allclose(c1[key][:3],
c2[key][:3],
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
assert_allclose(c1[key][9:12],
c2[key][9:12],
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = Raw(out_fname, add_eeg_ref=False)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.info['buffer_size_sec']))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(
bnd, bnd + 1), slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0], raw_c[pick_ch,
sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0], raw_c[pick_ch,
sl_time][0])
# all data / preload
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname, preload=True)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
assert_allclose(raw[:][0], raw_c[:][0])
raw.plot(show=False) # Test plotting with ref_meg channels.
assert_raises(ValueError, raw.plot, order='selection')
assert_raises(TypeError, read_raw_ctf, 1)
assert_raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
assert_raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
def test_read_ctf():
"""Test CTF reader"""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
assert_raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (str(ch_num + 1), raw.ch_names[ch_num],) + tuple(
'%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with warnings.catch_warnings(record=True) as w: # reclassified channel
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
assert_true(all('MISC channel' in str(ww.message) for ww in w))
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos), pos_read,
rtol=1e-5, atol=1e-5)
assert_true((pos_read == pos_read_old).mean() < 0.1)
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with warnings.catch_warnings(record=True): # no coord tr
assert_raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = Raw(fname + '_raw.fif', add_eeg_ref=False, preload=True)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
for key in ('version', 'usecs'):
assert_equal(raw.info['meas_id'][key], raw_c.info['meas_id'][key])
py_time = raw.info['meas_id']['secs']
c_time = raw_c.info['meas_id']['secs']
max_offset = 24 * 60 * 60 # probably overkill but covers timezone
assert_true(c_time - max_offset <= py_time <= c_time)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'], raw_c.info[t]['trans'],
rtol=1e-4, atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads',
'ch_names', 'custom_ref_applied', 'description',
'events', 'experimenter', 'highpass', 'line_freq',
'lowpass', 'nchan', 'proj_id', 'proj_name',
'projs', 'sfreq', 'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert_equal(raw.info['buffer_size_sec'],
raw_c.info['buffer_size_sec'])
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'], c2['data']['data'], atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5, atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal',):
assert_allclose(c1[key], c2[key], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc',):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
assert_allclose(c1[key][:3], c2[key][:3], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
assert_allclose(c1[key][9:12], c2[key][9:12], atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = Raw(out_fname, add_eeg_ref=False)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.info['buffer_size_sec']))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
# all data / preload
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname, preload=True)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
assert_allclose(raw[:][0], raw_c[:][0])
assert_raises(TypeError, read_raw_ctf, 1)
assert_raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
assert_raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
