def _test_raw_reader(reader, test_preloading=True, test_kwargs=True,
boundary_decimal=2, test_scaling=True, test_rank=True,
**kwargs):
"""Test reading, writing and slicing of raw classes.
Parameters
----------
reader : function
Function to test.
test_preloading : bool
Whether not preloading is implemented for the reader. If True, both
cases and memory mapping to file are tested.
test_kwargs : dict
Test _init_kwargs support.
boundary_decimal : int
Number of decimals up to which the boundary should match.
**kwargs :
Arguments for the reader. Note: Do not use preload as kwarg.
Use ``test_preloading`` instead.
Returns
-------
raw : instance of Raw
A preloaded Raw object.
"""
tempdir = _TempDir()
rng = np.random.RandomState(0)
montage = None
if "montage" in kwargs:
montage = kwargs['montage']
del kwargs['montage']
if test_preloading:
raw = reader(preload=True, **kwargs)
rep = repr(raw)
assert rep.count('<') == 1
assert rep.count('>') == 1
if montage is not None:
raw.set_montage(montage)
# don't assume the first is preloaded
buffer_fname = op.join(tempdir, 'buffer')
picks = rng.permutation(np.arange(len(raw.ch_names) - 1))[:10]
picks = np.append(picks, len(raw.ch_names) - 1) # test trigger channel
bnd = min(int(round(raw.buffer_size_sec *
raw.info['sfreq'])), raw.n_times)
slices = [slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None), slice(1, bnd)]
if raw.n_times >= 2 * bnd: # at least two complete blocks
slices += [slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100)]
other_raws = [reader(preload=buffer_fname, **kwargs),
reader(preload=False, **kwargs)]
for sl_time in slices:
data1, times1 = raw[picks, sl_time]
for other_raw in other_raws:
data2, times2 = other_raw[picks, sl_time]
assert_allclose(data1, data2)
assert_allclose(times1, times2)
# test projection vs cals and data units
other_raw = reader(preload=False, **kwargs)
other_raw.del_proj()
eeg = meg = fnirs = False
if 'eeg' in raw:
eeg, atol = True, 1e-18
elif 'grad' in raw:
meg, atol = 'grad', 1e-24
elif 'mag' in raw:
meg, atol = 'mag', 1e-24
else:
assert 'fnirs_cw_amplitude' in raw, 'New channel type necessary?'
fnirs, atol = 'fnirs_cw_amplitude', 1e-10
picks = pick_types(
other_raw.info, meg=meg, eeg=eeg, fnirs=fnirs)
col_names = [other_raw.ch_names[pick] for pick in picks]
proj = np.ones((1, len(picks)))
proj /= proj.shape[1]
proj = Projection(
data=dict(data=proj, nrow=1, row_names=None,
col_names=col_names, ncol=len(picks)),
active=False)
assert len(other_raw.info['projs']) == 0
other_raw.add_proj(proj)
assert len(other_raw.info['projs']) == 1
# Orders of projector application, data loading, and reordering
# equivalent:
# 1. load->apply->get
data_load_apply_get = \
other_raw.copy().load_data().apply_proj().get_data(picks)
# 2. apply->get (and don't allow apply->pick)
apply = other_raw.copy().apply_proj()
data_apply_get = apply.get_data(picks)
data_apply_get_0 = apply.get_data(picks[0])[0]
with pytest.raises(RuntimeError, match='loaded'):
apply.copy().pick(picks[0]).get_data()
# 3. apply->load->get
data_apply_load_get = apply.copy().load_data().get_data(picks)
data_apply_load_get_0, data_apply_load_get_1 = \
apply.copy().load_data().pick(picks[:2]).get_data()
# 4. reorder->apply->load->get
all_picks = np.arange(len(other_raw.ch_names))
reord = np.concatenate((
picks[1::2],
picks[0::2],
np.setdiff1d(all_picks, picks)))
rev = np.argsort(reord)
assert_array_equal(reord[rev], all_picks)
assert_array_equal(rev[reord], all_picks)
reorder = other_raw.copy().pick(reord)
assert reorder.ch_names == [other_raw.ch_names[r] for r in reord]
assert reorder.ch_names[0] == other_raw.ch_names[picks[1]]
assert_allclose(reorder.get_data([0]), other_raw.get_data(picks[1]))
reorder_apply = reorder.copy().apply_proj()
assert reorder_apply.ch_names == reorder.ch_names
assert reorder_apply.ch_names[0] == apply.ch_names[picks[1]]
assert_allclose(reorder_apply.get_data([0]), apply.get_data(picks[1]),
atol=1e-18)
data_reorder_apply_load_get = \
reorder_apply.load_data().get_data(rev[:len(picks)])
data_reorder_apply_load_get_1 = \
reorder_apply.copy().load_data().pick([0]).get_data()[0]
assert reorder_apply.ch_names[0] == apply.ch_names[picks[1]]
assert (data_load_apply_get.shape ==
data_apply_get.shape ==
data_apply_load_get.shape ==
data_reorder_apply_load_get.shape)
del apply
# first check that our data are (probably) in the right units
data = data_load_apply_get.copy()
data = data - np.mean(data, axis=1, keepdims=True) # can be offsets
np.abs(data, out=data)
if test_scaling:
maxval = atol * 1e16
assert_array_less(data, maxval)
minval = atol * 1e6
assert_array_less(minval, np.median(data))
else:
atol = 1e-7 * np.median(data) # 1e-7 * MAD
# ranks should all be reduced by 1
if test_rank == 'less':
cmp = np.less
elif test_rank is False:
cmp = None
else: # anything else is like True or 'equal'
assert test_rank is True or test_rank == 'equal', test_rank
cmp = np.equal
rank_load_apply_get = np.linalg.matrix_rank(data_load_apply_get)
rank_apply_get = np.linalg.matrix_rank(data_apply_get)
rank_apply_load_get = np.linalg.matrix_rank(data_apply_load_get)
if cmp is not None:
assert cmp(rank_load_apply_get, len(col_names) - 1)
assert cmp(rank_apply_get, len(col_names) - 1)
assert cmp(rank_apply_load_get, len(col_names) - 1)
# and they should all match
t_kw = dict(
atol=atol, err_msg='before != after, likely _mult_cal_one prob')
assert_allclose(data_apply_get[0], data_apply_get_0, **t_kw)
assert_allclose(data_apply_load_get_1,
data_reorder_apply_load_get_1, **t_kw)
assert_allclose(data_load_apply_get[0], data_apply_load_get_0, **t_kw)
assert_allclose(data_load_apply_get, data_apply_get, **t_kw)
assert_allclose(data_load_apply_get, data_apply_load_get, **t_kw)
if 'eeg' in raw:
other_raw.del_proj()
direct = \
other_raw.copy().load_data().set_eeg_reference().get_data()
other_raw.set_eeg_reference(projection=True)
assert len(other_raw.info['projs']) == 1
this_proj = other_raw.info['projs'][0]['data']
assert this_proj['col_names'] == col_names
assert this_proj['data'].shape == proj['data']['data'].shape
assert_allclose(this_proj['data'], proj['data']['data'])
proj = other_raw.apply_proj().get_data()
assert_allclose(proj[picks], data_load_apply_get, atol=1e-10)
assert_allclose(proj, direct, atol=1e-10, err_msg=t_kw['err_msg'])
else:
raw = reader(**kwargs)
assert_named_constants(raw.info)
# smoke test for gh #9743
ids = [id(ch['loc']) for ch in raw.info['chs']]
assert len(set(ids)) == len(ids)
full_data = raw._data
assert raw.__class__.__name__ in repr(raw) # to test repr
assert raw.info.__class__.__name__ in repr(raw.info)
assert isinstance(raw.info['dig'], (type(None), list))
data_max = full_data.max()
data_min = full_data.min()
# these limits could be relaxed if we actually find data with
# huge values (in SI units)
assert data_max < 1e5
assert data_min > -1e5
if isinstance(raw.info['dig'], list):
for di, d in enumerate(raw.info['dig']):
assert isinstance(d, DigPoint), (di, d)
# gh-5604
meas_date = raw.info['meas_date']
assert meas_date is None or meas_date >= _stamp_to_dt((0, 0))
# test repr_html
assert 'Good channels' in raw.info._repr_html_()
# test resetting raw
if test_kwargs:
raw2 = reader(**raw._init_kwargs)
assert set(raw.info.keys()) == set(raw2.info.keys())
assert_array_equal(raw.times, raw2.times)
# Test saving and reading
out_fname = op.join(tempdir, 'test_raw.fif')
raw = concatenate_raws([raw])
raw.save(out_fname, tmax=raw.times[-1], overwrite=True, buffer_size_sec=1)
# Test saving with not correct extension
out_fname_h5 = op.join(tempdir, 'test_raw.h5')
with pytest.raises(IOError, match='raw must end with .fif or .fif.gz'):
raw.save(out_fname_h5)
raw3 = read_raw_fif(out_fname)
assert_named_constants(raw3.info)
assert set(raw.info.keys()) == set(raw3.info.keys())
assert_allclose(raw3[0:20][0], full_data[0:20], rtol=1e-6,
atol=1e-20) # atol is very small but > 0
assert_allclose(raw.times, raw3.times, atol=1e-6, rtol=1e-6)
assert not math.isnan(raw3.info['highpass'])
assert not math.isnan(raw3.info['lowpass'])
assert not math.isnan(raw.info['highpass'])
assert not math.isnan(raw.info['lowpass'])
assert raw3.info['kit_system_id'] == raw.info['kit_system_id']
# Make sure concatenation works
first_samp = raw.first_samp
last_samp = raw.last_samp
concat_raw = concatenate_raws([raw.copy(), raw])
assert concat_raw.n_times == 2 * raw.n_times
assert concat_raw.first_samp == first_samp
assert concat_raw.last_samp - last_samp + first_samp == last_samp + 1
idx = np.where(concat_raw.annotations.description == 'BAD boundary')[0]
expected_bad_boundary_onset = raw._last_time
assert_array_almost_equal(concat_raw.annotations.onset[idx],
expected_bad_boundary_onset,
decimal=boundary_decimal)
if raw.info['meas_id'] is not None:
for key in ['secs', 'usecs', 'version']:
assert raw.info['meas_id'][key] == raw3.info['meas_id'][key]
assert_array_equal(raw.info['meas_id']['machid'],
raw3.info['meas_id']['machid'])
assert isinstance(raw.annotations, Annotations)
# Make a "soft" test on units: They have to be valid SI units as in
# mne.io.meas_info.valid_units, but we accept any lower/upper case for now.
valid_units = _get_valid_units()
valid_units_lower = [unit.lower() for unit in valid_units]
if raw._orig_units is not None:
assert isinstance(raw._orig_units, dict)
for ch_name, unit in raw._orig_units.items():
assert unit.lower() in valid_units_lower, ch_name
# Test picking with and without preload
if test_preloading:
preload_kwargs = (dict(preload=True), dict(preload=False))
else:
preload_kwargs = (dict(),)
n_ch = len(raw.ch_names)
picks = rng.permutation(n_ch)
for preload_kwarg in preload_kwargs:
these_kwargs = kwargs.copy()
these_kwargs.update(preload_kwarg)
# don't use the same filename or it could create problems
if isinstance(these_kwargs.get('preload', None), str) and \
op.isfile(these_kwargs['preload']):
these_kwargs['preload'] += '-1'
whole_raw = reader(**these_kwargs)
print(whole_raw) # __repr__
assert n_ch >= 2
picks_1 = picks[:n_ch // 2]
picks_2 = picks[n_ch // 2:]
raw_1 = whole_raw.copy().pick(picks_1)
raw_2 = whole_raw.copy().pick(picks_2)
data, times = whole_raw[:]
data_1, times_1 = raw_1[:]
data_2, times_2 = raw_2[:]
assert_array_equal(times, times_1)
assert_array_equal(data[picks_1], data_1)
assert_array_equal(times, times_2,)
assert_array_equal(data[picks_2], data_2)
# Make sure that writing info to h5 format
# (all fields should be compatible)
if check_version('h5py'):
fname_h5 = op.join(tempdir, 'info.h5')
with _writing_info_hdf5(raw.info):
write_hdf5(fname_h5, raw.info)
new_info = Info(read_hdf5(fname_h5))
assert object_diff(new_info, raw.info) == ''
# Make sure that changing directory does not break anything
if test_preloading:
these_kwargs = kwargs.copy()
key = None
for key in ('fname',
'input_fname', # artemis123
'vhdr_fname', # BV
'pdf_fname', # BTi
'directory', # CTF
'filename', # nedf
):
try:
fname = kwargs[key]
except KeyError:
key = None
else:
break
# len(kwargs) == 0 for the fake arange reader
if len(kwargs):
assert key is not None, sorted(kwargs.keys())
dirname = op.dirname(fname)
these_kwargs[key] = op.basename(fname)
these_kwargs['preload'] = False
orig_dir = os.getcwd()
try:
os.chdir(dirname)
raw_chdir = reader(**these_kwargs)
finally:
os.chdir(orig_dir)
raw_chdir.load_data()
return raw
def _test_raw_reader(reader,
test_preloading=True,
test_kwargs=True,
boundary_decimal=2,
**kwargs):
"""Test reading, writing and slicing of raw classes.
Parameters
----------
reader : function
Function to test.
test_preloading : bool
Whether not preloading is implemented for the reader. If True, both
cases and memory mapping to file are tested.
test_kwargs : dict
Test _init_kwargs support.
boundary_decimal : int
Number of decimals up to which the boundary should match.
**kwargs :
Arguments for the reader. Note: Do not use preload as kwarg.
Use ``test_preloading`` instead.
Returns
-------
raw : instance of Raw
A preloaded Raw object.
"""
tempdir = _TempDir()
rng = np.random.RandomState(0)
montage = None
if "montage" in kwargs:
montage = kwargs['montage']
del kwargs['montage']
if test_preloading:
raw = reader(preload=True, **kwargs)
rep = repr(raw)
assert rep.count('<') == 1
assert rep.count('>') == 1
if montage is not None:
raw.set_montage(montage)
# don't assume the first is preloaded
buffer_fname = op.join(tempdir, 'buffer')
picks = rng.permutation(np.arange(len(raw.ch_names) - 1))[:10]
picks = np.append(picks, len(raw.ch_names) - 1) # test trigger channel
bnd = min(int(round(raw.buffer_size_sec * raw.info['sfreq'])),
raw.n_times)
slices = [
slice(0, bnd),
slice(bnd - 1, bnd),
slice(3, bnd),
slice(3, 300),
slice(None),
slice(1, bnd)
]
if raw.n_times >= 2 * bnd: # at least two complete blocks
slices += [
slice(bnd, 2 * bnd),
slice(bnd, bnd + 1),
slice(0, bnd + 100)
]
other_raws = [
reader(preload=buffer_fname, **kwargs),
reader(preload=False, **kwargs)
]
for sl_time in slices:
data1, times1 = raw[picks, sl_time]
for other_raw in other_raws:
data2, times2 = other_raw[picks, sl_time]
assert_allclose(data1, data2)
assert_allclose(times1, times2)
else:
raw = reader(**kwargs)
full_data = raw._data
assert raw.__class__.__name__ in repr(raw) # to test repr
assert raw.info.__class__.__name__ in repr(raw.info)
assert isinstance(raw.info['dig'], (type(None), list))
data_max = full_data.max()
data_min = full_data.min()
# these limits could be relaxed if we actually find data with
# huge values (in SI units)
assert data_max < 1e5
assert data_min > -1e5
if isinstance(raw.info['dig'], list):
for di, d in enumerate(raw.info['dig']):
assert isinstance(d, DigPoint), (di, d)
# gh-5604
meas_date = raw.info['meas_date']
assert meas_date is None or meas_date >= _stamp_to_dt((0, 0))
# test resetting raw
if test_kwargs:
raw2 = reader(**raw._init_kwargs)
assert set(raw.info.keys()) == set(raw2.info.keys())
assert_array_equal(raw.times, raw2.times)
# Test saving and reading
out_fname = op.join(tempdir, 'test_raw.fif')
raw = concatenate_raws([raw])
raw.save(out_fname, tmax=raw.times[-1], overwrite=True, buffer_size_sec=1)
raw3 = read_raw_fif(out_fname)
assert set(raw.info.keys()) == set(raw3.info.keys())
assert_allclose(raw3[0:20][0], full_data[0:20], rtol=1e-6,
atol=1e-20) # atol is very small but > 0
assert_array_almost_equal(raw.times, raw3.times)
assert not math.isnan(raw3.info['highpass'])
assert not math.isnan(raw3.info['lowpass'])
assert not math.isnan(raw.info['highpass'])
assert not math.isnan(raw.info['lowpass'])
assert raw3.info['kit_system_id'] == raw.info['kit_system_id']
# Make sure concatenation works
first_samp = raw.first_samp
last_samp = raw.last_samp
concat_raw = concatenate_raws([raw.copy(), raw])
assert concat_raw.n_times == 2 * raw.n_times
assert concat_raw.first_samp == first_samp
assert concat_raw.last_samp - last_samp + first_samp == last_samp + 1
idx = np.where(concat_raw.annotations.description == 'BAD boundary')[0]
expected_bad_boundary_onset = raw._last_time
assert_array_almost_equal(concat_raw.annotations.onset[idx],
expected_bad_boundary_onset,
decimal=boundary_decimal)
if raw.info['meas_id'] is not None:
for key in ['secs', 'usecs', 'version']:
assert raw.info['meas_id'][key] == raw3.info['meas_id'][key]
assert_array_equal(raw.info['meas_id']['machid'],
raw3.info['meas_id']['machid'])
assert isinstance(raw.annotations, Annotations)
# Make a "soft" test on units: They have to be valid SI units as in
# mne.io.meas_info.valid_units, but we accept any lower/upper case for now.
valid_units = _get_valid_units()
valid_units_lower = [unit.lower() for unit in valid_units]
if raw._orig_units is not None:
assert isinstance(raw._orig_units, dict)
for ch_name, unit in raw._orig_units.items():
assert unit.lower() in valid_units_lower, ch_name
# Test picking with and without preload
if test_preloading:
preload_kwargs = (dict(preload=True), dict(preload=False))
else:
preload_kwargs = (dict(), )
n_ch = len(raw.ch_names)
picks = rng.permutation(n_ch)
for preload_kwarg in preload_kwargs:
these_kwargs = kwargs.copy()
these_kwargs.update(preload_kwarg)
# don't use the same filename or it could create problems
if isinstance(these_kwargs.get('preload', None), str) and \
op.isfile(these_kwargs['preload']):
these_kwargs['preload'] += '-1'
whole_raw = reader(**these_kwargs)
print(whole_raw) # __repr__
assert n_ch >= 2
picks_1 = picks[:n_ch // 2]
picks_2 = picks[n_ch // 2:]
raw_1 = whole_raw.copy().pick(picks_1)
raw_2 = whole_raw.copy().pick(picks_2)
data, times = whole_raw[:]
data_1, times_1 = raw_1[:]
data_2, times_2 = raw_2[:]
assert_array_equal(times, times_1)
assert_array_equal(data[picks_1], data_1)
assert_array_equal(
times,
times_2,
)
assert_array_equal(data[picks_2], data_2)
# Make sure that writing info to h5 format
# (all fields should be compatible)
if check_version('h5py'):
fname_h5 = op.join(tempdir, 'info.h5')
with _writing_info_hdf5(raw.info):
write_hdf5(fname_h5, raw.info)
new_info = Info(read_hdf5(fname_h5))
assert object_diff(new_info, raw.info) == ''
return raw