def test_temporal_derivative_distribution_repair(fname, tmp_path):
"""Test running artifact rejection."""
raw = read_raw_nirx(fname)
raw_od = optical_density(raw)
raw_hb = beer_lambert_law(raw_od)
# With optical densities
# Add a baseline shift artifact about half way through data
max_shift = np.max(np.diff(raw_od._data[0]))
shift_amp = 5 * max_shift
raw_od._data[0, 0:30] = raw_od._data[0, 0:30] - shift_amp
# make one channel zero std
raw_od._data[1] = 0.
raw_od._data[2] = 1.
assert np.max(np.diff(raw_od._data[0])) > shift_amp
# Ensure that applying the algorithm reduces the step change
raw_od = tddr(raw_od)
assert np.max(np.diff(raw_od._data[0])) < shift_amp
assert_allclose(raw_od._data[1], 0.) # unchanged
assert_allclose(raw_od._data[2], 1.) # unchanged
# With Hb
# Add a baseline shift artifact about half way through data
max_shift = np.max(np.diff(raw_hb._data[0]))
shift_amp = 5 * max_shift
raw_hb._data[0, 0:30] = raw_hb._data[0, 0:30] - (1.1 * shift_amp)
# make one channel zero std
raw_hb._data[1] = 0.
raw_hb._data[2] = 1.
assert np.max(np.diff(raw_hb._data[0])) > shift_amp
# Ensure that applying the algorithm reduces the step change
raw_hb = tddr(raw_hb)
assert np.max(np.diff(raw_hb._data[0])) < shift_amp
assert_allclose(raw_hb._data[1], 0.) # unchanged
assert_allclose(raw_hb._data[2], 1.) # unchanged
def test_order_agnostic(nirx_snirf):
"""Test that order does not matter to (pre)processing results."""
raw_nirx, raw_snirf = nirx_snirf
raw_random = raw_nirx.copy().pick(
np.random.RandomState(0).permutation(len(raw_nirx.ch_names)))
raws = dict(nirx=raw_nirx, snirf=raw_snirf, random=raw_random)
del raw_nirx, raw_snirf, raw_random
orders = dict()
# continuous wave
for key, r in raws.items():
assert set(r.get_channel_types()) == {'fnirs_cw_amplitude'}
orders[key] = [
r.ch_names.index(name) for name in raws['nirx'].ch_names
]
assert_array_equal(raws['nirx'].ch_names,
np.array(r.ch_names)[orders[key]])
assert_allclose(raws['nirx'].get_data(),
r.get_data(orders[key]),
err_msg=key)
assert_array_equal(orders['nirx'], np.arange(len(raws['nirx'].ch_names)))
# optical density
for key, r in raws.items():
raws[key] = r = optical_density(r)
assert_allclose(raws['nirx'].get_data(),
r.get_data(orders[key]),
err_msg=key)
assert set(r.get_channel_types()) == {'fnirs_od'}
# scalp-coupling index
sci = dict()
for key, r in raws.items():
sci[key] = r = scalp_coupling_index(r)
assert_allclose(sci['nirx'], r[orders[key]], err_msg=key, rtol=0.01)
# TDDR (on optical)
tddrs = dict()
for key, r in raws.items():
tddrs[key] = r = tddr(r)
assert_allclose(tddrs['nirx'].get_data(),
r.get_data(orders[key]),
err_msg=key,
atol=1e-4)
assert set(r.get_channel_types()) == {'fnirs_od'}
# beer-lambert
for key, r in raws.items():
raws[key] = r = beer_lambert_law(r)
assert_allclose(raws['nirx'].get_data(),
r.get_data(orders[key]),
err_msg=key,
rtol=2e-7)
assert set(r.get_channel_types()) == {'hbo', 'hbr'}
# TDDR (on haemo)
tddrs = dict()
for key, r in raws.items():
tddrs[key] = r = tddr(r)
assert_allclose(tddrs['nirx'].get_data(),
r.get_data(orders[key]),
err_msg=key,
atol=1e-9)
assert set(r.get_channel_types()) == {'hbo', 'hbr'}
def test_temporal_derivative_distribution_repair(fname, tmpdir):
"""Test running artifact rejection."""
raw = read_raw_nirx(fname)
raw = optical_density(raw)
# Add a baseline shift artifact about half way through data
max_shift = np.max(np.diff(raw._data[0]))
shift_amp = 5 * max_shift
raw._data[0, 0:30] = raw._data[0, 0:30] - (shift_amp)
assert np.max(np.diff(raw._data[0])) > shift_amp
# Ensure that applying the algorithm reduces the step change
raw = tddr(raw)
assert np.max(np.diff(raw._data[0])) < shift_amp