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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_less
from mne.datasets import testing
from mne.datasets.testing import data_path
from mne.io import read_raw_nirx
from mne.preprocessing.nirs import (
beer_lambert_law,
optical_density,
scalp_coupling_index,
)
fname_nirx_15_0 = (
data_path(download=False) / "NIRx" / "nirscout" / "nirx_15_0_recording"
)
fname_nirx_15_2 = (
data_path(download=False) / "NIRx" / "nirscout" / "nirx_15_2_recording"
)
fname_nirx_15_2_short = (
data_path(download=False) / "NIRx" / "nirscout" / "nirx_15_2_recording_w_short"
)
@testing.requires_testing_data
@pytest.mark.parametrize(
"fname", ([fname_nirx_15_2_short, fname_nirx_15_2, fname_nirx_15_0])
)
@pytest.mark.parametrize("fmt", ("nirx", "fif"))
def test_scalp_coupling_index(fname, fmt, tmp_path):
"""Test converting NIRX files."""
assert fmt in ("nirx", "fif")
raw = read_raw_nirx(fname)
with pytest.raises(RuntimeError, match="Scalp"):
scalp_coupling_index(raw)
raw = optical_density(raw)
sci = scalp_coupling_index(raw)
# All values should be between -1 and +1
assert_array_less(sci, 1.0)
assert_array_less(sci * -1.0, 1.0)
# Fill in some data with known correlation values
rng = np.random.RandomState(0)
new_data = rng.rand(raw._data[0].shape[0])
# Set first two channels to perfect correlation
raw._data[0] = new_data
raw._data[1] = new_data
# Set next two channels to perfect correlation
raw._data[2] = new_data
raw._data[3] = new_data * 0.3 # check scale invariance
# Set next two channels to anti correlation
raw._data[4] = new_data
raw._data[5] = new_data * -1.0
# Set next two channels to be uncorrelated
raw._data[6] = new_data
raw._data[7] = rng.rand(raw._data[0].shape[0])
# Set next channel to have zero std
raw._data[8] = 0.0
raw._data[9] = 1.0
raw._data[10] = 2.0
raw._data[11] = 3.0
# Check values
sci = scalp_coupling_index(raw)
assert_allclose(sci[0:6], [1, 1, 1, 1, -1, -1], atol=0.01)
assert np.abs(sci[6]) < 0.5
assert np.abs(sci[7]) < 0.5
assert_allclose(sci[8:12], 0, atol=1e-10)
# Ensure function errors if wrong type is passed in
raw = beer_lambert_law(raw, ppf=6)
with pytest.raises(RuntimeError, match="Scalp"):
scalp_coupling_index(raw)
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