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# Authors: Robert Luke <mail@robertluke.net>
# Eric Larson <larson.eric.d@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
from ...io import BaseRaw
from ...io.constants import FIFF
from ...utils import _validate_type, warn
from ..nirs import source_detector_distances, _validate_nirs_info
def beer_lambert_law(raw, ppf=6.):
r"""Convert NIRS optical density data to haemoglobin concentration.
Parameters
----------
raw : instance of Raw
The optical density data.
ppf : float
The partial pathlength factor.
Returns
-------
raw : instance of Raw
The modified raw instance.
"""
from scipy import linalg
raw = raw.copy().load_data()
_validate_type(raw, BaseRaw, 'raw')
_validate_type(ppf, 'numeric', 'ppf')
ppf = float(ppf)
picks = _validate_nirs_info(raw.info, fnirs='od', which='Beer-lambert')
# This is the one place we *really* need the actual/accurate frequencies
freqs = np.array(
[raw.info['chs'][pick]['loc'][9] for pick in picks], float)
abs_coef = _load_absorption(freqs)
distances = source_detector_distances(raw.info, picks='all')
bad = ~np.isfinite(distances[picks])
bad |= distances[picks] <= 0
if bad.any():
warn('Source-detector distances are zero on NaN, some resulting '
'concentrations will be zero. Consider setting a montage '
'with raw.set_montage.')
distances[picks[bad]] = 0.
if (distances[picks] > 0.1).any():
warn('Source-detector distances are greater than 10 cm. '
'Large distances will result in invalid data, and are '
'likely due to optode locations being stored in a '
' unit other than meters.')
rename = dict()
for ii, jj in zip(picks[::2], picks[1::2]):
EL = abs_coef * distances[ii] * ppf
iEL = linalg.pinv(EL)
raw._data[[ii, jj]] = iEL @ raw._data[[ii, jj]] * 1e-3
# Update channel information
coil_dict = dict(hbo=FIFF.FIFFV_COIL_FNIRS_HBO,
hbr=FIFF.FIFFV_COIL_FNIRS_HBR)
for ki, kind in zip((ii, jj), ('hbo', 'hbr')):
ch = raw.info['chs'][ki]
ch.update(coil_type=coil_dict[kind], unit=FIFF.FIFF_UNIT_MOL)
new_name = f'{ch["ch_name"].split(" ")[0]} {kind}'
rename[ch['ch_name']] = new_name
raw.rename_channels(rename)
# Validate the format of data after transformation is valid
_validate_nirs_info(raw.info, fnirs='hb')
return raw
def _load_absorption(freqs):
"""Load molar extinction coefficients."""
# Data from https://omlc.org/spectra/hemoglobin/summary.html
# The text was copied to a text file. The text before and
# after the table was deleted. The the following was run in
# matlab
# extinct_coef=importdata('extinction_coef.txt')
# save('extinction_coef.mat', 'extinct_coef')
#
# Returns data as [[HbO2(freq1), Hb(freq1)],
# [HbO2(freq2), Hb(freq2)]]
from scipy.io import loadmat
from scipy.interpolate import interp1d
extinction_fname = op.join(op.dirname(__file__), '..', '..', 'data',
'extinction_coef.mat')
a = loadmat(extinction_fname)['extinct_coef']
interp_hbo = interp1d(a[:, 0], a[:, 1], kind='linear')
interp_hb = interp1d(a[:, 0], a[:, 2], kind='linear')
ext_coef = np.array([[interp_hbo(freqs[0]), interp_hb(freqs[0])],
[interp_hbo(freqs[1]), interp_hb(freqs[1])]])
abs_coef = ext_coef * 0.2303
return abs_coef
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