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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os.path as op
import numpy as np
from scipy.interpolate import interp1d
from scipy.io import loadmat
from ..._fiff.constants import FIFF
from ...io import BaseRaw
from ...utils import _validate_type, pinv, warn
from ..nirs import _validate_nirs_info, source_detector_distances
def beer_lambert_law(raw, ppf=6.0):
r"""Convert NIRS optical density data to haemoglobin concentration.
Parameters
----------
raw : instance of Raw
The optical density data.
ppf : tuple | float
The partial pathlength factors for each wavelength.
.. versionchanged:: 1.7
Support for different factors for the two wavelengths.
Returns
-------
raw : instance of Raw
The modified raw instance.
"""
raw = raw.copy().load_data()
_validate_type(raw, BaseRaw, "raw")
_validate_type(ppf, ("numeric", "array-like"), "ppf")
ppf = np.array(ppf, float)
if ppf.ndim == 0: # upcast single float to shape (2,)
ppf = np.array([ppf, ppf])
if ppf.shape != (2,):
raise ValueError(
f"ppf must be float or array-like of shape (2,), got shape {ppf.shape}"
)
ppf = ppf[:, np.newaxis] # shape (2, 1)
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]] = 0.0
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 = 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)]]
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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