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tyler
2025-08-19 09:13:22 -07:00
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mne/preprocessing/nirs/nirs.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import re
import numpy as np
from ..._fiff.pick import _picks_to_idx, pick_types
from ...utils import _check_option, _validate_type, fill_doc
# Standardized fNIRS channel name regexs
_S_D_F_RE = re.compile(r"S(\d+)_D(\d+) (\d+\.?\d*)")
_S_D_H_RE = re.compile(r"S(\d+)_D(\d+) (\w+)")
@fill_doc
def source_detector_distances(info, picks=None):
r"""Determine the distance between NIRS source and detectors.
Parameters
----------
%(info_not_none)s
%(picks_all_data)s
Returns
-------
dists : array of float
Array containing distances in meters.
Of shape equal to number of channels, or shape of picks if supplied.
"""
return np.array(
[
np.linalg.norm(
np.diff(info["chs"][pick]["loc"][3:9].reshape(2, 3), axis=0)[0]
)
for pick in _picks_to_idx(info, picks, exclude=[])
],
float,
)
@fill_doc
def short_channels(info, threshold=0.01):
r"""Determine which NIRS channels are short.
Channels with a source to detector distance of less than
``threshold`` are reported as short. The default threshold is 0.01 m.
Parameters
----------
%(info_not_none)s
threshold : float
The threshold distance for what is considered short in meters.
Returns
-------
short : array of bool
Array indicating which channels are short.
Of shape equal to number of channels.
"""
return source_detector_distances(info) < threshold
def _channel_frequencies(info):
"""Return the light frequency for each channel."""
# Only valid for fNIRS data before conversion to haemoglobin
picks = _picks_to_idx(
info, ["fnirs_cw_amplitude", "fnirs_od"], exclude=[], allow_empty=True
)
freqs = list()
for pick in picks:
freqs.append(round(float(_S_D_F_RE.match(info["ch_names"][pick]).groups()[2])))
return np.array(freqs, int)
def _channel_chromophore(info):
"""Return the chromophore of each channel."""
# Only valid for fNIRS data after conversion to haemoglobin
picks = _picks_to_idx(info, ["hbo", "hbr"], exclude=[], allow_empty=True)
chroma = []
for ii in picks:
chroma.append(info["ch_names"][ii].split(" ")[1])
return chroma
def _check_channels_ordered(info, pair_vals, *, throw_errors=True, check_bads=True):
"""Check channels follow expected fNIRS format.
If the channels are correctly ordered then an array of valid picks
will be returned.
If throw_errors is True then any errors in fNIRS formatting will be
thrown to inform the user. If throw_errors is False then an empty array
will be returned if the channels are not sufficiently formatted.
"""
# Every second channel should be same SD pair
# and have the specified light frequencies.
# All wavelength based fNIRS data.
picks_wave = _picks_to_idx(
info, ["fnirs_cw_amplitude", "fnirs_od"], exclude=[], allow_empty=True
)
# All chromophore fNIRS data
picks_chroma = _picks_to_idx(info, ["hbo", "hbr"], exclude=[], allow_empty=True)
if (len(picks_wave) > 0) & (len(picks_chroma) > 0):
picks = _throw_or_return_empty(
"MNE does not support a combination of amplitude, optical "
"density, and haemoglobin data in the same raw structure.",
throw_errors,
)
# All continuous wave fNIRS data
if len(picks_wave):
error_word = "frequencies"
use_RE = _S_D_F_RE
picks = picks_wave
else:
error_word = "chromophore"
use_RE = _S_D_H_RE
picks = picks_chroma
pair_vals = np.array(pair_vals)
if pair_vals.shape != (2,):
raise ValueError(
f"Exactly two {error_word} must exist in info, got {list(pair_vals)}"
)
# In principle we do not need to require that these be sorted --
# all we need to do is change our sorted() below to make use of a
# pair_vals.index(...) in a sort key -- but in practice we always want
# (hbo, hbr) or (lower_freq, upper_freq) pairings, both of which will
# work with a naive string sort, so let's just enforce sorted-ness here
is_str = pair_vals.dtype.kind == "U"
pair_vals = list(pair_vals)
if is_str:
if pair_vals != ["hbo", "hbr"]:
raise ValueError(
f'The {error_word} in info must be ["hbo", "hbr"], but got '
f"{pair_vals} instead"
)
elif not np.array_equal(np.unique(pair_vals), pair_vals):
raise ValueError(
f"The {error_word} in info must be unique and sorted, but got "
f"got {pair_vals} instead"
)
if len(picks) % 2 != 0:
picks = _throw_or_return_empty(
"NIRS channels not ordered correctly. An even number of NIRS "
f"channels is required. {len(info.ch_names)} channels were"
f"provided",
throw_errors,
)
# Ensure wavelength info exists for waveform data
all_freqs = [info["chs"][ii]["loc"][9] for ii in picks_wave]
if np.any(np.isnan(all_freqs)):
picks = _throw_or_return_empty(
f"NIRS channels is missing wavelength information in the "
f'info["chs"] structure. The encoded wavelengths are {all_freqs}.',
throw_errors,
)
# Validate the channel naming scheme
for pick in picks:
ch_name_info = use_RE.match(info["chs"][pick]["ch_name"])
if not bool(ch_name_info):
picks = _throw_or_return_empty(
"NIRS channels have specified naming conventions. "
"The provided channel name can not be parsed: "
f"{repr(info.ch_names[pick])}",
throw_errors,
)
break
value = ch_name_info.groups()[2]
if len(picks_wave):
value = value
else: # picks_chroma
if value not in ["hbo", "hbr"]:
picks = _throw_or_return_empty(
"NIRS channels have specified naming conventions."
"Chromophore data must be labeled either hbo or hbr. "
f"The failing channel is {info['chs'][pick]['ch_name']}",
throw_errors,
)
break
# Reorder to be paired (naive sort okay here given validation above)
picks = picks[np.argsort([info["ch_names"][pick] for pick in picks])]
# Validate our paired ordering
for ii, jj in zip(picks[::2], picks[1::2]):
ch1_name = info["chs"][ii]["ch_name"]
ch2_name = info["chs"][jj]["ch_name"]
ch1_re = use_RE.match(ch1_name)
ch2_re = use_RE.match(ch2_name)
ch1_S, ch1_D, ch1_value = ch1_re.groups()[:3]
ch2_S, ch2_D, ch2_value = ch2_re.groups()[:3]
if len(picks_wave):
ch1_value, ch2_value = float(ch1_value), float(ch2_value)
if (
(ch1_S != ch2_S)
or (ch1_D != ch2_D)
or (ch1_value != pair_vals[0])
or (ch2_value != pair_vals[1])
):
picks = _throw_or_return_empty(
"NIRS channels not ordered correctly. Channels must be "
"ordered as source detector pairs with alternating"
f" {error_word} {pair_vals[0]} & {pair_vals[1]}, but got "
f"S{ch1_S}_D{ch1_D} pair "
f"{repr(ch1_name)} and {repr(ch2_name)}",
throw_errors,
)
break
if check_bads:
for ii, jj in zip(picks[::2], picks[1::2]):
want = [info.ch_names[ii], info.ch_names[jj]]
got = list(set(info["bads"]).intersection(want))
if len(got) == 1:
raise RuntimeError(
f"NIRS bad labelling is not consistent, found {got} but "
f"needed {want}"
)
return picks
def _throw_or_return_empty(msg, throw_errors):
if throw_errors:
raise ValueError(msg)
else:
return []
def _validate_nirs_info(
info,
*,
throw_errors=True,
fnirs=None,
which=None,
check_bads=True,
allow_empty=True,
):
"""Apply all checks to fNIRS info. Works on all continuous wave types."""
_validate_type(fnirs, (None, str), "fnirs")
kinds = dict(
od="optical density",
cw_amplitude="continuous wave",
hb="chromophore",
)
_check_option("fnirs", fnirs, (None,) + tuple(kinds))
if fnirs is not None:
kind = kinds[fnirs]
fnirs = ["hbo", "hbr"] if fnirs == "hb" else f"fnirs_{fnirs}"
if not len(pick_types(info, fnirs=fnirs)):
raise RuntimeError(
f"{which} must operate on {kind} data, but none was found."
)
freqs = np.unique(_channel_frequencies(info))
if freqs.size > 0:
pair_vals = freqs
else:
pair_vals = np.unique(_channel_chromophore(info))
out = _check_channels_ordered(
info, pair_vals, throw_errors=throw_errors, check_bads=check_bads
)
return out
def _fnirs_spread_bads(info):
"""Spread bad labeling across fnirs channels."""
# For an optode pair if any component (light frequency or chroma) is marked
# as bad, then they all should be. This function will find any pairs marked
# as bad and spread the bad marking to all components of the optode pair.
picks = _validate_nirs_info(info, check_bads=False)
new_bads = set(info["bads"])
for ii, jj in zip(picks[::2], picks[1::2]):
ch1_name, ch2_name = info.ch_names[ii], info.ch_names[jj]
if ch1_name in new_bads:
new_bads.add(ch2_name)
elif ch2_name in new_bads:
new_bads.add(ch1_name)
info["bads"] = sorted(new_bads)
return info
def _fnirs_optode_names(info):
"""Return list of unique optode names."""
picks_wave = _picks_to_idx(
info, ["fnirs_cw_amplitude", "fnirs_od"], exclude=[], allow_empty=True
)
picks_chroma = _picks_to_idx(info, ["hbo", "hbr"], exclude=[], allow_empty=True)
if len(picks_wave) > 0:
regex = _S_D_F_RE
elif len(picks_chroma) > 0:
regex = _S_D_H_RE
else:
return [], []
sources = np.unique([int(regex.match(ch).groups()[0]) for ch in info.ch_names])
detectors = np.unique([int(regex.match(ch).groups()[1]) for ch in info.ch_names])
src_names = [f"S{s}" for s in sources]
det_names = [f"D{d}" for d in detectors]
return src_names, det_names
def _optode_position(info, optode):
"""Find the position of an optode."""
idx = [optode in a for a in info.ch_names].index(True)
if "S" in optode:
loc_idx = range(3, 6)
elif "D" in optode:
loc_idx = range(6, 9)
return info["chs"][idx]["loc"][loc_idx]
def _reorder_nirx(raw):
# Maybe someday we should make this public like
# mne.preprocessing.nirs.reorder_standard(raw, order='nirx')
info = raw.info
picks = pick_types(info, fnirs=True, exclude=[])
prefixes = [info["ch_names"][pick].split()[0] for pick in picks]
nirs_names = [info["ch_names"][pick] for pick in picks]
nirs_sorted = sorted(
nirs_names,
key=lambda name: (prefixes.index(name.split()[0]), name.split(maxsplit=1)[1]),
)
raw.reorder_channels(nirs_sorted)