针对pulse-transit的工具

dist/client/mne/preprocessing/nirs/_beer_lambert_law.py

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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