initial commit

mne/preprocessing/nirs/_optical_density.py

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+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+import numpy as np
+
+from ..._fiff.constants import FIFF
+from ...io import BaseRaw
+from ...utils import _validate_type, verbose, warn
+from ..nirs import _validate_nirs_info
+
+
+@verbose
+def optical_density(raw, *, verbose=None):
+    r"""Convert NIRS raw data to optical density.
+
+    Parameters
+    ----------
+    raw : instance of Raw
+        The raw data.
+    %(verbose)s
+
+    Returns
+    -------
+    raw : instance of Raw
+        The modified raw instance.
+    """
+    raw = raw.copy().load_data()
+    _validate_type(raw, BaseRaw, "raw")
+    picks = _validate_nirs_info(raw.info, fnirs="cw_amplitude")
+
+    # The devices measure light intensity. Negative light intensities should
+    # not occur. If they do it is likely due to hardware or movement issues.
+    # Set all negative values to abs(x), this also has the benefit of ensuring
+    # that the means are all greater than zero for the division below.
+    if np.any(raw._data[picks] <= 0):
+        warn("Negative intensities encountered. Setting to abs(x)")
+        min_ = np.inf
+        for pi in picks:
+            np.abs(raw._data[pi], out=raw._data[pi])
+            min_ = min(min_, raw._data[pi].min() or min_)
+        # avoid == 0
+        for pi in picks:
+            np.maximum(raw._data[pi], min_, out=raw._data[pi])
+
+    for pi in picks:
+        data_mean = np.mean(raw._data[pi])
+        raw._data[pi] /= data_mean
+        np.log(raw._data[pi], out=raw._data[pi])
+        raw._data[pi] *= -1
+        raw.info["chs"][pi]["coil_type"] = FIFF.FIFFV_COIL_FNIRS_OD
+
+    return raw