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