Function _otp

mne/preprocessing/otp.py:112–140

Perform OTP on one segment of data.

(data, picks_good, picks_bad, *, start=0, stop=None)

def _otp(data, picks_good, picks_bad, *, start=0, stop=None):
    """Perform OTP on one segment of data."""
    if not np.isfinite(data).all():
        raise RuntimeError("non-finite data (inf or nan) found in raw instance")
    # demean our data
    data_means = np.mean(data, axis=-1, keepdims=True)
    data -= data_means
    # make a copy
    data_good = data[picks_good]
    # scale the copy that will be used to form the temporal basis vectors
    # so that _orth_svdvals thresholding should work properly with
    # different channel types (e.g., M-EEG)
    norms = _normalize_vectors(data_good)
    cov = np.dot(data_good, data_good.T)
    if len(picks_bad) > 0:
        full_basis = _svd_cov(cov, data_good)[2]
    for mi, pick in enumerate(picks_good):
        # operate on original data
        idx = list(range(mi)) + list(range(mi + 1, len(data_good)))
        # Equivalent: svd(data[idx], full_matrices=False)[2]
        t_basis = _svd_cov(cov[np.ix_(idx, idx)], data_good[idx])[2]
        x = np.dot(np.dot(data_good[mi], t_basis.T), t_basis)
        x *= norms[mi]
        x += data_means[pick]
        data[pick] = x
    for pick in picks_bad:
        data[pick] = np.dot(np.dot(data[pick], full_basis.T), full_basis)
        data[pick] += data_means[pick]
    return [data]