Python Raw._data[event_ch,event_idxs]方法代码示例

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import _data[event_ch,event_idxs] [as 别名]
def simulate_movement(raw, pos, stc, trans, src, bem, cov='simple',
                      mindist=1.0, interp='linear', random_state=None,
                      n_jobs=1, verbose=None):
    """Simulate raw data with head movements

    Parameters
    ----------
    raw : instance of Raw
        The raw instance to use. The measurement info, including the
        head positions, will be used to simulate data.
    pos : str | dict | None
        Name of the position estimates file. Should be in the format of
        the files produced by maxfilter-produced. If dict, keys should
        be the time points and entries should be 4x3 ``dev_head_t``
        matrices. If None, the original head position (from
        ``raw.info['dev_head_t']``) will be used.
    stc : instance of SourceEstimate
        The source estimate to use to simulate data. Must have the same
        sample rate as the raw data.
    trans : dict | str
        Either a transformation filename (usually made using mne_analyze)
        or an info dict (usually opened using read_trans()).
        If string, an ending of `.fif` or `.fif.gz` will be assumed to
        be in FIF format, any other ending will be assumed to be a text
        file with a 4x4 transformation matrix (like the `--trans` MNE-C
        option).
    src : str | instance of SourceSpaces
        If string, should be a source space filename. Can also be an
        instance of loaded or generated SourceSpaces.
    bem : str
        Filename of the BEM (e.g., "sample-5120-5120-5120-bem-sol.fif").
    cov : instance of Covariance | 'simple' | None
        The sensor covariance matrix used to generate noise. If None,
        no noise will be added. If 'simple', a basic (diagonal) ad-hoc
        noise covariance will be used.
    mindist : float
        Minimum distance between sources and the inner skull boundary
        to use during forward calculation.
    interp : str
        Either 'linear' or 'zero', the type of forward-solution
        interpolation to use between provided time points.
    random_state : None | int | np.random.RandomState
        To specify the random generator state.
    n_jobs : int
        Number of jobs to use.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see mne.verbose).

    Returns
    -------
    raw : instance of Raw
        The simulated raw file.

    Notes
    -----
    Events coded with the number of the forward solution used will be placed
    in the raw files in the trigger channel STI101 at the t=0 times of the
    SourceEstimates.

    The resulting SNR will be determined by the structure of the noise
    covariance, and the amplitudes of the SourceEstimate. Note that this
    will vary as a function of position.
    """
    if isinstance(raw, string_types):
        with warnings.catch_warnings(record=True):
            raw = Raw(raw, allow_maxshield=True, preload=True, verbose=False)
    else:
        raw = raw.copy()

    if not isinstance(stc, _BaseSourceEstimate):
        raise TypeError('stc must be a SourceEstimate')
    if not np.allclose(raw.info['sfreq'], 1. / stc.tstep):
        raise ValueError('stc and raw must have same sample rate')
    rng = check_random_state(random_state)
    if interp not in ('linear', 'zero'):
        raise ValueError('interp must be "linear" or "zero"')

    if pos is None:  # use pos from file
        dev_head_ts = [raw.info['dev_head_t']] * 2
        offsets = np.array([0, raw.n_times])
        interp = 'zero'
    else:
        if isinstance(pos, string_types):
            pos = get_chpi_positions(pos, verbose=False)
        if isinstance(pos, tuple):  # can be an already-loaded pos file
            transs, rots, ts = pos
            ts -= raw.first_samp / raw.info['sfreq']  # MF files need reref
            dev_head_ts = [np.r_[np.c_[r, t[:, np.newaxis]], [[0, 0, 0, 1]]]
                           for r, t in zip(rots, transs)]
            del transs, rots
        elif isinstance(pos, dict):
            ts = np.array(list(pos.keys()), float)
            ts.sort()
            dev_head_ts = [pos[float(tt)] for tt in ts]
        else:
            raise TypeError('unknown pos type %s' % type(pos))
        if not (ts >= 0).all():  # pathological if not
            raise RuntimeError('Cannot have t < 0 in transform file')
        tend = raw.times[-1]
        assert not (ts < 0).any()
#.........这里部分代码省略.........