Class AverageTFRArray

mne/time_frequency/tfr.py:2980–3033

Data object for *precomputed* spectrotemporal representations of averaged data. Parameters ---------- %(info_not_none)s %(data_tfr)s %(times)s %(freqs_tfr_array)s nave : int The number of averaged TFRs. %(comment_averagetfr_attr)s %(method_tfr_array)s

@fill_doc
class AverageTFRArray(AverageTFR):
    """Data object for *precomputed* spectrotemporal representations of averaged data.

    Parameters
    ----------
    %(info_not_none)s
    %(data_tfr)s
    %(times)s
    %(freqs_tfr_array)s
    nave : int
        The number of averaged TFRs.
    %(comment_averagetfr_attr)s
    %(method_tfr_array)s
    %(weights_tfr_array)s

    Attributes
    ----------
    %(baseline_tfr_attr)s
    %(ch_names_tfr_attr)s
    %(comment_averagetfr_attr)s
    %(freqs_tfr_attr)s
    %(info_not_none)s
    %(method_tfr_attr)s
    %(nave_tfr_attr)s
    %(sfreq_tfr_attr)s
    %(shape_tfr_attr)s
    %(weights_tfr_attr)s

    See Also
    --------
    AverageTFR
    EpochsTFRArray
    mne.Epochs.compute_tfr
    mne.Evoked.compute_tfr
    """

    def __init__(
        self,
        info,
        data,
        times,
        freqs,
        *,
        nave=None,
        comment=None,
        method=None,
        weights=None,
    ):
        state = dict(info=info, data=data, times=times, freqs=freqs)
        optional = dict(nave=nave, comment=comment, method=method, weights=weights)
        for name, value in optional.items():
            if value is not None:
                state[name] = value
        self.__setstate__(state)


@fill_doc