@verbose
    def resample(self, sfreq, npad=None, window='boxcar', n_jobs=1,
                 verbose=None):
        """Resample data

        Parameters
        ----------
        sfreq : float
            New sample rate to use.
        npad : int | str
            Amount to pad the start and end of the data.
            Can also be "auto" to use a padding that will result in
            a power-of-two size (can be much faster).
        window : string or tuple
            Window to use in resampling. See scipy.signal.resample.
        n_jobs : int
            Number of jobs to run in parallel.
        verbose : bool, str, int, or None
            If not None, override default verbose level (see mne.verbose).
            Defaults to self.verbose.

        Notes
        -----
        For some data, it may be more accurate to use npad=0 to reduce
        artifacts. This is dataset dependent -- check your data!

        Note that the sample rate of the original data is inferred from tstep.
        """
        if npad is None:
            npad = 100
            warn_('npad is currently taken to be 100, but will be changed to '
                  '"auto" in 0.12. Please set the value explicitly.',
                  DeprecationWarning)
        # resampling in sensor instead of source space gives a somewhat
        # different result, so we don't allow it
        self._remove_kernel_sens_data_()

        o_sfreq = 1.0 / self.tstep
        self._data = resample(self._data, sfreq, o_sfreq, npad, n_jobs=n_jobs)

        # adjust indirectly affected variables
        self.tstep = 1.0 / sfreq
        self._update_times()
dataset/ETHPy150Open mne-tools/mne-python/mne/source_estimate.py/_BaseSourceEstimate.resample/original