def bin(self, width, tstart=None, tstop=None, func=np.mean):
        """Returns a SourceEstimate object with data summarized over time bins

        Time bins of ``width`` seconds. This method is intended for
        visualization only. No filter is applied to the data before binning,
        making the method inappropriate as a tool for downsampling data.

        Parameters
        ----------
        width : scalar
            Width of the individual bins in seconds.
        tstart : scalar | None
            Time point where the first bin starts. The default is the first
            time point of the stc.
        tstop : scalar | None
            Last possible time point contained in a bin (if the last bin would
            be shorter than width it is dropped). The default is the last time
            point of the stc.
        func : callable
            Function that is applied to summarize the data. Needs to accept a
            numpy.array as first input and an ``axis`` keyword argument.

        Returns
        -------
        stc : instance of SourceEstimate
            The binned SourceEstimate.
        """
        if tstart is None:
            tstart = self.tmin
        if tstop is None:
            tstop = self.times[-1]

        times = np.arange(tstart, tstop + self.tstep, width)
        nv, _ = self.shape
        nt = len(times) - 1
        data = np.empty((nv, nt), dtype=self.data.dtype)
        for i in range(nt):
            idx = (times[i] >= self.times) & (self.times < times[i + 1])
            data[:, i] = func(self.data[:, idx], axis=1)

        tmin = times[0] + width / 2.
        stc = _make_stc(data, vertices=self.vertices,
                        tmin=tmin, tstep=width, subject=self.subject)
        return stc
dataset/ETHPy150Open mne-tools/mne-python/mne/source_estimate.py/_BaseSourceEstimate.bin/swapped operands of `>=`