def rms(self, stride=<NUM_LIT:1>):
stridesamp = int(stride * self.sample_rate.value)<EOL>nsteps = int(self.size // stridesamp)<EOL>data = numpy.zeros(nsteps)<EOL>for step in range(nsteps):<EOL><INDENT>idx = int(stridesamp * step)<EOL>idx_end = idx + stridesamp<EOL>stepseries = self[idx:idx_end]<EOL>rms_ = numpy.sqrt(numpy.mean(numpy.abs(stepseries.value)**<NUM_LIT:2>))<EOL>data[step] = rms_<EOL><DEDENT>name = '<STR_LIT>' % (self.name, stride)<EOL>return self.__class__(data, channel=self.channel, t0=self.t0,<EOL>name=name, sample_rate=(<NUM_LIT:1>/float(stride)))<EOL>
Calculate the root-mean-square value of this `TimeSeries`
        once per stride.

        Parameters
        ----------
        stride : `float`
            stride (seconds) between RMS calculations

        Returns
        -------
        rms : `TimeSeries`
            a new `TimeSeries` containing the RMS value with dt=stride