code: def rms(self, stride=1):
          
          stridesamp = int(stride * self.sample_rate.value)
          nsteps = int(self.size // stridesamp)
          # stride through TimeSeries, recording RMS
          data = numpy.zeros(nsteps)
          for step in range(nsteps):
              # find step TimeSeries
              idx = int(stridesamp * step)
              idx_end = idx + stridesamp
              stepseries = self[idx:idx_end]
              rms_ = numpy.sqrt(numpy.mean(numpy.abs(stepseries.value)**2))
              data[step] = rms_
          name = '%s %.2f-second RMS' % (self.name, stride)
          return self.__class__(data, channel=self.channel, t0=self.t0,
                                name=name, sample_rate=(1/float(stride)))
docs: 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