def _tcorrel(self, nstep=100, **kwargs):
        """Correlation "time" of data.

        The 0-th column of the data is interpreted as a time and the
        decay of the data is computed from the autocorrelation
        function (using FFT).

        .. SeeAlso:: :func:`numkit.timeseries.tcorrel`
        """
        t = self.array[0,::nstep]
        r = gromacs.collections.Collection([numkit.timeseries.tcorrel(t, Y, nstep=1, **kwargs) for Y in self.array[1:,::nstep]])
        return r
def _tcorrel(self, nstep=100, **kwargs):
        """Correlation "time" of data.

        The 0-th column of the data is interpreted as a time and the
        decay of the data is computed from the autocorrelation
        function (using FFT).

        .. SeeAlso:: :func:`numkit.timeseries.tcorrel`
        """
        t = self.array[0,::nstep]
        r = gromacs.collections.Collection([numkit.timeseries.tcorrel(t, Y, nstep=1, **kwargs) for Y in self.array[1:,::nstep]])
        return r
def _tcorrel ( self , nstep = 100 , * * kwargs ) : t = self . array [ 0 , : : nstep ] r = gromacs . collections . Collection ( [ numkit . timeseries . tcorrel ( t , Y , nstep = 1 , * * kwargs ) for Y in self . array [ 1 : , : : nstep ] ] ) return r
Correlation "time" of data.

        The 0-th column of the data is interpreted as a time and the
        decay of the data is computed from the autocorrelation
        function (using FFT).

        .. SeeAlso:: :func:`numkit.timeseries.tcorrel`
Correlation time of data .