def interpolate(self, df):
f0 = self.f0.decompose().value<EOL>N = (self.size - <NUM_LIT:1>) * (self.df.decompose().value / df) + <NUM_LIT:1><EOL>fsamples = numpy.arange(<NUM_LIT:0>, numpy.rint(N), dtype=self.dtype) * df + f0<EOL>out = type(self)(numpy.interp(fsamples, self.frequencies.value,<EOL>self.value))<EOL>out.__array_finalize__(self)<EOL>out.f0 = f0<EOL>out.df = df<EOL>return out<EOL>
Interpolate this `FrequencySeries` to a new resolution.

        Parameters
        ----------
        df : `float`
            desired frequency resolution of the interpolated `FrequencySeries`,
            in Hz

        Returns
        -------
        out : `FrequencySeries`
            the interpolated version of the input `FrequencySeries`

        See Also
        --------
        numpy.interp
            for the underlying 1-D linear interpolation scheme