title: Interpolating and estimating uncertainty
body: <p>Let's say I have a gappy time series <code>x</code>, with a particular measurement through time. I also have a measure of uncertainty <code>sx</code> (say the standard deviation of <code>x</code> at a particular interval). I just basically want to gap-fill the time series, but I want to propagate the measurement uncertainty, and hopefully, the interpolation uncertainty. In the past, I have used Tikhonov regularisation to achieve this (i.e. adding a smoothness constraint), but I'd rather just use an out-of-the-box routine from scipy. I can see that the scipy spline interpolation routines take a smoothing parameter (that would be derived for <code>sx</code> here), but it doesn't calculate the interpolated series' uncertainty.</p>
  
  <p>I'm asking because I don't think it's a trivial calculation, and to see if anyone know whether this capability is available.</p>