Python: How to interpolate errors using scipy interpolate.interp1d
<p>I have a number of data sets, each containing <em>x, y</em>, and <em>y_error</em> values, and I'm simply trying to calculate the average value of <em>y</em> at each <em>x</em> across these data sets. However the data sets are not quite the same length. I thought the best way to get them to an equal length would be to use scipy's <code>interoplate.interp1d</code> for each data set. However, I still need to be able to calculate the error on each of these averaged values, and I'm quite lost on how to accomplish that after doing an interpolation.</p>

<p>I'm pretty new to Python and coding in general, so I appreciate your help!</p>

<p>As long as you can assume that your errors represent one-sigma intervals of normal distributions, you can always generate synthetic datasets, resample and interpolate those, and compute the 1-sigma errors of the results.</p>

<p>Or just interpolate values+err and values-err, if all you need is a quick and dirty rough estimate.</p>

python scipy interpolation