Python: How to interpolate errors using scipy interpolate.interp1d
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.
Or just interpolate values+err and values-err, if all you need is a quick and dirty rough estimate.
I have a number of data sets, each containing x, y, and y_error values, and I'm simply trying to calculate the average value of y at each x 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 interoplate.interp1d 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.
I'm pretty new to Python and coding in general, so I appreciate your help!