A friend of mine is evaluating data with Pythons package
This results due to my
array being an
AffineScalarFunc (as opposed to a
Variable), and thus they not only store the value but also all the variables that the value depends on .
Now, my values are not fully independent (which wasn't clear at all at first sight*), and thus
sum(array) also considers the off-diagonal elements of my covariance matrix in accordance to this formula (sorry that the article is in German, but English Wikipedias formula isn't as intuitive), whereas
sqrt(sum(unumpy.std_devs(array)**2)) obviously doesn't and just adds up the diagonal elements.
A way to reproduce what uncertainties does is:
from uncertainties import covariance_matrix sum=0 for i in range(0,len(array)): for j in range(0,len(array)): sum+=covariancematrix(array)[i][j] print(sqrt(sum))
*Correlation due to the use of data taken from the same interpolation (of measurements) and because the length of a measurement was calculated as the difference of two times (and meassurement were consecutive, so the times are now correlated!)