Tilman - 3 months ago 8

Python Question

A friend of mine is evaluating data with Pythons package

`uncertainties`

`sum(array)`

`sqrt(sum(unumpy.std_devs(array)**2))`

Now, I know that the variance approach is only suited for when the error is small compared to the partial derivate (because of the Taylor series) which isn't given in this case, but how does

`uncertainties`

Answer

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 [1].

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))
```

And then `unumpy.std_devs(sum(array))==sqrt(sum)`

is `True`

.

*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!)

Source (Stackoverflow)

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