Isolate High Variance vs Low Variance Sections of Signals I have the following one-dimensional signals:




The goal is to identify the sections with smaller variances compared to the rest (For Python, that is to get their index value in the array). In the first signal, there are two such sections (one at the left end and one at the middle). In the second signal, there is one such section at the middle. 

What I've tried so far

I've tried computing the local stdev in each signals with window size 25 and produced the following plots:




From here on, I can use threshold to select sections with small stdev. However, one can see that both have different y-axis, and need different threshold values to isolate the low variance sections. Furthermore, applying a rolling window is notoriously computational expensive (I have to apply this on ~5,000,000 similar one-dimensional signals). So the question is, what is a quick method to identify sections that can be applied to both signals?

EDIT

I've put two sample signals at here and here.
What you did is the reasonable solution.
From here you can do 2 things to mitigate your issues:


Computation Efficiency
You can use online calculation of the Mean and the STD.
Remember when you move one sample to the right 24 samples are shared.
Hence if you take advantage of that you'll get a performance boost.
Threshold
Use adaptive threshold per signal if your classification of the STD is relative. Namely per signal you want to find zones with high variance relative to other zones in the same signal (And not an absolute measure).
You could use statistical properties of the STD of the signal like Mean / Median or even use adaptive Threhshold method like Otsu's Method.