In order to combine time series data, I am left with the following essential step:

```
>>> xs1
array([ 0, 10, 12, 16, 25, 29])
>>> xs2
array([ 0,  5, 10, 15, 20, 25, 30])

```

How to best get the following solutions:

```
>>> xs1_ = np.array([0,0,10,12,12,16,16,25,29,29])
>>> xs2_ = np.array([0,5,10,10,15,15,20,25,25,30])

```

This is to align the measurements taken at times `x1` and `x2`.

Imagine that the measurement from series `xs1` at time `0` is valid until the next measurement in this series has been made, which is time `10`. We could interpolate both series to their greatest common divisor, but that is most likely `1` and creates a huge bloat. Therefore it would be better to have an interpolation only for the union of `xs1` and `xs2`. In `xs1_` and `xs2_` are aligned by list index the x-values to compare. I.e. we compare time `5` in series `xs2_` with time `0` in series `xs1_` as the next measurement in series `xs1_` is only later, at time `10`. From a visual point of view, imagine a step plot for both measurements (the y-values are not shown here) where we always compare the lines laying above each other.

Although I am struggling how to name this task, I believe it is a problem of general interest and therefore think it is appropriate to ask here for its best solution.