I am using LoadDNSSCD algorithm to load data into an MDEventworkspace (assume it is elastic powder for simplicity). The data is not real event data, it is histogram data, but not regularly binned.
I have sample data and background data.
The background data might be measured at different 2theta positions, which are not regularly spaced.
I need to interpolate the background 2theta positions to the sample 2theta positions.
Binning is not what I want since I will lose information.
At the moment I am interpolating in python, create new files and then load the new files with LoadDNSSCD.
This has the disadvantage, that it is outside of an algorithm, so not tracked and you do not have error propagation.
One solution would be to load every 2theta position on its own, then convert to MDHistoWorkspace and then interpolate. But this would create a lot of workspaces and would be very slow.
Another solution is to have fine regular binning with lot of empty bins, but that enlarges the workspace a lot.
Any better idea how this can be handled. Is there any other Workspace for non regular spaced data?