Why not support unnormalized data?

Just stumbled upon this. Let's say that my response data is not normalized to a range 0-1. Example:

| sample | drug | dose | replicate | response |
---------|----- |------|----------|----------|
| CL1 | D1 | 10 | 1 | 1276 |
| CL1 | D1 | 1.1 | 1 | 71650 |
| CL1 | D1 | 0.37 | 1 | 125234 |
| CL1 | D1 | 0.12 | 1 | 177396 |
| CL1 | D1 | 0.04 | 1 | 203986 |
| CL1 | D1 | 0.01 | 1 | 231502 |
| CL1 | D1 | 0.00 | 1 | 312330 |

where 0.0 := DMSO.

Why are we making the user normalize their data first, can't CurveCurator do this?
> All responses must be normalized against the control already without the response for the control.

https://github.com/daisybio/drevalpy/blob/a37e1f5e41fbe8944839e81507caf7a720fd551b/drevalpy/datasets/curvecurator.py#L47-L50

I already have DMSO, why am I always adding cells with dose=0.0, response=1.0 if I have the normalize=True/False option? Why not just check whether dose=0.00 occurs in the data for every drug/cl/replicate combination and then not add this?