Hi,

I’d like to ask for help to better understand how to construct RooHistPdf in a specific case. This question might be similar to [1], but I was not able to translate the solution from there to my specific problem.

I have a TH1 describing some distribution of a derived quantity (e.g. “pt”). In my implementation, the underlying real free parameters are not pt, but px and py. I would like to be able to define a PDF based on my TH1 and have it depend on pt. Naively, I tried the following implementation using RooDataHist as an intermediate step (in python):

```
import ROOT
ROOT.gSystem.Load('libRooFit')
from ROOT import RooFormulaVar, RooRealVar, RooDataHist, RooHistPdf, RooArgList
# Dummy histogram
th1 = ROOT.TH1D("test","test",10,0,100)
th1.Fill(5, 3)
th1.Fill(15, 1)
th1.Fill(25, 0.1)
# Elementary variables
px = RooRealVar("px","px",10,0,100)
py = RooRealVar("py","py",10,0,100)
# Derived variables
pt = RooFormulaVar("pt","sqrt(px**2 + py**2)", RooArgList(px,py))
# This does not work.
rdh = RooDataHist("test","test", RooArgList(pt), th1)
# Ultimately, I want to make a RooHistPdf
pdf = RooHistPdf("pdf",
"pdf",
RooArgList(pt),
RooArgList(pt),
rdh
)
```

This fails, with the following error message:

```
[#0] ERROR:InputArguments -- RooAbsDataStore::initialize(test): Data set cannot contain non-fundamental types, ignoring pt
```

The message is very clear, so I have no doubts what I am doing wrong, but I fail to figure out what I should be doing instead. What better ways are there to achieve what I am trying to do?

The final goal of my implementation is to be able to minimize some negative log-likelihood function by finding optimal values of (px,py). This PDF would be a part of the likelihood. I cannot just switch from (px,py)->pt as the basic variable because other parts of the likelihood depend on (px,py) separately, so I would run into the same problem in reverse.

Any advice would be appreciated!

Thanks

Andreas