Typical analysis: add leaves to existing tree. Efficient ? Doable ?

David Rousseau, CERN (David.Rousseau@cern.ch)
Wed, 2 Sep 1998 16:52:59 +0200

Hi Rooters,

Browsing the documentation, it's not clear to me how I should do the
following (not unusual) thing:
Starting from a TTree (a converted Ntuple) with lots of events and variables,
I want to compute a few complex variables for each of a few selected events,
and store them to be able to use them many times (for example
to do an unbinned maximum likelihood fit).
With Fortran/Hbook I would read the ntuple, select the events, and put
the few complex variables for each of a few selected events in a common. Then
my MINUIT FCN would use this common.
With Root, I imagine I can do exactly the same thing, create a new
TTree with only the variables I need and the events I need. Then my MINUIT
FCN would use this TTree. But I would have lost all links to the original
event data.
But since I was told in C++ course that I have to think completely different,
I thought I could do the following: add a selection flag as a leaf
to all events (not even sure if and how it is doable), add my complex
variables as new leaves to the event which are selected. Then my MINUIT FCN
would read first the event flag, then the complex variables only for the good
events (as explained in the web pages).
So, what is the right way ?

Note: the examples on the web are in the style: I loop once on the events and
fill histograms. I would like to do (but maybe I shouldn't):
I loop on the events, and add information to be used later.

Thanks a lot

David Rousseau